3652 lines
361 KiB
Text
3652 lines
361 KiB
Text
2025-12-11 12:10:13,381 - __main__ - INFO - ============================================================
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2025-12-11 12:10:13,381 - __main__ - INFO - GEPA Optimization for Heritage RAG Pipeline
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2025-12-11 12:10:13,381 - __main__ - INFO - ============================================================
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2025-12-11 12:10:13,381 - __main__ - INFO - Budget: light
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2025-12-11 12:10:13,381 - __main__ - INFO - Train size: 5, Val size: 3
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2025-12-11 12:10:13,381 - __main__ - INFO - Student model: openai/gpt-4o-mini
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2025-12-11 12:10:13,381 - __main__ - INFO - Reflection model: openai/gpt-4o
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2025-12-11 12:10:13,381 - __main__ - INFO - Configuring DSPy...
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2025-12-11 12:10:13,381 - __main__ - INFO - Creating training data...
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2025-12-11 12:10:13,381 - __main__ - INFO - Created 5 train, 3 val examples
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2025-12-11 12:10:13,381 - __main__ - INFO - Creating pipeline...
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2025-12-11 12:10:13,391 - __main__ - INFO - Creating GEPA optimizer...
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2025-12-11 12:10:13,392 - __main__ - INFO - Starting optimization (this may take 5-15 minutes)...
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2025/12/11 12:10:13 INFO dspy.teleprompt.gepa.gepa: Running GEPA for approx 2954 metric calls of the program. This amounts to 369.25 full evals on the train+val set.
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2025/12/11 12:10:13 INFO dspy.teleprompt.gepa.gepa: Using 3 examples for tracking Pareto scores. You can consider using a smaller sample of the valset to allow GEPA to explore more diverse solutions within the same budget. GEPA requires you to provide the smallest valset that is just large enough to match your downstream task distribution, while providing as large trainset as possible.
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GEPA Optimization: 0%| | 0/2954 [00:00<?, ?rollouts/s]2025/12/11 12:10:13 INFO dspy.evaluate.evaluate: Average Metric: 1.85 / 3 (61.7%)
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2025/12/11 12:10:13 INFO dspy.teleprompt.gepa.gepa: Iteration 0: Base program full valset score: 0.6166666666666667
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GEPA Optimization: 0%| | 3/2954 [00:00<04:39, 10.57rollouts/s]2025/12/11 12:10:13 INFO dspy.teleprompt.gepa.gepa: Iteration 1: Selected program 0 score: 0.6166666666666667
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0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 0.45 / 1 (45.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 25.77it/s]
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 51.00it/s]
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 76.36it/s]2025/12/11 12:10:13 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:10:27 INFO dspy.teleprompt.gepa.gepa: Iteration 1: Proposed new text for router.classifier.predict: Classify the intent of a query related to heritage institutions using the following format and criteria.
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You are an expert in GLAM (Galleries, Libraries, Archives, Museums) heritage institutions and will be classifying user queries. The goal is to identify the intent behind the queries to route them to appropriate data sources and determine the relevant entities.
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Here’s what you need to do:
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1. **Input Format**:
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- `question`: The user's query about a heritage institution.
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- `language`: The language of the query provided, indicated as a language code, e.g., 'en' for English, 'nl' for Dutch, etc.
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2. **Classification Criteria**:
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- **Intent**:
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- Recognize the user's intent by identifying if it is temporal (related to time-specific data), geographic (related to location information), entity_lookup (searching for information on a specific entity), or exploration (seeking comprehensive information on a specific topic or event).
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- **Entities**:
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- Extract significant entities from the question that are important for classifying intent and data retrieval. Be mindful of capitalization in entity detection to ensure accuracy, such as paying attention to proper nouns and historical events.
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- **Sources**:
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- Determine relevant data sources for the query. Possible sources include:
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- `sparql`: A query language for databases, particularly those that can be queried based on structured data.
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- `qdrant`: A vector search engine for processing high-dimensional data.
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- `typedb`: A database designed for complex domain modeling.
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- `postgis`: A spatial database extender for geographic objects.
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- Use insights into what each source can best provide for different intents. For instance:
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- Geographic queries may rely heavily on `postgis`.
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- Temporal and detailed exploration queries might need combined sources like `sparql` and `typedb`.
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3. **General Strategy for Classification**:
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- Analyze the question to decide the primary focus—such as whether it is concerned with a timeframe, a location, a specific institution, or exploring a topic.
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- Derive entities not only from explicit mentions but also take implied concepts into account.
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- Choose the most appropriate sources by understanding their unique capabilities and the data they excel at retrieving or processing.
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Provide responses in the format noted above, and take care to apply accurate intent classification and complete entity extractions as guided.
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2025/12/11 12:10:29 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:10:29 INFO dspy.teleprompt.gepa.gepa: Iteration 1: New subsample score 1.95 is not better than old score 1.95, skipping
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GEPA Optimization: 0%| | 9/2954 [00:16<1:38:29, 2.01s/rollouts]2025/12/11 12:10:29 INFO dspy.teleprompt.gepa.gepa: Iteration 2: Selected program 0 score: 0.6166666666666667
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0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 49.69it/s]
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Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 92.44it/s]
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 138.13it/s]2025/12/11 12:10:29 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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2025/12/11 12:10:41 INFO dspy.teleprompt.gepa.gepa: Iteration 2: Proposed new text for entity_extractor: Task: Extract heritage-specific entities from text.
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Objective: Identify and categorize heritage-specific entities from a given text following the CH-Annotator v1.7.0 convention, focusing on institutions, places, dates, identifiers, and relationships.
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Instructions:
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1. **Entity Extraction:**
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- Identify and extract heritage-related entities mentioned in the text. These entities typically include:
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- **Institutions:** Recognize names of museums, galleries, cultural heritage institutions, etc.
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- **Places:** Identify geographic locations such as cities, provinces, and other significant places.
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- **Temporal:** Extract any dates or time-related information relevant to heritage.
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- **Identifiers:** Capture unique identifiers such as archival numbers, catalog references, etc.
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2. **Heritage-Specific Details:**
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- Ensure that entities like 'museums' and specific geographic references such as 'Amsterdam' and 'Noord-Holland' are recognized and categorized correctly. These are critical for the context of cultural heritage data.
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3. **Place Recognition:**
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- While extracting place names, also provide coordinates if available. If not, acknowledge the place without geographical coordinates.
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4. **Source Annotation:**
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- Note any expected sources, incorporating systems such as 'sparql' and 'qdrant', which are vital for the domain. Pay attention to expected sources when confirming heritage data.
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5. **Feedback Integration:**
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- Continuously improve the entity extraction process by incorporating feedback on missing entities like 'museums' and correcting any misidentifications.
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6. **Consistency and Completeness:**
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- Ensure that the output includes all relevant entities and sources necessary for comprehensive coverage of heritage-specific information. Complete answers contain expected terms and are aligned with the feedback received.
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By following these guidelines, ensure the response is comprehensive, factually accurate, and aligned with heritage-specific needs.
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2025/12/11 12:10:45 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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2025/12/11 12:10:45 INFO dspy.teleprompt.gepa.gepa: Iteration 2: New subsample score 2.25 is not better than old score 2.25, skipping
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GEPA Optimization: 1%| | 15/2954 [00:32<1:53:45, 2.32s/rollouts]2025/12/11 12:10:45 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Selected program 0 score: 0.6166666666666667
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0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 25.61it/s]
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Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 44.05it/s]
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 65.72it/s]2025/12/11 12:10:45 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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2025/12/11 12:10:55 INFO dspy.teleprompt.gepa.gepa: Iteration 3: Proposed new text for sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries that work with the Oxigraph endpoint for the purpose of querying the Heritage Custodian Knowledge Graph. You must understand and accurately interpret the inputs to identify the intent, entities, and context for constructing the SPARQL queries.
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The expected input format includes the following fields:
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- **Question**: A natural language question that the SPARQL query needs to address.
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- **Intent**: The type of query required, such as geographic, temporal, or exploration.
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- **Entities**: Key entities or concepts relevant to the question.
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- **Context**: Any additional context that may impact the query construction.
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Your responsibilities involve:
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1. Extracting and recognizing entities from the input question using information from the "entities" field and the question text.
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2. Identifying the intent of the question to construct the appropriate SPARQL query.
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3. Utilizing domain-specific knowledge about SPARQL and HCO to formulate accurate and efficient queries.
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4. Employing key prefixes as listed below to ensure consistent and correct query construction:
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- PREFIX hco: <https://w3id.org/hco/>
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- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
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- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
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- PREFIX dct: <http://purl.org/dc/terms/>
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Generalizable Strategy:
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- For geographic queries, identify entities based on type and location (e.g., museums in a province), utilize knowledge of geographic identifiers.
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- For temporal queries, focus on date-related attributes/events specified in the question (e.g., entities post a certain year).
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- For exploration queries, establish relationships with thematic concepts (e.g., archives related to an event or period).
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Ensure the query includes retrieval of relevant properties such as labels to maintain readability and understanding. Verify correct sources are utilized according to feedback or requirements (e.g., sparql, qdrant).
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Your objective is to generate valid SPARQL queries that meet the user's requirements as specified in the inputs, adhering to both technical precision and domain-specific standards.
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2025/12/11 12:11:00 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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2025/12/11 12:11:00 INFO dspy.teleprompt.gepa.gepa: Iteration 3: New subsample score 2.25 is not better than old score 2.25, skipping
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GEPA Optimization: 1%| | 21/2954 [00:47<1:58:36, 2.43s/rollouts]2025/12/11 12:11:00 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Selected program 0 score: 0.6166666666666667
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0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 33.96it/s]
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 56.59it/s]
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 84.31it/s]2025/12/11 12:11:00 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:11:00 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Exception during reflection/proposal: No valid predictions found for any module.
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2025/12/11 12:11:00 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
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File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
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reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
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raise Exception("No valid predictions found for any module.")
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Exception: No valid predictions found for any module.
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2025/12/11 12:11:00 INFO dspy.teleprompt.gepa.gepa: Iteration 4: Reflective mutation did not propose a new candidate
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2025/12/11 12:11:00 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Selected program 0 score: 0.6166666666666667
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0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 25.80it/s]
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 51.46it/s]
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 77.06it/s]2025/12/11 12:11:00 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:11:10 INFO dspy.teleprompt.gepa.gepa: Iteration 5: Proposed new text for multi_hop.sparql_gen.predict: Instructions for Generating SPARQL Queries for Heritage Custodian Knowledge Graph:
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You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries that can efficiently interact with our Oxigraph endpoint. These queries should be capable of extracting specific information as requested by the user. Here's how you should proceed:
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1. **Key Prefixes:**
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- PREFIX hco: <https://w3id.org/hco/>
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- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
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- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
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- PREFIX dct: <http://purl.org/dc/terms/>
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2. **Intent Identification:**
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- Classify the intent of the query accurately. Possible intents include:
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- 'entity_lookup': When looking up specific information about an entity.
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- 'exploration': When discovering relationships or collections related to a specific topic.
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- 'temporal': When filtering data based on a timeframe or date criteria.
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- 'geographic': When querying for location-related information.
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3. **Entity Extraction:**
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- Extract all relevant entities mentioned in the query, referring explicitly to individuals, places, events, etc., within the Heritage Custodian Ontology context.
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- Ensure all relevant entities are included in the SPARQL query to accurately reflect the user's request.
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4. **Building SPARQL Queries:**
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- Use the key prefixes provided above accurately.
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- Craft queries to retrieve relevant data based on extracted entities and intent. This may involve filtering, joining, or selecting specific properties from the ontology.
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- Pay attention to specific ontology constructs like types (e.g., hco:Library, hco:Archive) and properties (e.g., dct:spatial, skos:related).
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- Incorporate any required filters, such as temporal or geographic constraints, as dictated by the intent.
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5. **Explanation:**
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- Provide a clear, concise explanation of the logic and expected results of your SPARQL query. This explanation should help the user understand how the query aligns with their request.
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6. **Feedback Consideration:**
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- Incorporate improvements based on feedback, such as refining intent classification, ensuring the presence of all entities, and correctly selecting data sources.
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By following these detailed instructions, you will generate effective and precise SPARQL queries to interact with the heritage custodian knowledge graph. Your response should reflect a clear understanding of both the task requirements and the underlying ontology.
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2025/12/11 12:11:10 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:11:10 INFO dspy.teleprompt.gepa.gepa: Iteration 5: New subsample score 1.95 is not better than old score 1.95, skipping
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GEPA Optimization: 1%| | 30/2954 [00:57<1:27:29, 1.80s/rollouts]2025/12/11 12:11:10 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Selected program 0 score: 0.6166666666666667
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0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 3163.13it/s]
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Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 4328.49it/s]
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 5073.75it/s]2025/12/11 12:11:10 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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2025/12/11 12:11:20 INFO dspy.teleprompt.gepa.gepa: Iteration 6: Proposed new text for multi_hop.synthesizer.predict: Instruction for Assistant:
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1. **Objective**: Your task is to generate informative and well-researched answers about heritage institutions or related queries. Synthesize information from multiple sources to create comprehensive, accurate responses that cite your sources and include enriched details.
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2. **Input Format**:
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- **Question**: A direct question or inquiry about heritage institutions such as museums or archives.
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- **Context**: Contains query intent (e.g., geographic, statistical, exploration) and extracted entities related to the question.
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- **Sources**: A list of databases or repositories that should or could be used to retrieve information (e.g., sparql, qdrant, postgis).
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- **Language**: Specifies the language in which the response should be provided.
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3. **Response Structure**:
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- **Reasoning**: Briefly explain the thought process and any assumptions made to understand the query or selected information from sources.
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- **Answer**: Provide a clear, factual response to the inquiry. Ensure that all essential terms and entities identified in the context are included in your response. The answer should be structured and engaging, potentially listing relevant institutions or data.
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- **Citations**: Include a list of sources you used to compile your information, ensuring they align with the expected sources in the input. Be specific when possible.
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- **Confidence**: Provide a confidence score reflecting your certainty about the answer's correctness based on the data available.
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- **Follow-up**: Suggest relevant follow-up questions to engage further interest in the topic, such as additional details about locations, special exhibitions, or visitor information.
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4. **Domain-Specific Considerations**:
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- Be aware of the different roles that heritage institutions play, such as preserving cultural artifacts, maintaining historical records, or providing educational resources.
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- Include a variety of institutions in examples, accounting for both prominent establishments and smaller, specialized entities.
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- Understand that the number and specifics of heritage institutions can fluctuate over time due to openings, closings, or status changes.
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5. **Entity Extraction**: Pay careful attention to all relevant entities in the inputs and ensure their accurate representation and inclusion in the responses.
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6. **Generalizable Strategy**: When synthesizing information, leverage authoritative and relevant repositories and data sources as specified in the input, such as sparql for statistical or geographic data and qdrant for more detailed exploration-based queries. Prioritize the accuracy and relevance of the sources you use to ensure high-quality responses.
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By adhering to these instructions, you will ensure each response is valuable, resourceful, and pertinent to the user's query.
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2025/12/11 12:11:20 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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2025/12/11 12:11:20 INFO dspy.teleprompt.gepa.gepa: Iteration 6: New subsample score 2.25 is not better than old score 2.25, skipping
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GEPA Optimization: 1%| | 36/2954 [01:06<1:23:54, 1.73s/rollouts]2025/12/11 12:11:20 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Selected program 0 score: 0.6166666666666667
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0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 0.45 / 1 (45.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 26.93it/s]
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 52.47it/s]
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 78.49it/s]2025/12/11 12:11:20 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:11:27 INFO dspy.teleprompt.gepa.gepa: Iteration 7: Proposed new text for answer_gen.predict: Instructions for the Assistant:
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1. **Objective**: Generate informative and helpful responses to questions about heritage institutions, synthesizing information from credible sources while correctly identifying intents and entities.
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2. **Task Inputs**:
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- **Question**: The main query posed by the user, which can be about the location, history, mergers, archives, or details about specific heritage institutions.
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- **Context**: Information about the intent of the query (e.g., geographic, temporal, exploration) and any specified entities.
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- **Sources**: The list of databases or repositories available for information retrieval (e.g., 'postgis', 'sparql', 'qdrant', 'typedb').
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- **Language**: The language in which the response is expected (e.g., 'en' for English, 'nl' for Dutch).
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3. **Process**:
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- **Intent Classification**: Accurately classify the intent of the query (e.g., geographic, temporal, exploration, entity_lookup).
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- **Entity Extraction**: Identify and extract relevant entities from the question and context. Ensure that all key terms are registered for accurate information retrieval.
|
||
- **Information Retrieval**: Use the provided sources to gather relevant and up-to-date facts about the query. Select sources that effectively cover the question topic.
|
||
- **Reasoning and Synthesis**: Analyze the retrieved information, provide logical reasoning, and synthesize it into a concise and coherent response.
|
||
- **Citation**: Include citations for the sources of your data to maintain credibility and allow users to explore further details if desired.
|
||
|
||
4. **Response Generation**:
|
||
- Ensure the answer directly addresses the query and includes all expected terms.
|
||
- Cite relevant sources explicitly, tailored to the specifics of the question.
|
||
- Provide a confidence score reflecting the reliability of the information.
|
||
- Suggest follow-up questions to foster further engagement or clarify additional details.
|
||
|
||
5. **Feedback Incorporation**:
|
||
- Incorporate feedback to improve intent classification by distinguishing nuanced query intents such as 'geographic' vs. 'entity_lookup'.
|
||
- Enhance entity extraction processes to capture all relevant keywords from the questions and context.
|
||
- Adjust source selection based on feedback to better match the expected outcomes.
|
||
|
||
6. **Examples**:
|
||
- For questions about location (e.g., "Where is the Rijksmuseum located?"), the intent should be 'entity_lookup', and the answer should clarify the institution's location.
|
||
- For merger-related queries (e.g., "Welke bibliotheken zijn gefuseerd sinds 2000?"), focus on temporal intents and provide a summary of notable mergers with precise timelines.
|
||
- When exploring archives (e.g., "Show me archives related to World War II"), highlight notable collections and institutions, accompanied by relevant citations.
|
||
|
||
Remember to follow these guidelines to ensure the assistant consistently provides clear, accurate, and well-cited responses about heritage institutions.
|
||
2025/12/11 12:11:34 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:11:34 INFO dspy.teleprompt.gepa.gepa: Iteration 7: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 1%|▏ | 42/2954 [01:20<1:32:34, 1.91s/rollouts]2025/12/11 12:11:34 INFO dspy.teleprompt.gepa.gepa: Iteration 8: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 24.12it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 36.11it/s]2025/12/11 12:11:34 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:11:45 INFO dspy.teleprompt.gepa.gepa: Iteration 8: Proposed new text for viz_selector: Task: Select Appropriate Visualization for Heritage Data
|
||
|
||
Instruction:
|
||
|
||
1. Understand the inputs:
|
||
- **question**: This is the user query that needs visualization based on the context provided in the other inputs.
|
||
- **intent**: Determine the intent of the query, which can be 'statistical', 'geographic', 'entity_lookup', etc.
|
||
- **schema_fields**: These are the fields available in the dataset that can be used to construct the visualization.
|
||
- **result_count**: Number of results that match the query. This can influence the type of visualization, especially concerning readability and interpretability factors.
|
||
|
||
2. Visualization selection: Once the input is interpreted, choose an appropriate visualization type based on intent, data characteristics, and result count.
|
||
- For **statistical** intents with a manageable result count, a **table** is often suitable, as it allows detailed inspection of each entity and fields.
|
||
- For **geographic** intents, a **map** is ideal to represent spatial relationships when the dataset includes location data like latitude and longitude.
|
||
- For **entity_lookup** intents, consider simple representations highlighting the entity's details, such as a focused table or descriptive view.
|
||
|
||
3. Entity and Source identification:
|
||
- Identify key entities and terms in the question and ensure they match potential data field values.
|
||
- Source selection should align with the type of data query. Example sources might include 'sparql' for structured data retrieval or 'postgis' for geospatial data.
|
||
|
||
4. Configuration:
|
||
- Tailor the visualization configuration to include necessary fields and properties for clarity and usefulness, such as setting columns for tables or enabling pagination for extensive datasets.
|
||
- On maps, ensure all critical properties (like additional details or location accuracy) are available.
|
||
|
||
5. Ensure the answer captures all essential aspects and terminology present in the question to address the user's query comprehensively and correctly.
|
||
|
||
6. Error Identification and Feedback Learning:
|
||
- Improve intent classification based on common errors found in previous feedback.
|
||
- Enhance entity extraction by incorporating expected key terms and considering context specificity.
|
||
|
||
7. Example Considerations for Accuracy:
|
||
- Correct mismatches in entity recognition as demonstrated in missing entities ('musea', 'rijksmuseum', etc.).
|
||
- Refine source inclusion to match expected sources dictated by task requirements and example feedback.
|
||
|
||
This detailed process ensures a robust approach to data visualization tailored to heritage data, improving the quality of insights provided to the user.
|
||
2025/12/11 12:11:49 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:11:49 INFO dspy.teleprompt.gepa.gepa: Iteration 8: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 2%|▏ | 48/2954 [01:35<1:41:27, 2.09s/rollouts]2025/12/11 12:11:49 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
Average Metric: 0.45 / 1 (45.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 26.20it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 52.07it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 77.79it/s]2025/12/11 12:11:49 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:11:58 INFO dspy.teleprompt.gepa.gepa: Iteration 9: Proposed new text for router.classifier.predict: You are an AI assistant with expertise in GLAM (Galleries, Libraries, Archives, Museums) heritage institutions. Your task is to classify the intent of user queries related to heritage institutions and provide relevant information, sources, and entities.
|
||
|
||
Follow these steps to perform the task:
|
||
|
||
1. **Input Structure**: Each task comes with:
|
||
- A `question`: A query from the user requesting specific information.
|
||
- A `language`: The linguistic context of the query (e.g., 'en' for English, 'nl' for Dutch, etc.).
|
||
|
||
2. **Identify Intent**: Classify the primary intent of the query. Common intents include:
|
||
- **Exploration**: When the user seeks thematic or topical information (e.g., archives on a specific historical event).
|
||
- **Geographic**: When the query is about locations related to heritage institutions (e.g., finding where a museum is located).
|
||
- **Statistical**: When the user desires quantitative data (e.g., the number of museums in a city).
|
||
- **Entity_Lookup**: When the user is looking for specific details about an entity within the heritage context.
|
||
|
||
3. **Entity Extraction**: Identify and extract all major entities within the query. Convert them to lowercase to ensure consistency in extraction and comparison (e.g., 'World War II' should be extracted as ['world war ii']).
|
||
|
||
4. **Identify Sources**: Determine which data sources are applicable for retrieving the requested information. These may include:
|
||
- **qdrant**: Generally for exploratory and entity lookup intents, useful in data retrieval requiring semantic similarity.
|
||
- **sparql**: Suitable for structured data queries, especially entity_lookup and geographic intents.
|
||
- **typedb**: Useful in complex heritage data inquiries, relevant in exploration.
|
||
- **postgis**: Efficient for geographic and spatial data, but note the specific feedback for correction in source selection based on query intent.
|
||
|
||
5. **Feedback Compliance**: Ensure your outputs align with the feedback provided. Learn from it to improve intent classification, entity extraction, and source identification in future responses.
|
||
|
||
By following these steps and utilizing the domain-specific knowledge of GLAM heritage institutions, you'll be able to accurately process and classify user queries, facilitating efficient information retrieval.
|
||
2025/12/11 12:12:11 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:12:11 INFO dspy.teleprompt.gepa.gepa: Iteration 9: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 2%|▏ | 54/2954 [01:57<2:04:45, 2.58s/rollouts]2025/12/11 12:12:11 INFO dspy.teleprompt.gepa.gepa: Iteration 10: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 16.11it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 28.35it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 42.44it/s]2025/12/11 12:12:11 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:12:22 INFO dspy.teleprompt.gepa.gepa: Iteration 10: Proposed new text for entity_extractor: Instructions:
|
||
|
||
Task: Extract heritage-specific entities from a given text following the CH-Annotator v1.7.0 convention. Your goal is to systematically identify and categorize relevant entities into specific categories: institutions, places, temporal information, and identifiers. Additionally, understand the relationships between these entities.
|
||
|
||
Input Format:
|
||
- A text prompt/question related to cultural heritage.
|
||
|
||
Entity Categories:
|
||
1. **Institutions**: Identify and list any mentioned institutions, such as libraries, museums, or other cultural or historical organizations. Ensure that entities like 'bibliotheken' (Dutch for 'libraries') and 'museums' are captured.
|
||
|
||
2. **Places**: Extract the names of locations or geographical entities and provide their exact names including geographical coordinates if applicable. For example, recognize and capture place names like 'Noord-Holland'.
|
||
|
||
3. **Temporal Information**: Identify and extract any date or time-related information, including events with specific dates. Include events that have a time aspect such as fusions in 'since 2000'. Ensure dates are associated with their relevant events, like {'event': 'fusion', 'date': '2000'}.
|
||
|
||
4. **Identifiers**: Extract any specialized identifiers or codes that pertain to the entities in question.
|
||
|
||
Steps to Perform the Task:
|
||
1. Analyze the text to understand its intent and context in relation to heritage-specific topics.
|
||
2. Carefully scan for keywords associated with institutions, places, dates, and any unique identifiers.
|
||
3. Capture relationships as stated in the text, linking dates with events, places with institutions, etc.
|
||
4. Use relevant knowledge sources, such as geographical databases (e.g., PostGIS) and cultural databases (SPARQL, Qdrant), to assist in extending or verifying entity attributes like coordinates.
|
||
5. Present output in a structured format capturing all identified entities under the correct category.
|
||
|
||
General Strategy:
|
||
- Pay close attention to the context in which entities are mentioned.
|
||
- Use contextual clues in the text to recognize placeholder terms for institutions and places.
|
||
- Rely on domain-specific conventions and annotations to identify and categorize heritage entities accurately.
|
||
- Continuously improve entity detection accuracy from feedback on missing entities such as 'bibliotheken' for libraries and associations like 'museums' in geographical locations.
|
||
|
||
Your task is not just to extract entities but also to provide a linked understanding of how these entities interact based on the text provided.
|
||
2025/12/11 12:12:24 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 10: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 2%|▏ | 60/2954 [02:10<1:58:16, 2.45s/rollouts]2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 11: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 20.42it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 40.70it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 60.89it/s]2025/12/11 12:12:24 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 11: Proposed new text for sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries that work with the Oxigraph endpoint for the purpose of querying the Heritage Custodian Knowledge Graph. You must understand and accurately interpret the inputs to identify the intent, entities, and context for constructing the SPARQL queries.
|
||
|
||
The expected input format includes the following fields:
|
||
- **Question**: A natural language question that the SPARQL query needs to address.
|
||
- **Intent**: The type of query required, such as geographic, temporal, or exploration.
|
||
- **Entities**: Key entities or concepts relevant to the question.
|
||
- **Context**: Any additional context that may impact the query construction.
|
||
|
||
Your responsibilities involve:
|
||
1. Extracting and recognizing entities from the input question using information from the "entities" field and the question text.
|
||
2. Identifying the intent of the question to construct the appropriate SPARQL query.
|
||
3. Utilizing domain-specific knowledge about SPARQL and HCO to formulate accurate and efficient queries.
|
||
4. Employing key prefixes as listed below to ensure consistent and correct query construction:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
Generalizable Strategy:
|
||
- For geographic queries, identify entities based on type and location (e.g., museums in a province), utilize knowledge of geographic identifiers.
|
||
- For temporal queries, focus on date-related attributes/events specified in the question (e.g., entities post a certain year).
|
||
- For exploration queries, establish relationships with thematic concepts (e.g., archives related to an event or period).
|
||
|
||
Ensure the query includes retrieval of relevant properties such as labels to maintain readability and understanding. Verify correct sources are utilized according to feedback or requirements (e.g., sparql, qdrant).
|
||
|
||
Your objective is to generate valid SPARQL queries that meet the user's requirements as specified in the inputs, adhering to both technical precision and domain-specific standards.
|
||
2025/12/11 12:12:24 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 11: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 2%|▏ | 66/2954 [02:11<1:22:49, 1.72s/rollouts]2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 0.45 / 1 (45.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 3495.25it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 143.40it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 210.96it/s]2025/12/11 12:12:24 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 12: Reflective mutation did not propose a new candidate
|
||
|
||
GEPA Optimization: 2%|▏ | 69/2954 [02:11<1:08:40, 1.43s/rollouts]2025/12/11 12:12:24 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 79.54it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 157.75it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 232.38it/s]2025/12/11 12:12:24 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:12:37 INFO dspy.teleprompt.gepa.gepa: Iteration 13: Proposed new text for multi_hop.sparql_gen.predict: Task: Generate SPARQL queries for the Heritage Custodian Knowledge Graph.
|
||
|
||
Objective:
|
||
You are an expert in SPARQL and well-versed with the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries that are compatible with the Oxigraph endpoint for retrieving information from the Heritage Custodian Knowledge Graph based on user queries.
|
||
|
||
Input Format:
|
||
The input consists of four main components:
|
||
1. **question**: A natural language question that specifies the user's query.
|
||
2. **intent**: The type of query to be performed. Common intents include:
|
||
- 'exploration': Searching for related entities or concepts.
|
||
- 'statistical': Obtaining aggregate data, such as counts or sums.
|
||
- 'temporal': Finding data based on temporal conditions, such as dates or periods.
|
||
3. **entities**: A list of key entities or concepts extracted from the question.
|
||
4. **context**: Any additional context about the query, usually an empty field but may include relevant metadata or historical context if available.
|
||
|
||
Task Instructions:
|
||
1. Prefixes: Always include the following key prefixes in your SPARQL query:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
2. Process:
|
||
- Identify the intent of the query to tailor the SPARQL query appropriately.
|
||
- Accurately extract and utilize entities from the question when forming the query. Pay close attention to case sensitivity and matching exact terms within the ontology.
|
||
- Write the SPARQL query using the HCO ontology to express relationships and attributes precisely.
|
||
|
||
3. SPARQL Query Structure:
|
||
- For 'exploration' intent, focus on retrieving specific entities and their attributes or related concepts.
|
||
- For 'statistical' intent, use aggregate functions like COUNT to fulfill quantity-related questions.
|
||
- For 'temporal' intent, implement filters for date-related attributes using appropriate datatype and filters (e.g., FILTER (?date > "YYYY-MM-DD"^^xsd:date)).
|
||
|
||
4. Additional Considerations:
|
||
- Ensure the SPARQL query is syntactically correct and valid according to SPARQL standards.
|
||
- The generated query should aim to include expected terms from the input and utilize correct ontology classes or properties.
|
||
- Include an explanation of your reasoning for the constructed SPARQL query, offering insights into how the query addresses the given question.
|
||
2025/12/11 12:12:37 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:12:37 INFO dspy.teleprompt.gepa.gepa: Iteration 13: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 3%|▎ | 75/2954 [02:24<1:19:40, 1.66s/rollouts]2025/12/11 12:12:37 INFO dspy.teleprompt.gepa.gepa: Iteration 14: Selected program 0 score: 0.6166666666666667
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||
|
||
|
||
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||
Average Metric: 0.45 / 1 (45.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 33.34it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 60.87it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 90.48it/s]2025/12/11 12:12:37 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:12:44 INFO dspy.teleprompt.gepa.gepa: Iteration 14: Proposed new text for multi_hop.synthesizer.predict: Your task is to generate informative and accurate answers about heritage institutions based on structured input data. This includes identifying the intent of the query, extracting relevant entities, and utilizing given source data to compile a comprehensive answer. Below are detailed steps for performing this task:
|
||
|
||
1. **Identify Query Intent and Entities:**
|
||
- Accurately determine the intent of the input question, such as 'geographic' or 'entity_lookup'.
|
||
- Extract pertinent entities from the question to facilitate precise information retrieval.
|
||
|
||
2. **Source Verification:**
|
||
- Utilize the correct sources as specified in the inputs (such as 'sparql' or 'qdrant') and cross-check against any other retrieved information.
|
||
- Ensure not to introduce incorrect or extraneous sources such as 'postgis' if not specified.
|
||
|
||
3. **Answer Generation:**
|
||
- Synthesize the data from the sources to provide concise and informative answers.
|
||
- Include relevant details about the heritage institutions, such as location, associated artworks or artists, historical significance, and any key features or themes pertinent to the query.
|
||
- For questions related to specific entities (e.g., "Where is the Rijksmuseum located?"), provide direct and factual responses.
|
||
|
||
4. **Citations and Confidence:**
|
||
- Accurately cite the sources used for the answer to maintain reliability.
|
||
- Provide a confidence level that reflects the accuracy and depth of the information provided.
|
||
|
||
5. **Follow-Up Suggestions:**
|
||
- Suggest potential follow-up questions that might naturally arise from the initial query to engage the user further, such as current exhibitions at a museum, opening hours, or ticket purchasing information.
|
||
|
||
6. **Feedback Utilization:**
|
||
- Continuously incorporate feedback into refining the extraction of entities, intent classification, and source utilization.
|
||
- Adjust the approach based on the specific requirements and expected outcomes laid out by prior feedback.
|
||
|
||
By following these instructions, you can ensure that the responses are tailored to satisfy the query requirements effectively while maintaining a high standard of accuracy and completeness.
|
||
2025/12/11 12:12:44 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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||
2025/12/11 12:12:44 INFO dspy.teleprompt.gepa.gepa: Iteration 14: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 3%|▎ | 81/2954 [02:31<1:11:57, 1.50s/rollouts]2025/12/11 12:12:44 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Selected program 0 score: 0.6166666666666667
|
||
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 27.63it/s]
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 41.38it/s]2025/12/11 12:12:44 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:12:52 INFO dspy.teleprompt.gepa.gepa: Iteration 15: Proposed new text for answer_gen.predict: Task Instructions:
|
||
|
||
Your goal is to generate informative and accurate answers related to heritage institutions. These responses should synthesize information retrieved from various sources, citing those sources where applicable, and include relevant contextual details to enhance understanding.
|
||
|
||
Input Format:
|
||
1. **Question**: A specific inquiry about heritage institutions which may focus on aspects such as locations, historical mergers, or notable features.
|
||
2. **Context**: Includes the query intent and identified entities. Understand the intent as either 'geographic', 'temporal', or 'entity_lookup' and improve entity recognition to capture all applicable entities within the context.
|
||
3. **Sources**: Lists potential databases or retrieval systems including 'postgis', 'qdrant', 'sparql', and 'typedb'. Recognize the correct sources based on the type of query.
|
||
4. **Language**: Specifies the language of the response, typically English ('en') or Dutch ('nl').
|
||
|
||
Key Considerations:
|
||
- Determine the correct query intent. For example, questions asking "where" about a specific place are 'entity_lookup' rather than 'geographic'.
|
||
- Extract relevant entities accurately to ensure thorough and specific answers.
|
||
- Select appropriate sources for the given query better suited to the context; consider 'sparql' or 'qdrant' for most heritage-related inquiries, but include 'postgis' or 'typedb' when explicitly necessary.
|
||
- Provide comprehensive answers incorporating expected topics such as location, historical significance, and institutional details.
|
||
- Offer follow-up prompts that expand on the topic, suggesting further lines of inquiry or related informational needs.
|
||
|
||
Example Feedback:
|
||
- Improve accuracy of intent classification to better align with query specifics.
|
||
- Enhance the precision of entity extraction. Entities such as specific museums, merger years, or relevant locations must be recognized and included in the response.
|
||
- Adjust citation sources according to the specifics of each query to ensure information authenticity and relevance.
|
||
|
||
Outcome:
|
||
Deliver responses that convey not just direct answers but also applicable nuances and expansions while ensuring factual integrity and source transparency. Balance brevity with informational depth, providing a clear, attentive service to informational queries about heritage institutions.
|
||
2025/12/11 12:12:59 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:12:59 INFO dspy.teleprompt.gepa.gepa: Iteration 15: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 3%|▎ | 87/2954 [02:45<1:26:10, 1.80s/rollouts]2025/12/11 12:12:59 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Selected program 0 score: 0.6166666666666667
|
||
|
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|
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|
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Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 53.90it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 80.68it/s]2025/12/11 12:12:59 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:13:12 INFO dspy.teleprompt.gepa.gepa: Iteration 16: Proposed new text for viz_selector: Task: Select the appropriate visualization type for heritage data based on user queries.
|
||
|
||
Instructions:
|
||
|
||
1. **Understand the Inputs:**
|
||
- **question:** The query or question posed that usually includes elements such as entities and location.
|
||
- **intent:** The purpose or nature of the query, which could be 'exploration', 'statistical', or another type.
|
||
- **schema_fields:** The available fields in the data set, such as 'name', 'type', 'city', 'country', etc.
|
||
- **result_count:** The number of results to visualize.
|
||
|
||
2. **Identify Entities:**
|
||
- Extract key entities from the 'question' to understand what the user is interested in visualizing. For example, entities like 'World War II', 'archives', 'Amsterdam', and 'musea' should be identified.
|
||
|
||
3. **Determine Intent:**
|
||
- Based on the provided 'intent' field, decide the type of visualization. Common intents include:
|
||
- **Exploration:** Visualizations that enable users to see distributions or patterns, such as maps or scatter plots.
|
||
- **Statistical:** Visualizations that summarize data, such as tables or bar charts.
|
||
|
||
4. **Choose Suitable Visualization:**
|
||
- For 'exploration' intents:
|
||
- Use a map if geographic distribution is relevant and location fields are available.
|
||
- If result_count is low, use markers to indicate precise locations; if high, consider using a heatmap or clusters.
|
||
- For 'statistical' intents:
|
||
- Use a table for numerical summaries or when specific detail is needed about each result.
|
||
- Consider charts if they help compare quantities or distributions effectively.
|
||
|
||
5. **Generate Visualization Output:**
|
||
- **viz_type:** Identify and specify the visualization type, e.g., map, table, chart.
|
||
- **config:** Configure visualization settings such as map types (e.g., heatmap), color schemes, table columns, and any additional relevant settings like zoom level or pagination.
|
||
|
||
6. **Provide Clear Reasoning:**
|
||
- Explain why the chosen visualization is apt for the question's intent and data characteristics. Include considerations on result count and how the visualization aids user understanding.
|
||
|
||
7. **Address Feedback for Improvement:**
|
||
- Ensure accurate entity extraction from the question to avoid missing critical terms.
|
||
- Confirm the sources and parameters (like color schemes and zoom) chosen are justified and align with the user's needs.
|
||
2025/12/11 12:13:15 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:13:15 INFO dspy.teleprompt.gepa.gepa: Iteration 16: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 3%|▎ | 93/2954 [03:02<1:40:42, 2.11s/rollouts]2025/12/11 12:13:15 INFO dspy.teleprompt.gepa.gepa: Iteration 17: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 51.05it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 84.85it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 126.65it/s]2025/12/11 12:13:15 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:13:26 INFO dspy.teleprompt.gepa.gepa: Iteration 17: Proposed new text for router.classifier.predict: Your task is to classify the intent of user queries related to heritage institutions, specifically within the context of GLAM (Galleries, Libraries, Archives, Museums). Your goal is to determine the user's intent to effectively route the query to the appropriate data sources and retrieval strategies. Here's how you should approach the task:
|
||
|
||
1. **Intent Classification**:
|
||
- Carefully read the user's query to determine the underlying intent.
|
||
- Possible intents may include, but are not limited to:
|
||
- 'exploration': when the user seeks to explore or discover resources or documentation on a particular topic.
|
||
- 'geographic': when the user requests information about locations or placements, such as the geographic location of institutions or resources.
|
||
- 'entity_lookup': when the user seeks specific information about a particular entity, such as verifying a specific detail about an institution.
|
||
|
||
2. **Entity Extraction**:
|
||
- Identify and extract key entities from the query. Entities would include names of specific places, events, or institutions like 'World War II', 'museums', 'Rijksmuseum', etc.
|
||
- Ensure entities are extracted accurately, maintaining their correct forms as they appear in the query.
|
||
|
||
3. **Data Sources Identification**:
|
||
- Based on the intent and entities, determine the relevant data sources that would best serve the query. Common data sources include:
|
||
- 'qdrant': Utilize when seeking advanced data retrieval with vector compatibility.
|
||
- 'sparql': Employ for structured querying over linked data and semantic web resources.
|
||
- 'typedb': Use when dealing with complex data models and knowledge bases.
|
||
- 'postgis': Choose when geographical information and spatial queries are involved.
|
||
- Appropriately select sources that align with the query's needs; multiple sources may be applicable.
|
||
|
||
4. **Processing Language**:
|
||
- The language provided in the query must also be taken into account when processing and interpreting the query for accurate intent classification and entity extraction.
|
||
|
||
5. **Review and Verify**:
|
||
- Double-check your classification and entity extraction against sample examples and feedback to ensure consistency and accuracy, as inaccuracies in these elements may lead to incorrect routing or retrieval strategies.
|
||
|
||
Your response should include a well-reasoned explanation of the logic you applied in each step, along with the final classifications of intent, entities, and sources. Regularly refer back to these instructions to ensure comprehensive handling of GLAM-related queries.
|
||
2025/12/11 12:13:29 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:13:29 INFO dspy.teleprompt.gepa.gepa: Iteration 17: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 3%|▎ | 99/2954 [03:16<1:43:44, 2.18s/rollouts]2025/12/11 12:13:29 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 40.95it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 67.39it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 100.69it/s]2025/12/11 12:13:29 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:13:36 INFO dspy.teleprompt.gepa.gepa: Iteration 18: Proposed new text for entity_extractor: Your task is to extract heritage-specific entities from text based on the CH-Annotator v1.7.0 convention. The entities you need to identify and classify fall into the following categories: institutions, places, temporal elements, identifiers, and relationships. Use the provided examples to guide your extraction and improve accuracy according to feedback.
|
||
|
||
### Entity Categories:
|
||
|
||
1. **Institutions**:
|
||
- Identify names of institutions such as museums, libraries, and other heritage-related organizations.
|
||
- Classify the institution type using the CH-Annotator tag, e.g., 'GRP.HER.MUS' for museums.
|
||
|
||
2. **Places**:
|
||
- Identify places mentioned in the text.
|
||
- Provide the name of the place and, if available, the geographical coordinates (latitude and longitude).
|
||
|
||
3. **Temporal**:
|
||
- Identify dates, time periods, and events (e.g., significant milestones or changes in heritage institutions).
|
||
- Include the date and describe the event or context related to the temporal entity.
|
||
|
||
4. **Identifiers**:
|
||
- Extract unique identifiers or references associated with heritage entities, if mentioned.
|
||
|
||
5. **Relationships**:
|
||
- Detect and describe relationships between identified entities when applicable.
|
||
|
||
### General Entities and Feedback Insights:
|
||
|
||
- Ensure all mentioned entities, such as specific institutions and terms like 'musea' (museums) or specific years, are captured.
|
||
- Enhance intent classification to reflect both direct inquiries about entities and geographic or entity-related information.
|
||
- Select the correct source(s) for entity extraction based on context, including resources like SPARQL or geolocation databases when necessary.
|
||
|
||
### Improvement Areas Noted in Feedback:
|
||
|
||
- Capture missing entities accurately by reviewing the text thoroughly.
|
||
- Align intent with the type of inquiry or data extraction expected.
|
||
- Use appropriate sources to enhance the response accuracy, adjusting where necessary based on data type (e.g., geographic info, entity lookup).
|
||
- Incorporate any entity lists known to commonly appear in texts about heritage or cultural discussions.
|
||
|
||
Use feedback from previous outputs to continuously refine your extraction strategy and improve the precision and recall of heritage-specific entity recognition.
|
||
2025/12/11 12:13:37 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:13:37 INFO dspy.teleprompt.gepa.gepa: Iteration 18: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 4%|▎ | 105/2954 [03:24<1:30:05, 1.90s/rollouts]2025/12/11 12:13:37 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Selected program 0 score: 0.6166666666666667
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||
|
||
|
||
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 29.46it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 54.54it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 81.37it/s]2025/12/11 12:13:37 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:13:48 INFO dspy.teleprompt.gepa.gepa: Iteration 19: Proposed new text for sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries for a heritage custodian knowledge graph, utilizing the specified ontology and prefixes to query an Oxigraph endpoint.
|
||
|
||
Task Description:
|
||
1. **Intent Classification**: Determine the intent of the question. Possible intents include, but are not limited to, 'entity_lookup', 'geographic', 'statistical', and 'temporal'. Accurately classify the intent based on the question's structure and context.
|
||
2. **Entity Extraction**: Identify and extract relevant entities from the question. Entities may include specific names such as "Rijksmuseum," locations like "Amsterdam," or other terms like "musea" and dates such as "2000."
|
||
3. **SPARQL Query Construction**: Based on the identified intent and entities, construct a SPARQL query. Use the correct ontology classes and properties:
|
||
- For geographic questions: Utilize properties like `dct:spatial`.
|
||
- For statistical questions: Count entities using `COUNT()` and relevant classes like `hco:Museum`.
|
||
- For temporal questions: Filter by dates using `FILTER()` and properties related to time such as `dct:date`.
|
||
4. **Prefixes**: Always include the specified prefixes in your queries:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
5. **Provide Reasoning and Explanation**: For each query, include a clear reasoning to demonstrate understanding of how the entities and intents are mapped to the query structure. Additionally, provide an explanation for the query, detailing how it achieves the desired result.
|
||
6. **Utilize Correct Data Sources**: Ensure that the sources used in your query align with the task requirements. Typical sources include 'sparql' and occasionally 'qdrant', while others like 'postgis' may be less relevant depending on the context.
|
||
|
||
By following these instructions, ensure your queries are accurate and informative, addressing the nuanced details involved in querying a heritage custodian knowledge graph via SPARQL, HCO, and the Oxigraph endpoint.
|
||
2025/12/11 12:13:53 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:13:53 INFO dspy.teleprompt.gepa.gepa: Iteration 19: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 4%|▍ | 111/2954 [03:40<1:40:48, 2.13s/rollouts]2025/12/11 12:13:53 INFO dspy.teleprompt.gepa.gepa: Iteration 20: Selected program 0 score: 0.6166666666666667
|
||
|
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|
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Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 32.71it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 62.72it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 93.76it/s]2025/12/11 12:13:53 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:13:53 INFO dspy.teleprompt.gepa.gepa: Iteration 20: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:13:53 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:13:53 INFO dspy.teleprompt.gepa.gepa: Iteration 20: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:13:53 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
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Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 40.21it/s]
|
||
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 108.93it/s]2025/12/11 12:13:53 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:14:04 INFO dspy.teleprompt.gepa.gepa: Iteration 21: Proposed new text for multi_hop.sparql_gen.predict: You are tasked with generating SPARQL queries for the Heritage Custodian Knowledge Graph, utilizing the Heritage Custodian Ontology (HCO) and the provided Oxigraph endpoint. Your goal is to transform user questions into appropriate SPARQL queries that adhere to the ontology's structure while effectively leveraging the given prefixes.
|
||
|
||
Key prefixes:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
Follow these detailed steps:
|
||
|
||
1. **Entity and Intent Extraction**: Carefully extract all relevant entities from the user's question. These entities are likely terms that refer to specific categories or classes within the ontology, such as specific events (e.g., 'World War II'), locations (e.g., 'Noord-Holland province'), or object types (e.g., 'archives', 'museums', 'bibliotheken'). Determine the intent of the question, which typically falls into categories such as temporal queries (related to time), exploration queries (search and discover), or geographic queries (location-based).
|
||
|
||
2. **Understanding the Structure**: Understand the relationships stipulated by the HCO and how they represent various concepts, such as associating archives with historical events, museums with geo-locations, and libraries with temporal events like mergers.
|
||
|
||
3. **Constructing SPARQL Queries**:
|
||
- Use the extracted entities and identified intent to formulate a SPARQL query.
|
||
- Ensure the query selects the appropriate class or type for the entities (e.g., `hco:Archive`, `hco:Museum`, `hco:Library`).
|
||
- Establish necessary relationships using appropriate HCO predicates (e.g., `skos:related`, `hco:isLocatedIn`, `dct:date`) to accurately represent the question.
|
||
- Integrate necessary filters or additional service specifications for extended functionalities, such as fetching labels in specific languages or dates more recent than a provided temporal marker.
|
||
|
||
4. **Providing Explanation**: Include a brief explanation accompanying each query to elucidate the reasoning behind its structure, including how entity connections and filters reflect the user's original question.
|
||
|
||
5. **Review Feedback and Iterate**: Use feedback from previous tasks to note where improvements are necessary, particularly in entity extraction or selection of sources. Adjust your strategy to incorporate this feedback.
|
||
|
||
By strictly adhering to these instructions, ensure that all generated SPARQL queries are valid and efficient in accessing and retrieving the desired information from the Heritage Custodian Knowledge Graph using the Oxigraph endpoint.
|
||
2025/12/11 12:14:04 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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2025/12/11 12:14:04 INFO dspy.teleprompt.gepa.gepa: Iteration 21: New subsample score 2.25 is not better than old score 2.25, skipping
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||
|
||
GEPA Optimization: 4%|▍ | 120/2954 [03:51<1:24:15, 1.78s/rollouts]2025/12/11 12:14:04 INFO dspy.teleprompt.gepa.gepa: Iteration 22: Selected program 0 score: 0.6166666666666667
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 243.51it/s]2025/12/11 12:14:04 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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||
2025/12/11 12:14:11 INFO dspy.teleprompt.gepa.gepa: Iteration 22: Proposed new text for multi_hop.synthesizer.predict: Your task is to generate informative answers about heritage institutions using a specific input format. This includes synthesizing retrieved information into helpful and accurate responses that cite sources and include relevant details. Pay close attention to the following guidelines:
|
||
|
||
1. **Input Structure:**
|
||
- **Question**: This is the specific inquiry about a heritage institution, which can vary in intent. Examples include geographic location, statistical data, historical facts, etc.
|
||
- **Context**:
|
||
- **Query Intent**: Identify whether the question is about 'entity_lookup', 'geographic', 'statistical', or another category.
|
||
- **Entities**: Extract all relevant entities mentioned in the question (e.g., 'Rijksmuseum', 'Amsterdam', 'musea').
|
||
- **Sources**: Use specified sources such as 'sparql', 'qdrant', and others as necessary for retrieving reliable information.
|
||
- **Language**: The language of the question, which indicates the language in which the response should be provided.
|
||
|
||
2. **Output Generation:**
|
||
- **Reasoning**: Provide a brief explanation supporting the answer based on well-known or sourced data.
|
||
- **Answer**: Formulate a clear and concise response that directly addresses the question.
|
||
- **Citations**: List the specific sources from which information was retrieved to formulate the answer.
|
||
- **Confidence Score**: Assign a confidence level to your response to indicate reliability based on the quality of sources and the clarity of the information retrieved.
|
||
- **Follow-up Suggestions**: Suggest 2-3 related questions that can further inform the user’s understanding.
|
||
|
||
3. **Accuracy and Completeness:**
|
||
- Ensure the intent classification accurately matches the nature of the question.
|
||
- Thoroughly extract all mentioned entities to ensure no critical information is omitted.
|
||
- Select appropriate sources based on the context and nature of the inquiry.
|
||
- All expected terms and entities should be present in the answer to meet completeness standards.
|
||
|
||
4. **Expected Challenges:**
|
||
- Misclassification of intent may occur; strive for precision by familiarizing yourself with distinct intent types.
|
||
- Entities can sometimes be missed; practice thorough entity recognition to improve accuracy.
|
||
- Source selection needs to be precise, using only relevant and designated databases to maintain answer integrity.
|
||
|
||
Using these structured guidelines, generate responses that are accurate, resourceful, and aligned with the input structure for heritage institution-related questions.
|
||
2025/12/11 12:14:11 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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||
2025/12/11 12:14:11 INFO dspy.teleprompt.gepa.gepa: Iteration 22: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 4%|▍ | 126/2954 [03:57<1:14:45, 1.59s/rollouts]2025/12/11 12:14:11 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Selected program 0 score: 0.6166666666666667
|
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 72.16it/s]
|
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 107.83it/s]2025/12/11 12:14:11 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:14:26 INFO dspy.teleprompt.gepa.gepa: Iteration 23: Proposed new text for answer_gen.predict: Your task is to generate informative, fact-based answers about heritage institutions, specifically focusing on museums, by utilizing a combination of structured data retrieval and synthesis for accuracy and detail. The process should involve the following steps:
|
||
|
||
1. **Understand the Context and Intent**:
|
||
- Identify the intent of the query, which can typically be categorized as 'statistical', 'geographic', or 'entity_lookup'.
|
||
- Extract entities from the question, such as specific museum names, cities, or regions, as these are critical for generating the correct response.
|
||
|
||
2. **Select Appropriate Sources**:
|
||
- Utilize the designated sources for retrieving information. The primary sources include 'sparql' and 'qdrant' for data specific queries:
|
||
- 'sparql' can be used for retrieving structured data from databases about specific museums and heritage institutions.
|
||
- 'qdrant' can help leverage more generalized data.
|
||
- Ensure you're retrieving the most relevant data for crafting the answer.
|
||
|
||
3. **Synthesize Information and Construct the Response**:
|
||
- Formulate logical reasoning based on the retrieved data to provide a well-rounded and informative answer.
|
||
- Present the answer in a clear, concise manner, including all expected terms and addressing the entities and context of the query.
|
||
|
||
4. **Citations and Confidence Levels**:
|
||
- Provide citations for the data sources used to enhance credibility and traceability of the provided information.
|
||
- Express a confidence level in your response, indicating how certain you are about the accuracy of the information.
|
||
|
||
5. **Suggest Follow-up Inquiries**:
|
||
- Generate potential follow-up questions related to the initial query to assist in expanding knowledge further.
|
||
|
||
6. **General Instructions for Improved Performance**:
|
||
- Pay special attention to improving entity extraction to avoid missing keywords or crucial entities.
|
||
- Ensure that the intent classification is precise to select the right approach for addressing the query.
|
||
- Continuously update and refine the knowledge base to be aware of possible changes in the status or relevance of museums and heritage institutions over time.
|
||
|
||
By following these instructions, you will provide users with highly informative and accurate answers that suit their informational needs regarding heritage institutions, specifically museums, enabling them to gain deeper insights and make informed decisions.
|
||
2025/12/11 12:14:32 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:14:32 INFO dspy.teleprompt.gepa.gepa: Iteration 23: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 4%|▍ | 132/2954 [04:19<1:41:35, 2.16s/rollouts]2025/12/11 12:14:32 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Selected program 0 score: 0.6166666666666667
|
||
|
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||
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Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 32.79it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 59.89it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 89.56it/s]2025/12/11 12:14:32 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:14:45 INFO dspy.teleprompt.gepa.gepa: Iteration 24: Proposed new text for viz_selector: Instructions for Selecting Visualization for Heritage Data:
|
||
|
||
Objective:
|
||
The task is to choose an appropriate visualization type for heritage data. This decision should be based on the query's intent, the characteristics of the data, and the result count.
|
||
|
||
Instructions:
|
||
|
||
1. **Understanding Input Format:**
|
||
- Each task will contain the following inputs:
|
||
- **Question**: A query that specifies what information the user is seeking, often related to heritage or historical data.
|
||
- **Intent**: The underlying purpose of the query, which could be "exploration," "temporal," or "geographic."
|
||
- **Schema Fields**: The structure of the data available, which may include fields like 'name', 'type', 'city', 'country', 'lat', 'lon'.
|
||
- **Result Count**: The number of data entries returned that fulfill the query.
|
||
|
||
2. **Extracting Entities:**
|
||
- Ensure all relevant entities are extracted from the question. Pay attention to historical events, locations, or key terms related to the heritage context.
|
||
- Example terms to extract include "World War II," "libraries," "museums," specific locations like "Noord-Holland," and years or temporal markers like "2000".
|
||
|
||
3. **Selecting Visualization Type:**
|
||
- Based on intent:
|
||
- **Exploration**: Use visualizations that allow users to explore data spatially or contextually, such as maps.
|
||
- **Temporal**: Select visualizations that effectively represent changes over time, such as timelines.
|
||
- **Geographic**: Maps are suitable for displaying spatial data that involves geographic locations.
|
||
|
||
4. **Configuring Visualization:**
|
||
- Provide a configuration object for the chosen visualization. Include elements such as map type (e.g., heatmap or standard), color schemes, axes titles for timelines, and data points.
|
||
- Ensure the configuration is appropriate for the number of results. For fewer entries (e.g., around 10), ensure clarity and prevent clutter.
|
||
- Example configurations might include setting marker sizes and colors for maps or specifying year ranges for timelines.
|
||
|
||
5. **Reasoning:**
|
||
- Provide a brief reasoning for selecting the visualization. Mention how it suits the intended purpose and how it handles the data signature effectively.
|
||
- Address how the visualization helps in understanding relationships, trends, or distributions based on the query's focus.
|
||
|
||
6. **Quality Control:**
|
||
- Cross-check entity extraction against the input question to ensure all significant entities are captured.
|
||
- Verify the sources you select for visualizations are appropriate to the input data fields and intended query.
|
||
|
||
By following these steps, ensure the visualization recommendations are accurate, informative, and enhance user understanding of heritage data.
|
||
2025/12/11 12:14:52 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:14:52 INFO dspy.teleprompt.gepa.gepa: Iteration 24: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 5%|▍ | 138/2954 [04:39<1:57:16, 2.50s/rollouts]2025/12/11 12:14:52 INFO dspy.teleprompt.gepa.gepa: Iteration 25: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 22.12it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 40.86it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 61.12it/s]2025/12/11 12:14:52 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:15:09 INFO dspy.teleprompt.gepa.gepa: Iteration 25: Proposed new text for router.classifier.predict: Classify the intent of a user query related to GLAM (Galleries, Libraries, Archives, Museums) heritage institutions and identify relevant entities to effectively route to the appropriate data sources and retrieval strategies.
|
||
|
||
Task Instructions:
|
||
|
||
1. **Language Identification**: Determine the language of the user's query to handle multilingual inputs effectively. Current examples are in Dutch (nl) and English (en).
|
||
|
||
2. **Intent Classification**: Classify the intent of the user's query into one of the following categories based on the content and nature of the question:
|
||
- **Statistical**: Questions seeking quantitative data, such as the number of institutions within a specific location.
|
||
- **Temporal**: Questions focusing on events or changes over time, such as mergers since a specific year.
|
||
- **Geographic**: Questions about locations, specifically asking for institutions within a certain geographic boundary.
|
||
|
||
3. **Entity Extraction**: Identify and extract key entities from the query. Common entities are:
|
||
- Names of geographic locations (e.g., ‘Amsterdam’, ‘Noord-Holland’).
|
||
- Types of institutions or events related to heritage (e.g., ‘musea’, ‘bibliotheken’).
|
||
- Temporal markers or specific years (e.g., ‘2000’).
|
||
|
||
4. **Data Source Selection**: Recommend appropriate data sources for retrieving information based on the intent and entities identified. Possible sources include:
|
||
- **PostGIS**: Often suitable for geographically-focused queries needing spatial data.
|
||
- **Qdrant**: Useful for structured queries or data enhancement.
|
||
- **SPARQL**: Effective for querying RDF data models, ideal for structured heritage data.
|
||
- **TypeDB**: Useful for complex data relationships or temporal data querying.
|
||
|
||
5. **Response Generation**: Formulate the output by clearly stating:
|
||
- Reasoning: The logic for intent classification and source recommendation.
|
||
- Intent: The classified intent category.
|
||
- Entities: A list of extracted entities.
|
||
- Sources: The data sources selected for query fulfillment.
|
||
|
||
General Strategy:
|
||
- Focus on accurately identifying the query's intent and essential entities, considering both explicit and implicit indicators within the query.
|
||
- Use examples and feedback for enhancing entity recognition and improving source selection accuracy.
|
||
- Address language-specific aspects where applicable to facilitate comprehensive understanding and response generation across different languages.
|
||
2025/12/11 12:15:26 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:15:26 INFO dspy.teleprompt.gepa.gepa: Iteration 25: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 5%|▍ | 144/2954 [05:12<2:38:46, 3.39s/rollouts]2025/12/11 12:15:26 INFO dspy.teleprompt.gepa.gepa: Iteration 26: Selected program 0 score: 0.6166666666666667
|
||
|
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|
||
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 22.31it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 44.50it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 66.63it/s]2025/12/11 12:15:26 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:15:35 INFO dspy.teleprompt.gepa.gepa: Iteration 26: Proposed new text for entity_extractor: Task: Extract heritage-specific entities from text using the CH-Annotator v1.7.0 convention.
|
||
|
||
Input Format:
|
||
- You will be provided with a textual input that may contain references to cultural heritage entities such as institutions, places, dates, and identifiers.
|
||
|
||
Objective:
|
||
- Accurately extract and categorize heritage-specific entities mentioned in the text.
|
||
|
||
Extraction Categories:
|
||
1. Institutions: Identify entities that are recognized as heritage institutions (e.g., museums, archives) and denote their type using the appropriate CH-Annotator convention (e.g., 'GRP.HER.MUS' for museums).
|
||
2. Places: Identify and extract names of places, and if possible, provide their geographical coordinates (latitude and longitude).
|
||
3. Temporal: Identify and extract dates or time periods relevant to the heritage entities.
|
||
4. Identifiers: Extract identifiers associated with heritage-specific entities.
|
||
|
||
Special Instructions:
|
||
- Ensure that all entities are extracted, including those mentioned implicitly or explicitly in the text.
|
||
- Use entity lookup capabilities to identify entities that might not have been explicitly mentioned but are inferable from the context.
|
||
- Sources should be accurately selected to match the following expectations:
|
||
- For institutions and place identification, reference the 'sparql' and 'qdrant' sources.
|
||
- Avoid using 'postgis' unless geographical coordinates need to be mapped.
|
||
- Answer completeness is crucial. Ensure that the extracted output contains all expected terms based on the input text.
|
||
|
||
Example Evaluation:
|
||
- Carefully analyze example texts and their feedback to continuously improve entity extraction accuracy and source selection.
|
||
- Address any mismatch between intent and entity extraction by refining classification and lookup mechanisms.
|
||
2025/12/11 12:15:39 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:15:39 INFO dspy.teleprompt.gepa.gepa: Iteration 26: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 5%|▌ | 150/2954 [05:26<2:22:24, 3.05s/rollouts]2025/12/11 12:15:39 INFO dspy.teleprompt.gepa.gepa: Iteration 27: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
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Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 22.83it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 44.25it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 66.14it/s]2025/12/11 12:15:39 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:15:50 INFO dspy.teleprompt.gepa.gepa: Iteration 27: Proposed new text for sparql_gen.predict: You are tasked with generating SPARQL queries for our Heritage Custodian Knowledge Graph. You should ensure that the queries work seamlessly with our Oxigraph endpoint by following several steps outlined below. Utilize your expertise in SPARQL and the Heritage Custodian Ontology (HCO) to craft these queries.
|
||
|
||
1. **Understand Inputs**:
|
||
- Analyze the natural language question provided.
|
||
- Identify and extract the intent behind the question (e.g., temporal, geographic, exploration).
|
||
- Identify and extract entities mentioned in the question.
|
||
- Evaluate the context if provided.
|
||
|
||
2. **SPARQL Query Construction**:
|
||
- Use the key prefixes provided in each SPARQL query:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
- Based on the intent, formulate a query that accurately captures the question's requirements:
|
||
- For temporal tasks, filter entities based on date conditions.
|
||
- For geographic tasks, use location-based filters to retrieve entities within specified areas.
|
||
- For exploration tasks, establish relationships between entities using relevant ontology properties.
|
||
|
||
3. **Entity Extraction**:
|
||
- Ensure accurate identification of key entities within the question. This includes extracting dates, specific locations, or notable events such as "World War II".
|
||
- Consider using named entity recognition techniques to improve accuracy in capturing all relevant entities.
|
||
|
||
4. **Validate Ontology Use**:
|
||
- Ensure the use of the Heritage Custodian Ontology (HCO) classes and properties when forming your queries.
|
||
- Use ontology concepts correctly to link entities, as seen in the handling of classes like `hco:Library`, `hco:Museum`, and `hco:Archive`.
|
||
|
||
5. **Explanation of Queries**:
|
||
- Provide a clear and concise explanation of how your SPARQL query works and what results it will yield.
|
||
- Explain any labels or service calls used in your query, especially when involving external services or language parameterization.
|
||
|
||
6. **Feedback Analysis**:
|
||
- Review any feedback from previous examples similar to the task at hand. Adjust your approach to address commonly noted issues, especially in entity extraction.
|
||
- Continuously refine query structure to align with expected terms and selected data sources.
|
||
|
||
By following these instructions, you will generate accurate and efficient SPARQL queries that fulfill the requirements of the Heritage Custodian Knowledge Graph task. Focus on precision, especially concerning ontology terms, and ensure all queries are robust and aligned with HCO specifications.
|
||
2025/12/11 12:15:55 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:15:55 INFO dspy.teleprompt.gepa.gepa: Iteration 27: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 5%|▌ | 156/2954 [05:41<2:15:48, 2.91s/rollouts]2025/12/11 12:15:55 INFO dspy.teleprompt.gepa.gepa: Iteration 28: Selected program 0 score: 0.6166666666666667
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||
|
||
|
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Average Metric: 0.45 / 1 (45.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 42.47it/s]
|
||
Average Metric: 1.65 / 3 (55.0%): 67%|██████▋ | 2/3 [00:00<00:00, 84.66it/s]
|
||
Average Metric: 1.65 / 3 (55.0%): 100%|██████████| 3/3 [00:00<00:00, 126.65it/s]2025/12/11 12:15:55 INFO dspy.evaluate.evaluate: Average Metric: 1.65 / 3 (55.0%)
|
||
2025/12/11 12:15:55 INFO dspy.teleprompt.gepa.gepa: Iteration 28: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:15:55 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:15:55 INFO dspy.teleprompt.gepa.gepa: Iteration 28: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:15:55 INFO dspy.teleprompt.gepa.gepa: Iteration 29: Selected program 0 score: 0.6166666666666667
|
||
|
||
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||
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 128.60it/s]2025/12/11 12:15:55 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:16:06 INFO dspy.teleprompt.gepa.gepa: Iteration 29: Proposed new text for multi_hop.sparql_gen.predict: Your task is to generate SPARQL queries for a heritage custodian knowledge graph, specifically designed to work with an Oxigraph endpoint. You are an expert in SPARQL and the Heritage Custodian Ontology (HCO).
|
||
|
||
The inputs for the task include:
|
||
- A question that typically pertains to geographic or statistical information about entities in the heritage domain.
|
||
- An intent that indicates the type of information requested, such as 'geographic' or 'statistical'.
|
||
- An array of entities that the question is concerned with, extracted from the question text.
|
||
- Optional context that may provide additional background information useful for query generation.
|
||
|
||
To create a valid and relevant SPARQL query:
|
||
1. Use these key prefixes:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
2. For a 'geographic' intent:
|
||
- Focus on retrieving location-related information linked to the entities provided.
|
||
- Ensure the use of properties such as `dct:spatial` or `hco:isLocatedIn`, where relevant, to fetch location data.
|
||
- Consider using geo-based filtering or relationships to refine results, such as matching specific province names.
|
||
|
||
3. For a 'statistical' intent:
|
||
- Concentrate on counting or aggregating entity occurrences, like the number of museums in a location.
|
||
- Use constructs such as COUNT to summarize data based on the type or classification of entities (e.g., museums).
|
||
|
||
4. Pay attention to the proper classification of entities and intent extraction. Ensure that all entities from the input are properly recognized and used in the query.
|
||
|
||
5. Justify each part of the SPARQL query with reasoning and provide an explanation of how the query addresses the original question.
|
||
|
||
6. Select appropriate data sources, with a focus on using 'sparql' and 'qdrant', avoiding use of incorrect data sources like 'postgis'.
|
||
|
||
Remember to always include explanations for both the SPARQL query itself and the strategy used to derive the query, ensuring that the logic aligns well with the knowledge graph's structure and intended use of the data.
|
||
|
||
Keep the expected answers clear and concise, and ensure that they meet the demands of the question by containing all expected terms. Additionally, be prepared to adjust your approach based on feedback to continually improve entity extraction and intent classification.
|
||
2025/12/11 12:16:06 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:16:06 INFO dspy.teleprompt.gepa.gepa: Iteration 29: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 6%|▌ | 165/2954 [05:52<1:45:16, 2.26s/rollouts]2025/12/11 12:16:06 INFO dspy.teleprompt.gepa.gepa: Iteration 30: Selected program 0 score: 0.6166666666666667
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 102.02it/s]2025/12/11 12:16:06 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:16:15 INFO dspy.teleprompt.gepa.gepa: Iteration 30: Proposed new text for multi_hop.synthesizer.predict: The task is to generate informative and accurate answers about heritage institutions, focusing on specific questions that involve different types of queries such as exploratory or temporal questions. The assistant should synthesize information from the provided sources and include relevant details in its response, while properly citing these sources. Additionally, the assistant may need to identify and correct any missing or incorrect entities for comprehensive data retrieval.
|
||
|
||
Key considerations and steps for the task include:
|
||
|
||
1. **Entity Extraction:** Identify and extract relevant entities from the input to better understand the context of the query and ensure accurate information retrieval. For instance, entities might include terms like 'World War II', 'archives', '2000', 'bibliotheken', etc. Proper entity extraction aids in generating responses that meet the query's needs.
|
||
|
||
2. **Source Selection:** Determine and utilize the correct data sources as indicated in the task input (e.g., 'qdrant', 'sparql', 'typedb'). This ensures that the gathered information is relevant and accurate to the query context.
|
||
|
||
3. **Query Intent and Context Analysis:** Discern the type of query (e.g., exploratory, temporal) and its context. This informs the strategy for gathering and synthesizing data. It involves understanding whether the question aims for historical exploration or focuses on temporal events like library mergers.
|
||
|
||
4. **Information Synthesis and Response Generation:** Compile the obtained information into a coherent and structured response. Responses should highlight key points relevant to the query, provide detailed explanations, and be presented in a helpful manner covering all necessary aspects of the question.
|
||
|
||
5. **Citing Sources:** Clearly cite all the sources of information used to construct the response. This builds credibility and allows for verification of the provided information.
|
||
|
||
6. **Confidence Assessment and Follow-up Suggestions:** Provide a confidence score regarding the accuracy of the response. Additionally, suggest follow-up questions that align with the original query and provide deeper insights or clarify potential ambiguities.
|
||
|
||
These steps should be followed to ensure comprehensive and informative answers are provided in the context of heritage institutions, while also being adaptable to various specific queries related to historical and cultural heritage.
|
||
2025/12/11 12:16:15 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:16:15 INFO dspy.teleprompt.gepa.gepa: Iteration 30: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 6%|▌ | 171/2954 [06:02<1:36:13, 2.07s/rollouts]2025/12/11 12:16:15 INFO dspy.teleprompt.gepa.gepa: Iteration 31: Selected program 0 score: 0.6166666666666667
|
||
|
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|
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 127.56it/s]2025/12/11 12:16:15 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:16:24 INFO dspy.teleprompt.gepa.gepa: Iteration 31: Proposed new text for answer_gen.predict: Task Instruction for the Assistant:
|
||
|
||
1. **Objective**: Generate detailed and informative answers about heritage institutions based on user queries. The task includes synthesizing information into helpful, accurate responses, citing sources, and including relevant details.
|
||
|
||
2. **Input Format**:
|
||
- **Question**: A natural language query about heritage institutions.
|
||
- **Context**: Information about the user's query intent (e.g., exploration, temporal, geographic) and any identified entities.
|
||
- **Sources**: List of potential data sources to be used (e.g., 'qdrant', 'sparql', 'typedb', 'postgis').
|
||
- **Language**: The language code of the user's query (e.g., 'en' for English, 'nl' for Dutch).
|
||
|
||
3. **Task Details**:
|
||
- **Entity Identification**: Ensure accurate extraction of entities mentioned in the question to better tailor the response. This includes recognizing key terms and phrases relevant to the query.
|
||
- **Source Selection**: Choose appropriate sources based on the context and intent of the query. Ensure sources are relevant to the particular domain, be it geographical information, temporal data, or specific database content.
|
||
- **Answer Synthesis**: Produce responses that not only address the specifics of the query but also provide additional context and details, such as notable examples and broader implications (e.g., the significance of the archives or libraries in their respective fields).
|
||
- **Citation and Verification**: Include citations from the used sources to support the information provided. Aim for high confidence levels in the responses to ensure accuracy and reliability.
|
||
- **Follow-Up Suggestions**: Generate meaningful follow-up questions that can help the user explore the topic further. These should be based on the nuances of the original query and the provided response.
|
||
|
||
4. **Examples & Considerations**:
|
||
- Example scenarios include identifying archives related to historical events, recognizing library mergers over a given time period, or listing museums within a specific geographical region.
|
||
- Always ensure to handle multi-lingual queries effectively by using the language context provided.
|
||
- Be aware of the domain-specific nuances, such as understanding different types of heritage institutions (archives, museums, libraries) and their unique attributes or functions.
|
||
2025/12/11 12:16:33 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:16:33 INFO dspy.teleprompt.gepa.gepa: Iteration 31: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 6%|▌ | 177/2954 [06:19<1:46:40, 2.30s/rollouts]2025/12/11 12:16:33 INFO dspy.teleprompt.gepa.gepa: Iteration 32: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 147.11it/s]2025/12/11 12:16:33 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:16:40 INFO dspy.teleprompt.gepa.gepa: Iteration 32: Proposed new text for viz_selector: Task: Selecting Appropriate Visualization for Heritage Data
|
||
|
||
Your task is to choose the most suitable visualization type for heritage data queries. Follow these steps to ensure your choice is aligned with the data characteristics and query requirements:
|
||
|
||
1. **Input Structure:**
|
||
- **Question:** A query posed by the user regarding heritage data.
|
||
- **Intent:** The purpose of the query (e.g., geographic, entity_lookup, statistical).
|
||
- **Schema Fields:** A list of fields available in the dataset (e.g., ['name', 'type', 'city', 'country', 'lat', 'lon']).
|
||
- **Result Count:** The number of results returned by the query.
|
||
|
||
2. **Determine Visualization Type:**
|
||
- Consider the **intent** of the query:
|
||
- **Geographic:** Use a map visualization if the question involves physical locations and the data includes latitude and longitude.
|
||
- **Entity Lookup:** Use visual tools like lists or highlighted elements for specific entities.
|
||
- **Statistical:** Opt for charts or tables to present summaries or statistical information.
|
||
|
||
3. **Entities and Sources:**
|
||
- Accurate entity extraction is crucial. For example, identify entities such as 'Rijksmuseum', 'Amsterdam', and 'musea' correctly from the question.
|
||
- Choose the relevant data sources for querying the information. Possible sources might include 'sparql', 'qdrant', or 'postgis', depending on the data type and availability.
|
||
|
||
4. **Visualization Configuration:**
|
||
- For **maps**, ensure a configuration that enables the plotting of geographical points using lat/lon.
|
||
- For **tables**, include essential columns and consider additional options like pagination for large datasets.
|
||
- For **charts**, select suitable chart types according to the statistical nature of the query.
|
||
|
||
5. **Reasoning:**
|
||
- Provide a clear rationale for your visualization choice, taking into account the query's intent, schema fields, and result count. Explain how the chosen visualization will effectively communicate the necessary information.
|
||
|
||
By following this structured approach, you'll be able to select the most appropriate visualization type for heritage data queries, thereby aiding in effective data representation and user comprehension.
|
||
2025/12/11 12:16:43 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:16:43 INFO dspy.teleprompt.gepa.gepa: Iteration 32: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 6%|▌ | 183/2954 [06:30<1:39:54, 2.16s/rollouts]2025/12/11 12:16:43 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Selected program 0 score: 0.6166666666666667
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||
|
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|
||
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 113.44it/s]2025/12/11 12:16:44 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:16:51 INFO dspy.teleprompt.gepa.gepa: Iteration 33: Proposed new text for router.classifier.predict: Task: Classify the Intent of a Heritage Institution Query for GLAM Institutions
|
||
|
||
You are an expert in GLAM (Galleries, Libraries, Archives, Museums) heritage institutions. Your task is to classify the intent of a user's query to route it to the appropriate data sources and retrieval strategies effectively. Follow these instructions carefully to ensure accuracy and comprehensiveness in your response:
|
||
|
||
1. **Intent Classification**:
|
||
- Determine the primary intent of the query. The potential intent categories include:
|
||
- **Geographic**: Queries seeking information based on location (e.g., "Which museums are in Noord-Holland province?").
|
||
- **Temporal**: Queries focused on events or information over a specific timeframe (e.g., "Welke bibliotheken zijn gefuseerd sinds 2000?").
|
||
- **Exploration**: Queries seeking comprehensive information on a topic (e.g., "Show me archives related to World War II").
|
||
|
||
2. **Entity Extraction**:
|
||
- Identify and extract key entities mentioned in the query. These may include:
|
||
- Geographic locations (e.g., "Noord-Holland")
|
||
- Dates or time periods (e.g., "since 2000")
|
||
- Specific institutions or themes (e.g., "museums", "World War II", "bibliotheken")
|
||
- Ensure that all relevant entities are extracted accurately to improve query handling.
|
||
|
||
3. **Data Source Recommendation**:
|
||
- Propose relevant data sources based on the query intent and identified entities. Possible data sources include:
|
||
- **SPARQL**: Utilized for complex queries that involve semantic data through a query language.
|
||
- **Qdrant**: Acts as a vector database, useful for similarity and recommendation tasks.
|
||
- **TypeDB**: Employed for deep knowledge queries which need logical processing.
|
||
- **PostGIS**: Suitable for geographic queries requiring spatial data analysis.
|
||
|
||
4. **Output Requirements**:
|
||
- Provide clear and concise output for:
|
||
- **Reasoning**: Explain the rationale behind the identified intent.
|
||
- **Intent**: State the determined intent of the query.
|
||
- **Entities**: List all key entities extracted from the question.
|
||
- **Sources**: Propose appropriate data sources for query resolution.
|
||
|
||
Use your expertise to adapt to the query context and ensure all relevant elements are considered. Aim for thoroughness and precision in recognizing intent, extracting entities, and recommending data sources to enhance the utility of GLAM institution resources for users.
|
||
2025/12/11 12:17:05 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:17:05 INFO dspy.teleprompt.gepa.gepa: Iteration 33: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 6%|▋ | 189/2954 [06:51<1:57:40, 2.55s/rollouts]2025/12/11 12:17:05 INFO dspy.teleprompt.gepa.gepa: Iteration 34: Selected program 0 score: 0.6166666666666667
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||
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||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 144.42it/s]2025/12/11 12:17:05 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:17:12 INFO dspy.teleprompt.gepa.gepa: Iteration 34: Proposed new text for entity_extractor: Task Instructions:
|
||
|
||
Objective: Extract heritage-specific entities from given text based on the CH-Annotator v1.7.0 convention. The goal is to accurately identify and categorize relevant entities such as institutions, places, dates, identifiers, and relationships.
|
||
|
||
Components to Extract:
|
||
|
||
1. Institutions:
|
||
- Identify institutions such as museums or other heritage sites.
|
||
- Record the name and type of institution. Use the type 'GRP.HER.MUS' for museums.
|
||
- Example: 'Rijksmuseum' should be classified as an institution with the type 'GRP.HER.MUS'.
|
||
|
||
2. Places:
|
||
- Recognize geographic locations such as cities, provinces, or other significant places.
|
||
- Include the name of the place and its geographical coordinates (latitude and longitude if provided).
|
||
- Example: 'Amsterdam' should be identified as a place and linked to its specific coordinates: {'latitude': 52.3676, 'longitude': 4.9041}.
|
||
|
||
3. Temporal:
|
||
- Extract any date-related information from the text.
|
||
|
||
4. Identifiers:
|
||
- Look for any unique identifiers related to heritage entities.
|
||
|
||
Entity Relationships:
|
||
- Identify relationships between extracted entities if indicated in the text.
|
||
|
||
Sources:
|
||
- Utilize appropriate sources for extraction: 'sparql' and 'qdrant' are expected for most queries, whereas 'postgis' may not always be relevant.
|
||
|
||
Hints for Improved Extraction:
|
||
- Ensure that all entities mentioned in the text are captured fully, without omissions.
|
||
- Accurate classification of the task intent is crucial: questions about the number or presence of institutions should be identified as 'entity_lookup'.
|
||
- Focus on capturing all mentioned entities, even when they do not directly answer the query, as these are valuable for database mapping.
|
||
|
||
Consider Feedback:
|
||
- Address any missing entities from prior attempts.
|
||
- Improve accuracy of geographic and institutional classifications.
|
||
- Adapt entity extraction techniques to better align with feedback, ensuring completeness and relevance of extracted data.
|
||
2025/12/11 12:17:16 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:17:16 INFO dspy.teleprompt.gepa.gepa: Iteration 34: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 7%|▋ | 195/2954 [07:03<1:49:43, 2.39s/rollouts]2025/12/11 12:17:16 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Selected program 0 score: 0.6166666666666667
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||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 185.40it/s]2025/12/11 12:17:16 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:17:26 INFO dspy.teleprompt.gepa.gepa: Iteration 35: Proposed new text for sparql_gen.predict: You are tasked with generating SPARQL queries for a Heritage Custodian knowledge graph that can be executed on our Oxigraph endpoint. You are expected to be knowledgeable in both SPARQL and the Heritage Custodian Ontology (HCO) to ensure the correctness and relevance of the queries. Here is your task broken down into steps:
|
||
|
||
1. **Understand the Question**: For each task, you will be given a question that pertains to the Heritage Custodian knowledge graph. Carefully analyze the question to identify the intent. Common intents include statistical (e.g., counting numbers), geographic (e.g., finding locations), entity lookup (e.g., finding specific details about entities), and exploration (e.g., seeking relationships or connections between entities).
|
||
|
||
2. **Entity Extraction**: Identify and extract entities from the question. Entities usually include specific names of places, institutions, or significant events (e.g., 'Amsterdam', 'Rijksmuseum', 'World War II'). Note that entity names may have variations such as capitalization differences (e.g., 'Amsterdam' vs. 'amsterdam') which are crucial for the query's success.
|
||
|
||
3. **Construct the SPARQL Query**:
|
||
- Use the provided key prefixes to build your query:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
- Identify the required ontology classes and properties based on the identified intent and entities:
|
||
- For statistical and count-related questions, use `COUNT(?variable) AS ?count`.
|
||
- For geographic questions, consider using location properties like `dct:spatial` or `dct:location`.
|
||
- For entity lookup, use specific entity IRIs directly, such as `hco:Rijksmuseum`.
|
||
- For exploration and relationship-based queries, utilize properties like `skos:related` to establish connections.
|
||
|
||
4. **Explain the Query**: After constructing the SPARQL query, provide a clear explanation of how the query works and why it's suitable for the question. Highlight what the query aims to retrieve (e.g., labels, properties) and the relationship it leverages within the HCO ontology.
|
||
|
||
5. **Quality Assurance**: Validate your query against expected areas of improvement:
|
||
- Ensure intent is correctly identified.
|
||
- Ensure all relevant entities from the question are extracted and accounted for.
|
||
- Ensure the SPARQL query references correct ontology classes and properties as specified by HCO.
|
||
- Confirm that the explanation accurately reflects the query's operation and output.
|
||
|
||
This procedure will ensure the queries are both syntactically and semantically valid, effectively retrieving the needed information from the knowledge graph. Be concise, clear, and accurate in each output to maintain the quality of responses.
|
||
2025/12/11 12:17:31 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:17:31 INFO dspy.teleprompt.gepa.gepa: Iteration 35: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 7%|▋ | 201/2954 [07:18<1:50:25, 2.41s/rollouts]2025/12/11 12:17:31 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Selected program 0 score: 0.6166666666666667
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 167.10it/s]2025/12/11 12:17:31 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:17:31 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:17:31 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:17:31 INFO dspy.teleprompt.gepa.gepa: Iteration 36: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:17:31 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Selected program 0 score: 0.6166666666666667
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 40.09it/s]2025/12/11 12:17:31 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:17:44 INFO dspy.teleprompt.gepa.gepa: Iteration 37: Proposed new text for multi_hop.sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries that work with our Oxigraph endpoint based on specific user input questions. The queries need to effectively extract the desired data from a knowledge graph that uses the Heritage Custodian Ontology. Here's a detailed description of the task:
|
||
|
||
- **Input Format:**
|
||
- Each input will include the following fields:
|
||
- `question`: A natural language query related to the heritage custodian knowledge graph.
|
||
- `intent`: The type of question, such as "statistical", "temporal", or "exploration".
|
||
- `entities`: A list of entities recognized from the question.
|
||
- `context`: Any additional context if available (might be empty).
|
||
|
||
- **Task Requirements:**
|
||
- **Entity Extraction:** Accurately extract and identify all relevant entities from the question. Map these correctly to the ontology where possible.
|
||
- **SPARQL Query Construction:** Use the given prefixes in your queries:
|
||
```sparql
|
||
PREFIX hco: <https://w3id.org/hco/>
|
||
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
PREFIX dct: <http://purl.org/dc/terms/>
|
||
```
|
||
- **Intent Understanding:** Correctly interpret the intent of the question to guide the query design. Adjust the query structure based on whether the question is statistical (e.g., counts), temporal (e.g., dates), or exploratory (e.g., finding related items).
|
||
- **Knowledge of Ontology Concepts:**
|
||
- Use entities and relations accurately. For instance, use `hco:Museum` for museums or `hco:Library` for libraries.
|
||
- Leverage `dct:location`, `dct:date`, `skos:related`, and similar properties relevant to the query.
|
||
- **Output Requirements:**
|
||
- The query should be syntactically correct and should align with the question intent.
|
||
- Provide a reasoning section explaining the logic behind the constructed query.
|
||
- Offer an explanation section to clarify how the query addresses the question.
|
||
|
||
- **Handling Feedback:**
|
||
- Review all feedback provided on generated queries, especially regarding missing entities or incorrect mappings. Continuously improve entity extraction and ontology alignment based on this feedback.
|
||
- Aim to have all expected terms from the question reflected in your SPARQL queries.
|
||
|
||
- **General Strategy:**
|
||
- Understand the semantic structure of the question to guide SPARQL query construction.
|
||
- Ensure that the queries are efficient and only retrieve necessary data points, filtering out any unrelated results.
|
||
- Maintain consistency across different types of questions by adhering to best practices in ontology querying.
|
||
|
||
Remember, the goal is to provide precise and accurate SPARQL queries that facilitate the exploration and analysis of the cultural heritage knowledge graph. Always verify that your responses are complete and address all aspects of the user's query.
|
||
2025/12/11 12:17:44 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:17:44 INFO dspy.teleprompt.gepa.gepa: Iteration 37: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 7%|▋ | 210/2954 [07:31<1:33:31, 2.04s/rollouts]2025/12/11 12:17:44 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Selected program 0 score: 0.6166666666666667
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||
|
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|
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||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 36.42it/s]
|
||
Average Metric: 1.65 / 3 (55.0%): 67%|██████▋ | 2/3 [00:00<00:00, 66.46it/s]
|
||
Average Metric: 1.65 / 3 (55.0%): 100%|██████████| 3/3 [00:00<00:00, 99.33it/s]2025/12/11 12:17:44 INFO dspy.evaluate.evaluate: Average Metric: 1.65 / 3 (55.0%)
|
||
2025/12/11 12:17:53 INFO dspy.teleprompt.gepa.gepa: Iteration 38: Proposed new text for multi_hop.synthesizer.predict: Instruction for the Assistant:
|
||
|
||
1. **Task Overview**: Your task is to generate informative and accurate answers about heritage institutions based on given questions. The answers should synthesize information retrieved from designated sources and include relevant details with proper citations.
|
||
|
||
2. **Input Format**:
|
||
- **Question**: A specific query related to heritage institutions. It can range from geographic location inquiries to entity-specific information.
|
||
- **Context**: Provides the intent behind the query and identifies entities associated with the question. This helps in tailoring the response accurately.
|
||
- **Sources**: Lists the datasets or information repositories that should be consulted for generating the response. Typical sources include 'postgis', 'qdrant', and 'sparql'.
|
||
- **Language**: Specifies the language in which the answer should be provided (e.g., 'en' for English).
|
||
|
||
3. **Response Generation**:
|
||
- **Reasoning**: Begin with an explanatory segment that summarizes the interpreted context and establishes the foundation for your answer. This should logically connect the question to the knowledge you present.
|
||
- **Answer**: Provide a direct response to the query, incorporating specifics such as names, locations, or attributes relevant to the heritage institutions.
|
||
- **Citations**: Clearly reference the sources from which the information was drawn, ensuring transparency and reliability.
|
||
|
||
4. **Accuracy and Relevance**:
|
||
- Ensure that the intent and entities mentioned in the context are accurately recognized and reflected in your answer.
|
||
- Even when general knowledge is leveraged, validate its relevance and accuracy against the mentioned sources.
|
||
- Anticipate potential follow-up questions and suggest them, expanding on the information provided.
|
||
|
||
5. **Additional Considerations**:
|
||
- Intent Classification: Accurately determine the query intent (e.g., 'geographic' or 'entity_lookup') to improve the precision of the response.
|
||
- Entity Extraction: Identify and incorporate key entities from the query to enhance the specificity of your answer.
|
||
- Correct Source Usage: Ensure that the sources listed in your citations align with those expected for the information provided.
|
||
|
||
By adhering to these guidelines, your responses will provide the user with helpful, precise, and verifiable information about heritage institutions.
|
||
2025/12/11 12:17:53 INFO dspy.evaluate.evaluate: Average Metric: 1.65 / 3 (55.0%)
|
||
2025/12/11 12:17:53 INFO dspy.teleprompt.gepa.gepa: Iteration 38: New subsample score 1.65 is not better than old score 1.65, skipping
|
||
|
||
GEPA Optimization: 7%|▋ | 216/2954 [07:39<1:25:26, 1.87s/rollouts]2025/12/11 12:17:53 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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||
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|
||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 22.96it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 45.64it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 68.22it/s]2025/12/11 12:17:53 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:18:06 INFO dspy.teleprompt.gepa.gepa: Iteration 39: Proposed new text for answer_gen.predict: Your task is to generate informative and detailed answers about heritage institutions. Follow these steps to provide accurate and comprehensive responses:
|
||
|
||
1. **Input Analysis**:
|
||
- Determine the primary intent of the query (exploration, statistical, geographic, etc.) based on the question.
|
||
- Identify and extract all entities related to the query (e.g., specific museums, locations, historical events).
|
||
|
||
2. **Source Selection**:
|
||
- Choose appropriate data sources based on the intent and entities identified. For example:
|
||
- Use 'sparql' and 'qdrant' for general exploration and statistical queries.
|
||
- Choose 'sparql', 'qdrant', or 'postgis' for geographic information, though prefer 'sparql' and 'qdrant' for entity lookup.
|
||
|
||
3. **Information Synthesis**:
|
||
- Combine and integrate information from multiple sources to create a detailed response.
|
||
- Ensure your answers include all relevant details and are aligned with the query's intent.
|
||
|
||
4. **Citation and Confidence**:
|
||
- Always cite your sources to provide transparency and reliability in your responses.
|
||
- Indicate a confidence level based on the accuracy and relevance of the information.
|
||
|
||
5. **Response Composition**:
|
||
- Address all aspects of the query and provide additional context when relevant.
|
||
- Include follow-up questions that might interest the user or expand on the provided information.
|
||
|
||
6. **Specific Guidance**:
|
||
- For queries related to historic events (e.g., World War II archives), provide information about key archives, their characteristics, and their significance.
|
||
- For questions about the number of institutions (e.g., musea in a city), offer recent statistics and notable examples.
|
||
- For geographic queries (e.g., location of a museum), ensure to confirm the exact location and add context about the institution.
|
||
|
||
7. **Quality Assurance**:
|
||
- Verify intent classification to ensure accurate and contextual responses.
|
||
- Double-check entity extraction to capture all relevant entities in the query.
|
||
|
||
In summary, make sure to address the query comprehensively, use appropriate sources, accurately extract entities, and create informative responses that are well-cited and confidently presented.
|
||
2025/12/11 12:18:15 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:18:15 INFO dspy.teleprompt.gepa.gepa: Iteration 39: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 8%|▊ | 222/2954 [08:01<1:47:41, 2.36s/rollouts]2025/12/11 12:18:15 INFO dspy.teleprompt.gepa.gepa: Iteration 40: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 29.89it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 44.77it/s]2025/12/11 12:18:15 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:18:27 INFO dspy.teleprompt.gepa.gepa: Iteration 40: Proposed new text for viz_selector: Task: Select the appropriate visualization for heritage data presentations.
|
||
|
||
Objective:
|
||
Your task is to choose and configure the best visualization type for heritage-related data queries. Your selection should be based on the following criteria: the intent of the query, the characteristics of the data provided (like schema fields), and the number of results.
|
||
|
||
Instructions:
|
||
|
||
1. **Identify Query Intent**:
|
||
- Analyze the question and determine the intent. Common intents include:
|
||
- Geographic: Queries about locations or spatial distribution.
|
||
- Temporal: Queries focusing on time-related data changes.
|
||
|
||
2. **Consider Data Characteristics**:
|
||
- Review the `schema_fields` provided. This information will guide you in understanding what type of data visualization will best represent the data. For instance:
|
||
- Fields like `lat` and `lon` suggest a need for geographic representations.
|
||
- Temporal data requires a visualization type that can effectively showcase changes over time.
|
||
|
||
3. **Evaluate Result Count**:
|
||
- The number of results (`result_count`) can impact the choice of visualization. Ensure that the visualization type can effectively display the given quantity of data without becoming cluttered or losing clarity.
|
||
|
||
4. **Select Visualization Type**:
|
||
- Based on the intent, data characteristics, and result count, choose an appropriate visualization type.
|
||
- Map: Ideal for geographic distributions when data includes location-based fields.
|
||
- Timeline: Suited for temporal data that needs to show change over time.
|
||
|
||
5. **Configure Visualization Settings**:
|
||
- Provide a configuration for the selected visualization type. This should include:
|
||
- A title that accurately reflects the focus of the data visualization.
|
||
- Axis titles which are descriptive of the data presented (e.g., 'Year' for time data on a timeline).
|
||
- If applicable, include sample data points to demonstrate the structure expected in the visualization.
|
||
|
||
6. **Entity Extraction**:
|
||
- Ensure accurate entity extraction from the query. Key entities related to the task question must be identified and incorporated into the configuration settings. These can be geographical names, time periods, etc.
|
||
|
||
7. **Determine Data Sources**:
|
||
- Correctly identify potential data sources based on the query. Understand the context in which different databases might be used, including:
|
||
- Geographic data could draw from sources like 'postgis'.
|
||
- Heritage or cultural datasets might be sourced from databases like 'sparql' and 'qdrant'.
|
||
|
||
8. **Verify Completeness**:
|
||
- Ensure that the final output includes all expected terms and covers all elements required by the query. Cross-check against task feedback to improve upon any past oversights.
|
||
|
||
The intent is to create insights and clarity through visual representation, emphasizing user understanding and engagement through the best visualization means possible for the heritage data context.
|
||
2025/12/11 12:18:37 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:18:37 INFO dspy.teleprompt.gepa.gepa: Iteration 40: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 8%|▊ | 228/2954 [08:24<2:05:44, 2.77s/rollouts]2025/12/11 12:18:37 INFO dspy.teleprompt.gepa.gepa: Iteration 41: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 45.43it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 67.98it/s]2025/12/11 12:18:37 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:18:46 INFO dspy.teleprompt.gepa.gepa: Iteration 41: Proposed new text for router.classifier.predict: Classify the intent of a heritage institution query with precision.
|
||
|
||
You are an expert in GLAM (Galleries, Libraries, Archives, Museums) heritage institutions. Your task is to accurately classify the user's query intent, identify any relevant entities, and suggest appropriate data sources and retrieval strategies. The classifications should assist in routing the query to the correct systems and datasets for heritage information. Here is how you should approach each query:
|
||
|
||
1. **Intent Classification**:
|
||
- Analyze the user's query to determine the underlying intent. Common intents include:
|
||
- **Entity Lookup**: When the query concerns identifying specific entities such as names or details of institutions.
|
||
- **Geographic**: Queries concerning physical locations of institutions or cultural heritage sites.
|
||
- **Exploration**: Queries aimed at researching or discovering information related to specific historical events or themes.
|
||
|
||
2. **Entity Extraction**:
|
||
- Extract essential entities from the query that are relevant to heritage institutions. Key entities may include:
|
||
- Institution names (e.g., 'Rijksmuseum', 'museums').
|
||
- Geographic locations (e.g., 'Noord-Holland').
|
||
- Historical events or themes (e.g., 'World War II').
|
||
|
||
3. **Source Suggestions**:
|
||
- Recommend appropriate data sources and retrieval strategies based on the query intent. Common data sources and systems include:
|
||
- **SPARQL** for querying structured data often used in semantic web and linked data environments.
|
||
- **Qdrant** for vector similarity search, useful in exploration tasks involving large datasets.
|
||
- Avoid recommending sources like 'postgis' unless geographic spatial analysis is definitively required.
|
||
|
||
Your responses should reflect a subtle understanding of heritage contexts and data systems applicable to the GLAM sector. Use past feedback to improve entity extraction and intent matching to refine your process continuously.
|
||
2025/12/11 12:18:48 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:18:48 INFO dspy.teleprompt.gepa.gepa: Iteration 41: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 8%|▊ | 234/2954 [08:35<1:52:46, 2.49s/rollouts]2025/12/11 12:18:48 INFO dspy.teleprompt.gepa.gepa: Iteration 42: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 21.82it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 43.51it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 65.12it/s]2025/12/11 12:18:48 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:18:58 INFO dspy.teleprompt.gepa.gepa: Iteration 42: Proposed new text for entity_extractor: Task: Extract heritage-specific entities from text
|
||
|
||
Objective: To annotate text with specific categories of entities as per the CH-Annotator v1.7.0 convention. The focus is on accurately identifying and categorizing cultural heritage entities such as institutions, places, temporal markers, identifiers, and relationships mentioned within the text.
|
||
|
||
1. Input Format:
|
||
- Provide the input text from which the entities need to be extracted.
|
||
|
||
2. Output Categories:
|
||
- Institutions: Identify and extract entities related to cultural heritage institutions such as museums, libraries, and other organizations. These should be listed distinctly in the 'institutions' category.
|
||
- Places: Extract geographical entities and locations mentioned in the text, even if they are implicit or require inference. Each place should include its name and, if available, corresponding coordinates in the 'places' category.
|
||
- Temporal: Identify and extract temporal information within the text such as dates, specific periods, and events related to the context. These should be detailed in the 'temporal' category.
|
||
- Identifiers: Recognize unique identifiers associated with cultural heritage entities. If mentioned, they should be included under the 'identifiers' category.
|
||
- Relationships: Capture and detail relationships between the entities mentioned in the text that are relevant to cultural heritage, even though this may not have been emphasized in feedback, it could be part of the CH-Annotator v1.7.0 convention.
|
||
|
||
3. Considerations:
|
||
- Pay special attention to the complete and accurate extraction of all mentioned entities and information.
|
||
- Review examples and feedback to improve entity detection and ensure no entities are missed. For instance, ensure that both 'Amsterdam' and 'musea' are identified even when they are not explicitly tagged.
|
||
- If an event or concept is described but not explicitly named as an entity in the feedback, still attempt to capture all aspects for thoroughness.
|
||
- Apply a strategy to identify the intent behind text queries to recognize pertinent entities contextually, ensuring all expected terms and concepts are covered.
|
||
2025/12/11 12:19:00 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:19:00 INFO dspy.teleprompt.gepa.gepa: Iteration 42: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 8%|▊ | 240/2954 [08:46<1:45:14, 2.33s/rollouts]2025/12/11 12:19:00 INFO dspy.teleprompt.gepa.gepa: Iteration 43: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 11335.96it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 472.78it/s]
|
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 682.26it/s]2025/12/11 12:19:00 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
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2025/12/11 12:19:11 INFO dspy.teleprompt.gepa.gepa: Iteration 43: Proposed new text for sparql_gen.predict: You are tasked with generating SPARQL queries compatible with the Heritage Custodian Ontology (HCO) and an Oxigraph endpoint to retrieve specific information from a heritage custodian knowledge graph. You should leverage your expertise in SPARQL and the HCO to synthesize accurate and contextually appropriate queries.
|
||
|
||
Key part of the task:
|
||
1. **Understand the Query Intent**: Identify the intent of the query from the provided inputs. Possible intents can include exploration of entities, statistical data retrieval, or geographic data retrieval.
|
||
|
||
2. **Entity Extraction**: Accurately extract relevant entities from the input that are essential for constructing the SPARQL query. Common entities include locations like cities or provinces (e.g., 'Amsterdam', 'Noord-Holland') or thematic topics (e.g., 'World War II').
|
||
|
||
3. **SPARQL Query Construction**: Use the following prefixes for constructing your SPARQL query:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
The query should:
|
||
- Use `hco` to correctly specify entity types like hco:Museum or hco:Archive.
|
||
- Filter results based on location or thematic relevance using properties like `hco:isLocatedIn`, `dct:location`, or `skos:related`.
|
||
- Optionally, fetch labels and ensure the query retrieves them in a readable form, often using language services like `SERVICE wikibase:label`.
|
||
|
||
4. **Sources Selection**: Ensure the correct data sources are used (sparql, qdrant, postgis, etc.) based on what the task demands, and ensure that the results contain all the necessary terms to answer the question.
|
||
|
||
5. **Reasoning and Explanation**: Provide a logical explanation of the reasoning behind the query constructed, outlining why certain RDF triples or filters were used.
|
||
|
||
Pay close attention to feedback related to entity extraction and selection of appropriate data sources, ensuring that all necessary entities and expected terms are present in the final SPARQL query output.
|
||
|
||
Use the information and examples from the task context to improve the extraction of entities and choices made during source selection. The goal is to continuously refine the approach based on feedback on previous examples.
|
||
2025/12/11 12:19:19 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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2025/12/11 12:19:19 INFO dspy.teleprompt.gepa.gepa: Iteration 43: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
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GEPA Optimization: 8%|▊ | 246/2954 [09:06<1:57:37, 2.61s/rollouts]2025/12/11 12:19:19 INFO dspy.teleprompt.gepa.gepa: Iteration 44: Selected program 0 score: 0.6166666666666667
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 73.90it/s]
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 110.47it/s]2025/12/11 12:19:20 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:19:20 INFO dspy.teleprompt.gepa.gepa: Iteration 44: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:19:20 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:19:20 INFO dspy.teleprompt.gepa.gepa: Iteration 44: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:19:20 INFO dspy.teleprompt.gepa.gepa: Iteration 45: Selected program 0 score: 0.6166666666666667
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||
|
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 67.52it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 101.03it/s]2025/12/11 12:19:20 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:19:20 INFO dspy.teleprompt.gepa.gepa: Iteration 45: Proposed new text for multi_hop.sparql_gen.predict: You are tasked with generating SPARQL queries for a Heritage Custodian knowledge graph that can be executed on our Oxigraph endpoint. You are expected to be knowledgeable in both SPARQL and the Heritage Custodian Ontology (HCO) to ensure the correctness and relevance of the queries. Here is your task broken down into steps:
|
||
|
||
1. **Understand the Question**: For each task, you will be given a question that pertains to the Heritage Custodian knowledge graph. Carefully analyze the question to identify the intent. Common intents include statistical (e.g., counting numbers), geographic (e.g., finding locations), entity lookup (e.g., finding specific details about entities), and exploration (e.g., seeking relationships or connections between entities).
|
||
|
||
2. **Entity Extraction**: Identify and extract entities from the question. Entities usually include specific names of places, institutions, or significant events (e.g., 'Amsterdam', 'Rijksmuseum', 'World War II'). Note that entity names may have variations such as capitalization differences (e.g., 'Amsterdam' vs. 'amsterdam') which are crucial for the query's success.
|
||
|
||
3. **Construct the SPARQL Query**:
|
||
- Use the provided key prefixes to build your query:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
- Identify the required ontology classes and properties based on the identified intent and entities:
|
||
- For statistical and count-related questions, use `COUNT(?variable) AS ?count`.
|
||
- For geographic questions, consider using location properties like `dct:spatial` or `dct:location`.
|
||
- For entity lookup, use specific entity IRIs directly, such as `hco:Rijksmuseum`.
|
||
- For exploration and relationship-based queries, utilize properties like `skos:related` to establish connections.
|
||
|
||
4. **Explain the Query**: After constructing the SPARQL query, provide a clear explanation of how the query works and why it's suitable for the question. Highlight what the query aims to retrieve (e.g., labels, properties) and the relationship it leverages within the HCO ontology.
|
||
|
||
5. **Quality Assurance**: Validate your query against expected areas of improvement:
|
||
- Ensure intent is correctly identified.
|
||
- Ensure all relevant entities from the question are extracted and accounted for.
|
||
- Ensure the SPARQL query references correct ontology classes and properties as specified by HCO.
|
||
- Confirm that the explanation accurately reflects the query's operation and output.
|
||
|
||
This procedure will ensure the queries are both syntactically and semantically valid, effectively retrieving the needed information from the knowledge graph. Be concise, clear, and accurate in each output to maintain the quality of responses.
|
||
2025/12/11 12:19:20 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:19:20 INFO dspy.teleprompt.gepa.gepa: Iteration 45: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 9%|▊ | 255/2954 [09:06<1:12:08, 1.60s/rollouts]2025/12/11 12:19:20 INFO dspy.teleprompt.gepa.gepa: Iteration 46: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 42.54it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 82.10it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 122.73it/s]2025/12/11 12:19:20 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:19:34 INFO dspy.teleprompt.gepa.gepa: Iteration 46: Proposed new text for multi_hop.synthesizer.predict: Task Instruction for the Assistant:
|
||
|
||
1. **Objective**: Generate informative, synthesized responses to questions about heritage institutions, specifically libraries, museums, and archives, using retrieved information. Ensure answers are accurate, detailed, and cite appropriate sources.
|
||
|
||
2. **Entity and Intent Recognition**:
|
||
- Identify query intent, such as temporal (Example 1: focusing on dates or periods), geographic (Example 2: location-based requests), or exploratory (Example 3: general inquiries).
|
||
- Extract and recognize specific entities from the question. This includes key terms like 'libraries', 'museums', 'archives', 'World War II', and geographic regions such as 'Noord-Holland'. Note that temporal indicators (e.g., 'since 2000') are critical.
|
||
|
||
3. **Source Selection**:
|
||
- Use relevant data sources based on the type of heritage institution and the nature of the inquiry. Possible sources include:
|
||
- SPARQL databases for structured query access.
|
||
- Qdrant for semantic search and vector similarity.
|
||
- PostGIS for geographic data (though validation for source relevance is necessary).
|
||
- Adjust source selection strategies according to both the context and the expected type of heritage entity in the query.
|
||
|
||
4. **Answer Generation**:
|
||
- Provide a concise and informative answer. Include specific examples from the selected data sources. Mention the institutions, highlights of their collections, and any notable mergers (in the case of libraries).
|
||
- Answers should include all necessary terms and entities relevant to the inputs.
|
||
|
||
5. **Citations and Confidence**:
|
||
- Clearly list sources that were referenced to ensure transparency and validate the provided information.
|
||
- Assign a confidence metric to the response, indicating the reliability of the information provided.
|
||
|
||
6. **Follow-Up Suggestions**:
|
||
- Offer additional related questions or areas of inquiry to engage the user further and expand on the topic.
|
||
- Ensure follow-up suggestions are relevant and designed to provide a deeper understanding of the heritage institutions.
|
||
|
||
7. **Feedback Integration**:
|
||
- Continuously integrate user feedback to improve entity recognition and source relevancy.
|
||
- Make necessary corrections based on identified entity extraction issues (e.g., missing entities like '2000', 'museums', 'World War II') and refine the selection of sources.
|
||
|
||
By integrating these instructions, the assistant will deliver accurate, complete, and valuable information about heritage institutions, enhancing the user's experience with precise and contextually relevant responses.
|
||
2025/12/11 12:19:34 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:19:34 INFO dspy.teleprompt.gepa.gepa: Iteration 46: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 9%|▉ | 261/2954 [09:21<1:21:42, 1.82s/rollouts]2025/12/11 12:19:34 INFO dspy.teleprompt.gepa.gepa: Iteration 47: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 64.86it/s]
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||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 101.06it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 151.00it/s]2025/12/11 12:19:34 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:19:45 INFO dspy.teleprompt.gepa.gepa: Iteration 47: Proposed new text for answer_gen.predict: Task Instructions:
|
||
|
||
1. **Objective**: Your task is to generate informative answers related to heritage institutions. This includes museums, libraries, and various cultural entities with a historical or cultural significance.
|
||
|
||
2. **Input Format**:
|
||
- **Question**: User's query related to heritage institutions.
|
||
- **Context**: Provides an indication of the type of information required, such as `geographic`, `entity_lookup`, or `temporal`.
|
||
- **Entities**: List of specific entities extracted from the question.
|
||
- **Sources**: Databases or APIs available for retrieving information. These can vary but may commonly include `postgis`, `qdrant`, `sparql`, `typedb`, among others.
|
||
- **Language**: The language code (e.g., 'en' for English, 'nl' for Dutch) in which the response should be provided.
|
||
|
||
3. **Response Generation**:
|
||
- **Entity Extraction**: Ensure that all potential entities within the question are identified. This includes specific names of institutions, geographic locations, or time-related information.
|
||
- **Contextual Understanding**: Clearly identify the intent of the query. Common intents include `geographic` (location-based queries), `entity_lookup` (specific information about an entity), and `temporal` (time-related inquiries).
|
||
- **Source Selection**: Utilize the most relevant and correct sources to gather accurate information. For example, for museum location information, `sparql` and `qdrant` might be more suitable than `postgis`.
|
||
- **Reasoning**: Provide a rational explanation for how the answer was derived, incorporating relevant details from the sources and background knowledge if necessary.
|
||
- **Answer Quality**: Ensure the answer contains all relevant and expected terms. It should be informative, concise, and directly address the user's query.
|
||
|
||
4. **Answer Structure**:
|
||
- **Answer**: Present a clear and accurate answer. If listing items, use bullet points or numbering for clarity.
|
||
- **Citations**: Cite the sources used to construct the response, ensuring transparency in information sourcing.
|
||
- **Confidence Level**: Optionally include a confidence score to indicate the reliability of the response.
|
||
|
||
5. **Follow-Up Suggestions**:
|
||
- Offer additional questions related to the original query that the user might find helpful. These could pertain to visiting details, additional historical context, or related topics of interest.
|
||
|
||
6. **Feedback Utilization**:
|
||
- Use any available feedback to refine and improve entity extraction, intent classification, and source selection. Correct any errors in these areas as noted in the feedback provided.
|
||
2025/12/11 12:19:52 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:19:52 INFO dspy.teleprompt.gepa.gepa: Iteration 47: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 9%|▉ | 267/2954 [09:39<1:36:39, 2.16s/rollouts]2025/12/11 12:19:52 INFO dspy.teleprompt.gepa.gepa: Iteration 48: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
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||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 47.92it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 88.33it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 132.04it/s]2025/12/11 12:19:52 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:20:03 INFO dspy.teleprompt.gepa.gepa: Iteration 48: Proposed new text for viz_selector: To assist users in selecting the most suitable visualization for heritage data, follow these detailed instructions:
|
||
|
||
1. **Identify Query Intent**: Determine the intent of the user's query, which could be 'exploration', 'statistical', 'geographic', or 'entity_lookup'.
|
||
- 'Exploration' is for queries seeking an overview or pattern across a dataset.
|
||
- 'Statistical' intends to quantify or list data specifics in a straightforward manner.
|
||
- 'Geographic' focuses on the location-based display of data.
|
||
- 'Entity_lookup' involves looking up specific information about an entity.
|
||
|
||
2. **Entity Extraction**: Accurately extract relevant entities from the user query. For example, notable entities like 'World War II', 'Amsterdam', or 'Rijksmuseum' should be clearly identified.
|
||
|
||
3. **Data Analysis**: Examine data characteristics provided in 'schema_fields', such as 'name', 'type', 'city', 'country', 'lat', 'lon', and the 'result_count':
|
||
- If geographic fields ('lat', 'lon') are present and the intent is 'geographic' or 'exploration', consider using a map.
|
||
- For a 'statistical' intent with numerical or count-based queries, utilize a table.
|
||
- For 'entity_lookup', despite prior feedback, reconsider if a simple textual display might suit better unless geographic data clearly suggests a map.
|
||
|
||
4. **Visualization Type Selection**: Based on your analysis of the query intent, entities, data fields, and result count:
|
||
- Maps work well for geographic distribution (if latitude and longitude are available).
|
||
- Tables are effective for detailed listings, particularly when displaying specific attributes like names, types, etc., up to around 10 results.
|
||
- Modify the visualization choice if the typical conventions do not fit the query, for instance, using a textual response for direct questions under 'entity_lookup'.
|
||
|
||
5. **Configuration Setup**: Depending on the chosen visualization type, tailor the configuration:
|
||
- For maps, define specific attributes such as map type (e.g., heatmap), color scheme, zoom level, and marker properties.
|
||
- For tables, determine column inclusion, title, and pagination relevance ensuring the display remains neat and relevant to a maximum of around 10 results.
|
||
|
||
6. **Source Selection**: Identify correct data sources based on entity extraction and intent, using appropriate databases like postgis, sparql, or qdrant.
|
||
|
||
7. **Quality Assurance**:
|
||
- Validate the reasoning behind the chosen visualization to ensure all aspects of the query intent, data attributes, and entity recognition align.
|
||
- Cross-check feedback to improve future decision-making in the visualization selection process.
|
||
|
||
By adhering to these guidelines, you will improve the accuracy and relevance of heritage data visualizations based on user queries.
|
||
2025/12/11 12:20:06 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:20:06 INFO dspy.teleprompt.gepa.gepa: Iteration 48: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 9%|▉ | 273/2954 [09:53<1:38:58, 2.22s/rollouts]2025/12/11 12:20:06 INFO dspy.teleprompt.gepa.gepa: Iteration 49: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
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|
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 84.52it/s]2025/12/11 12:20:06 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:20:18 INFO dspy.teleprompt.gepa.gepa: Iteration 49: Proposed new text for router.classifier.predict: You are tasked with classifying the intent of queries related to GLAM (Galleries, Libraries, Archives, Museums) heritage institutions. Your objective is to accurately identify the intent behind the user's query to effectively route it to the appropriate data sources and retrieval strategies.
|
||
|
||
Task Details:
|
||
1. **Intent Classification**:
|
||
- Identify the user's intent based on the query. Possible intents include, but are not limited to:
|
||
- **Exploration**: Queries seeking detailed or broad information about a topic.
|
||
- **Statistical**: Queries looking for quantitative or numerical data.
|
||
- **Geographic**: Queries related to specific geographic locations.
|
||
|
||
2. **Entity Extraction**:
|
||
- Extract and list all relevant entities from the query. Ensure that all important terms and phrases that represent key components of the query are identified. For example:
|
||
- Proper nouns and significant terms such as names of events, locations, or categories (e.g., 'World War II', 'Amsterdam').
|
||
|
||
3. **Data Source Recommendation**:
|
||
- Based on the query intent, recommend the appropriate data sources that may contain the requested information. Options include:
|
||
- **Qdrant**: Suitable for broad exploration queries.
|
||
- **Sparql**: Ideal for structured, resource-related information, often used for geographic and exploration queries.
|
||
- **TypeDB**: Suitable for knowledge queries requiring detailed relationships.
|
||
- **PostGIS**: Best for geographic queries involving spatial information.
|
||
|
||
General Strategy:
|
||
- Analyze the query to determine its scope, specificity, and the type of information requested.
|
||
- Cross-reference entities with potential data sources to ensure comprehensive and relevant source recommendations.
|
||
- Ensure all identified terms and intents align with commonly understood frameworks within the GLAM domain.
|
||
|
||
Consider different languages and contexts, and apply domain knowledge from the GLAM sector to ensure the accuracy and reliability of your responses.
|
||
2025/12/11 12:20:20 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:20:20 INFO dspy.teleprompt.gepa.gepa: Iteration 49: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 9%|▉ | 279/2954 [10:07<1:39:10, 2.22s/rollouts]2025/12/11 12:20:20 INFO dspy.teleprompt.gepa.gepa: Iteration 50: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 46.74it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 93.16it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 139.39it/s]2025/12/11 12:20:20 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:20:27 INFO dspy.teleprompt.gepa.gepa: Iteration 50: Proposed new text for entity_extractor: Task Instruction: Extract heritage-specific entities from text using CH-Annotator v1.7.0 conventions.
|
||
|
||
Task Requirements:
|
||
1. Entity Types: Your task is to extract and categorize the following types of heritage-specific entities from the provided text inputs:
|
||
a. Institutions: Extract names of institutions and classify them with their type, such as cultural heritage museums (e.g., 'GRP.HER.MUS').
|
||
|
||
b. Places: Extract names of places and provide geographic details, including latitude and longitude.
|
||
|
||
c. Temporal Information: Identify significant dates or periods mentioned in relation to events such as establishments, fusions, or other historical contexts.
|
||
|
||
d. Identifiers: Identify any specific identifiers related to cultural heritage.
|
||
|
||
e. Relationships: Understand and specifically identify relationships between entities where applicable.
|
||
|
||
2. Intent Detection: Ensure the intent matches the expected, such as 'entity_lookup'.
|
||
|
||
3. Source Attribution: Entities must be associated with correct sources. For facts like locations or institution details, ensure provenance from databases such as 'sparql', 'postgis', or 'qdrant', as appropriate.
|
||
|
||
4. Accuracy in Entity Extraction:
|
||
a. All significant entities such as place names (e.g., 'Amsterdam'), institutions (e.g., 'Rijksmuseum'), and dates (e.g., '2000'), must be extracted without omission.
|
||
|
||
b. Pay particular attention to ensure that all parts of the prompt (e.g., specific terms like 'bibliotheken') are captured to fit the context of cultural heritage.
|
||
|
||
5. Feedback Integration: Apply insights from the sample feedback to enhance accuracy and completeness in future entity extractions. Correct any noted issues by improving your entity recognition models or strategies.
|
||
|
||
By carefully following these guidelines, ensure your response fully reflects an understanding of both entities and relationships described in the text related to cultural heritage standards.
|
||
2025/12/11 12:20:29 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:20:29 INFO dspy.teleprompt.gepa.gepa: Iteration 50: New subsample score 1.95 is not better than old score 1.95, skipping
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||
|
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GEPA Optimization: 10%|▉ | 285/2954 [10:16<1:30:11, 2.03s/rollouts]2025/12/11 12:20:29 INFO dspy.teleprompt.gepa.gepa: Iteration 51: Selected program 0 score: 0.6166666666666667
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 82.16it/s]2025/12/11 12:20:29 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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2025/12/11 12:20:44 INFO dspy.teleprompt.gepa.gepa: Iteration 51: Proposed new text for sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries for a heritage custodian knowledge graph that work specifically with our Oxigraph endpoint.
|
||
|
||
Input Format:
|
||
- Each query request will have several components: a question, an intent, entities, and context.
|
||
- The question is expressed in natural language and indicates the information sought.
|
||
- The intent classifies the nature of the question, such as "temporal," "statistical," or "geographic."
|
||
- Entities are specific keywords or phrases identified within the question.
|
||
- Context can provide additional background information, but it may sometimes be empty.
|
||
|
||
Key Prefixes:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
Output Format:
|
||
1. Reasoning: Provide a detailed explanation of how you will translate the question into a SPARQL query using the relevant ontological entities and relationships.
|
||
2. SPARQL: Generate the SPARQL query based on the reasoning, ensuring it is complete and uses the appropriate prefixes.
|
||
3. Explanation: Offer a concise summary of how the SPARQL query addresses the question.
|
||
|
||
Guidelines:
|
||
- Correctly identify and extract all relevant entities from the question, even if they are implied, and include them in the entities field.
|
||
- Ensure the query leverages the right sources and prefixes, matching the intent and entities identified in the input.
|
||
- When fetching locations, ensure geographic names like 'Amsterdam' and 'Noord-Holland' match how they are represented in the data (e.g., using appropriate labels or identifiers).
|
||
- For temporal queries, correctly filter using date comparisons where necessary.
|
||
- For statistical queries, typically use aggregate functions like COUNT to fulfill the requirement.
|
||
- Always provide contextually aware queries that incorporate identified entities and appropriately address the intent.
|
||
|
||
Note: Consider potential errors in entity extraction and provide improved guidance on identifying these entities accurately. Additionally, ensure query outputs contain all expected terms to fully address the user's question.
|
||
2025/12/11 12:20:50 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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||
2025/12/11 12:20:50 INFO dspy.teleprompt.gepa.gepa: Iteration 51: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 10%|▉ | 291/2954 [10:36<1:48:03, 2.43s/rollouts]2025/12/11 12:20:50 INFO dspy.teleprompt.gepa.gepa: Iteration 52: Selected program 0 score: 0.6166666666666667
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 319.08it/s]
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 474.24it/s]2025/12/11 12:20:50 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:20:50 INFO dspy.teleprompt.gepa.gepa: Iteration 52: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:20:50 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:20:50 INFO dspy.teleprompt.gepa.gepa: Iteration 52: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:20:50 INFO dspy.teleprompt.gepa.gepa: Iteration 53: Selected program 0 score: 0.6166666666666667
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||
|
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||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 159.86it/s]2025/12/11 12:20:50 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:21:02 INFO dspy.teleprompt.gepa.gepa: Iteration 53: Proposed new text for multi_hop.sparql_gen.predict: You are tasked with generating SPARQL queries for a Heritage Custodian knowledge graph. Your goal is to create queries that interact correctly and effectively with our Oxigraph endpoint. You are expected to be an expert in SPARQL query construction and in the Heritage Custodian Ontology (HCO).
|
||
|
||
The task involves several specific steps and components:
|
||
|
||
1. **Understanding the Inputs:**
|
||
- Each task includes specific inputs such as a *question*, *intent*, *entities*, and *context*.
|
||
- The question provides a natural language inquiry that needs to be addressed using a SPARQL query.
|
||
- The intent (e.g., 'exploration', 'temporal', 'statistical') helps in determining the nature of the SPARQL query.
|
||
- Entities list relevant concepts or items derived from the question.
|
||
- Context provides additional information if available.
|
||
|
||
2. **Key Prefixes for the Queries:**
|
||
- Use the following prefixes for constructing the queries:
|
||
- `PREFIX hco: <https://w3id.org/hco/>`
|
||
- `PREFIX skos: <http://www.w3.org/2004/02/skos/core#>`
|
||
- `PREFIX geo: <http://www.opengis.net/ont/geosparql#>`
|
||
- `PREFIX dct: <http://purl.org/dc/terms/>`
|
||
|
||
3. **Generating the SPARQL Query:**
|
||
- Analyze the question and interpret it in the context of heritage and archive data.
|
||
- Identify the key entities from the question that should be included in the query. Aim for precise entity extraction.
|
||
- Consider the query's intent to ensure it aligns with the nature of the data requested (e.g., filtering dates for temporal, counting results for statistical queries).
|
||
- Construct a SPARQL query using the identified concepts that retrieves relevant data from the knowledge graph according to the Heritage Custodian Ontology. Ensure your SPARQL queries are valid and syntactically correct.
|
||
|
||
4. **Reasoning and Explanation:**
|
||
- Provide reasoning for how you interpreted the inputs and what information you aim to retrieve with your query.
|
||
- Offer an explanation of the query: state what entities it targets, what kind of relationships or data fields it explores, and elucidate the use of filters or aggregations if applicable.
|
||
|
||
5. **Feedback Integration:**
|
||
- When receiving feedback indicating missing entities or other issues, work on improving entity extraction and query accuracy.
|
||
- Make sure your queries cover all relevant aspects of the question and the expected terms.
|
||
|
||
By following these detailed steps, you can ensure the generation of accurate and contextually appropriate SPARQL queries that align with the expertise in the Heritage Custodian domain and the ontology in use.
|
||
2025/12/11 12:21:02 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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||
2025/12/11 12:21:02 INFO dspy.teleprompt.gepa.gepa: Iteration 53: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 10%|█ | 300/2954 [10:49<1:30:13, 2.04s/rollouts]2025/12/11 12:21:02 INFO dspy.teleprompt.gepa.gepa: Iteration 54: Selected program 0 score: 0.6166666666666667
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 338.38it/s]2025/12/11 12:21:02 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:21:10 INFO dspy.teleprompt.gepa.gepa: Iteration 54: Proposed new text for multi_hop.synthesizer.predict: Instructions for Assistant:
|
||
|
||
Task Objective:
|
||
The goal is to generate informative and accurate answers about heritage institutions, focusing on museums and other culturally significant places. Your responses should synthesize information from multiple data sources, incorporate relevant details, provide accurate citations, and suggest meaningful follow-up questions.
|
||
|
||
1. Input Interpretation:
|
||
- **Question**: Understand the user's question and the relevant context.
|
||
- **Context**:
|
||
- Identify the query intent (e.g., geographic, entity lookup) to guide the nature of the response.
|
||
- Extract entities from the question that are key to forming the response (e.g., 'Rijksmuseum', 'museums', 'Noord-Holland').
|
||
|
||
2. Data Sources:
|
||
- Use the provided data sources such as 'sparql', 'qdrant', and 'postgis' to retrieve information.
|
||
- Focus on using the most relevant sources based on the expected task requirement (e.g., geographic data might be better suited for 'sparql' and 'qdrant').
|
||
|
||
3. Response Generation:
|
||
- **Reasoning**: Offer reasoning that supports your answer, integrating context and common knowledge where applicable.
|
||
- **Answer Composition**: Provide a succinct and direct answer that includes all expected terms and is relevant to the question.
|
||
- **Citations**: Ensure to accurately cite the sources of information using only the sources relevant to the response.
|
||
- **Confidence Level**: Indicate your confidence in the accuracy of the response on a scale (e.g., 0.85 or 0.95).
|
||
- **Follow-up Questions**: Suggest additional related questions that might interest the user to provide further engagement.
|
||
|
||
4. Feedback Optimization:
|
||
- Address feedback by improving intent classification and entity extraction where noted.
|
||
- Ensure source alignment by only including data sources expected for the type of query at hand.
|
||
- Regularly review feedback to continually refine the extraction and synthesis processes in your responses.
|
||
|
||
5. Domain-Specific Information:
|
||
- Be aware that heritage institutions may often involve well-known entities or places, so ensure your facts are both current and accurate.
|
||
- Museums are central to these types of questions, and often involve history, art, and cultural insights which should be reflected in the responses.
|
||
|
||
Using a generalizable strategy:
|
||
- For questions focused on location or geographic intent, ensure that answers are clear on where entities such as museums are located.
|
||
- When the question involves listing or explaining different entities, ensure the responses encompass a variety of relevant examples that highlight diversity and significance in the context of the question.
|
||
2025/12/11 12:21:10 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:21:10 INFO dspy.teleprompt.gepa.gepa: Iteration 54: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 10%|█ | 306/2954 [10:56<1:20:28, 1.82s/rollouts]2025/12/11 12:21:10 INFO dspy.teleprompt.gepa.gepa: Iteration 55: Selected program 0 score: 0.6166666666666667
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||
|
||
|
||
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|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 140.61it/s]2025/12/11 12:21:10 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:21:24 INFO dspy.teleprompt.gepa.gepa: Iteration 55: Proposed new text for answer_gen.predict: markdown
|
||
Your task is to generate informative, detailed, and accurate responses about various heritage institutions, focusing on libraries, museums, and archives. Here is a detailed breakdown of the task to help guide your process:
|
||
|
||
1. **Input Format**: You will receive specific questions related to heritage institutions, accompanied by four structured input segments:
|
||
- `question`: The main query that needs to be answered.
|
||
- `context`: Provides additional clues about the intent behind the question, including identified query intents and entities.
|
||
- `sources`: Lists recommended data sources for retrieving relevant information.
|
||
- `language`: Specifies the language in which the response should be provided.
|
||
|
||
2. **Clarify Intent and Recognize Entities**:
|
||
- Accurately identify the user's query intent, such as temporal (time-related), geographic (location-related), or exploration (general interest in knowing more).
|
||
- Extract and acknowledge key entities mentioned in the question to improve the focus and relevance of the retrieval process.
|
||
|
||
3. **Information Retrieval and Answer Construction**:
|
||
- Use the identified intent and entities to retrieve precise and informative content about the heritage institutions from the specified sources.
|
||
- Construct the response by synthesizing information into coherent and logically structured answers. Ensure your answer is helpful, informative, and includes all expected terms relevant to the query.
|
||
- Cite the correct sources, not just those listed, but those that directly inform and support the factual statements in your answer.
|
||
|
||
4. **Adapt Responses to the Question Language**:
|
||
- Respond in the language specified in the input to ensure comprehension and relevance for the user.
|
||
- Include cultural and context-specific details pertinent to the language and region being referred to in the question.
|
||
|
||
5. **Include Follow-up Suggestions**:
|
||
- Suggest further inquiries or related topics the user might be interested in exploring, ensuring a continuation of engagement with the subject.
|
||
|
||
6. **Examples of Specific Knowledge Related to Heritage Institutions**:
|
||
- Be aware of key institutions such as the Rijksmuseum located in Amsterdam, the types of content held in archives such as the U.S. National Archives, or collaborative efforts like library mergers.
|
||
- Understand typical reasons behind certain movements or behaviors in heritage sectors, such as library mergers to improve efficiency and resource sharing.
|
||
|
||
7. **Confidence and Accuracy**:
|
||
- Express confidence in your responses, backed by well-researched information and robust citations.
|
||
- Strive to reach a high level of accuracy by confirming facts through recognized and reliable data sources.
|
||
|
||
By following these detailed instructions, you should be able to craft responses that not only meet the user's immediate needs but also enrich their understanding of heritage institutions across various domains.
|
||
2025/12/11 12:21:32 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:21:32 INFO dspy.teleprompt.gepa.gepa: Iteration 55: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 11%|█ | 312/2954 [11:19<1:43:59, 2.36s/rollouts]2025/12/11 12:21:32 INFO dspy.teleprompt.gepa.gepa: Iteration 56: Selected program 0 score: 0.6166666666666667
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||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 54.99it/s]
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||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 82.33it/s]2025/12/11 12:21:32 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:21:42 INFO dspy.teleprompt.gepa.gepa: Iteration 56: Proposed new text for viz_selector: Your task is to select an appropriate visualization type for heritage data based on a given query. For each task, you will be provided with inputs including a question, intent, schema_fields, and result_count.
|
||
|
||
1. **Identify Query Intent**: Understand the query to determine the intent. It can generally fall under categories such as statistical, geographic, or exploration.
|
||
|
||
2. **Entity Extraction**: Extract key entities from the question such as specific locations, types of heritage sites, or specific events mentioned. This can enhance the understanding of data context and relevance.
|
||
|
||
3. **Assess Data Characteristics**: Examine the schema_fields provided, which typically contain details relevant for visualization like 'name', 'type', 'city', 'country', 'lat', 'lon'. Some fields might be used in the visualization depending on the query intent.
|
||
|
||
4. **Determine Result Count**: Consider the result_count to decide the scale of visualization. For small numbers (e.g., 10 results), simple visualizations like tables or basic maps are often sufficient, whereas larger data sets may require more sophisticated visualizations such as heatmaps or charts.
|
||
|
||
5. **Select Visualization Type**:
|
||
- If the intent is **statistical**, a table is often suitable to clearly present detailed information like names and types of the sites or items in a structured manner.
|
||
- For a **geographic** intent, use a map visualization to help visualize spatial relationships and geographical distributions.
|
||
- With an **exploration** intent, consider using maps with special configurations (e.g., heatmaps) to allow for deeper insights into data patterns.
|
||
|
||
6. **Configure Visualization**: Based on the chosen visualization type, determine any specific configuration settings such as pagination for tables or custom settings like map types, zoom level, color schemes, and marker sizes for maps.
|
||
|
||
7. **Provide Detailed Reasoning**: Explain why the chosen visualization type is appropriate given the intent, data characteristics, and result count.
|
||
|
||
This approach will create a visualization strategy that is comprehensive and adaptable to the query characteristics, ensuring that the visual representation of data meets the user's inquiry in an informative and user-friendly manner.
|
||
2025/12/11 12:21:48 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:21:48 INFO dspy.teleprompt.gepa.gepa: Iteration 56: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 11%|█ | 318/2954 [11:35<1:47:17, 2.44s/rollouts]2025/12/11 12:21:48 INFO dspy.teleprompt.gepa.gepa: Iteration 57: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 148.20it/s]2025/12/11 12:21:48 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:21:58 INFO dspy.teleprompt.gepa.gepa: Iteration 57: Proposed new text for router.classifier.predict: Task: Classify the intent of a heritage institution query and identify relevant entities and data sources.
|
||
|
||
You are an expert in GLAM (Galleries, Libraries, Archives, Museums) heritage institutions. Your task is to classify user queries related to heritage institutions and determine the appropriate data retrieval strategies. Follow the steps outlined below:
|
||
|
||
1. **Intent Classification**:
|
||
- Determine the intent of the query based on its focus:
|
||
- **Temporal**: Focuses on time-related data (e.g., events or changes over a specific period).
|
||
- **Geographic**: Seeks information related to locations or places.
|
||
- **Entity Lookup**: Aims to find specific details about a particular entity (institution).
|
||
|
||
2. **Entity Extraction**:
|
||
- Identify key entities within the query that are relevant to the field of heritage institutions. Entities may include:
|
||
- Names of institutions (e.g., "Rijksmuseum", "bibliotheken").
|
||
- Geographic locations (e.g., "Noord-Holland province").
|
||
- Dates or years (e.g., "2000").
|
||
|
||
3. **Source Identification**:
|
||
- Select appropriate data sources for fulfilling the query based on its intent and information needs. Consider the following sources:
|
||
- **SPARQL**: Suitable for structured queries on semantic data and ontologies related to heritage information.
|
||
- **Qdrant**: Useful for vector similarity search that can refine searches within large datasets based on similarity and relevance.
|
||
- **PostGIS**: Appropriate for geographic information system (GIS) queries involving spatial data processing and location-based information.
|
||
- **TypeDB**: Consider if the query requires logical inference or complex relationships inherent among data entities.
|
||
|
||
4. **Processing Guidelines**:
|
||
- Distill the query into its core intent and extract all recognizable entities accurately.
|
||
- Ensure entity names are captured in correct format (e.g., capitalization).
|
||
- Correctly classify intent as per feedback norms and improve it iteratively based on examples provided.
|
||
- Choose data sources that align with the expected query requirements and enhance those with contextual understanding where feasible.
|
||
|
||
The goal is to accurately classify the query intent, extract pertinent entities, and identify correct data sources such that the query can be effectively routed and processed within the domain of GLAM heritage institutions.
|
||
2025/12/11 12:22:08 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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||
2025/12/11 12:22:08 INFO dspy.teleprompt.gepa.gepa: Iteration 57: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 11%|█ | 324/2954 [11:55<1:57:49, 2.69s/rollouts]2025/12/11 12:22:08 INFO dspy.teleprompt.gepa.gepa: Iteration 58: Selected program 0 score: 0.6166666666666667
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|
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 156.98it/s]2025/12/11 12:22:08 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:22:24 INFO dspy.teleprompt.gepa.gepa: Iteration 58: Proposed new text for entity_extractor: Instructions for the Assistant:
|
||
|
||
Objective:
|
||
Your task is to extract heritage-specific entities from the provided text. You will identify and categorize entities according to the CH-Annotator v1.7.0 convention, which includes institutions, places, dates (temporal), identifiers, and relationships.
|
||
|
||
Task Details:
|
||
1. **Entity Extraction:**
|
||
- Identify and extract specific heritage-related entities mentioned in the text:
|
||
- **Institutions:** Look for names of museums, archives, and other heritage-specific organizations.
|
||
- **Places:** Recognize geographical locations such as cities, landmarks, and sites with historical relevance.
|
||
- **Temporal (Dates):** Extract any date or date range referenced in the text that could be related to historical events or timelines.
|
||
- **Identifiers:** Look for any unique identifiers mentioned that could be related to heritage or archival systems (for example, catalog numbers or code names).
|
||
- **Relationships:** Identify potential relationships mentioned between entities, such as ownership, location, or historical connections.
|
||
|
||
2. **Typing and Example Schema:**
|
||
- For each entity identified, provide a structured output including:
|
||
- Example for institutions: `{'name': 'Rijksmuseum', 'type': 'GRP.HER.MUS'}`
|
||
- Example for places: `{'name': 'Amsterdam', 'coordinates': {'latitude': 52.3676, 'longitude': 4.9041}}`
|
||
|
||
3. **Coordinate Provision:**
|
||
- For places, where possible, include geographical coordinates (latitude and longitude). If precise coordinates are unknown, you may set them to `None`.
|
||
|
||
4. **Intent Recognition and Sources:**
|
||
- Determine the user's intent accurately, whether it's a request for geographic, entity lookup, or another type of information.
|
||
- Based on the intent, identify the proper sources to fulfill the request. Possible sources include databases or geographic information systems (GIS).
|
||
|
||
5. **Feedback Consideration:**
|
||
- Ensure coverage of all expected entities to avoid omissions as highlighted in feedback examples.
|
||
- Address missed entities or relationships from previous tasks by improving recognition algorithms or patterns.
|
||
- Adjust source selection to align with intended outcomes as informed by feedback.
|
||
|
||
By following these instructions, you will deliver comprehensive and structured data that meets the requirements of CH-Annotator v1.7.0 and enhances the accuracy and relevance of information extracted from text snippets.
|
||
2025/12/11 12:22:31 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:22:31 INFO dspy.teleprompt.gepa.gepa: Iteration 58: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 11%|█ | 330/2954 [12:17<2:11:27, 3.01s/rollouts]2025/12/11 12:22:31 INFO dspy.teleprompt.gepa.gepa: Iteration 59: Selected program 0 score: 0.6166666666666667
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||
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|
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||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 84.14it/s]2025/12/11 12:22:31 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:22:50 INFO dspy.teleprompt.gepa.gepa: Iteration 59: Proposed new text for sparql_gen.predict: Your task is to generate SPARQL queries for a heritage custodian knowledge graph using provided input information. You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your goal is to create valid SPARQL queries that will work with our Oxigraph endpoint using the following key prefixes:
|
||
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
Make sure to consider the following guidelines when generating SPARQL queries:
|
||
|
||
1. **Intent Classification**: Properly identify the user's intent based on the question and context. Possible intents include:
|
||
- Exploration: For general inquiries about connections, relationships, or categorizations.
|
||
- Geographic: When location data is needed.
|
||
- Temporal: When time-based data is important.
|
||
- Entity lookup: Specifically retrieving data related to a specific entity regardless of context.
|
||
|
||
2. **Entity Extraction**: Identify and retrieve key entities mentioned in the inputs. Accurately determine which entities are referenced to build precise and relevant SPARQL queries. Examples include identifying terms such as 'World War II', 'Rijksmuseum', and numerical data for time filtering.
|
||
|
||
3. **SPARQL Query Construction**: Formulate the SPARQL query to accurately reflect the retrieved intent and entities. For instance:
|
||
- Use appropriate RDF types and predicates to structure the query.
|
||
- Utilize filtering when necessary to match the exact requirements, like dates or related terms.
|
||
- Make the query robust enough to handle variations or absence of some data points, ensuring it can still perform effectively.
|
||
|
||
4. **Use of Services**: When required, utilize SPARQL service clauses to interface with external data sources or endpoint services.
|
||
|
||
5. **Explanation and Reasoning**: Provide a clear explanation of your reasoning process for the SPARQL query. This includes context on why specific paths or predicates are used.
|
||
|
||
6. **Feedback Incorporation**: Integrate feedback for better accuracy in the domain-specific task. This includes better entity extraction (finding all relevant entities in each task) and intent classification.
|
||
|
||
Remember to continuously refine queries based on domain-specific requirements and feedback. Keep yourself updated with ontology updates to ensure that SPARQL queries are utilizing the most current and accurate information.
|
||
2025/12/11 12:22:55 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:22:55 INFO dspy.teleprompt.gepa.gepa: Iteration 59: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 11%|█▏ | 336/2954 [12:42<2:25:02, 3.32s/rollouts]2025/12/11 12:22:55 INFO dspy.teleprompt.gepa.gepa: Iteration 60: Selected program 0 score: 0.6166666666666667
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||
|
||
|
||
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 152.34it/s]2025/12/11 12:22:55 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:22:55 INFO dspy.teleprompt.gepa.gepa: Iteration 60: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:22:55 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:22:55 INFO dspy.teleprompt.gepa.gepa: Iteration 60: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:22:55 INFO dspy.teleprompt.gepa.gepa: Iteration 61: Selected program 0 score: 0.6166666666666667
|
||
|
||
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 155.55it/s]2025/12/11 12:22:55 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:22:55 INFO dspy.teleprompt.gepa.gepa: Iteration 61: Proposed new text for multi_hop.sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries that work with our Oxigraph endpoint based on specific user input questions. The queries need to effectively extract the desired data from a knowledge graph that uses the Heritage Custodian Ontology. Here's a detailed description of the task:
|
||
|
||
- **Input Format:**
|
||
- Each input will include the following fields:
|
||
- `question`: A natural language query related to the heritage custodian knowledge graph.
|
||
- `intent`: The type of question, such as "statistical", "temporal", or "exploration".
|
||
- `entities`: A list of entities recognized from the question.
|
||
- `context`: Any additional context if available (might be empty).
|
||
|
||
- **Task Requirements:**
|
||
- **Entity Extraction:** Accurately extract and identify all relevant entities from the question. Map these correctly to the ontology where possible.
|
||
- **SPARQL Query Construction:** Use the given prefixes in your queries:
|
||
```sparql
|
||
PREFIX hco: <https://w3id.org/hco/>
|
||
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
PREFIX dct: <http://purl.org/dc/terms/>
|
||
```
|
||
- **Intent Understanding:** Correctly interpret the intent of the question to guide the query design. Adjust the query structure based on whether the question is statistical (e.g., counts), temporal (e.g., dates), or exploratory (e.g., finding related items).
|
||
- **Knowledge of Ontology Concepts:**
|
||
- Use entities and relations accurately. For instance, use `hco:Museum` for museums or `hco:Library` for libraries.
|
||
- Leverage `dct:location`, `dct:date`, `skos:related`, and similar properties relevant to the query.
|
||
- **Output Requirements:**
|
||
- The query should be syntactically correct and should align with the question intent.
|
||
- Provide a reasoning section explaining the logic behind the constructed query.
|
||
- Offer an explanation section to clarify how the query addresses the question.
|
||
|
||
- **Handling Feedback:**
|
||
- Review all feedback provided on generated queries, especially regarding missing entities or incorrect mappings. Continuously improve entity extraction and ontology alignment based on this feedback.
|
||
- Aim to have all expected terms from the question reflected in your SPARQL queries.
|
||
|
||
- **General Strategy:**
|
||
- Understand the semantic structure of the question to guide SPARQL query construction.
|
||
- Ensure that the queries are efficient and only retrieve necessary data points, filtering out any unrelated results.
|
||
- Maintain consistency across different types of questions by adhering to best practices in ontology querying.
|
||
|
||
Remember, the goal is to provide precise and accurate SPARQL queries that facilitate the exploration and analysis of the cultural heritage knowledge graph. Always verify that your responses are complete and address all aspects of the user's query.
|
||
2025/12/11 12:22:56 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:22:56 INFO dspy.teleprompt.gepa.gepa: Iteration 61: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 12%|█▏ | 345/2954 [12:42<1:29:11, 2.05s/rollouts]2025/12/11 12:22:56 INFO dspy.teleprompt.gepa.gepa: Iteration 62: Selected program 0 score: 0.6166666666666667
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||
|
||
|
||
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|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 163.87it/s]2025/12/11 12:22:56 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:23:03 INFO dspy.teleprompt.gepa.gepa: Iteration 62: Proposed new text for multi_hop.synthesizer.predict: Your task is to respond to queries related to heritage institutions with accurate and informative answers. Use the following guidelines to generate your responses:
|
||
|
||
1. **Interpret Inputs Correctly**:
|
||
- Identify the intent of the query. Common intents include:
|
||
- Geographic: Questions about locations of institutions.
|
||
- Entity Lookup: Specific inquiries about a known institution.
|
||
- Statistical: Queries seeking numerical data or statistics.
|
||
|
||
2. **Entity Identification**:
|
||
- Extract and recognize relevant entities such as names of places, museums, or specific cultural terms. Common entities include the names of regions, cities, and institutions.
|
||
|
||
3. **Source and Cite Information**:
|
||
- Use appropriate sources for accurate data retrieval:
|
||
- `sparql` and `qdrant` are preferred sources for precise information about museum locations or counts.
|
||
- Reconsider use of `postgis` unless spatial or GIS-specific data is explicitly required.
|
||
- Cite all sources clearly in your answer and ensure they match the context of the question.
|
||
|
||
4. **Language Consideration**:
|
||
- Provide answers in the language specified. Common languages include English (`en`) and Dutch (`nl`).
|
||
|
||
5. **Information Synthesis**:
|
||
- Use reliable data that directly addresses the query.
|
||
- For statistics, include current and approximate figures where precise numbers may vary.
|
||
- Ensure your answer includes major terms and entities relevant to the query.
|
||
|
||
6. **Confidence Level**:
|
||
- Indicate the confidence level of your response based on the clarity of the data and sources used.
|
||
|
||
7. **Follow-Up Suggestions**:
|
||
- Offer relevant follow-up questions to anticipate further user interest or information gaps, such as visiting hours, ticket purchases, or special exhibitions.
|
||
|
||
By following these instructions, you can generate helpful, accurate, and well-cited responses to inquiries about heritage institutions.
|
||
2025/12/11 12:23:03 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:23:03 INFO dspy.teleprompt.gepa.gepa: Iteration 62: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 12%|█▏ | 351/2954 [12:50<1:19:31, 1.83s/rollouts]2025/12/11 12:23:03 INFO dspy.teleprompt.gepa.gepa: Iteration 63: Selected program 0 score: 0.6166666666666667
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||
|
||
|
||
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||
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||
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|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 469.62it/s]2025/12/11 12:23:03 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:23:09 INFO dspy.teleprompt.gepa.gepa: Iteration 63: Proposed new text for answer_gen.predict: Your task is to generate informative answers to questions about heritage institutions, specifically museums. Here are the key points to consider when performing your task:
|
||
|
||
1. **Intent Classification**:
|
||
- Accurately determine the intent of the query, which could include geographic requests (e.g., locations, presence of museums in a region), statistical inquiries (e.g., number of museums, visitation statistics), or categorical/entity lookup (e.g., identifying or listing museums).
|
||
|
||
2. **Entity Extraction**:
|
||
- Identify and extract relevant entities from the provided inputs, such as names of specific museums, geographic regions (such as cities or provinces), and other specific terms like "musea" or "Noord-Holland."
|
||
|
||
3. **Information Retrieval and Synthesis**:
|
||
- Use the available sources (e.g., 'sparql', 'qdrant') to gather accurate data related to the query. Prioritize sources that are most likely to contain the needed information based on query context and entities involved.
|
||
- Synthesize this information into coherent, concise, and informative responses, ensuring that any claims are backed by the sources.
|
||
|
||
4. **Citations and Confidence**:
|
||
- Always provide citations for the sources used to retrieve the information. Ensure the sources listed are correct and relevant to the information provided.
|
||
- Indicate your level of confidence in the response by providing a confidence score.
|
||
|
||
5. **Follow-up Suggestions**:
|
||
- Offer relevant follow-up questions or suggestions that stem from the original query, enhancing user engagement and providing further value.
|
||
|
||
6. **Language Consideration**:
|
||
- Ensure that the response is in the language specified in the input, accommodating both English (en) and Dutch (nl) as needed.
|
||
|
||
In addition, stay updated on the following domain-specific knowledge:
|
||
- Prominent museums and their locations, especially in regions like Amsterdam and Noord-Holland.
|
||
- Common facts about museums, such as notable collections or current exhibitions.
|
||
- Utilize a strategic approach by combining retrieved data with general domain knowledge of the cultural and heritage sector to enhance response accuracy.
|
||
|
||
By following these guidelines, you will effectively generate meaningful and well-supported responses to inquiries about heritage institutions.
|
||
2025/12/11 12:23:15 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 63: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 12%|█▏ | 357/2954 [13:02<1:21:51, 1.89s/rollouts]2025/12/11 12:23:15 INFO dspy.teleprompt.gepa.gepa: Iteration 64: Selected program 0 score: 0.6166666666666667
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||
|
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|
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||
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 120.01it/s]2025/12/11 12:23:15 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:23:28 INFO dspy.teleprompt.gepa.gepa: Iteration 64: Proposed new text for viz_selector: Your task is to select the most appropriate visualization type for presenting heritage data based on the following inputs: question, intent, schema_fields, and result_count. You should consider the intent of the query, the characteristics of the data as indicated by schema_fields, and the number of results available (result_count) to determine the best visualization method.
|
||
|
||
1. **Identify the Intent**: Understand the purpose of the query. Possible intents include:
|
||
- Temporal: The focus is on changes over time.
|
||
- Exploration: The focus is on discovering or browsing data points.
|
||
- Geographic: The focus is on the spatial distribution of data.
|
||
|
||
2. **Determine the Visualization Type**:
|
||
- For "temporal" intent, consider using visualizations such as a timeline to effectively display changes or events over time.
|
||
- For "exploration" intent, consider using visualizations such as heatmaps or other types of maps that allow for examining distributed data points.
|
||
- For "geographic" intent, a map is often most appropriate, allowing users to see locations and understand spatial relationships.
|
||
|
||
3. **Analyze Schema Fields and Result Count**:
|
||
- Use fields like 'lat' and 'lon' for geographic plots.
|
||
- Use time-related fields (e.g., years) for temporal plots.
|
||
- Factor in the result_count to ensure the visualization will not be cluttered and remains interpretable.
|
||
|
||
4. **Generate Configuration**: Based on the chosen visualization type, create a configuration object that outlines necessary parameters such as title, axes titles, map types, color schemes, etc.
|
||
|
||
5. **Entity Extraction and Source Selection**:
|
||
- Ensure the extraction of relevant entities from the question, such as specific terms or historical events, to guide the accuracy of the visualization.
|
||
- Correctly identify and select the appropriate sources for data integration.
|
||
|
||
Be mindful of any feedback provided:
|
||
|
||
- Always ensure entity extraction is comprehensive; important entities should not be omitted.
|
||
- Validate that the selected sources are those expected for the task at hand.
|
||
- The final visualization should effectively address all terms and conditions of the query.
|
||
|
||
Continue to improve based on feedback, ensuring that all expected entities are included and that sources are accurately aligned with the task requirements.
|
||
2025/12/11 12:23:31 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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||
2025/12/11 12:23:31 INFO dspy.teleprompt.gepa.gepa: Iteration 64: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 12%|█▏ | 363/2954 [13:17<1:30:20, 2.09s/rollouts]2025/12/11 12:23:31 INFO dspy.teleprompt.gepa.gepa: Iteration 65: Selected program 0 score: 0.6166666666666667
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 180.91it/s]2025/12/11 12:23:31 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:23:40 INFO dspy.teleprompt.gepa.gepa: Iteration 65: Proposed new text for router.classifier.predict: Task: Classify the intent of a heritage institution query in the context of GLAM (Galleries, Libraries, Archives, Museums) domains.
|
||
|
||
Instructions:
|
||
|
||
1. **Intent Classification**: For each query, determine the primary intent based on the following categories:
|
||
- **Statistical**: Queries requesting quantitative data, such as counts or measurements.
|
||
- **Geographic**: Inquiries about locations or areas within specific geographic boundaries.
|
||
- **Entity_Lookup**: Questions seeking specific information about a particular heritage institution or entity.
|
||
|
||
2. **Entity Extraction**: Identify key entities mentioned in the query, such as names of institutions, geographic locations, and specific exhibits or collections. Ensure correct capitalization as entities are often proper nouns.
|
||
|
||
3. **Sources Determination**: Recommend appropriate data sources for retrieving information. Potential sources include:
|
||
- **PostGIS**: Suitable for geographically-related queries, especially those requiring spatial data processing.
|
||
- **SPARQL**: Ideal for structured queries related to entity lookup and relationships among data.
|
||
- **Qdrant**: Useful for nearest neighbor searches, which can involve unstructured or semi-structured data.
|
||
|
||
4. **Output Structure**: Provide a response structured with clear sections for 'reasoning', 'intent', 'entities', and 'sources'.
|
||
|
||
5. **General Principles**:
|
||
- Analyze the semantic content of the query to determine intent, considering both explicit and implicit elements.
|
||
- Ensure that the reasoning aligns with the intent classification and the nature of the query.
|
||
- Cross-reference entity names for accurate extraction and avoid missing essential details.
|
||
- Ensure selected sources align with the intent, supporting the retrieval of accurate and relevant information.
|
||
|
||
Use these guidelines to ensure a comprehensive and detailed response that meets the user's needs in querying heritage institution data within the GLAM sector.
|
||
2025/12/11 12:23:52 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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||
2025/12/11 12:23:52 INFO dspy.teleprompt.gepa.gepa: Iteration 65: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 12%|█▏ | 369/2954 [13:38<1:47:48, 2.50s/rollouts]2025/12/11 12:23:52 INFO dspy.teleprompt.gepa.gepa: Iteration 66: Selected program 0 score: 0.6166666666666667
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Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 57.43it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 85.94it/s]2025/12/11 12:23:52 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:23:58 INFO dspy.teleprompt.gepa.gepa: Iteration 66: Proposed new text for entity_extractor: Your task is to extract heritage-specific entities from a given text input. The entities should be identified as per the CH-Annotator v1.7.0 convention, focusing on institutions, places, dates, identifiers, and relationships. Follow these detailed guidelines to ensure accurate entity extraction:
|
||
|
||
1. **Identify Institutions**: Extract names of libraries, archives, museums, or any other heritage-related institutions mentioned in the text.
|
||
|
||
2. **Spot Places**: Identify geographic locations or specific places related to cultural heritage or historical significance mentioned in the text.
|
||
|
||
3. **Extract Temporal Entities**: Look for any dates or time periods referenced in the text that denote historical events, such as "World War II" or specific years (e.g., "since 2000"). Events associated with dates should also be noted.
|
||
|
||
4. **Find Identifiers**: Capture unique identifiers mentioned in the text, such as archive codes, library codes, or any other special reference numbers.
|
||
|
||
5. **Understand Relationships**: Identify and specify any relationships mentioned in the text, such as collaborations or fusions between institutions.
|
||
|
||
6. **Recognize Contextual Keywords**: Pay attention to heritage-related keywords such as 'archives', 'fusion', 'collections', which might indicate the presence of a relevant entity.
|
||
|
||
7. **Consider Multiple Languages**: Be ready to process inputs in various languages and adapt entity extraction accordingly.
|
||
|
||
Remember to output the extracted entities under the corresponding categories: institutions, places, temporal, identifiers, and relationships. Ensure that your extraction is comprehensive and includes all possible entities from the text input.
|
||
2025/12/11 12:24:00 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:24:00 INFO dspy.teleprompt.gepa.gepa: Iteration 66: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 13%|█▎ | 375/2954 [13:46<1:32:14, 2.15s/rollouts]2025/12/11 12:24:00 INFO dspy.teleprompt.gepa.gepa: Iteration 67: Selected program 0 score: 0.6166666666666667
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|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 63.39it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 94.84it/s]2025/12/11 12:24:00 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:24:15 INFO dspy.teleprompt.gepa.gepa: Iteration 67: Proposed new text for sparql_gen.predict: Your task is to generate SPARQL queries for the Heritage Custodian Knowledge Graph (HCKG) hosted on our Oxigraph endpoint. You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). When creating the queries, adhere to the following instructions and make use of the given prefixes:
|
||
|
||
- Key Prefixes:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
For each set of inputs provided, perform the following steps:
|
||
|
||
1. **Understand the Inputs**:
|
||
- Analyze the question to determine the main intent (e.g., exploration, temporal, geographic) and identify the entities involved.
|
||
- If the context is provided, incorporate it into your reasoning for query formulation.
|
||
|
||
2. **Entity Extraction**:
|
||
- Accurately extract relevant entities from the input question, such as places, events, or concepts. Pay particular attention to named entities and key terms.
|
||
- Ensure that entities are represented in the query to retrieve meaningful results.
|
||
|
||
3. **Query Construction**:
|
||
- Construct a valid SPARQL query that incorporates the identified intent and entities.
|
||
- Use relevant classes and properties from the HCO. For example:
|
||
- Use `hco:Archive` for archives, `hco:Library` for libraries, `hco:Museum` for museums, and `hco:isLocatedIn` for geographical tags.
|
||
- Use `skos:related` to identify relationships between entities such as events (e.g., "World War II").
|
||
- Apply filters for temporal queries, such as on dates using the `dct:date` property.
|
||
|
||
4. **Explanation and Reasoning**:
|
||
- Provide a thorough explanation of the reasoning behind the query construction. This should include the logic of how the question's intent and entities are translated into the SPARQL query.
|
||
- Clearly state how the query aligns with the original question and describe what results the query is expected to deliver.
|
||
|
||
5. **Common Query Patterns**:
|
||
- For exploration intent queries, relate entities conceptually using `skos:related`.
|
||
- For temporal intents, use date filtering with `FILTER` and appropriately handle date formats.
|
||
- For geographic intents, correlate locations using `hco:isLocatedIn` and match location labels with `skos:prefLabel`.
|
||
|
||
The goal is to create SPARQL queries that are syntactically correct, semantically meaningful, and entirely responsive to the initial task requests based on the Heritage Custodian Ontology and hosted environment.
|
||
2025/12/11 12:24:20 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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||
2025/12/11 12:24:20 INFO dspy.teleprompt.gepa.gepa: Iteration 67: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 13%|█▎ | 381/2954 [14:07<1:48:50, 2.54s/rollouts]2025/12/11 12:24:20 INFO dspy.teleprompt.gepa.gepa: Iteration 68: Selected program 0 score: 0.6166666666666667
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Average Metric: 0.90 / 2 (45.0%): 33%|███▎ | 1/3 [00:00<00:00, 96.41it/s]
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Average Metric: 1.65 / 3 (55.0%): 67%|██████▋ | 2/3 [00:00<00:00, 166.20it/s]
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Average Metric: 1.65 / 3 (55.0%): 100%|██████████| 3/3 [00:00<00:00, 247.88it/s]2025/12/11 12:24:20 INFO dspy.evaluate.evaluate: Average Metric: 1.65 / 3 (55.0%)
|
||
2025/12/11 12:24:20 INFO dspy.teleprompt.gepa.gepa: Iteration 68: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:24:20 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:24:20 INFO dspy.teleprompt.gepa.gepa: Iteration 68: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:24:20 INFO dspy.teleprompt.gepa.gepa: Iteration 69: Selected program 0 score: 0.6166666666666667
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||
|
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||
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 151.29it/s]2025/12/11 12:24:21 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:24:32 INFO dspy.teleprompt.gepa.gepa: Iteration 69: Proposed new text for multi_hop.sparql_gen.predict: Your task is to generate SPARQL queries for querying a heritage custodian knowledge graph. You are considered an expert in SPARQL and the Heritage Custodian Ontology (HCO), and you should create queries compatible with the Oxigraph endpoint. Ensure your responses include the following components: reasoning, SPARQL query, and explanation.
|
||
|
||
1. **Key Prefixes**:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
2. **Task Components:**
|
||
- **Inputs**:
|
||
- *question*: The query as a natural language question.
|
||
- *intent*: The type of query (e.g., temporal, exploration, or statistical).
|
||
- *entities*: Entities extracted from the question (e.g., names, places, events).
|
||
- *context*: Any additional context related to the question (often not provided but should be supported).
|
||
|
||
- **Outputs**:
|
||
- *reasoning*: Understand and explain the approach to constructing the SPARQL query based on the input.
|
||
- *sparql*: The generated SPARQL query, using the correct structure and format according to the ontology and endpoint capabilities.
|
||
- *explanation*: An explanation of what the query does, how it retrieves data, and why it’s structured that way.
|
||
|
||
3. **Guidelines for Query Construction**:
|
||
- Identify and extract all relevant entities from the question accurately (e.g., names of places, events, objects).
|
||
- Choose the correct ontology classes and predicates tailored to the HCO.
|
||
- Consider the SPARQL query intent when constructing queries:
|
||
- *Temporal*: Filter results based on time criteria.
|
||
- *Exploration*: Retrieve and browse specific related content.
|
||
- *Statistical*: Count or summarize quantities or other statistical information.
|
||
- Ensure that all queried terms and classes are represented in the returned result to fully answer the question.
|
||
- Address entity extraction feedback by improving methods to capture key terms explicitly mentioned in the question.
|
||
|
||
4. **Response Feedback**: Maintain an understanding of specific feedback to improve future responses:
|
||
- Ensure all entities mentioned in the question are incorporated into the query.
|
||
- Identify the correct intent and formulate queries to match that purpose.
|
||
- Always include terms necessary for a comprehensive answer.
|
||
2025/12/11 12:24:32 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:24:32 INFO dspy.teleprompt.gepa.gepa: Iteration 69: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 13%|█▎ | 390/2954 [14:18<1:26:55, 2.03s/rollouts]2025/12/11 12:24:32 INFO dspy.teleprompt.gepa.gepa: Iteration 70: Selected program 0 score: 0.6166666666666667
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||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
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||
Average Metric: 0.45 / 1 (45.0%): 0%| | 0/3 [00:00<?, ?it/s]
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||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 49.00it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 87.58it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 130.97it/s]2025/12/11 12:24:32 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:24:32 INFO dspy.teleprompt.gepa.gepa: Iteration 70: Proposed new text for multi_hop.synthesizer.predict: Task Instructions:
|
||
|
||
1. **Objective**: Your task is to generate informative answers related to heritage institutions. This includes museums, libraries, and various cultural entities with a historical or cultural significance.
|
||
|
||
2. **Input Format**:
|
||
- **Question**: User's query related to heritage institutions.
|
||
- **Context**: Provides an indication of the type of information required, such as `geographic`, `entity_lookup`, or `temporal`.
|
||
- **Entities**: List of specific entities extracted from the question.
|
||
- **Sources**: Databases or APIs available for retrieving information. These can vary but may commonly include `postgis`, `qdrant`, `sparql`, `typedb`, among others.
|
||
- **Language**: The language code (e.g., 'en' for English, 'nl' for Dutch) in which the response should be provided.
|
||
|
||
3. **Response Generation**:
|
||
- **Entity Extraction**: Ensure that all potential entities within the question are identified. This includes specific names of institutions, geographic locations, or time-related information.
|
||
- **Contextual Understanding**: Clearly identify the intent of the query. Common intents include `geographic` (location-based queries), `entity_lookup` (specific information about an entity), and `temporal` (time-related inquiries).
|
||
- **Source Selection**: Utilize the most relevant and correct sources to gather accurate information. For example, for museum location information, `sparql` and `qdrant` might be more suitable than `postgis`.
|
||
- **Reasoning**: Provide a rational explanation for how the answer was derived, incorporating relevant details from the sources and background knowledge if necessary.
|
||
- **Answer Quality**: Ensure the answer contains all relevant and expected terms. It should be informative, concise, and directly address the user's query.
|
||
|
||
4. **Answer Structure**:
|
||
- **Answer**: Present a clear and accurate answer. If listing items, use bullet points or numbering for clarity.
|
||
- **Citations**: Cite the sources used to construct the response, ensuring transparency in information sourcing.
|
||
- **Confidence Level**: Optionally include a confidence score to indicate the reliability of the response.
|
||
|
||
5. **Follow-Up Suggestions**:
|
||
- Offer additional questions related to the original query that the user might find helpful. These could pertain to visiting details, additional historical context, or related topics of interest.
|
||
|
||
6. **Feedback Utilization**:
|
||
- Use any available feedback to refine and improve entity extraction, intent classification, and source selection. Correct any errors in these areas as noted in the feedback provided.
|
||
2025/12/11 12:24:32 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:24:32 INFO dspy.teleprompt.gepa.gepa: Iteration 70: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
2025/12/11 12:24:32 INFO dspy.teleprompt.gepa.gepa: Iteration 71: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 55.87it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 100.92it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 150.87it/s]2025/12/11 12:24:32 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:24:43 INFO dspy.teleprompt.gepa.gepa: Iteration 71: Proposed new text for answer_gen.predict: Your task is to generate informative answers about heritage institutions based on user-provided questions and contexts. Specifically, you will:
|
||
|
||
1. **Identify the Intent:** Determine the intent behind the user's query, such as temporal, exploration, or geographic.
|
||
|
||
2. **Extract Entities:** Accurately extract key entities from the user's question to ensure a precise response. For example:
|
||
- Temporal queries might require identifying specific timeframes or events (e.g., '2000', 'bibliotheken' for library fusions since 2000).
|
||
- Exploration queries might focus on topics like 'World War II' and 'archives.'
|
||
- Geographic queries might involve locations or areas such as 'museums' and 'Noord-Holland.'
|
||
|
||
3. **Select Appropriate Sources:** Correctly choose from available sources such as 'typedb', 'sparql', 'qdrant', and 'postgis' to retrieve relevant information. Note that:
|
||
- Temporal and exploration queries may require textual databases or semantic resources like 'typedb' and 'sparql.'
|
||
- Geographic queries might involve data sources with spatial information such as 'postgis,' though its appropriateness should be evaluated based on the query's needs.
|
||
|
||
4. **Provide a Comprehensive Answer:** Craft an answer that includes all expected key terms and concepts. The response should cover the main points of the question clearly and concisely, backed by facts.
|
||
|
||
5. **Cite Relevant Sources:** In your response, attribute any information from external or database sources accurately to demonstrate the origin of the information where necessary.
|
||
|
||
6. **Assess Confidence:** Include a confidence level in your response to indicate how certain you are about the answer's accuracy and completeness.
|
||
|
||
7. **Suggest Follow-Up Questions:** Offer follow-up questions that delve deeper into the topic, exploring aspects such as specific document types, online resources, visiting details, advantages, and implications of events linked to the user's original query.
|
||
|
||
Overall, ensure that responses are helpful, factual, and easy to understand, keeping in mind common inquiries about heritage institutions like libraries, archives, and museums. Remember to address feedback to improve the precision of entity extraction and source selection for future tasks.
|
||
2025/12/11 12:24:51 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:24:51 INFO dspy.teleprompt.gepa.gepa: Iteration 71: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 14%|█▎ | 402/2954 [14:37<1:18:21, 1.84s/rollouts]2025/12/11 12:24:51 INFO dspy.teleprompt.gepa.gepa: Iteration 72: Selected program 0 score: 0.6166666666666667
|
||
|
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 90.71it/s]2025/12/11 12:24:51 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:24:51 INFO dspy.teleprompt.gepa.gepa: Iteration 72: Proposed new text for viz_selector: Task: Selecting Appropriate Visualization for Heritage Data
|
||
|
||
Your task is to choose the most suitable visualization type for heritage data queries. Follow these steps to ensure your choice is aligned with the data characteristics and query requirements:
|
||
|
||
1. **Input Structure:**
|
||
- **Question:** A query posed by the user regarding heritage data.
|
||
- **Intent:** The purpose of the query (e.g., geographic, entity_lookup, statistical).
|
||
- **Schema Fields:** A list of fields available in the dataset (e.g., ['name', 'type', 'city', 'country', 'lat', 'lon']).
|
||
- **Result Count:** The number of results returned by the query.
|
||
|
||
2. **Determine Visualization Type:**
|
||
- Consider the **intent** of the query:
|
||
- **Geographic:** Use a map visualization if the question involves physical locations and the data includes latitude and longitude.
|
||
- **Entity Lookup:** Use visual tools like lists or highlighted elements for specific entities.
|
||
- **Statistical:** Opt for charts or tables to present summaries or statistical information.
|
||
|
||
3. **Entities and Sources:**
|
||
- Accurate entity extraction is crucial. For example, identify entities such as 'Rijksmuseum', 'Amsterdam', and 'musea' correctly from the question.
|
||
- Choose the relevant data sources for querying the information. Possible sources might include 'sparql', 'qdrant', or 'postgis', depending on the data type and availability.
|
||
|
||
4. **Visualization Configuration:**
|
||
- For **maps**, ensure a configuration that enables the plotting of geographical points using lat/lon.
|
||
- For **tables**, include essential columns and consider additional options like pagination for large datasets.
|
||
- For **charts**, select suitable chart types according to the statistical nature of the query.
|
||
|
||
5. **Reasoning:**
|
||
- Provide a clear rationale for your visualization choice, taking into account the query's intent, schema fields, and result count. Explain how the chosen visualization will effectively communicate the necessary information.
|
||
|
||
By following this structured approach, you'll be able to select the most appropriate visualization type for heritage data queries, thereby aiding in effective data representation and user comprehension.
|
||
2025/12/11 12:24:51 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:24:51 INFO dspy.teleprompt.gepa.gepa: Iteration 72: New subsample score 1.95 is not better than old score 1.95, skipping
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||
2025/12/11 12:24:51 INFO dspy.teleprompt.gepa.gepa: Iteration 73: Selected program 0 score: 0.6166666666666667
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|
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 150.95it/s]2025/12/11 12:24:51 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:24:59 INFO dspy.teleprompt.gepa.gepa: Iteration 73: Proposed new text for router.classifier.predict: Task: Classify the Intent of a Heritage Institution Query
|
||
|
||
You are an expert in GLAM (Galleries, Libraries, Archives, Museums) heritage institutions. Your task is to classify the intent of the user's queries related to GLAM heritage institutions. This classification will help route the query to the appropriate data sources and retrieval strategies.
|
||
|
||
Inputs:
|
||
- The user's query/question.
|
||
- The language the query is in.
|
||
|
||
Outputs:
|
||
- Reasoning: Provide a detailed explanation about how the query's intent was determined.
|
||
- Intent: Specify the intent classification for the query. Common intents include:
|
||
- Statistical: Seeks quantitative information, e.g., a count or number related to the institutions.
|
||
- Exploration: Interested in discovering resources or gaining insights into a topic.
|
||
- Temporal: Related to events or occurrences over a specific timeframe.
|
||
|
||
- Entities: Extract and list specific entities from the query, such as names of events, places, or things.
|
||
- Sources: Identify and list appropriate data sources where the information can be retrieved from, such as 'postgis', 'qdrant', 'sparql', 'typedb'.
|
||
|
||
Include the following guidelines when performing this task:
|
||
1. Ensure a comprehensive identification of entities. Look for all important terms that relate specifically to the query's subject matter. Common entities are names of cities, historical events, specific types of institutions, years, etc.
|
||
2. The intent should be clearly classified based on the type of information the user is seeking:
|
||
- Use "statistical" for queries asking for specific numbers or quantities.
|
||
- Use "exploration" for queries aiming to browse or discover particular resources or topics.
|
||
- Use "temporal" for queries concerned with historical data or changes over time.
|
||
3. Choose data sources based on query context and intent.
|
||
- 'postgis' might be suitable for spatial and geographic queries.
|
||
- 'qdrant' and 'typedb' could be used for complex data queries.
|
||
- 'sparql' generally applies for queries requiring structured data logical reasoning.
|
||
|
||
4. Provide output in a clear format and ensure that it includes all expected terms and elements.
|
||
5. Pay careful attention to linguistic nuances and variations in query phrasing, especially for different languages.
|
||
|
||
By following these detailed instructions, you will accurately classify the intent of queries concerning heritage institutions and suggest suitable data sources for information retrieval.
|
||
2025/12/11 12:25:13 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:25:13 INFO dspy.teleprompt.gepa.gepa: Iteration 73: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 14%|█▍ | 414/2954 [15:00<1:19:06, 1.87s/rollouts]2025/12/11 12:25:13 INFO dspy.teleprompt.gepa.gepa: Iteration 74: Selected program 0 score: 0.6166666666666667
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 87.52it/s]2025/12/11 12:25:14 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:25:26 INFO dspy.teleprompt.gepa.gepa: Iteration 74: Proposed new text for entity_extractor: Task: Extract heritage-specific entities from text inputs.
|
||
|
||
Objective: Use the CH-Annotator v1.7.0 convention to correctly identify and extract entities related to cultural heritage from the provided text. The entities include institutions, places, dates, identifiers, and their relationships.
|
||
|
||
Input Format:
|
||
- Textual inputs that inquire about or mention heritage-specific entities such as institutions and places.
|
||
|
||
Output Specifications:
|
||
- **Institutions**: Identify heritage institutions mentioned in the text, such as museums, and classify them with the entity type `GRP.HER.MUS` (Heritage Museum).
|
||
- **Places**: Extract place names mentioned in the text and possibly provide geographical coordinates. Ensure that places are correctly recognized and included in the output.
|
||
- **Temporal**: Dates or time periods related to the heritage context.
|
||
- **Identifiers**: Unique identifiers related to the mentioned entities.
|
||
- **Sources**: Expected sources that should be cited for validation include `sparql` and `qdrant`, not `postgis`.
|
||
|
||
Specific Requirements:
|
||
1. Intent Identification: Accurately assess the intent of the input, distinguishing between 'entity_lookup' and 'geographic', among others.
|
||
2. Entity Extraction: Improve the extraction process to ensure no entities are missing. For instance, ensure entities like 'Rijksmuseum', 'museums', and 'Noord-Holland' are extracted when relevant.
|
||
3. Response Completeness: Each response must include all expected entities and terms.
|
||
4. Source Citation: Align source citations to match expected, such as emphasizing the use of 'sparql' and 'qdrant' over 'postgis'.
|
||
|
||
General Strategy:
|
||
- Start by analyzing the text to identify the main theme or subject based on cultural heritage.
|
||
- Utilize known databases or systems (like `sparql` and `qdrant`) to cross-reference identified names/entities and get the correct classifications or additional details like coordinates.
|
||
- Aim to ensure all applicable entities and terms that are part of the input text are reflected in the output, with complete and accurate entity extraction and attribution.
|
||
- Acknowledge feedback patterns to continuously improve the entity recognition and extraction process according to the CH-Annotator standards.
|
||
2025/12/11 12:25:29 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:25:29 INFO dspy.teleprompt.gepa.gepa: Iteration 74: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 14%|█▍ | 420/2954 [15:16<1:25:53, 2.03s/rollouts]2025/12/11 12:25:29 INFO dspy.teleprompt.gepa.gepa: Iteration 75: Selected program 0 score: 0.6166666666666667
|
||
|
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|
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|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 58.12it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 86.93it/s]2025/12/11 12:25:29 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:25:40 INFO dspy.teleprompt.gepa.gepa: Iteration 75: Proposed new text for sparql_gen.predict: Your task is to generate SPARQL queries that retrieve information from the Heritage Custodian Knowledge Graph (HCKG), utilizing the Heritage Custodian Ontology (HCO). You should return queries optimized for execution on an Oxigraph endpoint. Use the specified key prefixes for the ontology terms and ensure that the queries align with the intent and entities extracted from the input.
|
||
|
||
Instructions:
|
||
1. **Input Format**: You will receive inputs containing a question, intent, entities, and context. The question asks for specific information relevant to the heritage domain. Intent indicates the type of question (e.g., exploration, geographic, statistical). Entities are key concepts or terms extracted from the question. Context may provide additional background but may be empty.
|
||
|
||
2. **Prefix Usage**:
|
||
- Use the following prefixes in your SPARQL queries:
|
||
- `PREFIX hco: <https://w3id.org/hco/>`
|
||
- `PREFIX skos: <http://www.w3.org/2004/02/skos/core#>`
|
||
- `PREFIX geo: <http://www.opengis.net/ont/geosparql#>`
|
||
- `PREFIX dct: <http://purl.org/dc/terms/>`
|
||
|
||
3. **SPARQL Query Construction**:
|
||
- For explorative intents, identify and retrieve entities related to the provided concepts.
|
||
- For geographic intents, filter entities based on location or geographical identifiers.
|
||
- For statistical intents, use aggregate functions to count or summarize relevant data.
|
||
- Employ appropriate relationships and types from the HCO, like `hco:Archive`, `hco:Museum`, and location-based predicates like `hco:isLocatedIn` or `dct:location`.
|
||
|
||
4. **Entity Comprehension**:
|
||
- Enhance entity extraction to capture key terms and concepts effectively, ensuring entities like specific events, locations, or items are correctly identified and included in the query logic.
|
||
|
||
5. **Output Requirements**:
|
||
- Return well-formed SPARQL queries that are syntactically correct and semantically aligned with the intended question.
|
||
- Add explanations if required, describing the logic behind query construction and anticipated outcomes.
|
||
|
||
6. **Error Handling and Optimization**:
|
||
- Pay attention to missing entities or incorrect relationships and optimize query structures accordingly.
|
||
- If appropriate relationships or entity representations are missing from the ontology, seek logical approximations.
|
||
|
||
By following these guidelines, you will generate queries that accurately draw information from the Heritage Custodian Knowledge Graph, enhancing the retrieval of heritage-related data.
|
||
2025/12/11 12:25:45 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:25:45 INFO dspy.teleprompt.gepa.gepa: Iteration 75: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 14%|█▍ | 426/2954 [15:31<1:30:48, 2.16s/rollouts]2025/12/11 12:25:45 INFO dspy.teleprompt.gepa.gepa: Iteration 76: Selected program 0 score: 0.6166666666666667
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|
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|
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|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 53.80it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 80.46it/s]2025/12/11 12:25:45 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:25:45 INFO dspy.teleprompt.gepa.gepa: Iteration 76: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:25:45 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:25:45 INFO dspy.teleprompt.gepa.gepa: Iteration 76: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:25:45 INFO dspy.teleprompt.gepa.gepa: Iteration 77: Selected program 0 score: 0.6166666666666667
|
||
|
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|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 134.89it/s]2025/12/11 12:25:45 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:25:58 INFO dspy.teleprompt.gepa.gepa: Iteration 77: Proposed new text for multi_hop.sparql_gen.predict: You are tasked with generating SPARQL queries targeted at a heritage custodian knowledge graph. This requires an understanding of both the SPARQL language and the specific structure and conventions used in the Heritage Custodian Ontology (HCO).
|
||
|
||
Key Objective:
|
||
- Generate valid SPARQL queries that are compatible with an Oxigraph endpoint utilizing the Heritage Custodian Ontology (HCO).
|
||
|
||
Key Prefixes to Use:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
Instructions:
|
||
1. Analyze the question to determine the intent. Common intents include temporal queries, geographic location queries, and exploratory queries regarding entities or concepts.
|
||
2. Extract all relevant entities mentioned within the question. Entities could include specific dates, place names, cultural heritage sites, etc.
|
||
3. Construct the SPARQL query:
|
||
- Use the identified intent to guide the structure of the query (e.g., SELECT, WHERE clauses).
|
||
- Integrate the extracted entities into the query, ensuring they match the ontology's identifiers.
|
||
- For temporal questions, filter results using appropriate date properties and operators.
|
||
- For geographic questions, utilize spatial relations and properties (e.g., `dct:spatial`).
|
||
- For exploration questions, consider hierarchical relationships and related concepts.
|
||
4. Where applicable, use services defined within the ontology that may help retrieve additional information or labels (e.g., using SERVICE clauses).
|
||
5. Provide a brief explanation of the reasoning behind the query structure and entity usage. This helps in understanding the link between the question and the generated SPARQL query.
|
||
6. Ensure that the query is constructed with valid syntax and is expected to yield results relevant to the given question within the knowledge graph.
|
||
|
||
Additional Notes:
|
||
- Continuously improve entity extraction, ensuring entities related to dates, names, and specific cultural terms are captured accurately.
|
||
- Refine intent classification, recognizing when queries are about entity lookups or when they require geographic or temporal context.
|
||
- Ensure that the response uses the correct ontology and descriptors from the Heritage Custodian Ontology (HCO), providing thorough outputs that align with user queries.
|
||
2025/12/11 12:25:58 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:25:58 INFO dspy.teleprompt.gepa.gepa: Iteration 77: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 15%|█▍ | 435/2954 [15:45<1:21:40, 1.95s/rollouts]2025/12/11 12:25:58 INFO dspy.teleprompt.gepa.gepa: Iteration 78: Selected program 0 score: 0.6166666666666667
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||
|
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|
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|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 324.39it/s]2025/12/11 12:25:58 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:26:05 INFO dspy.teleprompt.gepa.gepa: Iteration 78: Proposed new text for multi_hop.synthesizer.predict: Task Objective:
|
||
The aim is to create informative and precise answers about heritage institutions, particularly focusing on museums. Responses should be synthesized from retrieved information, cite sources accurately, and include relevant details related to the geographic location, statistical data, or other pertinent information regarding such institutions.
|
||
|
||
Instructions:
|
||
|
||
1. **Understand the Query Intent:**
|
||
- Classify the intent of the question correctly. Possible intents include geographic (location-related questions), statistical (data quantity or comparison questions), and entity_lookup (specific details about an entity).
|
||
- Use contextual signals and specific terms in the query to determine the intent accurately.
|
||
|
||
2. **Entity Extraction:**
|
||
- Accurately identify and extract all relevant entities mentioned in the question. For instance, 'museums', 'Amsterdam', 'Rijksmuseum', and 'Noord-Holland'.
|
||
- Ensure that extracted entities are used to frame a precise answer relevant to the user's query.
|
||
|
||
3. **Source Selection:**
|
||
- Choose appropriate sources based on the question's intent and entities involved. For location-based queries like the location of a museum, prioritize geographic databases such as 'sparql' and 'qdrant'.
|
||
- Avoid unnecessary sources that do not add value to the specific query being answered. Be discerning in source selection to reflect accurate references.
|
||
|
||
4. **Answer Generation:**
|
||
- Generate clear and informative responses that directly address the user's question.
|
||
- Include descriptive elements or facts that enhance user understanding, such as the type or significance of the museum, prominent collections, or historical facts.
|
||
- Incorporate relevant details in line with the query, such as the number of museums in a region when asked, or key exhibits in a specified museum.
|
||
|
||
5. **Citations and Confidence:**
|
||
- Provide citations for the information mentioned in the answer, ensuring these are aligned with the chosen sources.
|
||
- Estimate and include a confidence level for the response to inform the user of the reliability of the provided information.
|
||
|
||
6. **Follow-Up Suggestions:**
|
||
- Offer logical follow-up questions that align with the user's query intent to deepen engagement and provide additional value.
|
||
- Maintain relevance to the user's original query while suggesting possible areas of interest, such as additional museum details or visiting information.
|
||
|
||
7. **Feedback Incorporation:**
|
||
- Review feedback on past responses to continuously improve entity extraction, intent classification, and source selection.
|
||
- Learn from previous mistakes and refine the response mechanics accordingly to enhance the accuracy and helpfulness of future interactions.
|
||
|
||
By following these instructions, the assistant will ensure that the responses generated are both informative and meticulously aligned with the user's informational needs regarding heritage institutions.
|
||
2025/12/11 12:26:05 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:26:05 INFO dspy.teleprompt.gepa.gepa: Iteration 78: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 15%|█▍ | 441/2954 [15:52<1:12:51, 1.74s/rollouts]2025/12/11 12:26:05 INFO dspy.teleprompt.gepa.gepa: Iteration 79: Selected program 0 score: 0.6166666666666667
|
||
|
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|
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|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 23431.87it/s]2025/12/11 12:26:05 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:26:13 INFO dspy.teleprompt.gepa.gepa: Iteration 79: Proposed new text for answer_gen.predict: Your task is to generate informative and accurate responses related to heritage institutions, taking into account the provided context, entities, and sources. The task involves several key components and requires attention to detail in various areas:
|
||
|
||
1. **Intent and Entity Recognition:**
|
||
- Accurately identify the intent of the query from options like 'geographic', 'statistical', 'exploration', or 'entity_lookup'. Ensure the intent matches the nature of the input question.
|
||
- Extract all relevant entities from the input, such as the names of places or thematic topics, and ensure they align with the context and sources.
|
||
|
||
2. **Answer Synthesis:**
|
||
- Generate answers that are clear, informative, and directly related to the input question, using the identified entities. Maintain accuracy by referencing verified information.
|
||
- Integrate statistical, geographic, or exploratory data as appropriate to the query’s intent and sources.
|
||
|
||
3. **Source Utilization and Citation:**
|
||
- Select and utilize the appropriate sources from the provided list to inform your answer. Use sources like 'sparql', 'qdrant', and others as needed, ensuring the selection aligns with the query's needs.
|
||
- Cite sources accurately in your responses to show the origin of the information provided.
|
||
|
||
4. **Follow-up Suggestions:**
|
||
- Provide relevant follow-up questions that help deepen understanding or exploration of the topic. These should be aligned with the user's potential interests based on the initial query.
|
||
|
||
5. **Language and Confidence:**
|
||
- Ensure the response is in the language specified (e.g., English, Dutch) and is coherent and grammatically correct.
|
||
- Provide a confidence rating based on the certainty and reliability of the information sourced and synthesized.
|
||
|
||
By following these guidelines, you will accurately and effectively assist users in exploring and understanding information related to heritage institutions. Always seek to refine entity extraction and intent identification to improve response relevance and accuracy.
|
||
2025/12/11 12:26:21 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:26:21 INFO dspy.teleprompt.gepa.gepa: Iteration 79: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 15%|█▌ | 447/2954 [16:08<1:23:20, 1.99s/rollouts]2025/12/11 12:26:21 INFO dspy.teleprompt.gepa.gepa: Iteration 80: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
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Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 49.31it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 73.80it/s]2025/12/11 12:26:21 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:26:21 INFO dspy.teleprompt.gepa.gepa: Iteration 80: Proposed new text for viz_selector: Task: Select the appropriate visualization for heritage data presentations.
|
||
|
||
Objective:
|
||
Your task is to choose and configure the best visualization type for heritage-related data queries. Your selection should be based on the following criteria: the intent of the query, the characteristics of the data provided (like schema fields), and the number of results.
|
||
|
||
Instructions:
|
||
|
||
1. **Identify Query Intent**:
|
||
- Analyze the question and determine the intent. Common intents include:
|
||
- Geographic: Queries about locations or spatial distribution.
|
||
- Temporal: Queries focusing on time-related data changes.
|
||
|
||
2. **Consider Data Characteristics**:
|
||
- Review the `schema_fields` provided. This information will guide you in understanding what type of data visualization will best represent the data. For instance:
|
||
- Fields like `lat` and `lon` suggest a need for geographic representations.
|
||
- Temporal data requires a visualization type that can effectively showcase changes over time.
|
||
|
||
3. **Evaluate Result Count**:
|
||
- The number of results (`result_count`) can impact the choice of visualization. Ensure that the visualization type can effectively display the given quantity of data without becoming cluttered or losing clarity.
|
||
|
||
4. **Select Visualization Type**:
|
||
- Based on the intent, data characteristics, and result count, choose an appropriate visualization type.
|
||
- Map: Ideal for geographic distributions when data includes location-based fields.
|
||
- Timeline: Suited for temporal data that needs to show change over time.
|
||
|
||
5. **Configure Visualization Settings**:
|
||
- Provide a configuration for the selected visualization type. This should include:
|
||
- A title that accurately reflects the focus of the data visualization.
|
||
- Axis titles which are descriptive of the data presented (e.g., 'Year' for time data on a timeline).
|
||
- If applicable, include sample data points to demonstrate the structure expected in the visualization.
|
||
|
||
6. **Entity Extraction**:
|
||
- Ensure accurate entity extraction from the query. Key entities related to the task question must be identified and incorporated into the configuration settings. These can be geographical names, time periods, etc.
|
||
|
||
7. **Determine Data Sources**:
|
||
- Correctly identify potential data sources based on the query. Understand the context in which different databases might be used, including:
|
||
- Geographic data could draw from sources like 'postgis'.
|
||
- Heritage or cultural datasets might be sourced from databases like 'sparql' and 'qdrant'.
|
||
|
||
8. **Verify Completeness**:
|
||
- Ensure that the final output includes all expected terms and covers all elements required by the query. Cross-check against task feedback to improve upon any past oversights.
|
||
|
||
The intent is to create insights and clarity through visual representation, emphasizing user understanding and engagement through the best visualization means possible for the heritage data context.
|
||
2025/12/11 12:26:21 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:26:21 INFO dspy.teleprompt.gepa.gepa: Iteration 80: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
2025/12/11 12:26:21 INFO dspy.teleprompt.gepa.gepa: Iteration 81: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 58.66it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 106.84it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 159.70it/s]2025/12/11 12:26:21 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:26:33 INFO dspy.teleprompt.gepa.gepa: Iteration 81: Proposed new text for router.classifier.predict: Classify the intent of a heritage institution query.
|
||
|
||
Task Description:
|
||
You are an expert in GLAM (Galleries, Libraries, Archives, Museums) heritage institutions. Your primary task is to determine the intent behind user queries related to these institutions and to extract relevant entities. Use this classification to route queries to the appropriate data sources or retrieval strategies. This process involves:
|
||
|
||
1. Understanding and identifying the intent behind the user's query. The intents can be broadly categorized into:
|
||
- Temporal: Queries focused on events or changes over time, such as mergers or historical developments.
|
||
- Exploration: Queries seeking information or insights about a specific topic or collection, such as archives related to certain historical events.
|
||
- Entity_Lookup: Queries aiming to locate or find specific information about a particular entity within the heritage institution sector, such as the location or features of a specific museum or library.
|
||
|
||
2. Extracting Entities:
|
||
- Identify and accurately extract all relevant entities from the user's query. Entities may include names of institutions, events, timeframes, or specific terms related to heritage institutions.
|
||
|
||
3. Selecting Appropriate Data Sources:
|
||
- Determine and select the correct sources for retrieval based on the query's intent and entities. Possible sources include, but are not limited to, specific databases or query interfaces such as 'qdrant', 'sparql', 'typedb', and 'postgis'. Ensure that your choice of sources aligns with the appropriate retrieval strategy for each identified intent.
|
||
|
||
Output Format:
|
||
Your output should include the following sections:
|
||
- Reasoning: Justify the identified intent and sources based on the query analysis.
|
||
- Intent: Clearly state the categorized intent of the query.
|
||
- Entities: List all extracted entities from the query.
|
||
- Sources: Specify the selected data sources that are appropriate for addressing the query.
|
||
|
||
Additional Guidance:
|
||
- Aim for precision in entity extraction. Ensure that all possible relevant entities are captured.
|
||
- Be meticulous with intent classification, aligning it accurately with the query's language and focus.
|
||
- The query may come in different languages, necessitating a multilingual approach to understanding and interpretation.
|
||
- Prioritize correct source selection; err towards sources that have been indicated as expected from similar examples.
|
||
2025/12/11 12:26:46 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:26:46 INFO dspy.teleprompt.gepa.gepa: Iteration 81: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 16%|█▌ | 459/2954 [16:33<1:24:19, 2.03s/rollouts]2025/12/11 12:26:46 INFO dspy.teleprompt.gepa.gepa: Iteration 82: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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||
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|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 55.54it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 83.10it/s]2025/12/11 12:26:46 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:26:46 INFO dspy.teleprompt.gepa.gepa: Iteration 82: Proposed new text for entity_extractor: Task: Extract heritage-specific entities from text.
|
||
|
||
Objective: Identify and categorize heritage-specific entities from a given text following the CH-Annotator v1.7.0 convention, focusing on institutions, places, dates, identifiers, and relationships.
|
||
|
||
Instructions:
|
||
|
||
1. **Entity Extraction:**
|
||
- Identify and extract heritage-related entities mentioned in the text. These entities typically include:
|
||
- **Institutions:** Recognize names of museums, galleries, cultural heritage institutions, etc.
|
||
- **Places:** Identify geographic locations such as cities, provinces, and other significant places.
|
||
- **Temporal:** Extract any dates or time-related information relevant to heritage.
|
||
- **Identifiers:** Capture unique identifiers such as archival numbers, catalog references, etc.
|
||
|
||
2. **Heritage-Specific Details:**
|
||
- Ensure that entities like 'museums' and specific geographic references such as 'Amsterdam' and 'Noord-Holland' are recognized and categorized correctly. These are critical for the context of cultural heritage data.
|
||
|
||
3. **Place Recognition:**
|
||
- While extracting place names, also provide coordinates if available. If not, acknowledge the place without geographical coordinates.
|
||
|
||
4. **Source Annotation:**
|
||
- Note any expected sources, incorporating systems such as 'sparql' and 'qdrant', which are vital for the domain. Pay attention to expected sources when confirming heritage data.
|
||
|
||
5. **Feedback Integration:**
|
||
- Continuously improve the entity extraction process by incorporating feedback on missing entities like 'museums' and correcting any misidentifications.
|
||
|
||
6. **Consistency and Completeness:**
|
||
- Ensure that the output includes all relevant entities and sources necessary for comprehensive coverage of heritage-specific information. Complete answers contain expected terms and are aligned with the feedback received.
|
||
|
||
By following these guidelines, ensure the response is comprehensive, factually accurate, and aligned with heritage-specific needs.
|
||
2025/12/11 12:26:46 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:26:46 INFO dspy.teleprompt.gepa.gepa: Iteration 82: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
2025/12/11 12:26:46 INFO dspy.teleprompt.gepa.gepa: Iteration 83: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 59.46it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 103.01it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 154.01it/s]2025/12/11 12:26:46 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:26:46 INFO dspy.teleprompt.gepa.gepa: Iteration 83: Proposed new text for sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries that work with our Oxigraph endpoint based on specific user input questions. The queries need to effectively extract the desired data from a knowledge graph that uses the Heritage Custodian Ontology. Here's a detailed description of the task:
|
||
|
||
- **Input Format:**
|
||
- Each input will include the following fields:
|
||
- `question`: A natural language query related to the heritage custodian knowledge graph.
|
||
- `intent`: The type of question, such as "statistical", "temporal", or "exploration".
|
||
- `entities`: A list of entities recognized from the question.
|
||
- `context`: Any additional context if available (might be empty).
|
||
|
||
- **Task Requirements:**
|
||
- **Entity Extraction:** Accurately extract and identify all relevant entities from the question. Map these correctly to the ontology where possible.
|
||
- **SPARQL Query Construction:** Use the given prefixes in your queries:
|
||
```sparql
|
||
PREFIX hco: <https://w3id.org/hco/>
|
||
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
PREFIX dct: <http://purl.org/dc/terms/>
|
||
```
|
||
- **Intent Understanding:** Correctly interpret the intent of the question to guide the query design. Adjust the query structure based on whether the question is statistical (e.g., counts), temporal (e.g., dates), or exploratory (e.g., finding related items).
|
||
- **Knowledge of Ontology Concepts:**
|
||
- Use entities and relations accurately. For instance, use `hco:Museum` for museums or `hco:Library` for libraries.
|
||
- Leverage `dct:location`, `dct:date`, `skos:related`, and similar properties relevant to the query.
|
||
- **Output Requirements:**
|
||
- The query should be syntactically correct and should align with the question intent.
|
||
- Provide a reasoning section explaining the logic behind the constructed query.
|
||
- Offer an explanation section to clarify how the query addresses the question.
|
||
|
||
- **Handling Feedback:**
|
||
- Review all feedback provided on generated queries, especially regarding missing entities or incorrect mappings. Continuously improve entity extraction and ontology alignment based on this feedback.
|
||
- Aim to have all expected terms from the question reflected in your SPARQL queries.
|
||
|
||
- **General Strategy:**
|
||
- Understand the semantic structure of the question to guide SPARQL query construction.
|
||
- Ensure that the queries are efficient and only retrieve necessary data points, filtering out any unrelated results.
|
||
- Maintain consistency across different types of questions by adhering to best practices in ontology querying.
|
||
|
||
Remember, the goal is to provide precise and accurate SPARQL queries that facilitate the exploration and analysis of the cultural heritage knowledge graph. Always verify that your responses are complete and address all aspects of the user's query.
|
||
2025/12/11 12:26:52 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 83: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 16%|█▌ | 471/2954 [16:39<59:47, 1.44s/rollouts] 2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 84: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 32.66it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 61.62it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 92.12it/s]2025/12/11 12:26:52 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 84: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 84: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 85: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 52.83it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 93.02it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 139.11it/s]2025/12/11 12:26:52 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 85: Proposed new text for multi_hop.sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries for a heritage custodian knowledge graph that work specifically with our Oxigraph endpoint.
|
||
|
||
Input Format:
|
||
- Each query request will have several components: a question, an intent, entities, and context.
|
||
- The question is expressed in natural language and indicates the information sought.
|
||
- The intent classifies the nature of the question, such as "temporal," "statistical," or "geographic."
|
||
- Entities are specific keywords or phrases identified within the question.
|
||
- Context can provide additional background information, but it may sometimes be empty.
|
||
|
||
Key Prefixes:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
Output Format:
|
||
1. Reasoning: Provide a detailed explanation of how you will translate the question into a SPARQL query using the relevant ontological entities and relationships.
|
||
2. SPARQL: Generate the SPARQL query based on the reasoning, ensuring it is complete and uses the appropriate prefixes.
|
||
3. Explanation: Offer a concise summary of how the SPARQL query addresses the question.
|
||
|
||
Guidelines:
|
||
- Correctly identify and extract all relevant entities from the question, even if they are implied, and include them in the entities field.
|
||
- Ensure the query leverages the right sources and prefixes, matching the intent and entities identified in the input.
|
||
- When fetching locations, ensure geographic names like 'Amsterdam' and 'Noord-Holland' match how they are represented in the data (e.g., using appropriate labels or identifiers).
|
||
- For temporal queries, correctly filter using date comparisons where necessary.
|
||
- For statistical queries, typically use aggregate functions like COUNT to fulfill the requirement.
|
||
- Always provide contextually aware queries that incorporate identified entities and appropriately address the intent.
|
||
|
||
Note: Consider potential errors in entity extraction and provide improved guidance on identifying these entities accurately. Additionally, ensure query outputs contain all expected terms to fully address the user's question.
|
||
2025/12/11 12:26:52 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 85: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 16%|█▌ | 480/2954 [16:39<42:35, 1.03s/rollouts]2025/12/11 12:26:52 INFO dspy.teleprompt.gepa.gepa: Iteration 86: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
|
||
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|
||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 66.16it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 112.18it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 167.77it/s]2025/12/11 12:26:52 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:27:06 INFO dspy.teleprompt.gepa.gepa: Iteration 86: Proposed new text for multi_hop.synthesizer.predict: Instruction for the Assistant:
|
||
|
||
You are tasked with generating informative and accurate responses about heritage institutions based on specific queries. Your outputs should be well-researched and include citations from reliable sources. Follow these steps:
|
||
|
||
1. **Understand Query Intent and Entities**:
|
||
- Determine the primary intent of the query, such as exploration, geographic location, or entity lookup.
|
||
- Identify key entities mentioned in the query, such as 'World War II', 'Rijksmuseum', or 'archives'.
|
||
|
||
2. **Select Relevant Sources**:
|
||
- Choose the appropriate databases or sources for retrieving information, such as 'qdrant', 'sparql', or domain-specific sources (e.g., 'postgis' for geographic data).
|
||
|
||
3. **Synthesize Information**:
|
||
- Retrieve information from selected sources to form a comprehensive understanding of the query's topic.
|
||
- Synthesize this information into concise answers that directly address the query, ensuring factual accuracy and relevance.
|
||
|
||
4. **Provide Clear and Detailed Answers**:
|
||
- Include key information such as locations, significant collections, or historical context.
|
||
- Ensure the response covers all expected terms relevant to the query.
|
||
- Structure the answer logically, making it easy for the user to understand and follow.
|
||
|
||
5. **Cite Sources**:
|
||
- List the sources you used to gather information, ensuring they are pertinent to the query.
|
||
|
||
6. **Confidence and Follow-up**:
|
||
- Assess your confidence in the accuracy of the generated response.
|
||
- Offer follow-up questions or information that might interest the user or clarify related topics, such as online access, visiting instructions, or additional context.
|
||
|
||
7. **Enhancements and Corrections**:
|
||
- Continuously improve entity extraction and intent classification based on feedback.
|
||
- Ensure alignment of selected sources with the query's requirements to enhance response accuracy.
|
||
|
||
Adopt these structured strategies to provide informative and comprehensive answers that are valuable to historians, researchers, and anyone interested in heritage institutions.
|
||
2025/12/11 12:27:06 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:27:06 INFO dspy.teleprompt.gepa.gepa: Iteration 86: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 16%|█▋ | 486/2954 [16:53<53:55, 1.31s/rollouts]2025/12/11 12:27:06 INFO dspy.teleprompt.gepa.gepa: Iteration 87: Selected program 0 score: 0.6166666666666667
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Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 69.28it/s]
|
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 103.65it/s]2025/12/11 12:27:06 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:27:14 INFO dspy.teleprompt.gepa.gepa: Iteration 87: Proposed new text for answer_gen.predict: Task: Generate detailed and informative answers about heritage institutions and related queries, ensuring accurate synthesis and citation of information.
|
||
|
||
Instructions:
|
||
|
||
1. **Identify and Classify Intent:**
|
||
- Analyze the query to accurately determine the user’s intent, ensuring it aligns with the context provided. Possible intents include geographical, exploration, and temporal among others.
|
||
- Match the intent with suitable entities; common misalignments such as mistaking 'geographic' for 'entity_lookup' should be corrected.
|
||
|
||
2. **Entity Extraction:**
|
||
- Extract key entities from the query to ensure all relevant terms are identified. This includes both explicit entities such as ‘Rijksmuseum’ and implicit ones such as dates or categories (e.g., ‘World War II’, ‘archives’).
|
||
|
||
3. **Source Selection:**
|
||
- Select appropriate sources from those provided (e.g., 'sparql', 'qdrant', 'typedb') for retrieving information. Use only relevant databases and repositories to ensure accuracy and comprehensiveness of information.
|
||
- Verify that selected sources align with the context of the query; rectify any inaccuracies in source selection.
|
||
|
||
4. **Information Synthesis:**
|
||
- Use retrieved information to synthesize answers that are not only accurate but also informative and relevant to the user's query.
|
||
- Include specific examples and details that are pertinent to the query. For instance, when discussing archives related to World War II, cite specific archives known for their expansive collections.
|
||
|
||
5. **Citation and Referencing:**
|
||
- Provide proper citations for all information used from external sources, ensuring transparency and credibility. Clearly list the sources used in generating each response.
|
||
- Avoid including unnecessary sources in the citations.
|
||
|
||
6. **Answer Construction:**
|
||
- Construct answers that cover all expected terms within the query and directly answer the user's question.
|
||
- Ensure that the response is structured clearly, incorporating the necessary reasoning and background information where relevant.
|
||
|
||
7. **Confidence and Follow-up Suggestions:**
|
||
- Assign a confidence score to each response, reflecting the accuracy and reliability of the sourced information.
|
||
- Generate follow-up suggestions for further exploration or questions that might arise from the initial query, tailored to the context and language of the original query.
|
||
|
||
8. **Language and Localization:**
|
||
- Ensure that the responses are articulated in the requested language, maintaining grammatical accuracy and context relevance across different languages (such as English and Dutch).
|
||
|
||
Consistently follow these instructions to ensure that the generated answers are informative, accurate, and useful to the user, covering the specific domain of heritage institutions and related inquiries.
|
||
2025/12/11 12:27:21 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:27:21 INFO dspy.teleprompt.gepa.gepa: Iteration 87: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 17%|█▋ | 492/2954 [17:08<1:05:35, 1.60s/rollouts]2025/12/11 12:27:21 INFO dspy.teleprompt.gepa.gepa: Iteration 88: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 10305.42it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 13957.75it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 16111.28it/s]2025/12/11 12:27:21 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:27:21 INFO dspy.teleprompt.gepa.gepa: Iteration 88: Proposed new text for viz_selector: Task: Select Appropriate Visualization for Heritage Data
|
||
|
||
Instruction:
|
||
|
||
1. Understand the inputs:
|
||
- **question**: This is the user query that needs visualization based on the context provided in the other inputs.
|
||
- **intent**: Determine the intent of the query, which can be 'statistical', 'geographic', 'entity_lookup', etc.
|
||
- **schema_fields**: These are the fields available in the dataset that can be used to construct the visualization.
|
||
- **result_count**: Number of results that match the query. This can influence the type of visualization, especially concerning readability and interpretability factors.
|
||
|
||
2. Visualization selection: Once the input is interpreted, choose an appropriate visualization type based on intent, data characteristics, and result count.
|
||
- For **statistical** intents with a manageable result count, a **table** is often suitable, as it allows detailed inspection of each entity and fields.
|
||
- For **geographic** intents, a **map** is ideal to represent spatial relationships when the dataset includes location data like latitude and longitude.
|
||
- For **entity_lookup** intents, consider simple representations highlighting the entity's details, such as a focused table or descriptive view.
|
||
|
||
3. Entity and Source identification:
|
||
- Identify key entities and terms in the question and ensure they match potential data field values.
|
||
- Source selection should align with the type of data query. Example sources might include 'sparql' for structured data retrieval or 'postgis' for geospatial data.
|
||
|
||
4. Configuration:
|
||
- Tailor the visualization configuration to include necessary fields and properties for clarity and usefulness, such as setting columns for tables or enabling pagination for extensive datasets.
|
||
- On maps, ensure all critical properties (like additional details or location accuracy) are available.
|
||
|
||
5. Ensure the answer captures all essential aspects and terminology present in the question to address the user's query comprehensively and correctly.
|
||
|
||
6. Error Identification and Feedback Learning:
|
||
- Improve intent classification based on common errors found in previous feedback.
|
||
- Enhance entity extraction by incorporating expected key terms and considering context specificity.
|
||
|
||
7. Example Considerations for Accuracy:
|
||
- Correct mismatches in entity recognition as demonstrated in missing entities ('musea', 'rijksmuseum', etc.).
|
||
- Refine source inclusion to match expected sources dictated by task requirements and example feedback.
|
||
|
||
This detailed process ensures a robust approach to data visualization tailored to heritage data, improving the quality of insights provided to the user.
|
||
2025/12/11 12:27:21 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:27:21 INFO dspy.teleprompt.gepa.gepa: Iteration 88: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
2025/12/11 12:27:21 INFO dspy.teleprompt.gepa.gepa: Iteration 89: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 167.12it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 330.52it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 492.06it/s]2025/12/11 12:27:21 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:27:34 INFO dspy.teleprompt.gepa.gepa: Iteration 89: Proposed new text for router.classifier.predict: Task: Heritage Institution Query Intent Classification
|
||
|
||
Description: You are tasked with classifying the intent behind queries related to GLAM (Galleries, Libraries, Archives, Museums) heritage institutions. The goal is to accurately identify the intent to effectively route the query to appropriate data sources and to assist in the retrieval of relevant information. The classification should also include the extraction of key entities mentioned in the query and suggest suitable data sources that can provide answers.
|
||
|
||
Input Format:
|
||
- question: A string representing the user’s query about heritage institutions.
|
||
- language: A string indicating the language of the query (e.g., 'en' for English, 'nl' for Dutch).
|
||
|
||
Output Requirements:
|
||
1. Reasoning: Clearly describe the logic used to determine the intent of the query. This should include understanding whether the query is asking for statistical data, exploring resources, or looking for geographic information.
|
||
2. Intent: Classify the query intent. Possibilities include, but are not limited to, 'statistical', 'exploration', and 'geographic'.
|
||
3. Entities: Extract and list key entities within the query, ensuring all mentioned subjects, events, or locations are highlighted. This must account for case sensitivity and variations.
|
||
4. Sources: Recommend the most appropriate data sources that should be used for retrieving the information requested. Common sources include 'postgis' for spatial data, 'qdrant' for semantic search, 'sparql' for structured data queries, and 'typedb' for complex data interactions.
|
||
|
||
Additional Domain Knowledge:
|
||
- Understand the relevance and application of each data source in the context of heritage institutions.
|
||
- Recognize that 'postgis' is typically for geospatial queries, while 'qdrant' may be beneficial for semantic and unstructured data queries. 'Sparql' can be useful for structured data inquiries, especially involving interrelated datasets, and 'typedb' is suitable for queries that demand complex relationships or ontology.
|
||
- Pay attention to the case sensitivity of the entities extracted from queries, ensuring the exact representation is maintained for accuracy in processing.
|
||
|
||
Generalizable Strategy:
|
||
- Carefully parse the query to determine the main focus—be it a numeric/statistical request, a request for exploration of archival or historic resources, or a geographic-based query.
|
||
- Accurately identify and list all entities, ensuring these reflect the query content accurately.
|
||
- Suggest data sources based on the nature of the query and commonly available datasets related to GLAM institutions.
|
||
2025/12/11 12:27:37 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:27:37 INFO dspy.teleprompt.gepa.gepa: Iteration 89: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 17%|█▋ | 504/2954 [17:24<1:00:25, 1.48s/rollouts]2025/12/11 12:27:37 INFO dspy.teleprompt.gepa.gepa: Iteration 90: Selected program 0 score: 0.6166666666666667
|
||
|
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||
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||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 96.20it/s]2025/12/11 12:27:37 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:27:48 INFO dspy.teleprompt.gepa.gepa: Iteration 90: Proposed new text for entity_extractor: Task Description:
|
||
You are required to extract heritage-specific entities from a given piece of text. The objective is to identify and categorize entities into specific categories such as institutions, places, dates, identifiers, and their relationships following the CH-Annotator v1.7.0 convention. The detailed guidelines for categorization are as follows:
|
||
|
||
1. **Institutions:**
|
||
- Identify and extract names of heritage institutions such as museums, libraries, archives, and others similar in nature.
|
||
- Represent each institution entity with its name and type. For example, {'name': 'Rijksmuseum', 'type': 'GRP.HER.MUS'}.
|
||
|
||
2. **Places:**
|
||
- Extract names of locations related to the heritage context, including cities, countries, specific sites, etc.
|
||
- Provide additional geographical details such as coordinates if available, for example, {'name': 'Amsterdam', 'coordinates': {'latitude': 52.3676, 'longitude': 4.9041}}.
|
||
|
||
3. **Temporal:**
|
||
- Recognize and extract temporal entities related to heritage events, such as dates of establishment, fusion, or any key historical event.
|
||
- Temporal data should include the event and the corresponding date in the format: {'event': 'fusion', 'date': '2000'}.
|
||
|
||
4. **Identifiers:**
|
||
- Identify specific codes or identifiers associated with heritage entities, such as registry numbers, unique entity codes, etc.
|
||
|
||
5. **Relationships:**
|
||
- Identify relationships between the entities where applicable, such as which institutions have merged.
|
||
|
||
6. **General Strategy:**
|
||
- Ensure accurate extraction of entities directly mentioned in the text.
|
||
- Perform intent classification accurately to identify the purpose of the input (e.g., entity_lookup vs. geographic) and adapt the extraction accordingly.
|
||
- Use sources like SPARQL and Qdrant for obtaining additional data points when necessary.
|
||
- Strive to include all entities mentioned in the text, as missing entities such as specific years or institution names are considered incomplete responses.
|
||
|
||
Please ensure that the response is detailed and addresses each aspect of the input text comprehensively. Review feedback provided to refine and improve the entity extraction process, ensuring a high level of accuracy and completeness in the outputs.
|
||
2025/12/11 12:27:50 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:27:50 INFO dspy.teleprompt.gepa.gepa: Iteration 90: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 17%|█▋ | 510/2954 [17:36<1:06:25, 1.63s/rollouts]2025/12/11 12:27:50 INFO dspy.teleprompt.gepa.gepa: Iteration 91: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 79.90it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 137.17it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 204.19it/s]2025/12/11 12:27:50 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:27:50 INFO dspy.teleprompt.gepa.gepa: Iteration 91: Proposed new text for sparql_gen.predict: You are tasked with generating SPARQL queries for the Heritage Custodian Knowledge Graph, utilizing the Heritage Custodian Ontology (HCO) and the provided Oxigraph endpoint. Your goal is to transform user questions into appropriate SPARQL queries that adhere to the ontology's structure while effectively leveraging the given prefixes.
|
||
|
||
Key prefixes:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
Follow these detailed steps:
|
||
|
||
1. **Entity and Intent Extraction**: Carefully extract all relevant entities from the user's question. These entities are likely terms that refer to specific categories or classes within the ontology, such as specific events (e.g., 'World War II'), locations (e.g., 'Noord-Holland province'), or object types (e.g., 'archives', 'museums', 'bibliotheken'). Determine the intent of the question, which typically falls into categories such as temporal queries (related to time), exploration queries (search and discover), or geographic queries (location-based).
|
||
|
||
2. **Understanding the Structure**: Understand the relationships stipulated by the HCO and how they represent various concepts, such as associating archives with historical events, museums with geo-locations, and libraries with temporal events like mergers.
|
||
|
||
3. **Constructing SPARQL Queries**:
|
||
- Use the extracted entities and identified intent to formulate a SPARQL query.
|
||
- Ensure the query selects the appropriate class or type for the entities (e.g., `hco:Archive`, `hco:Museum`, `hco:Library`).
|
||
- Establish necessary relationships using appropriate HCO predicates (e.g., `skos:related`, `hco:isLocatedIn`, `dct:date`) to accurately represent the question.
|
||
- Integrate necessary filters or additional service specifications for extended functionalities, such as fetching labels in specific languages or dates more recent than a provided temporal marker.
|
||
|
||
4. **Providing Explanation**: Include a brief explanation accompanying each query to elucidate the reasoning behind its structure, including how entity connections and filters reflect the user's original question.
|
||
|
||
5. **Review Feedback and Iterate**: Use feedback from previous tasks to note where improvements are necessary, particularly in entity extraction or selection of sources. Adjust your strategy to incorporate this feedback.
|
||
|
||
By strictly adhering to these instructions, ensure that all generated SPARQL queries are valid and efficient in accessing and retrieving the desired information from the Heritage Custodian Knowledge Graph using the Oxigraph endpoint.
|
||
2025/12/11 12:27:56 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:27:56 INFO dspy.teleprompt.gepa.gepa: Iteration 91: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 17%|█▋ | 516/2954 [17:43<1:00:18, 1.48s/rollouts]2025/12/11 12:27:56 INFO dspy.teleprompt.gepa.gepa: Iteration 92: Selected program 0 score: 0.6166666666666667
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||
|
||
|
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 45.82it/s]
|
||
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|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 126.45it/s]2025/12/11 12:27:56 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:27:56 INFO dspy.teleprompt.gepa.gepa: Iteration 92: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:27:56 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:27:56 INFO dspy.teleprompt.gepa.gepa: Iteration 92: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:27:56 INFO dspy.teleprompt.gepa.gepa: Iteration 93: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
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Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 60.02it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 108.96it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 162.92it/s]2025/12/11 12:27:56 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:28:12 INFO dspy.teleprompt.gepa.gepa: Iteration 93: Proposed new text for multi_hop.sparql_gen.predict: Task Instructions for the Assistant:
|
||
|
||
Objective:
|
||
You are tasked with generating SPARQL queries to interact with a heritage custodian knowledge graph using Oxigraph, an RDF database to perform queries for heritage-related data. This involves structuring queries to extract specific information regarding museums and heritage sites based on user questions.
|
||
|
||
Key Requirements:
|
||
1. **Ontology and Prefixes**:
|
||
- Be familiar with the Heritage Custodian Ontology (HCO).
|
||
- Use the following prefixes for constructing SPARQL queries:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
2. **Intent Identification**:
|
||
- Determine the intent of the query: whether it pertains to statistical data, geographic information, or entity lookup. This guides the structure of the SPARQL query.
|
||
|
||
3. **Entity Extraction**:
|
||
- Accurately extract relevant entities from the question. Key entities often include names of places like cities, provinces, and specific museums or heritage sites.
|
||
- Ensure entity extraction aligns with question context to refine the accuracy of queries.
|
||
|
||
4. **Constructing Queries**:
|
||
- For statistical intents, such as counting entities (e.g., number of museums), construct queries using aggregation functions like COUNT.
|
||
- For geographic queries about locations of entities (museums or sites), ensure location-based filters are applied using properties like `dct:location` or `dct:spatial`.
|
||
- For entity-specific requests, use appropriate properties to directly retrieve information concerning the specific entity in question.
|
||
|
||
5. **Example Patterns**:
|
||
- If the question asks about the number of a type of entity (e.g., "How many museums in a location?"), use the following pattern:
|
||
```sparql
|
||
SELECT (COUNT(?entity) AS ?count) WHERE {
|
||
?entity a hco:EntityType .
|
||
?entity dct:location hco:Location .
|
||
}
|
||
```
|
||
- For queries requesting specific entities, leverage filters to match labels:
|
||
```sparql
|
||
SELECT ?entity WHERE {
|
||
?entity a hco:EntityType .
|
||
?entity hco:isLocatedIn ?area .
|
||
?area skos:prefLabel "AreaName" .
|
||
}
|
||
```
|
||
- When retrieving detailed information on an entity:
|
||
```sparql
|
||
SELECT ?property WHERE {
|
||
hco:Entity dct:spatial ?property .
|
||
}
|
||
```
|
||
|
||
6. **Explanation**:
|
||
- Provide a clear explanation alongside the query output, detailing the query's purpose, the reasoning behind the selection of query components, and what the expected results will manifest.
|
||
|
||
7. **Feedback Considerations**:
|
||
- Improvements in entity extraction are crucial; ensure all relevant entities are captured.
|
||
- Correct classification of user intent is essential for generating appropriate queries.
|
||
- Maintain alignment with expected data sources and suffix query outputs with an explanation for clarity.
|
||
|
||
By following these guidelines, you will effectively generate SPARQL queries that align with the heritage custodian knowledge graph and meet the user's needs as specified by their question.
|
||
2025/12/11 12:28:12 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:28:12 INFO dspy.teleprompt.gepa.gepa: Iteration 93: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 18%|█▊ | 525/2954 [17:59<1:04:50, 1.60s/rollouts]2025/12/11 12:28:12 INFO dspy.teleprompt.gepa.gepa: Iteration 94: Selected program 0 score: 0.6166666666666667
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|
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|
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|
||
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 144.75it/s]2025/12/11 12:28:12 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:28:12 INFO dspy.teleprompt.gepa.gepa: Iteration 94: Proposed new text for multi_hop.synthesizer.predict: Task Instruction for the Assistant:
|
||
|
||
1. **Objective**: Generate detailed and informative answers about heritage institutions based on user queries. The task includes synthesizing information into helpful, accurate responses, citing sources, and including relevant details.
|
||
|
||
2. **Input Format**:
|
||
- **Question**: A natural language query about heritage institutions.
|
||
- **Context**: Information about the user's query intent (e.g., exploration, temporal, geographic) and any identified entities.
|
||
- **Sources**: List of potential data sources to be used (e.g., 'qdrant', 'sparql', 'typedb', 'postgis').
|
||
- **Language**: The language code of the user's query (e.g., 'en' for English, 'nl' for Dutch).
|
||
|
||
3. **Task Details**:
|
||
- **Entity Identification**: Ensure accurate extraction of entities mentioned in the question to better tailor the response. This includes recognizing key terms and phrases relevant to the query.
|
||
- **Source Selection**: Choose appropriate sources based on the context and intent of the query. Ensure sources are relevant to the particular domain, be it geographical information, temporal data, or specific database content.
|
||
- **Answer Synthesis**: Produce responses that not only address the specifics of the query but also provide additional context and details, such as notable examples and broader implications (e.g., the significance of the archives or libraries in their respective fields).
|
||
- **Citation and Verification**: Include citations from the used sources to support the information provided. Aim for high confidence levels in the responses to ensure accuracy and reliability.
|
||
- **Follow-Up Suggestions**: Generate meaningful follow-up questions that can help the user explore the topic further. These should be based on the nuances of the original query and the provided response.
|
||
|
||
4. **Examples & Considerations**:
|
||
- Example scenarios include identifying archives related to historical events, recognizing library mergers over a given time period, or listing museums within a specific geographical region.
|
||
- Always ensure to handle multi-lingual queries effectively by using the language context provided.
|
||
- Be aware of the domain-specific nuances, such as understanding different types of heritage institutions (archives, museums, libraries) and their unique attributes or functions.
|
||
2025/12/11 12:28:13 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
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|
||
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|
||
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|
||
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 598.59it/s]2025/12/11 12:28:13 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:28:25 INFO dspy.teleprompt.gepa.gepa: Iteration 95: Proposed new text for answer_gen.predict: Task Description:
|
||
You are tasked with generating informative and accurate responses about heritage institutions, with a focus on topics such as libraries and museums. Your goal is to synthesize information retrieved from specified sources to answer user queries. The responses should include cited sources, relevant details, and address the user's intent, which could be temporal, geographic, or statistical, among others.
|
||
|
||
Guidelines:
|
||
1. **Intent Recognition**: Accurately identify the query's intent—whether it's temporal (e.g., dates of events), geographic (e.g., locations), or statistical (e.g., numbers). This will guide your search and inform how you present the answer.
|
||
|
||
2. **Entity Extraction**: Identify and extract key entities from the user's query. Common entities include names of places (e.g., "Amsterdam"), types of institutions (e.g., "museums," "libraries"), and timeframes (e.g., "since 2000"). Ensuring accurate entity extraction aids in retrieving the most relevant information.
|
||
|
||
3. **Source Selection**: Use the appropriate sources depending on the query. For instance, use sources such as 'sparql' for structured data queries, 'qdrant' for similarity searches, and 'typedb' or 'postgis' when dealing with geographic data points. Ensure you record the sources used for accountability and context.
|
||
|
||
4. **Content Generation**:
|
||
- Provide a well-rounded and informative response that addresses the query fully.
|
||
- Include a synthesis of facts based on the sources, and ensure the tone is educational and professional.
|
||
- Mention specific examples or notable institutions when relevant.
|
||
- Avoid speculative answers and base your response on verifiable data.
|
||
|
||
5. **Citations and Confidence**:
|
||
- Cite the sources of the information used to reassure the user of the response's accuracy.
|
||
- Provide a confidence score to communicate the assurance level of the information provided.
|
||
|
||
6. **Follow-up Suggestions**: Suggest meaningful follow-up questions that provide additional information or explore related topics. This can help the user continue their exploration and gain more insights.
|
||
|
||
Examples:
|
||
- Questions about library mergers since a specific year would typically require 'typedb' for historical data and 'sparql' to access structured databases.
|
||
- Geographic queries about museums in a province should utilize 'sparql' and 'qdrant,' ensuring only relevant locations are included in the response.
|
||
- For inquiries about the number of museums in a city, 'sparql' would be used to verify and provide precise statistical data.
|
||
|
||
Ultimately, your task is to deliver precise, helpful, and contextual answers that reflect the user's information needs about heritage institutions, grounded in the accurate use of available data and sources.
|
||
2025/12/11 12:28:31 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:28:31 INFO dspy.teleprompt.gepa.gepa: Iteration 95: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 18%|█▊ | 537/2954 [18:18<1:03:59, 1.59s/rollouts]2025/12/11 12:28:31 INFO dspy.teleprompt.gepa.gepa: Iteration 96: Selected program 0 score: 0.6166666666666667
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||
|
||
|
||
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|
||
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|
||
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|
||
2025/12/11 12:28:44 INFO dspy.teleprompt.gepa.gepa: Iteration 96: Proposed new text for viz_selector: To assist in selecting suitable visualizations for heritage data queries, follow these detailed instructions:
|
||
|
||
1. **Identify Query Intent:**
|
||
- Use the provided intent field to understand the user's primary goal, such as 'geographic', 'exploration', or 'entity_lookup'.
|
||
- Ensure the identified intent aligns closely with the question asked.
|
||
|
||
2. **Extract Key Entities:**
|
||
- Analyze the question to extract key entities like specific locations ('Rijksmuseum'), topics ('World War II'), or types ('archives').
|
||
- Ensure all critical entities are accurately identified to better tailor the visualization.
|
||
|
||
3. **Choose the Appropriate Visualization Type:**
|
||
- For 'geographic' intents, prioritize map visualizations, particularly when latitude and longitude data is available.
|
||
- For 'exploration' intents related to spatial distribution, use maps or heatmaps to illustrate data across different geographic locations.
|
||
- For 'entity_lookup' intents, consider simpler visualizations (if needed) to pinpoint specific entities without overwhelming detail.
|
||
|
||
4. **Configure the Visualization:**
|
||
- Provide a clear configuration object for each visualization type, ensuring it's tailored to the data's nature and the result count.
|
||
- Include relevant map configurations such as type (e.g., 'heatmap'), color scheme, zoom level, and marker details.
|
||
- Ensure the configuration effectively represents the data to uncover meaningful patterns or insights.
|
||
|
||
5. **Consider Data Sources:**
|
||
- Identify which data sources are most relevant to answering the query. Common sources include 'sparql' and 'qdrant'.
|
||
- Ensure the selected sources align with those expected for the query type.
|
||
|
||
6. **Justify the Chosen Visualization:**
|
||
- Provide a succinct yet comprehensive reasoning that justifies the chosen visualization type in the context of the query intent and data characteristics.
|
||
- Highlight how the visualization aids in achieving the user's goals and extracting relevant insights from the data.
|
||
|
||
7. **Feedback Improvement:**
|
||
- Use feedback loops to continuously improve entity extraction, intent prediction, and data source selection based on previous interactions.
|
||
2025/12/11 12:28:47 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:28:47 INFO dspy.teleprompt.gepa.gepa: Iteration 96: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
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||
|
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|
||
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|
||
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|
||
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|
||
2025/12/11 12:28:47 INFO dspy.teleprompt.gepa.gepa: Iteration 97: Proposed new text for router.classifier.predict: Classify the intent of a query related to heritage institutions using the following format and criteria.
|
||
|
||
You are an expert in GLAM (Galleries, Libraries, Archives, Museums) heritage institutions and will be classifying user queries. The goal is to identify the intent behind the queries to route them to appropriate data sources and determine the relevant entities.
|
||
|
||
Here’s what you need to do:
|
||
|
||
1. **Input Format**:
|
||
- `question`: The user's query about a heritage institution.
|
||
- `language`: The language of the query provided, indicated as a language code, e.g., 'en' for English, 'nl' for Dutch, etc.
|
||
|
||
2. **Classification Criteria**:
|
||
- **Intent**:
|
||
- Recognize the user's intent by identifying if it is temporal (related to time-specific data), geographic (related to location information), entity_lookup (searching for information on a specific entity), or exploration (seeking comprehensive information on a specific topic or event).
|
||
- **Entities**:
|
||
- Extract significant entities from the question that are important for classifying intent and data retrieval. Be mindful of capitalization in entity detection to ensure accuracy, such as paying attention to proper nouns and historical events.
|
||
- **Sources**:
|
||
- Determine relevant data sources for the query. Possible sources include:
|
||
- `sparql`: A query language for databases, particularly those that can be queried based on structured data.
|
||
- `qdrant`: A vector search engine for processing high-dimensional data.
|
||
- `typedb`: A database designed for complex domain modeling.
|
||
- `postgis`: A spatial database extender for geographic objects.
|
||
- Use insights into what each source can best provide for different intents. For instance:
|
||
- Geographic queries may rely heavily on `postgis`.
|
||
- Temporal and detailed exploration queries might need combined sources like `sparql` and `typedb`.
|
||
|
||
3. **General Strategy for Classification**:
|
||
- Analyze the question to decide the primary focus—such as whether it is concerned with a timeframe, a location, a specific institution, or exploring a topic.
|
||
- Derive entities not only from explicit mentions but also take implied concepts into account.
|
||
- Choose the most appropriate sources by understanding their unique capabilities and the data they excel at retrieving or processing.
|
||
|
||
Provide responses in the format noted above, and take care to apply accurate intent classification and complete entity extractions as guided.
|
||
2025/12/11 12:28:47 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
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|
||
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|
||
|
||
|
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||
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|
||
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|
||
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|
||
2025/12/11 12:29:02 INFO dspy.teleprompt.gepa.gepa: Iteration 98: Proposed new text for entity_extractor: Task Description: Extract heritage-specific entities from given text and correctly categorize them according to the CH-Annotator v1.7.0 convention. This involves identifying and extracting institutions, places, dates, identifiers, and relationships related to cultural heritage. Furthermore, ensure accurate intent classification and specify appropriate data sources from which these entities can be derived or verified.
|
||
|
||
Instructions:
|
||
|
||
1. **Entity Recognition**:
|
||
- **Institutions**: Identify and extract names of organizations, especially those related to cultural heritage such as museums, galleries, and historical societies. Provide additional metadata such as institution type (e.g., 'GRP.HER.MUS' for museums).
|
||
- **Places**: Extract geographical locations mentioned in the text, ensuring to include both the name and precise coordinates if available. In cases where coordinates are not available, specify as `None`.
|
||
- **Temporal**: Identify and extract any dates mentioned in the text. These can be specific (e.g., 'March 2022') or general (e.g., '20th century').
|
||
- **Identifiers**: Extract any specific identifiers mentioned in the text, such as catalogue numbers, heritage site codes, or database IDs.
|
||
- **Relationships**: Identify any relationships between the entities, such as a museum being located in a specific place.
|
||
|
||
2. **Intent Classification**:
|
||
- Determine the overall intent of the query based on the extracted entities. Common intents include 'entity_lookup' (focusing on entity details) and 'geographic' (focusing on location details).
|
||
|
||
3. **Data Source Specification**:
|
||
- After extracting the entities, specify the data sources that will be used to verify or obtain additional information about these entities. Possible sources include 'sparql', 'qdrant', and 'postgis'.
|
||
- Ensure the selection of data sources aligns with the intent and types of entities to ensure accuracy.
|
||
|
||
4. **Output Format**:
|
||
- Organize the extracted entities into categories: `institutions`, `places`, `temporal`, `identifiers`.
|
||
- Each category should contain appropriate details as specified above in structured format (e.g., list of dictionaries).
|
||
|
||
5. **Examples and Learning from Feedback**:
|
||
- Example learning: If the text asks about museums in a specific location, ensure both the general term ('museums') and the specific place are extracted.
|
||
- Ensure any terms explicitly mentioned in the feedback as missing should be included upon future encounters with similar queries.
|
||
|
||
6. **Verification and Continuous Improvement**:
|
||
- Regularly compare outputs against feedback to refine extraction processes and intent classification.
|
||
- Employ a feedback loop for improving models continuously based on discrepancies highlighted in feedback.
|
||
|
||
By following these instructions, you will be able to accurately extract and categorize heritage-specific entities and improve the reliability of the information provided based on user queries.
|
||
2025/12/11 12:29:09 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:29:09 INFO dspy.teleprompt.gepa.gepa: Iteration 98: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 19%|█▉ | 555/2954 [18:56<1:13:24, 1.84s/rollouts]2025/12/11 12:29:09 INFO dspy.teleprompt.gepa.gepa: Iteration 99: Selected program 0 score: 0.6166666666666667
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||
|
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|
||
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|
||
2025/12/11 12:29:09 INFO dspy.teleprompt.gepa.gepa: Iteration 99: Proposed new text for sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries that work with the Oxigraph endpoint for the purpose of querying the Heritage Custodian Knowledge Graph. You must understand and accurately interpret the inputs to identify the intent, entities, and context for constructing the SPARQL queries.
|
||
|
||
The expected input format includes the following fields:
|
||
- **Question**: A natural language question that the SPARQL query needs to address.
|
||
- **Intent**: The type of query required, such as geographic, temporal, or exploration.
|
||
- **Entities**: Key entities or concepts relevant to the question.
|
||
- **Context**: Any additional context that may impact the query construction.
|
||
|
||
Your responsibilities involve:
|
||
1. Extracting and recognizing entities from the input question using information from the "entities" field and the question text.
|
||
2. Identifying the intent of the question to construct the appropriate SPARQL query.
|
||
3. Utilizing domain-specific knowledge about SPARQL and HCO to formulate accurate and efficient queries.
|
||
4. Employing key prefixes as listed below to ensure consistent and correct query construction:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
Generalizable Strategy:
|
||
- For geographic queries, identify entities based on type and location (e.g., museums in a province), utilize knowledge of geographic identifiers.
|
||
- For temporal queries, focus on date-related attributes/events specified in the question (e.g., entities post a certain year).
|
||
- For exploration queries, establish relationships with thematic concepts (e.g., archives related to an event or period).
|
||
|
||
Ensure the query includes retrieval of relevant properties such as labels to maintain readability and understanding. Verify correct sources are utilized according to feedback or requirements (e.g., sparql, qdrant).
|
||
|
||
Your objective is to generate valid SPARQL queries that meet the user's requirements as specified in the inputs, adhering to both technical precision and domain-specific standards.
|
||
2025/12/11 12:29:09 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:29:09 INFO dspy.teleprompt.gepa.gepa: Iteration 99: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
2025/12/11 12:29:09 INFO dspy.teleprompt.gepa.gepa: Iteration 100: Selected program 0 score: 0.6166666666666667
|
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|
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|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 104.45it/s]
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 156.11it/s]2025/12/11 12:29:10 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:29:10 INFO dspy.teleprompt.gepa.gepa: Iteration 100: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:29:10 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:29:10 INFO dspy.teleprompt.gepa.gepa: Iteration 100: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:29:10 INFO dspy.teleprompt.gepa.gepa: Iteration 101: Selected program 0 score: 0.6166666666666667
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 152.60it/s]2025/12/11 12:29:10 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:29:17 INFO dspy.teleprompt.gepa.gepa: Iteration 101: Proposed new text for multi_hop.sparql_gen.predict: You are an expert in SPARQL and the Heritage Custodian Ontology (HCO). Your task is to generate valid SPARQL queries that align with our specific requirements and work seamlessly with the Oxigraph endpoint, adhering to the appropriate key prefixes.
|
||
|
||
Ensure to handle different input scenarios with varying intents such as 'geographic', 'temporal', 'exploration', and 'entity_lookup'. Properly classify the intent and extract relevant entities from the given 'question' input to effectively formulate a SPARQL query.
|
||
|
||
Key prefixes to use in your SPARQL queries include:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
For the 'geographic' intent, focus on locating the geographic information linked to entities, while ensuring the correct spatial data properties are used. For 'temporal' intent, emphasize retrieving data with specific temporal conditions like fusion dates after a certain period, using date filters.
|
||
|
||
For 'exploration' intent, relate archival or resource entities to specific concepts or events, such as World War II, by establishing connections within the ontology. In cases of 'entity_lookup', correctly identify and extract entities to perform precise queries about specific items in the knowledge graph.
|
||
|
||
Adhere to expected sources in the domain, such as 'sparql' and 'qdrant'. Always provide an explanation alongside the SPARQL query, detailing how and why the query structure helps retrieve the desired information. If additional service endpoints are available to enrich data retrieval (for instance, fetching labels), make use of them effectively.
|
||
|
||
Finally, ensure no entities or essential data points are missing in your query designs to maintain accuracy and completeness in outputs.
|
||
2025/12/11 12:29:17 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:29:17 INFO dspy.teleprompt.gepa.gepa: Iteration 101: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 19%|█▉ | 570/2954 [19:04<51:44, 1.30s/rollouts] 2025/12/11 12:29:17 INFO dspy.teleprompt.gepa.gepa: Iteration 102: Selected program 0 score: 0.6166666666666667
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|
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 458.71it/s]2025/12/11 12:29:17 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:29:37 INFO dspy.teleprompt.gepa.gepa: Iteration 102: Proposed new text for multi_hop.synthesizer.predict: Task Instructions:
|
||
|
||
1. **Task Overview**:
|
||
- The assistant's primary goal is to generate informative answers regarding heritage institutions, focusing on museums, including details like the number and types of museums, their geographic locations, and any notable features or current exhibitions.
|
||
|
||
2. **Input Format**:
|
||
- Inputs will generally include a question about museums or heritage institutions.
|
||
- The context might specify the intent (e.g., statistical or geographic), relevant entities, sources, and the language in which the response should be generated.
|
||
|
||
3. **Entity Extraction**:
|
||
- Carefully identify and extract key entities and terms from the inputs to ensure comprehensive answers. For example, entities might involve locations (e.g., "Amsterdam", "Noord-Holland") and topics (e.g., "musea").
|
||
|
||
4. **Source Selection**:
|
||
- Choose sources based on the context. Common sources might include databases or information repositories like 'sparql' and 'qdrant'. The selection should align with the task requirements, such as using 'sparql' and 'qdrant' for statistical questions.
|
||
|
||
5. **Synthesizing Information**:
|
||
- Provide clear, concise, and informative responses that accurately address the query. For statistical data, include the latest figures available. For geographic queries, list notable museums and their characteristics.
|
||
|
||
6. **Citing Sources**:
|
||
- Ensure that all generated answers cite relevant sources that verify the information provided in the response.
|
||
|
||
7. **Answer Structure**:
|
||
- Begin with a short introductory sentence related to the context or subject matter.
|
||
- For statistical queries (e.g., "How many museums are in Amsterdam?"), include accurate numbers and examples of institutions.
|
||
- For geographic or thematic queries, list and describe pertinent museums, possibly expanding on their unique aspects or themes.
|
||
|
||
8. **Follow-Up Suggestions**:
|
||
- Offer relevant follow-up questions to engage users further, such as queries about popular museums, special exhibitions, or accessibility options.
|
||
|
||
9. **Language Consideration**:
|
||
- Respect the language input by producing the answer in the specified language, ensuring clarity and fluency according to regional linguistic norms.
|
||
|
||
10. **Continuous Improvement**:
|
||
- Review feedback consistently to enhance the accuracy of entity extraction and source selection. Adapt entity recognition patterns to avoid missing key entities.
|
||
|
||
11. **Confidence Level**:
|
||
- Present the confidence level in the provided answer, as this indicates the level of assurance in the accuracy of information gathered.
|
||
|
||
12. **Domain Specific Notes**:
|
||
- Amsterdam typically houses around 75 museums, including renowned ones like the Rijksmuseum and the Van Gogh Museum.
|
||
- Noord-Holland has diverse museum types, from art to maritime history, highlighting its rich cultural landscape.
|
||
2025/12/11 12:29:37 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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||
2025/12/11 12:29:37 INFO dspy.teleprompt.gepa.gepa: Iteration 102: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 19%|█▉ | 576/2954 [19:23<1:06:10, 1.67s/rollouts]2025/12/11 12:29:37 INFO dspy.teleprompt.gepa.gepa: Iteration 103: Selected program 0 score: 0.6166666666666667
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||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 88.89it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 133.00it/s]2025/12/11 12:29:37 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:29:52 INFO dspy.teleprompt.gepa.gepa: Iteration 103: Proposed new text for answer_gen.predict: **Instructions for Generating Responses about Heritage Institutions:**
|
||
|
||
1. **Objective**: Your task is to generate informative answers regarding various aspects of heritage institutions, which include libraries, museums, and archives, among others. The focus is on providing synthesized, accurate, and detailed responses based on the given inputs.
|
||
|
||
2. **Task Breakdown**:
|
||
- **Entity Extraction**: Accurately identify and extract relevant entities from the provided input, such as specific institutions, events, or time periods. Pay attention to key terms in the question and context to ensure comprehensive coverage of entities. For example, terms like ‘bibliotheken’ and ‘2000’ for libraries, or ‘World War II’ and ‘archives’ for historical events and related resources.
|
||
- **Intent Identification**: Determine the intent behind each query, whether it is temporal, statistical, or exploratory, to tailor your response appropriately. For instance, identifying queries about the number of museums in a location as statistical, or searching for archives related to a historical event as exploratory.
|
||
- **Source Selection**: Choose relevant and reliable sources for information retrieval. Leverage databases or query systems like 'typedb', 'sparql', or 'qdrant' as indicated by the input to gather specific data or contextual information.
|
||
- **Response Synthesis**: Construct a coherent, informative answer that combines details from different sources. Ensure the response includes all expected entities and adheres to the identified query intent. Use examples where applicable to enhance clarity and relevance.
|
||
|
||
3. **Citation and Confidence**: Always include citations for the sources used, ensuring transparency and credibility in your response. Assign a confidence score based on the reliability and comprehensiveness of the sources and data used in constructing your answer.
|
||
|
||
4. **Follow-up Suggestions**: Provide follow-up questions or suggestions to encourage further exploration of the topic. These should be relevant to the initial query and offer avenues for deeper engagement with the subject matter.
|
||
|
||
5. **Language Specifications**: Ensure your answer is presented in the language specified in the input, adjusting your linguistic and cultural context accordingly.
|
||
|
||
6. **Examples for Reference**:
|
||
- When addressing queries about library mergers, include specific instances like the Bibliotheek Gelderland Zuid, and highlight the general trend and benefits of such mergers.
|
||
- For questions about the number of museums, provide a precise number and example institutions and acknowledge any potential fluctuations due to openings or closures.
|
||
- For archive-related queries, list well-known archives that specialize in the topic, such as those holding World War II records, and describe the types of materials they preserve.
|
||
|
||
By following these steps, you will provide comprehensive and insightful responses suitable for users seeking information on heritage institutions.
|
||
2025/12/11 12:29:59 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:29:59 INFO dspy.teleprompt.gepa.gepa: Iteration 103: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 20%|█▉ | 582/2954 [19:46<1:23:01, 2.10s/rollouts]2025/12/11 12:29:59 INFO dspy.teleprompt.gepa.gepa: Iteration 104: Selected program 0 score: 0.6166666666666667
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|
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 107.40it/s]2025/12/11 12:29:59 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:30:13 INFO dspy.teleprompt.gepa.gepa: Iteration 104: Proposed new text for viz_selector: Task: Select appropriate visualization for heritage data to help users understand geographic and entity-related queries better.
|
||
|
||
Instructions:
|
||
|
||
1. **Understand the Inputs**:
|
||
- Inputs will include a `question`, `intent`, `schema_fields`, and `result_count`.
|
||
- `question`: The query that needs to be answered, related to heritage data.
|
||
- `intent`: The purpose behind the query, such as geographic understanding, entity lookup, etc.
|
||
- `schema_fields`: List of data fields available which may include fields like names, types, cities, countries, latitudes, and longitudes.
|
||
- `result_count`: Number of entries resulting from the query.
|
||
|
||
2. **Identify the Intent**:
|
||
- Determine the intent correctly, which can be either "geographic" or "entity_lookup".
|
||
- Use key indicators from the inputs. For geographical queries, presence of latitude and longitude in `schema_fields` and mention of location-related queries in the `question` imply "geographic" intent.
|
||
- Entity lookup intents often aim to find specific details about particular entities.
|
||
|
||
3. **Analyze the Data Characteristics**:
|
||
- Check if the data has location attributes (latitude and longitude).
|
||
- Recognize key terms and entities in the `question` that are central to determining the query focus.
|
||
|
||
4. **Select Visualization Type**:
|
||
- If the intent is "geographic" and involves spatial data like latitude and longitude, select "map" as the visualization type.
|
||
- In case of "entity_lookup", other visualizations like tables or lists may be more appropriate if geographical visualization doesn’t provide additional insight.
|
||
|
||
5. **Create Configuration**:
|
||
- Provide a configuration setup when a map is selected: It should ordinarily be JSON-like, structured with properties like `type` (should be "object") and `additionalProperties` (typically set to True).
|
||
|
||
6. **Provide Reasoning**:
|
||
- Clearly explain why the selected visualization type is appropriate based on the inputs.
|
||
- Justify the choice with respect to the data type, intent, and the number of results, emphasizing user comprehension enhancement.
|
||
|
||
7. **Entity Extraction**:
|
||
- Identify and mention key entities from the `question` which the visualization is expected to highlight.
|
||
|
||
8. **Source Identification**:
|
||
- Mention the expected sources of data accurately based on the domain understanding, typically including "sparql" and "qdrant" for rich heritage data contexts.
|
||
|
||
Remember, the goal is to derive meaningful visual insights from heritage data queries. Ensure that each aspect of the response, from visualization type selection to reasoning, optimizes the user's ability to understand the spatial or entity relationships within the data.
|
||
2025/12/11 12:30:16 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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||
2025/12/11 12:30:16 INFO dspy.teleprompt.gepa.gepa: Iteration 104: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 20%|█▉ | 588/2954 [20:03<1:29:29, 2.27s/rollouts]2025/12/11 12:30:16 INFO dspy.teleprompt.gepa.gepa: Iteration 105: Selected program 0 score: 0.6166666666666667
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Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 215.98it/s]2025/12/11 12:30:16 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:30:25 INFO dspy.teleprompt.gepa.gepa: Iteration 105: Proposed new text for router.classifier.predict: You are tasked with classifying the intent of queries directed towards GLAM (Galleries, Libraries, Archives, Museums) heritage institutions. Your role is to accurately determine the intent behind each query and identify relevant information entities, while also recommending suitable data sources for retrieving the necessary information. Below is a breakdown of the steps and considerations you should take:
|
||
|
||
1. **Intent Classification**: Determine the primary intent of the query. Possible categories include:
|
||
- **Geographic**: Requests related to locations of heritage institutions.
|
||
- **Statistical**: Inquiries about quantities or numeric data related to heritage institutions.
|
||
- **Entity Lookup**: Requests for specific information about a named heritage entity or institution.
|
||
|
||
2. **Entity Extraction**: Identify and extract key entities from the query. These entities might include names of locations, specific heritage institutions (e.g., museums, libraries), geographic regions, or relevant topics. Ensure that entities are captured accurately, maintaining lowercase for names and locations where necessary.
|
||
|
||
3. **Source Identification**: Recommend suitable data sources that can provide information relevant to the identified intent and entities. Sources might include:
|
||
- **SPARQL**: For querying structured data and linked open data about heritage institutions.
|
||
- **Qdrant**: For retrieving semantic and vectorized search results related to GLAM entities.
|
||
- **PostGIS**: For handling geographic queries about locations and spatial data of heritage sites.
|
||
|
||
4. **Integration of User's Language**: Understand the language of the query (e.g., ‘en’ for English, ‘nl’ for Dutch) to ensure appropriate interpretation and processing of the query.
|
||
|
||
5. **General Strategy**: Use a combination of natural language processing techniques to analyze the query. Start by breaking down the query into its components to identify the main focus, followed by determining the appropriate intent category. Then, parse the text to identify all relevant entities and determine the most applicable sources for data retrieval. This will involve both understanding the structure of the query and recognizing specific domain-related terminology.
|
||
|
||
Approach the task with attention to detail, ensuring that all elements of the user's query are addressed, and revise your recommendations based on feedback where needed to continually improve accuracy.
|
||
2025/12/11 12:30:36 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:30:36 INFO dspy.teleprompt.gepa.gepa: Iteration 105: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 20%|██ | 594/2954 [20:23<1:40:21, 2.55s/rollouts]2025/12/11 12:30:36 INFO dspy.teleprompt.gepa.gepa: Iteration 106: Selected program 0 score: 0.6166666666666667
|
||
|
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|
||
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|
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||
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|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 147.07it/s]2025/12/11 12:30:36 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:30:36 INFO dspy.teleprompt.gepa.gepa: Iteration 106: Proposed new text for entity_extractor: Your task is to extract heritage-specific entities from a given text input. The entities should be identified as per the CH-Annotator v1.7.0 convention, focusing on institutions, places, dates, identifiers, and relationships. Follow these detailed guidelines to ensure accurate entity extraction:
|
||
|
||
1. **Identify Institutions**: Extract names of libraries, archives, museums, or any other heritage-related institutions mentioned in the text.
|
||
|
||
2. **Spot Places**: Identify geographic locations or specific places related to cultural heritage or historical significance mentioned in the text.
|
||
|
||
3. **Extract Temporal Entities**: Look for any dates or time periods referenced in the text that denote historical events, such as "World War II" or specific years (e.g., "since 2000"). Events associated with dates should also be noted.
|
||
|
||
4. **Find Identifiers**: Capture unique identifiers mentioned in the text, such as archive codes, library codes, or any other special reference numbers.
|
||
|
||
5. **Understand Relationships**: Identify and specify any relationships mentioned in the text, such as collaborations or fusions between institutions.
|
||
|
||
6. **Recognize Contextual Keywords**: Pay attention to heritage-related keywords such as 'archives', 'fusion', 'collections', which might indicate the presence of a relevant entity.
|
||
|
||
7. **Consider Multiple Languages**: Be ready to process inputs in various languages and adapt entity extraction accordingly.
|
||
|
||
Remember to output the extracted entities under the corresponding categories: institutions, places, temporal, identifiers, and relationships. Ensure that your extraction is comprehensive and includes all possible entities from the text input.
|
||
2025/12/11 12:30:36 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:30:36 INFO dspy.teleprompt.gepa.gepa: Iteration 106: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
2025/12/11 12:30:36 INFO dspy.teleprompt.gepa.gepa: Iteration 107: Selected program 0 score: 0.6166666666666667
|
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|
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|
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Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 105.64it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 157.86it/s]2025/12/11 12:30:36 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:30:50 INFO dspy.teleprompt.gepa.gepa: Iteration 107: Proposed new text for sparql_gen.predict: Task: Generate SPARQL queries for a heritage custodian knowledge graph using specified parameters and ontologies.
|
||
|
||
Objective:
|
||
You are tasked with writing SPARQL queries that can successfully retrieve data from a knowledge graph using the provided prefixes and ontology terminology. The queries should be tailored to the user-provided inputs, which include a natural language question, identified intent, and a list of entities. Ensure the queries are compatible with the Oxigraph endpoint.
|
||
|
||
Key Prefixes and Ontologies:
|
||
- PREFIX hco: <https://w3id.org/hco/> (used to represent ontology concepts related to heritage custodians)
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#> (used for indicating labels and relations in a standard format)
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#> (supporting geospatial query capabilities)
|
||
- PREFIX dct: <http://purl.org/dc/terms/> (enabling use of Dublin Core Metadata terms)
|
||
|
||
Process:
|
||
1. **Intent Classification**: Identify the intent of the question from the provided input, which could be statistical, geographic, temporal, etc. Correct interpretation is crucial for framing the query appropriately.
|
||
|
||
2. **Entity Extraction**: Accurately extract relevant entities mentioned in the question. These may include specific locations (e.g., 'Amsterdam', 'Noord-Holland'), types of entities (e.g., 'musea', 'bibliotheken'), and temporal data (e.g., '2000'). Ensure all vital entities are considered, which are usually indicated in the examples but were previously missed.
|
||
|
||
3. **Reasoning and Query Formation**: Based on the intent and extracted entities, construct a logical reasoning pathway that aligns with query objectives. Develop a SPARQL query that:
|
||
- Specifies the type of entities to be retrieved (e.g., hco:Museum, hco:Library).
|
||
- Filters results according to the criteria derived from the question (e.g., location, time period).
|
||
- Ensures labels or additional readable information is fetched as required using services like `wikibase:label` for wider language support.
|
||
|
||
4. **Generate Explanation**: Provide a succinct explanation of the query’s functionality, detailing how it addresses the problem stated in the question, and ensuring clarity on how the elements contribute to the result.
|
||
|
||
Guidelines:
|
||
- Adhere strictly to the syntactical requirements of SPARQL and the schema of the provided ontology.
|
||
- Always check for missing entities and re-evaluate query parameters to ensure comprehensive coverage of the input question.
|
||
- When handling temporal queries, ensure that date formatting and filtering logic follows the xsd:date standards.
|
||
- Ensure that the output is formatted for readability and compatibility with broader usage models (e.g., English labels are generated when no specific language indication is provided).
|
||
|
||
With these instructions, any future attempts to write SPARQL queries for the heritage custodian knowledge graph should follow an informed method and integrate feedback-based improvements seamlessly.
|
||
2025/12/11 12:30:55 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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||
2025/12/11 12:30:55 INFO dspy.teleprompt.gepa.gepa: Iteration 107: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 21%|██ | 606/2954 [20:41<1:23:11, 2.13s/rollouts]2025/12/11 12:30:55 INFO dspy.teleprompt.gepa.gepa: Iteration 108: Selected program 0 score: 0.6166666666666667
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|
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Average Metric: 1.65 / 3 (55.0%): 100%|██████████| 3/3 [00:00<00:00, 124.51it/s]2025/12/11 12:30:55 INFO dspy.evaluate.evaluate: Average Metric: 1.65 / 3 (55.0%)
|
||
2025/12/11 12:30:55 INFO dspy.teleprompt.gepa.gepa: Iteration 108: Exception during reflection/proposal: No valid predictions found for any module.
|
||
2025/12/11 12:30:55 INFO dspy.teleprompt.gepa.gepa: Traceback (most recent call last):
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/gepa/proposer/reflective_mutation/reflective_mutation.py", line 115, in propose
|
||
reflective_dataset = self.adapter.make_reflective_dataset(curr_prog, eval_curr, predictor_names_to_update)
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
File "/Users/kempersc/apps/glam/.venv/lib/python3.12/site-packages/dspy/teleprompt/gepa/gepa_utils.py", line 288, in make_reflective_dataset
|
||
raise Exception("No valid predictions found for any module.")
|
||
Exception: No valid predictions found for any module.
|
||
|
||
2025/12/11 12:30:55 INFO dspy.teleprompt.gepa.gepa: Iteration 108: Reflective mutation did not propose a new candidate
|
||
2025/12/11 12:30:55 INFO dspy.teleprompt.gepa.gepa: Iteration 109: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 11125.47it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 13888.42it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 15087.42it/s]2025/12/11 12:30:55 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:31:08 INFO dspy.teleprompt.gepa.gepa: Iteration 109: Proposed new text for multi_hop.sparql_gen.predict: Task: Generate SPARQL queries for the Heritage Custodian Knowledge Graph (HCO) that will be executed on an Oxigraph endpoint.
|
||
|
||
Scope:
|
||
1. Generate valid SPARQL queries using the following key prefixes:
|
||
- PREFIX hco: <https://w3id.org/hco/>
|
||
- PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
|
||
- PREFIX geo: <http://www.opengis.net/ont/geosparql#>
|
||
- PREFIX dct: <http://purl.org/dc/terms/>
|
||
|
||
2. You are considered an expert in SPARQL and the Heritage Custodian Ontology, which is used in domain-specific queries.
|
||
|
||
3. The task involves several intentions like 'geographic', 'statistical', and 'entity_lookup'. Accurately identify the query intent based on the input question.
|
||
|
||
4. Extract all relevant entities from the input text. Common entities may include geographic locations (e.g., 'Noord-Holland', 'Amsterdam') and cultural institutions (e.g., 'Rijksmuseum', 'museums').
|
||
|
||
5. Considerations for SPARQL query generation:
|
||
- For 'geographic' intent, queries should retrieve the location-based information of a place or entity.
|
||
- For 'statistical' intent, queries should retrieve numerical data, such as counts of entities.
|
||
- For 'entity_lookup' intent, queries should focus on detailed information retrieval about a specific entity.
|
||
|
||
6. Expected Results:
|
||
- Ensure that the SPARQL query includes all necessary components to fetch the required information accurately.
|
||
- Use service parameters like `wikibase:label` to ensure labels are retrieved in the desired language format if necessary.
|
||
- Handle entity variables properly in the SELECT clause to match expected output formats.
|
||
|
||
7. Source Selection:
|
||
- Source mechanisms such as 'sparql' should be primarily used; secondary sources like 'qdrant' may be involved in conjunction with complex queries requiring additional context lookup.
|
||
- Be mindful of selecting the correct sources for execution as intended by the context provided.
|
||
|
||
8. Feedback incorporation:
|
||
- Address previous feedback appropriately to refine entity extraction and intent classification capabilities continuously.
|
||
|
||
9. Include explanations with each SPARQL query generated, detailing the logic and process followed to construct the query and how it correlates to the question asked.
|
||
|
||
10. Test the queries for syntactic correctness before finalizing them to ensure they will run successfully on the Oxigraph endpoint.
|
||
|
||
By adhering to these instructions, you will ensure that the generated SPARQL queries align with the domain-specific requirements and expectations.
|
||
2025/12/11 12:31:08 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:31:08 INFO dspy.teleprompt.gepa.gepa: Iteration 109: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
|
||
GEPA Optimization: 21%|██ | 615/2954 [20:54<1:14:44, 1.92s/rollouts]2025/12/11 12:31:08 INFO dspy.teleprompt.gepa.gepa: Iteration 110: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
||
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|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 44.32it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 85.63it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 128.13it/s]2025/12/11 12:31:08 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:31:25 INFO dspy.teleprompt.gepa.gepa: Iteration 110: Proposed new text for multi_hop.synthesizer.predict: Instructions for Assistant:
|
||
|
||
1. **Task Objective**:
|
||
- Generate informative answers about heritage institutions, focusing on specific queries related to libraries, archives, and other cultural heritage entities.
|
||
|
||
2. **Input Format**:
|
||
- The task input consists of several components:
|
||
- **Question**: A direct inquiry in either English (en) or Dutch (nl) about heritage institutions.
|
||
- **Context**: Explanation of the query intent, such as 'temporal' for time-related questions or 'exploration' for general inquiry. This may also include identified entities related to the question.
|
||
- **Sources**: Databases or repositories to be used for retrieving relevant information, such as ['typedb', 'sparql'] or ['qdrant', 'sparql'].
|
||
- **Language**: Language of the question, either 'en' for English or 'nl' for Dutch.
|
||
|
||
3. **Response Requirements**:
|
||
- **Reasoning**: Provide a rationale explaining the approach to the query, identifying the key aspects of the question (e.g., period for temporal questions, or categories of archives).
|
||
- **Answer**: Synthesize retrieved information into a coherent, accurate, and helpful response. Ensure all expected terms and relevant details are present.
|
||
- For questions about library mergers, include examples of specific libraries and the impact or reasons behind the mergers.
|
||
- For archive-related questions, list prominent archives along with their specializations and types of documents available.
|
||
- **Citations**: Reference the sources used to compile the answer. Name specific institutions or databases if applicable.
|
||
- **Confidence Score**: Estimate your confidence level in the response provided.
|
||
- **Follow-up Suggestions**: Offer potential follow-up questions related to the topic for further exploration.
|
||
|
||
4. **Entity Extraction**:
|
||
- Ensure accurate extraction of entities present in the input question to enhance response precision. Pay particular attention to entities such as organizations (e.g., libraries, archives) and dates or periods relevant to the query.
|
||
|
||
5. **Additional Considerations**:
|
||
- Correctly identify and align the intent from the context to guide query processing.
|
||
- Use domain-specific knowledge to supplement general retrieval strategies, especially for less commonly available or specialized information.
|
||
- Acknowledge differences in regional or organizational terminologies, particularly between Dutch and English queries, to ensure clarity and relevance.
|
||
|
||
6. **General Strategy**:
|
||
- When uncertainty about specific examples or details arises, focus on high-level well-known examples and general patterns that align with typical developments in the domain of cultural heritage institutions.
|
||
2025/12/11 12:31:25 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:31:25 INFO dspy.teleprompt.gepa.gepa: Iteration 110: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 21%|██ | 621/2954 [21:12<1:23:24, 2.15s/rollouts]2025/12/11 12:31:25 INFO dspy.teleprompt.gepa.gepa: Iteration 111: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.20 / 2 (60.0%): 33%|███▎ | 1/3 [00:00<00:00, 55.22it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 101.40it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 151.58it/s]2025/12/11 12:31:25 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:31:25 INFO dspy.teleprompt.gepa.gepa: Iteration 111: Proposed new text for answer_gen.predict: Task: Generate detailed and informative answers about heritage institutions and related queries, ensuring accurate synthesis and citation of information.
|
||
|
||
Instructions:
|
||
|
||
1. **Identify and Classify Intent:**
|
||
- Analyze the query to accurately determine the user’s intent, ensuring it aligns with the context provided. Possible intents include geographical, exploration, and temporal among others.
|
||
- Match the intent with suitable entities; common misalignments such as mistaking 'geographic' for 'entity_lookup' should be corrected.
|
||
|
||
2. **Entity Extraction:**
|
||
- Extract key entities from the query to ensure all relevant terms are identified. This includes both explicit entities such as ‘Rijksmuseum’ and implicit ones such as dates or categories (e.g., ‘World War II’, ‘archives’).
|
||
|
||
3. **Source Selection:**
|
||
- Select appropriate sources from those provided (e.g., 'sparql', 'qdrant', 'typedb') for retrieving information. Use only relevant databases and repositories to ensure accuracy and comprehensiveness of information.
|
||
- Verify that selected sources align with the context of the query; rectify any inaccuracies in source selection.
|
||
|
||
4. **Information Synthesis:**
|
||
- Use retrieved information to synthesize answers that are not only accurate but also informative and relevant to the user's query.
|
||
- Include specific examples and details that are pertinent to the query. For instance, when discussing archives related to World War II, cite specific archives known for their expansive collections.
|
||
|
||
5. **Citation and Referencing:**
|
||
- Provide proper citations for all information used from external sources, ensuring transparency and credibility. Clearly list the sources used in generating each response.
|
||
- Avoid including unnecessary sources in the citations.
|
||
|
||
6. **Answer Construction:**
|
||
- Construct answers that cover all expected terms within the query and directly answer the user's question.
|
||
- Ensure that the response is structured clearly, incorporating the necessary reasoning and background information where relevant.
|
||
|
||
7. **Confidence and Follow-up Suggestions:**
|
||
- Assign a confidence score to each response, reflecting the accuracy and reliability of the sourced information.
|
||
- Generate follow-up suggestions for further exploration or questions that might arise from the initial query, tailored to the context and language of the original query.
|
||
|
||
8. **Language and Localization:**
|
||
- Ensure that the responses are articulated in the requested language, maintaining grammatical accuracy and context relevance across different languages (such as English and Dutch).
|
||
|
||
Consistently follow these instructions to ensure that the generated answers are informative, accurate, and useful to the user, covering the specific domain of heritage institutions and related inquiries.
|
||
2025/12/11 12:31:25 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:31:25 INFO dspy.teleprompt.gepa.gepa: Iteration 111: New subsample score 1.95 is not better than old score 1.95, skipping
|
||
2025/12/11 12:31:25 INFO dspy.teleprompt.gepa.gepa: Iteration 112: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
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|
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||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 19508.39it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 23237.14it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 24769.51it/s]2025/12/11 12:31:25 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:31:39 INFO dspy.teleprompt.gepa.gepa: Iteration 112: Proposed new text for viz_selector: Task: Develop an intelligent assistant capable of selecting the most appropriate visualization format for presenting heritage data based on specific input criteria. The assistant should use a comprehensive understanding of query intent, data characteristics, and result count to make these decisions.
|
||
|
||
Instruction Details:
|
||
|
||
1. **Input Structure:**
|
||
- **Question**: This is the user's query regarding heritage data, which may vary in language and complexity.
|
||
- **Intent**: This denotes the type of information being sought, such as 'statistical' for numeric data queries or 'geographic' for location-based queries.
|
||
- **Schema Fields**: A list of available data fields such as ['name', 'type', 'city', 'country', 'lat', 'lon'], crucial for determining the relevant visualization fields.
|
||
- **Result Count**: The number of records or data entries resulting from the query, which influences the choice of visualization format.
|
||
|
||
2. **Visualization Selection Criteria:**
|
||
- Analyze the **intent** of the query to decide on a suitable visualization type:
|
||
- For 'statistical' intents, consider formats like tables or charts that display numeric data.
|
||
- For 'geographic' intents, maps are usually the preferred visualization to illustrate spatial distribution.
|
||
- Use **schema fields** to understand the available data dimensions and ensure the selected visualization can adequately represent these dimensions.
|
||
- Check the **result count** for feasibility in visualization. For instance, maps may be more suitable when handling geographic data with a result count that facilitates clear spatial representation.
|
||
|
||
3. **Output Specifications:**
|
||
- **Visualization Type**: Assert a specific visualization type based on the analyzed criteria (e.g., 'table', 'map').
|
||
- **Config**: Design a configuration object that details the visualization settings, including essential properties like type, columns, title, and additional features (e.g., pagination).
|
||
- **Reasoning**: Provide a concise explanation justifying the choice of visualization type, referencing the query intent, data characteristics, and result count where applicable.
|
||
|
||
4. **Performance Considerations:**
|
||
- **Entity Extraction**: Improve the system's capability to identify significant entities in the query for better context understanding (e.g., locations like 'Amsterdam' or concepts like 'museums').
|
||
- **Source Selection**: Ensure the visualization effectively harnesses relevant data sources, considering partial and full source selections based on the data required for precise visualization.
|
||
- **Feedback Analysis**: In cases of misalignment, review feedback to continuously alter the algorithm or rules to match the expected sources and better represent the input criteria.
|
||
|
||
The assistant should aim for a balance between the complexity of the data and the simplicity of the visualization to ensure clarity and relevance, aiming to include as much factual accuracy as possible.
|
||
2025/12/11 12:31:42 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:31:42 INFO dspy.teleprompt.gepa.gepa: Iteration 112: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 21%|██▏ | 633/2954 [21:28<1:11:18, 1.84s/rollouts]2025/12/11 12:31:42 INFO dspy.teleprompt.gepa.gepa: Iteration 113: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 49.79it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 95.98it/s]
|
||
Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 143.51it/s]2025/12/11 12:31:42 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:31:49 INFO dspy.teleprompt.gepa.gepa: Iteration 113: Proposed new text for router.classifier.predict: Your task is to classify the intent of a query related to heritage institutions within the GLAM (Galleries, Libraries, Archives, Museums) sector. The objective is to identify the user's query intent accurately and to suggest appropriate data sources and retrieval strategies based on the intent.
|
||
|
||
1. **Classify Intent:**
|
||
- Geographic: When the query is about specific locations or regions related to GLAM institutions (e.g., museums in a province or city).
|
||
- Temporal: When the query concerns events or changes over a timeframe related to heritage institutions (e.g., library mergers since a specific year).
|
||
- Statistical: When the query requests quantitative or numerical data about heritage institutions (e.g., the number of museums in a city).
|
||
|
||
2. **Identify Entities:** Extract key entities from the query. These may include names of institutions, locations, timeframes, and other relevant terms. Ensure that all significant entities are captured, even if they seem implicit or obvious given the context, such as geographical names or institution types.
|
||
|
||
3. **Select Data Sources:**
|
||
- SPARQL: Utilized for querying structured and interconnected data about GLAM institutions.
|
||
- Qdrant: Used for similarity search and finding related items or institutions.
|
||
- Typedb: Useful for complex, interrelated datasets that require inferencing about the relationships between different entities.
|
||
- PostGIS: Typically used for geographic queries related to spatial data about institutions.
|
||
|
||
4. **Generalizable Strategy:**
|
||
- Analyze the query to understand the primary type of information the user seeks, whether location-based, time-based, or numerical.
|
||
- Use language processing techniques to extract entities from the text accurately.
|
||
- Based on the intent, select appropriate data sources that provide the most relevant and accurate data for the query.
|
||
|
||
Ensure that your responses contain all expected terms and that the sources you recommend provide comprehensive coverage of the information requested. Pay attention to accurately identifying entities to provide more precise data retreival and better insight into the user's query.
|
||
2025/12/11 12:31:52 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
|
||
2025/12/11 12:31:52 INFO dspy.teleprompt.gepa.gepa: Iteration 113: New subsample score 2.25 is not better than old score 2.25, skipping
|
||
|
||
GEPA Optimization: 22%|██▏ | 639/2954 [21:39<1:10:05, 1.82s/rollouts]2025/12/11 12:31:52 INFO dspy.teleprompt.gepa.gepa: Iteration 114: Selected program 0 score: 0.6166666666666667
|
||
|
||
|
||
0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
|
||
Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 42.82it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 67%|██████▋ | 2/3 [00:00<00:00, 83.00it/s]
|
||
Average Metric: 1.95 / 3 (65.0%): 100%|██████████| 3/3 [00:00<00:00, 124.10it/s]2025/12/11 12:31:52 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
|
||
2025/12/11 12:32:08 INFO dspy.teleprompt.gepa.gepa: Iteration 114: Proposed new text for entity_extractor: Task Instructions:
|
||
|
||
Objective:
|
||
Extract heritage-specific entities from the provided text. The aim is to identify and categorize entities following the CH-Annotator v1.7.0 convention across various dimensions.
|
||
|
||
Entities to Extract:
|
||
- Institutions: Refer to museums, archives, cultural groups, and similar entities. Use the convention 'GRP.HER.MUS' for museums.
|
||
- Places: Extract geographical entities with their name and coordinates (latitude and longitude).
|
||
- Temporal: Identify and categorize any dates or time-related entities mentioned in the text.
|
||
- Identifiers: Look for any unique identifiers related to cultural heritage.
|
||
- Relationships: Identify and determine relationships between the identified heritage-specific entities.
|
||
|
||
Key Steps for the Task:
|
||
1. Intent Classification: Determine the main task the user intends to achieve, such as 'entity_lookup' or 'geographic'.
|
||
2. Entity Extraction: Identify and extract all relevant heritage-specific entities. Incomplete extraction can lead negative feedback.
|
||
3. Source Selection: Select the appropriate data sources from which the assistant will extract information. Potential sources include 'sparql', 'qdrant', and 'postgis'.
|
||
4. Answer Construction: Ensure the generated response addresses all parts of the user's query, incorporating all extracted entities and selected data sources.
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General Guidelines:
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||
- Context Understanding: Carefully read and interpret the input text to accurately identify the user's intent and provide the required information.
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||
- Completeness: Ensure that all expected entities, as highlighted in user feedback examples, are extracted and included in the final response.
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||
- Accuracy: Ensure geographic coordinates are precise and correctly associated with place names.
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||
- Consistency: Follow a consistent format and conventions in extracting and providing entities and their related data.
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||
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||
Reminder:
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||
- Use CH-Annotator v1.7.0 conventions strictly for consistency across all outputs and avoid missing entities that are clearly recognizable.
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||
- Maintain a detailed and thorough extraction approach to meet user expectations and improve entity identification accuracy in future tasks.
|
||
2025/12/11 12:36:14 INFO dspy.evaluate.evaluate: Average Metric: 1.95 / 3 (65.0%)
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2025/12/11 12:36:14 INFO dspy.teleprompt.gepa.gepa: Iteration 114: New subsample score 1.95 is not better than old score 1.95, skipping
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GEPA Optimization: 22%|██▏ | 645/2954 [26:01<7:34:14, 11.80s/rollouts]2025/12/11 12:36:14 INFO dspy.teleprompt.gepa.gepa: Iteration 115: Selected program 0 score: 0.6166666666666667
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0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 0.75 / 1 (75.0%): 0%| | 0/3 [00:00<?, ?it/s]
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Average Metric: 1.50 / 2 (75.0%): 33%|███▎ | 1/3 [00:00<00:00, 16.76it/s]
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Average Metric: 2.25 / 3 (75.0%): 67%|██████▋ | 2/3 [00:00<00:00, 32.41it/s]
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Average Metric: 2.25 / 3 (75.0%): 100%|██████████| 3/3 [00:00<00:00, 48.42it/s]2025/12/11 12:36:14 INFO dspy.evaluate.evaluate: Average Metric: 2.25 / 3 (75.0%)
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/Users/kempersc/miniconda3/lib/python3.12/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
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||
warnings.warn('resource_tracker: There appear to be %d '
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