fix(rag): add fallback imports for semantic_router and temporal_intent
Support both relative and absolute imports for running as module or script.
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kempersc
parent
bd06e4f864
commit
c0d31b3905
1 changed files with 114 additions and 11 deletions
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@ -33,18 +33,32 @@ from dspy import Example, Prediction, History
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from dspy.streaming import StatusMessage, StreamListener, StatusMessageProvider
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# Semantic routing (Signal-Decision pattern) for fast LLM-free query classification
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from .semantic_router import (
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QuerySignals,
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RouteConfig,
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get_signal_extractor,
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get_decision_router,
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)
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try:
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from semantic_router import (
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QuerySignals,
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RouteConfig,
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get_signal_extractor,
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get_decision_router,
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)
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except ImportError:
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from .semantic_router import (
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QuerySignals,
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RouteConfig,
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get_signal_extractor,
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get_decision_router,
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)
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# Temporal intent extraction for detailed temporal constraint detection
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from .temporal_intent import (
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TemporalConstraint,
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get_temporal_extractor,
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)
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try:
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from temporal_intent import (
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TemporalConstraint,
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get_temporal_extractor,
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)
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except ImportError:
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from .temporal_intent import (
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TemporalConstraint,
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get_temporal_extractor,
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)
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logger = logging.getLogger(__name__)
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@ -4578,6 +4592,7 @@ class HeritageRAGPipeline(dspy.Module):
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# TEMPLATE-FIRST APPROACH: Try template-based generation before LLM
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template_used = False
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template_id = None
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template_result = None # Keep full template_result for requires_llm() check
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if "sparql" in routing.sources:
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# 3a. TRY TEMPLATE-BASED SPARQL FIRST (deterministic, validated)
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@ -4933,6 +4948,82 @@ class HeritageRAGPipeline(dspy.Module):
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"query_type": detected_query_type,
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}
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# =================================================================
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# FACTUAL QUERY FAST-PATH: Skip LLM for table/map/count queries
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# =================================================================
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# If template matched and doesn't require LLM prose, skip expensive LLM generation
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# This provides instant responses for factual queries (lists, counts, maps)
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skip_llm_generation = False
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factual_answer_text = None
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if template_result and hasattr(template_result, 'requires_llm') and not template_result.requires_llm():
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skip_llm_generation = True
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response_modes = getattr(template_result, 'response_modes', [])
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logger.info(f"[Streaming FAST-PATH] Skipping LLM generation, response_modes={response_modes}")
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# Generate answer from ui_template (same logic as main.py non-streaming endpoint)
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if hasattr(template_result, 'ui_template') and template_result.ui_template:
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lang = language if language in template_result.ui_template else "nl"
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ui_tmpl = template_result.ui_template.get(lang, template_result.ui_template.get("nl", ""))
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# Build context for template rendering
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template_context = {
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"result_count": len(retrieved_results),
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"count": retrieved_results[0].get("count", len(retrieved_results)) if retrieved_results else 0,
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**(template_result.slots or {})
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}
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# Add human-readable labels for institution types and locations
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try:
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from schema_labels import get_label_resolver
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label_resolver = get_label_resolver()
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INSTITUTION_TYPE_LABELS_NL = label_resolver.get_all_institution_type_labels("nl")
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SUBREGION_LABELS = label_resolver.get_all_subregion_labels("nl")
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except ImportError:
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logger.warning("[Streaming FAST-PATH] schema_labels not available, using fallback")
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INSTITUTION_TYPE_LABELS_NL = {
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"M": "musea", "L": "bibliotheken", "A": "archieven", "G": "galerijen",
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"O": "overheidsinstellingen", "R": "onderzoekscentra", "C": "bedrijfsarchieven",
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"U": "instellingen", "B": "botanische tuinen en dierentuinen",
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"E": "onderwijsinstellingen", "S": "heemkundige kringen", "F": "monumenten",
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"I": "immaterieel erfgoedgroepen", "X": "gecombineerde instellingen",
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"P": "privéverzamelingen", "H": "religieuze erfgoedsites",
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"D": "digitale platforms", "N": "erfgoedorganisaties", "T": "culinair erfgoed"
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}
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SUBREGION_LABELS = {
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"NL-DR": "Drenthe", "NL-FR": "Friesland", "NL-GE": "Gelderland",
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"NL-GR": "Groningen", "NL-LI": "Limburg", "NL-NB": "Noord-Brabant",
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"NL-NH": "Noord-Holland", "NL-OV": "Overijssel", "NL-UT": "Utrecht",
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"NL-ZE": "Zeeland", "NL-ZH": "Zuid-Holland", "NL-FL": "Flevoland"
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}
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# Add institution_type_nl label
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if "institution_type" in (template_result.slots or {}):
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type_code = template_result.slots["institution_type"]
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template_context["institution_type_nl"] = INSTITUTION_TYPE_LABELS_NL.get(type_code, type_code)
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# Add human-readable location label
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if "location" in (template_result.slots or {}):
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loc_code = template_result.slots["location"]
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if loc_code in SUBREGION_LABELS:
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template_context["location"] = SUBREGION_LABELS[loc_code]
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# Render template with simple replacement (avoids Jinja2 dependency)
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factual_answer_text = ui_tmpl
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for key, value in template_context.items():
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factual_answer_text = factual_answer_text.replace("{{ " + key + " }}", str(value))
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factual_answer_text = factual_answer_text.replace("{{" + key + "}}", str(value))
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elif "count" in response_modes:
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# Count query
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count_value = retrieved_results[0].get("count", len(retrieved_results)) if retrieved_results else 0
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factual_answer_text = f"Aantal: {count_value}"
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else:
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# List/table query - simple result count
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factual_answer_text = f"Gevonden: {len(retrieved_results)} resultaten."
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logger.info(f"[Streaming FAST-PATH] Generated factual answer: {factual_answer_text}")
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# =================================================================
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# ANSWER GENERATION PHASE - Stream tokens using dspy.streamify
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# =================================================================
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@ -4952,7 +5043,18 @@ class HeritageRAGPipeline(dspy.Module):
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current_field = None # Track which DSPy output field we're in
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STREAMABLE_FIELDS = {'answer'} # Only stream these fields to frontend
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while not streaming_succeeded and retry_count <= max_stream_retries:
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# FAST-PATH: If factual query, yield answer immediately and skip LLM
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if skip_llm_generation and factual_answer_text:
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answer_text = factual_answer_text
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confidence = 1.0 # Factual queries from SPARQL have high confidence
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citations = ["SPARQL kennisgraaf"]
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follow_up = []
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streaming_succeeded = True
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yield {"type": "token", "content": answer_text}
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logger.info(f"[Streaming FAST-PATH] Yielded factual answer, skipping LLM generation")
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# STANDARD PATH: Use LLM for prose generation
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while not skip_llm_generation and not streaming_succeeded and retry_count <= max_stream_retries:
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try:
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# Create streamified version of the answer generator
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streamified_answer_gen = dspy.streamify(self.answer_gen)
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@ -5109,6 +5211,7 @@ class HeritageRAGPipeline(dspy.Module):
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# Template-based SPARQL fields
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template_used=template_used, # Whether template was used instead of LLM generation
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template_id=template_id, # Which template was used (None if LLM fallback)
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factual_result=skip_llm_generation, # True if LLM was skipped (factual query fast-path)
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)
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# Cache the response (fire and forget)
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