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fix: correctly map LLM fact_type \"assistant\" to \"experience\" for DB storage (#609)

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The Pydantic model extraction paths (batch API and parallel extraction) used
fact_from_llm.fact_type directly, bypassing the \"assistant\" → \"experience\"
conversion and causing DB CHECK constraint violations.

Unified the conversion logic across all paths:
- \"assistant\" → \"experience\"
- \"world\" → \"world\"
- anything else: fall back to fact_kind (\"assistant\" → \"experience\"), else \"world\"
This commit is contained in:
Nicolò Boschi 2026-03-19 10:32:08 +01:00 committed by GitHub
parent 276a4ba7e8
commit 446c75f3e2
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GPG key ID: B5690EEEBB952194
1 changed files with 21 additions and 41 deletions
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62
hindsight-api-slim/hindsight_api/engine/retain/fact_extraction.py
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@ -1083,30 +1083,16 @@ async def _extract_facts_from_chunk(
logger.warning(f"Skipping fact {i}: missing 'what' field")
continue
# Critical field: fact_type
# LLM uses "assistant" but we convert to "experience" for storage
original_fact_type = llm_fact.get("fact_type")
fact_type = original_fact_type
# Convert "assistant" → "experience" for storage
if fact_type == "assistant":
# Critical field: fact_type — "assistant" maps to "experience", everything else is "world".
# If fact_type is unexpected, fall back to fact_kind before defaulting to "world".
raw_fact_type = llm_fact.get("fact_type")
if raw_fact_type == "assistant":
fact_type = "experience"
# Validate fact_type (after conversion)
if fact_type not in ["world", "experience", "opinion"]:
# Try to fix common mistakes - check if they swapped fact_type and fact_kind
fact_kind = llm_fact.get("fact_kind")
if fact_kind == "assistant":
fact_type = "experience"
elif fact_kind in ["world", "experience", "opinion"]:
fact_type = fact_kind
else:
# Default to 'world' if we can't determine
fact_type = "world"
logger.warning(
f"Fact {i}: defaulting to fact_type='world' "
f"(original fact_type={original_fact_type!r}, fact_kind={fact_kind!r})"
)
elif raw_fact_type == "world":
fact_type = "world"
else:
raw_fact_kind = llm_fact.get("fact_kind")
fact_type = "experience" if raw_fact_kind == "assistant" else "world"
# Get fact_kind for temporal handling (but don't store it)
fact_kind = llm_fact.get("fact_kind", "conversation")
@ -1754,23 +1740,17 @@ async def extract_facts_from_contents_batch_api(
who = get_value("who")
why = get_value("why")
# Critical field: fact_type
original_fact_type = llm_fact.get("fact_type")
fact_type = original_fact_type
# Convert "assistant" → "experience"
if fact_type == "assistant":
# Critical field: fact_type — only "assistant" maps to "experience", everything else is "world"
# Critical field: fact_type — "assistant" maps to "experience", everything else is "world".
# If fact_type is unexpected, fall back to fact_kind before defaulting to "world".
raw_fact_type = llm_fact.get("fact_type")
if raw_fact_type == "assistant":
fact_type = "experience"
# Validate fact_type
if fact_type not in ["world", "experience", "opinion"]:
fact_kind = llm_fact.get("fact_kind")
if fact_kind == "assistant":
fact_type = "experience"
elif fact_kind in ["world", "experience", "opinion"]:
fact_type = fact_kind
else:
fact_type = "world"
elif raw_fact_type == "world":
fact_type = "world"
else:
raw_fact_kind = llm_fact.get("fact_kind")
fact_type = "experience" if raw_fact_kind == "assistant" else "world"
# Build combined fact text
combined_parts = [what]
@ -1933,7 +1913,7 @@ async def extract_facts_from_contents_batch_api(
for fact_from_llm in chunk_facts:
extracted_fact = ExtractedFactType(
fact_text=fact_from_llm.fact,
fact_type=fact_from_llm.fact_type,
fact_type="experience" if fact_from_llm.fact_type == "assistant" else "world",
entities=[e.text for e in (fact_from_llm.entities or [])],
occurred_start=_parse_datetime(fact_from_llm.occurred_start) if fact_from_llm.occurred_start else None,
occurred_end=_parse_datetime(fact_from_llm.occurred_end) if fact_from_llm.occurred_end else None,
@ -2110,7 +2090,7 @@ async def extract_facts_from_contents(
# mentioned_at is always the event_date (when the conversation/document occurred)
extracted_fact = ExtractedFactType(
fact_text=fact_from_llm.fact,
fact_type=fact_from_llm.fact_type,
fact_type="experience" if fact_from_llm.fact_type == "assistant" else "world",
entities=[e.text for e in (fact_from_llm.entities or [])],
# occurred_start/end: from LLM only, leave None if not provided
occurred_start=_parse_datetime(fact_from_llm.occurred_start)