fix: document not tracked if has 0 extracted facts (#399)
* fix: document not tracked if has 0 extracted facts * fix: document not tracked if has 0 extracted facts
This commit is contained in:
Nicolò Boschi
GitHub
parent
117dd6988d
commit
f78278ea89
2 changed files with 109 additions and 35 deletions
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@ -156,13 +156,22 @@ async def retain_batch(
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)
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if not extracted_facts:
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# Still need to create document if document_id was provided
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# Still need to create document if document_id was provided or chunks exist
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from collections import defaultdict
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docs_tracked = 0
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async with acquire_with_retry(pool) as conn:
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async with conn.transaction():
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await fact_storage.ensure_bank_exists(conn, bank_id)
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# Handle document tracking even with no facts
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# Group contents by document_id (consistent with normal path)
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contents_by_doc_early = defaultdict(list)
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for idx, content_dict in enumerate(contents_dicts):
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doc_id = content_dict.get("document_id")
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contents_by_doc_early[doc_id].append((idx, content_dict))
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if document_id:
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# Legacy: single document_id parameter
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combined_content = "\n".join([c.get("content", "") for c in contents_dicts])
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# Collect tags from all content items and merge with document_tags
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all_tags = set(document_tags or [])
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@ -187,45 +196,57 @@ async def retain_batch(
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await fact_storage.handle_document_tracking(
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conn, bank_id, document_id, combined_content, is_first_batch, retain_params, merged_tags
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)
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docs_tracked += 1
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else:
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# Check for per-item document_ids
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from collections import defaultdict
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# Handle per-item document_ids and/or chunks (mirrors normal path logic)
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has_any_doc_ids = any(item.get("document_id") for item in contents_dicts)
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contents_by_doc = defaultdict(list)
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for idx, content_dict in enumerate(contents_dicts):
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doc_id = content_dict.get("document_id")
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if doc_id:
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contents_by_doc[doc_id].append((idx, content_dict))
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if has_any_doc_ids or chunks:
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for original_doc_id, doc_contents in contents_by_doc_early.items():
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should_create_doc = (original_doc_id is not None) or chunks
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if not should_create_doc:
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continue
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for doc_id, doc_contents in contents_by_doc.items():
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combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
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# Collect tags from all content items for this document and merge with document_tags
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all_tags = set(document_tags or [])
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for _, item in doc_contents:
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item_tags = item.get("tags", []) or []
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all_tags.update(item_tags)
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merged_tags = list(all_tags)
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actual_doc_id = original_doc_id
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if actual_doc_id is None:
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# No document_id but have chunks - generate one
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actual_doc_id = str(uuid.uuid4())
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retain_params = {}
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if doc_contents:
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first_item = doc_contents[0][1]
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if first_item.get("context"):
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retain_params["context"] = first_item["context"]
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if first_item.get("event_date"):
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retain_params["event_date"] = (
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first_item["event_date"].isoformat()
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if hasattr(first_item["event_date"], "isoformat")
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else str(first_item["event_date"])
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)
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if first_item.get("metadata"):
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retain_params["metadata"] = first_item["metadata"]
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await fact_storage.handle_document_tracking(
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conn, bank_id, doc_id, combined_content, is_first_batch, retain_params, merged_tags
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)
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combined_content = "\n".join([c.get("content", "") for _, c in doc_contents])
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all_tags = set(document_tags or [])
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for _, item in doc_contents:
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item_tags = item.get("tags", []) or []
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all_tags.update(item_tags)
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merged_tags = list(all_tags)
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retain_params = {}
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if doc_contents:
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first_item = doc_contents[0][1]
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if first_item.get("context"):
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retain_params["context"] = first_item["context"]
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if first_item.get("event_date"):
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retain_params["event_date"] = (
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first_item["event_date"].isoformat()
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if hasattr(first_item["event_date"], "isoformat")
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else str(first_item["event_date"])
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)
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if first_item.get("metadata"):
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retain_params["metadata"] = first_item["metadata"]
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await fact_storage.handle_document_tracking(
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conn,
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bank_id,
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actual_doc_id,
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combined_content,
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is_first_batch,
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retain_params,
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merged_tags,
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)
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docs_tracked += 1
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total_time = time.time() - start_time
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doc_status = f"{docs_tracked} document(s) tracked" if docs_tracked > 0 else "no document tracked"
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logger.info(
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f"RETAIN_BATCH COMPLETE: 0 facts extracted from {len(contents)} contents in {total_time:.3f}s (document tracked, no facts)"
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f"RETAIN_BATCH COMPLETE: 0 facts extracted from {len(contents)} contents in {total_time:.3f}s ({doc_status}, no facts)"
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)
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return [[] for _ in contents], usage
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@ -2,9 +2,13 @@
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Tests for document tracking and upsert functionality.
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"""
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import logging
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import pytest
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from datetime import datetime, timezone
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from unittest.mock import patch
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import pytest
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from hindsight_api import RequestContext
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from hindsight_api.engine.response_models import TokenUsage
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@pytest.mark.asyncio
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@ -311,3 +315,52 @@ async def test_document_persisted_with_zero_facts_async_submit(memory, request_c
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finally:
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await memory.delete_bank(bank_id, request_context=request_context)
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@pytest.mark.asyncio
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async def test_document_stored_without_chunks_when_zero_facts(memory_no_llm_verify, request_context):
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"""
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Regression test: when 0 facts are extracted from chunked content, the document row
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must be stored but no chunk rows should be written.
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"""
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bank_id = f"test_zero_facts_no_chunks_{datetime.now(timezone.utc).timestamp()}"
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document_id = "doc-zero-facts-chunked"
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# Content large enough to exceed default retain_chunk_size (3000 chars) so chunking is triggered
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content = "Alice works at Google. " * 200 # ~4600 chars
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async def mock_llm_zero_facts(*args, **kwargs):
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response = {"facts": []}
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if kwargs.get("return_usage", False):
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return response, TokenUsage(input_tokens=10, output_tokens=2)
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return response
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try:
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with patch("hindsight_api.engine.llm_wrapper.LLMProvider.call", new=mock_llm_zero_facts):
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units = await memory_no_llm_verify.retain_async(
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bank_id=bank_id,
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content=content,
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document_id=document_id,
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request_context=request_context,
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)
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assert units == [], "Should return no memory units when LLM extracts zero facts"
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# Document row must exist
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doc = await memory_no_llm_verify.get_document(document_id, bank_id, request_context=request_context)
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assert doc is not None, "Document row must be stored even when zero facts are extracted"
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assert doc["id"] == document_id
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assert doc["memory_unit_count"] == 0
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# No chunk rows should be stored
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pool = await memory_no_llm_verify._get_pool()
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async with pool.acquire() as conn:
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chunk_count = await conn.fetchval(
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"SELECT COUNT(*) FROM chunks WHERE document_id = $1 AND bank_id = $2",
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document_id,
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bank_id,
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)
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assert chunk_count == 0, "No chunk rows should be stored when zero facts are extracted"
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finally:
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await memory_no_llm_verify.delete_bank(bank_id, request_context=request_context)
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