ab5e31f203
* chore: remove dead code * chore: remove extract_opinions from test and regenerate openapi - Remove extract_opinions parameter from test_fact_extraction_analysis - Regenerate OpenAPI spec after removing entity observations code * chore: update generated files and apply formatting - Regenerate Python and TypeScript client SDKs after main merge - Apply ruff formatting to llm_wrapper.py * fix: accept and filter deprecated 'opinion' fact type in recall The dead code removal eliminated support for the 'opinion' fact type, but existing clients may still pass it. Instead of rejecting it with a ValueError, silently filter it out before validation to maintain backward compatibility.
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
"""
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Utility functions for memory system.
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"""
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import logging
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from datetime import datetime
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from .llm_wrapper import LLMConfig
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from .retain.fact_extraction import Fact
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from .retain.fact_extraction import extract_facts_from_text
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async def extract_facts(
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text: str,
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event_date: datetime,
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context: str = "",
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llm_config: "LLMConfig" = None,
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agent_name: str = None,
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) -> tuple[list["Fact"], list[tuple[str, int]]]:
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"""
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Extract semantic facts from text using LLM.
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Uses LLM for intelligent fact extraction that:
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- Filters out social pleasantries and filler words
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- Creates self-contained statements with absolute dates
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- Handles conversational text well
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- Resolves relative time expressions to absolute dates
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Args:
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text: Input text (conversation, article, etc.)
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event_date: Reference date for resolving relative times
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context: Context about the conversation/document
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llm_config: LLM configuration to use
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agent_name: Optional agent name to help identify agent-related facts
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Returns:
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Tuple of (facts, chunks) where:
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- facts: List of Fact model instances
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- chunks: List of tuples (chunk_text, fact_count) for each chunk
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Raises:
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Exception: If LLM fact extraction fails
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"""
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if not text or not text.strip():
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return [], []
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facts, chunks, _ = await extract_facts_from_text(
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text,
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event_date,
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context=context,
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llm_config=llm_config,
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agent_name=agent_name,
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)
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if not facts:
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logging.warning(
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f"LLM extracted 0 facts from text of length {len(text)}. This may indicate the text contains no meaningful information, or the LLM failed to extract facts. Full text: {text}"
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)
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return [], chunks
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return facts, chunks
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