9cfdd464a9
* fix(retain): preserve normalized experience fact types and remove deprecated opinion type The ExtractedFactType conversion was re-checking for raw "assistant" fact_type after the parsing layer had already normalized it to "experience". Since fact_from_llm.fact_type was always "experience" (never "assistant"), the ternary always fell through to "world", silently losing experience classification. Also removes the deprecated "opinion" fact type from internal extraction models, database constraints/indexes (via migration), and dead code paths. The public API surface (descriptions, response models, backwards-compat filter) is unchanged. * refactor(retain): drop unused confidence_score column The confidence_score column was only ever non-null for opinion facts (which are now removed). It was always written as NULL and never read back from the database. Remove it from: - DB model and migration (DROP COLUMN) - INSERT queries in fact_storage.py - retain_async/retain_batch_async parameters - RetainContext/RetainResult extension models - RetainBatch dataclass
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
"""
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Graph retrieval strategies for memory recall.
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This module provides an abstraction for graph-based memory retrieval,
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allowing different algorithms to be swapped without changing the rest
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of the recall pipeline.
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"""
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import logging
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from abc import ABC, abstractmethod
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from .tags import TagGroup, TagsMatch
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from .types import GraphRetrievalTimings, RetrievalResult
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logger = logging.getLogger(__name__)
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class GraphRetriever(ABC):
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"""
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Abstract base class for graph-based memory retrieval.
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Implementations traverse the memory graph (entity links, temporal links,
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causal links) to find relevant facts that might not be found by
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semantic or keyword search alone.
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"""
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@property
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@abstractmethod
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def name(self) -> str:
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"""Return identifier for this retrieval strategy (e.g., 'link_expansion')."""
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pass
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@abstractmethod
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async def retrieve(
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self,
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pool,
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query_embedding_str: str,
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bank_id: str,
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fact_type: str,
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budget: int,
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query_text: str | None = None,
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semantic_seeds: list[RetrievalResult] | None = None,
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temporal_seeds: list[RetrievalResult] | None = None,
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adjacency=None, # TypedAdjacency, optional pre-loaded graph
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tags: list[str] | None = None, # Visibility scope tags for filtering
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tags_match: TagsMatch = "any", # How to match tags: 'any' (OR) or 'all' (AND)
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tag_groups: list[TagGroup] | None = None, # Compound boolean tag filter groups
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) -> tuple[list[RetrievalResult], GraphRetrievalTimings | None]:
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"""
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Retrieve relevant facts via graph traversal.
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Args:
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pool: Database connection pool
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query_embedding_str: Query embedding as string (for finding entry points)
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bank_id: Memory bank identifier
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fact_type: Fact type to filter ('world', 'experience', 'observation')
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budget: Maximum number of nodes to explore/return
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query_text: Original query text (optional, for some strategies)
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semantic_seeds: Pre-computed semantic entry points (from semantic retrieval)
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temporal_seeds: Pre-computed temporal entry points (from temporal retrieval)
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adjacency: Pre-loaded typed adjacency graph (optional)
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tags: Optional list of tags for visibility filtering (OR matching)
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Returns:
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Tuple of (List of RetrievalResult with activation scores, optional timing info)
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"""
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pass
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