Module knowledge_embedding 

Embedding-based Knowledge Retrieval for ctx_knowledge.

Wraps ProjectKnowledge with a vector index for semantic recall. Facts are automatically embedded on remember and searched via cosine similarity on recall, with hybrid exact + semantic ranking.

Structs

FactEmbedding

KnowledgeEmbeddingIndex

ScoredFact

Constants

SEMANTIC_DUP_THRESHOLD

Cosine threshold above which a freshly-remembered fact is treated as a semantic near-duplicate of an existing one. Deliberately conservative — only genuine paraphrases (“DB is Postgres” / “we persist to PostgreSQL”) clear it, so the advisory stays signal, not noise. Non-destructive: it nudges the agent to judge, never auto-merges (distinct facts can be near in embedding space, e.g. “Postgres 14” vs “Postgres 15”).

Functions

compact_against_knowledge

embed_and_store

find_semantic_duplicates

Embedding-based near-duplicate detection for remember. Mirrors the lexical find_cross_key_similar but scores cosine similarity, so paraphrases that share few tokens are still caught. Read-only against the pre-upsert index, so the incoming fact never matches itself. Returns advisory hits for the agent to resolve via judge — it never mutates or merges facts.

format_scored_facts

reset

semantic_recall

semantic_recall_semantic_only