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Renamed onboarding chunk

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Viswamedha Nalabotu 2026-03-22 15:43:19 +00:00
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@ -554,7 +554,7 @@ embeddings. This avoids naive fixed-size splits that can break context
mid-concept \cite{reimers2019sbert,sbert2024docs,fastapi2024docs}. mid-concept \cite{reimers2019sbert,sbert2024docs,fastapi2024docs}.
\underline{Vector storage and retrieval with pgvector}\\ \underline{Vector storage and retrieval with pgvector}\\
Returned chunk embeddings are stored in RoleRagDocument.embedding (768 Returned chunk embeddings are stored in KnowledgeChunk.embedding (768
dimensions) in PostgreSQL using pgvector, linked relationally to role dimensions) in PostgreSQL using pgvector, linked relationally to role
and source file metadata. Retrieval is performed in SQL using and source file metadata. Retrieval is performed in SQL using
cosine-distance ranking and top-k selection, allowing role filtering and cosine-distance ranking and top-k selection, allowing role filtering and