palimind-rag

name	palimind-rag
description	How Palimind's RAG pipeline works (ingest → chunk → embed → retrieve → rerank → respond). Use when changing retrieval quality, indexing, chunking, embeddings, or context assembly. Triggers: RAG, chunker, embedding, retrieval, rerank, vector store, TurboVec, FTS5.

Palimind RAG Pipeline

Flow

files → ingestion/ parsers (PDF/DOCX/PPTX/XLSX/images OCR/video Whisper)
      → chunkers (chunker.py, rich_chunker.py; size/overlap from config)
      → embedder.py (nomic-embed-text via Ollama; 4-bit TurboVec quantization)
      → storage/vector_store.py + SQLite FTS5 (storage/db.py)

query → querying.py: rewrite (LLM) → hybrid search (semantic + BM25 in
        document/engine.py) → reciprocal rank fusion → cross-encoder rerank
        (reranker.py, BAAI/bge-reranker-base) → context budget assembly
        → generative/responder.py streams answer with sources

Tunables (all per-workspace in .palimind/config.json)

chunk_size 3000 / chunk_overlap 500 / turbovec_bit_width 4 / retrieval_limit 10 / context_token_budget 8000 / rerank true / query_rewrite true.

Rules

• Retrieval changes must preserve the "sources" contract returned to the UI.
• Never load embedding/reranker models at module import time — they are lazy for startup speed; keep it that way.
• Index DB is .palimind/index.db (SQLite FTS5); schema changes need a migration story before touching storage/.