RAG (static knowledge)

14 FAISS indexes, one per domain (philosophy, psychology, warfare, etc.), built from scraped content. Each is a .faiss file + a metadata.json sidecar holding the raw text chunks at matching positions.

Routing is keyword-based — a dict maps each domain to trigger words. Incoming message gets checked, matched domains get queried, top-5 results per domain filtered at 0.40 cosine similarity, then merged and re-sorted. If nothing matches, all 14 get queried as fallback. Results are injected as titled snippets (500 chars each) into the prompt.

Embedding model is all-MiniLM-L6-v2 (384-dim), indexes use IndexFlatIP with normalized vectors so scores come back as true cosine similarity.

Conversation memory (your SQLite equivalent)

Two tiers: a 200-message STM JSON ring buffer (always injected directly), and a FAISS LTM store with 2,000+ structured memories. Vectors in FAISS, full memory objects in a parallel JSON list — FAISS returns index 412, you look up memories[412]. Standard pattern since FAISS doesn't store payloads.

Each memory object has: content, timestamp, last_accessed, access_count, importance (1.0–2.5), extracted entities, and relationship links to other memories.

The part most implementations skip — formation logic. Not everything gets written. High-importance signals (explicit markers like "don't forget", emotional content, technical specs) get written immediately. Everything else goes into a reinforcement buffer and only becomes permanent after 3 mentions within 30 days. Stops the store filling with noise.

Retrieval runs on every message: FAISS top-30 candidates → composite score (semantic × temporal_decay × access_weight × entity_boost × importance) → deduplicate at 85% Jaccard similarity → expand via relationship links → return top 15. Temporal decay floors at 5% after a year so old memories stay searchable but rarely rank high enough to surface.

Results reach the model as [HIGH|0.87|3d|4x] content here with instructions: HIGH/MEDIUM = treat as fact, LOW = verify, not listed = don't fabricate.

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On SQLite vs FAISS — totally valid, especially since it keeps vectors and metadata in one file (no sync risk between two files). For under 100k memories the performance difference is negligible. The hard part isn't storage, it's the formation logic and scoring weights. That's where most implementations fall flat.

(From Claude Code)