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retrieval fusion

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2026-04-27 07:03:46 -07:00
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docs/architecture/overview.md
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@@ -73,7 +73,7 @@ service by ID after the vector search.
The core four-service architecture is complete and operational. Key capabilities:
- **Hybrid memory retrieval** — recent episodes + semantic search combined into every prompt
- **Retrieval fusion** — Reciprocal Rank Fusion (RRF) merges semantic (Qdrant vector search) and keyword (SQLite FTS5) episode retrieval into a single ranked result set. Weights are configurable per strategy via settings; keyword search is off by default (`keywordWeight: 0`) and can be enabled without a service restart
- **Entity layer + Knowledge graph** — automatic extraction of named entities and relationships from conversations via qwen2.5:3b. Entities and relationships are stored in SQLite with `mention_count` tracking. A graph traversal layer expands Qdrant entity search hits into a 1-hop neighborhood subgraph, injecting structured connected knowledge into every prompt
- **Projects** — sessions grouped with shared or isolated memory pools
- **Auto-naming** — sessions named automatically from first exchange via inference