Adds LiteLLM as a semantic embedding provider, including provider configuration, vector normalization, live-provider evaluation tooling, and documentation for OpenAI, Cohere, Azure Foundry, Azure OpenAI, and NVIDIA NIM-style cases.
Maintainer follow-up on this PR added provider hardening, asymmetric document/query embedding support, dimension-forwarding controls, SQLite/Postgres vector invalidation coverage, and the repeatable live LiteLLM harness.
Verification:
- Full base-repo Tests workflow passed for 3d4e092ceb: https://github.com/basicmachines-co/basic-memory/actions/runs/27072071785
- Live LiteLLM harness passed locally for OpenAI, Cohere, and Azure Foundry.
Co-authored-by: Aarish Alam <arishalam121@gmail.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Signed-off-by: RheagalFire <arishalam121@gmail.com>
Signed-off-by: phernandez <paul@basicmachines.co>
RRF compressed all fused scores to ~0.016, destroying ranking differentiation.
The new formula `max(vec, fts) + FUSION_BONUS * min(vec, fts)` preserves
dominant signals and rewards dual-source agreement.
Changes:
- Remove RRF_K constant; add FUSION_BONUS (0.3) and FTS_GATE_THRESHOLD (0.0)
- Use raw vector similarity scores instead of re-normalizing by vec_max
- Zero-score results now produce zero fused score (no 0.1 weight floor)
- Rename test_hybrid_rrf.py → test_hybrid_fusion.py with updated assertions
- Update docs and docstrings to reflect score-based fusion
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: phernandez <paul@basicmachines.co>
Paul Hernandez