framework
Embedding model library named alongside OpenAI as a typical source of RAG embeddings suited to cosine distance.
No evidence-backed relationships are recorded.
The FAISS-plus-BM25 retrieval in this writeup does address vocabulary mismatch, but the agent loop around it ran at over 213 seconds a step on CPU, and that figure decided the deployment, not the retrieval design.
Reality
The provider cache discounts a repeated prefix, and the worst case is bounded arithmetic. The semantic cache deletes the call outright, but a miss there means a wrong answer, not a rounding error, so the threshold sweep matters more than the hit rate.
Reality
A dev.to writeup drops vector stores for a SQLite table with tag and timestamp columns. Its own numbers put the practical ceiling at a million rows, not a hundred million.
Reality
A developer wired runtime schema introspection, vector field matching and a local LLM repair loop into Odoo 17, then published the failure list. The failures define the boundary.
Reality
A dev.to walkthrough wires chokidar, tree-sitter, Qdrant and an MCP server into a code search stack. Every part is off the shelf; the unresolved work is reindexing and retrieval quality.
Reality
HNSW indexing runs inside the SQL engine, so there is no second service and no network hop. There is also no index persistence unless you turn on an experimental flag.
Reality