For the uneducated, how large is too large? Curious.
itake•5m ago
FAISS runs in RAM. If your dataset can't fit into ram, FAISS is not the right tool.
motakuk•13h ago
LightRAG, Archestra as a UI with LightRAG mcp
ramesh31•10h ago
SQLite with FTS5
nineteen999•9h ago
A little BM25 can get you quite a way with an LLM.
jeffchuber•1h ago
try out chroma or better yet as opus to!
electroglyph•1h ago
simple lil setup with qdrant
CuriouslyC•1h ago
Don't use a vector database for code, embeddings are slow and bad for code. Code likes bm25+trigram, that gets better results while keeping search responses snappy.
lee1012•1h ago
static embedding models im finding quite fast
lee101/gobed https://github.com/lee101/gobed is 1ms on gpu :) would need to be trained for code though the bigger code llm embeddings can be high quality too so its just yea about where is ideal on the pareto fronteir really , often yea though your right it tends to be bm25 or rg even for code but yea more complex solutions are kind of possible too if its really important the search is high quality
itake•6m ago
With AI needing more access to documentation, WDYT about using RAG for documentation retrieval?
lee1012•1h ago
lee101/gobed https://github.com/lee101/gobed static embedding models so they are embedded in milliseconds and on gpu search with a cagra style on gpu index with a few things for speed like int8 quantization on the embeddings and fused embedding and search in the same kernel as the embedding really is just a trained map of embeddings per token/averaging
eajr•14h ago