LightRAG, Archestra as a UI with LightRAG mcp

Giving the LLM tools with an OData query interface has worked well for me. In C# it's pretty trivial to set up an MCP server with OData querying for an arbitrary data model. At work we have an Excel sheet with 40k rows which the LLM was able to quickly and reliably analyse using this method.

AnythingLLM for documents, amazing tool!

You don’t need a vector database or graph, it really depends on your existing infrastructure , file types and needs.

The newer “agent” search approach can just query a file system or api. It’s slightly slower but easier to setup and maintain as no extra infrastructure.

The Claude Code model highlights the power of simple search (grep) and selective reads (only reading in excerpts). The only time I vectorize is when I explicitly want to similarity-based searching, but that's actually pretty rare.

I built a Pandas extension SearchArray, I just use that (plus in memory embeddings) for any toy thing

https://github.com/softwaredoug/searcharray

I'm using Sonnet with 1M Context Window at work, just stuffing everything in a window (it works fine for now), and I'm hoping to investigate Recursive Language Models with DSPy when I'm using local models with Ollama

Any suggestion what to use as embeddings model runtime and semantic search in C++?

sqlite + FTS + sqlite-vec + local LLM for reranking results (reasoning model)

Has anyone tried this? https://aws.amazon.com/s3/features/vectors/