81–90 of 166 posts
save_memory, recall_memory, search
Save memory vectorizes a session, summarizes it, and stores it in SQLite. Recall memory takes vector or a previous tool run id and loads the full text output. Search takes a vector array or string array and searches through the graph using fuzzy matching and vector dot products.
It’s not fancy, but it works really well. gpt-oss
Not sure how useful it is for what you need specifically: https://blog.yakkomajuri.com/blog/local-rag
So I use hosted one to prevent this. My business use vector db, so created a new db to vectorize and host my knowledge base. 1. All my knowledge base is markdown files. So I split that by header tags. 2. The split is hashed and hash value is stored in SQLite 3. The hashed version is vectorized and pushed to cloud db. 4. When ever I make changes , I run a script which splits and checks hash, if it is changed the. I upsert the document. If not I don’t do anything. This helps me keep the store up to date
For search I have a cli query which searches and fetches from vector store.
Most of my complex documents are, luckily, Markdown files. I can recommend https://github.com/tobi/qmd/ . It’s a simple CLI tool for searching in these kinds of files. My previous workflow was based on fzf, but this tool gives better results and enables even more fuzzy queries. I don’t use it for code, though.
ragtune explain "your query" --collection prod
Open source: https://github.com/metawake/ragtune
It’s a CLI tool and MCP server for creating discrete, versioned “libraries” of RAG-able content.
Under the hood, it uses an embedding model locally. It chunks your content and stores embeddings in SQLite. The search functionality uses vector + keyword search + a re-ranking model.
You can also point it at any GitHub repo and it will create a RAG DB out of it.
You can also use the MCP server to create and query the libraries.
https://huggingface.co/MongoDB/mdbr-leaf-ir
It ranks #1 on a bunch of leaderboards for models of its size. It can be used interchangeably with the model it has been distilled from (https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1...).
You can see an example comparing semantic (i.e., embeddings-based) search vs bm25 vs hybrid here: http://search-sensei.s3-website-us-east-1.amazonaws.com (warning! It will download ~50MB of data for the model weights and onnx runtime on first load, but should otherwise run smoothly even on a phone)
This mini app illustrates the advantage of semantic vs bm25 search. For instance, embedding models "know" that j lo refers to jennifer lopez.
We have also published the recipe to train this type of models if you were interested in doing so; we show that it can be done on relatively modest hardware and training data is very easy to obtain: https://arxiv.org/abs/2509.12539
https://duckdb.org/2024/05/03/vector-similarity-search-vss