I built a new plugin for my sqlite-utils CLI tool that lets you ask human-language questions directly of SQLite databases and CSV/JSON files on your computer. It’s called sqlite-utils-ask. Here’s …

Build powerful AI that you can trust with agentic data access for your LLMs.

A library for graph deep learning research. Contribute to divelab/DIG development by creating an account on GitHub.

Build a retrieval system with semantic, full-text, and fuzzy search in Postgres to be used as a backbone in RAG pipelines.

Install with Python, Node.js, Deno, Bun Rust, Go, C, WASM...

txtai is an all-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

Retrieval Augmented Generation (RAG) is a technique for adding extra “knowledge” to systems built on LLMs, allowing them to answer questions against custom information not included in their training data. …

A unified SQL query interface and portable runtime to locally materialize, accelerate, and query data tables sourced from any database, data warehouse, or data lake. - spiceai/spiceai

💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows - neuml/txtai

A series of top performing Text to SQL LLMs. Contribute to cfahlgren1/natural-sql development by creating an account on GitHub.

If you want to build a RAG-based tool, first build search.

With the popularity of Large Language Model, vector databases have also become a hot topic. With just a few lines of simple Python code, a vector database can act as a cheap but highly effective "external brain" for your LLM. But do we really need a specialized vector database?

ChatGPT is all the rage right now, and understandably so. Feeding it prompts and watching it spit out quality articles, recipes, travel itineraries, code examples, and more is fun and addictive. Yep, it’s a rabbit hole, and one we’re all getting a kick out of. One of ChatGPT’s many neat tricks is its ability to […]