The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs.
GraphRAG is a structured, hierarchical approach to Retrieval Augmented Generation (RAG), as opposed to naive semantic-search approaches using plain text snippets. The GraphRAG process involves extracting a knowledge graph out of raw text, building a community hierarchy, generating summaries for these communities, and then leveraging these structures when perform RAG-based tasks.
A new community-based approach to build truly open-source LLMs.
InstructLab Command-Line Interface. Use this to chat with a model and execute the InstructLab workflow to train a model using custom taxonomy data.
Retrieval Augmented Generation (RAG) chatbot powered by Weaviate.
Welcome to Verba: The Golden RAGtriever, an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. In just a few easy steps, explore your datasets and extract insights with ease, either locally with HuggingFace and Ollama or through LLM providers such as OpenAI, Cohere, and Google.
Infra for RAG apps that work in prod.
You know Postgres. Now you know machine learning.
Index, filter & rank vectors. Create embeddings. Generate real-time, fact-based outputs.
Korvus is a search SDK that unifies the entire RAG pipeline in a single database query. Built on top of Postgres with bindings for Python, JavaScript and Rust, Korvus delivers high-performance, customizable search capabilities with minimal infrastructure concerns.