RAG & Knowledge
llm-graph-builder
Build Neo4j knowledge graphs from unstructured data using LLMs — then chat with your graph.
★ 5.3k starsApache-2.0
View guide →Build a ChatGPT over your own data using natural language and LlamaIndex.
rags is an open-source rag & knowledge project by run-llama (run-llama/rags on GitHub). It has 6,549 GitHub stars and 654 forks, is released under the MIT license, and was last updated on GitHub on 5 April 2024.
Decide which documents or data sources you want to chat with and what questions the app should answer.
Clone the repo and install dependencies with Poetry.
Add your OpenAI key (or configure Anthropic, Replicate or a local Hugging Face model) in the app's configuration.
Use the builder in natural language to configure chunking, embeddings and retrieval, then chat with the generated RAG agent.
Describe what you want in plain language and the builder agent assembles the RAG application for you.
Tune the system prompt, top-k retrieval, chunk size, embedding model, LLM and whether to add a summarization tool.
Supports OpenAI, Anthropic and Replicate models, plus local Hugging Face models referenced as local:<model_name>.
Uses OpenAI text-embedding-ada-002 by default and also supports local Hugging Face embedding models.
The generated app runs as a Streamlit UI with a home page, a RAG config page and the generated agent page.
Uses the LlamaIndex framework from run-llama for indexing and querying your data.
Clone RAGs and enter the project directory.
git clone https://github.com/run-llama/rags.git
cd ragsInstall the project and its dev dependencies with Poetry.
poetry install --with devStart the Streamlit interface, then build your RAG agent from the home page.
streamlit run 1_🏠_Home.pyRAGs is a run-llama project — the team behind LlamaIndex.
Yes, the code is MIT licensed. You pay only for any model APIs you choose to call.
By default it uses OpenAI models and embeddings, which need an OpenAI API key; Anthropic and Replicate also need keys, while local Hugging Face models can run without one.
RAGs is a builder on top of LlamaIndex: faster to get a working chat app via natural language, but less flexible than hand-writing your own LlamaIndex pipeline.
Clone the repo, run poetry install --with dev, add your model key, and launch the Streamlit app.
Build Neo4j knowledge graphs from unstructured data using LLMs — then chat with your graph.
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Free and open — grab the source, star the repo, and start creating.