Retrieval-augmented generation — grounding a model in your own documents and data.
The framework most LLM applications are still assembled from.
Turn any set of documents into a private chat workspace.
Ask questions of your own documents with nothing leaving the machine.
The data framework for connecting models to your own sources.
Composable, typed pipelines for search and RAG systems.
Twelve build areas, from chatbots and agents to vector search and fine-tuning.
Retrieval-augmented generation — grounding a model in your own documents and data.
5 projectsThe scaffolding around a model: UI builders, experiment tracking and app frameworks.
5 projectsRunning models fast and cheaply: local runtimes, inference servers and OpenAI-compatible APIs.
4 projectsEmbedding stores and similarity search — the memory layer under most RAG systems.
3 projectsConversational apps you can actually ship — chat UIs, assistants and the plumbing behind them.
2 projectsProjects where the model plans, calls tools and works a task through to a result on its own.
2 projectsDiffusion pipelines, node graphs and browser front-ends for generating and editing images.
2 projectsSpeech recognition, text-to-speech and voice cloning you can run yourself.
2 projectsDetection, segmentation and tracking — the frameworks real vision products are built on.
2 projectsAdapting an existing model to your data — LoRA, full fine-tunes and the tooling around them.
2 projectsWiring models into multi-step processes that run without a human in the loop.
2 projectsSeveral specialised agents co-ordinating on one job — roles, hand-offs and shared state.
2 projectsEvery project in RAG & Knowledge is open source and buildable — each one links straight to its repository so you can read the code before you commit an afternoon to it.
