Important

We're migrating fastRAG to Haystack v2.0 API and will release a major update soon. Stay tuned!

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Build and explore efficient retrieval-augmented generative models and applications

📍 Installation • 🚀 Components • 📚 Examples • 🚗 Getting Started • 💊 Demos • ✏️ Scripts • 📊 Benchmarks

fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines, incorporating state-of-the-art LLMs and Information Retrieval. fastRAG is designed to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation.

Comments, suggestions, issues and pull-requests are welcomed! ❤️

📣 Updates

• 2024-04: (Extra Demo) Chat with your documents on Intel Meteor Lake iGPU.
• 2023-12: Gaudi2, ONNX runtime and LlamaCPP support; Optimized Embedding models; Multi-modality and Chat demos; REPLUG text generation.
• 2023-06: ColBERT index modification: adding/removing documents; see IndexUpdater.
• 2023-05: RAG with LLM and dynamic prompt synthesis example.
• 2023-04: Qdrant DocumentStore support.

Key Features

• Optimized RAG: Build RAG pipelines with SOTA efficient components for greater compute efficiency.
• Optimized for Intel Hardware: Leverage Intel extensions for PyTorch (IPEX), 🤗 Optimum Intel and 🤗 Optimum-Habana for running as optimal as possible on Intel® Xeon® Processors and Intel® Gaudi® AI accelerators.
• Customizable: fastRAG is built using Haystack and HuggingFace. All of fastRAG's components are 100% Haystack compatible.

🚀 Components

For a brief overview of the various unique components in fastRAG refer to the Components Overview page.

LLM Backends
Intel Gaudi Accelerators	Running LLMs on Gaudi 2
ONNX Runtime	Running LLMs with optimized ONNX-runtime
Llama-CPP	Running RAG Pipelines with LLMs on a Llama CPP backend
Optimized Components
Embedders	Optimized int8 bi-encoders
Rankers	Optimized/sparse cross-encoders
RAG-efficient Components
ColBERT	Token-based late interaction
Fusion-in-Decoder (FiD)	Generative multi-document encoder-decoder
REPLUG	Improved multi-document decoder
PLAID	Incredibly efficient indexing engine

📍 Installation

Preliminary requirements:

• Python 3.8 or higher.
• PyTorch 2.0 or higher.

To set up the software, clone the project and run the following, preferably in a newly created virtual environment:

pip install .

There are several dependencies to consider, depending on your specific usage:

# Additional engines/components
pip install .[intel]               # Intel optimized backend [Optimum-intel, IPEX]
pip install .[elastic]             # Support for ElasticSearch store
pip install .[qdrant]              # Support for Qdrant store
pip install .[colbert]             # Support for ColBERT+PLAID; requires FAISS
pip install .[faiss-cpu]           # CPU-based Faiss library
pip install .[faiss-gpu]           # GPU-based Faiss library
pip install .[knowledge_graph]     # Libraries for working with spacy and KG

# User interface (for demos)
pip install .[ui]

# Benchmarking
pip install .[benchmark]

# Development tools
pip install .[dev]

License

The code is licensed under the Apache 2.0 License.

Disclaimer

This is not an official Intel product.