
fastRAG
Efficient Retrieval Augmentation and Generation Framework
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fastRAG is a research framework designed to build and explore efficient retrieval-augmented generative models. It incorporates state-of-the-art Large Language Models (LLMs) and Information Retrieval to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation. The framework is optimized for Intel hardware, customizable, and includes key features such as optimized RAG pipelines, efficient components, and RAG-efficient components like ColBERT and Fusion-in-Decoder (FiD). fastRAG supports various unique components and backends for running LLMs, making it a versatile tool for research and development in the field of retrieval-augmented generation.
README:

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! ❤️
[!IMPORTANT] Now compatible with Haystack v2+. Please report any possible issues you find.
- 2024-05: fastRAG V3 is Haystack 2.0 compatible 🔥
- 2023-12: Gaudi2 and ONNX runtime 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.
- 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.
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 |
OpenVINO | Running quantized LLMs using OpenVINO |
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 |
Preliminary requirements:
- Python 3.8 or higher.
- PyTorch 2.0 or higher.
To set up the software, install from pip
or clone the project for the bleeding-edge updates. Run the following, preferably in a newly created virtual environment:
pip install fastrag
There are additional dependencies that you can install based on your specific usage of fastRAG:
# Additional engines/components
pip install fastrag[intel] # Intel optimized backend [Optimum-intel, IPEX]
pip install fastrag[openvino] # Intel optimized backend using OpenVINO
pip install fastrag[elastic] # Support for ElasticSearch store
pip install fastrag[qdrant] # Support for Qdrant store
pip install fastrag[colbert] # Support for ColBERT+PLAID; requires FAISS
pip install fastrag[faiss-cpu] # CPU-based Faiss library
pip install fastrag[faiss-gpu] # GPU-based Faiss library
To work with the latest version of fastRAG, you can install it using the following command:
pip install .
pip install .[dev]
The code is licensed under the Apache 2.0 License.
This is not an official Intel product.
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