Integration: fastRAG
fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines
fastRAG is a research framework, that extends Haystack, with abilities to build efficient and optimized retrieval augmented generative pipelines (with emphasis on Intel hardware), incorporating state-of-the-art LLMs and Information Retrieval modules.
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), š¤ Optimum Intel and š¤ Optimum-Habana for running as optimal as possible on IntelĀ® XeonĀ® Processors and IntelĀ® GaudiĀ® 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:
git clone https://github.com/IntelLabs/fastRAG.git
cd fastrag
There are several dependencies to consider, depending on your specific usage:
Basic installation:
pip install .
fastRAG with Intel-optimized backend:
pip install .[intel]
Other installation options can be found here.