Integration: fastRAG
A research framework designed to facilitate the building of retrieval augmented generative pipelines.
fastRAG is a research framework designed to facilitate the building of retrieval augmented generative pipelines. Its main goal is to make retrieval augmented generation as efficient as possible through the use of state-of-the-art and efficient retrieval and generative models. The framework includes a variety of sparse and dense retrieval models, as well as different extractive and generative information processing models. fastRAG aims to provide researchers and developers with a comprehensive tool-set for exploring and advancing the field of retrieval augmented generation.
It includes custom nodes such as:
- Image Generators
- Knoweldge Graph Creator
- Document Shapers
- Reader with FiD implementation
- Efficient document vector store (PLAID)
- Benchmarking scripts
Installation
Preliminary requirements:
- Python 3.8+
- PyTorch
In a new virtual environment, run:
pip install .
There are various dependencies, based on usage:
# Additional engines/components
pip install .[faiss-cpu] # CPU-based Faiss
pip install .[faiss-gpu] # GPU-based Faiss
pip install .[qdrant] # Qdrant support
pip install libs/colbert # ColBERT/PLAID indexing engine
pip install .[image-generation] # Stable diffusion library
pip install .[knowledge_graph] # spacy and KG libraries
# REST API + UI
pip install .[ui]
# Benchmarking
pip install .[benchmark]
# Dev tools
pip install .[dev]