recallib

recallib is a Python library designed to facilitate the process of developing and evaluating retrieval-augmented generation (RAG) systems. It aims to provide a standardized and efficient way to handle the various components involved in RAG, from data ingestion and indexing to query processing and response generation. The library focuses on enabling developers to iterate quickly on their RAG pipelines by abstracting away common complexities.

At its core, recallib offers functionalities for document loading, chunking, and embedding. It supports integration with various embedding models, allowing users to choose the most suitable one for their specific use case. The library also provides tools for indexing these embeddings, commonly using vector databases, to enable efficient similarity search.

For the retrieval phase, recallib facilitates the implementation of different retrieval strategies, such as k-nearest neighbors (k-NN) search. It then allows for the integration of these retrieved documents with a large language model (LLM) for the generation of contextually relevant responses. The library aims to simplify the chaining of these steps, making it easier to build and experiment with RAG workflows.

Beyond basic pipeline construction, recallib includes features for evaluation. It supports metrics and methodologies for assessing the performance of RAG systems, enabling developers to measure the effectiveness of their retrieval and generation components. This focus on evaluation is crucial for optimizing RAG models and ensuring they meet desired performance benchmarks. The library is intended to be modular, allowing for customization and extension to accommodate a wide range of RAG architectures and requirements.