Preeti
Retrieval-Augmented Generation (RAG) is a technique that combines information retrieval and natural language generation. It consists of a Retriever, which searches for relevant documents, and a Generator, which creates responses based on the retrieved context. RAG is effective for tasks like open-domain question answering, leveraging unstructured data for accurate answers.
Chroma DB is an open-source database optimized for handling dense vector embeddings, ideal for similarity search and large-scale retrieval tasks. It excels in managing vector embeddings and offers efficient similarity search capabilities. Chroma DB complements RAG systems by efficiently storing and retrieving document embeddings, enhancing overall retrieval performance.
To build a RAG system, one can use Hugging Face models and Chroma DB. The process involves setting up the environment, loading and preprocessing the dataset, initializing Chroma DB, querying the database, and generating answers using a pre-trained Hugging Face model for question answering. By following these steps, one can create an effective QA system that leverages the strengths of both RAG and Chroma DB. Further refinements can be made by optimizing the document retrieval process, experimenting with different models, and fine-tuning based on specific datasets. For a more detailed guide and code examples, refer to the comprehensive guide provided in the link.
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Source link: https://medium.com/@prajwal.p1115/rag-with-huggingface-models-and-chroma-db-3f6ade28b5fe?source=rss——hugging_face-5
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