Retrieval with Feedback Loop
This system implements a Retrieval-Augmented Generation (RAG) approach with an integrated feedback loop. It aims to improve the quality and relevance of responses over time by incorporating user feedback and dynamically adjusting the retrieval process.
Traditional RAG systems can sometimes produce inconsistent or irrelevant responses due to limitations in the retrieval process or the underlying knowledge base. By implementing a feedback loop, we can: 1. Continuously improve the quality of retrieved documents 2. Enhance the relevance of generated responses 3. Adapt the system to user preferences and needs over time
What you'll learn
- 1PDF Content Extraction: Extracts text from PDF documents
- 2Vectorstore: Stores and indexes document embeddings for efficient retrieval
- 3Retriever: Fetches relevant documents based on user queries
- 4Language Model: Generates responses using retrieved documents
- 5Feedback Collection: Gathers user feedback on response quality and relevance
- 6Feedback Storage: Persists user feedback for future use
About this tutorial
This hands-on Jupyter notebook is part of RAG Techniques, a free open-source repository by Nir Diamant covering rag techniques with runnable code examples and detailed explanations.
RAG Made Simple
The book that extends this repo: 22 RAG techniques with the intuition behind each, side-by-side comparisons of when each wins (and quietly fails), and original illustrations.
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