This project implements a Retrieval-Augmented Generation (RAG) system using Mistral LLM for local document processing and question answering. The system:
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This project implements a Retrieval-Augmented Generation (RAG) system using Mistral LLM for local document processing and question answering. The system:
Explain the RAG (Retrieval-Augmented Generation) pattern like I'm a junior developer. Include: 1. What problems does it solve? 2. How is it different from regular LLM approaches? 3. Draw a simple diagram showing data flow 4. What are its main advantages and limitations?
Looking at this project's document processing approach: - Why split documents into chunks? - What determines optimal chunk size? - How does overlap help maintain context? - What are the tradeoffs in chunk size vs performance? Explain with examples.
I'm new to vector embeddings. Explain: 1. What are text embeddings? 2. How are they generated? 3. Why use MiniLM-L6-v2 specifically? 4. How does Chroma DB store and search these vectors? Use simple analogies where possible.
Help me understand LangChain's role in this project: 1. What components does it provide? 2. How does it simplify RAG implementation? 3. What's the RetrievalQA chain? 4. How does it integrate with Mistral? Include code structure examples.
Regarding Mistral LLM: 1. What makes it suitable for RAG? 2. How does it compare to other open models? 3. What are temperature and top_p parameters? 4. How to optimize its responses?
Explain vector similarity search: 1. Basic concepts and math 2. How Chroma DB implements it 3. Why k-NN for document retrieval? 4. Performance optimization techniques
Looking at the Streamlit implementation: 1. How does it manage state? 2. What's the document upload flow? 3. How is chat history maintained? 4. Best practices for RAG UI design?
Analyze this project's structure: 1. Why these specific components? 2. How do they communicate? 3. Where to add new features? 4. Improvement possibilities?
Regarding RAG system errors: 1. Common failure points? 2. Best practices for handling? 3. User feedback mechanisms? 4. Recovery strategies?
About RAG system performance: 1. Main bottlenecks? 2. Optimization strategies? 3. Caching approaches? 4. Scaling considerations?
Explain how LangChain's components are used in this project: - Document processing - Chain creation - Vector store integration What alternatives exist for each?
Explain sentence-transformers usage: 1. Model selection criteria 2. Embedding generation process 3. Performance characteristics 4. Alternative approaches
Regarding Chroma DB in this project: 1. Why choose it over alternatives? 2. How does persistence work? 3. Query optimization techniques? 4. Scaling considerations?
Analyze the Streamlit implementation: 1. State management approach 2. UI/UX considerations 3. Performance optimizations 4. Alternative frameworks?
Give me a step-by-step guide to: 1. Set up development environment 2. Process first document 3. Make first query 4. Debug common issues Include exact commands and code snippets.
Document Processing
Embeddings
RAG Implementation
Error Handling
ā” Start with basic prompts and progress to more complex ones based on your understanding. Use the responses to build a mental model of the system.