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Build a Local RAG 💻 with Ollama, Huggingface, FAISS and Google Gemma 3 ✨
- Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by providing accurate and context-specific answers, addressing issues like 'hallucination' and outdated information.
- RAG enables LLMs to offer more relevant responses by combining retrieval-based search with generative language modeling.
- The article discusses creating a RAG-powered chat application using technologies like Reflex, LangChain, Ollama, FAISS, and Hugging Face Datasets & Transformers.
- Key components include Reflex for frontend, LangChain for application flow, Ollama for running local LLMs, FAISS for similarity searches, and Hugging Face for NLP tasks.
- The objective is to build a web-based chat app that utilizes a locally running LLM grounded in retrieved context to provide answers to user queries.
- The code structure includes necessary files like .env, requirements.txt, and scripts for RAG logic, state management, and UI in Reflex.
- Various libraries like reflex, langchain, datasets, faiss-cpu, sentence-transformers, ollama are integrated to handle different tasks in the RAG application.
- Diving into the code, steps involve loading datasets, creating embeddings, building or loading vector stores, initializing the Ollama LLM, and setting up the RAG processing chain.
- The UI utilizes Reflex components and customizable styling to create an interactive chat interface for the RAG application.
- To enhance accuracy and readiness for production, suggestions include using larger Ollama models, dedicated vector databases, tailored datasets, and improving the chat interface.
- The project showcases the synergy of technologies like Reflex, LangChain, FAISS, Hugging Face, and Ollama in creating a locally hosted RAG chat application.
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