Building a RAG-Powered Document Assistant from Scratch
📰 Medium · Python
Learn to build a RAG-powered document assistant from scratch using FastAPI, Qdrant, Llama-3, and Next.js
Action Steps
- Build a backend API using FastAPI to handle document queries
- Configure Qdrant as a vector database to store and retrieve document embeddings
- Integrate Llama-3 as the LLM to generate human-like responses
- Develop a frontend application using Next.js to interact with the document assistant
- Test and fine-tune the RAG-powered document assistant for optimal performance
Who Needs to Know This
Developers and data scientists on a team can benefit from this tutorial to build a custom document assistant, improving their productivity and efficiency. This project requires collaboration between backend, frontend, and AI engineers.
Key Insight
💡 By combining the strengths of FastAPI, Qdrant, Llama-3, and Next.js, developers can create a powerful document assistant that leverages RAG technology for efficient and accurate document processing.
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🚀 Build a RAG-powered document assistant from scratch with FastAPI, Qdrant, Llama-3, and Next.js! 📄💻
Key Takeaways
Learn to build a RAG-powered document assistant from scratch using FastAPI, Qdrant, Llama-3, and Next.js
Full Article
My end-to-end journey building a RAG document assistant using FastAPI, Qdrant, Llama-3, and Next.js Continue reading on Medium »
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