To enhance embedding in your Retrieval-Augmented Generation (RAG) application based on the "Python RAG Tutorial.txt" notes, follow these steps in simple English: 1. **Use High-Quality Embeddings**: - Choose high-quality embeddings to ensure accurate matching between your queries and the relevant data chunks. Consider using services like OpenAI or AWS Bedrock, as they provide reliable embeddings. 2. **Consistent Embedding Function**: - Use the same embedding function for both creating the database and querying it. This ensures consistency and better performance. 3. **Manage Large Documents**: - Split large documents into smaller chunks. Use tools like Langchain's recursive text splitter. Smaller chunks improve indexing and retrieval accuracy. 4. **Update the Vector Database**: - Add a unique ID to each data chunk. Use the file path, page number, and chunk number to create these IDs. This helps in updating the database without dupl...
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