Add RAG with Milvus, LangChain & Anthropic Claude notebook #1565
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Signed-off-by: Jael Gu <[email protected]>
Signed-off-by: CheneyZhang <[email protected]>
Signed-off-by: ChengZi <[email protected]>
Signed-off-by: ChengZi <[email protected]>
Signed-off-by: ChengZi <[email protected]>
Signed-off-by: ChengZi <[email protected]>
Signed-off-by: ChengZi <[email protected]>
Signed-off-by: ChengZi <[email protected]>
Signed-off-by: ChengZi <[email protected]>
Signed-off-by: ShawnZheng <[email protected]>
Signed-off-by: ChengZi <[email protected]>
Signed-off-by: ChengZi <[email protected]>
- Complete RAG pipeline implementation using Milvus vector database - Integration with LangChain framework for document processing - Anthropic Claude for response generation - Modular design with configurable components - Support for PDF and text document processing - Interactive query sessions and utility functions - Comprehensive setup instructions and examples
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[APPROVALNOTIFIER] This PR is NOT APPROVED This pull-request has been approved by: behroozazarkhalili The full list of commands accepted by this bot can be found here.
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Welcome @behroozazarkhalili! It looks like this is your first PR to milvus-io/bootcamp 🎉 |
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We can remove this line :)
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I removed the colab badge.
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Line #26. connections.connect("default", host=self.host, port=self.port)
This is deprecated API. Recommend to use MilvusClient(). Also it's suggested to use endpoint that composes the host and port for initializing MilvusClient()
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Line #14. print("📝 Expected files: ai_overview.txt, vector_databases.txt, rag_systems.txt")
Where do they get such expected files?
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- Replace deprecated connections.connect() with MilvusClient(uri=endpoint) - Implement URI endpoint composition pattern for MilvusClient initialization - Update MilvusVectorStore class to use modern MilvusClient methods - Simplify schema creation using MilvusClient's streamlined approach - Fix vector search by adding anns_field parameter specification - Add proper resource cleanup with client.close() method - Remove deprecated utility imports and Collection-based operations - Ensure compatibility with current Milvus Python SDK
- Enhanced API key configuration with environment variable fallback - Improved error handling and resource management - Added comprehensive setup instructions and prerequisites - Included detailed Milvus installation guide with Docker commands - Enhanced modular architecture with better separation of concerns - Added support for both sample documents and PDF processing workflows - Improved logging and debugging capabilities - Added interactive session functions with better user experience - Enhanced documentation with step-by-step explanations - Optimized vector search and embedding generation processes
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Summary
This PR adds a comprehensive RAG (Retrieval-Augmented Generation) notebook that demonstrates how to build an end-to-end RAG system using:
Features
File Structure
Testing
This notebook provides a production-ready foundation for developers looking to implement RAG systems with Milvus and Anthropic Claude.