New Agentic Hybrid for Sport Scores #104
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MongoDB Atlas Vector Search with VoyageAI Embeddings PR Summary
This PR introduces a comprehensive notebook demonstrating the integration of MongoDB Atlas Vector Search with VoyageAI embeddings for sports data retrieval. The notebook showcases:
Key Features
MongoDB Atlas connection and data management for sports content
VoyageAI embedding generation (using the voyage-3 model)
Vector search implementation for semantic similarity queries
Hybrid search combining vector search with full-text search
RAG (Retrieval-Augmented Generation) implementation using OpenAI
Agentic RAG using the OpenAI Agents SDK for more dynamic query refinement
Implementation Details
Sample sports data including teams, matches, and news stories
Vector embedding generation and storage in MongoDB
Vector search index setup and configuration
Comparison between vector-only and hybrid search approaches
OpenAI integration for natural language responses
Agentic approach demonstrating intelligent query decomposition
The notebook provides step-by-step walkthroughs with code examples and output demonstrations, making it an excellent reference implementation for similar use cases.