New Agentic Hybrid for Sport Scores

[Pash10g]

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.