Steerable AI with Pinecone + Semantic Router
AI Summary
Summary: Integration of Semantic Router and Pinecone
- Introduction to Integration
- Integration of Semantic Router Library with Pinecone.
- Aim: Enhance scalability and ease of use.
- Benefits of Integration
- Scalability: Handle a vast number of utterances and routes.
- Persistence: Route layers stored in Pinecone index for easy transfer and session continuity.
- Practical Examples and Use Cases
- High-scale routing and utterance creation.
- Semantic splitting for document and conversation trunking.
- Video frame chunking based on content.
- Content moderation for images.
- Implementation Steps
- Install necessary libraries (Hugging Face datasets, Semantic Router, Pinecone extras).
- Download dataset with routes and utterances.
- Initialize route layer with an encoder and Pinecone index.
- Obtain Pinecone API key and set up index.
- Embed and send data to Pinecone index.
- Use Pinecone for persistent storage and retrieval of routes.
- Demonstration of Functionality
- Delete local route layer, index, and routes to demonstrate Pinecone’s persistence.
- Initialize new Pinecone index with custom name.
- Retrieve and reformat routes from Pinecone index.
- Reinitialize route layer with new routes from Pinecone.
- Test route layer to confirm correct identification of utterances.
- Conclusion
- The integration simplifies scaling and persistence of route layers.
- Encourages exploration of new use cases for Semantic Router with Pinecone’s capabilities.
- Closing Remarks
- The video demonstrates the ease and potential of the Semantic Router and Pinecone integration.
- The presenter looks forward to seeing innovative applications of the technology.