Steerable AI with Pinecone + Semantic Router
AI Summary
Summary: Integration of Semantic Router Library with Pinecone
- Introduction to Integration
- New integration between Semantic Router Library and Pinecone.
- Focus on scalability and ease of use.
- Potential for high scale with Pinecone’s capabilities.
- Benefits of Integration
- Scalability: Handle a large number of utterances and routes.
- Ease of Use: Simplified management of route layers across sessions.
- Persistence: Route layers stored within Pinecone index for easy access.
- Practical Examples and Use Cases
- Creation of numerous routes and utterances.
- Semantic splitting for intelligent document and conversation trunking.
- Video frame chunking based on content.
- Content moderation for images.
- Implementation Steps
- Installation of necessary libraries and extras.
- Downloading and using a dataset with routes and utterances.
- Initialization of a route layer with an encoder and Pinecone index.
- API key retrieval from Pinecone for serverless operations.
- Embedding creation and index initialization with Pinecone.
- Demonstration of persistent index and route layer reloading.
- Custom index naming and retrieval of previous routes from Pinecone.
- Conversion of utterances to a usable format for route layer initialization.
- Testing the route layer to ensure correct identification of utterances.
- Conclusion
- The integration is designed for simplicity and unlocks scalability and persistence for route layers.
- The tutorial demonstrates the ease of managing and persisting route layers with Pinecone.
For more information on the tools mentioned, you can refer to Semantic Router Library and Pinecone.