NEW AI Framework - Steerable Chatbots with Semantic Router
AI Summary
Summary: Introduction to Semantic Router
- Overview of Semantic Router
- A tool for building AI assistants and dialogue systems.
- Acts as a fast, deterministic decision-making layer for natural language processing.
- Provides a list of queries that trigger specific responses or actions.
- Utilizes a semantic vector space for response determination.
- Getting Started with Semantic Router
- Repository:
orelo Labs semantic router.- Installation: Version 0.0.4, open source for contributions.
- Google Colab integration requires session restart after installation.
- Defining Routes
- Example routes: “politics” to prevent certain discussions, “chitchat” for casual conversation.
- Initialization of an embedding model using either Cohere or OpenAI.
- API key required for model initialization.
- Route Layer
- Contains different routes for decision-making.
- Two types: standard route layer (semantic search) and hybrid route layer (semantic and term-based vector space).
- Testing Routes
- Example queries trigger appropriate routes (e.g., politics, chitchat).
- Static and dynamic routes demonstrated.
- If-else logic used to generate responses based on route choice.
- Integration with AI Agents
- Example of a personal trainer agent with brand affiliation.
- Semantic router augments queries with additional context or suggestions.
- Demonstrates the ability to protect against certain queries and function calling without slow processing.
- Advantages of Semantic Router
- Combines the power of agent-based systems with the speed of naive retrieval.
- Enhances AI behavior for production-ready applications.
- Future Developments
- Dynamic routing, hybrid layer, and other features in progress.
- Encouragement for community contributions and feedback.
- Conclusion
- The semantic router is a significant development for AI dialogue systems.
- The creator is excited about its potential and plans to release more instructional content.