Enhance CodeGate’s muxing functionality to support user-defined “personas,” allowing the system to classify incoming requests based on a persona and then route them to an LLM chosen by the user. For instance, a “Frontend React Expert” persona might be manually mapped to a favorite advanced LLM, while a “Backend Microservices Guru” persona could be routed to a lightweight local model—entirely at the user’s discretion.

Why Is This Feature Important?

1. Fine-Grained Control. Users maintain complete authority over which LLM handles requests for a given persona (e.g., “Frontend React Expert” → Model X, “Backend Microservices Guru” → Model Y).
2. Better Alignment With Developer Expertise. By defining personas that capture specific skill sets or roles within a project, responses can be more targeted and relevant to the given domain, without sacrificing user choice in model selection.
3. Resource and Cost Efficiency. Users decide exactly when to employ advanced or specialized models, and when to default to smaller, cost-effective ones, based on the persona’s needs.

Possible Solution

Persona Definitions

Store personas in CodeGate configuration (similar to how CodeGate currently handles different providers).

Examples:

• Frontend React Expert: Focuses on UI and React-specific queries.
• Backend Microservices Guru: Focuses on scalability, architecture, and performance.

Local LLM Classifier

A small, local model quickly inspects incoming prompts to determine which persona best fits.
Example: "How do I optimize state management in my React app?" → Frontend React Expert.

User-Defined LLM Routing

After classification, CodeGate routes requests to the LLM the user has configured for that persona.
Example: Frontend React Expert → [User-selected advanced model].
Users can easily update which LLM is tied to each persona at any time.

Challenges & Considerations

1. Classifier Accuracy. Ensuring the local LLM correctly identifies the right persona. Misclassifications could lead to irrelevant or suboptimal answers—even if the correct LLM is specified.
2. Performance & Latency. Running a local model for classification adds a small overhead. Must be optimized to avoid bottlenecks in large-scale or rapid-fire scenarios. An alternative would be to use one of the user-defined providers. Although this would mean that CodeGate is going to consume more of the user's tokens which might not be expected and cause a bad impression.
3. User Experience. Providing a clear interface or config structure for defining personas and selecting their corresponding LLMs. Ensuring that changes to persona-LLM mappings are intuitive and quick to implement.
4. Extensibility. Potential to introduce more advanced persona logic in the future (e.g., dynamic persona creation).

Additional Context

No response