Routing

llms = {
    "claude": {
        "model_name": "claude-3-5-sonnet-latest",
        "description": "Anthropic's Claude LLM",
        "pros": "High-quality responses, good for complex queries",
        "cons": "May be slower than other models",
    },
    "gpt-4": {
        "model_name": "gpt-4",
        "description": "OpenAI's GPT-4 LLM",
        "pros": "Fast, versatile, good for a wide range of tasks",
        "cons": "May not handle complex queries as well as Claude",
    },
    "gemini": {
        "model_name": "gemini-1.5-pro",
        "description": "Google's Gemini LLM",
        "pros": "Good for tasks requiring understanding of context",
        "cons": "May not be as fast as GPT-4",
    },
    "deepseek": {
        "model_name": "deepseek-llm",
        "description": "DeepSeek's LLM",
        "pros": "Good for technical queries, strong in reasoning",
        "cons": "Less known, may not be as versatile as others",
    },
}


systemPrompt = f"""
Hey you're a routing LLM.
You will route requests to the appropriate LLM based on the type of query.

You will use the following LLMs:
{llms.keys()}

You will route requests based on the following criteria:
1. If the query is complex or requires deep reasoning, route to Claude.
2. If the query is simple or requires a quick response, route to GPT-4.
3. If the query requires understanding of context, route to Gemini.
4. If the query is technical or requires strong reasoning, route to DeepSeek.
You will return the response from the routed LLM.

"""