Taking Function Calling to the NEXT Level with Groq API 🚀 🚀 🚀

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AI Summary

Summary: Fast Function Calling with Groc API

• Introduction
  • Examining the fastest function calling in LLM using Groc API.
  • Official support for function calling now implemented by the Groc team.
• Supported Models
  • Supports three models: Lama 270, Bill the mixe, and Gemma 7 bill.
• Function Calling Use Cases
  • Convert natural language into API calls.
  • Call external APIs for various tasks like weather information or resume parsing.
• Code Walkthrough Steps
  1. Initialize the Groc API client.
  2. Define function and conversation parameters.
  3. Process model requests to determine if external tools are needed.
  4. Incorporate function responses into the conversation.
• Flow of Function Calling
  • User query → LLM decides on function use → If needed, select tool → Get tool response → Pass response to LLM → Final user response.
• Code Example Overview
  • Install Groc Python client and set up API key.
  • Import necessary libraries and select a model (e.g., mixl Moe).
  • Define the function for the LLM to call (e.g., get NBA game scores).
  • Send conversation and function details to the model.
  • Check if LLM requires a tool and process accordingly.
  • Handle multiple tools by expanding the list of functions and modifying the code.
• Example Scenarios
  • Query for NBA game score: LLM calls the appropriate function.
  • Query unrelated to NBA: LLM responds based on its training without external tools.
• Conclusion
  • Groc API is fast and currently free to use.
  • Paid account and better rate limits expected in the future.

For more details, refer to the previous video linked in the original text.