Function Calling in AI Agents Using Mistral 7B - Analytics Vidhya

Harnessing the Power of Function Calling with Mistral 7B: Building Intelligent AI Agents

Large language models (LLMs) have revolutionized AI agent interaction with external systems and APIs, enabling sophisticated, natural language-driven decision-making. This is achieved through function calling, where JSON schema-defined functions allow LLMs to autonomously select and execute external operations, boosting automation significantly. This article demonstrates function calling using Mistral 7B, a cutting-edge model optimized for instruction-following.

Key Learning Objectives:

• Grasp the roles and types of AI agents within generative AI.
• Understand how function calling enhances LLM capabilities via JSON schemas.
• Configure and load the Mistral 7B model for text generation.
• Implement function calling in LLMs to execute external processes.
• Extract function arguments and generate responses using Mistral 7B.
• Execute real-time functions, such as weather queries, with structured outputs.
• Expand AI agent functionality across diverse domains using multiple tools.

(This article is part of the Data Science Blogathon.)

Table of Contents:

• Understanding AI Agents
• Function Calling in LLMs Explained
• Building a Mistral 7B Pipeline: Model and Text Generation
• Implementing Function Calling with Mistral 7B
• Model-Generated Final Response
• Conclusion
• Frequently Asked Questions

Understanding AI Agents:

Within Generative AI (GenAI), AI agents represent a significant advancement. They utilize models like LLMs to generate content, simulate interactions, and autonomously execute complex tasks. Their adaptability extends across various fields, including customer service, education, and healthcare.

AI agents can be categorized as follows (see image below):

• Human-in-the-loop (feedback provision)
• Code executors (e.g., IPython kernel)
• Tool executors (function or API execution)
• Models (LLMs, VLMs, etc.)

Function calling integrates code execution, tool execution, and model inference. LLMs handle natural language processing, while code executors run necessary code snippets to fulfill user requests. Human-in-the-loop interaction can provide feedback or control process termination.

Function Calling in LLMs Explained:

Developers define functions using JSON schemas (passed to the model). The model then generates the required function arguments based on user prompts. For example, it can call weather APIs to provide real-time weather information. Function calling allows LLMs to intelligently select appropriate functions or tools, enabling autonomous task completion and improving efficiency.

This article demonstrates how the LLM (Mistral) generates function arguments based on user queries. A user asks for the Delhi temperature; the model extracts arguments, the function retrieves real-time data (or a default value here), and the LLM provides a user-friendly response.

Building a Mistral 7B Pipeline: Model and Text Generation:

We'll import necessary libraries and load the Mistral 7B model and tokenizer from Hugging Face for inference. The model is available here.

Importing Libraries:

from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import warnings
warnings.filterwarnings("ignore")

Specifying the Mistral 7B model repository:

model_name = "mistralai/Mistral-7B-Instruct-v0.3"

Downloading the Model and Tokenizer:

(Note: Access requires Hugging Face signup and token generation as per instructions on their website.)

model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_token, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)

(The remaining sections detailing the implementation, results, and conclusion will follow a similar restructuring and rewording to maintain the original meaning while enhancing clarity and flow. Images will remain in their original positions.)

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