AI Tool Use Explained | Generated by AI

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Yes, you’re spot on with your understanding of tool use and function calls in AI chatbot APIs like Mistral’s (or similar ones from OpenAI, Anthropic, etc.). Let me break it down step by step to confirm and clarify, as it indeed involves inversion of control (IoC) in a way that shifts the decision-making from your code to the AI model.

How It Works

1. Defining Tools:
   • You (the developer) provide a list of custom “tools” (essentially function definitions) in your initial API request. Each tool includes details like the function name, parameters (with types and descriptions), and what it does. This is done via a schema, often in JSON format (e.g., based on OpenAI’s tool schema, which Mistral also supports).
   • Example: You might define a tool called get_weather that takes a location parameter and returns current weather data.
2. Model’s Decision (Inversion of Control):
   • The AI model processes your prompt/query and decides if it needs external help from one of your tools to respond accurately. This is the IoC part: Instead of your code directly calling functions in a linear flow, the model “inverts” control by requesting a tool call when it deems necessary. It’s like the model is orchestrating the workflow.
   • If no tool is needed, the model just generates a direct response.
3. Tool Call Response from the API:
   • If a tool is required, the API doesn’t give a final answer immediately. Instead, it responds with a “tool call” object. This includes:
     • The tool/function name.
     • The arguments (e.g., JSON with values like {"location": "New York"}).
   • At this point, the conversation is paused—the model is waiting for you to act.
4. Executing the Tool (Your Side):
   • Your code receives this tool call response, parses it, and executes the corresponding function/tool with the provided arguments.
   • You handle the actual logic (e.g., calling a weather API, querying a database, or running some computation).
   • Importantly, the model doesn’t execute the tool itself; it just specifies what to call. This keeps things secure and flexible.
5. Sending Results Back:
   • After executing the tool, you append the result (e.g., as a “tool response” message) to the conversation history and make another API request, passing the updated history back to the model.
   • This result is framed as a system message or tool output, so the model can incorporate it.
6. Final Response from the API:
   • The model receives the tool result, processes it alongside the original query and history, and generates the complete, final response.
   • If needed, it might request additional tool calls in a loop (e.g., for multi-step tasks), but eventually, it outputs the answer without further calls.

Why It’s Inversion of Control

• In traditional programming, your code controls the flow: “If condition X, call function Y.”
• Here, the AI model takes control: It analyzes the query and decides “I need to call Y with these args—tell me the result, then I’ll proceed.” Your code becomes reactive, handling the calls as requested. This makes the system more dynamic and allows the model to handle complex, reasoning-based workflows.

Edge Cases and Tips

• Parallel Tool Calls: Some APIs (like Mistral’s) support calling multiple tools in one response if the query requires it.
• Error Handling: If your tool execution fails, you can send an error message back, and the model might retry or adjust.
• Chat History: Always maintain the full conversation thread across requests to keep context.
• Compatibility: Mistral’s API follows a similar structure to OpenAI’s tools endpoint, so code examples from one often work with the other with minor tweaks.

This pattern is powerful for building agents or apps that integrate AI with real-world actions (e.g., booking flights, fetching data). If you’re implementing this, check Mistral’s docs for exact schema details.

Mistral AI Documentation - Tools

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