Develop generative AI apps that use tools | AI-103 | Episode 4

Generative AI models are powerful, but their trained knowledge is limited. Tools extend models beyond text generation, allowing them to access real-time information, take actions, ground responses in facts, extend functionality, and build intelligent workflows.

Know the Tools

Remember: current information → web_search · uploaded/private documents → file_search · calculations/code → code_interpreter · application-specific actions → function

Responses API

Tools are provided through the tools collection. The model can determine which available tool is appropriate for a request.

response = client.responses.create(
    model=model_name,
    input="Answer the user's request using the available tools.",
    tools=[
        {"type": "code_interpreter", "container": {"type": "auto"}},
        {"type": "web_search"},
        {"type": "file_search", "vector_store_ids": [vector_store.id]}
    ]
)

print(response.output_text)

Core flow: User → Responses API → Model → Tool → Result → Model → Response

For file_search, documents are stored in a vector store and prepared for semantic retrieval:

Files → Chunking → Embeddings → Vector Store → Retrieval → Model

This lets the model answer using relevant document content rather than relying only on its trained knowledge. Uploaded company policies or private documents → File Search + Vector Store.

Function Calling

Functions are different because the application executes the function, not the model. The model identifies the required function and returns a function-call request:

User → Model → Function Call → Application → Function → Result → Model → Response

The application executes the requested code and returns its result. This process can run in a loop when multiple tool calls are needed.

Key distinction: built-in tools extend the model with predefined capabilities; function calling connects the model to your own application logic and actions.

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