Ricerca file

Ricerca file consente agli agenti di eseguire ricerche nei file caricati per trovare informazioni pertinenti. Questo strumento è particolarmente utile per la creazione di agenti che possono rispondere a domande sui documenti, analizzare il contenuto dei file ed estrarre informazioni.

Annotazioni

La disponibilità di Ricerca file dipende dal provider dell'agente sottostante. Vedere Panoramica dei provider per il supporto specifico del provider.

L'esempio seguente illustra come creare un agente con lo strumento Ricerca file:

using System;
using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;

// Requires: dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
    ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";

// Create an agent with the file search hosted tool
// Provide vector store IDs containing your uploaded documents
AIAgent agent = new AzureOpenAIClient(new Uri(endpoint), new AzureCliCredential())
    .GetChatClient(deploymentName)
    .AsAIAgent(
        instructions: "You are a helpful assistant that searches through files to find information.",
        tools: [new FileSearchToolDefinition(vectorStoreIds: ["<your-vector-store-id>"])]);

Console.WriteLine(await agent.RunAsync("What does the document say about today's weather?"));

L'esempio seguente illustra come creare un agente con lo strumento Ricerca file e i documenti di esempio:

Definire documenti di esempio

# Copyright (c) Microsoft. All rights reserved.

import asyncio

from agent_framework import Agent, Content
from agent_framework.openai import OpenAIResponsesClient

"""
OpenAI Responses Client with File Search Example

This sample demonstrates using get_file_search_tool() with OpenAI Responses Client
for direct document-based question answering and information retrieval.
"""

# Helper functions


async def create_vector_store(client: OpenAIResponsesClient) -> tuple[str, Content]:
    """Create a vector store with sample documents."""
    file = await client.client.files.create(
        file=("todays_weather.txt", b"The weather today is sunny with a high of 75F."), purpose="user_data"
    )
    vector_store = await client.client.vector_stores.create(
        name="knowledge_base",
        expires_after={"anchor": "last_active_at", "days": 1},
    )
    result = await client.client.vector_stores.files.create_and_poll(vector_store_id=vector_store.id, file_id=file.id)
    if result.last_error is not None:
        raise Exception(f"Vector store file processing failed with status: {result.last_error.message}")

    return file.id, Content.from_hosted_vector_store(vector_store_id=vector_store.id)


async def delete_vector_store(client: OpenAIResponsesClient, file_id: str, vector_store_id: str) -> None:
    """Delete the vector store after using it."""
    await client.client.vector_stores.delete(vector_store_id=vector_store_id)
    await client.client.files.delete(file_id=file_id)


async def main() -> None:
    client = OpenAIResponsesClient()

    message = "What is the weather today? Do a file search to find the answer."

    stream = False
    print(f"User: {message}")
    file_id, vector_store_id = await create_vector_store(client)

    agent = Agent(
        client=client,
        instructions="You are a helpful assistant that can search through files to find information.",
        tools=[client.get_file_search_tool(vector_store_ids=[vector_store_id])],
    )

    if stream:
        print("Assistant: ", end="")
        async for chunk in agent.run(message, stream=True):
            if chunk.text:
                print(chunk.text, end="")
        print("")
    else:
        response = await agent.run(message)
        print(f"Assistant: {response}")
    await delete_vector_store(client, file_id, vector_store_id)


if __name__ == "__main__":
    asyncio.run(main())

Eseguire l'agente

# Copyright (c) Microsoft. All rights reserved.

import asyncio

from agent_framework import Agent, Content
from agent_framework.openai import OpenAIResponsesClient

"""
OpenAI Responses Client with File Search Example

This sample demonstrates using get_file_search_tool() with OpenAI Responses Client
for direct document-based question answering and information retrieval.
"""

# Helper functions


async def create_vector_store(client: OpenAIResponsesClient) -> tuple[str, Content]:
    """Create a vector store with sample documents."""
    file = await client.client.files.create(
        file=("todays_weather.txt", b"The weather today is sunny with a high of 75F."), purpose="user_data"
    )
    vector_store = await client.client.vector_stores.create(
        name="knowledge_base",
        expires_after={"anchor": "last_active_at", "days": 1},
    )
    result = await client.client.vector_stores.files.create_and_poll(vector_store_id=vector_store.id, file_id=file.id)
    if result.last_error is not None:
        raise Exception(f"Vector store file processing failed with status: {result.last_error.message}")

    return file.id, Content.from_hosted_vector_store(vector_store_id=vector_store.id)


async def delete_vector_store(client: OpenAIResponsesClient, file_id: str, vector_store_id: str) -> None:
    """Delete the vector store after using it."""
    await client.client.vector_stores.delete(vector_store_id=vector_store_id)
    await client.client.files.delete(file_id=file_id)


async def main() -> None:
    client = OpenAIResponsesClient()

    message = "What is the weather today? Do a file search to find the answer."

    stream = False
    print(f"User: {message}")
    file_id, vector_store_id = await create_vector_store(client)

    agent = Agent(
        client=client,
        instructions="You are a helpful assistant that can search through files to find information.",
        tools=[client.get_file_search_tool(vector_store_ids=[vector_store_id])],
    )

    if stream:
        print("Assistant: ", end="")
        async for chunk in agent.run(message, stream=True):
            if chunk.text:
                print(chunk.text, end="")
        print("")
    else:
        response = await agent.run(message)
        print(f"Assistant: {response}")
    await delete_vector_store(client, file_id, vector_store_id)


if __name__ == "__main__":
    asyncio.run(main())

Passaggi successivi

Ricerca Web