Wyszukiwanie plików

Wyszukiwanie plików umożliwia agentom wyszukiwanie przekazanych plików w celu znalezienia odpowiednich informacji. To narzędzie jest szczególnie przydatne w przypadku tworzenia agentów, którzy mogą odpowiedzieć na pytania dotyczące dokumentów, analizować zawartość plików i wyodrębniać informacje.

Uwaga / Notatka

Dostępność wyszukiwania plików zależy od bazowego dostawcy agenta. Zobacz Omówienie dostawców , aby uzyskać pomoc techniczną specyficzną dla dostawcy.

W poniższym przykładzie pokazano, jak utworzyć agenta za pomocą narzędzia wyszukiwania plików:

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?"));

W poniższym przykładzie pokazano, jak utworzyć agenta za pomocą narzędzia wyszukiwania plików i przykładowych dokumentów:

Definiowanie przykładowych dokumentów

# 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())

Uruchamianie agenta

# 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())

Dalsze kroki

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