Hledání souborů

Vyhledávání souborů umožňuje agentům prohledávat nahrané soubory, aby našli relevantní informace. Tento nástroj je užitečný zejména pro vytváření agentů, kteří můžou odpovídat na otázky týkající se dokumentů, analyzovat obsah souborů a extrahovat informace.

Poznámka:

Dostupnost vyhledávání souborů závisí na příslušném poskytovateli agenta. Viz Přehled poskytovatelů podpory pro konkrétního poskytovatele.

Následující příklad ukazuje, jak vytvořit agenta pomocí nástroje Pro vyhledávání souborů:

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?"));

Následující příklad ukazuje, jak vytvořit agenta pomocí nástroje Pro vyhledávání souborů a ukázkových dokumentů:

Definování ukázkových dokumentů

# 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())

Spusťte 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())

Další kroky

Vyhledávání na webu