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Använda ett befintligt index med Azure AI Search-verktyget

Den här artikeln beskriver hur du använder ett befintligt sökindex med Azure AI Search-verktyget .

Förutsättningar

Lägga till Azure AI Search-verktyget i en agent

  1. Logga in på Azure AI Foundry-portalen och välj ditt projekt.

  2. I den vänstra rutan väljer du Agenter.

  3. Välj din agent i listan och välj sedan Kunskap>Lägg till.

    En skärmbild som visar tillgängliga verktygskategorier i Azure AI Foundry-portalen.

  4. Välj Azure AI Search.

    En skärmbild som visar tillgängliga kunskapsverktyg i Azure AI Foundry-portalen.

  5. Följ anvisningarna för att lägga till Azure AI Search-verktyget.

Skapa en Azure AI-klient

Börja med att skapa en Azure AI-klient med hjälp av slutpunkten för ditt Azure AI Foundry-projekt.

import os
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential

# Retrieve the endpoint from environment variables
project_endpoint = os.environ["PROJECT_ENDPOINT"]

# Initialize the AIProjectClient
project_client = AIProjectClient(
    endpoint=project_endpoint,
    credential=DefaultAzureCredential(exclude_interactive_browser_credential=False)
)

Konfigurera Azure AI Search-verktyget

Med hjälp av anslutnings-ID:t för din Azure AI Search-tjänst konfigurerar du Azure AI Search-verktyget så att det använder ditt sökindex.

from azure.ai.agents.models import AzureAISearchTool, AzureAISearchQueryType
from azure.ai.projects.models import ConnectionType

# Define the Azure AI Search connection ID and index name
azure_ai_conn_id = project_client.connections.get_default(ConnectionType.AZURE_AI_SEARCH).id

# Find the index name on the Search Management > Indexes page of your Azure AI Search service
index_name = "sample_index"

# Initialize the Azure AI Search tool
ai_search = AzureAISearchTool(
    index_connection_id=azure_ai_conn_id,
    index_name=index_name,
    query_type=AzureAISearchQueryType.SIMPLE,  # Use SIMPLE query type
    top_k=3,  # Retrieve the top 3 results
    filter="",  # Optional filter for search results
)

Skapa en agent med Azure AI Search-verktyget aktiverat

Ändra modellen till den som distribueras i projektet. Du hittar modellnamnet på fliken Modeller i Azure AI Foundry-portalen. Du kan också ändra agentens namn och instruktioner så att de passar dina behov.

# Define the model deployment name
model_deployment_name = os.environ["MODEL_DEPLOYMENT_NAME"]

# Create an agent with the Azure AI Search tool
agent = project_client.agents.create_agent(
    model=model_deployment_name,
    name="my-agent",
    instructions="You are a helpful agent",
    tools=ai_search.definitions,
    tool_resources=ai_search.resources,
)
print(f"Created agent, ID: {agent.id}")

Ställ frågor till agenten om data i indexet

Nu när agenten har skapats kan du ställa frågor om data i ditt sökindex.

from azure.ai.agents.models import MessageRole, ListSortOrder

# Create a thread for communication
thread = project_client.agents.threads.create()
print(f"Created thread, ID: {thread.id}")

# Send a message to the thread
message = project_client.agents.messages.create(
    thread_id=thread.id,
    role=MessageRole.USER,
    content="What is the temperature rating of the cozynights sleeping bag?",
)
print(f"Created message, ID: {message['id']}")

# Create and process a run with the specified thread and agent
run = project_client.agents.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
print(f"Run finished with status: {run.status}")

# Check if the run failed
if run.status == "failed":
    print(f"Run failed: {run.last_error}")

# Fetch and log all messages in the thread
messages = project_client.agents.messages.list(thread_id=thread.id, order=ListSortOrder.ASCENDING)
for message in messages.data:
    print(f"Role: {message.role}, Content: {message.content}")

Rensa resurser

När du har slutfört de här åtgärderna tar du bort agenten för att rensa resurser.

# Delete the agent
project_client.agents.delete_agent(agent.id)
print("Deleted agent")

Skapa en projektklient

Skapa ett klientobjekt som innehåller slutpunkten för ditt Azure AI Foundry-projekt, vilket möjliggör anslutningar till projektet och andra resurser.

using Azure;
using Azure.AI.Agents.Persistent;
using Azure.Identity;
using Microsoft.Extensions.Configuration;
using System;
using System.Threading;

// Get connection information from app configuration
IConfigurationRoot configuration = new ConfigurationBuilder()
    .SetBasePath(AppContext.BaseDirectory)
    .AddJsonFile("appsettings.json", optional: false, reloadOnChange: true)
    .Build();

var projectEndpoint = configuration["ProjectEndpoint"];
var modelDeploymentName = configuration["ModelDeploymentName"];
var azureAiSearchConnectionId = configuration["AzureAiSearchConnectionId"];

// Create the agent client
PersistentAgentsClient agentClient = new(projectEndpoint, new DefaultAzureCredential());

Konfigurera Azure AI Search-verktyget

Med hjälp av anslutnings-ID:t för din Azure AI Search-tjänst konfigurerar du Azure AI Search-verktyget så att det använder ditt sökindex.

AzureAISearchToolResource searchResource = new(
    indexConnectionId: azureAiSearchConnectionId,
    indexName: "sample_index",
    topK: 5,
    filter: "category eq 'sleeping bag'",
    queryType: AzureAISearchQueryType.Simple
);

ToolResources toolResource = new() { AzureAISearch = searchResource };

Skapa en agent med Azure AI Search-verktyget aktiverat

Ändra modellen till den som distribueras i projektet. Du hittar modellnamnet på fliken Modeller i Azure AI Foundry-portalen. Du kan också ändra agentens namn och instruktioner så att de passar dina behov.

// Create an agent with Tools and Tool Resources
PersistentAgent agent = agentClient.Administration.CreateAgent(
    model: modelDeploymentName,
    name: "my-agent",
    instructions: "Use the index provided to answer questions.",
    tools: [new AzureAISearchToolDefinition()],
    toolResources: toolResource
);

Ställ frågor till agenten om data i indexet

Nu när agenten har skapats kan du ställa frågor om data i ditt sökindex.

// Create thread for communication
PersistentAgentThread thread = agentClient.Threads.CreateThread();

// Create message and run the agent
PersistentThreadMessage message = agentClient.Messages.CreateMessage(
    thread.Id,
    MessageRole.User,
    "What is the temperature rating of the cozynights sleeping bag?");
ThreadRun run = agentClient.Runs.CreateRun(thread, agent);

Vänta på att agenten ska avsluta och sända ut resultatet

Vänta tills agenten är klar och skriv ut utdata till konsolen.

// Wait for the agent to finish running
do
{
    Thread.Sleep(TimeSpan.FromMilliseconds(500));
    run = agentClient.Runs.GetRun(thread.Id, run.Id);
}
while (run.Status == RunStatus.Queued
    || run.Status == RunStatus.InProgress);

// Confirm that the run completed successfully
if (run.Status != RunStatus.Completed)
{
    throw new Exception("Run did not complete successfully, error: " + run.LastError?.Message);
}

// Retrieve the messages from the agent client
Pageable<PersistentThreadMessage> messages = agentClient.Messages.GetMessages(
    threadId: thread.Id,
    order: ListSortOrder.Ascending
);

// Process messages in order
foreach (PersistentThreadMessage threadMessage in messages)
{
    Console.Write($"{threadMessage.CreatedAt:yyyy-MM-dd HH:mm:ss} - {threadMessage.Role,10}: ");
    foreach (MessageContent contentItem in threadMessage.ContentItems)
    {
        if (contentItem is MessageTextContent textItem)
        {
            // Annotate only agent messages
            if (threadMessage.Role == MessageRole.Agent && textItem.Annotations.Count > 0)
            {
                string annotatedText = textItem.Text;

                // If there are text URL citation annotations, reformat the response to show the title and URL for citations
                foreach (MessageTextAnnotation annotation in textItem.Annotations)
                {
                    if (annotation is MessageTextUriCitationAnnotation urlAnnotation)
                    {
                        annotatedText = annotatedText.Replace(
                            urlAnnotation.Text,
                            $" [see {urlAnnotation.UriCitation.Title}] ({urlAnnotation.UriCitation.Uri})");
                    }
                }
                Console.Write(annotatedText);
            }
            else
            {
                Console.Write(textItem.Text);
            }
        }
        else if (contentItem is MessageImageFileContent imageFileItem)
        {
            Console.Write($"<image from ID: {imageFileItem.FileId}");
        }
        Console.WriteLine();
    }
}

Du kan valfritt mata ut de körsteg som används av agenten

// Retrieve the run steps used by the agent and print them to the console
Console.WriteLine("Run Steps used by Agent:");
Pageable<RunStep> runSteps = agentClient.Runs.GetRunSteps(run);

foreach (var step in runSteps)
{
    Console.WriteLine($"Step ID: {step.Id}, Total Tokens: {step.Usage.TotalTokens}, Status: {step.Status}, Type: {step.Type}");

    if (step.StepDetails is RunStepMessageCreationDetails messageCreationDetails)
    {
        Console.WriteLine($"   Message Creation Id: {messageCreationDetails.MessageCreation.MessageId}");
    }
    else if (step.StepDetails is RunStepToolCallDetails toolCallDetails)
    {
        // This agent only has the Azure AI Search tool, so we can cast it directly
        foreach (RunStepAzureAISearchToolCall toolCall in toolCallDetails.ToolCalls)
        {
            Console.WriteLine($"   Tool Call Details: {toolCall.GetType()}");

            foreach (var result in toolCall.AzureAISearch)
            { 
                Console.WriteLine($"      {result.Key}: {result.Value}");
            }
        }
    }
}

Rensa resurser

Ta bort resurserna från det här exemplet.

// Clean up resources
agentClient.Threads.DeleteThread(thread.Id);
agentClient.Administration.DeleteAgent(agent.Id);

Skapa en Azure AI-klient

Börja med att skapa en Azure AI-klient med hjälp av slutpunkten för ditt Azure AI Foundry-projekt.

const projectEndpoint = process.env["PROJECT_ENDPOINT"];

if (!projectString) {
  throw new Error("AZURE_AI_PROJECTS_CONNECTION_STRING must be set in the environment variables");
}

const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());

Konfigurera Azure AI Search-verktyget

Med hjälp av anslutnings-ID:t för din Azure AI Search-tjänst konfigurerar du Azure AI Search-verktyget så att det använder ditt sökindex.

const connectionId = process.env["AZURE_AI_CONNECTION_ID"] || "<connection-name>";

const azureAISearchTool = ToolUtility.createAzureAISearchTool(connectionId, "ai-search-sample", {
    queryType: "simple",
    topK: 3,
    filter: "",
    indexConnectionId: "",
    indexName: "",
  });

Skapa en agent med Azure AI Search-verktyget aktiverat

Ändra modellen till den som distribueras i projektet. Du hittar modellnamnet på fliken Modeller i Azure AI Foundry-portalen. Du kan också ändra agentens namn och instruktioner så att de passar dina behov.


const agent = await client.agents.createAgent("gpt-4o-mini", {
  name: "my-agent",
  instructions: "You are a helpful agent",
  tools: [azureAISearchTool.definition],
  toolResources: azureAISearchTool.resources,
});
console.log(`Created agent, agent ID : ${agent.id}`);

Ställ frågor till agenten om data i indexet

Nu när agenten har skapats kan du ställa frågor om data i ditt sökindex.

// Create thread for communication
  const thread = await client.threads.create();
  console.log(`Created thread, thread ID: ${thread.id}`);

// Create message to thread
const message = await client.messages.create(
  thread.id,
  "user",
  "What is the temperature rating of the cozynights sleeping bag?",
);
console.log(`Created message, message ID : ${message.id}`);

// Create and process the agent run in thread with tools
let run = await client.runs.create(thread.id, agent.id);
while (run.status === "queued" || run.status === "in_progress") {
  await delay(1000);
  run = await client.runs.get(thread.id, run.id);
}
if (run.status === "failed") {
  console.log(`Run failed:`, JSON.stringify(run, null, 2));
}
console.log(`Run finished with status: ${run.status}`);

// Fetch run steps to get the details of agent run
const runSteps = await client.runSteps.list(thread.id, run.id);

for await (const step of runSteps) {
  console.log(`Step ID: ${step.id}, Status: ${step.status}`);
  const stepDetails = step.stepDetails;
  if (isOutputOfType(stepDetails, "tool_calls")) {
    const toolCalls = stepDetails.toolCalls;
    for (const toolCall of toolCalls) {
      console.log(`Tool Call ID: ${toolCall.id}, Tool type: ${toolCall.type}`);
      if (isOutputOfType(toolCall, "azure_ai_search")) {
        {
          const azureAISearch = toolCall.azureAISearch;
          if (azureAISearch) {
            console.log(`Azure AI Search Tool Call input: ${azureAISearch.input}`);
            console.log(`Azure AI Search Tool Call output: ${azureAISearch.output}`);
          }
        }
      }
    }
  }
}

// Delete the assistant when done
await client.deleteAgent(agent.id);
console.log(`Deleted agent, agent ID: ${agent.id}`);

// Fetch and log all messages
const messagesIterator = client.messages.list(thread.id);
console.log(`Messages:`);

// Get the first message
for await (const m of messagesIterator) {
  if (m.content.length > 0) {
    const agentMessage = m.content[0];
    if (isOutputOfType(agentMessage, "text")) {
      const textContent = agentMessage;
      console.log(`Text Message Content - ${textContent.text.value}`);
    }
  }
  break; // Just process the first message
}
  • Slutförande av REST API-snabbstarten för att hämta värden för AGENT_TOKENmiljövariablerna , AZURE_AI_FOUNDRY_PROJECT_ENDPOINToch API_VERSION .

Hämta anslutnings-ID:t för Azure AI Search-resursen

Så här hämtar du anslutnings-ID:t:

  1. Logga in på Azure AI Foundry-portalen och välj ditt projekt.

  2. På sidan Översikt väljer du Öppna i hanteringscenter.

  3. I den vänstra rutan väljer du Anslutna resurser och sedan din Azure AI Search-tjänst.

    En skärmbild av en resursanslutningssida för AI Search i Azure AI Foundry.

  4. Kopiera allt som kommer efter wsid= i webbläsarens URL.

    En skärmbild av en AI Search-resursanslutning och hur du kopierar anslutnings-ID:t.

Konfigurera Azure AI Search-verktyget

Med hjälp av anslutnings-ID:t som du fick i föregående steg konfigurerar du Azure AI Search-verktyget så att det använder ditt sökindex.

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/assistants?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
        "instructions": "You are a helpful agent.",
        "name": "my-agent",
        "tools": [
          {"type": "azure_ai_search"}
        ],
        "model": "gpt-4o-mini",
        "tool_resources": {
            "azure_ai_search": {
              "indexes": [
                  {
                      "index_connection_id": "/subscriptions/<your-subscription-id>/resourceGroups/<your-resource-group>/providers/Microsoft.MachineLearningServices/workspaces/<your-project-name>/connections/<your-azure-ai-search-connection-name>",
                      "index_name": "<your-index-name>",
                      "query_type": "semantic"
                  }
              ]
            }
        }
      }'

Ställ frågor till agenten om data i indexet

Nu när agenten har skapats kan du ställa frågor om data i ditt sökindex.

Skapa en tråd

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d ''

Lägg till en användarfråga i tråden

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/messages?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
      "role": "user",
      "content": "what are my health insurance plan coverage types?"
    }'

Starta tråden

curl --request POST \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/runs?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "assistant_id": "asst_abc123",
  }'

Hämta status för körningen

curl --request GET \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/runs/run_abc123?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN"

Hämta agentsvaret

curl --request GET \
  --url $AZURE_AI_FOUNDRY_PROJECT_ENDPOINT/threads/thread_abc123/messages?api-version=$API_VERSION \
  -H "Authorization: Bearer $AGENT_TOKEN"

Kodexempel

package com.example.agents;

import com.azure.ai.agents.persistent.MessagesClient;
import com.azure.ai.agents.persistent.PersistentAgentsAdministrationClient;
import com.azure.ai.agents.persistent.PersistentAgentsClient;
import com.azure.ai.agents.persistent.PersistentAgentsClientBuilder;
import com.azure.ai.agents.persistent.RunsClient;
import com.azure.ai.agents.persistent.ThreadsClient;
import com.azure.ai.agents.persistent.models.AISearchIndexResource;
import com.azure.ai.agents.persistent.models.AzureAISearchToolDefinition;
import com.azure.ai.agents.persistent.models.AzureAISearchToolResource;
import com.azure.ai.agents.persistent.models.CreateAgentOptions;
import com.azure.ai.agents.persistent.models.CreateRunOptions;
import com.azure.ai.agents.persistent.models.MessageImageFileContent;
import com.azure.ai.agents.persistent.models.MessageRole;
import com.azure.ai.agents.persistent.models.MessageTextContent;
import com.azure.ai.agents.persistent.models.PersistentAgent;
import com.azure.ai.agents.persistent.models.PersistentAgentThread;
import com.azure.ai.agents.persistent.models.RunStatus;
import com.azure.ai.agents.persistent.models.ThreadMessage;
import com.azure.ai.agents.persistent.models.ThreadRun;
import com.azure.ai.agents.persistent.models.ToolResources;
import com.azure.ai.agents.persistent.models.MessageContent;
import com.azure.core.http.rest.PagedIterable;
import com.azure.identity.DefaultAzureCredentialBuilder;

import java.net.URL;
import java.io.File;
import java.io.FileNotFoundException;
import java.net.URISyntaxException;
import java.nio.file.Path;
import java.util.Arrays;

public class AgentExample {

    public static void main(String[] args) throws FileNotFoundException, URISyntaxException {

        // variables for authenticating requests to the agent service 
        String projectEndpoint = System.getenv("PROJECT_ENDPOINT");
        String modelName = System.getenv("MODEL_DEPLOYMENT_NAME");
        String aiSearchConnectionId = System.getenv("AZURE_AI_CONNECTION_ID");
        String indexName = "my-index";

        PersistentAgentsClientBuilder clientBuilder = new PersistentAgentsClientBuilder().endpoint(projectEndpoint)
            .credential(new DefaultAzureCredentialBuilder().build());
        PersistentAgentsClient agentsClient = clientBuilder.buildClient();
        PersistentAgentsAdministrationClient administrationClient = agentsClient.getPersistentAgentsAdministrationClient();
        ThreadsClient threadsClient = agentsClient.getThreadsClient();
        MessagesClient messagesClient = agentsClient.getMessagesClient();
        RunsClient runsClient = agentsClient.getRunsClient();

        AISearchIndexResource indexResource = new AISearchIndexResource()
            .setIndexConnectionId(aiSearchConnectionId)
            .setIndexName(indexName);
        ToolResources toolResources = new ToolResources()
            .setAzureAISearch(new AzureAISearchToolResource()
                .setIndexList(Arrays.asList(indexResource)));

        String agentName = "ai_search_example";
        CreateAgentOptions createAgentOptions = new CreateAgentOptions(modelName)
            .setName(agentName)
            .setInstructions("You are a helpful agent")
            .setTools(Arrays.asList(new AzureAISearchToolDefinition()))
            .setToolResources(toolResources);
        PersistentAgent agent = administrationClient.createAgent(createAgentOptions);

        PersistentAgentThread thread = threadsClient.createThread();
        ThreadMessage createdMessage = messagesClient.createMessage(
            thread.getId(),
            MessageRole.USER,
            "<question about information in search index>");

        try {
            //run agent
            CreateRunOptions createRunOptions = new CreateRunOptions(thread.getId(), agent.getId())
                .setAdditionalInstructions("");
            ThreadRun threadRun = runsClient.createRun(createRunOptions);

            waitForRunCompletion(thread.getId(), threadRun, runsClient);
            printRunMessages(messagesClient, thread.getId());

        } catch (InterruptedException e) {
            throw new RuntimeException(e);
        } finally {
            //cleanup
            threadsClient.deleteThread(thread.getId());
            administrationClient.deleteAgent(agent.getId());
        }
    }
    // A helper function to print messages from the agent
    public static void printRunMessages(MessagesClient messagesClient, String threadId) {

        PagedIterable<ThreadMessage> runMessages = messagesClient.listMessages(threadId);
        for (ThreadMessage message : runMessages) {
            System.out.print(String.format("%1$s - %2$s : ", message.getCreatedAt(), message.getRole()));
            for (MessageContent contentItem : message.getContent()) {
                if (contentItem instanceof MessageTextContent) {
                    System.out.print((((MessageTextContent) contentItem).getText().getValue()));
                } else if (contentItem instanceof MessageImageFileContent) {
                    String imageFileId = (((MessageImageFileContent) contentItem).getImageFile().getFileId());
                    System.out.print("Image from ID: " + imageFileId);
                }
                System.out.println();
            }
        }
    }

    // a helper function to wait until a run has completed running
    public static void waitForRunCompletion(String threadId, ThreadRun threadRun, RunsClient runsClient)
        throws InterruptedException {

        do {
            Thread.sleep(500);
            threadRun = runsClient.getRun(threadId, threadRun.getId());
        }
        while (
            threadRun.getStatus() == RunStatus.QUEUED
                || threadRun.getStatus() == RunStatus.IN_PROGRESS
                || threadRun.getStatus() == RunStatus.REQUIRES_ACTION);

        if (threadRun.getStatus() == RunStatus.FAILED) {
            System.out.println(threadRun.getLastError().getMessage());
        }
    }
    private static Path getFile(String fileName) throws FileNotFoundException, URISyntaxException {
        URL resource = AgentExample.class.getClassLoader().getResource(fileName);
        if (resource == null) {
            throw new FileNotFoundException("File not found");
        }
        File file = new File(resource.toURI());
        return file.toPath();
    }
}