Anpassa agentbeteende vid körning med strukturerade indata

from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import PromptAgentDefinition, StructuredInputDefinition
from azure.identity import DefaultAzureCredential

# Format: "https://resource_name.ai.azure.com/api/projects/project_name"
PROJECT_ENDPOINT = "your_project_endpoint"

# Create clients to call Foundry API
project = AIProjectClient(endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential())
openai = project.get_openai_client()

# Create agent with handlebar templates in instructions
agent = project.agents.create_version(
    agent_name="structured-input-agent",
    definition=PromptAgentDefinition(
        model="gpt-5-mini",
        instructions=(
            "You are a helpful assistant. "
            "The user's name is {{userName}} and their role is {{userRole}}. "
            "Greet them and confirm their details."
        ),
        structured_inputs={
            "userName": StructuredInputDefinition(
                description="The user's name", required=True, schema={"type": "string"},
            ),
            "userRole": StructuredInputDefinition(
                description="The user's role", required=True, schema={"type": "string"},
            ),
        },
    ),
)
print(f"Agent created: {agent.name}, version: {agent.version}")

# Create conversation and send request with runtime values
conversation = openai.conversations.create()
response = openai.responses.create(
    conversation=conversation.id,
    input="Hello! Can you confirm my details?",
    extra_body={
        "agent_reference": {"name": agent.name, "type": "agent_reference"},
        "structured_inputs": {"userName": "Alice Smith", "userRole": "Senior Developer"},
    },
)
print(response.output_text)

Förväntade utdata

Agent created: structured-input-agent, version: 1
Hello Alice Smith! I can confirm your details: your name is Alice Smith and your role is Senior Developer. How can I help you today?

Agenten ersätter platshållarna {{userName}} och {{userRole}} i anvisningarna med "Alice Smith" och "Senior Developer" innan begäran bearbetas.

using System;
using Azure.AI.Projects;
using Azure.AI.Projects.Agents;
using Azure.AI.Extensions.OpenAI;
using Azure.Identity;
using OpenAI.Responses;

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
var projectEndpoint = "your_project_endpoint";

// Create project client to call Foundry API
AIProjectClient projectClient = new(
    endpoint: new Uri(projectEndpoint),
    tokenProvider: new DefaultAzureCredential());

// Create agent with handlebar templates in instructions
DeclarativeAgentDefinition agentDefinition = new(model: "gpt-5-mini")
{
    Instructions = "You are a helpful assistant. "
        + "The user's name is {{userName}} and their role is {{userRole}}. "
        + "Greet them and confirm their details.",
    StructuredInputs =
    {
        ["userName"] = new StructuredInputDefinition
            { Description = "The user's name", IsRequired = true },
        ["userRole"] = new StructuredInputDefinition
            { Description = "The user's role", IsRequired = true }
    }
};
AgentVersion agent = projectClient.AgentAdministrationClient.CreateAgentVersion(
    agentName: "structured-input-agent", options: new(agentDefinition));

// Send response with runtime structured input values
AgentReference agentRef = new(name: agent.Name, version: agent.Version);
ProjectResponsesClient responseClient =
    projectClient.ProjectOpenAIClient.GetProjectResponsesClientForAgent(agentRef);

CreateResponseOptions responseOptions = new()
{
    Input = [ResponseItem.CreateUserMessageItem("Hello! Can you confirm my details?")]
};
responseOptions.Patch.Set(
    "$.structured_inputs[\"userName\"]"u8,
    BinaryData.FromObjectAsJson("Alice Smith"));
responseOptions.Patch.Set(
    "$.structured_inputs[\"userRole\"]"u8,
    BinaryData.FromObjectAsJson("Senior Developer"));

ResponseResult response = responseClient.CreateResponse(responseOptions);
Console.WriteLine(response.GetOutputText());

// Clean up
projectClient.AgentAdministrationClient.DeleteAgentVersion(
    agentName: agent.Name, agentVersion: agent.Version);

Förväntade utdata

Hello Alice Smith! I can confirm your details: your name is Alice Smith and your role is Senior Developer. How can I help you today?

Ordlistan StructuredInputs i agentdefinitionen mappar mallnamn till deras scheman. Vid körningen använder du metoden Patch.Set på CreateResponseOptions för att tillhandahålla de faktiska värdena via $.structured_inputs JSON-sökvägen.

import { DefaultAzureCredential } from "@azure/identity";
import { AIProjectClient } from "@azure/ai-projects";

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
const PROJECT_ENDPOINT = "your_project_endpoint";

export async function main(): Promise<void> {
  // Create clients to call Foundry API
  const project = new AIProjectClient(PROJECT_ENDPOINT, new DefaultAzureCredential());
  const openai = project.getOpenAIClient();

  // Create agent with handlebar templates in instructions
  const agent = await project.agents.createVersion("structured-input-agent", {
    kind: "prompt",
    model: "gpt-5-mini",
    instructions:
      "You are a helpful assistant. " +
      "The user's name is {{userName}} and their role is {{userRole}}. " +
      "Greet them and confirm their details.",
    structured_inputs: {
      userName: { description: "The user's name", required: true },
      userRole: { description: "The user's role", required: true },
    },
  });
  console.log(`Agent created: ${agent.name}, version: ${agent.version}`);

  // Create conversation and send request with runtime values
  const conversation = await openai.conversations.create();
  const response = await openai.responses.create(
    {
      conversation: conversation.id,
      input: "Hello! Can you confirm my details?",
    },
    {
      body: {
        agent_reference: { name: agent.name, type: "agent_reference" },
        structured_inputs: { userName: "Alice Smith", userRole: "Senior Developer" },
      },
    },
  );
  console.log(response.output_text);

  // Clean up
  await project.agents.deleteVersion(agent.name, agent.version);
}

main().catch(console.error);

Förväntade utdata

Agent created: structured-input-agent, version: 1
Hello Alice Smith! I can confirm your details: your name is Alice Smith and your role is Senior Developer. How can I help you today?

Agentdefinitionen använder structured_inputs för att deklarera mallscheman. Vid körning skickar du de faktiska värdena i parametern body tillsammans med agent_reference.

Lägg till beroendet till din pom.xml.

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-agents</artifactId>
    <version>2.0.0</version>
</dependency>

import com.azure.ai.agents.AgentsClient;
import com.azure.ai.agents.AgentsClientBuilder;
import com.azure.ai.agents.AgentsServiceVersion;
import com.azure.ai.agents.ResponsesClient;
import com.azure.ai.agents.models.AgentReference;
import com.azure.ai.agents.models.AgentVersionDetails;
import com.azure.ai.agents.models.AzureCreateResponseOptions;
import com.azure.ai.agents.models.PromptAgentDefinition;
import com.azure.ai.agents.models.StructuredInputDefinition;
import com.azure.core.util.BinaryData;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.openai.models.responses.Response;
import com.openai.models.responses.ResponseCreateParams;

import java.util.LinkedHashMap;
import java.util.Map;

public class StructuredInputInstructionsExample {
    public static void main(String[] args) {
        // Format: "https://resource_name.ai.azure.com/api/projects/project_name"
        String projectEndpoint = "your_project_endpoint";

        AgentsClientBuilder builder = new AgentsClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(projectEndpoint)
            .serviceVersion(AgentsServiceVersion.getLatest());

        AgentsClient agentsClient = builder.buildAgentsClient();
        ResponsesClient responsesClient = builder.buildResponsesClient();

        // Define structured input schemas
        Map<String, StructuredInputDefinition> inputDefs = new LinkedHashMap<>();
        inputDefs.put("userName",
            new StructuredInputDefinition().setDescription("The user's name").setRequired(true));
        inputDefs.put("userRole",
            new StructuredInputDefinition().setDescription("The user's role").setRequired(true));

        // Create agent with handlebar templates in instructions
        AgentVersionDetails agent = agentsClient.createAgentVersion(
            "structured-input-agent",
            new PromptAgentDefinition("gpt-5-mini")
                .setInstructions("You are a helpful assistant. "
                    + "The user's name is {{userName}} and their role is {{userRole}}. "
                    + "Greet them and confirm their details.")
                .setStructuredInputs(inputDefs));

        // Supply structured input values at runtime
        Map<String, BinaryData> inputValues = new LinkedHashMap<>();
        inputValues.put("userName", BinaryData.fromObject("Alice Smith"));
        inputValues.put("userRole", BinaryData.fromObject("Senior Developer"));

        Response response = responsesClient.createAzureResponse(
            new AzureCreateResponseOptions()
                .setAgentReference(
                    new AgentReference(agent.getName()).setVersion(agent.getVersion()))
                .setStructuredInputs(inputValues),
            ResponseCreateParams.builder()
                .input("Hello! Can you confirm my details?"));

        System.out.println("Response: " + response.output());

        // Clean up
        agentsClient.deleteAgentVersion(agent.getName(), agent.getVersion());
    }
}

Förväntade utdata

Response: Hello Alice Smith! I can confirm your details: your name is Alice Smith and your role is Senior Developer. How can I help you today?

Java SDK använder StructuredInputDefinition för agentschemat och Map<String, BinaryData> för körningsvärden som skickas via AzureCreateResponseOptions.

Skapa en agent med strukturerade indata

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/agents?api-version=v1" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "name": "structured-input-agent",
    "definition": {
      "kind": "prompt",
      "model": "<MODEL_DEPLOYMENT>",
      "instructions": "You are a helpful assistant. The user'\''s name is {{userName}} and their role is {{userRole}}. Greet them and confirm their details.",
      "structured_inputs": {
        "userName": {
          "type": "string",
          "description": "The user'\''s name",
          "default_value": "Unknown"
        },
        "userRole": {
          "type": "string",
          "description": "The user'\''s role",
          "default_value": "User"
        }
      }
    }
  }'

Skapa ett svar med strukturerade indatavärden

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/openai/v1/responses" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "agent_reference": {
      "type": "agent_reference",
      "name": "structured-input-agent"
    },
    "input": [
      {
        "type": "message",
        "role": "user",
        "content": "Hello! Can you confirm my details?"
      }
    ],
    "structured_inputs": {
      "userName": "Alice Smith",
      "userRole": "Senior Developer"
    }
  }'

Agentdefinitionens structured_inputs objekt deklarerar scheman med beskrivningar och standardvärden. Svarsbegäran innehåller de faktiska körningsvärden structured_inputs som ersätter mallarna {{userName}} och {{userRole}} .

from io import BytesIO
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    PromptAgentDefinition,
    CodeInterpreterTool,
    AutoCodeInterpreterToolParam,
    StructuredInputDefinition,
)
from azure.identity import DefaultAzureCredential

# Format: "https://resource_name.ai.azure.com/api/projects/project_name"
PROJECT_ENDPOINT = "your_project_endpoint"

# Create clients to call Foundry API
project = AIProjectClient(endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential())
openai = project.get_openai_client()

# Upload a CSV file for the code interpreter
csv_file = BytesIO(b"x\n1\n2\n3\n")
csv_file.name = "numbers.csv"
uploaded = openai.files.create(purpose="assistants", file=csv_file)
print(f"File uploaded (id: {uploaded.id})")

# Create agent with a template placeholder for the file ID
tool = CodeInterpreterTool(
    container=AutoCodeInterpreterToolParam(file_ids=["{{analysis_file_id}}"])
)
agent = project.agents.create_version(
    agent_name="code-interp-structured",
    definition=PromptAgentDefinition(
        model="gpt-5-mini",
        instructions="You are a helpful data analyst.",
        tools=[tool],
        structured_inputs={
            "analysis_file_id": StructuredInputDefinition(
                description="File ID for the code interpreter",
                required=True,
                schema={"type": "string"},
            ),
        },
    ),
)

# Supply the actual file ID at runtime
conversation = openai.conversations.create()
response = openai.responses.create(
    conversation=conversation.id,
    input="Read numbers.csv and return the sum of x.",
    extra_body={
        "agent_reference": {"name": agent.name, "type": "agent_reference"},
        "structured_inputs": {"analysis_file_id": uploaded.id},
    },
    tool_choice="required",
)
print(response.output_text)

Förväntade utdata

File uploaded (id: <file-id>)
The sum of x in numbers.csv is 6.

Platshållaren {{analysis_file_id}} i verktygets matris ersätts file_ids av det faktiska fil-ID:t vid körning. Med den här metoden kan du återanvända samma agentdefinition med olika filer för varje begäran.

using System;
using Azure.AI.Projects;
using Azure.AI.Projects.Agents;
using Azure.AI.Extensions.OpenAI;
using Azure.Identity;
using OpenAI.Responses;

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
var projectEndpoint = "your_project_endpoint";

// Create project client to call Foundry API
AIProjectClient projectClient = new(
    endpoint: new Uri(projectEndpoint),
    tokenProvider: new DefaultAzureCredential());

// Create agent with a structured input placeholder for the file ID
DeclarativeAgentDefinition agentDefinition = new(model: "gpt-5-mini")
{
    Instructions = "You are a helpful data analyst.",
    Tools = {
        ResponseTool.CreateCodeInterpreterTool(
            new CodeInterpreterToolContainer(
                CodeInterpreterToolContainerConfiguration
                    .CreateAutomaticContainerConfiguration(
                        fileIds: ["{{analysis_file_id}}"])))
    },
    StructuredInputs =
    {
        ["analysis_file_id"] = new StructuredInputDefinition
            { Description = "File ID for the code interpreter", IsRequired = true }
    }
};
AgentVersion agent = projectClient.AgentAdministrationClient.CreateAgentVersion(
    agentName: "code-interp-structured", options: new(agentDefinition));

// Supply the actual file ID at runtime
AgentReference agentRef = new(name: agent.Name, version: agent.Version);
ProjectResponsesClient responseClient =
    projectClient.ProjectOpenAIClient.GetProjectResponsesClientForAgent(agentRef);

CreateResponseOptions responseOptions = new()
{
    Input = [ResponseItem.CreateUserMessageItem(
        "Read numbers.csv and return the sum of x.")]
};
responseOptions.Patch.Set(
    "$.structured_inputs[\"analysis_file_id\"]"u8,
    BinaryData.FromObjectAsJson("<uploaded-file-id>"));

ResponseResult response = responseClient.CreateResponse(responseOptions);
Console.WriteLine(response.GetOutputText());

// Clean up
projectClient.AgentAdministrationClient.DeleteAgentVersion(
    agentName: agent.Name, agentVersion: agent.Version);

Förväntade utdata

The sum of x in numbers.csv is 6.

import { DefaultAzureCredential } from "@azure/identity";
import { AIProjectClient } from "@azure/ai-projects";

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
const PROJECT_ENDPOINT = "your_project_endpoint";

export async function main(): Promise<void> {
  const project = new AIProjectClient(PROJECT_ENDPOINT, new DefaultAzureCredential());
  const openai = project.getOpenAIClient();

  // Upload a file for code interpreter
  const file = new File(["x\n1\n2\n3\n"], "numbers.csv");
  const uploaded = await openai.files.create({ file, purpose: "assistants" });
  console.log(`File uploaded (id: ${uploaded.id})`);

  // Create agent with a template placeholder for the file ID
  const agent = await project.agents.createVersion("code-interp-structured", {
    kind: "prompt",
    model: "gpt-5-mini",
    instructions: "You are a helpful data analyst.",
    tools: [
      {
        type: "code_interpreter",
        container: { type: "auto", file_ids: ["{{analysis_file_id}}"] },
      },
    ],
    structured_inputs: {
      analysis_file_id: {
        description: "File ID for the code interpreter",
        required: true,
      },
    },
  });

  // Supply the actual file ID at runtime
  const conversation = await openai.conversations.create();
  const response = await openai.responses.create(
    {
      conversation: conversation.id,
      input: "Read numbers.csv and return the sum of x.",
      tool_choice: "required",
    },
    {
      body: {
        agent_reference: { name: agent.name, type: "agent_reference" },
        structured_inputs: { analysis_file_id: uploaded.id },
      },
    },
  );
  console.log(response.output_text);

  // Clean up
  await project.agents.deleteVersion(agent.name, agent.version);
}

main().catch(console.error);

Förväntade utdata

File uploaded (id: <file-id>)
The sum of x in numbers.csv is 6.

import com.azure.ai.agents.AgentsClient;
import com.azure.ai.agents.AgentsClientBuilder;
import com.azure.ai.agents.AgentsServiceVersion;
import com.azure.ai.agents.ResponsesClient;
import com.azure.ai.agents.models.*;
import com.azure.core.util.BinaryData;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.openai.models.responses.Response;
import com.openai.models.responses.ResponseCreateParams;

import java.util.Arrays;
import java.util.LinkedHashMap;
import java.util.Map;

public class CodeInterpreterStructuredInputExample {
    public static void main(String[] args) {
        // Format: "https://resource_name.ai.azure.com/api/projects/project_name"
        String projectEndpoint = "your_project_endpoint";

        AgentsClientBuilder builder = new AgentsClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(projectEndpoint)
            .serviceVersion(AgentsServiceVersion.getLatest());

        AgentsClient agentsClient = builder.buildAgentsClient();
        ResponsesClient responsesClient = builder.buildResponsesClient();

        // Create code interpreter tool with a template placeholder
        CodeInterpreterTool tool = new CodeInterpreterTool()
            .setContainer(new AutoCodeInterpreterToolParameter()
                .setFileIds(Arrays.asList("{{analysis_file_id}}")));

        Map<String, StructuredInputDefinition> inputDefs = new LinkedHashMap<>();
        inputDefs.put("analysis_file_id",
            new StructuredInputDefinition()
                .setDescription("File ID for the code interpreter")
                .setRequired(true));

        AgentVersionDetails agent = agentsClient.createAgentVersion(
            "code-interp-structured",
            new PromptAgentDefinition("gpt-5-mini")
                .setInstructions("You are a helpful data analyst.")
                .setTools(Arrays.asList(tool))
                .setStructuredInputs(inputDefs));

        // Supply the actual file ID at runtime
        Map<String, BinaryData> inputValues = new LinkedHashMap<>();
        inputValues.put("analysis_file_id",
            BinaryData.fromObject("<uploaded-file-id>"));

        Response response = responsesClient.createAzureResponse(
            new AzureCreateResponseOptions()
                .setAgentReference(
                    new AgentReference(agent.getName()).setVersion(agent.getVersion()))
                .setStructuredInputs(inputValues),
            ResponseCreateParams.builder()
                .input("Read numbers.csv and return the sum of x."));

        System.out.println("Response: " + response.output());

        // Clean up
        agentsClient.deleteAgentVersion(agent.getName(), agent.getVersion());
    }
}

Förväntade utdata

Response: The sum of x in numbers.csv is 6.

Skapa en agent med dynamiska kodtolkarfiler

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/agents?api-version=v1" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "name": "code-interp-structured",
    "definition": {
      "kind": "prompt",
      "model": "<MODEL_DEPLOYMENT>",
      "instructions": "You are a helpful data analyst.",
      "tools": [
        {
          "type": "code_interpreter",
          "container": {
            "type": "auto",
            "file_ids": ["{{analysis_file_id}}"]
          }
        }
      ],
      "structured_inputs": {
        "analysis_file_id": {
          "description": "File ID for the code interpreter",
          "required": true,
          "schema": {"type": "string"}
        }
      }
    }
  }'

Skapa ett svar med fil-ID:t

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/openai/v1/responses" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "agent_reference": {
      "type": "agent_reference",
      "name": "code-interp-structured"
    },
    "input": [
      {
        "type": "message",
        "role": "user",
        "content": "Read numbers.csv and return the sum of x."
      }
    ],
    "structured_inputs": {
      "analysis_file_id": "<FILE_ID>"
    },
    "tool_choice": "required"
  }'

Mallen {{analysis_file_id}} i file_ids ersätts med det faktiska fil-ID:t vid körning. Du kan definiera flera platshållare för fil-ID och lämna oanvända platshållare tomma. Tomma värden tas automatiskt bort från matrisen.

from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    PromptAgentDefinition,
    FileSearchTool,
    StructuredInputDefinition,
)
from azure.identity import DefaultAzureCredential

# Format: "https://resource_name.ai.azure.com/api/projects/project_name"
PROJECT_ENDPOINT = "your_project_endpoint"

# Create clients to call Foundry API
project = AIProjectClient(endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential())
openai = project.get_openai_client()

# Create a vector store and upload a file
vector_store = openai.vector_stores.create(name="ProductInfoStore")
with open("product_info.md", "rb") as f:
    file = openai.vector_stores.files.upload_and_poll(
        vector_store_id=vector_store.id, file=f
    )
print(f"Vector store created (id: {vector_store.id})")

# Create agent with a template placeholder for vector store ID
tool = FileSearchTool(vector_store_ids=["{{vector_store_id}}"])
agent = project.agents.create_version(
    agent_name="file-search-structured",
    definition=PromptAgentDefinition(
        model="gpt-5-mini",
        instructions="You are a helpful assistant that searches product information.",
        tools=[tool],
        structured_inputs={
            "vector_store_id": StructuredInputDefinition(
                description="Vector store ID for file search",
                required=True,
                schema={"type": "string"},
            ),
        },
    ),
)

# Supply the actual vector store ID at runtime
conversation = openai.conversations.create()
response = openai.responses.create(
    conversation=conversation.id,
    input="Tell me about Contoso products",
    extra_body={
        "agent_reference": {"name": agent.name, "type": "agent_reference"},
        "structured_inputs": {"vector_store_id": vector_store.id},
    },
)
print(response.output_text)

Förväntade utdata

Vector store created (id: <vector-store-id>)
Based on the product information, Contoso offers several product lines including...

Platshållaren {{vector_store_id}} ersätts med det faktiska vektorlager-ID:t under körning. Du kan definiera platshållare för flera vektorlager för att aktivera nivåindelade eller kontextspecifika kunskapsbaser.

using System;
using Azure.AI.Projects;
using Azure.AI.Projects.Agents;
using Azure.AI.Extensions.OpenAI;
using Azure.Identity;
using OpenAI.Responses;

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
var projectEndpoint = "your_project_endpoint";

AIProjectClient projectClient = new(
    endpoint: new Uri(projectEndpoint),
    tokenProvider: new DefaultAzureCredential());

// Create agent with a template placeholder for vector store ID
DeclarativeAgentDefinition agentDefinition = new(model: "gpt-5-mini")
{
    Instructions = "You are a helpful assistant that searches product information.",
    Tools = {
        ResponseTool.CreateFileSearchTool(
            vectorStoreIds: ["{{vector_store_id}}"])
    },
    StructuredInputs =
    {
        ["vector_store_id"] = new StructuredInputDefinition
            { Description = "Vector store ID for file search", IsRequired = true }
    }
};
AgentVersion agent = projectClient.AgentAdministrationClient.CreateAgentVersion(
    agentName: "file-search-structured", options: new(agentDefinition));

// Supply the actual vector store ID at runtime
AgentReference agentRef = new(name: agent.Name, version: agent.Version);
ProjectResponsesClient responseClient =
    projectClient.ProjectOpenAIClient.GetProjectResponsesClientForAgent(agentRef);

CreateResponseOptions responseOptions = new()
{
    Input = [ResponseItem.CreateUserMessageItem("Tell me about Contoso products")]
};
responseOptions.Patch.Set(
    "$.structured_inputs[\"vector_store_id\"]"u8,
    BinaryData.FromObjectAsJson("<vector-store-id>"));

ResponseResult response = responseClient.CreateResponse(responseOptions);
Console.WriteLine(response.GetOutputText());

// Clean up
projectClient.AgentAdministrationClient.DeleteAgentVersion(
    agentName: agent.Name, agentVersion: agent.Version);

Förväntade utdata

Based on the product information, Contoso offers several product lines including...

import { DefaultAzureCredential } from "@azure/identity";
import { AIProjectClient } from "@azure/ai-projects";

// Format: "https://resource_name.ai.azure.com/api/projects/project_name"
const PROJECT_ENDPOINT = "your_project_endpoint";

export async function main(): Promise<void> {
  const project = new AIProjectClient(PROJECT_ENDPOINT, new DefaultAzureCredential());
  const openai = project.getOpenAIClient();

  // Create a vector store (assumes file already uploaded)
  const vectorStore = await openai.vectorStores.create({ name: "ProductInfoStore" });
  console.log(`Vector store created (id: ${vectorStore.id})`);

  // Create agent with a template placeholder for vector store ID
  const agent = await project.agents.createVersion("file-search-structured", {
    kind: "prompt",
    model: "gpt-5-mini",
    instructions: "You are a helpful assistant that searches product information.",
    tools: [
      {
        type: "file_search",
        vector_store_ids: ["{{vector_store_id}}"],
      },
    ],
    structured_inputs: {
      vector_store_id: {
        description: "Vector store ID for file search",
        required: true,
      },
    },
  });

  // Supply the actual vector store ID at runtime
  const conversation = await openai.conversations.create();
  const response = await openai.responses.create(
    {
      conversation: conversation.id,
      input: "Tell me about Contoso products",
    },
    {
      body: {
        agent_reference: { name: agent.name, type: "agent_reference" },
        structured_inputs: { vector_store_id: vectorStore.id },
      },
    },
  );
  console.log(response.output_text);

  // Clean up
  await project.agents.deleteVersion(agent.name, agent.version);
}

main().catch(console.error);

Förväntade utdata

Vector store created (id: <vector-store-id>)
Based on the product information, Contoso offers several product lines including...

import com.azure.ai.agents.AgentsClient;
import com.azure.ai.agents.AgentsClientBuilder;
import com.azure.ai.agents.AgentsServiceVersion;
import com.azure.ai.agents.ResponsesClient;
import com.azure.ai.agents.models.AgentReference;
import com.azure.ai.agents.models.AgentVersionDetails;
import com.azure.ai.agents.models.AzureCreateResponseOptions;
import com.azure.ai.agents.models.FileSearchTool;
import com.azure.ai.agents.models.PromptAgentDefinition;
import com.azure.ai.agents.models.StructuredInputDefinition;
import com.azure.core.util.BinaryData;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.openai.models.responses.Response;
import com.openai.models.responses.ResponseCreateParams;

import java.util.Arrays;
import java.util.LinkedHashMap;
import java.util.Map;

public class FileSearchStructuredInputExample {
    public static void main(String[] args) {
        // Format: "https://resource_name.ai.azure.com/api/projects/project_name"
        String projectEndpoint = "your_project_endpoint";

        AgentsClientBuilder builder = new AgentsClientBuilder()
            .credential(new DefaultAzureCredentialBuilder().build())
            .endpoint(projectEndpoint)
            .serviceVersion(AgentsServiceVersion.getLatest());

        AgentsClient agentsClient = builder.buildAgentsClient();
        ResponsesClient responsesClient = builder.buildResponsesClient();

        // Create agent with a template placeholder for vector store ID
        FileSearchTool tool = new FileSearchTool()
            .setVectorStoreIds(Arrays.asList("{{vector_store_id}}"));

        Map<String, StructuredInputDefinition> inputDefs = new LinkedHashMap<>();
        inputDefs.put("vector_store_id",
            new StructuredInputDefinition()
                .setDescription("Vector store ID for file search")
                .setRequired(true));

        AgentVersionDetails agent = agentsClient.createAgentVersion(
            "file-search-structured",
            new PromptAgentDefinition("gpt-5-mini")
                .setInstructions(
                    "You are a helpful assistant that searches product information.")
                .setTools(Arrays.asList(tool))
                .setStructuredInputs(inputDefs));

        // Supply the actual vector store ID at runtime
        Map<String, BinaryData> inputValues = new LinkedHashMap<>();
        inputValues.put("vector_store_id",
            BinaryData.fromObject("<vector-store-id>"));

        Response response = responsesClient.createAzureResponse(
            new AzureCreateResponseOptions()
                .setAgentReference(
                    new AgentReference(agent.getName()).setVersion(agent.getVersion()))
                .setStructuredInputs(inputValues),
            ResponseCreateParams.builder()
                .input("Tell me about Contoso products"));

        System.out.println("Response: " + response.output());

        // Clean up
        agentsClient.deleteAgentVersion(agent.getName(), agent.getVersion());
    }
}

Förväntade utdata

Response: Based on the product information, Contoso offers several product lines including...

Skapa en agent med dynamiska filsökningsvektorlager

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/agents?api-version=v1" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "name": "file-search-structured",
    "definition": {
      "kind": "prompt",
      "model": "<MODEL_DEPLOYMENT>",
      "instructions": "You are a helpful assistant that searches product information.",
      "tools": [
        {
          "type": "file_search",
          "vector_store_ids": [
            "vs_base_kb",
            "{{tier_specific_kb}}"
          ]
        }
      ],
      "structured_inputs": {
        "tier_specific_kb": {
          "description": "Vector store ID for customer tier",
          "required": true,
          "schema": {"type": "string"}
        }
      }
    }
  }'

Skapa ett svar med vektorlagrings-ID:t

curl -X POST "$FOUNDRY_PROJECT_ENDPOINT/openai/v1/responses" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AGENT_TOKEN" \
  -d '{
    "agent_reference": {
      "type": "agent_reference",
      "name": "file-search-structured"
    },
    "input": [
      {
        "type": "message",
        "role": "user",
        "content": "Tell me about Contoso products"
      }
    ],
    "structured_inputs": {
      "tier_specific_kb": "vs_premium_kb_2024"
    }
  }'

I det här exemplet kombineras ett statiskt vektorlager (vs_base_kb) med ett dynamiskt ({{tier_specific_kb}}). Mallens platshållare ersätts vid körning, och processen tar automatiskt bort alla tomma strängvärden i matrisen.