Knowledge retrieval

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Prerequisites

1. Obtain an API key and configure it as an environment variable.
2. Create a knowledge base and obtain its ID. You can create a knowledge base in one of the following ways:
   • Create in the console: Create a knowledge base on the knowledge base page in the Model Studio console. For more information, see Create and use a knowledge base.
   • Create using the API: You can call the API through the Alibaba Cloud Model Studio software development kit (SDK) to create a knowledge base. For more information, see Knowledge Base API Guide.

     Before you create a knowledge base using the API, obtain the workspace ID (workspace_id) from the Model Studio console. Only two types of knowledge bases are supported: document search and data query. These types are suitable for basic document Q&A pairs and do not support responses with rich text or images.

   You can find the knowledge base ID on the knowledge base details page in the Model Studio console.

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How to use

Before you start, create and use a knowledge base and obtain its ID. Currently, the vector_store_ids parameter supports only one knowledge base ID.

# Dependency imports and client creation are omitted here. For the complete runnable code, see "Getting started" below.
response = client.responses.create(
    model="qwen3.8-max",
    input="Tell me about the Alibaba Cloud Model Studio X1 phone",
    tools=[
        {
            "type": "file_search",
            # Replace this with your knowledge base ID. Only one is currently supported.
            "vector_store_ids": ["your_knowledge_base_id"]
        }
    ]
)

print(response.output_text)

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Supported models

• Qwen-Max: Qwen3.8-Max series and Qwen3.7-Max series
• Qwen-Plus: Qwen3.7-Plus series, Qwen3.6-Plus series, and Qwen3.5-Plus series
• Qwen-Flash: Qwen3.7-Flash series, Qwen3.6-Flash series and Qwen3.5-Flash series
• Qwen3.8 open-source series
• Qwen3.6 open-source series (except qwen3.6-27b)
• Qwen3.5 open source series

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Getting started

Replace vector_store_ids in the sample code with your knowledge base ID.

import os
from openai import OpenAI

client = OpenAI(
    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx" (not recommended)
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)

response = client.responses.create(
    model="qwen3.8-max",
    input="Tell me about the Alibaba Cloud Model Studio X1 phone",
    tools=[
        {
            "type": "file_search",
            # Replace this with your knowledge base ID. Only one is currently supported.
            "vector_store_ids": ["your_knowledge_base_id"]
        }
    ]
)

print("[Model Response]")
print(response.output_text)
print(f"\n[Token Usage] Input: {response.usage.input_tokens}, Output: {response.usage.output_tokens}, Total: {response.usage.total_tokens}")

[Model Response]
Based on the content in the knowledge base, the key points of this product include the following:

1. **Core features**: The product provides...
2. **Scenarios**: Suitable for...
3. **Technical attributes**: Based on...

...

[Token Usage] Input: 1568, Output: 1724, Total: 3292

​

Streaming output

import os
from openai import OpenAI

client = OpenAI(
    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx" (not recommended)
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)

stream = client.responses.create(
    model="qwen3.8-max",
    input="Tell me about the Alibaba Cloud Model Studio X1 phone",
    tools=[
        {
            "type": "file_search",
            # Replace this with your knowledge base ID. Only one is currently supported.
            "vector_store_ids": ["your_knowledge_base_id"]
        }
    ],
    stream=True
)

for event in stream:
    # The model response starts
    if event.type == "response.content_part.added":
        print("[Model Response]")
    # Print the model response in a stream
    elif event.type == "response.output_text.delta":
        print(event.delta, end="", flush=True)
    # When the response is complete, print the token usage
    elif event.type == "response.completed":
        usage = event.response.usage
        print(f"\n\n[Token Usage] Input: {usage.input_tokens}, Output: {usage.output_tokens}, Total: {usage.total_tokens}")

​

Parameters

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Billing

• Model call fees: Content retrieved from the knowledge base is added to the prompt. This increases the number of input tokens for the model. You are charged based on the model's standard pricing. For pricing details, see the Model Studio console.
• Tool call fees: The knowledge base feature is currently free of charge.