ApsaraDB for ClickHouse:Create an Agent-lens application

Step 1: Enable the Agent-lens service

1. Log on to the ApsaraDB for ClickHouse console.

2. On the Clusters page, select Enterprise Edition Clusters, and then click the ID of the target instance to open its details page.

3. In the left-side navigation pane of the instance details page, click Agent Observability Suite.

4. In the dialog box that appears, click OK to enable the Agent-lens service.

Step 2: Configure the whitelist

The first time you access the Langfuse service page, you must add your local machine's IP address to the service's whitelist.

Wait until the Instance Status changes to Running. Confirm whether the IP address displayed in the dialog box is your local machine's IP address. If it is, click OK. If not, you must manually add the IP address to the whitelist.

Step 3: Create a user

1. Click Public Address to open the Langfuse service page.

2. On your first visit, click Sign up, and enter your name, email address, and password to create a Langfuse user.

Step 4: Create an organization and a project

You can select an existing organization at the top of the page or follow these steps to create a new one.

1. Click New Organization.

2. Enter an organization name and click Create.

3. Click Next.

4. Enter a project name and click Create.

Step 5: Create an API key

A Langfuse API key is used to send observability data to Langfuse.

Click Create new API key to create a new API key.

Step 6: Send your first observability data

Langfuse seamlessly integrates with model providers such as Alibaba Cloud Model Studio, OpenAI, and Anthropic. The following example uses Alibaba Cloud Model Studio to demonstrate how to send your first observability data.

1. Install the Langfuse SDK.

   pip install langfuse

2. Set environment variables.

   Set the Langfuse API key and service endpoint as environment variables to direct the SDK to the correct Langfuse project. Also, set the Alibaba Cloud Model Studio API key as an environment variable to allow the application to call the Model Studio model service.

   Note

   Before sending data, ensure that the IP address of the host where the SDK is running has been added to the Langfuse service whitelist.

   export LANGFUSE_SECRET_KEY="sk-lf-..."
   export LANGFUSE_PUBLIC_KEY="pk-lf-..."
   export LANGFUSE_BASE_URL="http://{your_langfuse_endpoint}:3000"
   export DASHSCOPE_API_KEY="sk-..."

3. Create a file named quick_start.py and add the following code.

   import os
   from langfuse.openai import OpenAI
   
   try:
       client = OpenAI(
           api_key=os.getenv("DASHSCOPE_API_KEY"),
           base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
       )
   
       completion = client.chat.completions.create(
           model="qwen-plus",
           messages=[
               {'role': 'system', 'content': 'You are a helpful assistant.'},
               {'role': 'user', 'content': 'Who are you?'}
           ]
       )
       print(completion.choices[0].message.content)
   except Exception as e:
       print(f"Error: {e}")
       print("For more information, see the documentation at: https://www.alibabacloud.com/help/en/model-studio/error-code")

4. Run the quick_start.py file to send the observability data to the Langfuse service.

   python quick_start.py

   Example output:

   Hello! I am Qwen, a large-scale language model from Alibaba Group. I can answer questions, create text such as stories, official documents, emails, and scripts, perform logical reasoning, write code, express opinions, and play games. If you have any questions or need help, feel free to ask me at any time!

   You can view the collected observability data on the Langfuse service page: