Platform For AI:RAG-based LLM chatbot

Step 1: Deploy the RAG service

1. Log on to the PAI console. On the page that appears, select a region and a workspace. Then, click Enter Elastic Algorithm Service (EAS).

2. On the Elastic Algorithm Service (EAS) page, click Deploy Service. In the Scenario-based Model Deployment section, click RAG-based Smart Dialogue Deployment.

3. On the RAG-based LLM Chatbot Deployment page, configure the parameters. The following tables describe the parameters.

   • Basic Information

     Parameter	Description
     Model Source	The source of the model. Valid values: Open Source Model: PAI provides a variety of preset open-source models, including Qwen, DeepSeek, Llama, ChatGLM, Baichuan, Falcon, Yi, Mistral, and Gemma. You can select and deploy a model with the appropriate parameter size. Custom Fine-tuned Model: PAI supports your fine-tuned models for specific scenarios.
     Model Type	If you use an Open Source Model, select a model with the appropriate parameter size. If you use a Custom Fine-tuned Model, you need to specify the model type, parameter size, and precision.
     Model Settings	If you use a Custom Fine-tuned Model, you need to specify the path of the model. The system reads the model configuration file from this path when deploying the model. Valid values: Note We recommend that you first run the fine-tuned model in Transformers of Huggingface to confirm that the output meets your expectations before you deploy the model as an EAS service. OSS: Select the OSS path in which the fine-tuned model file is stored. NAS: Select the NAS file system in which the fine-tuned model file is stored, the source path and the mount path. For more information about how to create an OSS bucket and NAS file system, see Get started by using the OSS console and Create a file system.

   • Resource Configuration

     Parameter	Description
     Resource Configuration	After you select a model, the system recommends appropriate resource configurations. If you switch to another specification, the model service may fail to start.
     Inference Acceleration	Inference acceleration can be enabled for the Qwen, Llama2, ChatGLM, or Baichuan2 model that is deployed on A10 or GU30 instances. The acceleration feature is not billed. Valid values: BladeLLM Inference Acceleration (recommended) Open-source vLLM Inference Acceleration

   • Vector Database Settings

     You can use one of the following types of vector database: Faiss, Elasticsearch, Hologres, OpenSearch, or RDS PostgreSQL. Select a type based on your business requirements.

     FAISS

     You can use Faiss to quickly build a local vector database in an EAS instance without the need to purchase or activate online vector databases.

     Parameter	Description
     Vector Database Type	Select FAISS.
     OSS Path	The OSS path of the vector database. Select an OSS path in the current region. You can create an OSS path if no OSS path is available. For more information, see Get started by using the OSS console. Note If you use a Custom Fine-tuned Model, make sure that the OSS paths of the vector database and the model are different.

     Elasticsearch

     Specify the connection information of an Elasticsearch cluster. For information about how to create and prepare an Elasticsearch cluster, see Prepare a vector database by using Elasticsearch.

     Parameter	Description
     Vector Database Type	Select Elasticsearch.
     Private Endpoint and Port	The private endpoint and port number of the Elasticsearch cluster. Format: http://Private endpoint:Port number. For information about how to obtain the private endpoint and port number of the Elasticsearch cluster, see View the basic information of a cluster.
     Index Name	The name of the index. You can enter a new index name or an existing index name. If you use an existing index name, the index schema must meet the requirements of the RAG-based chatbot. For example, you can enter the name of the index that is automatically created when you deploy the RAG-based chatbot by using EAS.
     Account	The logon name that you specified when you created the Elasticsearch cluster. Default logon name: elastic.
     Password	The password that you configured when you created the Elasticsearch cluster. If you forget the password, see Reset the access password for an Elasticsearch cluster.

     Hologres

     Specify the connection information of a Hologres instance. To purchase a Hologres instance, see Purchase a Hologres instance.

     Parameter	Description
     Vector Database Type	Select Hologres.
     Invocation Information	The host information of Select VPC. Go to the Instance Details page in the Hologres console. In the Network Information section, click Copy next to Select VPC to obtain the host before the domain name :80.
     Database Name	The name of the database in the Hologres instance. For more information about how to create a database, see Create a database.
     Account	The custom account that you created. For more information, see Create a custom account. In the Select Member Role section, select Examples of the Super Administrator (SuperUser).
     Password	The password of the custom account that you created.
     Table Name	The name of the table. You can enter a new table name or an existing table name. If you use an existing table name, the table schema must meet the requirements of the RAG-based chatbot. For example, you can enter the name of the Hologres table that is automatically created when you deploy the RAG-based chatbot by using EAS.

     OpenSearch

     Specify the connection information of an OpenSearch instance of Vector Search Edition. For information about how to create and prepare an OpenSearch instance, see Prepare an OpenSearch Vector Search Edition instance.

     Parameter	Description
     Vector Database Type	Select OpenSearch.
     Endpoint	The public endpoint of the OpenSearch instance. You must first configure Internet access for the OpenSearch instance. For more information, see Prepare an OpenSearch Vector Search Edition instance.
     Instance ID	Obtain the instance ID from the OpenSearch instance list.
     Username	Enter the username and password of the OpenSearch instance.
     Password
     Table Name	Enter the name of the index table of the OpenSearch instance. For information about how to prepare the index table, see Prepare an OpenSearch Vector Search Edition instance.

     RDS PostgreSQL

     Specify the connection information of the ApsaraDB RDS for PostgreSQL instance. For information about how to create and prepare an ApsaraDB RDS for PostgreSQL instance, see Prepare a vector database by using ApsaraDB RDS for PostgreSQL.

     Parameter	Description
     Vector Database Type	Select RDS PostgreSQL.
     Host Address	The internal endpoint of the ApsaraDB RDS for PostgreSQL instance. You can log on to the ApsaraDB ApsaraDB RDS for PostgreSQL console and view the endpoint on the Database Connection page of the instance.
     Port	The port number. Default value: 5432.
     Database	The name of the database. For information about how to create a database and an account, see Create a database and an account. When you create an account, select Privileged Account for Account Type. When you create a database, select the created privileged account from the Authorized By drop-down list.
     Table Name	The name of the database table.
     Account	Specify the privileged account and password you created. Create a database and an account.
     Password

   • VPC Configuration

     Parameter	Description
     VPC	If you want to use a Model Studio LLM or web search for Q&A, you must configure the VPC and enable Internet access. For more information, see Configure Internet access. Network requirements of a vector database: If you use Faiss to build a vector database, you do not need to configure the VPC. If you use Hologres, Elasticsearch, or RDS PostgreSQL to build a vector database, EAS supports access to the vector database over Internet or a VPC (recommended). VPC access requires that the VPC configured in EAS must be the same as the VPC configured in the vector database. If you use OpenSearch to build a vector database, EAS supports only Internet access. For more information about the configuration method, see Step 2: Prepare configuration items.
     vSwitch
     Security Group Name

Step 2: Test the chatbot on the Web UI

After you deploy the RAG-based chatbot, click View Web App in the Service Type column to go to the Web UI.

Follow the following steps to upload your knowledge base file on the Web UI and test the Q&A chatbot.

Step 3: Call API operations to perform model inference

Obtain the invocation information

1. Click the name of the RAG-based chatbot to go to the Service Details page.

2. In the Basic Information section, click View Endpoint Information.

3. On the Public Endpoint tab of the Invocation Method dialogue box, obtain the service endpoint and token.

Upload knowledge base files

You can use an API to upload knowledge base files from your on-premises machine. You can use the returned value of the task_id parameter to query the upload status.

In the following example, replace <service_url> with the endpoint of the RAG-based chatbot and <service_token> with the token of the RAG-based chatbot. For more information about how to obtain the endpoint and token, see Obtain the invocation information.

• Upload data

  curl -X 'POST' '<service_url>api/v1/upload_data' -H 'Authorization: <service_token>' -H 'Content-Type: multipart/form-data' -F 'files=@<file_path>'
  # Return: {"task_id": "****557733764fdb9fefa063538914da"}

• Query the upload status

  curl '<service_url>api/v1/get_upload_state?task_id=****557733764fdb9fefa063538914da' -H 'Authorization: <service_token>'
  # Return: {"task_id":"****557733764fdb9fefa063538914da","status":"completed"}

Single-round conversation request

import requests

SERVICE_URL = 'http://xxxx.****.cn-beijing.pai-eas.aliyuncs.com/'
headers = {
    'accept': 'application/json',
    'Content-Type': 'application/json',
    'Authorization': 'MDA5NmJkNzkyMGM1Zj****YzM4M2YwMDUzZTdiZmI5YzljYjZmNA==',
}

def test_post_api_query(url):
    data = {
       "question":"What is PAI?" 
    }
    response = requests.post(url, headers=headers, json=data)

    if response.status_code != 200:
        raise ValueError(f'Error post to {url}, code: {response.status_code}')
    ans = dict(response.json())

    print(f"======= Question =======\n {data['question']}")
    if 'answer' in ans.keys():
        print(f"======= Answer =======\n {ans['answer']}")
    if 'docs' in ans.keys():
        print(f"======= Retrieved Docs =======\n {ans['docs']}\n\n")
 
# LLM 
test_post_api_query(SERVICE_URL + 'api/v1/query/llm')
# Retrieval
test_post_api_query(SERVICE_URL + 'api/v1/query/retrieval')
# RAG (Knowledge Base)
test_post_api_query(SERVICE_URL + 'api/v1/query')

Multi-round conversation request

LLM and Chat (Knowledge Base) support multi-round conversation requests. Sample code:

# Send the request. 
curl -X 'POST'  '<service_url>api/v1//query' -H 'Authorization: <service_token>' -H 'accept: application/json'  -H 'Content-Type: application/json'  -d '{"question": "What is PAI?"}'

# Provide the session ID returned for the request. This ID uniquely identifies a conversation in the conversation history. After the session ID is provided, the corresponding conversation is stored and is automatically included in subsequent requests to call an LLM.
curl -X 'POST'  '<service_url>api/v1//query' -H 'Authorization: <service_token>' -H 'accept: application/json'  -H 'Content-Type: application/json'  -d '{"question": "What are the advantages of PAI?","session_id": "ed7a80e2e20442eab****"}'

# Provide the chat_history parameter, which contains the conversation history between you and the chatbot. The parameter value is a list in which each element indicates a single round of conversation in the {"user":"Inputs","bot":"Outputs"} format. Multiple conversations are sorted in chronological order. 
curl -X 'POST'  '<service_url>api/v1//query' -H 'Authorization: <service_token>' -H 'accept: application/json'  -H 'Content-Type: application/json'  -d '{"question":"What are the features of PAI?", "chat_history": [{"user":"What is PAI?", "bot":"PAI is an Alibaba Cloud platform for AI......"}]}'

# If you provide both the session_id and chat_history parameters, the conversation history is appended to the conversation that corresponds to the specified session ID. 
curl -X 'POST'  '<service_url>api/v1//query' -H 'Authorization: <service_token>' -H 'accept: application/json'  -H 'Content-Type: application/json'  -d '{"question":"What are the features of PAI?", "chat_history": [{"user":"What is PAI?", "bot":"PAI is an Alibaba Cloud platform for AI......"}], "session_id": "1702ffxxad3xxx6fxxx97daf7c"}'

import requests

SERVICE_URL = 'http://xxxx.****.cn-beijing.pai-eas.aliyuncs.com'
headers = {
    'accept': 'application/json',
    'Content-Type': 'application/json',
    'Authorization': 'MDA5NmJkN****jNlMDgzYzM4M2YwMDUzZTdiZmI5YzljYjZmNA==',
}

def test_post_api_query_with_chat_history(url):
    # Round 1 query
    data = {
       "question": "What is PAI?"
    }
    response = requests.post(url, headers=headers, json=data)

    if response.status_code != 200:
        raise ValueError(f'Error post to {url}, code: {response.status_code}')
    ans = dict(response.json())
    print(f"=======Round 1: Question =======\n {data['question']}")
    if 'answer' in ans.keys():
        print(f"=======Round 1: Answer =======\n {ans['answer']} session_id: {ans['session_id']}")
    if 'docs' in ans.keys():
        print(f"=======Round 1: Retrieved Docs =======\n {ans['docs']}")
   
    # Round 2 query
    data_2 = {
       "question": "What are the advantages of PAI?",
       "session_id": ans['session_id']
    }
    response_2 = requests.post(url, headers=headers, json=data_2)

    if response.status_code != 200:
        raise ValueError(f'Error post to {url}, code: {response.status_code}')
    ans_2 = dict(response_2.json())
    print(f"=======Round 2: Question =======\n {data_2['question']}")
    if 'answer' in ans.keys():
        print(f"=======Round 2: Answer =======\n {ans_2['answer']} session_id: {ans_2['session_id']}")
    if 'docs' in ans.keys():
        print(f"=======Round 2: Retrieved Docs =======\n {ans['docs']}")
    print("\n")

# LLM
test_post_api_query_with_chat_history(SERVICE_URL + "api/v1/query/llm")
# RAG (Knowledge Base)
test_post_api_query_with_chat_history(SERVICE_URL + "api/v1/query")

Usage notes

This practice is subject to the maximum number of tokens of an LLM service and is designed to help you understand the basic retrieval feature of a RAG-based LLM chatbot.

• The chatbot is limited by the server resource size of the LLM service and the default number of tokens. The conversation length supported by the chatbot is also limited.

• If you do not need to perform multiple rounds of conversations, we recommended that you disable the with chat history feature of the chatbot on the web UI. This effectively reduces the possibility of reaching the limit.

  Web UI: On the Chat tab of the web UI, clear Chat history.

  

References

You can also use EAS to deploy the following items:

• You can deploy an LLM application that can be called by using the web UI or API operations. After the LLM application is deployed, use the LangChain framework to integrate enterprise knowledge bases into the LLM application to implement intelligent Q&A and automation features. For more information, see Quickly deploy open source LLMs in EAS.

• You can deploy an AI video generation model service by using ComfyUI and Stable Video Diffusion models. This helps you complete tasks such as short video generation and animation on social media platforms. For more information, see Use ComfyUI to deploy an AI video generation model service.

• You can deploy a model service based on Stable Diffusion WebUI by configuring a few parameters. For more information, see Use Stable Diffusion web UI to deploy an AI painting service.