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layout default
title Chapter 5: Memory, RAG, and Long-Context Workflows
nav_order 5
parent Qwen-Agent Tutorial

Chapter 5: Memory, RAG, and Long-Context Workflows

Welcome to Chapter 5: Memory, RAG, and Long-Context Workflows. In this part of Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter covers knowledge-heavy workflows requiring retrieval and long-context handling.

Learning Goals

  • apply RAG patterns for large document tasks
  • choose between fast and higher-cost long-context approaches
  • combine retrieval with tool reasoning effectively
  • validate output quality on document-intensive tasks

Workflow Guidance

  • start with efficient RAG pipelines for scale
  • escalate to more expensive agentic flows when needed
  • track recall and citation quality in evaluation loops

Source References

Summary

You now can design Qwen-Agent workflows for high-context and document-heavy workloads.

Next: Chapter 6: Application Patterns and Safety Boundaries

Depth Expansion Playbook

Source Code Walkthrough

examples/assistant_qwen3.5.py

The test function in examples/assistant_qwen3.5.py handles a key part of this chapter's functionality:

def test(query: str = 'What time is it?'):
    # Define the agent
    bot = init_agent_service()

    # Chat
    messages = [{'role': 'user', 'content': query}]
    response_plain_text = ''
    for response in bot.run(messages=messages):
        response_plain_text = typewriter_print(response, response_plain_text)


def app_tui():
    # Define the agent
    bot = init_agent_service()

    # Chat
    messages = []
    while True:
        query = input('user question: ')
        messages.append({'role': 'user', 'content': query})
        response = []
        response_plain_text = ''
        for response in bot.run(messages=messages):
            response_plain_text = typewriter_print(response, response_plain_text)
        messages.extend(response)


def app_gui():
    # Define the agent
    bot = init_agent_service()

This function is important because it defines how Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows implements the patterns covered in this chapter.

examples/assistant_qwen3.5.py

The app_tui function in examples/assistant_qwen3.5.py handles a key part of this chapter's functionality:

def app_tui():
    # Define the agent
    bot = init_agent_service()

    # Chat
    messages = []
    while True:
        query = input('user question: ')
        messages.append({'role': 'user', 'content': query})
        response = []
        response_plain_text = ''
        for response in bot.run(messages=messages):
            response_plain_text = typewriter_print(response, response_plain_text)
        messages.extend(response)


def app_gui():
    # Define the agent
    bot = init_agent_service()
    chatbot_config = {
        'prompt.suggestions': [
            'Help me organize my desktop.',
            'Develop a dog website and save it on the desktop',
        ]
    }
    WebUI(
        bot,
        chatbot_config=chatbot_config,
    ).run()

This function is important because it defines how Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows implements the patterns covered in this chapter.

examples/assistant_qwen3.5.py

The app_gui function in examples/assistant_qwen3.5.py handles a key part of this chapter's functionality:

def app_gui():
    # Define the agent
    bot = init_agent_service()
    chatbot_config = {
        'prompt.suggestions': [
            'Help me organize my desktop.',
            'Develop a dog website and save it on the desktop',
        ]
    }
    WebUI(
        bot,
        chatbot_config=chatbot_config,
    ).run()


if __name__ == '__main__':
    # test()
    # app_tui()
    app_gui()

This function is important because it defines how Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows implements the patterns covered in this chapter.

examples/assistant_qwen3_coder.py

The init_agent_service function in examples/assistant_qwen3_coder.py handles a key part of this chapter's functionality:

def init_agent_service():
    llm_cfg = {
        # Use the model service provided by DashScope:
        'model': 'qwen3-coder-480b-a35b-instruct',
        'model_type': 'qwen_dashscope',
        'generate_cfg': {
            # Using the API's native tool call interface
            'use_raw_api': True,
            'max_input_tokens': 200000
        },
    }
    # llm_cfg = {
    #     # Use the OpenAI-compatible model service provided by DashScope:
    #     'model': 'qwen3-coder-480b-a35b-instruct',
    #     'model_server': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
    #     'api_key': os.getenv('DASHSCOPE_API_KEY'),
    #     'generate_cfg': {
    #         # Using the API's native tool call interface
    #         'use_raw_api': True,
    #         'max_input_tokens': 200000
    #     },
    # }

    tools = [
        {
            'mcpServers': {  # You can specify the MCP configuration file
                'time': {
                    'command': 'uvx',
                    'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
                },

This function is important because it defines how Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[test]
    B[app_tui]
    C[app_gui]
    D[init_agent_service]
    E[test]
    A --> B
    B --> C
    C --> D
    D --> E
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