Assistant

from qwen_agent.agents import Assistant

class Assistant(FnCallAgent):
    def __init__(
        self,
        function_list: Optional[List[Union[str, Dict, BaseTool]]] = None,
        llm: Optional[Union[Dict, BaseChatModel]] = None,
        system_message: Optional[str] = DEFAULT_SYSTEM_MESSAGE,
        name: Optional[str] = None,
        description: Optional[str] = None,
        files: Optional[List[str]] = None,
        rag_cfg: Optional[Dict] = None
    )

def run(
    self,
    messages: List[Union[Dict, Message]],
    knowledge: str = '',
    **kwargs
) -> Iterator[List[Message]]

from qwen_agent.agents import Assistant
from qwen_agent.llm.schema import Message

# Create assistant with documents
assistant = Assistant(
    llm={'model': 'qwen-max', 'api_key': 'your-api-key'},
    files=[
        'https://example.com/product_manual.pdf',
        '/local/docs/faq.txt'
    ]
)

# Ask questions about the documents
messages = [Message(role='user', content='What are the key features?')]

for response in assistant.run(messages):
    print(response[-1].content)

# Assistant with tools and RAG
assistant = Assistant(
    llm={'model': 'qwen-max'},
    function_list=['code_interpreter', 'web_extractor'],
    files=['research_paper.pdf']
)

messages = [
    Message(
        role='user',
        content='Analyze the data in the paper and create a visualization'
    )
]

for response in assistant.run(messages):
    for msg in response:
        if msg.content:
            print(msg.content)
        if msg.function_call:
            print(f"Calling: {msg.function_call.name}")

assistant = Assistant(
    llm={'model': 'qwen-max'},
    files=['large_document.pdf'],
    rag_cfg={
        'max_ref_token': 8000,           # Retrieve more context
        'parser_page_size': 300,          # Smaller chunks
        'rag_searchers': ['keyword_search', 'vector_search']  # Hybrid search
    }
)

messages = [Message(role='user', content='Summarize the main findings')]

for response in assistant.run(messages):
    print(response[-1].content)

assistant = Assistant(
    llm={'model': 'qwen-max'},
    files=['customer_support.pdf'],
    system_message='You are a helpful customer support agent.'
)

messages = []

# First question
messages.append(Message(role='user', content='How do I reset my password?'))
for response in assistant.run(messages):
    pass
messages.extend(response)

print(f"Assistant: {messages[-1].content}")

# Follow-up question
messages.append(Message(role='user', content='What if I forgot my email?'))
for response in assistant.run(messages):
    pass
messages.extend(response)

print(f"Assistant: {messages[-1].content}")

assistant = Assistant(
    llm={'model': 'qwen-max'}
)

# Provide knowledge directly instead of from files
external_knowledge = """
Product: SuperWidget Pro
Features:
- Advanced AI processing
- Cloud synchronization
- Multi-platform support
Price: $99.99
"""

messages = [Message(role='user', content='Tell me about SuperWidget Pro')]

for response in assistant.run(messages, knowledge=external_knowledge):
    print(response[-1].content)

assistant = Assistant(
    llm={'model': 'qwen-max'},
    files=[
        'company_policy.pdf',
        'employee_handbook.docx',
        'https://intranet.example.com/benefits.html'
    ],
    function_list=['web_extractor']  # Can fetch additional web content
)

messages = [
    Message(
        role='user',
        content='What are the vacation policies and health benefits?'
    )
]

for response in assistant.run(messages):
    print(response[-1].content)

assistant = Assistant(
    llm={'model': 'qwen-max'},
    files=['technical_specs.pdf']
)

messages = [Message(role='user', content='What are the system requirements?')]

# Get complete response at once
response = assistant.run_nonstream(messages)
print(response[-1].content)

# Example of retrieved knowledge format
"""
# Knowledge Base

## The content from product_manual.pdf:

## The content from faq.txt:

"""

from qwen_agent.memory import Memory

# Use custom memory configuration
assistant = Assistant(
    llm={'model': 'qwen-max'},
    files=['docs.pdf']
)

# Access the memory system
print(f"Loaded files: {assistant.mem.system_files}")

# The assistant auto-detects language and uses appropriate prompts
assistant_cn = Assistant(
    llm={'model': 'qwen-max'},
    files=['中文文档.pdf']
)

messages = [Message(role='user', content='这个产品有什么功能？')]

for response in assistant_cn.run(messages, lang='zh'):
    print(response[-1].content)