BasicDocQA

from qwen_agent.agents import BasicDocQA

class BasicDocQA(Assistant):
    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] = DEFAULT_NAME,
        description: Optional[str] = DEFAULT_DESC,
        files: Optional[List[str]] = None,
        rag_cfg: Optional[Dict] = None
    )

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

Please fully understand the content of the following reference materials 
and organize a clear response that meets the user's questions.
# Reference materials:
{retrieved_content}

请充分理解以下参考资料内容，组织出满足用户提问的条理清晰的回复。
#参考资料：
{retrieved_content}

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

# Create agent with documents
agent = BasicDocQA(
    llm={'model': 'qwen-max', 'api_key': 'your-api-key'},
    files=['user_manual.pdf', 'faq.txt']
)

# Ask question
messages = [Message(role='user', content='How do I reset my password?')]

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

agent = BasicDocQA(
    llm={'model': 'qwen-max'},
    files=[
        'product_specifications.pdf',
        'installation_guide.pdf',
        'https://example.com/docs/troubleshooting.html'
    ]
)

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

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

agent = BasicDocQA(
    llm={'model': 'qwen-max'},
    files=['产品说明.pdf', '用户指南.docx']
)

messages = [Message(role='user', content='如何使用这个功能？')]

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

custom_prompt = """
You are a technical support assistant. Based on the reference materials below,
provide a detailed, step-by-step answer to the user's question.
Reference materials:
"""

agent = BasicDocQA(
    llm={'model': 'qwen-max'},
    files=['technical_docs.pdf'],
    system_message=custom_prompt
)

messages = [Message(role='user', content='How do I configure the database?')]

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

agent = BasicDocQA(
    llm={'model': 'qwen-max'},
    files=['large_document.pdf'],
    rag_cfg={
        'max_ref_token': 6000,        # More context
        'parser_page_size': 400,       # Chunk size
        'rag_searchers': ['keyword_search']  # Search method
    }
)

messages = [Message(role='user', content='Summarize chapter 3')]

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

agent = BasicDocQA(
    llm={'model': 'qwen-max'},
    files=['company_handbook.pdf']
)

messages = []

# First question
messages.append(Message(role='user', content='What are the vacation policies?'))
for response in agent.run(messages):
    pass
messages.extend(response)
print(f"A: {messages[-1].content}")

# Follow-up question
messages.append(Message(role='user', content='How many days do I get per year?'))
for response in agent.run(messages):
    pass
messages.extend(response)
print(f"A: {messages[-1].content}")

agent = BasicDocQA(
    llm={'model': 'qwen-max'},
    files=['documentation.pdf']
)

messages = [Message(role='user', content='What is the API endpoint?')]

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

agent = BasicDocQA(
    llm={'model': 'qwen-max'},
    files=['report.pdf']
)

# Good for queries about specific sections
messages = [
    Message(
        role='user',
        content='Explain Table 1 from the report'
    )
]

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

agent = BasicDocQA(
    llm={'model': 'qwen-max'},
    files=[
        'report.pdf',
        'data.xlsx',
        'presentation.pptx',
        'https://docs.example.com'
    ]
)

try:
    agent = BasicDocQA(
        llm={'model': 'qwen-max'},
        files=['nonexistent.pdf']
    )
    messages = [Message(role='user', content='Question?')]
    response = agent.run_nonstream(messages)
except Exception as e:
    print(f"Error: {e}")