agent

Agent

An agent assembles modules together to create a workflow for some specific task. It normally uses llm to determine actions and procedures via prompts.

ChatAgent

ChatAgent is able to build a chat system with action options from given tools such as doc retrieval through vector store. It also accepts llm and memory modules as parameters to enable more flexibility.

Configuration

The default ChatAgent is based on LangChain ConversationChatAgent, using customized prompts. You can modify prompts for your scenarios.

# Set system prompt
PREFIX = """Your system prompt goes here."""

# Define response format in user message
FORMAT_INSTRUCTIONS = """Your instructions for format response here."""

# Pass tools like search in user message
SUFFIX = """Your method to pass tools like search here."""

# Pass search results in user message
TEMPLATE_TOOL_RESPONSE = """Your method to pass search results and query here."""

Example Usage

from chat_agent import ChatAgent

# Define tools
tools = [
    Tool(
        name='Test',
        func=lambda x: 'This is a test tool.',
        description='Test action'
    )
]

# Define an agent
agent = ChatAgent.from_llm_and_tools(
    llm=chat_llm,
    tools=tools)

# Define a chain
agent_chain = AgentExecutor.from_agent_and_tools(
    agent=agent,
    tools=tools,
    verbose=False
    )

# Run a test
final_answer = agent_chain.run(input='Test question')

Customize Modules

• To customize ChatAgent, you can modify its methods in chat_agent.py.

• To change output parser, you can modify OutputParser.