Structured chat

The structured chat agent is capable of using multi-input tools.

from langchain import hub
from langchain.agents import AgentExecutor, create_structured_chat_agent
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai import ChatOpenAI

API Reference:

• AgentExecutor
• create_structured_chat_agent
• TavilySearchResults
• ChatOpenAI

Initialize Tools

We will test the agent using Tavily Search

tools = [TavilySearchResults(max_results=1)]

Create Agent

# Get the prompt to use - you can modify this!
prompt = hub.pull("hwchase17/structured-chat-agent")

# Choose the LLM that will drive the agent
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-1106")

# Construct the JSON agent
agent = create_structured_chat_agent(llm, tools, prompt)

Run Agent

# Create an agent executor by passing in the agent and tools
agent_executor = AgentExecutor(
    agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
)

agent_executor.invoke({"input": "what is LangChain?"})

> Entering new AgentExecutor chain...
Action:

{ "action": "tavily_search_results_json", "action_input": {"query": "LangChain"} }

{ "action": "Final Answer", "action_input": "LangChain is an open source orchestration framework for the development of applications using large language models. It simplifies the process of programming and integration with external data sources and software workflows. LangChain provides integrations for over 25 different embedding methods and supports various large language model providers such as OpenAI, Google, and IBM. It supports Python and Javascript languages." }

> Finished chain.

{'input': 'what is LangChain?',
 'output': 'LangChain is an open source orchestration framework for the development of applications using large language models. It simplifies the process of programming and integration with external data sources and software workflows. LangChain provides integrations for over 25 different embedding methods and supports various large language model providers such as OpenAI, Google, and IBM. It supports Python and Javascript languages.'}

Use with chat history

from langchain_core.messages import AIMessage, HumanMessage

agent_executor.invoke(
    {
        "input": "what's my name? Do not use tools unless you have to",
        "chat_history": [
            HumanMessage(content="hi! my name is bob"),
            AIMessage(content="Hello Bob! How can I assist you today?"),
        ],
    }
)

API Reference:

• AIMessage
• HumanMessage

> Entering new AgentExecutor chain...
Could not parse LLM output: I understand. Your name is Bob.Invalid or incomplete responseCould not parse LLM output: Apologies for any confusion. Your name is Bob.Invalid or incomplete response{
  "action": "Final Answer",
  "action_input": "Your name is Bob."
}

> Finished chain.

{'input': "what's my name? Do not use tools unless you have to",
 'chat_history': [HumanMessage(content='hi! my name is bob'),
  AIMessage(content='Hello Bob! How can I assist you today?')],
 'output': 'Your name is Bob.'}