OpenAI tools

Certain OpenAI models have been finetuned to work with with tool calling. This is very similar but different from function calling, and thus requires a separate agent type.

# ! pip install openai tavily-python

from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_community.chat_models import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults

Initialize Tools

For this agent let’s give it the ability to search the web with Tavily.

tools = [TavilySearchResults(max_results=1)]

Create Agent

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

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

# Construct the OpenAI Tools agent
agent = create_openai_tools_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)

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

> Entering new AgentExecutor chain...

Invoking: `tavily_search_results_json` with `{'query': 'LangChain'}`


[{'url': 'https://www.ibm.com/topics/langchain', 'content': 'LangChain is essentially a library of abstractions for Python and Javascript, representing common steps and concepts  LangChain is an open source orchestration framework for the development of applications using large language models  other LangChain features, like the eponymous chains.  LangChain provides integrations for over 25 different embedding methods, as well as for over 50 different vector storesLangChain is a tool for building applications using large language models (LLMs) like chatbots and virtual agents. It simplifies the process of programming and integration with external data sources and software workflows. It supports Python and Javascript languages and supports various LLM providers, including OpenAI, Google, and IBM.'}]LangChain is an open source orchestration framework for the development of applications using large language models. It is essentially a library of abstractions for Python and Javascript, representing common steps and concepts. LangChain simplifies the process of programming and integration with external data sources and software workflows. It supports various large language model providers, including OpenAI, Google, and IBM. You can find more information about LangChain on the IBM website: [LangChain - IBM](https://www.ibm.com/topics/langchain)

> Finished chain.

{'input': 'what is LangChain?',
 'output': 'LangChain is an open source orchestration framework for the development of applications using large language models. It is essentially a library of abstractions for Python and Javascript, representing common steps and concepts. LangChain simplifies the process of programming and integration with external data sources and software workflows. It supports various large language model providers, including OpenAI, Google, and IBM. You can find more information about LangChain on the IBM website: [LangChain - IBM](https://www.ibm.com/topics/langchain)'}

Using with chat history

from langchain_core.messages import AIMessage, HumanMessage

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

> Entering new AgentExecutor chain...
Your name is Bob.

> Finished chain.

{'input': "what's my name? Don't use tools to look this up unless you NEED 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.'}