OpenAI tools

Newer OpenAI models have been fine-tuned to detect when one or more function(s) should be called and respond with the inputs that should be passed to the function(s). In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call these functions. The goal of the OpenAI tools APIs is to more reliably return valid and useful function calls than what can be done using a generic text completion or chat API.

OpenAI termed the capability to invoke a single function as functions, and the capability to invoke one or more functions as tools.

tip

In the OpenAI Chat API, functions are now considered a legacy options that is deprecated in favor of tools.

If you're creating agents using OpenAI models, you should be using this OpenAI Tools agent rather than the OpenAI functions agent.

Using tools allows the model to request that more than one function will be called upon when appropriate.

In some situations, this can help signficantly reduce the time that it takes an agent to achieve its goal.

See

• OpenAI chat create
• OpenAI function calling

%pip install --upgrade --quiet  langchain-openai tavily-python

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

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.'}