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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.

The key difference between tools and functions lies in their availability and application within the chat environment. Tools are specialized capabilities directly accessible in the chat interface, designed for interactive and immediate use, such as generating images or executing code. In contrast, functions are more about invoking specific computational tasks or algorithms, typically as part of a larger process or application. This distinction underscores the tailored nature of each tool and function, ensuring they are optimally suited for their respective roles within the chat environment.

# ! pip install 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.'}