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OpenAI functions | Документация для разработчиков

OpenAI functions

Обновлено 1 марта 2024

Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should be called and respond with the inputs that should be passed to the function. In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions. The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.

A number of open source models have adopted the same format for function calls and have also fine-tuned the model to detect when a function should be called.

The OpenAI Functions Agent is designed to work with these models.

Install openai, tavily-python packages which are required as the LangChain packages call them internally.

OpenAI API has deprecated functions in favor of tools. The difference between the two is that the tools API allows the model to request that multiple functions be invoked at once, which can reduce response times in some architectures. It's recommended to use the tools agent for OpenAI models.

See the following links for more information:

OpenAI chat create

OpenAI function calling

The functions format remains relevant for open source models and providers that have adopted it, and this agent is expected to work for such models.

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

Initialize Tools

We will first create some tools we can use

from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_openai import ChatOpenAI
tools = [TavilySearchResults(max_results=1)]

Create Agent

# Get the prompt to use - you can modify this!
prompt = hub.pull("hwchase17/openai-functions-agent")
prompt.messages
    [SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='You are a helpful assistant')),
MessagesPlaceholder(variable_name='chat_history', optional=True),
HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], template='{input}')),
MessagesPlaceholder(variable_name='agent_scratchpad')]
# Choose the LLM that will drive the agent
llm = ChatOpenAI(model="gpt-3.5-turbo-1106")

# Construct the OpenAI Functions agent
agent = create_openai_functions_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 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. LangChain provides integrations for over 25 different embedding methods and for over 50 different vector stores. It is essentially a library of abstractions for Python and JavaScript, representing common steps and concepts. LangChain supports Python and JavaScript languages and various LLM providers, including OpenAI, Google, and IBM. You can find more information about LangChain [here](https://www.ibm.com/topics/langchain).

> Finished chain.
    {'input': 'what is LangChain?',
'output': 'LangChain 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. LangChain provides integrations for over 25 different embedding methods and for over 50 different vector stores. It is essentially a library of abstractions for Python and JavaScript, representing common steps and concepts. LangChain supports Python and JavaScript languages and various LLM providers, including OpenAI, Google, and IBM. You can find more information about LangChain [here](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?",
"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?",
'chat_history': [HumanMessage(content='hi! my name is bob'),
AIMessage(content='Hello Bob! How can I assist you today?')],
'output': 'Your name is Bob.'}
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