AttributeError: 'ChatOpenAI' object has no attribute 'bind_tools'

[adream307]

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Example Code

from langchain_community.chat_models import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

from langchain.agents import tool


@tool
def get_word_length(word: str) -> int:
    """Returns the length of a word."""
    return len(word)


from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are very powerful assistant, but don't know current events",
        ),
        ("user", "{input}"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ]
)

llm_with_tools = llm.bind_tools(tools)

Description

I follow this site https://python.langchain.com/docs/modules/agents/how_to/custom_agent try to make an custom agent, but get this error:

----> 1 llm_with_tools = llm.bind_tools(tools)

AttributeError: 'ChatOpenAI' object has no attribute 'bind_tools'

System Info

langchain==0.1.9
langchain-community==0.0.24
langchain-core==0.1.26
langchainhub==0.1.14
platform linux
python 3.10.12

[dosubot[bot]]

🤖

Hey @adream307, great to see you diving into the depths of LangChain again! 🌊

Based on the context provided, it seems like you're trying to use the bind_tools method with the ChatOpenAI class. However, as per the LangChain codebase and the issues found in the repository, there is no bind_tools method available in the ChatOpenAI class.

The bind_tools method is available in the ChatMistralAI class, which is a subclass of BaseChatModel. This method is designed to bind tool-like objects to the chat model, assuming the model is compatible with the OpenAI tool-calling API. You can find more details about this in the ChatMistralAI class definition in the LangChain codebase.

If you're trying to bind functions to the ChatOpenAI model, you might want to use the bind_functions method instead. This method accepts a list of function definitions, which can be a dictionary, a Pydantic model, or a callable. Pydantic models and callables are automatically converted to their schema dictionary representation.

Here's an example of how you can use the bind_functions method:

llm = ChatOpenAI(openai_api_key="xxx",temperature=0.5)
tools = [get_band_packages]
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            system_msg
        ),
        MessagesPlaceholder(variable_name="chat_history"),
        ("user", "{input}"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ]
)
chat_history = []
functions = [format_tool_to_openai_function(t) for t in tools]
llm_with_tools = llm.bind_functions(functions=functions)
agent = (
        {
            "input": lambda x: x["input"],
            "agent_scratchpad": lambda x: format_to_openai_function_messages(
                x["intermediate_steps"]
            ),
            "chat_history": lambda x: x["chat_history"],
        }
        | prompt
        | llm_with_tools
        | OpenAIFunctionsAgentOutputParser()
)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "xxxxxxx","chat_history":[]})

In this example, the bind_functions method is used to bind the functions to the llm instance of the ChatOpenAI class. The functions parameter is a list of functions that have been formatted for OpenAI using the format_tool_to_openai_function function.

Please try this approach and let me know if it resolves your issue. If you have any further questions or need additional clarification, feel free to ask.

Sources

• Using tools in non-ChatGPT models
• openai.error.InvalidRequestError: Unrecognized request argument supplied: functions
• raise AttributeError(name) from None AttributeError: OPENAI_FUNCTIONS
• Functions not called when i use langchain_openai.ChatOpenAI as llm
• libs/partners/openai/langchain_openai/chat_models/base.py
• libs/partners/mistralai/langchain_mistralai/chat_models.py

About Dosu

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[okwinds]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.

Commit to Help

• I commit to help with one of those options 👆

Example Code

from langchain_community.chat_models import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

from langchain.agents import tool


@tool
def get_word_length(word: str) -> int:
    """Returns the length of a word."""
    return len(word)


from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are very powerful assistant, but don't know current events",
        ),
        ("user", "{input}"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ]
)

llm_with_tools = llm.bind_tools(tools)

Description

I follow this site https://python.langchain.com/docs/modules/agents/how_to/custom_agent try to make an custom agent, but get this error:

----> 1 llm_with_tools = llm.bind_tools(tools)

AttributeError: 'ChatOpenAI' object has no attribute 'bind_tools'

System Info

langchain==0.1.9 langchain-community==0.0.24 langchain-core==0.1.26 langchainhub==0.1.14 platform linux python 3.10.12

I encountered the same issue as you and resolved it in the following way:

Firstly, I uninstalled the packages:
pip uninstall langchain langchain-openai langchain-community langchain_experimental
Then, I installed:
pip install langchain langchain_openai
Now, my versions are as follows:
langchain: 0.1.12
langchain-openai: 0.0.8
Please note the difference between ”langchain-openai“ and ”langchain_openai“.

[adream307]

thanks

[noshila]

similar problem for llamacpp, koboldcppai, and ollama.

[piotrekz79]

For llamacpp I had the same problem when using

from langchain_community.llms import LlamaCpp

and llm = LlamaCpp(...)

However, following https://python.langchain.com/v0.2/docs/integrations/chat/ we see that ChatLlamaCpp has green checkbox in tooling (contrary to ChatOllama and kobold not being mentioned at all)

Therefore,
from langchain_community.chat_models import ChatLlamaCpp

and llm = ChatLlamaCpp(...)

and later in my case

    toolkit = SQLDatabaseToolkit(db=db, llm=llm)
    tools = toolkit.get_tools()
    agent_executor = create_react_agent(llm, tools, messages_modifier=template)

results in a running code.
For the reference,

[tool.poetry.dependencies]
python = "^3.10"
langchain = "^0.2"
mysql-connector-python = "^8.4.0"
langchain-community = "^0.2.6"
llama-cpp-python = "^0.2.79"

[piotrekz79]

... after further tests, it seems that the code with ChatLlamaCpp indeed does not crash but I have an impression there is no interaction with the database. Following the example from https://python.langchain.com/v0.2/docs/tutorials/sql_qa/#agents I never see ToolMessage and the answers - if I ever get them - are unrelated to the specific DB anyway. I'll investigate further.

[keurcien]

... after further tests, it seems that the code with ChatLlamaCpp indeed does not crash but I have an impression there is no interaction with the database. Following the example from https://python.langchain.com/v0.2/docs/tutorials/sql_qa/#agents I never see ToolMessage and the answers - if I ever get them - are unrelated to the specific DB anyway. I'll investigate further.

Same behaviour with LangGraph and ChatLlamaCpp!

[AntarikshVerma]

I am facing same issue with Azureopenai in LangGraph

similar problem for llamacpp, koboldcppai, and ollama.

[RajMaharajwala]

... after further tests, it seems that the code with ChatLlamaCpp indeed does not crash but I have an impression there is no interaction with the database. Following the example from https://python.langchain.com/v0.2/docs/tutorials/sql_qa/#agents I never see ToolMessage and the answers - if I ever get them - are unrelated to the specific DB anyway. I'll investigate further.

Hi are you able to find any workaround solution by experimenting or by tool calling that work with db well? Using ChatLlamaCpp or ChatOllama???