llm.bind_tools exception: no description

[GaffeyFF]

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Example Code

def price_search(query: str) -> str:
    """When you ask for the price, Return the price , Use it to answer questions related to understanding the price of a show.
    the input is show name always be a string, This function always returns the price.
    """

    item = query.strip().lower()
    

    if item in price_list:
        return f"{item.capitalize()} is {price_list[item]} tokens"
    else:

        similar_items = get_close_matches(item, price_list.keys(), n=1, cutoff=0.8)
        if similar_items:
            similar_item = similar_items[0]
            return f"{item.capitalize()} is {price_list[similar_item]} tokens"
        else:
            price_info = "\n".join([f"{item.capitalize()} is ${price}" for item, price in price_list.items()])
            return f"{price_info}"


price = Tool.from_function(func=price_search,
                         name="price_search",
                         description="When you ask for the price, Return the price , Use it to answer questions related to understanding the price of a show.the input is show name always be a string, This function always returns the price."
                         )

llm_with_tools = llm.bind_tools([price])

Description

"price" has "description" content, but throws an exception

System Info

System Information

OS: Windows
OS Version: 10.0.22631
Python Version: 3.11.7 | packaged by Anaconda, Inc. | (main, Dec 15 2023, 18:05:47) [MSC v.1916 64 bit (AMD64)]

Package Information

langchain_core: 0.2.9
langchain: 0.2.5
langchain_community: 0.2.5
langsmith: 0.1.81
langchain_chroma: 0.1.1
langchain_experimental: 0.0.61
langchain_openai: 0.1.7
langchain_text_splitters: 0.2.0
langgraph: 0.0.69

Packages not installed (Not Necessarily a Problem)

The following packages were not found:

langserve

[dosubot[bot]]

@GaffeyFF Hello there! I'm here to help you out with any bugs, questions, or contributions. Let's work on this together while we wait for the human maintainer.

To resolve the NotImplementedError in agent.py at line 89 when using the llm.bind_tools function with the price_search tool, you need to ensure that the bind_tools method is implemented for the language model (llm) you are using. The error occurs because the bind_tools method is not defined for the llm object.

Here is a possible implementation of the bind_tools method for your language model:

class MyLanguageModel(BaseLanguageModel):
    def bind_tools(self, tools: Sequence[BaseTool]):
        # Implement the logic to bind tools to the language model
        self.tools = tools
        return self

# Example usage
model = MyLanguageModel()
tools = [price_search]  # Assuming price_search is defined as a tool
agent = create_tool_calling_agent(model, tools, prompt)

Make sure that your language model class (MyLanguageModel in this example) has the bind_tools method implemented. This method should handle the logic for binding the tools to the language model.

If you are using the LangChain framework, you can use the from_llm_and_tools method to construct an agent with the necessary tools. Here is an example of how to use it:

from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.tools import BaseTool
from langchain.agents.structured_chat.base import create_structured_chat_agent

# Assuming price_search is an instance of BaseTool
llm = BaseLanguageModel()  # Replace with your actual language model
price_search_tool = BaseTool(name="price_search", description="Search for prices")

# Create a prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", "Respond to the human as helpfully and accurately as possible. You have access to the following tools:\n\n{tools}\n\nUse a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).\n\nValid \"action\" values: \"Final Answer\" or {tool_names}\n\nProvide only ONE action per $JSON_BLOB, as shown:\n\n```\n{\n  \"action\": $TOOL_NAME,\n  \"action_input\": $INPUT\n}\n```\n\nFollow this format:\n\nQuestion: input question to answer\nThought: consider previous and subsequent steps\nAction:\n```\n$JSON_BLOB\n```\nObservation: action result\n... (repeat Thought/Action/Observation N times)\nThought: I know what to respond\nAction:\n```\n{\n  \"action\": \"Final Answer\",\n  \"action_input\": \"Final response to human\"\n}\nBegin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation"),
    ("human", "{input}\n\n{agent_scratchpad}\n\n(reminder to respond in a JSON blob no matter what)")
])

agent = create_structured_chat_agent(
    llm=llm,
    tools=[price_search_tool],
    prompt=prompt
)

This function constructs a structured chat agent from an LLM and a tool, handling the binding of tools internally. Make sure to replace BaseLanguageModel with your actual language model and configure the price_search_tool accordingly ^{[1][2][3][4][5]}.

To continue talking to Dosu, mention @dosu.

[tangent95]

I'm a Chinese student, my English level seems a bit low, please forgive me if my saying is offensive or having gramma errors.

I don't know which LLM you are using, but I discover that in langchain_community.chat_models.hunyuan.ChatHunyuan, the implement of .bind_tools is:

def bind_tools(
    self,
    tools: Sequence[
        Union[typing.Dict[str, Any], type, Callable, BaseTool]  # noqa: UP006
    ],
    **kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
    raise NotImplementedError