Loading Qwen-2.5 32B model using VLLM and creating an agent with integrated tools.

[SalmonSung]

I'm currently trying to load the Qwen model using VLLM and create an agent with integrated tools.

What I've tried so far:
create_react_agent: This resulted in an error, stating that the 'VLLM' object has no attribute 'bind_tools.'

Is there an alternative method to achieve similar functionality? Unfortunately, I need to use VLLM for its speed and efficiency.

[dosubot[bot]]

Hey @ISaySalmonYouSayYes! I'm here to help you with any bugs, questions, or contributions while you wait for a human maintainer. Let's tackle this together!

To integrate tools with agents in LangChain using VLLM, you can implement the bind_tools method for the ChatMLX class. Here's an example of how you can do this:

def bind_tools(
    self,
    tools: typing.Sequence[typing.Union[dict[str, typing.Any], type, typing.Callable, BaseTool]],
    *,
    tool_choice: typing.Optional[typing.Union[dict, str, typing.Literal['auto', 'none'], bool]] = None,
    **kwargs: typing.Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
    formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
    if tool_choice is not None and tool_choice:
        if len(formatted_tools) != 1:
            raise ValueError(
                'When specifying `tool_choice`, you must provide exactly one '
                f'tool. Received {len(formatted_tools)} tools.'
            )
        if isinstance(tool_choice, str):
            if tool_choice not in ('auto', 'none'):
                tool_choice = {
                    'type': 'function',
                    'function': {'name': tool_choice},
                }
        elif isinstance(tool_choice, bool):
            tool_choice = formatted_tools[0]
        elif isinstance(tool_choice, dict):
            if formatted_tools[0]['function']['name'] != tool_choice['function']['name']:
                raise ValueError(
                    f'Tool choice {tool_choice} was specified, but the only '
                    f'provided tool was {formatted_tools[0]["function"]["name"]}.'
                )
        else:
            raise ValueError(
                f'Unrecognized tool_choice type. Expected str, bool or dict. '
                f'Received: {tool_choice}'
            )
        kwargs['tool_choice'] = tool_choice
    return super().bind(tools=formatted_tools, **kwargs)

This implementation allows ChatMLX to work with react agents by formatting the tools and handling the tool_choice parameter appropriately ^[1].

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[dosubot[bot]]

Ensure that all components, including the model and input tensors, are explicitly moved to the same device. Here's how you can modify your code to address the device mismatch error:

import torch
from langchain_huggingface.llms import HuggingFacePipeline
from langchain_core.prompts import PromptTemplate

# Set the device to 0 for the first CUDA device, or -1 for CPU
device = 0  # Use 0 for CUDA device, -1 for CPU

# Initialize the HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
    model_id="microsoft/Phi-3.5-mini-instruct",
    task="text-generation",
    device=device,  # Ensure the device is set correctly
    model_kwargs={"torch_dtype": torch.float16},  # Use half-precision
    pipeline_kwargs={
        "max_new_tokens": 4096,
        "temperature": 0.7,
        "top_k": 50,
        "top_p": 0.9
    },
)

# Define the prompt template
template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)

# Create the chain
chain = prompt | hf

# Define the question
question = "What is electroencephalography?"

# Move the input to the correct device
input_data = {"question": question}

# Invoke the chain and print the result
print(chain.invoke(input_data))

Ensure that your environment has the necessary CUDA setup and that PyTorch is installed with GPU support. If the error persists, double-check that your GPU is available and properly configured.

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[SalmonSung]

@dosu
From my understanding, the ChatHuggingFace with HuggingFacePipeline as input does not support tool calling even though invoking .bind_tools on ChatHuggingFace causes no immediate issues.

Is this correct?

[dosubot[bot]]

The ChatHuggingFace class with HuggingFacePipeline as input does indeed support tool calling. The bind_tools method is implemented in the ChatHuggingFace class, allowing you to bind tool-like objects to the chat model. This method assumes compatibility with the OpenAI tool-calling API, indicating that tool calling is supported ^[1].

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[SalmonSung]

@dosu
How can I load a model from huggingface with ChatHuggingFace so that it supports tool calling?

[dosubot[bot]]

To load a model from Hugging Face with ChatHuggingFace and enable tool calling, follow these steps:

1. Set up your environment: Ensure you have a Hugging Face account, generate an API key, and store it as an environment variable HUGGINGFACEHUB_API_TOKEN.

2. Install necessary packages:

   %pip install --upgrade --quiet langchain-huggingface text-generation transformers

3. Instantiate the model: Use HuggingFacePipeline to instantiate the model. Here's an example:

   from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline
   
   llm = HuggingFacePipeline.from_model_id(
       model_id="HuggingFaceH4/zephyr-7b-beta",
       task="text-generation",
       pipeline_kwargs=dict(
           max_new_tokens=512,
           do_sample=False,
           repetition_penalty=1.03,
       ),
   )
   
   chat_model = ChatHuggingFace(llm=llm)

4. Bind tools to the model: Use the bind_tools method to add tool calling capabilities. Define your tools using Pydantic models and bind them to the chat model.

   from pydantic import BaseModel, Field
   
   class GetWeather(BaseModel):
       '''Get the current weather in a given location'''
       location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
   
   class GetPopulation(BaseModel):
       '''Get the current population in a given location'''
       location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
   
   chat_with_tools = chat_model.bind_tools([GetWeather, GetPopulation])

5. Invoke the model: Use the chat_with_tools to process messages and support tool calling.

This setup allows ChatHuggingFace to support tool calling by integrating tool-like objects, enabling the model to handle specific tasks like fetching weather or population data ^[1][2].

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