Ollama & Langchain Tools: Why It's Not Working

Sep 13, 2025 by Square 47 views

Ollama, Langchain, and Tools: A Deep Dive into the Integration

Hey everyone! Let's dive into a common issue folks are hitting when they try to use tools with Ollama within the Langchain framework. Specifically, we're looking at why init_chat_model might be throwing a wrench in your plans when you try to bind_tools. I understand the frustration when something doesn't quite click, so let's break down the problem, explore what's going on, and see if we can find a solution.

The Problem: `NotImplementedError` with `bind_tools`

So, the core issue is pretty straightforward: when you're using init_chat_model with Ollama and trying to incorporate tools, you're likely running into a NotImplementedError. This error pops up when Langchain's chat models don't have the functionality to handle the tool binding directly. The error message itself is a bit of a tell-tale sign, signaling that the feature isn't yet fully implemented for that specific setup. The code you've provided perfectly illustrates the issue. You're setting up your model, defining a tool (in this case, tavily_search), and then attempting to bind it using .bind_tools(). That's when things go south.

Let's reiterate the code block where the problem occurs, so you can reference it.

from langchain.chat_models import init_chat_model
from langchain_core.tools import tool, InjectedToolArg
from tavily import TavilyClient
from typing_extensions import Annotated, Literal

model = init_chat_model(model="kitsonk/watt-tool-8B", model_provider="ollama", api_key="ollama")

@tool(parse_docstring=True)
def tavily_search(
    query: str,
    max_results: Annotated[int, InjectedToolArg] = 3,
    topic: Annotated[Literal["general", "news", "finance"], InjectedToolArg] = "general",
) -> str:
    """Fetch results from Tavily search API with content summarization.

    Args:
        query: A single search query to execute
        max_results: Maximum number of results to return
        topic: Topic to filter results by ('general', 'news', 'finance')

    Returns:
        Formatted string of search results with summaries
    """
    tavily_client = TavilyClient()
    result = tavily_client.search(
            query,
            max_results=max_results,
            include_raw_content=True,
            topic=topic
        )

    # Format output for consumption
    return result

model_with_tools = model.bind_tools([tavily_search])

The error happens on the last line when you try to bind the tool to the model. The error message points directly to an unimplemented feature within Langchain's implementation for Ollama, suggesting that the direct binding of tools isn't supported in the same way as it might be with other model providers or setups.

Why `bind_tools` Fails with Ollama

Now, let's dig into why this is happening. The bind_tools method is part of Langchain's toolkit for integrating tools with chat models. It's designed to take a list of tools and then prepare the model to use those tools during its conversation. However, the underlying implementation depends on the specific model provider and its capabilities.

For Ollama, the support for tools might not be directly integrated into init_chat_model in the same way it is for other providers. This could be because of several reasons, including how Ollama's API interacts with Langchain, the way the tool specifications are passed to the model, or the model's inherent support for tool usage. It's important to remember that even though a model like kitsonk/watt-tool-8B might be designed to use tools, the Langchain integration still needs to be set up to facilitate that.

Essentially, the NotImplementedError tells us that the current version of Langchain used with Ollama doesn't have a built-in pathway for the bind_tools functionality. It does not mean the tools don't work with the model, instead the way you're attempting to integrate them is not the way that's supported in Langchain. I have a way to get this done, but let's first clarify what the problem is.

Workarounds and Alternative Approaches

Don't worry; all is not lost. There are definitely ways you can get tools working with Ollama and Langchain. Here are a few alternative approaches you can try:

Using `OpenAI` class

While init_chat_model might have limitations, using Langchain's ChatOllama class, which is designed specifically for Ollama, might provide the necessary hooks for tool integration. You would initialize it as follows:

from langchain_community.chat_models import ChatOllama
from langchain_core.tools import tool
from typing_extensions import Annotated, Literal

# Assuming you have Ollama running locally with the model
ollama_model = ChatOllama(model="kitsonk/watt-tool-8B")

@tool(parse_docstring=True)
def tavily_search(
    query: str,
    max_results: Annotated[int, InjectedToolArg] = 3,
    topic: Annotated[Literal["general", "news", "finance"], InjectedToolArg] = "general",
) -> str:
    """Fetch results from Tavily search API with content summarization.

    Args:
        query: A single search query to execute
        max_results: Maximum number of results to return
        topic: Topic to filter results by ('general', 'news', 'finance')

    Returns:
        Formatted string of search results with summaries
    """
    # Placeholder for Tavily search implementation
    return "Search results..."

# You might be able to use the tools argument with the ChatOllama class.

from langchain.chains import create_tool_calling_agent

agent = create_tool_calling_agent(ollama_model, tools=[tavily_search])

Manual Tool Calling

If direct binding still doesn't work, you might need to implement a manual tool-calling mechanism. This means you'd prompt the model to determine when to use a tool and then write the code to execute that tool. The prompt would need to be designed to guide the model to call your tools. This gives you more control over the tool-calling process. It's a bit more involved, but it can provide a great deal of flexibility and control.

Agentic Workflows

Langchain's agentic workflows are designed to handle tool use. You could create an agent that uses Ollama, defines the tools, and then lets the agent decide when and how to use those tools. This approach abstracts away some of the complexities of tool binding. This approach might need more advanced knowledge of Langchain's agents. However, they are an extremely powerful tool. You could look at the AgentType.OPENAI_FUNCTIONS in your code.

Debugging Steps and Troubleshooting

If you're still running into issues, here are some additional troubleshooting steps:

Check Langchain and Ollama Versions: Ensure you're using the latest versions of Langchain and the Ollama Python package. Older versions might have compatibility issues. You can use pip install -U langchain ollama to upgrade.
Review Model Documentation: Check the documentation for the specific Ollama model you're using (kitsonk/watt-tool-8B). It might have specific instructions or requirements for tool usage.
Examine the Ollama Logs: Look at the logs for your Ollama instance. They might provide hints about why the tool calls are failing. You should enable verbose logging.
Experiment with Different Tool Formats: Langchain supports various ways to define tools. Try experimenting with different tool definitions to see if that resolves the issue.

Considerations and Best Practices

To ensure smooth sailing with tools and Langchain, keep these points in mind:

Model Compatibility: Ensure the Ollama model you're using supports tool use. Not all models are created equal; some are explicitly designed for it, while others might not have that capability. The documentation of the model you are working with should confirm it.
Tool Definitions: Clearly define the tools your model will use. This includes the tool's name, description, and arguments. The more detailed your definitions, the better the model will understand how to use your tool.
Prompt Engineering: Craft effective prompts. You'll need to guide the model to recognize when a tool should be used and what inputs are required. This takes practice, but it's an important skill.

I have given you many ideas on how to solve your problem and all the things you should consider.

Final Thoughts

The NotImplementedError you're encountering is a signal that you might need to adjust your approach when working with Ollama and Langchain tools. Don't let this setback discourage you. There are several avenues you can explore to get things working.

I hope this helps you out. Let me know if you have any other questions. I'm always here to help.