OllamaFunctions

This notebook shows how to use an experimental wrapper around Ollama that gives it the same API as OpenAI Functions.

Note that more powerful and capable models will perform better with complex schema and/or multiple functions. The examples below use Mistral. For a complete list of supported models and model variants, see the Ollama model library.

Setup

Follow these instructions to set up and run a local Ollama instance.

Usage

You can initialize OllamaFunctions in a similar way to how you’d initialize a standard ChatOllama instance:

from langchain_experimental.llms.ollama_functions import OllamaFunctions

model = OllamaFunctions(model="mistral")

You can then bind functions defined with JSON Schema parameters and a function_call parameter to force the model to call the given function:

model = model.bind(
    functions=[
        {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, " "e.g. San Francisco, CA",
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                    },
                },
                "required": ["location"],
            },
        }
    ],
    function_call={"name": "get_current_weather"},
)

Calling a function with this model then results in JSON output matching the provided schema:

from langchain.schema import HumanMessage

model.invoke("what is the weather in Boston?")

AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_current_weather', 'arguments': '{"location": "Boston, MA", "unit": "celsius"}'}})

Using for extraction

One useful thing you can do with function calling here is extracting properties from a given input in a structured format:

from langchain.chains import create_extraction_chain

# Schema
schema = {
    "properties": {
        "name": {"type": "string"},
        "height": {"type": "integer"},
        "hair_color": {"type": "string"},
    },
    "required": ["name", "height"],
}

# Input
input = """Alex is 5 feet tall. Claudia is 1 feet taller than Alex and jumps higher than him. Claudia is a brunette and Alex is blonde."""

# Run chain
llm = OllamaFunctions(model="mistral", temperature=0)
chain = create_extraction_chain(schema, llm)
chain.run(input)

[{'name': 'Alex', 'height': 5, 'hair_color': 'blonde'},
 {'name': 'Claudia', 'height': 6, 'hair_color': 'brunette'}]