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ChatOllama

Ollama allows you to run open-source large language models, such as Llama 2, locally.

Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile.

It optimizes setup and configuration details, including GPU usage.

For a complete list of supported models and model variants, see the Ollama model library.

Overview​

Integration details​

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
ChatOllamalangchain-ollama✅❌✅PyPI - DownloadsPyPI - Version

Model features​

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
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Setup​

First, follow these instructions to set up and run a local Ollama instance:

  • Download and install Ollama onto the available supported platforms (including Windows Subsystem for Linux)
  • Fetch available LLM model via ollama pull <name-of-model>
    • View a list of available models via the model library
    • e.g., ollama pull llama3
  • This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.

On Mac, the models will be download to ~/.ollama/models

On Linux (or WSL), the models will be stored at /usr/share/ollama/.ollama/models

  • Specify the exact version of the model of interest as such ollama pull vicuna:13b-v1.5-16k-q4_0 (View the various tags for the Vicuna model in this instance)
  • To view all pulled models, use ollama list
  • To chat directly with a model from the command line, use ollama run <name-of-model>
  • View the Ollama documentation for more commands. Run ollama help in the terminal to see available commands too.

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

Installation​

The LangChain Ollama integration lives in the langchain-ollama package:

%pip install -qU langchain-ollama

Instantiation​

Now we can instantiate our model object and generate chat completions:

  • TODO: Update model instantiation with relevant params.
from langchain_ollama import ChatOllama

llm = ChatOllama(
    model="llama3.1",
    temperature=0,
    # other params...
)
API Reference:ChatOllama

Invocation​

from langchain_core.messages import AIMessage

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg
API Reference:AIMessage
AIMessage(content='The translation of "I love programming" from English to French is:\n\n"J\'adore programmer."', response_metadata={'model': 'llama3.1', 'created_at': '2024-08-19T16:05:32.81965Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2167842917, 'load_duration': 54222584, 'prompt_eval_count': 35, 'prompt_eval_duration': 893007000, 'eval_count': 22, 'eval_duration': 1218962000}, id='run-0863daa2-43bf-4a43-86cc-611b23eae466-0', usage_metadata={'input_tokens': 35, 'output_tokens': 22, 'total_tokens': 57})
print(ai_msg.content)
The translation of "I love programming" from English to French is:

"J'adore programmer."

Chaining​

We can chain our model with a prompt template like so:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)
API Reference:ChatPromptTemplate
AIMessage(content='Das Programmieren ist mir ein Leidenschaft! (That\'s "Programming is my passion!" in German.) Would you like me to translate anything else?', response_metadata={'model': 'llama3.1', 'created_at': '2024-08-19T16:05:34.893548Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2045997333, 'load_duration': 22584792, 'prompt_eval_count': 30, 'prompt_eval_duration': 213210000, 'eval_count': 32, 'eval_duration': 1808541000}, id='run-d18e1c6b-50e0-4b1d-b23a-973fa058edad-0', usage_metadata={'input_tokens': 30, 'output_tokens': 32, 'total_tokens': 62})

Tool calling​

We can use tool calling with an LLM that has been fine-tuned for tool use: 

ollama pull llama3.1

Details on creating custom tools are available in this guide. Below, we demonstrate how to create a tool using the @tool decorator on a normal python function.

from typing import List

from langchain_core.tools import tool
from langchain_ollama import ChatOllama


@tool
def validate_user(user_id: int, addresses: List[str]) -> bool:
    """Validate user using historical addresses.

    Args:
        user_id (int): the user ID.
        addresses (List[str]): Previous addresses as a list of strings.
    """
    return True


llm = ChatOllama(
    model="llama3.1",
    temperature=0,
).bind_tools([validate_user])

result = llm.invoke(
    "Could you validate user 123? They previously lived at "
    "123 Fake St in Boston MA and 234 Pretend Boulevard in "
    "Houston TX."
)
result.tool_calls
API Reference:tool | ChatOllama
[{'name': 'validate_user',
  'args': {'addresses': '["123 Fake St, Boston, MA", "234 Pretend Boulevard, Houston, TX"]',
   'user_id': '123'},
  'id': '40fe3de0-500c-4b91-9616-5932a929e640',
  'type': 'tool_call'}]

Multi-modal​

Ollama has support for multi-modal LLMs, such as bakllava and llava.

ollama pull bakllava

Be sure to update Ollama so that you have the most recent version to support multi-modal.

import base64
from io import BytesIO

from IPython.display import HTML, display
from PIL import Image


def convert_to_base64(pil_image):
    """
    Convert PIL images to Base64 encoded strings

    :param pil_image: PIL image
    :return: Re-sized Base64 string
    """

    buffered = BytesIO()
    pil_image.save(buffered, format="JPEG")  # You can change the format if needed
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return img_str


def plt_img_base64(img_base64):
    """
    Disply base64 encoded string as image

    :param img_base64:  Base64 string
    """
    # Create an HTML img tag with the base64 string as the source
    image_html = f'<img src="data:image/jpeg;base64,{img_base64}" />'
    # Display the image by rendering the HTML
    display(HTML(image_html))


file_path = "../../../static/img/ollama_example_img.jpg"
pil_image = Image.open(file_path)

image_b64 = convert_to_base64(pil_image)
plt_img_base64(image_b64)
<img 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/> 
from langchain_core.messages import HumanMessage
from langchain_ollama import ChatOllama

llm = ChatOllama(model="bakllava", temperature=0)


def prompt_func(data):
    text = data["text"]
    image = data["image"]

    image_part = {
        "type": "image_url",
        "image_url": f"data:image/jpeg;base64,{image}",
    }

    content_parts = []

    text_part = {"type": "text", "text": text}

    content_parts.append(image_part)
    content_parts.append(text_part)

    return [HumanMessage(content=content_parts)]


from langchain_core.output_parsers import StrOutputParser

chain = prompt_func | llm | StrOutputParser()

query_chain = chain.invoke(
    {"text": "What is the Dollar-based gross retention rate?", "image": image_b64}
)

print(query_chain)
90%

API reference​

For detailed documentation of all ChatOllama features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_ollama.chat_models.ChatOllama.html

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