Cannot use local models (ChatOllama)

[robbiemu]

I am using the studio in module 2 of the Intro to LangGraph from Langchain Academy. (The same issue occurs with all models relying on a model if that model is set to ChatOllama):

I've modified requirements.txt to remove langchain-openai and add langchain-ollama:

langgraph
langchain-core
langchain-community
langchain-ollama

I'm aware of no changes needed in the langraph.json:

{
  "dockerfile_lines": [],
  "graphs": {
    "chatbot": "./chatbot.py:graph"
  },
  "env": "./.env",
  "python_version": "3.11",
  "dependencies": [
    "."
  ]
}

in the graph I've swapped in my local model instead of openai:

# We will use this model for both the conversation and the summarization
#from langchain_openai import ChatOpenAI
#model = ChatOpenAI(model="gpt-4o", temperature=0) 
from langchain_ollama import ChatOllama

model = ChatOllama(
    model = "mistral-nemo:12b-instruct-2407-q8_0",
    temperature = 0.8,
    num_ctx=8192,
    num_predict = 4096,
)

This studio is a reproduction of the coursework in the Jupyter notebook, and there, I receive no issues. In the studio, however, when I submit a message, I get

"Connect Error: [Errno 111] Connection refused"

Expected:

If opening this port for the chain in docker is configurable in the json file, it should be required even for OpenAI models -- otherwise you are tightly coupling the studio to OpenAI.
The outbound ports should either always be explicit, or every chat* model in langchain should have its default port open.

note: see also the original issue I opened at the GitHub for langchain academy

[hwchase17]

i think this is possible we just need instructions on how to do it

[robbiemu]

I'm not super sharp with Docker but... The Ollama port it is trying to contact is 11434. This is how you could expose that port, I believe: docker run -d -p 11434:11434 <langgraph_studio_image>

The -p flag has no equivalent inside of the Dockerfile, so it can't be moved there. You can do the same port forwarding if it was a docker compose instead, using the "Ports" section.

I have no idea how to add configuration options to the langgraph.json (for decoupling), but I'd be happy to help if I could :)

edit:

it just dawned on me what the real problem is. in my code, I am not specifying the hostname. Localhost in the container refer's to the virtual machine represented by the container. Docker provides a special hostname host.docker.internal that can be used to refer to the host machine from within the container.

llm = ChatOllama(
    model = "mistral-nemo:12b-instruct-2407-q8_0",
    base_url="http://host.docker.internal:11434",
    temperature = 0.8,
    num_ctx=8192,
    num_predict = 4096,
)

That works! This was a non-issue I guess :) And maybe, could just use a note in documentation somewhere -- or hopefully at least someone can search here and find the answer.

Please feel free to close this issue, for my part I have a valid solution.

[timbogit]

I ran into the same problem / had the same use case. Thanks so much for figuring this out, @robbiemu ! 🥳

Using the base_url="http://host.docker.internal:11434" for local Ollama models might be worth adding to an FAQ / README section.
Thoughts, @hwchase17? 🙇

[deanchanter]

base_url="http://host.docker.internal:11434" did not work for me. This only difference is that I am using Orbstack. Could that be the issue?

Update:
Latest version of Orbstack and LangGraph Studio did the trick

[PlebeiusGaragicus]

Came here for same problem. Yes - because it runs in Docker you need to adjust the base_url:

model = ChatOllama(
    model=...,
    base_url="http://host.docker.internal:11434"
)

[dqbd]

Updated in README.md!