README.md

miade-llm

MiADE supercharged with LLMs - for the detailed extraction of diagnosis.

This project uses LangServe to deploy langchain chains as REST API endpoints.

Environment Setup

Currently using Mixtral-8x7B-instruct-v0.1 hosted by Replicate so you need to make sure that REPLICATE_API_TOKEN is set in your environment.

Prompts are currently pulled from LangChain Hub so you also need to set LANGCHAIN_API_KEY.

The model id, prompt, and extra model paths can be configured in config/config.yaml

(Optional) If you also want to configure LangSmith to trace and monitor chains, set these environment variables:

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"

Usage

To install dependencies make sure you have poetry installed:

pip install poetry

Then install the project dependencies with poetry:

cd src
poetry install

To spin up a LangServer instance run (make sure you are in the src directory):

poetry run langchain serve

This will start the FastAPI app with a server is running locally at http://localhost:8000

We can see all endpoints at http://localhost:8000/docs.

Access the playground at http://localhost:8000/name-of-package/playground

Access the endpoints from code with:

from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/name-of-package")

Chains

relation-extractor

Extracts relations between concepts found in note and outputs in a JSON-format. Uses MedCAT for NER (requires model).

MedCAT model is required to run this chain. To download an example model trained on MIMIC:

pip install gdown
gdown 'https://drive.google.com/uc?export=download&id=17s999FIotRenltR6gr_f8ZjdaXc-u1Gx', -O ./data/models/miade_problems_model_f25ec9423958e8d6.zip

RAG chain

placeholder description.