Integrating into your Python-based Langchain application is the easiest and recommended route. You can just add GalileoObserveCallback(project_name="YOUR_PROJECT_NAME") to the callbacks of your chain invocation.

from galileo_observe import GalileoObserveCallback
from langchain.chat_models import ChatOpenAI

prompt = ChatPromptTemplate.from_template("tell me a joke about {foo}")
model = ChatOpenAI()
chain = prompt | model

monitor_handler = GalileoObserveCallback(project_name="YOUR_PROJECT_NAME")
chain.invoke({'foo':'bears'},
            config(dict(callbacks=[monitor_handler])))

The GalileoObserveCallback logs your input, output, and relevant statistics back to Galileo, where additional evaluation metrics are computed.

Integrating into your Typescript-based Langchain application is a very simple process. You can just add aGalileoObserveCallback object to the callbacks of your chain invocation.

import { GalileoObserveCallback } from "@rungalileo/galileo";
const observe_callback = new GalileoObserveCallback("observe_example", "app_v1")
await observe_callback.init();

Add the callback {callbacks: [observe_callback]} in the invoke step of your application:

const result = await chain.invoke(
    { question: "What is the powerhouse of the cell?"},
    {callbacks: [observe_callback]});

The GalileoObserveCallback callback logs your input, output, and relevant statistics back to Galileo, where additional evaluation metrics are computed.