Context provides user analytics for LLM-powered products and features.
With Context, you can start understanding your users and improving their experiences in less than 30 minutes. In this guide we will show you how to integrate with Context.

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Installation and Setup

%pip install -qU  langchain langchain-openai langchain-community context-python

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Getting API Credentials

To get your Context API token:
  1. Go to the settings page within your Context account (with.context.ai/settings).
  2. Generate a new API Token.
  3. Store this token somewhere secure.

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Setup Context

To use the ContextCallbackHandler, import the handler from LangChain and instantiate it with your Context API token. Ensure you have installed the context-python package before using the handler. 
from langchain_community.callbacks.context_callback import ContextCallbackHandler

import os

token = os.environ["CONTEXT_API_TOKEN"]

context_callback = ContextCallbackHandler(token)

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Usage

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Context callback within a chat model

The Context callback handler can be used to directly record transcripts between users and AI assistants. 
import os

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI

token = os.environ["CONTEXT_API_TOKEN"]

chat = ChatOpenAI(
    headers={"user_id": "123"}, temperature=0, callbacks=[ContextCallbackHandler(token)]
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that translates English to French."
    ),
    HumanMessage(content="I love programming."),
]

print(chat(messages))

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Context callback within Chains

The Context callback handler can also be used to record the inputs and outputs of chains. Note that intermediate steps of the chain are not recorded - only the starting inputs and final outputs. Note: Ensure that you pass the same context object to the chat model and the chain. Wrong: 
chat = ChatOpenAI(temperature=0.9, callbacks=[ContextCallbackHandler(token)])
chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[ContextCallbackHandler(token)])
Correct: 
handler = ContextCallbackHandler(token)
chat = ChatOpenAI(temperature=0.9, callbacks=[callback])
chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[callback])

import os

from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_core.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain_openai import ChatOpenAI

token = os.environ["CONTEXT_API_TOKEN"]

human_message_prompt = HumanMessagePromptTemplate(
    prompt=PromptTemplate(
        template="What is a good name for a company that makes {product}?",
        input_variables=["product"],
    )
)
chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])
callback = ContextCallbackHandler(token)
chat = ChatOpenAI(temperature=0.9, callbacks=[callback])
chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[callback])
print(chain.run("colorful socks"))