ComponentsCallbacksContextOn this pageContextContext 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.Installation and Setup​$ pip install context-python --upgradeGetting API Credentials​To get your Context API token:Go to the settings page within your Context account (https://with.context.ai/settings).Generate a new API Token.Store this token somewhere secure.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.import osfrom langchain.callbacks import ContextCallbackHandlertoken = os.environ["CONTEXT_API_TOKEN"]context_callback = ContextCallbackHandler(token)Usage​Using the Context callback within a chat model​The Context callback handler can be used to directly record transcripts between users and AI assistants.Example​import osfrom langchain.chat_models import ChatOpenAIfrom langchain.schema import (    SystemMessage,    HumanMessage,)from langchain.callbacks import ContextCallbackHandlertoken = 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))Using the 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])Example​import osfrom langchain.chat_models import ChatOpenAIfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplatefrom langchain.prompts.chat import (    ChatPromptTemplate,    HumanMessagePromptTemplate,)from langchain.callbacks import ContextCallbackHandlertoken = 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"))PreviousConfidentNextInfinoInstallation and SetupGetting API CredentialsSetup ContextUsageUsing the Context callback within a chat modelUsing the Context callback within Chains