text: It is built for scalability and reproducibility, leveraging Kubernetes as its underlying platform.The purpose of this notebook is to demonstrate the integration of a FlyteCallback into your Flyte task, enabling you to effectively monitor and track your LangChain experiments.Installation & Setupâ€‹Install the Flytekit library by running the command pip install flytekit.Install the Flytekit-Envd plugin by running the command pip install flytekitplugins-envd.Install LangChain by running the command pip install langchain.Install Docker on your system.Flyte Tasksâ€‹A Flyte task serves as the foundational building block of Flyte.
  To execute LangChain experiments, you need to write Flyte tasks that define the specific steps and operations involved.NOTE: The getting started guide offers detailed, step-by-step instructions on installing Flyte locally and running your initial Flyte pipeline.First, import the necessary dependencies to support your LangChain experiments.import osfrom flytekit import ImageSpec, taskfrom langchain.agents import AgentType, initialize_agent, load_toolsfrom langchain.callbacks import FlyteCallbackHandlerfrom langchain.chains import LLMChainfrom langchain.chat_models import ChatOpenAIfrom langchain.prompts import PromptTemplatefrom langchain.schema import HumanMessageSet up the necessary environment variables to utilize the OpenAI API and Serp API:# Set OpenAI API keyos.environ["OPENAI_API_KEY"] = "<your_openai_api_key>"# Set Serp API keyos.environ["SERPAPI_API_KEY"] = "<your_serp_api_key>"Replace <your_openai_api_key> and <your_serp_api_key> with your respective API keys obtained from OpenAI and Serp API.To guarantee reproducibility of your pipelines, Flyte tasks are containerized.