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 & Setup‚ÄãInstall 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 Tasks‚ÄãA 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.
Flyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines.
Flyte is an open-source orchestrator that facilitates building production-grade data and ML pipelines. ->: 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 & Setup‚ÄãInstall 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 Tasks‚ÄãA 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.