In this blog post I will guide you on how you can implement end-to-end agentic RAG using LangGraph. It's an agentic framework used to create complex workflows, and it has mechanisms of indirect graph data structure, which includes nodes and edges. A node is a function or agent that connects to another agent with the help of an edge that transfers communication between different nodes.
Before starting to implement the LangGraph, we need to install some libraries and, of course, Python, because we are using the Python programming language in our project. All the code of this project will soon be available on my GitHub, and this blog article will be updated as we move forward.
First we will create a simple rag pipeline, and then we will move to implement agents. In simple terms, if we talk about the RAG process.
We load documents or unstructured URLs and break them down into small chunks because we can't directly pass the entire documents or unstructured URL information to LLM due to the limited token context length.
After breaking down into chunks, then we will convert into embeddings. Basically, it's a numerical representation of the vector space to convert words into numbers so that LLM can understand. Then we will store it in the vector database.
First I will load the web URL using LangChain's web-based loader and then will break it into the chunks. Then convert into vector embeddings and store this into the Pinecone vector DB.
After that we will create a retriever tool that helps us to retrieve stored documents when a user asks a query.