AI Agent RAG Pipeline Tutorial 2026

A single LLM's knowledge is limited to its training data. By adding a RAG pipeline, the Agent can query your private documents to answer questions. This guide walks you through building it step by step.

💡 What You Will Learn

A single LLM's knowledge is limited to its training data. By adding a RAG pipeline, the Agent can query your private documents to answer questions. This guide walks you through building it step by ste

 → Agent → RAG
       → Agent → 

from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

loader = TextLoader("docs.txt")
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(docs)

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)

from langchain.agents import Tool, AgentExecutor
from langchain.tools.retriever import create_retriever_tool

retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
tool = create_retriever_tool(
    retriever, "search_docs", "Search documents"
)

Summary