Mini Project: Document Chatbot – Part 1 (Setup & RAG Core)

Let's think this through for a moment

In the first stage of this project, we'll combine the Models, Document Loaders, Text Splitters, Embeddings, and Vector Store you learned about in the Basic and Intermediate chapters to build the core RAG pipeline of our Document Chatbot. The goal of the project is an assistant that loads a user's own document (say, a company FAQ or personal notes) and answers questions based on that document's content. In Part 1, we'll take things through loading the document, splitting it into small chunks, converting those chunks into embeddings and storing them in a vector store, and finally building a RetrievalQA chain and running a basic test query. This stage is the foundation for the whole project, so it's worth setting it up carefully. In Part 2 and Part 3, we'll build on this base by adding memory and agent tools.

Let's build it

Create a new project folder and install langchain, langchain-openai, langchain-community, and chromadb. Load a sample document with TextLoader (or PyPDFLoader), then split it into chunks with RecursiveCharacterTextSplitter (chunk_size=500, chunk_overlap=50). Embed it with OpenAIEmbeddings and store it in a Chroma vector store (with a persist_directory). Take vectorstore.as_retriever() as your retriever and build a RetrievalQA.from_chain_type with a ChatOpenAI model. Finally, run a sample question and check whether you get back an answer grounded in the document's data.

• Were you able to successfully load a sample document with the document loader?
• Were you able to split the text into chunks and store them in a Chroma vector store?
• Were you able to run a test question through the RetrievalQA chain and get an answer grounded in the document data?

Code Example

import os
from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQA

os.environ["OPENAI_API_KEY"] = "your-api-key"

# 1. Load document
loader = TextLoader("notes.txt", encoding="utf-8")
documents = loader.load()

# 2. Split into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(documents)

# 3. Embed + store in vector DB
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
    chunks, embeddings, persist_directory="./chroma_db"
)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

# 4. Build a basic RetrievalQA chain
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)

# 5. Test it
result = qa_chain.invoke({"query": "notes.txt ထဲမှာ ဘာအကြောင်းအရာတွေ ပါသလဲ?"})
print(result["result"])

An AI-generated answer based on the data in notes.txt appears in the terminal.