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AI Agents · Lesson

Building a RAG Chain End-to-End

Stitch it together: loader -> splitter -> embeddings -> vector store -> retriever -> prompt -> model.

Building a RAG Chain End-to-End is a free AI Agents lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Project Goal

Combine loaders, splitters, vector stores, and LCEL into a complete production-shaped RAG chain.

Step 1: Load and Chunk

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

loader = PyPDFLoader('handbook.pdf')
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
chunks = splitter.split_documents(docs)
print(f'{len(chunks)} chunks loaded')

Step 2: Embed and Store

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

embeddings = OpenAIEmbeddings(model='text-embedding-3-small')
store = Chroma.from_documents(
    chunks,
    embeddings,
    persist_directory='./handbook_db'
)

Step 3: Set Up Retriever

retriever = store.as_retriever(
    search_type='similarity',
    search_kwargs={'k': 4}
)

Step 4: Define the Prompt

from langchain.prompts import ChatPromptTemplate

rag_prompt = ChatPromptTemplate.from_template('''
You are a helpful assistant. Use the context below to answer.
If the answer is not in the context, say you do not know.
Cite sources like [1], [2].

Context:
{context}

Question: {question}

Answer:
''')

Step 5: Format Documents Helper

def format_docs(docs):
    return '\n\n'.join(
        f'[{i+1}] (source: {d.metadata.get("source", "?")}, page {d.metadata.get("page", "?")})\n{d.page_content}'
        for i, d in enumerate(docs)
    )

from types import SimpleNamespace
docs = [
    SimpleNamespace(metadata={'source': 'handbook.pdf', 'page': 3}, page_content='Vacation policy details.'),
    SimpleNamespace(metadata={'source': 'handbook.pdf', 'page': 5}, page_content='Sick leave details.'),
]
print(format_docs(docs))

Step 6: Assemble the LCEL Chain

from langchain_core.runnables import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model='gpt-4o-mini', temperature=0)

rag_chain = (
    {'context': retriever | format_docs, 'question': RunnablePassthrough()}
    | rag_prompt
    | model
    | StrOutputParser()
)

Step 7: Invoke

answer = rag_chain.invoke('What is our refund policy?')
print(answer)

Step 8: Stream the Answer

for chunk in rag_chain.stream('What is our refund policy?'):
    print(chunk, end='', flush=True)

Step 9: Return Sources Separately

Sometimes you want both the answer AND the retrieved docs in the output:

from langchain_core.runnables import RunnableParallel

rag_with_sources = RunnableParallel(
    context=retriever,
    answer=rag_chain
)

result = rag_with_sources.invoke('What is our refund policy?')
print(result['answer'])
for doc in result['context']:
    print(doc.metadata)

Step 10: Conversational RAG

Add history-aware retrieval — rewrite follow-up questions to standalone form before retrieval:

from langchain.chains import create_history_aware_retriever
# 'And what about XL sizes?' -> 'What is the refund policy for XL sizes?'

Step 11: Eval the Chain

LangSmith integration captures every chain invocation. You can build datasets from real usage and run evals.

Production Considerations

  • Pin model versions
  • Set max_tokens
  • Add token usage tracking
  • Implement retries with fallback model
  • Cache embeddings (already deterministic in OpenAI)
  • Add Langfuse/LangSmith for traces

format_docs Purpose

Why use a format_docs helper between the retriever and the prompt?

Recap

You built a complete RAG chain with LCEL. This skeleton scales to production with the additions above.

Frequently asked questions

Is the “Building a RAG Chain End-to-End” lesson free?

Yes — the full text of “Building a RAG Chain End-to-End” is free to read here on the web, and the AI Agents course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents course, upgrade to CoddyKit PRO.

What will I learn in “Building a RAG Chain End-to-End”?

Stitch it together: loader -> splitter -> embeddings -> vector store -> retriever -> prompt -> model. You practise AI Agents with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start AI Agents?

No prior experience is required. AI Agents on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Building a RAG Chain End-to-End” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this AI Agents lesson?

Yes. Every AI Agents lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. LangChain Architecture: Models, Prompts, Chains
  2. Loaders, Splitters and Vector Stores
  3. LCEL (LangChain Expression Language)
  4. Building a RAG Chain End-to-End
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