Rag

Langchain

Vector Database

Building Your First RAG Pipeline with LangChain

Sagar Sonwane

4 min readJan 25, 2026

Retrieval-Augmented Generation (RAG) is one of the most practical ways to build reliable AI systems today.

Instead of relying purely on a model’s internal knowledge, RAG lets you:

Inject your own data
Keep responses grounded
Reduce hallucinations
Build deterministic-ish AI systems

In this blog, we’ll build a simple end-to-end RAG pipeline:

Upload a document
Chunk it
Generate embeddings
Store in a vector database
Query it via a chat interface

🧠 What is RAG (Conceptually)?

Retrieval-Augmented Generation (RAG) works in two phases:

Ingestion phase (offline)
Document → Chunk → Embed → Store in vector DB
Retrieval phase (runtime)
Query → Embed → Search → Retrieve → Generate response

This separation is important because it makes the system:

scalable
reusable
efficient

Instead of sending entire documents to the LLM every time, we only fetch the most relevant pieces, which keeps responses fast and cost-effective.

🚀 What We’re Building

A minimal system like this:

User uploads document → 
Text is chunked → 
Embeddings generated → 
Stored in Vector DB →

User asks question →
Relevant chunks retrieved →
LLM generates grounded answer

Tech Stack

LangChain → orchestration layer
FAISS → vector storage (local)
OpenAI embeddings + LLM (or any alternative)
Python

⚙️ Step 1: Install Dependencies

pip install langchain openai faiss-cpu tiktoken

📄 Step 2: Load and Read Document

Let’s assume a simple .txt file.

from langchain.document_loaders import TextLoader

loader = TextLoader("sample.txt")
documents = loader.load()

✂️ Step 3: Chunk the Document

What is Chunking and Why It Matters?

Chunking is simply breaking large documents into smaller pieces (chunks).

But this is not just preprocessing — it directly impacts answer quality.

Why?
- LLMs have context limits
- Large documents cannot be processed at once
- Retrieval works better on focused, smaller units

Each chunk becomes:
- independently searchable
- individually embeddable
- easier to retrieve

Poor chunking leads to:
- irrelevant results
- incomplete answers
- hallucinations

A key insight:

Too large → noisy context
Too small → loss of meaning

Chunking is actually the foundation of your entire RAG pipeline, because everything downstream depends on it.

Why chunking matters:

LLMs have context limits
Smaller chunks improve retrieval precision

from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50
)

docs = text_splitter.split_documents(documents)

Rule of thumb:

Smaller chunks → better precision
Larger chunks → better context

🔢 Step 4: Generate Embeddings

What are Embeddings?

Embeddings convert text into numbers — specifically, vectors.

Think of it like this:

"dog" → [0.12, -0.98, 0.44, ...]
"puppy" → [0.10, -0.95, 0.40, ...]

These vectors capture semantic meaning, not just keywords.

So:

“dog” and “puppy” → close in vector space
“dog” and “car” → far apart

This is what enables semantic search, instead of keyword matching.

In a RAG pipeline:

Each chunk → converted into an embedding
Query → also converted into an embedding
Similarity → used to find relevant content

Embeddings are the reason RAG feels “intelligent” rather than just “search-based.”

from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

🧱 Step 5: Store in Vector DB

What is a Vector Database?

A vector database stores embeddings and allows similarity search.

Unlike traditional databases:
- SQL → exact match
- Vector DB → semantic similarity

When you store embeddings:
- Each chunk = one vector
- Stored along with metadata (source, page, etc.)

At query time:

Convert query → embedding
Search nearest vectors
Return top-k similar chunks

This is usually done using approximate nearest neighbor (ANN) algorithms for speed.

That’s why vector DBs can:

search millions of chunks
return results in milliseconds

They are the retrieval engine of your RAG system.

We’ll use FAISS for simplicity.

from langchain.vectorstores import FAISS

vectorstore = FAISS.from_documents(docs, embeddings)

🔍 Step 6: Retrieval

How Retrieval Actually Works

Retrieval is where everything comes together.
- Query → embedding
- Compared with stored embeddings
- Top-K similar chunks selected

This “top-k” is critical:
- Low k → might miss context
- High k → introduces noise

The retrieved chunks are then:
- passed into the LLM
- used as context for answer generation

This step is what makes RAG:
- grounded
- context-aware
- reliable

Convert vector DB into a retriever:

retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

This fetches top 3 relevant chunks.

💬 Step 7: Build the Chat Interface

Now we connect retrieval + LLM.

Why Not Just Use an LLM Directly?

Without RAG:
- LLM relies only on training data
- Can hallucinate
- Cannot access private data

With RAG:
- Answers are grounded in your data
- No retraining needed
- Easily updatable knowledge

This is why most real-world AI systems today use RAG instead of raw LLM calls.

from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(model="gpt-4")

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever
)

🧪 Step 8: Ask Questions

query = "What is this document about?"
response = qa_chain.run(query)

print(response)

What’s Actually Happening

When you ask a question:

Query → converted to embedding
Vector DB → finds similar chunks
Retrieved chunks → sent to LLM
LLM → generates grounded answer

Common Mistakes (Important)

1. Treating RAG as “plug and play”

RAG quality depends heavily on:

Chunking strategy
Embedding model
Retrieval tuning

2. Ignoring chunk overlap

Without overlap:

Context breaks
Answers become incomplete

3. Not controlling retrieval size (`k`)

Too small → misses info
Too large → noisy context

4. No evaluation

Always test:

Correctness
Context relevance
Hallucination rate

Improvements You Can Add

Metadata filtering (source, page number)
Hybrid search (keyword + vector)
Better vector DB:
- Pinecone
- Weaviate
- Qdrant
Streaming responses
Chat memory

Key Takeaways

RAG is not just retrieval + LLM
Quality comes from:
- chunking
- retrieval tuning
- prompt design
Start simple → iterate based on evaluation