Building a Simple RAG System Using FAISS

📚 Table of Contents

1. What is RAG and Why It Matters
2. High-Level Architecture of a RAG System
3. Tech Stack & Prerequisites
4. Step 1: Installing Dependencies
5. Step 2: Preparing and Chunking Documents
6. Step 3: Generating Embeddings
7. Step 4: Storing Vectors in FAISS
8. Step 5: Retrieving Relevant Context
9. Step 6: Augmenting Prompts & Querying the LLM
10. Real-World Use Cases
11. Common Developer Questions (FAQ)
12. Related Tools & Libraries
13. Conclusion & Next Steps

What is RAG and Why It Matters

Retrieval-Augmented Generation (RAG)combines:

Information Retrieval (vector search)
Text Generation (LLMs)

Instead of relying purely on the model’s training data, RAG:

• Injects fresh, private, or domain-specific data
• Reduces hallucinations
• Improves factual accuracy

👉 Perfect for chatbots, internal knowledge bases, support tools, and search assistants.

High-Level Architecture of a RAG System

User Query
↓
Embedding Model
↓
FAISS Vector Search
↓
Relevant Chunks
↓
LLM Prompt Augmentation
↓
Final Answer

Key idea: Retrieve first, then generate.

Tech Stack & Prerequisites
Core Stack

• Python 3.9+
• FAISS – Vector similarity search
• Sentence Transformers – Text embeddings
• OpenAI / Any LLM API – Answer generation

You Should Know

• Basic Python
• REST APIs
• Vector embeddings (conceptually)

Step 1: Installing Dependencies
bash
pip install faiss-cpu sentence-transformers openai tiktoken

💡 Tip:
Use faiss-gpu if you’re running on CUDA for large-scale datasets.

Step 2: Preparing and Chunking Documents

LLMs work better with small, meaningful chunks.
python
def chunk_text(text, chunk_size=500, overlap=50):
chunks = []
start = 0

while start < len(text):
    end = start + chunk_size
    chunks.append(text[start:end])
    start += chunk_size - overlap

return chunks

Why chunking matters

• Improves retrieval precision
• Prevents token overflow
• Enables semantic search

Step 3: Generating Embeddings

We’ll use sentence-transformers.

python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")

embeddings = model.encode(chunks, convert_to_numpy=True)

✔ Fast
✔ Lightweight
✔ Production-friendly

Step 4: Storing Vectors in FAISS
python
import faiss
import numpy as np

dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)

index.add(embeddings)

print("Total vectors indexed:", index.ntotal)

Why FAISS?

• Extremely fast similarity search
• Scales to millions of vectors
• Battle-tested in production systems

Step 5: Retrieving Relevant Context

python
def retrieve_context(query, top_k=3):
query_embedding = model.encode([query])
distances, indices = index.search(query_embedding, top_k)

return [chunks[i] for i in indices[0]]

🔍 This step is the heart of RAG.

Step 6: Augmenting Prompts & Querying the LLM

python
import openai

def generate_answer(query):
context = retrieve_context(query)
prompt = f"""
Use the following context to answer the question:

Context:
{''.join(context)}

Question:
{query}


response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}],
    temperature=0.2
)

return response.choices[0].message.content

Prompt Engineering Tips

• Keep temperature low for factual answers
• Always label Context clearly
• Avoid injecting irrelevant chunks

Real-World Use Cases

✅ Internal documentation assistant
✅ Customer support chatbot
✅ Codebase Q&A system
✅ Legal or medical document search
✅ Product recommendation engines

Common Developer Questions (FAQ)
❓ Why not just fine-tune the LLM?

• Fine-tuning is expensive
• RAG allows real-time updates
• No retraining needed when data changes

❓ How many chunks should I retrieve?

• Usually 3–5
• More chunks = more tokens + noise

❓ Can I store metadata?

Yes. Use a parallel structure:

python
metadata = {index_id: {"source": "doc1.txt"}}

❓ Is FAISS production-ready?

Absolutely. Used at Meta, Amazon, and large-scale AI systems.

Related Tools & Libraries

• FAISS – Vector similarity search
• ChromaDB – Managed vector DB
• Pinecone – Fully hosted vector search
• Weaviate – Graph + vector DB
• LangChain – RAG orchestration
• LlamaIndex – Document indexing framework

Conclusion & Next Steps

You’ve now built a fully working RAG system using FAISS:

• Semantic search ✔
• Context-aware generation ✔
• Scalable architecture ✔

🚀 Next Improvements

• Add document loaders (PDF, HTML)
• Introduce hybrid search (BM25 + vectors)
• Cache embeddings
• Add streaming responses

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