Building an AI-Powered Semantic Search with Django, FAISS, and OpenAI

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Semantic search is one of the most exciting areas in modern AI. Instead of matching keywords, semantic search understands meaning, which makes it incredibly useful for e-commerce, chatbots, and knowledge bases.

At Innosoft, we’ve implemented multiple projects where we combine Django (backend), FAISS (vector search), and OpenAI (embeddings) to create high-performance search systems.

In this tutorial, I’ll walk you through:

Why semantic search is better than keyword search

How FAISS works under the hood

How to integrate FAISS + OpenAI with Django

A minimal code example you can try yourself

By the end, you’ll have a working prototype of a semantic search system.

Why Semantic Search?

Traditional search engines rely on keyword matching:

Query: “cheap hotel in Tashkent”

Match: Only documents with exact keywords “cheap”, “hotel”, “Tashkent”.

Semantic search goes beyond keywords:

It understands that “affordable accommodation in Tashkent” has the same meaning.

This is possible using embeddings – vector representations of text.

Tech Stack

Backend: Django (REST API)

Vector Database: FAISS

Embeddings: OpenAI text-embedding-3-large

Language Support: English, Russian, Uzbek

Step 1 – Install Dependencies

pip install django djangorestframework openai faiss-cpu

Step 2 – Generate Embeddings with OpenAI

from openai import OpenAI

client = OpenAI(api_key=”YOUR_API_KEY”)

def get_embedding(text):
response = client.embeddings.create(
model=”text-embedding-3-large”,
input=text
)
return response.data[0].embedding

Step 3 – Store Vectors in FAISS

import faiss
import numpy as np

dimension = 3072 # size of text-embedding-3-large
index = faiss.IndexFlatL2(dimension)

documents = [
“Best hotels in Tashkent”,
“Affordable apartments in Samarkand”,
“Luxury resorts in Bukhara”
]

vectors = np.array([get_embedding(doc) for doc in documents])
index.add(vectors)

Step 4 – Semantic Search in Django API

from rest_framework.decorators import api_view
from rest_framework.response import Response

@api_view([“POST”])
def semantic_search(request):
query = request.data.get(“query”, “”)
query_vector = np.array([get_embedding(query)])

distances, indices = index.search(query_vector, k=3)

results = [documents[i] for i in indices[0]]
return Response({"results": results})
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Example Query

Input:

{“query”: “cheap hotel in Tashkent”}

Output:

{
“results”: [
“Best hotels in Tashkent”,
“Luxury resorts in Bukhara”,
“Affordable apartments in Samarkand”
]
}

Real-World Use Cases

At Innosoft, we applied this approach to:

AI-powered chatbots in the banking sector

Product matching systems for e-commerce

Knowledge bases for enterprises

The result: faster, smarter, and more human-like search experiences.

Conclusion

Semantic search is no longer a luxury – it’s becoming the standard. With just Django + FAISS + OpenAI, you can build scalable, multilingual, and high-performance search systems.

🚀 At Innosoft, we continue pushing the boundaries of AI in real projects. If you’re interested in learning more, check out our work at:
🔗 https://innosoft.uz



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