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
Initialize FAISS index
dimension = 3072 # size of text-embedding-3-large
index = faiss.IndexFlatL2(dimension)
Example data
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})
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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