Building a Semantic Search Engine for Your Codebase in Under an Hour

Learn how to build a semantic search engine for your codebase using Python, embeddings, and a vector database in under 60 minutes.

Posted on: 2026-03-12 by AI Assistant

Introduction

Standard code search (like grep or IDE search) is limited to exact text matches. If you search for “database connection”, you might miss a function named init_pg_pool(). What if you could search your codebase for what it does rather than just what it’s called? In this tutorial, you will learn how to build a semantic search engine for your codebase. We’ll use Python, ChromaDB (a local vector database), and an open-source embedding model.

Prerequisites

Python 3.10+
pip install chromadb sentence-transformers
A local directory with some source code files

Core Content

To build semantic search, we need to read our code files, split them into chunks (like functions or classes), generate embeddings for each chunk, and store them in a Vector DB.

import os
import chromadb
from sentence_transformers import SentenceTransformer

# 1. Initialize the embedding model and vector database
model = SentenceTransformer('all-MiniLM-L6-v2')
chroma_client = chromadb.Client()
collection = chroma_client.create_collection(name="codebase")

# 2. Index the codebase (simplified)
code_snippets = [
    "def connect_to_db(uri): return psycopg2.connect(uri)",
    "function renderUI() { console.log('rendering'); }",
    "class User: def __init__(self): self.name = 'admin'"
]

# Generate embeddings and add to collection
embeddings = model.encode(code_snippets).tolist()
collection.add(
    embeddings=embeddings,
    documents=code_snippets,
    ids=["snippet_1", "snippet_2", "snippet_3"]
)

# 3. Perform a semantic search
query = "How do we initialize postgres?"
query_embedding = model.encode([query]).tolist()

results = collection.query(
    query_embeddings=query_embedding,
    n_results=1
)

print("Best Match:", results['documents'][0][0])

Putting It All Together

In a real application, you would write a script that traverses your local directory recursively, reads .py or .js files, and chunks them properly. The example above demonstrates the core logic in just a few lines of code.

Conclusion & Next Steps

You’ve built a functional semantic search engine! This enables developers to navigate complex, unfamiliar codebases just by describing what they are looking for. Next Steps: Try expanding this to handle chunking correctly by using a library like LangChain’s RecursiveCharacterTextSplitter. Questions? Drop a comment below!

ai search python vector-database