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pythonadvanced3 hr est.

Semantic Search Engine with Pinecone

Build a semantic search engine using OpenAI embeddings and a Pinecone vector database.

Editorial note

Written by TechIdea Curriculum Team

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TechIdea Curriculum Team

Our engineers and educators design these projects to simulate real-world tasks and prepare you for technical interviews.

This guide is created to help beginners understand SEO, blogging, AI tools, and online growth in simple English. We focus on practical steps, original examples, and safe website growth methods.

Last updated: 2026-06-05

Before You Begin

  • 1
    Python
  • 2
    Vector DB concepts

Project Architecture

Folder Structure

search/
├── index_data.py
└── search.py

Data Flow

[Documents] -> [Embeddings] -> [Pinecone] <- [Search Query Embedding]

Source Code Breakdown & Implementation

Install pinecone-client and openai.
Use text-embedding-ada-002 model to create vectors.
Command line interface for querying.
Handle empty results.

Complete Solution Code

Compare your approach

Testing Checklist

  • • Index 10 documents
  • • Search with synonyms to test semantic matching

Common Bugs

  • Bug: Dimension mismatch

    Fix: Ensure your Pinecone index dimensions match ada-002 (1536).

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