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Semantic Search Engine with Pinecone
Build a semantic search engine using OpenAI embeddings and a Pinecone vector database.
The Problem
Build a semantic search engine using OpenAI embeddings and a Pinecone vector database.
Real-World Use Case
Build a semantic search engine using OpenAI embeddings and a Pinecone vector database.
Technology Stack
Python
Prerequisite
Vector DB concepts
Prerequisite
Architecture & Design
Folder Structure
search/
├── index_data.py
└── search.pyStep-by-Step Implementation
1
Generate embeddings for documents
Install pinecone-client and openai.
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# Pinecone and OpenAI integration logicCode Explanation
Implementation step
2
Upsert to Pinecone
Use text-embedding-ada-002 model to create vectors.
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# Pinecone and OpenAI integration logicCode Explanation
Implementation step
3
Generate embedding for search query
Command line interface for querying.
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# Pinecone and OpenAI integration logicCode Explanation
Implementation step
4
Query Pinecone for top K matches
Handle empty results.
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# Pinecone and OpenAI integration logicCode Explanation
Implementation step
Common Errors
Dimension mismatch
Ensure your Pinecone index dimensions match ada-002 (1536).
Security & Performance
Index 10 documents
Search with synonyms to test semantic matching
Scrape a website and index it
Add a Next.js frontend
Interview Questions
Q: What is semantic search?
A: Searching by meaning rather than exact keyword matches.