Pinecone
Pinecone Tutorial
Pinecone is a managed vector database designed for powering AI search, recommendation, and retrieval applications. Instead of storing plain text, Pinecone indexes data as embeddings (vectors), making it fast and scalable for building semantic search, RAG (Retrieval-Augmented Generation), and personalisation engines.
Make Money With This đź’°
Sell AI-powered search/chatbots to businesses using Pinecone as backend.
Build SaaS tools that embed Pinecone for semantic search.
Offer consulting services for RAG (Retrieval-Augmented Generation).
Use Cases
AI-Powered Search Engines → semantic results with OpenAI embeddings.
E-commerce Recommendations → “customers also bought” powered by vectors.
Knowledge Management → query PDFs, docs, or Notion pages with AI.
Personalisation Engines → customised content feeds.
Key Features
Managed Vector Database → scalable, low-latency, cloud-hosted.
RAG-Ready → plug Pinecone into LLMs like ChatGPT or Claude.
Hybrid Search → combine keyword + vector search for accuracy.
Serverless Scaling → no infra management needed.
Multi-Cloud Support → AWS, GCP, Azure.
Getting Started
Step 1: Visit Pinecone.
Step 2: Create a free account (email or GitHub login).
Step 3: Install the SDK: pip install pinecone-client
Step 4: Generate an API key from the Pinecone dashboard.
Step 5: Create a project and index your first dataset (e.g., OpenAI embeddings).
Step 6: Use Python or Node.js to query vectors in real time.
Example Code
Type:
import openai, pinecone
#Initialise Pinecone
pinecone.init(api_key="YOUR_KEY", environment="us-east1-gcp")
#Create an index
index = pinecone.Index("agentprompt-demo")
#Generate embeddings with OpenAI
res = openai.Embedding.create(input=["AI is changing the world"], model="text-embedding-ada-002")
#Upsert into Pinecone
index.upsert([(str(1), res['data'][0]['embedding'])])
What you’ll see: Your text is stored as a vector and instantly searchable.
Tool Snapshot: Pros & Cautions
Best if: you’re building AI search, chatbots, or recommendation systems.
Not ideal if: you don’t need vector similarity search (e.g., simple CRUD apps).
Pricing Snapshot
Free Plan → 1 project, limited index size.
Standard Plans → usage-based, starting around $0.096/hour per pod.
Enterprise pricing available for scale.
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