How to Integrate Pinecone Vector Database

Pinecone is a fully-managed vector database optimized for machine learning applications. Perfect for building semantic search, RAG systems, recommendations, and AI-powered features.

Step 1: Create a Pinecone Account

Sign up at pinecone.io and create your free Starter plan (up to 100K vectors).

Step 2: Create an Index

Navigate to Indexes and click Create Index:

• Name: my-first-index
• Dimension: 1536 (for OpenAI embeddings) or 768 (for Cohere)
• Metric: cosine (for similarity search)

Step 3: Get Your API Key

Copy your API key from API Keys in the dashboard.

Step 4: Install the Client

pip install pinecone-client

Step 5: Connect and Upsert Vectors

import pinecone
from openai import OpenAI

# Initialize Pinecone
pinecone.init(api_key="your-api-key", environment="us-west1-gcp")
index = pinecone.Index("my-first-index")

# Initialize OpenAI for embeddings
openai_client = OpenAI()

# Generate embeddings and upsert
docs = ["Machine learning is awesome", "Pinecone is a vector database"]
embeddings = [
    openai_client.embeddings.create(
        input=doc, model="text-embedding-3-small"
    ).data[0].embedding
    for doc in docs
]

index.upsert([
    ("vec1", embeddings[0], {"text": docs[0]}),
    ("vec2", embeddings[1], {"text": docs[1]})
])

# Search
query = "AI databases"
query_embedding = openai_client.embeddings.create(
    input=query, model="text-embedding-3-small"
).data[0].embedding

results = index.query(vector=query_embedding, top_k=3, include_metadata=True)

Common Use Cases

Semantic Search: Document search, knowledge base queries, FAQ assistants

RAG Systems: Chatbot knowledge grounding, document Q&A, context-aware AI

Recommendations: Content recommendations, product similarity, personalized feeds

For detailed guides, visit Pinecone Documentation.