Migrate from Pinecone

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("my-index")

# All operations below stay the same
index.upsert(vectors=[...])
results = index.query(vector=[...], top_k=10)
index.delete(ids=["vec1", "vec2"])

import { Pinecone } from '@pinecone-database/pinecone';

const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });
const index = pc.index('my-index');

// All operations below stay the same
await index.upsert([...]);
const results = await index.query({ vector: [...], topK: 10 });
await index.deleteMany(['vec1', 'vec2']);

from pinecone import Pinecone
from openai import OpenAI

# Initialize clients
pc = Pinecone(api_key="pk-xxxxx")
index = pc.Index("rag-docs")
openai_client = OpenAI()

# Generate embedding
response = openai_client.embeddings.create(
    model="text-embedding-3-small",
    input="What is vector search?"
)
query_vector = response.data[0].embedding

# Store documents
index.upsert(vectors=[
    {
        "id": "doc_001",
        "values": [...],  # 1536-dim embedding
        "metadata": {
            "text": "Vector search finds similar items...",
            "source": "docs.pdf",
            "page": 12
        }
    },
    {
        "id": "doc_002",
        "values": [...],
        "metadata": {
            "text": "HNSW is a graph-based algorithm...",
            "source": "paper.pdf",
            "page": 3
        }
    }
])

# Query for context
results = index.query(
    vector=query_vector,
    top_k=5,
    include_metadata=True
)

# Build LLM context
context = "\n".join([match.metadata["text"] for match in results.matches])
print(f"Found {len(results.matches)} relevant documents")

export PINECONE_API_KEY="pk-xxxxx"
export PINECONE_ENV="us-east-1-aws"