/pinecone

name: pinecone
description: Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.
version: 1.0.0
author: hermes-CCC (ported from Hermes Agent by NousResearch)
license: MIT
metadata:
  hermes:
    tags: [RAG, Vector-Database, Pinecone, Embeddings, Production, Serverless, Managed]
    related_skills: [qdrant, chroma, instructor]

Pinecone — Managed Vector Database

Fully managed vector database for production RAG. Serverless (pay-per-query) or pod-based (dedicated).

Setup

pip install pinecone-client sentence-transformers openai

from pinecone import Pinecone, ServerlessSpec

pc = Pinecone(api_key="your-api-key")  # or os.environ["PINECONE_API_KEY"]

---

Create Index

# Serverless (pay-per-query — cheapest to start)
pc.create_index(
    name="my-index",
    dimension=1536,          # match your embedding model
    metric="cosine",         # cosine | euclidean | dotproduct
    spec=ServerlessSpec(
        cloud="aws",
        region="us-east-1"
    )
)

# Connect to index
index = pc.Index("my-index")

---

Upsert Vectors

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")  # dim=384

documents = [
    {"id": "doc1", "text": "Python async programming guide"},
    {"id": "doc2", "text": "Machine learning with PyTorch"},
]

vectors = []
for doc in documents:
    embedding = model.encode(doc["text"]).tolist()
    vectors.append({
        "id": doc["id"],
        "values": embedding,
        "metadata": {"text": doc["text"], "source": "manual"}
    })

# Batch upsert (max 100 per call)
index.upsert(vectors=vectors, namespace="docs")

---

Query

query_text = "how to write async Python?"
query_vector = model.encode(query_text).tolist()

results = index.query(
    vector=query_vector,
    top_k=5,
    namespace="docs",
    include_metadata=True,
)

for match in results["matches"]:
    print(f"Score: {match['score']:.3f} | {match['metadata']['text']}")

---

Metadata Filtering

results = index.query(
    vector=query_vector,
    top_k=5,
    filter={"source": {"$eq": "manual"}},
    include_metadata=True,
)

# Operators: $eq, $ne, $gt, $gte, $lt, $lte, $in, $nin, $and, $or
results = index.query(
    vector=query_vector,
    top_k=5,
    filter={
        "$and": [
            {"category": {"$in": ["tech", "science"]}},
            {"year": {"$gte": 2023}},
        ]
    },
    include_metadata=True,
)

---

Namespaces

# Different namespaces = separate vector spaces (free, no extra cost)
index.upsert(vectors=vectors, namespace="user-123")
index.upsert(vectors=vectors, namespace="user-456")

# Query specific namespace
results = index.query(vector=query_vector, top_k=5, namespace="user-123")

# Delete namespace
index.delete(delete_all=True, namespace="user-123")

---

Fetch / Delete / Update

# Fetch specific vectors
fetched = index.fetch(ids=["doc1", "doc2"], namespace="docs")

# Delete vectors
index.delete(ids=["doc1"], namespace="docs")

# Update metadata (re-upsert with same id)
index.upsert(vectors=[{"id": "doc1", "values": embedding, "metadata": {"updated": True}}])

---

Index Stats

stats = index.describe_index_stats()
print(f"Total vectors: {stats['total_vector_count']}")
print(f"Namespaces: {stats['namespaces']}")

---

When to Use Pinecone vs Alternatives

| | Pinecone | Qdrant | Chroma | |---|---|---|---| | Hosting | Managed cloud | Self/cloud | Self/cloud | | Cost | Pay-per-use | Self-hosted free | Free | | Scale | Billions | Millions+ | Millions | | Setup | Minutes | Minutes | Seconds | | Best for | Production SaaS | Production self-hosted | Local dev/RAG |