SKILLoptionalHermes Optional Skills

Pinecone

Name: Pinecone
Author: Orchestra Research

Managed vector DB for production RAG and search

RAGPineconeVector DatabaseManaged ServiceServerlessHybrid SearchProductionAuto-Scaling

Last registry verification2026-08-18v1.0.1Orchestra Research

Open original source ↗اقرأ بالعربية

Plain meaning

What does it add to Hermes?

Managed vector DB for production RAG and search

Pinecone is a skill related to databases and data. It gives the agent a structured way to read or query data. Some tools may also write data, so clear limits matter.

This plain-language explanation is based on the publisher description. The original text remains visible for verification.

Use it when

Use it when your goal in databases and data is clear and you can limit it to the data and actions it actually needs.

Skip it when

Do not add it merely to experiment when Hermes already has a simpler path, or when you cannot review its source and permissions.

Who is it for?

Best for users who want a repeatable way of working inside Hermes.

Safe first test

Use a test database and a read-only account, then request a simple query that changes no records.

Original publisher description

Managed vector DB for production RAG and search

✓

Data source

This entry was indexed from Hermes Optional Skills. Our explanation interprets the type and domain without inventing a capability not present upstream.

Security review

The source is official or editorially reviewed, but you still need to review permissions and version compatibility.

Safe setup path

Inspect, install, then test.

01
Open the source
Match the publisher, license, and description to your need. Check the real update history.
02
Review permissions and secrets
Never paste a secret value into this site. Use environment-variable names and grant the smallest scope.
03
Copy setup only after review
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04
Test with a non-sensitive task
Inspect the visible tools, then exclude write or delete tools you do not need.

Install command

Review the command, then copy it.

hermes skills install pinecone

Hermes Belarabi does not execute this command. Installation happens on your device and remains subject to Hermes scanning and your review.

The full skill definition

Exactly what Hermes loads when this skill runs.

Reproduced from the official documentation. Read it before enabling the skill: this text becomes the agent's instructions.

Managed vector DB for production RAG and search.

Skill metadata

A lookup table. Do not read it all; find the row that applies to you.

Source	Optional — install with `hermes skills install official/mlops/pinecone`
Path	`optional-skills/mlops/pinecone`
Version	`1.0.1`
Author	Orchestra Research
License	MIT
Dependencies	`pinecone`
Platforms	linux, macos, windows
Tags	`RAG`, `Pinecone`, `Vector Database`, `Managed Service`, `Serverless`, `Hybrid Search`, `Production`, `Auto-Scaling`, `Low Latency`, `Recommendations`

Reference: full SKILL.md

Explains the idea itself. Read it slowly; the later sections build on it.

The vector database for production AI applications.

When to use Pinecone

Explains the idea itself. Read it slowly; the later sections build on it.

Use when:

Need managed, serverless vector database
Production RAG applications
Auto-scaling required
Low latency critical (<100ms)
Don't want to manage infrastructure
Need hybrid search (dense + sparse vectors)

Metrics:

Fully managed SaaS
Auto-scales to billions of vectors
p95 latency <100ms
99.9% uptime SLA

Use alternatives instead:

Chroma: Self-hosted, open-source
FAISS: Offline, pure similarity search
Weaviate: Self-hosted with more features

Quick start

Ordered, practical steps. Run one and confirm it worked before moving on.

Installation

Shell1 line

pip install pinecone

Note: the old pinecone-client package is deprecated. Install pinecone (v5+; current 9.x). The import stays from pinecone import Pinecone.

Basic usage

Python30 lines

from pinecone import Pinecone, ServerlessSpec

# Initialize
pc = Pinecone(api_key="your-api-key")

# Create index
pc.create_index(
    name="my-index",
    dimension=1536,  # Must match embedding dimension
    metric="cosine",  # or "euclidean", "dotproduct"
    spec=ServerlessSpec(cloud="aws", region="us-east-1")
)

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

# Upsert vectors
index.upsert(vectors=[
    {"id": "vec1", "values": [0.1, 0.2, ...], "metadata": {"category": "A"}},
    {"id": "vec2", "values": [0.3, 0.4, ...], "metadata": {"category": "B"}}
])

# Query
results = index.query(
    vector=[0.1, 0.2, ...],
    top_k=5,
    include_metadata=True
)

print(results["matches"])

Core operations

Explains the idea itself. Read it slowly; the later sections build on it.

Create index

Python23 lines

# Serverless (recommended)
pc.create_index(
    name="my-index",
    dimension=1536,
    metric="cosine",
    spec=ServerlessSpec(
        cloud="aws",         # or "gcp", "azure"
        region="us-east-1"
    )
)

# Pod-based (for consistent performance)
from pinecone import PodSpec

pc.create_index(
    name="my-index",
    dimension=1536,
    metric="cosine",
    spec=PodSpec(
        environment="us-east1-gcp",
        pod_type="p1.x1"
    )
)

Upsert vectors

Python20 lines

# Single upsert
index.upsert(vectors=[
    {
        "id": "doc1",
        "values": [0.1, 0.2, ...],  # 1536 dimensions
        "metadata": {
            "text": "Document content",
            "category": "tutorial",
            "timestamp": "2025-01-01"
        }
    }
])

# Batch upsert (recommended)
vectors = [
    {"id": f"vec{i}", "values": embedding, "metadata": metadata}
    for i, (embedding, metadata) in enumerate(zip(embeddings, metadatas))
]

index.upsert(vectors=vectors, batch_size=100)

Query vectors

Python27 lines

# Basic query
results = index.query(
    vector=[0.1, 0.2, ...],
    top_k=10,
    include_metadata=True,
    include_values=False
)

# With metadata filtering
results = index.query(
    vector=[0.1, 0.2, ...],
    top_k=5,
    filter={"category": {"$eq": "tutorial"}}
)

# Namespace query
results = index.query(
    vector=[0.1, 0.2, ...],
    top_k=5,
    namespace="production"
)

# Access results
for match in results["matches"]:
    print(f"ID: {match['id']}")
    print(f"Score: {match['score']}")
    print(f"Metadata: {match['metadata']}")

Metadata filtering

Python16 lines

# Exact match
filter = {"category": "tutorial"}

# Comparison
filter = {"price": {"$gte": 100}}  # $gt, $gte, $lt, $lte, $ne

# Logical operators
filter = {
    "$and": [
        {"category": "tutorial"},
        {"difficulty": {"$lte": 3}}
    ]
}  # Also: $or

# In operator
filter = {"tags": {"$in": ["python", "ml"]}}

Namespaces

Explains the idea itself. Read it slowly; the later sections build on it.

Python16 lines

# Partition data by namespace
index.upsert(
    vectors=[{"id": "vec1", "values": [...]}],
    namespace="user-123"
)

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

# List namespaces
stats = index.describe_index_stats()
print(stats['namespaces'])

Hybrid search (dense + sparse)

Explains the idea itself. Read it slowly; the later sections build on it.

Python40 lines

# Upsert with sparse vectors
index.upsert(vectors=[
    {
        "id": "doc1",
        "values": [0.1, 0.2, ...],  # Dense vector
        "sparse_values": {
            "indices": [10, 45, 123],  # Token IDs
            "values": [0.5, 0.3, 0.8]   # TF-IDF scores
        },
        "metadata": {"text": "..."}
    }
])

# Hybrid query
# NOTE: index.query() does NOT accept an `alpha` kwarg. Pinecone stores a
# single sparse-dense vector, so weighting must be applied by pre-scaling the
# query vectors before sending them. Use the hybrid_score_norm helper below
# (alpha * dense + (1 - alpha) * sparse; alpha=1 → pure dense, 0 → pure sparse).

def hybrid_score_norm(dense, sparse, alpha: float):
    """Scale dense/sparse query vectors for weighted hybrid search."""
    if not 0 <= alpha <= 1:
        raise ValueError("alpha must be between 0 and 1")
    scaled_sparse = {
        "indices": sparse["indices"],
        "values": [v * (1 - alpha) for v in sparse["values"]],
    }
    return [v * alpha for v in dense], scaled_sparse

hdense, hsparse = hybrid_score_norm(
    dense=[0.1, 0.2, ...],
    sparse={"indices": [10, 45], "values": [0.5, 0.3]},
    alpha=0.5,  # 0=sparse, 1=dense, 0.5=balanced
)

results = index.query(
    vector=hdense,
    sparse_vector=hsparse,
    top_k=5,
)

LangChain integration

Explains the idea itself. Read it slowly; the later sections build on it.

Python22 lines

from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAIEmbeddings

# Create vector store
vectorstore = PineconeVectorStore.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    index_name="my-index"
)

# Query
results = vectorstore.similarity_search("query", k=5)

# With metadata filter
results = vectorstore.similarity_search(
    "query",
    k=5,
    filter={"category": "tutorial"}
)

# As retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 10})

LlamaIndex integration

Explains the idea itself. Read it slowly; the later sections build on it.

Python14 lines

from llama_index.vector_stores.pinecone import PineconeVectorStore

# Connect to Pinecone
pc = Pinecone(api_key="your-key")
pinecone_index = pc.Index("my-index")

# Create vector store
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)

# Use in LlamaIndex
from llama_index.core import StorageContext, VectorStoreIndex

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

Index management

Explains the idea itself. Read it slowly; the later sections build on it.

Python14 lines

# List indices
indexes = pc.list_indexes()

# Describe index
index_info = pc.describe_index("my-index")
print(index_info)

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

# Delete index
pc.delete_index("my-index")

Delete vectors

Explains the idea itself. Read it slowly; the later sections build on it.

Python11 lines

# Delete by ID
index.delete(ids=["vec1", "vec2"])

# Delete by filter
index.delete(filter={"category": "old"})

# Delete all in namespace
index.delete(delete_all=True, namespace="test")

# Delete entire index
index.delete(delete_all=True)

Best practices

Explains the idea itself. Read it slowly; the later sections build on it.

Use serverless - Auto-scaling, cost-effective
Batch upserts - More efficient (100-200 per batch)
Add metadata - Enable filtering
Use namespaces - Isolate data by user/tenant
Monitor usage - Check Pinecone dashboard
Optimize filters - Index frequently filtered fields
Test with free tier - 1 index, 100K vectors free
Use hybrid search - Better quality
Set appropriate dimensions - Match embedding model
Regular backups - Export important data

Performance

A lookup table. Do not read it all; find the row that applies to you.

Operation	Latency	Notes
Upsert	~50-100ms	Per batch
Query (p50)	~50ms	Depends on index size
Query (p95)	~100ms	SLA target
Metadata filter	~+10-20ms	Additional overhead

Pricing (as of 2025)

Explains the idea itself. Read it slowly; the later sections build on it.

Serverless:

$0.096 per million read units
$0.06 per million write units
$0.06 per GB storage/month

Free tier:

1 serverless index
100K vectors (1536 dimensions)
Great for prototyping

Resources

Explains the idea itself. Read it slowly; the later sections build on it.

Website: https://www.pinecone.io
Docs: https://docs.pinecone.io
Console: https://app.pinecone.io
Pricing: https://www.pinecone.io/pricing