Pinecone Vector Protocol Documentation

Pinecone Vector Protocol Documentation

This directory contains consolidated documentation for HeliosDB’s Pinecone vector database protocol support.

Quick Start

Connect to HeliosDB using the Pinecone client:

from pinecone import Pinecone

# Connect to HeliosDB (Pinecone-compatible)
pc = Pinecone(api_key="your-api-key", host="http://localhost:8080")

# Access index
index = pc.Index("my-vectors")

# Upsert vectors
index.upsert(vectors=[
    {"id": "vec1", "values": [0.1, 0.2, 0.3, ...], "metadata": {"category": "A"}},
    {"id": "vec2", "values": [0.4, 0.5, 0.6, ...], "metadata": {"category": "B"}}
])

# Query similar vectors
results = index.query(vector=[0.1, 0.2, 0.3, ...], top_k=10)

Contents

File	Description
README.md	Overview and quick start (this file)
CONFIGURATION.md	Connection and API configuration
COMPATIBILITY.md	Pinecone API compatibility
EXAMPLES.md	Vector search examples

Feature Overview

API Compatibility

Operation	Status	Notes
Upsert	Supported	Batch operations
Query	Supported	Top-K search
Fetch	Supported	By ID
Delete	Supported	By ID or filter
Update	Supported	Metadata update
List	Supported	Pagination
Describe Index	Supported	Index stats

Vector Search Features

• Similarity Metrics: Cosine, Euclidean, Dot product
• Filtering: Metadata-based filtering
• Namespaces: Logical partitioning
• Sparse Vectors: Hybrid search support
• Batching: Bulk operations

Connection Parameters

Parameter	Default	Description
host	localhost	Server hostname
port	8080	Vector API port
api_key	-	API authentication
index	-	Index name

Use Cases

• Semantic Search: Natural language queries
• Recommendations: Similar item lookup
• Image Search: Visual similarity
• RAG Applications: Retrieval-augmented generation
• Anomaly Detection: Outlier identification

Related Documentation

• CONFIGURATION.md - API configuration
• COMPATIBILITY.md - API compatibility
• EXAMPLES.md - Usage examples
• Parent Directory - Protocol overview

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Last Updated: December 2025 Consolidation Status: Complete