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Pinecone

Vector Database

Pinecone is a fully managed vector database that makes it easy to add vector search to production applications. It combines state-of-the-art vector search libraries, advanced features like filtering, and distributed infrastructure.

Pinecone Systems

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Pricing Model

Enterprise

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Vector Database

Category

Memory Capabilities

How Pinecone handles agent memory

Real-time vector indexing
Metadata filtering for precise retrieval
Hybrid search capabilities
Automatic scaling and optimization
Multi-region deployment

Key Features

Managed vector database service

Sub-second query performance

Real-time updates and deletes

Advanced filtering and metadata

Enterprise security and compliance

Common Use Cases

Semantic search applications

Recommendation systems

RAG (Retrieval Augmented Generation)

Similarity matching

Content discovery

Installation

pip install pinecone-client

Quick Start

import pinecone
import openai

# Initialize Pinecone
pinecone.init(api_key="your-key", environment="us-west1-gcp")

# Create or connect to index
index = pinecone.Index("agent-memory")

# Generate embedding
def get_embedding(text):
    response = openai.Embedding.create(
        input=text,
        model="text-embedding-ada-002"
    )
    return response['data'][0]['embedding']

# Store memory
memory_text = "User prefers technical explanations"
embedding = get_embedding(memory_text)
index.upsert([("memory-1", embedding, {"type": "preference"})])

# Query similar memories
query_embedding = get_embedding("How should I explain this?")
results = index.query(vector=query_embedding, top_k=5)