gh-giuseppe-trisciuoglio-developer-kit/skills/ai/rag/references/vector-databases.md

Vector Database Comparison and Configuration

Overview

Vector databases store and efficiently retrieve document embeddings for semantic search in RAG systems.

Popular Vector Database Options

1. Pinecone

• Type: Managed cloud service
• Features: Scalable, fast queries, managed infrastructure
• Use Case: Production applications requiring high availability

2. Weaviate

• Type: Open-source, hybrid search
• Features: Combines vector and keyword search, GraphQL API
• Use Case: Applications needing both semantic and traditional search

3. Milvus

• Type: High performance, on-premise
• Features: Distributed architecture, GPU acceleration
• Use Case: Large-scale deployments with custom infrastructure

4. Chroma

• Type: Lightweight, easy to use
• Features: Local deployment, simple API
• Use Case: Development and small-scale applications

5. Qdrant

• Type: Fast, filtered search
• Features: Advanced filtering, payload support
• Use Case: Applications requiring complex metadata filtering

6. FAISS

• Type: Meta's library, local deployment
• Features: High performance, CPU/GPU optimized
• Use Case: Research and applications needing full control

Configuration Examples

Pinecone Setup

import pinecone
from langchain.vectorstores import Pinecone

pinecone.init(api_key="your-api-key", environment="us-west1-gcp")
index = pinecone.Index("your-index-name")
vectorstore = Pinecone(index, embeddings.embed_query, "text")

Weaviate Setup

import weaviate
from langchain.vectorstores import Weaviate

client = weaviate.Client("http://localhost:8080")
vectorstore = Weaviate(client, "Document", "content", embeddings)

Chroma Local Setup

from langchain.vectorstores import Chroma

vectorstore = Chroma(
    collection_name="my_collection",
    embedding_function=embeddings,
    persist_directory="./chroma_db"
)

Selection Criteria

1. Scale: Number of documents and expected query volume
2. Performance: Latency requirements and throughput needs
3. Deployment: Cloud vs on-premise preferences
4. Features: Filtering, hybrid search, metadata support
5. Cost: Budget constraints and operational overhead
6. Maintenance: Team expertise and available resources

Best Practices

1. Indexing Strategy: Choose appropriate distance metrics (cosine, euclidean)
2. Sharding: Distribute data for large-scale deployments
3. Monitoring: Track query performance and system health
4. Backups: Implement regular backup procedures
5. Security: Secure access to sensitive data
6. Optimization: Tune parameters for your specific use case