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giuseppe-trisciuoglio/developer-kit

Comprehensive developer toolkit providing reusable skills for Java/Spring Boot, TypeScript/NestJS/React/Next.js, Python, PHP, AWS CloudFormation, AI/RAG, DevOps, and more.

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vector-databases.mdplugins/developer-kit-ai/skills/rag/references/

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

plugins

developer-kit-ai

skills

rag

references

document-chunking.md

embedding-models.md

langchain4j-rag-guide.md

retrieval-strategies.md

vector-databases.md

SKILL.md

README.md

tile.json