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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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embedding-models.mdplugins/developer-kit-ai/skills/rag/references/

Embedding Models Guide

Overview

Embedding models convert text into numerical vectors that capture semantic meaning for similarity search in RAG systems.

Popular Embedding Models

1. text-embedding-ada-002 (OpenAI)

  • Dimensions: 1536
  • Type: General purpose
  • Use Case: Most applications requiring high quality embeddings
  • Performance: Excellent balance of quality and speed

2. all-MiniLM-L6-v2 (Sentence Transformers)

  • Dimensions: 384
  • Type: Lightweight
  • Use Case: Applications requiring fast inference
  • Performance: Good quality, very fast

3. e5-large-v2

  • Dimensions: 1024
  • Type: High quality
  • Use Case: Applications needing superior performance
  • Performance: Excellent quality, multilingual support

4. Instructor

  • Dimensions: Variable (768)
  • Type: Task-specific
  • Use Case: Domain-specific applications
  • Performance: Can be fine-tuned for specific tasks

5. bge-large-en-v1.5

  • Dimensions: 1024
  • Type: State-of-the-art
  • Use Case: Applications requiring best possible quality
  • Performance: SOTA performance on benchmarks

Selection Criteria

  1. Quality vs Speed: Balance between embedding quality and inference speed
  2. Dimension Size: Impact on storage and retrieval performance
  3. Domain: Specific language or domain requirements
  4. Cost: API costs vs local deployment
  5. Batch Size: Throughput requirements
  6. Language: Multilingual support needs

Usage Examples

OpenAI Embeddings

from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vector = embeddings.embed_query("Your text here")

Sentence Transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
vector = model.encode("Your text here")

Hugging Face Models

from langchain.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)

Optimization Tips

  1. Batch Processing: Process multiple texts together for efficiency
  2. Model Quantization: Reduce model size for faster inference
  3. Caching: Cache embeddings for frequently used texts
  4. GPU Acceleration: Use GPU for faster processing when available
  5. Model Selection: Choose appropriate model size for your use case

Evaluation Metrics

  1. Semantic Similarity: How well embeddings capture meaning
  2. Retrieval Performance: Quality of retrieved documents
  3. Speed: Inference time per document
  4. Memory Usage: RAM requirements for the model
  5. Cost: API costs or infrastructure requirements

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