gh-giuseppe-trisciuoglio-developer-kit/skills/ai/rag/references/embedding-models.md

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