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name: embedding-models description: Embedding model configurations and cost calculators allowed-tools: Bash, Read, Write, Edit, WebFetch

Embedding Models Skill

Embedding model selection, configuration, and cost optimization for RAG pipelines.

Use When

• Selecting embedding models for vector search
• Configuring OpenAI, Cohere, or HuggingFace embeddings
• Calculating embedding generation costs
• Optimizing embedding performance vs cost tradeoffs
• Setting up local vs cloud embedding models
• Implementing embedding caching strategies
• User mentions: "embeddings", "vector models", "embedding costs", "semantic search models"

Model Selection Guide

Commercial Models

OpenAI Embeddings:

• text-embedding-3-small - 1536 dims, $0.02/1M tokens, balanced performance
• text-embedding-3-large - 3072 dims, $0.13/1M tokens, highest quality
• text-embedding-ada-002 - 1536 dims, $0.10/1M tokens, legacy model

Cohere Embeddings:

• embed-english-v3.0 - 1024 dims, multilingual support
• embed-english-light-v3.0 - 384 dims, faster/cheaper
• embed-multilingual-v3.0 - 1024 dims, 100+ languages

Open Source Models (HuggingFace)

Sentence Transformers:

• all-MiniLM-L6-v2 - 384 dims, 80MB, fast and efficient
• all-mpnet-base-v2 - 768 dims, 420MB, high quality
• multi-qa-mpnet-base-dot-v1 - 768 dims, optimized for Q&A
• paraphrase-multilingual-mpnet-base-v2 - 768 dims, 50+ languages

Specialized Models:

• BAAI/bge-small-en-v1.5 - 384 dims, SOTA small model
• BAAI/bge-base-en-v1.5 - 768 dims, excellent retrieval
• BAAI/bge-large-en-v1.5 - 1024 dims, top performance
• intfloat/e5-base-v2 - 768 dims, strong general purpose

Cost Calculator

Use the cost calculator script to estimate embedding costs:

# Calculate costs for different models and volumes
python scripts/calculate-embedding-costs.py \
  --documents 100000 \
  --avg-tokens 500 \
  --model text-embedding-3-small

# Compare multiple models
python scripts/calculate-embedding-costs.py \
  --documents 100000 \
  --avg-tokens 500 \
  --compare

Setup Scripts

OpenAI Embeddings

bash scripts/setup-openai-embeddings.sh

Configures OpenAI embedding client with API key management and retry logic.

HuggingFace Embeddings

bash scripts/setup-huggingface-embeddings.sh

Downloads and configures sentence-transformers models locally.

Cohere Embeddings

bash scripts/setup-cohere-embeddings.sh

Sets up Cohere embedding client with API credentials.

Configuration Templates

OpenAI Configuration

# templates/openai-embedding-config.py
from openai import OpenAI
client = OpenAI(api_key="your-key")

embeddings = client.embeddings.create(
    model="text-embedding-3-small",
    input=["Your text here"]
)

HuggingFace Configuration

# templates/huggingface-embedding-config.py
from sentence_transformers import SentenceTransformer

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

Custom Model Template

# templates/custom-embedding-model.py
# Wrapper for any embedding model with consistent interface

Optimization Strategies

Cost Optimization:

1. Use smaller models for high-volume applications
2. Implement embedding caching (see examples/embedding-cache.py)
3. Batch embedding generation (see examples/batch-embedding-generation.py)
4. Consider local models for sensitive data

Performance Optimization:

1. Use GPU acceleration for local models
2. Batch processing for throughput
3. Dimension reduction for storage/speed
4. Model distillation for faster inference

Model Comparison Matrix

Model	Dimensions	Size	Speed	Quality	Cost
text-embedding-3-small	1536	API	Fast	Good	$0.02/1M
text-embedding-3-large	3072	API	Medium	Excellent	$0.13/1M
all-MiniLM-L6-v2	384	80MB	Very Fast	Good	Free
all-mpnet-base-v2	768	420MB	Fast	Excellent	Free
bge-base-en-v1.5	768	420MB	Fast	Excellent	Free
embed-english-v3.0	1024	API	Fast	Excellent	$0.10/1M

Examples

Batch Embedding Generation:

# examples/batch-embedding-generation.py
# Process large document collections efficiently

Embedding Cache:

# examples/embedding-cache.py
# Cache embeddings to avoid redundant API calls

Decision Framework

Use OpenAI when:

• Need highest quality embeddings
• Low to medium volume (<10M tokens/month)
• Prefer managed service over self-hosting
• Working with latest models

Use Cohere when:

• Need multilingual support
• Require production SLA
• Want embedding customization
• Need both embedding and reranking

Use HuggingFace/Local when:

• High volume (>10M tokens/month)
• Data privacy requirements
• Have GPU infrastructure
• Cost optimization priority
• Offline/air-gapped environments

References

• Sentence Transformers: https://www.sbert.net/
• OpenAI Embeddings: https://platform.openai.com/docs/guides/embeddings
• Cohere Embeddings: https://docs.cohere.com/docs/embeddings
• MTEB Leaderboard: https://huggingface.co/spaces/mteb/leaderboard