/rag-sentence-transformers

name: rag-sentence-transformers
description: Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.
license: MIT
metadata:
  version: 1.0.0
  author: Orchestra Research
  tags: [Sentence Transformers, Embeddings, Semantic Similarity, RAG, Multilingual, Multimodal, Pre-Trained Models, Clustering, Semantic Search, Production]
  dependencies: [sentence-transformers, transformers, torch]

Sentence Transformers - State-of-the-Art Embeddings

Python framework for sentence and text embeddings using transformers.

When to use Sentence Transformers

**Use when:**

• Need high-quality embeddings for RAG
• Semantic similarity and search
• Text clustering and classification
• Multilingual embeddings (100+ languages)
• Running embeddings locally (no API)
• Cost-effective alternative to OpenAI embeddings

**Metrics**:

• **15,700+ GitHub stars**
• **5000+ pre-trained models**
• **100+ languages** supported
• Based on PyTorch/Transformers

**Use alternatives instead**:

• **OpenAI Embeddings**: Need API-based, highest quality
• **Instructor**: Task-specific instructions
• **Cohere Embed**: Managed service

Quick start

Installation

pip install sentence-transformers

Basic usage

from sentence_transformers import SentenceTransformer

# Load model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Generate embeddings
sentences = [
    "This is an example sentence",
    "Each sentence is converted to a vector"
]

embeddings = model.encode(sentences)
print(embeddings.shape)  # (2, 384)

# Cosine similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
print(f"Similarity: {similarity.item():.4f}")

Popular models

General purpose

# Fast, good quality (384 dim)
model = SentenceTransformer('all-MiniLM-L6-v2')

# Better quality (768 dim)
model = SentenceTransformer('all-mpnet-base-v2')

# Best quality (1024 dim, slower)
model = SentenceTransformer('all-roberta-large-v1')

Multilingual

# 50+ languages
model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')

# 100+ languages
model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')

Domain-specific

# Legal domain
model = SentenceTransformer('nlpaueb/legal-bert-base-uncased')

# Scientific papers
model = SentenceTransformer('allenai/specter')

# Code
model = SentenceTransformer('microsoft/codebert-base')

Semantic search

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer('all-MiniLM-L6-v2')

# Corpus
corpus = [
    "Python is a programming language",
    "Machine learning uses algorithms",
    "Neural networks are powerful"
]

# Encode corpus
corpus_embeddings = model.encode(corpus, convert_to_tensor=True)

# Query
query = "What is Python?"
query_embedding = model.encode(query, convert_to_tensor=True)

# Find most similar
hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=3)
print(hits)

Similarity computation

# Cosine similarity
similarity = util.cos_sim(embedding1, embedding2)

# Dot product
similarity = util.dot_score(embedding1, embedding2)

# Pairwise cosine similarity
similarities = util.cos_sim(embeddings, embeddings)

Batch encoding

# Efficient batch processing
sentences = ["sentence 1", "sentence 2", ...] * 1000

embeddings = model.encode(
    sentences,
    batch_size=32,
    show_progress_bar=True,
    convert_to_tensor=False  # or True for PyTorch tensors
)

Fine-tuning

from sentence_transformers import InputExample, losses
from torch.utils.data import DataLoader

# Training data
train_examples = [
    InputExample(texts=['sentence 1', 'sentence 2'], label=0.8),
    InputExample(texts=['sentence 3', 'sentence 4'], label=0.3),
]

train_dataloader = DataLoader(train_examples, batch_size=16)

# Loss function
train_loss = losses.CosineSimilarityLoss(model)

# Train
model.fit(
    train_objectives=[(train_dataloader, train_loss)],
    epochs=10,
    warmup_steps=100
)

# Save
model.save('my-finetuned-model')

LangChain integration

from langchain_community.embeddings import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-mpnet-base-v2"
)

# Use with vector stores
from langchain_chroma import Chroma

vectorstore = Chroma.from_documents(
    documents=docs,
    embedding=embeddings
)

LlamaIndex integration

from llama_index.embeddings.huggingface import HuggingFaceEmbedding

embed_model = HuggingFaceEmbedding(
    model_name="sentence-transformers/all-mpnet-base-v2"
)

from llama_index.core import Settings
Settings.embed_model = embed_model

# Use in index
index = VectorStoreIndex.from_documents(documents)

Model selection guide

| Model | Dimensions | Speed | Quality | Use Case | |-------|------------|-------|---------|----------| | all-MiniLM-L6-v2 | 384 | Fast | Good | General, prototyping | | all-mpnet-base-v2 | 768 | Medium | Better | Production RAG | | all-roberta-large-v1 | 1024 | Slow | Best | High accuracy needed | | paraphrase-multilingual | 768 | Medium | Good | Multilingual |

Best practices

1. **Start with all-MiniLM-L6-v2** - Good baseline 2. **Normalize embeddings** - Better for cosine similarity 3. **Use GPU if available** - 10× faster encoding 4. **Batch encoding** - More efficient 5. **Cache embeddings** - Expensive to recompute 6. **Fine-tune for domain** - Improves quality 7. **Test different models** - Quality varies by task 8. **Monitor memory** - Large models need more RAM

Performance

| Model | Speed (sentences/sec) | Memory | Dimension | |-------|------