Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.
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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.
安装前可以查看交给 AI 代理的指令内容。
Python framework for sentence and text embeddings using transformers.
Use when:
Metrics:
Use alternatives instead:
pip install sentence-transformers
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}")
# 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')
# 50+ languages
model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
# 100+ languages
model = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
# Legal domain
model = SentenceTransformer('nlpaueb/legal-bert-base-uncased')
# Scientific papers
model = SentenceTransformer('allenai/specter')
# Code
model = SentenceTransformer('microsoft/codebert-base')
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)
# 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)
# 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
)
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')
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
)
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 | 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 |
| Model | Speed (sentences/sec) | Memory | Dimension |
|---|---|---|---|
| MiniLM | ~2000 | 120MB | 384 |
| MPNet | ~600 | 420MB | 768 |
| RoBERTa | ~300 | 1.3GB | 1024 |
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Create interactive 3D scenes for websites, including product views and scroll-driven animations. Covers choosing Three.js, React Three Fiber, or Spline, preparing lightweight models, showing loading progress, and adapting experiences for mobile devices.
中文简介正在准备中。当前显示英语简介。
Plan A/B tests that compare page or product changes, with clear hypotheses, sample sizes, success metrics, and traffic allocation. Get a structured test plan and guidance for interpreting results and deciding what to try next.
中文简介正在准备中。当前显示英语简介。
Prepare an academic CV for faculty, postdoctoral, or research applications. Organize publications, grants, teaching, and research experience, with section order and formatting adapted to the role and academic field.
中文简介正在准备中。当前显示英语简介。
Audit and improve web accessibility using WCAG 2.1 guidelines. Covers image descriptions, color contrast, keyboard controls, forms, and screen reader support, with code examples and automated and manual checks to guide fixes.
中文简介正在准备中。当前显示英语简介。
Audit websites for accessibility barriers and improve image descriptions, color contrast, forms, keyboard navigation, and screen reader support. Provides code examples and automated and manual testing guidance for work toward WCAG 2.1 AA or AAA accessibility standards.
中文简介正在准备中。当前显示英语简介。
Design autonomous agents for Claude Code plugins, including when they should run, what tools they may use, and how they should respond. Provides configuration templates, prompt guidance, and validation steps for creating agent files.
中文简介正在准备中。当前显示英语简介。