| name | embedding |
| description | Use when user needs to convert text/images to vectors. Triggers on: embedding, vectorize, encode, text-to-vector, model selection, sentence-transformers, OpenAI embeddings, BGE, CLIP. |
Embedding - Vectorization Model Selection
Convert text, images, and other data into vectors - the foundation of all vector retrieval applications.
Quick Selection
By User Type
| User Type | Recommended | Reason |
|---|
| Beginners (don't want to manage infrastructure) | OpenAI / Cohere API | Sign up and use, no GPU needed |
| Advanced users (seeking best results) | BGE-M3 / Voyage AI | Open source control / best for specialized domains |
| Chinese scenarios | BGE-M3 or Qwen3-Embedding | Best Chinese performance |
| Image scenarios | CLIP / Chinese-CLIP | Cross-modal image-text |
By Scenario
| Scenario | Recommended Model | Notes |
|---|
| General text search | OpenAI text-embedding-3-small | Best value |
| High-precision RAG | Voyage-3-large / Cohere embed-v4 | MTEB top performers |
| Chinese knowledge base | BGE-M3 / BGE-large-zh | Chinese optimized |
| Multilingual | BGE-M3 / multilingual-e5 | 100+ languages |
| Code search | Voyage-code-3 | Code specialized |
| Legal/Finance | Voyage-finance/law | Domain specialized |
| Image search | CLIP-ViT-L-14 | General image-text |
| Chinese image-text | Chinese-CLIP | Chinese image-text |
Model Overview
API Models (Ready to Use)
Suitable for: Beginners, no GPU management, quick validation
| Model | Provider | Dimensions | Context | Price ($/1M tokens) | MTEB | Features |
|---|
| text-embedding-3-large | OpenAI | 3072 | 8K | $0.13 | 64.6 | Best general, mature ecosystem |
| text-embedding-3-small | OpenAI | 1536 | 8K | $0.02 | 62.3 | Best value |
| embed-v4 | Cohere | 1024 | 512 | $0.10 | 65.2 | Multilingual, compressible |
| voyage-3-large | Voyage AI | 1024 | 32K | $0.06 | 65.8 | Long text, domain specialized |
| voyage-3-lite | Voyage AI | 512 | 32K | $0.02 | 61.2 | Low cost, low latency |
Open Source Models (Local Deployment)
Suitable for: Advanced users, data privacy requirements, seeking best performance
| Model | Source | Dimensions | Context | Size | MTEB | Features |
|---|
| BGE-M3 | BAAI | 1024 | 8K | 2.2GB | 63.0 | Multi-lingual/functional/granular, best Chinese |
| BGE-large-zh-v1.5 | BAAI | 1024 | 512 | 1.3GB | - | Pure Chinese, lightweight |
| Qwen3-Embedding-8B | Alibaba | 4096 | 32K | 16GB | 66.2 | Latest, best performance |
| Qwen3-Embedding-0.6B | Alibaba | 1024 | 32K | 1.2GB | 58.5 | Lightweight, surpasses BGE-M3 at same size |
| jina-embeddings-v3 | Jina AI | 1024 | 8K | 1.2GB | 62.8 | Multi-task, adjustable dimensions |
| nomic-embed-text | Nomic | 768 | 8K | 548MB | 59.3 | Open source free, local first choice |
| all-MiniLM-L6-v2 | SBERT | 384 | 256 | 80MB | 56.3 | Ultra lightweight, prototyping |
Image Models
| Model | Source | Dimensions | Features |
|---|
| CLIP-ViT-L-14 | OpenAI | 768 | Highest accuracy |
| CLIP-ViT-B-32 | OpenAI | 512 | Fast |
| Chinese-CLIP-ViT-H | OFA-Sys | 1024 | Chinese optimized |
| SigLIP | Google | 1152 | Next generation, better performance |
Cost Calculation
API Model Monthly Cost Estimate
Assumption: 1M documents, average 500 tokens/document
| Model | Indexing Cost | Query Cost (100k/month) | Monthly Total |
|---|
| OpenAI small | $10 | $2 | ~$12 |
| OpenAI large | $65 | $13 | ~$78 |
| Cohere v4 | $50 | $10 | ~$60 |
| Voyage-3 | $30 | $6 | ~$36 |
| Voyage-3-lite | $10 | $2 | ~$12 |
Open Source Model Deployment Cost
| Model | Minimum GPU | Cloud GPU Monthly Cost |
|---|
| BGE-M3 | RTX 3090 (24GB) | ~$150-300 |
| Qwen3-0.6B | RTX 3060 (12GB) | ~$80-150 |
| all-MiniLM | CPU | $0 |
Selection Decision Tree
Start
│
├─ Have GPU?
│ ├─ No → API models
│ │ ├─ Tight budget → OpenAI small / Voyage-lite
│ │ ├─ Best results → Voyage-3-large / Cohere v4
│ │ └─ General use → OpenAI large
│ │
│ └─ Yes → Open source models
│ ├─ Mainly Chinese → BGE-M3 / Qwen3-Embedding
│ ├─ Multilingual → BGE-M3
│ ├─ Ultra lightweight → all-MiniLM / nomic-embed
│ └─ Images → CLIP / Chinese-CLIP
│
└─ Special domain?
├─ Code → Voyage-code-3
├─ Legal → Voyage-law-2
└─ Finance → Voyage-finance-2
Quick Code Examples
Local Model (Recommended for Beginners)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
text = "This is a sample text"
embedding = model.encode(text).tolist()
texts = ["Text 1", "Text 2", "Text 3"]
embeddings = model.encode(texts, batch_size=32, normalize_embeddings=True)
API Model
from openai import OpenAI
client = OpenAI()
response = client.embeddings.create(
model="text-embedding-3-small",
input=["Text 1", "Text 2"]
)
embeddings = [item.embedding for item in response.data]
Detailed Integration Guides
See verticals/ directory for detailed integration for each model (registration, installation, code examples):
API Models
openai.md - OpenAI text-embedding-3cohere.md - Cohere Embed v4voyage.md - Voyage AI (general/code/legal/finance)aliyun.md - Alibaba Cloud DashScope
Open Source Models
bge.md - BGE series (BAAI)qwen-embedding.md - Qwen3-Embedding (Alibaba open source)jina.md - Jina Embeddingsnomic.md - Nomic Embedminilm.md - all-MiniLM (lightweight)
Image Models
clip.md - CLIP / Chinese-CLIPsiglip.md - SigLIP
FAQ
Q: Are more dimensions always better?
Not necessarily. More dimensions mean higher storage costs and slower search. 512-1024 dimensions are usually sufficient; 3072 dimensions are only necessary for extremely high precision scenarios.
Q: Can I mix different models in the same Collection?
No. The same Collection must use the same model, otherwise the vector spaces are different and cannot be compared.
Q: Must queries and documents use the same model?
Yes, queries and documents must be encoded with the same model.
Q: Does OpenAI work well for Chinese?
It works, but BGE-M3 / Qwen3-Embedding perform better for Chinese scenarios.
Related Tools
- Batch processing:
core:ray
- Chunking strategy:
core:chunking
- Index management:
core:indexing
- Reranking:
core:rerank
