OpenPAI offers embedding and rerank models from the major vendors, ready to use for RAG / semantic retrieval / recommendation scenarios.
Embedding models
OpenAI
| Model ID | Dimensions | Notes |
|---|
text-embedding-3-small | 1536 (reducible to 512) | Default recommendation, great value |
text-embedding-3-large | 3072 (reducible to 256) | Higher retrieval quality |
text-embedding-ada-002 | 1536 | Older, for compatibility |
Google
| Model ID | Dimensions |
|---|
text-embedding-004 | 768 |
gemini-embedding-001 | 3072 (reducible) |
Chinese / open-source
| Model ID | Dimensions | Vendor |
|---|
bge-large-zh-v1.5 | 1024 | BAAI |
bge-m3 | 1024 | BAAI, multilingual |
text-embedding-v3 | 1024 | Tongyi Qianwen |
doubao-embedding | 2048 | ByteDance |
Example
from openai import OpenAI
client = OpenAI(api_key="sk-...", base_url="https://openp.ai/v1")
resp = client.embeddings.create(
model="text-embedding-3-small",
input=["OpenPAI is an API gateway", "What is RAG?"],
)
for item in resp.data:
print(len(item.embedding), item.embedding[:5])
curl https://openp.ai/v1/embeddings \
-H "Authorization: Bearer $OPENPAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "text-embedding-3-small",
"input": "OpenPAI is an API gateway"
}'
Rerank models
Rerank is used in RAG to re-sort the initial recall results, improving Top-K recall quality.
Available models
| Model ID | Vendor | Context |
|---|
rerank-multilingual-v3.0 | Cohere | 4K |
rerank-english-v3.0 | Cohere | 4K |
jina-reranker-v2-base-multilingual | Jina | 1K |
bge-reranker-large | BAAI | 512 |
bge-reranker-v2-m3 | BAAI | 8K |
API
OpenPAI provides a dedicated /v1/rerank endpoint, fully compatible with the Cohere / Jina request structure:
curl https://openp.ai/v1/rerank \
-H "Authorization: Bearer $OPENPAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "rerank-multilingual-v3.0",
"query": "What is a vector database?",
"documents": [
"A vector database stores high-dimensional vectors for similarity search.",
"Postgres is a relational database.",
"FAISS and Milvus are common vector-retrieval solutions."
],
"top_n": 2
}'
{
"results": [
{"index": 0, "relevance_score": 0.98},
{"index": 2, "relevance_score": 0.83}
]
}
Selection guidance
| Scenario | Recommendation |
|---|
| Primarily Chinese RAG | bge-m3 + bge-reranker-v2-m3 |
| Multilingual | text-embedding-3-small + rerank-multilingual-v3.0 |
| High-quality English retrieval | text-embedding-3-large + rerank-english-v3.0 |
| Ultra-low latency | text-embedding-3-small + no rerank |
Persist embedding vectors on your application side; only rerank the Top-50/100 recalls — there’s no need to rerank the full dataset.
