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ragflow/conf/models/baichuan.json

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{
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"name": "BaiChuan",
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"url": {
"default": "https://api.baichuan-ai.com/v1"
},
"url_suffix": {
"chat": "chat/completions",
"embedding": "embeddings"
},
"class": "baichuan",
"models": [
{
"name": "Baichuan4",
"content_length": 32768,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan4-Air",
"content_length": 32768,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan4-Turbo",
"content_length": 32768,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan-M3",
"content_length": 32768,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan-M3-plus",
"content_length": 32768,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan-M2-plus",
"content_length": 32768,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan-M2",
"content_length": 32768,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan3-Turbo",
"content_length": 32768,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan3-Turbo-128k",
"content_length": 131072,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan2-Turbo",
"content_length": 32768,
"max_output": 8192,
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"model_types": [
"chat"
],
"tools": {
"support": true
}
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},
{
"name": "Baichuan-Text-Embedding",
"max_tokens": 8192,
"model_types": [
"embedding"
feat: add batch_size to all embedding model configs (#17877) ## Summary Add a `batch_size` field to every embedding model entry in `conf/models/*.json`. The field represents the maximum number of text inputs that can be submitted to the embedding API in a single request. **75 embedding models across 30 config files** now carry a `batch_size`. Values were verified against each provider's official documentation (see the verification table at `Desktop/embedding_models_verified.md`). ## Distribution | batch_size | # models | Provider / Model | |---|---|---| | 1 | 2 | AWS Bedrock `amazon.titan-embed-text-v1/v2:0` — Bedrock `invoke` accepts a single input per call | | 10 | 3 | Aliyun `text-embedding-v3/v4`, Volcengine `doubao-embedding-vision-251215` | | 16 | 5 | BaiChuan `Baichuan-Text-Embedding`, Baidu Qianfan `embedding-v1`, Mistral `mistral-embed`, Replicate (x2) | | 32 | 10 | NVIDIA NIM (x3), SILICONFLOW (x2), PPIO (x3), GiteeAI `bge-m3`, HuaweiCloud `bge-m3` | | 50 | 4 | Tencent Hunyuan `kinfra` embeddings (x4) — `InputList.N` max 50 | | 96 | 8 | Cohere embed-v3/v4 (x5), Bedrock Cohere (x3) | | 100 | 3 | Google Gemini `text-embedding-004`, Upstage (x2) | | 512 | 5 | Zhipu GLM `embedding-2/3` (x2), Perplexity `pplx-embed` (x2), Astraflow `text-embedding-3-large` | | 1000 | 9 | Voyage AI (x9) — API reference max | | 1024 | 1 | DeepInfra `Qwen/Qwen3-Embedding-4B` | | 2048 | 15 | OpenAI (x3) + OpenAI-API-compatible proxies (CometAPI, n1n, Jiekou.AI, GreenPT, TogetherAI, NovitaAI) — OpenAI contract limit | | 16384 | 10 | Jina (x8), 302.AI, GiteeAI `jina-clip-v2` — no documented Jina batch limit, safe high cap | --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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],
"max_batch_size": 16
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}
]
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}