GET https://hk.hyucloud.com/v1/models

# 新加坡节点
GET https://hk.hyucloud.com/v1/models

# 美国洛杉矶节点
GET https://hk.hyucloud.com/v1/models

# 法兰克福节点
GET https://hk.hyucloud.com/v1/models

# 默认使用中国大陆节点
export ENDPOINT="https://hk.hyucloud.com"
# 或者使用新加坡节点
export ENDPOINT="https://hk.hyucloud.com"
# 或者使用美国洛杉矶节点
export ENDPOINT="https://hk.hyucloud.com"
# 或者使用法兰克福节点
export ENDPOINT="https://hk.hyucloud.com"

curl $ENDPOINT/v1/models \
  -H "Content-Type: application/json" | jq .

{
  "data": [
    {
      "created": 1762741377,
      "id": "deepseek-ai/DeepSeek-R1",
      "object": "model",
      "owned_by": "環宇互聯"
    },
    {
      "created": 1762741326,
      "id": "gpt-5",
      "object": "model",
      "owned_by": "環宇互聯"
    },
    ......
  ],
  "object": "list"
}

# or
# export ENDPOINT="https://hk.hyucloud.com"
# export ENDPOINT="https://hk.hyucloud.com"
# export ENDPOINT="https://hk.hyucloud.com"
# export ENDPOINT="https://hk.hyucloud.com"

curl $ENDPOINT/v1/chat/completions \
 -H "Content-Type: application/json" \
 -H "Authorization: Bearer {api_key}" \
 -d '{
   "model": "{model_name}",
   "messages": [
     {
       "role": "system",
       "content": "You are a helpful assistant."
     },
     {
       "role": "user",
       "content": "一句话描述環宇互聯这家公司。"
     }
   ],
   "stream": true
 }' 

{
  "id": "52ba2d24-f745-42b3-82c3-610a7b2658b0",
  "object": "chat.completion",
  "created": 1763020876,
  "model": "gemini-2.5-pro",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "環宇互聯 是一家中立、安全、可靠的云计算服务平台，致力于为全球企业级客户提供全面的云服务解决方案。"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 9,
    "completion_tokens": 1505,
    "total_tokens": 1514,
    "prompt_tokens_details": {
      "audio_tokens": 0,
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "audio_tokens": 0,
      "reasoning_tokens": 1357,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  },
  "system_fingerprint": "",
  "search_result": null
}

pip install -U openai

from openai import OpenAI
import os

# or
# export ENDPOINT="https://hk.hyucloud.com"
# export ENDPOINT="https://hk.hyucloud.com"
# export ENDPOINT="https://hk.hyucloud.com"
# export ENDPOINT="https://hk.hyucloud.com"

ENDPOINT = os.getenv("ENDPOINT", "https://hk.hyucloud.com")

client = OpenAI(
   api_key="{api_key}",
   base_url=ENDPOINT + "/v1/",
)

chat_completion = client.chat.completions.create(
   messages=[
       {
           "role": "user",
           "content": "一句话描述環宇互聯这家公司。",
       }
   ],
   model="{model_name}",
)

print(chat_completion.choices[0].message.content)

from langchain_openai import ChatOpenAI
from langchain import LLMChain
from langchain.prompts import ChatPromptTemplate

# or
# export ENDPOINT="https://hk.hyucloud.com"
# export ENDPOINT="https://hk.hyucloud.com"
# export ENDPOINT="https://hk.hyucloud.com"
# export ENDPOINT="https://hk.hyucloud.com"

ENDPOINT = os.getenv("ENDPOINT", "https://hk.hyucloud.com")

llm = ChatOpenAI(
    model_name="{model_name}",
    openai_api_key="{api_key}",
    openai_api_base=ENDPOINT + "/v1/",
)

prompt = ChatPromptTemplate.from_template(
    """
    {input}
    """
)

chain = LLMChain(llm=llm, prompt=prompt)

print(chain.run("一句话描述環宇互聯这家公司。"))

GET https://hk.hyucloud.com/v1/models

GET https://hk.hyucloud.com/v1/models

curl --location 'https://hk.hyucloud.com/v1/chat/completions' \
--header "Authorization: Bearer {api_key}" \
--header 'Content-Type: application/json' \
--data '{
    "model": "deepseek-ai/DeepSeek-R1",
    "messages": [
        {
            "role": "user",
            "content": "一句话描述環宇互聯这家公司。"
        }
    ]
}' | jq .

{
  "id": "0a7919c4-981e-438b-ad30-e943b96882b6",
  "object": "chat.completion",
  "created": 1763015400,
  "model": "deepseek-r1-250528",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "用中立、安全、本土化的云计算服务，专注服务中国企业数字化转型的科创板上市云服务商。"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 348,
    "total_tokens": 358,
    "prompt_tokens_details": null,
    "completion_tokens_details": {
      "audio_tokens": 0,
      "reasoning_tokens": 185,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  },
  "system_fingerprint": "",
  "search_result": null
}

curl https://hk.hyucloud.com/v1/responses\
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-5.4",
    "input": "Hello! Who are you?"
  }'

{"error":{"message":"Access forbidden: api key quota exceeded, key_id=uminferapikey-1j3gwjgyhkwc, daily_limit_amount=100 , monthly_limit_amount=1000","type":"permission_error","param":"c2ad1049-66a6-4640-b2b5-1711dc401092","code":"forbidden"}}

 curl https://hk.hyucloud.com/v1/responses\
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-5.4",
    "input": "Hello! Who are you?"
  }'

{"error":{"message":"No permission to use the model: apikey [uminferapikey-1j3gwjgyhkwc] not support model [gpt-5.4]","type":"invalid_request_error","param":"6d7248f8-ebaa-437e-bfc2-d79e7c54004c","code":"model_error"}}

 curl https://hk.hyucloud.com/v1/responses\
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-5.4",
    "input": "Hello! Who are you?"
  }'

{"error":{"message":"Access forbidden: api key ip not in whitelist, key_id=uminferapikey-1j3gwjgyhkwc, ip=127.0.0.1","type":"permission_error","param":"c2ad1049-66a6-4640-b2b5-1711dc401092","code":"forbidden"}}

GET https://hk.hyucloud.com/v1/models

curl https://hk.hyucloud.com/v1/models \
  -H "Content-Type: application/json" | jq .

{
  "data": [
    {
      "created": 1762741377,
      "id": "deepseek-ai/DeepSeek-R1",
      "object": "model",
      "owned_by": "環宇互聯"
    },
    {
      "created": 1762741326,
      "id": "gpt-5",
      "object": "model",
      "owned_by": "環宇互聯"
    },
    ......
  ],
  "object": "list"
}

GET https://hk.hyucloud.com/v1beta/models

curl --location 'https://hk.hyucloud.com/v1beta/models' \
  -H "Content-Type: application/json" 

{
    "models": [
        {
            "name": "models/gemini-2.5-flash-image",
            "baseModelId": "gemini-2.5-flash-image",
            "version": "",
            "displayName": "gemini-2.5-flash-image",
            "description": "gemini-2.5-flash-image",
            "inputTokenLimit": 0,
            "outputTokenLimit": 0,
            "supportedActions": [
                "generateContent"
            ]
        },
        {
            "name": "models/gemini-2.5-pro",
            "baseModelId": "gemini-2.5-pro",
            "version": "",
            "displayName": "gemini-2.5-pro",
            "description": "gemini-2.5-pro",
            "inputTokenLimit": 0,
            "outputTokenLimit": 0,
            "supportedActions": [
                "generateContent"
            ]
        },
        {
            "name": "models/gemini-2.5-flash",
            "baseModelId": "gemini-2.5-flash",
            "version": "",
            "displayName": "gemini-2.5-flash",
            "description": "gemini-2.5-flash",
            "inputTokenLimit": 0,
            "outputTokenLimit": 0,
            "supportedActions": [
                "generateContent"
            ]
        }
    ]
}

curl https://hk.hyucloud.com/v1/models \
  -H "Content-Type: application/json"

curl https://hk.hyucloud.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {api_key}" \
  -d '{
    "model": "{model_name}",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

import openai

client = openai.OpenAI(api_key="{api_key}", base_url="https://hk.hyucloud.com/v1/")
stream = client.chat.completions.create(
    model="{model_name}",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

curl https://hk.hyucloud.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer {api_key}" \
  -d '{
    "model": "{model_name}",
    "input": "Hello! Who are you?"
  }'

{
  "id": "resp-123",
  "object": "response",
  "created": 1677652288,
  "model": "gpt-4o",
  "output": [{"type": "text", "text": "Hi!"}],
  "status": "completed",
  "usage": {"total_tokens": 20}
}

import openai

client = openai.OpenAI(api_key="{api_key}", base_url="https://hk.hyucloud.com/v1/")
stream = client.responses.create(
    model="{model_name}",
    input_items=[{"type": "text", "text": "Hello!"}],
    stream=True
)
for event in stream:
    print(event)  # 如 response.delta

pip install anthropic

import anthropic

client = anthropic.Anthropic(
    api_key="<hyucloud_API_KEY>",
    base_url="https://hk.hyucloud.com"
)

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)

print(message.content[0].text)

import anthropic

client = anthropic.Anthropic(
    api_key="<hyucloud_API_KEY>",
    base_url="https://hk.hyucloud.com"
)

with client.messages.stream(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Write a short story about AI."}
    ]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

curl https://hk.hyucloud.com/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: $hyucloud_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

curl https://hk.hyucloud.com/v1/messages \
  -H "Content-Type: application/json" \
  -H "x-api-key: $hyucloud_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "max_tokens": 1024,
    "stream": true,
    "messages": [
      {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

import anthropic

client = anthropic.Anthropic(
    api_key="<hyucloud_API_KEY>",
    base_url="https://hk.hyucloud.com"
)

# 使用 URL 传递图像
message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "url",
                        "url": "https://hk.hyucloud.com/hyucloud-maxcot.jpg"
                    }
                },
                {
                    "type": "text",
                    "text": "What's in this image?"
                }
            ]
        }
    ]
)

print(message.content[0].text)

import anthropic
import base64

client = anthropic.Anthropic(
    api_key="<hyucloud_API_KEY>",
    base_url="https://hk.hyucloud.com"
)

# 使用 base64 编码图像
with open("image.png", "rb") as f:
    image_data = base64.standard_b64encode(f.read()).decode("utf-8")

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": "image/png",
                        "data": image_data
                    }
                },
                {
                    "type": "text",
                    "text": "What's in this image?"
                }
            ]
        }
    ]
)

print(message.content[0].text)

import anthropic

client = anthropic.Anthropic(
    api_key="<hyucloud_API_KEY>",
    base_url="https://hk.hyucloud.com"
)

tools = [
    {
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                }
            },
            "required": ["location"]
        }
    }
]

message = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    tools=tools,
    messages=[
        {"role": "user", "content": "What's the weather like in Beijing?"}
    ]
)

print(message.content)

pip install google-genai

from google import genai
from google.genai import types

client = genai.Client(
   api_key="<hyucloud_API_KEY>",
   http_options=types.HttpOptions(
       base_url="https://hk.hyucloud.com"
   ),
)

response = client.models.generate_content(
   model="gemini-3-flash-preview",
   contents=[
       {"text": "How does AI work?"},
   ],
   config=types.GenerateContentConfig(
       thinking_config=types.ThinkingConfig(thinking_budget=0),
   ),
)
print(response.text)

config=types.GenerateContentConfig(
    thinking_config=types.ThinkingConfig(
        include_thoughts=True
    )
)

curl "https://hk.hyucloud.com/v1beta/models/gemini-3-flash-preview:generateContent" \
    -H "x-goog-api-key: $hyucloud_API_KEY" \
    -H "Content-Type: application/json" \
    -X POST \
    -d '{
          "contents": [
            {
              "parts": [
                {
                  "text": "How does AI work?"
                }
              ]
            }
          ],
          "generationConfig": {
            "thinkingConfig": {
              "thinkingBudget": 0
            }
          }
        }'

from google import genai
from google.genai import types

client = genai.Client(
    api_key="<hyucloud_API_KEY>",
    http_options=types.HttpOptions(
        base_url="https://hk.hyucloud.com"
    ),
)

response = client.models.generate_content_stream(
    model="gemini-3-flash-preview", contents=["Explain how AI works"]
)
for chunk in response:
    print(chunk.text, end="")

curl "https://hk.hyucloud.com/v1beta/models/gemini-3-flash-preview:GenerateContent?alt=sse" \
    -H "Authorization: Bearer $hyucloud_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "contents": [
        {
          "role": "user",
          "parts": [
            {
              "text": "Explain how AI works"
            }
          ]
        }
      ]
    }'

from google import genai
from google.genai import types

client = genai.Client(
    api_key="<hyucloud_API_KEY>",
    http_options=types.HttpOptions(base_url="https://hk.hyucloud.com"),
)

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents="请告诉我今天 AI 领域最重要的三条新闻，并给出来源。",
    config=types.GenerateContentConfig(
        tools=[types.Tool(google_search=types.GoogleSearch())]
    ),
)

print(response.text)

# 可选：读取 grounding 元数据（若模型触发了搜索）
if response.candidates and response.candidates[0].grounding_metadata:
    gm = response.candidates[0].grounding_metadata
    if gm.grounding_chunks:
        for idx, chunk in enumerate(gm.grounding_chunks, 1):
            if chunk.web:
                print(f"[{idx}] {chunk.web.title}: {chunk.web.uri}")

curl "https://hk.hyucloud.com/v1beta/models/gemini-3-flash-preview:generateContent" \
  -H "x-goog-api-key: $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -X POST \
  -d '{
    "contents": [
      {
        "parts": [
          {
            "text": "请基于网络信息，概括一下今天全球 AI 相关新闻，并附带来源。"
          }
        ]
      }
    ],
    "tools": [
      {
        "google_search": {}
      }
    ]
  }'

from google import genai
from google.genai import types
import httpx

client = genai.Client(
    api_key="<hyucloud_API_KEY>",
    http_options=types.HttpOptions(
        base_url="https://hk.hyucloud.com"
    ),
)

doc_url = "https://hk.hyucloud.com/gemini-pdf.pdf"

# Retrieve and encode the PDF byte
doc_data = httpx.get(doc_url).content

prompt = "Summarize this document"
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents=[
        types.Part.from_bytes(
            data=doc_data,
            mime_type='application/pdf',
        ),
        prompt
    ]
)

print(response.text)

DOC_URL="https://hk.hyucloud.com/gemini-pdf.pdf"
PROMPT="Summarize this document"
DISPLAY_NAME="base64_pdf"

# Download the PDF
wget -O "${DISPLAY_NAME}.pdf" "${DOC_URL}"

# Check for FreeBSD base64 and set flags accordingly
if [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  B64FLAGS="--input"
else
  B64FLAGS="-w0"
fi

# Base64 encode the PDF
ENCODED_PDF=$(base64 $B64FLAGS "${DISPLAY_NAME}.pdf")

# Generate content using the base64 encoded PDF
curl "https://hk.hyucloud.com/v1beta/models/gemini-3-flash-preview:generateContent" \
    -H "x-goog-api-key: $hyucloud_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"inline_data": {"mime_type": "application/pdf", "data": "'"$ENCODED_PDF"'"}},
          {"text": "'$PROMPT'"}
        ]
      }]
    }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json

# Clean up the downloaded PDF
rm "${DISPLAY_NAME}.pdf"

from google import genai
from google.genai import types
import pathlib

client = genai.Client(
    api_key="<hyucloud_API_KEY>",
    http_options=types.HttpOptions(
        base_url="https://hk.hyucloud.com"
    ),
)

# Retrieve and encode the PDF byte
filepath = pathlib.Path('file.pdf')

prompt = "Summarize this document"
response = client.models.generate_content(
  model="gemini-3-flash-preview",
  contents=[
      types.Part.from_bytes(
        data=filepath.read_bytes(),
        mime_type='application/pdf',
      ),
      prompt])
print(response.text)

curl -X POST "https://hk.hyucloud.com/v1/files" \
  -H "Authorization: Bearer $hyucloud_API_KEY" \
  -F "purpose=batch:gcs" \
  -F "file=@your-input.jsonl"

{
  "id": "1773236148797418904_gemini-3.1-pro-preview.jsonl",
  "object": "file",
  "bytes": 224,
  "created_at": 1773236150,
  "expires_at": 0,
  "filename": "1773236148797418904_gemini-3.1-pro-preview.jsonl",
  "purpose": "batch:gcs"
}

curl -X POST "https://hk.hyucloud.com/v1beta/batchPredictionJobs" \
  -H "X-Goog-Api-Key: $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "displayName": "my-cloud-storage-batch-inference-job",
    "model": "publishers/google/models/gemini-3-flash-preview",
    "inputConfig": {
      "instancesFormat": "jsonl",
      "gcsSource": {
        "uris": "gs://gemini-batch-001/<上传返回的id>"
      }
    },
    "outputConfig": {
      "predictionsFormat": "jsonl",
      "gcsDestination": {
        "outputUriPrefix": "gs://gemini-batch-001/output"
      }
    }
  }'

{
  "name": "projects/279887244472/locations/global/batchPredictionJobs/7737084104264384512",
  "displayName": "my-cloud-storage-batch-inference-job",
  "model": "publishers/google/models/gemini-3-flash-preview",
  "inputConfig": {
    "instancesFormat": "jsonl",
    "gcsSource": {
      "uris": [
        "gs://gvideo.hyucloud.cn/batch_prompt_for_batch_gemini_predict.jsonl"
      ]
    }
  },
  "outputConfig": {
    "predictionsFormat": "jsonl",
    "gcsDestination": {
      "outputUriPrefix": "gs://gvideo.hyucloud.cn/test"
    }
  },
  "outputInfo": {
    "gcsOutputDirectory": "gs://gvideo.hyucloud.cn/test/prediction-model-2026-02-28T08:22:20.427871Z"
  },
  "state": "JOB_STATE_SUCCEEDED",
  "completionStats": {
    "successfulCount": "2"
  },
  "createTime": "2026-02-28T08:22:22.313780Z",
  "startTime": "2026-02-28T08:23:12.172401Z",
  "endTime": "2026-02-28T08:27:35.914509Z",
  "updateTime": "2026-02-28T08:27:35.914509Z",
  "encryptionSpec": {},
  "hyucloudionId": "1"
}

{
  "name": "projects/279887244472/locations/global/batchPredictionJobs/7737084104264384512",
  "displayName": "my-cloud-storage-batch-inference-job",
  "model": "publishers/google/models/gemini-3-flash-preview",
  "inputConfig": {
    "instancesFormat": "jsonl",
    "gcsSource": {
      "uris": [
        "gs://gvideo.hyucloud.cn/batch_prompt_for_batch_gemini_predict.jsonl"
      ]
    }
  },
  "outputConfig": {
    "predictionsFormat": "jsonl",
    "gcsDestination": {
      "outputUriPrefix": "gs://gvideo.hyucloud.cn/test"
    }
  },
  "outputInfo": {
    "gcsOutputDirectory": "gs://gvideo.hyucloud.cn/test/prediction-model-2026-02-28T08:22:20.4172871Z"
  },
  "state": "JOB_STATE_SUCCEEDED",
  "completionStats": {
    "successfulCount": "2"
  },
  "createTime": "2026-02-28T08:22:22.313780Z",
  "startTime": "2026-02-28T08:23:12.172401Z",
  "endTime": "2026-02-28T08:27:35.914509Z",
  "updateTime": "2026-02-28T08:27:35.914509Z",
  "encryptionSpec": {},
  "hyucloudionId": "1"
}

curl -X GET "https://hk.hyucloud.com/v1beta/batchPredictionJobs/<job_id>" \
  -H "x-goog-api-key: $hyucloud_API_KEY"

# 将 <output_directory> 替换为查询任务接口返回的 outputInfo.gcsOutputDirectory
curl -X GET "https://hk.hyucloud.com/v1/batchPredictionJobs/content?file_id=<output_directory>" \
  -H "Authorization: Bearer $hyucloud_API_KEY" \
  -o predictions.jsonl

curl https://hk.hyucloud.com/v1/files \
  -H "Authorization: Bearer <$hyucloud_API_KEY>" \
  -F purpose="batch:gcs" \
  -F "file=@gemini-3.1-pro-preview.jpg" 

{
  "id": "1773236148797418904_demo-image.jpg",
  "object": "file",
  "bytes": 224123,
  "created_at": 1773236150,
  "expires_at": 0,
  "filename": "1773236148797418904_demo-image.jpg",
  "purpose": "",
  "s3_url": "https://example-file-host/hyucloud/1773236148797418904_demo-image.jpg"
}

{
  "error": {
    "message": "this is error meesage",
    "type": "internal_error",
    "param": "e4b4cf3f-24a0-4620-b53e-fee919c9d914",
    "code": "model_server_error"
  }
} 

IMAGE_URL="https://example-file-host/hyucloud/1773236148797418904_demo-image.jpg"

curl -X POST "https://hk.hyucloud.com/v1beta/models/gemini-3-flash-preview:generateContent" \
  -H "x-goog-api-key: $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "contents": [
    {
      "parts": [
        {
          "fileData": {
            "fileUri": "https://storage.googleapis.com/gemini-batch-001/1774000031757082682_gemini-3-flash-preview.jsonl?Expires=1774604833&GoogleAccessId=bucket%40stacyzhang0708-1218-01.iam.gserviceaccount.com&Signature=c4QsHdf89aWeTNX0JzVqT%2BL4VtM2%2Bx7PEpjjNSVU%2FBNjrvJB2kKbpbIWmKvexQvOA4BO9no0KbZxazCvAQbNttK4Ecng7Za4K4FslNLBJ7fucapF7xdE0b%2BSO690ph%2BXWX5erZJixmzV%2BhiGZay0mBdYAxOfVwZb%2Bc5RA%2FQzPqBsdbdKb1%2FzzD4AcwCHu0w0So54JMQt3qcDQVi56NZaOZA4aC9SLZGcVdYakMF75XKn9Z99e2rXgFtEo2P7%2BuRBY8hD%2Bv%2B6dHC9k6fa9nABZEw38wi3Ozfsf7eQ0PmTmrQ2ue6n97zMjbg6rSn2RmKFWc4iWBl48ikkyFmA1GhKsA%3D%3D",
            "mimeType": "text/plain"
          }
        },
        {
          "text": "What is in the text?"
        }
      ],
      "role": "user"
    }
  ]
}
'

VIDEO_URL="https://example-file-host/hyucloud/1773236148797418904_demo-video.mp4"

curl -X POST "https://hk.hyucloud.com/v1beta/models/gemini-3-flash-preview:generateContent" \
  -H "Authorization: Bearer $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [
          {
            "file_data": {
              "mime_type": "video/mp4",
              "file_uri": "'"$VIDEO_URL"'"
            }
          },
          {
            "text": "请总结这个视频的主要事件，并按时间顺序列出关键片段。"
          }
        ]
      }
    ]
  }'

AUDIO_URL="https://example-file-host/hyucloud/1773236148797418904_demo-audio.mp3"

curl -X POST "https://hk.hyucloud.com/v1beta/models/gemini-3-flash-preview:generateContent" \
  -H "x-goog-api-key: $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [
          {
            "file_data": {
              "mime_type": "audio/mpeg",
              "file_uri": "'"$AUDIO_URL"'"
            }
          },
          {
            "text": "请转写这段音频，并总结其核心观点。"
          }
        ]
      }
    ]
  }'

from google import genai
from google.genai.types import   HttpOptions,Part

client = genai.Client(
    http_options=HttpOptions(
        api_version="v1beta",
        base_url="https://hk.hyucloud.com",
    ),
    api_key='<$hyucloud_API_KEY>', 
)

prompt = "Describe the key events in this video, providing both audio and visual details. Include timestamps for salient moments."
response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents=[
        Part.from_uri(
            file_uri="https://storage.googleapis.com/gemini-batch-001/1774000031757082682_gemini-3-flash-preview.jsonl?Expires=1774604833&GoogleAccessId=bucket%40stacyzhang0708-1218-01.iam.gserviceaccount.com&Signature=c4QsHdf89aWeTNX0JzVqT%2BL4VtM2%2Bx7PEpjjNSVU%2FBNjrvJB2kKbpbIWmKvexQvOA4BO9no0KbZxazCvAQbNttK4Ecng7Za4K4FslNLBJ7fucapF7xdE0b%2BSO690ph%2BXWX5erZJixmzV%2BhiGZay0mBdYAxOfVwZb%2Bc5RA%2FQzPqBsdbdKb1%2FzzD4AcwCHu0w0So54JMQt3qcDQVi56NZaOZA4aC9SLZGcVdYakMF75XKn9Z99e2rXgFtEo2P7%2BuRBY8hD%2Bv%2B6dHC9k6fa9nABZEw38wi3Ozfsf7eQ0PmTmrQ2ue6n97zMjbg6rSn2RmKFWc4iWBl48ikkyFmA1GhKsA%3D%3D",
            mime_type="text/plain",
        ),
        "What is in the text?",
    ],
)

print(response.text)

pip install openai

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_hyucloud_API_KEY",
    base_url="https://hk.hyucloud.com/v1"
)

response = client.embeddings.create(
    input="Your text string goes here",
    model="text-embedding-3-large"
)

print(response.data[0].embedding)

curl https://hk.hyucloud.com/v1/embeddings \
  -H "Authorization: Bearer $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Your text string goes here",
    "model": "text-embedding-3-large"
  }'

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [0.0023064255, -0.009327292, ..., -0.0028842222],
      "index": 0
    }
  ],
  "model": "text-embedding-3-large",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_hyucloud_API_KEY",
    base_url="https://hk.hyucloud.com/v1"
)

response = client.embeddings.create(
    model="text-embedding-3-large",
    input="Testing 123",
    dimensions=256  # 指定输出维度
)

print(response.data[0].embedding)

curl https://hk.hyucloud.com/v1/embeddings \
  -H "Authorization: Bearer $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Testing 123",
    "model": "text-embedding-3-large",
    "dimensions": 256
  }'

from openai import OpenAI
import numpy as np

client = OpenAI(
    api_key="YOUR_hyucloud_API_KEY",
    base_url="https://hk.hyucloud.com/v1"
)

def normalize_l2(x):
    x = np.array(x)
    if x.ndim == 1:
        norm = np.linalg.norm(x)
        if norm == 0:
            return x
        return x / norm
    else:
        norm = np.linalg.norm(x, 2, axis=1, keepdims=True)
        return np.where(norm == 0, x, x / norm)

response = client.embeddings.create(
    model="text-embedding-3-large",
    input="Testing 123",
    encoding_format="float"
)

cut_dim = response.data[0].embedding[:256]
norm_dim = normalize_l2(cut_dim)
print(norm_dim)

from openai import OpenAI
import numpy as np

client = OpenAI(
    api_key="YOUR_hyucloud_API_KEY",
    base_url="https://hk.hyucloud.com/v1"
)

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def get_embedding(text, model="text-embedding-3-large"):
    response = client.embeddings.create(input=text, model=model)
    return response.data[0].embedding

def search_documents(documents, query, n=3):
    query_embedding = get_embedding(query)
    
    results = []
    for doc in documents:
        doc_embedding = get_embedding(doc)
        similarity = cosine_similarity(query_embedding, doc_embedding)
        results.append((doc, similarity))
    
    results.sort(key=lambda x: x[1], reverse=True)
    return results[:n]

# 示例
documents = ["Python 是一种编程语言", "机器学习很有趣", "今天天气很好"]
results = search_documents(documents, "编程")
print(results)

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_hyucloud_API_KEY",
    base_url="https://hk.hyucloud.com/v1"
)

# 假设已通过嵌入搜索找到相关文章
relevant_article = "2022年冬季奥运会冰壶金牌由..."

query = f"""使用以下文章回答问题。如果找不到答案，请写"我不知道。"

文章：
\"\"\"
{relevant_article}
\"\"\"

问题：哪些运动员在 2022 年冬季奥运会上获得了冰壶金牌？
"""

response = client.chat.completions.create(
    messages=[
        {'role': 'system', 'content': '你回答有关 2022 年冬季奥运会的问题。'},
        {'role': 'user', 'content': query},
    ],
    model="gpt-4o",
    temperature=0,
)

print(response.choices[0].message.content)

import numpy as np
from sklearn.cluster import KMeans

# 假设 embeddings 是已获取的嵌入向量列表
embeddings = [...]  # 从 API 获取的嵌入向量

matrix = np.vstack(embeddings)
n_clusters = 4

kmeans = KMeans(
    n_clusters=n_clusters,
    init='k-means++',
    random_state=42
)
kmeans.fit(matrix)

# 每个文本的聚类标签
labels = kmeans.labels_

from openai import OpenAI
import numpy as np

client = OpenAI(
    api_key="YOUR_hyucloud_API_KEY",
    base_url="https://hk.hyucloud.com/v1"
)

def get_embedding(text, model="text-embedding-3-large"):
    response = client.embeddings.create(input=text, model=model)
    return response.data[0].embedding

def recommend_similar(items, source_index, n=3):
    """返回与源项目最相似的 n 个项目"""
    embeddings = [get_embedding(item) for item in items]
    source_embedding = embeddings[source_index]
    
    similarities = []
    for i, emb in enumerate(embeddings):
        if i != source_index:
            sim = np.dot(source_embedding, emb)
            similarities.append((i, items[i], sim))
    
    similarities.sort(key=lambda x: x[2], reverse=True)
    return similarities[:n]

from openai import OpenAI
import numpy as np

client = OpenAI(
    api_key="YOUR_hyucloud_API_KEY",
    base_url="https://hk.hyucloud.com/v1"
)

def get_embedding(text, model="text-embedding-3-large"):
    response = client.embeddings.create(input=text, model=model)
    return response.data[0].embedding

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def classify_text(text, labels):
    text_embedding = get_embedding(text)
    label_embeddings = [get_embedding(label) for label in labels]
    
    similarities = [cosine_similarity(text_embedding, le) for le in label_embeddings]
    best_index = np.argmax(similarities)
    return labels[best_index]

# 示例
labels = ["positive", "negative", "neutral"]
result = classify_text("这个产品太棒了！", labels)
print(result)  # 输出: positive

import tiktoken

def num_tokens_from_string(string: str, encoding_name: str = "cl100k_base") -> int:
    """返回文本字符串中的 token 数量。"""
    encoding = tiktoken.get_encoding(encoding_name)
    num_tokens = len(encoding.encode(string))
    return num_tokens

print(num_tokens_from_string("tiktoken is great!"))  # 输出: 4

curl --location --globoff 'https://hk.hyucloud.com/v1beta/models/gemini-embedding-2:embedContent' \
--header 'Authorization: Bearer YOUR_hyucloud_API_KEY' \
--header 'Content-Type: application/json' \
-d '{
  "content": {
    "parts": [
      {
        "text": "Whats this"
      },
      {
        "text": "Whats this like"
      },
      {
        "file_data": {
          "mime_type": "video/mp4",
          "file_uri": "https://example.com/video.mp4"
        }
      },
      {
        "file_data": {
          "mime_type": "image/png",
          "file_uri": "https://example.com/image.png"
        }
      },
      {
        "file_data": {
          "mime_type": "audio/wav",
          "file_uri": "https://example.com/audio.wav"
        }
      },
      {
        "file_data": {
          "mime_type": "application/pdf",
          "file_uri": "https://example.com/document.pdf"
        }
      }
    ]
  }
}'

{
  "embedding": {
    "values": [
      0.0015015427,
      0.014678672,
      -0.018105509,
      -0.013457718,
      0.015227248,
      0.003112343
    ]
  },
  "usageMetadata": {
    "promptTokenCount": 2517,
    "totalTokenCount": 2517,
    "promptTokensDetails": [
      {
        "modality": "VIDEO",
        "tokenCount": 330
      },
      {
        "modality": "TEXT",
        "tokenCount": 5
      },
      {
        "modality": "DOCUMENT",
        "tokenCount": 1548
      },
      {
        "modality": "IMAGE",
        "tokenCount": 258
      },
      {
        "modality": "AUDIO",
        "tokenCount": 376
      }
    ]
  }
}

{
  "text": "要嵌入的文本内容"
}

{
  "file_data": {
    "mime_type": "video/mp4",
    "file_uri": "https://example.com/video.mp4"
  }
}

curl --location --globoff 'https://hk.hyucloud.com/v1beta/models/gemini-embedding-2:embedContent' \
--header 'Authorization: Bearer YOUR_hyucloud_API_KEY' \
--header 'Content-Type: application/json' \
-d '{
  "content": {
    "parts": [
      {
        "text": "查找关于机器学习的视频教程"
      },
      {
        "file_data": {
          "mime_type": "image/png",
          "file_uri": "https://example.com/ml-diagram.png"
        }
      }
    ]
  }
}'

curl --location --globoff 'https://hk.hyucloud.com/v1beta/models/gemini-embedding-2:embedContent' \
--header 'Authorization: Bearer YOUR_hyucloud_API_KEY' \
--header 'Content-Type: application/json' \
-d '{
  "content": {
    "parts": [
      {
        "file_data": {
          "mime_type": "video/mp4",
          "file_uri": "https://example.com/tutorial.mp4"
        }
      }
    ]
  }
}'

curl --location --globoff 'https://hk.hyucloud.com/v1beta/models/gemini-embedding-2:embedContent' \
--header 'Authorization: Bearer YOUR_hyucloud_API_KEY' \
--header 'Content-Type: application/json' \
-d '{
  "content": {
    "parts": [
      {
        "text": "总结这份报告的关键点"
      },
      {
        "file_data": {
          "mime_type": "application/pdf",
          "file_uri": "https://example.com/report.pdf"
        }
      }
    ]
  }
}'

curl --location --globoff 'https://hk.hyucloud.com/v1beta/models/gemini-embedding-2:embedContent' \
--header 'Authorization: Bearer YOUR_hyucloud_API_KEY' \
--header 'Content-Type: application/json' \
-d '{
  "content": {
    "parts": [
      {
        "file_data": {
          "mime_type": "audio/wav",
          "file_uri": "https://example.com/speech.wav"
        }
      }
    ]
  }
}'

curl --location --globoff 'https://hk.hyucloud.com/v1beta/models/gemini-embedding-2:embedContent' \
--header 'Authorization: Bearer YOUR_hyucloud_API_KEY' \
--header 'Content-Type: application/json' \
-d '{
  "outputDimensionality": 768,
  "content": {
    "parts": [
      {
        "text": "Whats this"
      },
      {
        "file_data": {
          "mime_type": "video/mp4",
          "file_uri": "gs://cloud-samples-data/generative-ai/video/pixel8.mp4"
        }
      }
    ]
  }
}'

  curl -X POST https://hk.hyucloud.com/v1/rerank \
  -H "Authorization: Bearer $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "bge-reranker-v2-m3",
    "query": "what is panda?",
    "documents": [
      "hi",
      "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China."
    ],
    "top_n": 2
  }'

{
	"model": "BAAI/bge-reranker-v2-m3",
	"usage": {
		"total_tokens": 53
	},
	"results": [{
		"index": 1,
		"document": {
			"text": "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.",
			"multi_modal": null
		},
		"relevance_score": 0.9948425889015198
	}, {
		"index": 0,
		"document": {
			"text": "hi",
			"multi_modal": null
		},
		"relevance_score": 0.0002801174996420741
	}]
}

curl https://hk.hyucloud.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $YOUR_API_KEY" \
  -d '{
    "model": "deepseek-ai/DeepSeek-OCR",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "convert to markdown"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "data:image/jpeg;base64,'$(curl -s https://hk.hyucloud.com/hyucloud-maxcot.jpg | base64 | tr -d '\n')'"
            }
          }
        ]
      }
    ]
  }'

import base64
import os
from openai import OpenAI

# Function to encode the image
def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')

# Path to your image
image_path = os.path.expanduser("hyucloud.png")

# Getting the base64 string
base64_image = encode_image(image_path)

client = OpenAI(
    api_key=os.getenv("hyucloud_API_KEY", "<YOUR_hyucloud_API_KEY>"),
    base_url="https://hk.hyucloud.com/v1/",
)

response = client.chat.completions.create(
  model="deepseek-ai/DeepSeek-OCR",
  messages=[
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "convert to markdown"
        },
        {
          "type": "image_url",
          "image_url": {
            "url": f"data:image/jpeg;base64,{base64_image}"
          }
        }
      ]
    }
  ]
)

print(response.choices[0].message.content)

curl https://hk.hyucloud.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $YOUR_API_KEY" \
  -d '{
    "model": "deepseek-ai/DeepSeek-OCR",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "convert to markdown"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "data:image/jpeg;base64,'$(curl -s https://hk.hyucloud.com/hyucloud-maxcot.jpg | base64 | tr -d '\n')'"
            }
          }
        ]
      }
    ],
    "stream": true
  }'

import base64
import os
from openai import OpenAI

# Function to encode the image
def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')

# Path to your image
image_path = os.path.expanduser("hyucloud.png")

# Getting the base64 string
base64_image = encode_image(image_path)

client = OpenAI(
    api_key=os.getenv("hyucloud_API_KEY", "<YOUR_hyucloud_API_KEY>"),
    base_url="https://hk.hyucloud.com/v1/",
)

stream = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-OCR",
    messages=[
        {
          "role": "user",
          "content": [
            {
              "type": "text",
              "text": "convert to markdown"
            },
            {
              "type": "image_url",
              "image_url": {
                "url": f"data:image/jpeg;base64,{base64_image}"
              }
            }
          ]
        }
    ],
    stream=True,
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

{
    "model": "deepseek-ai/DeepSeek-V3.1-Terminus",
    ...
    "thinking": {
        "type": "enabled"
    }
}

import json
import requests

# 配置API密钥
api_key = "******"  # 替换为你的 APIKEY
url = "https://hk.hyucloud.com/v1/chat/completions"

headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
data = {
    "model": "deepseek-ai/DeepSeek-V3.1-Terminus",  # 指定模型
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "9.9和9.11哪个大",  # 用户提问
                }
            ],
        }
    ],
    "thinking": {"type": "enabled"},  # 开启思考功能，可选 enabled  disabled  auto
}

try:
    response = requests.post(url, headers=headers, json=data)
    response.raise_for_status() 

    print("请求成功!")
    print(json.dumps(response.json(), indent=2, ensure_ascii=False))

except requests.exceptions.RequestException as e:
    print(f"请求失败: {e}")

{
    "model": "deepseek-ai/DeepSeek-V3.2",
    ...
    "chat_template_kwargs": {
        "thinking": true
    }
}

import json
import requests

# 配置API密钥
api_key = "******"  # 替换为你的 APIKEY
url = "https://hk.hyucloud.com/v1/chat/completions"

headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
data = {
    "model": "deepseek-ai/DeepSeek-V3.2",  # 指定模型
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "9.9和9.11哪个大",  # 用户提问
                }
            ],
        }
    ],
    "chat_template_kwargs": {
        "thinking": true
    }
}

try:
    response = requests.post(url, headers=headers, json=data)
    response.raise_for_status()

    print("请求成功!")
    print(json.dumps(response.json(), indent=2, ensure_ascii=False))

except requests.exceptions.RequestException as e:
    print(f"请求失败: {e}")

{
    "model": "ByteDance/doubao-seed-1.6",
    ...
    "thinking": {
        "type": "enabled"
    }
}

import json
import requests

# 配置API密钥
api_key = "******"  # 替换为你的 APIKEY
url = "https://hk.hyucloud.com/v1/chat/completions"

headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
data = {
    "model": "ByteDance/doubao-seed-1.6",  # 指定模型
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "9.9和9.11哪个大",  # 用户提问
                }
            ],
        }
    ],
    "thinking": {"type": "enabled"},  # 开启思考功能，可选 enabled  disabled  auto
}

try:
    response = requests.post(url, headers=headers, json=data)
    response.raise_for_status() 

    print("请求成功!")
    print(json.dumps(response.json(), indent=2, ensure_ascii=False))

except requests.exceptions.RequestException as e:
    print(f"请求失败: {e}")

curl -s -X POST https://hk.hyucloud.com/v1/chat/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "model": "qwen-mt-flash",
  "messages": [
    {
      "role": "user",
      "content": "看完这个视频我没有笑"
    }
  ],
  "translation_options": {
    "source_lang": "auto",
    "target_lang": "English"
  }
}'

{
  "choices": [
    {
      "message": {
        "content": "I didn't laugh after watching this video.",
        "role": "assistant"
      },
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null
    }
  ],
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 32,
    "completion_tokens": 9,
    "total_tokens": 41
  },
  "created": 1782376798,
  "system_fingerprint": null,
  "model": "qwen-mt-flash",
  "id": "chatcmpl-1f33275d-d0bf-999d-bfa8-0f6029ff5ae4"
}

curl -X POST https://hk.hyucloud.com/v1/chat/completions \
  -H "Authorization: Bearer {api_key}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "easydoc-fin-chat",
    "messages": [
      {"role": "user", "content": "比亚迪2024年Q3营收同比增长率？"}
    ]
  }'

curl -X POST https://hk.hyucloud.com/v1/chat/completions \
  -H "Authorization: Bearer {api_key}" \
  -H "Content-Type: application/json" \
  --no-buffer \
  -d '{
    "model": "easydoc-fin-chat",
    "messages": [
      {"role": "user", "content": "比亚迪2024年Q3营收同比增长率？"}
    ],
    "stream": true
  }'

from openai import OpenAI

client = OpenAI(
    api_key="******",  # 替换为你的 API Key
    base_url="https://hk.hyucloud.com/v1",
)

# 流式调用
response = client.chat.completions.create(
    model="easydoc-fin-chat",
    messages=[
        {"role": "user", "content": "比亚迪2024年Q3营收同比增长率？"}
    ],
    stream=True,
)

for chunk in response:
    delta = chunk.choices[0].delta

    # 处理文本内容
    if delta.content:
        print(delta.content, end="", flush=True)

    # 处理文档引用（自定义扩展字段）
    if hasattr(delta, "annotations") and delta.annotations:
        for annotation in delta.annotations:
            print(f"\n引用: {annotation['file_name']} 第 {annotation['page_number']} 页")
            print(f"链接: {annotation['url']}")

{
  "id": "resp_xxxxxxxxxxxxxxxx",
  "object": "chat.completion",
  "created": 1700000000,
  "model": "easydoc-fin-chat",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "根据比亚迪2024年第三季度财报，营收为2011.25亿元，同比增长24.04%。",
        "annotations": [
          {
            "file_name": "比亚迪:2024年三季度报告.pdf",
            "url": "https://oss.easylink-ai.com/easylink-investplatform/file_md5/xxx.pdf?signature=...",
            "page_number": 2
          }
        ]
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 128,
    "completion_tokens": 64,
    "total_tokens": 192
  }
}

curl -X POST https://hk.hyucloud.com/v1/tasks/submit \
  -H "Authorization: Bearer {api_key}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "easydoc-parse-premium",
    "input": {
      "img_url": "https://example.com/document.pdf"
    }
  }'

curl "https://hk.hyucloud.com/v1/tasks/status?task_id={task_id}" \
  -H "Authorization: Bearer {api_key}"

curl "{output.urls[0]}" -o result.json

import json
import time
import requests

api_key = "******"  # 替换为你的 API Key
base_url = "https://hk.hyucloud.com/v1"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json",
}

# 提交任务
submit_data = {
    "model": "easydoc-parse-premium",
    "input": {
        "img_url": "https://example.com/document.pdf",
    },
}

resp = requests.post(f"{base_url}/tasks/submit", headers=headers, json=submit_data)
task_id = resp.json()["output"]["task_id"]
print(f"task_id: {task_id}")

# 轮询查询结果
while True:
    result = requests.get(
        f"{base_url}/tasks/status",
        headers=headers,
        params={"task_id": task_id},
    ).json()

    status = result["output"]["task_status"]
    print(f"status: {status}")

    if status == "Success":
        download_url = result["output"]["urls"][0]
        print(f"结果下载链接: {download_url}")
        parsed = requests.get(download_url).json()
        print(f"文件名: {parsed['file_name']}")
        print(f"节点数量: {len(parsed['nodes'])}")
        print(f"总页数: {result['usage']['page_count']}")
        break
    elif status == "Failure":
        print(f"失败: {result['output'].get('error_message')}")
        break

    time.sleep(10)

{
  "output": {
    "task_id": "parse_xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
  },
  "request_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
}

{
  "output": {
    "task_id": "parse_xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
    "task_status": "Success",
    "urls": [
      "https://text-store.cn-wlcb.ufileos.com/easylink/parse/parse_xxxxxxxx.json?Expires=xxxxxxxxxx&Signature=xxx&環宇互聯PublicKey=xxx"
    ],
    "submit_time": 1700000000,
    "finish_time": 1700000060
  },
  "usage": {
    "duration": 60,
    "page_count": 5
  },
  "request_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
}

{
  "file_name": "document.pdf",
  "nodes": [
    {
      "id": 1,
      "type": "Title",
      "text": "文档标题",
      "parent_id": -1,
      "composing_blocks": [
        {
          "page_number": 1,
          "coordinates": [582, 271, 1121, 328],
          "system": "PixelSpace",
          "layout_width": 1708,
          "layout_height": 2212
        }
      ],
      "path_info": {
        "path_context": "",
        "path": []
      }
    }
  ],
  "markdown": "# 文档标题\n\n正文内容..."
}

{
  "type": "object",
  "properties": {
    "患者姓名": {"type": "string"},
    "患者性别": {"type": "string"},
    "入院时间": {"type": "string"},
    "出院时间": {"type": "string"},
    "年龄": {"type": "string"},
    "住院时长": {"type": "string"}
  }
}

curl -X POST https://hk.hyucloud.com/v1/tasks/submit \
  -H "Authorization: Bearer {api_key}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "easydoc-emr-mask",
    "input": {
      "img_url": "https://example.com/medical_record.pdf",
      "prompt": "{\"type\":\"object\",\"properties\":{\"患者姓名\":{\"type\":\"string\"},\"患者性别\":{\"type\":\"string\"},\"年龄\":{\"type\":\"string\"}}}"
    }
  }'

curl "https://hk.hyucloud.com/v1/tasks/status?task_id={task_id}" \
  -H "Authorization: Bearer {api_key}"

import time
import requests

api_key = "******"  # 替换为你的 API Key
base_url = "https://hk.hyucloud.com/v1"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json",
}

# 提交任务
submit_data = {
    "model": "easydoc-emr-mask",
    "input": {
        "img_url": "https://example.com/medical_record.pdf",
        "prompt": '{"type":"object","properties":{"患者姓名":{"type":"string"},"患者性别":{"type":"string"},"年龄":{"type":"string"}}}',
    },
}

resp = requests.post(f"{base_url}/tasks/submit", headers=headers, json=submit_data)
task_id = resp.json()["output"]["task_id"]
print(f"task_id: {task_id}")

# 轮询查询结果
while True:
    result = requests.get(
        f"{base_url}/tasks/status",
        headers=headers,
        params={"task_id": task_id},
    ).json()

    status = result["output"]["task_status"]
    print(f"status: {status}")

    if status == "Success":
        print("脱敏后文件链接:")
        for url in result["output"]["urls"]:
            print(url)
        print(f"页数: {result['usage']['page_count']}")
        break
    elif status == "Failure":
        print(f"失败: {result['output'].get('error_message')}")
        break

    time.sleep(5)

{
  "output": {
    "task_id": "b_mask_xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
  },
  "request_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
}

{
  "output": {
    "task_id": "b_mask_xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
    "task_status": "Success",
    "urls": [
      "https://oss.easylink-ai.com/easylink-easydoc-task/mask_merge/xxxxxxxx.pdf?..."
    ],
    "submit_time": 1700000000,
    "finish_time": 1700000060
  },
  "usage": {
    "duration": 60,
    "page_count": 3
  },
  "request_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
}

{
  "type": "object",
  "properties": {
    "甲方名称": {"type": "string"},
    "乙方名称": {"type": "string"},
    "合同金额": {"type": "string"},
    "签订日期": {"type": "string"},
    "合同编号": {"type": "string"}
  }
}

curl -X POST https://hk.hyucloud.com/v1/tasks/submit \
  -H "Authorization: Bearer {api_key}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "easydoc-extract",
    "input": {
      "img_url": "https://example.com/contract.pdf"
    },
    "parameters": {
      "json_schema": "{\"type\":\"object\",\"properties\":{\"甲方名称\":{\"type\":\"string\"},\"乙方名称\":{\"type\":\"string\"},\"合同金额\":{\"type\":\"string\"},\"签订日期\":{\"type\":\"string\"}}}"
    }
  }'

curl -X POST https://hk.hyucloud.com/v1/tasks/submit \
  -H "Authorization: Bearer {api_key}" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "easydoc-extract",
    "input": {
      "img_url": "https://example.com/document.pdf"
    }
  }'

curl "https://hk.hyucloud.com/v1/tasks/status?task_id={task_id}" \
  -H "Authorization: Bearer {api_key}"

import json
import time
import requests

api_key = "******"  # 替换为你的 API Key
base_url = "https://hk.hyucloud.com/v1"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json",
}

# 提交任务（封闭式模式）
json_schema = {
    "type": "object",
    "properties": {
        "甲方名称": {"type": "string"},
        "乙方名称": {"type": "string"},
        "合同金额": {"type": "string"},
        "签订日期": {"type": "string"},
    },
}

submit_data = {
    "model": "easydoc-extract",
    "input": {
        "img_url": "https://example.com/contract.pdf",
    },
    "parameters": {
        "json_schema": json.dumps(json_schema, ensure_ascii=False),
    },
}

resp = requests.post(f"{base_url}/tasks/submit", headers=headers, json=submit_data)
task_id = resp.json()["output"]["task_id"]
print(f"task_id: {task_id}")

# 轮询查询结果
while True:
    result = requests.get(
        f"{base_url}/tasks/status",
        headers=headers,
        params={"task_id": task_id},
    ).json()

    status = result["output"]["task_status"]
    print(f"status: {status}")

    if status == "Success":
        print("抽取结果:")
        for item in result["output"]["result"]:
            print(f"  页数: {item.get('page_count')}")
            print(f"  字段名: {item.get('key_index')}")
            print(f"  抽取内容: {item.get('extracted_fields')}")
        print(f"总页数: {result['usage']['page_count']}")
        break
    elif status == "Failure":
        print(f"失败: {result['output'].get('error_message')}")
        break

    time.sleep(5)

{
  "output": {
    "task_id": "b_extract_xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
  },
  "request_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
}

{
  "output": {
    "task_id": "b_extract_xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
    "task_status": "Success",
    "result": [
      {
        "page_count": 5,
        "key_index": ["甲方名称", "乙方名称", "合同金额", "签订日期"],
        "extracted_fields": {
          "甲方名称": "北京示例科技有限公司",
          "乙方名称": "上海示例贸易有限公司",
          "合同金额": "人民币壹拾万元整",
          "签订日期": "2024年1月1日"
        }
      }
    ],
    "submit_time": 1700000000,
    "finish_time": 1700000060
  },
  "usage": {
    "duration": 60,
    "page_count": 5
  },
  "request_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
}

curl --location 'https://hk.hyucloud.com/v1/images/generations' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "wan2.7-image",
    "prompt": "A futuristic cloud data center floating above the sea at sunrise",
    "size": "1K",
    "seed": 123456,
    "watermark": false
  }'

curl --location 'https://hk.hyucloud.com/v1/images/generations' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "wan2.7-image-pro",
    "prompt": "把这只猫改成赛博朋克风格，霓虹灯光，电影感构图",
    "image": "https://hk.hyucloud.com/hyucloud-maxcot.jpg",
    "size": "2K",
    "thinking_mode": true,
    "watermark": false
  }'

curl --location 'https://hk.hyucloud.com/v1/images/generations' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "wan2.7-image-pro",
    "prompt": "融合两张参考图的主体特征，生成一张高级感品牌海报",
    "images": [
      "https://hk.hyucloud.com/hyucloud-maxcot.jpg",
      "https://hk.hyucloud.com/hyucloud-maxcot.jpg"
    ],
    "size": "2048*2048",
    "thinking_mode": true,
    "seed": 20260402
  }'

curl --location 'https://hk.hyucloud.com/v1/images/generations' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "wan2.7-image-pro",
    "prompt": "生成一组同一角色在春夏秋冬四季中的海报，人物形象保持一致",
    "size": "2K",
    "thinking_mode": true,
    "enable_sequential": true,
    "n": 4
  }'

{
  "created": 1775558400,
  "data": [
    {
      "url": "https://example.com/generated-image-1.png"
    },
    {
      "url": "https://example.com/generated-image-2.png"
    }
  ]
}

{
  "model": "wan2.7-image-pro",
  "input": {
    "prompt": "一只坐在咖啡馆窗边的布偶猫",
    "images": [
      "https://hk.hyucloud.com/hyucloud-maxcot.jpg"
    ]
  },
  "parameters": {
    "size": "2K",
    "prompt_extend": true,
    "enable_sequential": true,
    "n": 4,
    "seed": 123456,
    "watermark": false
  }
}

curl --location 'https://hk.hyucloud.com/v1/tasks/submit' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "wan2.7-image",
    "input": {
      "prompt": "A futuristic cloud data center floating above the sea at sunrise"
    },
    "parameters": {
      "size": "1K",
      "seed": 123456
    }
  }'

curl --location 'https://hk.hyucloud.com/v1/tasks/submit' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "wan2.7-image-pro",
    "input": {
      "prompt": "把这只猫改成赛博朋克风格，霓虹灯光，电影感构图",
      "img_url": "https://hk.hyucloud.com/hyucloud-maxcot.jpg"
    },
    "parameters": {
      "size": "2K",
      "prompt_extend": true,
      "watermark": false
    }
  }'

curl --location 'https://hk.hyucloud.com/v1/tasks/submit' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "wan2.7-image-pro",
    "input": {
      "prompt": "融合两张参考图的主体特征，生成一张高级感品牌海报",
      "images": [
        "https://hk.hyucloud.com/hyucloud-maxcot.jpg",
        "https://hk.hyucloud.com/hyucloud-maxcot.jpg"
      ]
    },
    "parameters": {
      "size": "2048*2048",
      "prompt_extend": true,
      "seed": 20260402
    }
  }'

curl --location 'https://hk.hyucloud.com/v1/tasks/submit' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "wan2.7-image-pro",
    "input": {
      "prompt": "生成一组同一角色在春夏秋冬四季中的海报，人物形象保持一致"
    },
    "parameters": {
      "size": "2K",
      "prompt_extend": true,
      "enable_sequential": true,
      "n": 4
    }
  }'

{
  "output": {
    "task_id": "3c7d6b6c-7cdb-4f37-bf41-7f2b0f1a9f0d"
  },
  "request_id": "req_202604021530000001"
}

curl --location 'https://hk.hyucloud.com/v1/tasks/status?task_id=<task_id>' \
  --header "Authorization: Bearer $hyucloud_API_KEY"

{
  "output": {
    "task_id": "3c7d6b6c-7cdb-4f37-bf41-7f2b0f1a9f0d",
    "task_status": "Success",
    "urls": [
      "https://example.com/generated-image-1.png",
      "https://example.com/generated-image-2.png",
      "https://example.com/generated-image-3.png",
      "https://example.com/generated-image-4.png"
    ],
    "submit_time": 1775115000,
    "finish_time": 1775115038
  },
  "usage": {
    "completion_tokens": 4,
    "total_tokens": 4
  },
  "request_id": ""
}

{
  "output": {
    "task_id": "3c7d6b6c-7cdb-4f37-bf41-7f2b0f1a9f0d",
    "task_status": "Failure",
    "submit_time": 1775115000,
    "finish_time": 1775115012,
    "error_message": "code=InvalidParameter, message=invalid image url"
  },
  "request_id": ""
}

curl --location 'https://hk.hyucloud.com/v1beta/models/gemini-2.5-flash-image:generateContent' \
--header "x-goog-api-key: $hyucloud_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "contents": [
        {
            "role": "user",
            "parts": [
                {
                    "text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"
                }
            ]
        }
    ],
    "generationConfig": {
        "responseModalities": [
            "TEXT",
            "IMAGE"
        ]
    }
}' | jq -r '.candidates[0].content.parts[] | select(.inlineData and (.thought | not)) | .inlineData.data' | head -1 \
| base64 -d > hyucloud_generated_image.png

from google import genai
from google.genai import types
from PIL import Image
from io import BytesIO
import os


client = genai.Client(
    api_key=os.getenv("hyucloud_API_KEY", "<hyucloud_API_KEY>"),  # 您的API_KEY
    http_options=types.HttpOptions(
        base_url="https://hk.hyucloud.com"
    ),
)

contents = [
    {
        "text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme.",
    },
]

generation_config = types.GenerationConfig(
    response_modalities=["text", "image"],
)

response = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents=contents,
    config={
        "response_modalities": ["text", "image"],
    },
)

for part in response.candidates[0].content.parts:
    # 跳过思考过程中的中间图片，只保留最终输出图片
    if getattr(part, "thought", False):
        continue
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        image = Image.open(BytesIO(part.inline_data.data))
        image.save("hyucloud_generated_image.png")

cat <<EOF | curl -X POST \
  --header "Authorization: Bearer ${hyucloud_API_KEY}" \
  --header "Content-Type: application/json" \
  --data @- \
  https://hk.hyucloud.com/v1beta/models/gemini-2.5-flash-image:generateContent | jq -r '.candidates[0].content.parts[] | select(.inlineData and (.thought | not)) | .inlineData.data' | head -1 | base64 --decode > hyucloud_generated_image.png
{
  "contents": [
    {
      "role": "user",
      "parts": [
        {"text": "Convert this photo to black and white, in a cartoonish style."},
        {
          "inlineData": {
            "mimeType": "image/jpeg",
            "data": "$(curl -s https://hk.hyucloud.com/hyucloud-maxcot.jpg | base64 | tr -d '\n')"
          }
        }
      ]
    }
  ],
  "generationConfig": {
    "responseModalities": ["TEXT", "IMAGE"]
  }
}
EOF

from google import genai
from google.genai import types
from PIL import Image
from io import BytesIO
import os

client = genai.Client(
    api_key=os.getenv("hyucloud_API_KEY", "<hyucloud_API_KEY>"),  # 您的API_KEY
    http_options=types.HttpOptions(
        base_url="https://hk.hyucloud.com"
    ),
)

response = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents=[
        types.Content(
            role="user",
            parts=[
                types.Part(
                    text="Convert this photo to black and white, in a cartoonish style."
                ),
                types.Part(
                    file_data=types.FileData(
                        mime_type="image/jpeg",
                        file_uri="https://hk.hyucloud.com/hyucloud-maxcot.jpg",
                    )
                ),
            ],
        )
    ],
    config=types.GenerateContentConfig(response_modalities=["TEXT", "IMAGE"]),
)

for part in response.candidates[0].content.parts:
    # 跳过思考过程中的中间图片，只保留最终输出图片
    if getattr(part, "thought", False):
        continue
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        image = Image.open(BytesIO(part.inline_data.data))
        image.save("hyucloud_generated_image.png")

{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "That sounds incredibly unique! Here's a picture of a nano banana dish in a fancy restaurant with a Gemini theme:\n\n"
          },
          {
            "inlineData": {
              "data": "xxx",
              "mimeType": "image/png"
            }
          }
        ],
        "role": "model"
      },
      "finishReason": "STOP"
    }
  ],
  "usageMetadata": {
    "candidatesTokenCount": 1315,
    "candidatesTokensDetails": [
      {
        "modality": "IMAGE",
        "tokenCount": 1290
      },
      {
        "modality": "TEXT",
        "tokenCount": 25
      }
    ],
    "promptTokenCount": 16,
    "promptTokensDetails": [
      {
        "modality": "TEXT",
        "tokenCount": 16
      }
    ],
    "totalTokenCount": 1331,
    "trafficType": "ON_DEMAND"
  }
}

{
  "error": {
    "message": "error_message",
    "type": "error_type",
    "param": "request_id",
    "code": "error_code"
  }
}

curl -s -X POST \
  "https://hk.hyucloud.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
  -H "x-goog-api-key: $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"parts": [{"text": "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."}]}],
    "tools": [{"google_search": {}}],
  "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"],
      "imageConfig": {"aspectRatio": "1:1", "imageSize": "1K"}
    }
  }' | jq -r '.candidates[0].content.parts[] | select(.inlineData and (.thought | not)) | .inlineData.data' | head -1 | base64 --decode > butterfly.png

from google import genai
from google.genai import types
import os

client = genai.Client(
    api_key=os.getenv("hyucloud_API_KEY", "<hyucloud_API_KEY>"),  # 您的API_KEY
    http_options=types.HttpOptions(
        base_url="https://hk.hyucloud.com"
    ),
)

prompt = "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."
aspect_ratio = "1:1"  # "1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"
resolution = "1K"  # "1K", "2K", "4K"

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=prompt,
    config=types.GenerateContentConfig(
        response_modalities=["TEXT", "IMAGE"],
        image_config=types.ImageConfig(
            aspect_ratio=aspect_ratio, image_size=resolution
        ),
    ),
)

for part in response.parts:
    # 跳过思考过程中的中间图片，只保留最终输出图片
    if getattr(part, "thought", False):
        continue
    if part.text is not None:
        print(part.text)
    elif image := part.as_image():
        image.save("butterfly.png")

{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "Here is the anatomical sketch of a dissected Monarch butterfly in Da Vinci style..."
          },
          {
            "inlineData": {
              "data": "iVBORw0KGgoAAAANSUhEUgAA...",
              "mimeType": "image/png"
            }
          }
        ],
        "role": "model"
      },
      "finishReason": "STOP"
    }
  ],
  "usageMetadata": {
    "candidatesTokenCount": 1315,
    "totalTokenCount": 1331
  }
}

{
  "error": {
    "message": "error_message",
    "type": "error_type",
    "param": "request_id",
    "code": "error_code"
  }
}

curl -s -X POST \
  "https://hk.hyucloud.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
  -H "x-goog-api-key: $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"parts": [{"text": "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."}]}],
    "tools": [{"google_search": {}}],
  "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"],
      "imageConfig": {"aspectRatio": "1:1", "imageSize": "1K"}
    }
  }' | jq -r '.candidates[0].content.parts[] | select(.inlineData and (.thought | not)) | .inlineData.data' | head -1 | base64 --decode > butterfly.png

from google import genai
from google.genai import types
import os

client = genai.Client(
    api_key=os.getenv("hyucloud_API_KEY", "<hyucloud_API_KEY>"),  # 您的API_KEY
    http_options=types.HttpOptions(
        base_url="https://hk.hyucloud.com"
    ),
)

prompt = "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."
aspect_ratio = "1:1"  # "1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"
resolution = "1K"  # "512"(0.5K), "1K", "2K", "4K"

response = client.models.generate_content(
    model="gemini-3.1-flash-image-preview",
    contents=prompt,
    config=types.GenerateContentConfig(
        response_modalities=["TEXT", "IMAGE"],
        image_config=types.ImageConfig(
            aspect_ratio=aspect_ratio, image_size=resolution
        ),
    ),
)

for part in response.parts:
    # 跳过思考过程中的中间图片，只保留最终输出图片
    if getattr(part, "thought", False):
        continue
    if part.text is not None:
        print(part.text)
    elif image := part.as_image():
        image.save("butterfly.png")

{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "Here is the anatomical sketch of a dissected Monarch butterfly in Da Vinci style..."
          },
          {
            "inlineData": {
              "data": "iVBORw0KGgoAAAANSUhEUgAA...",
              "mimeType": "image/png"
            }
          }
        ],
        "role": "model"
      },
      "finishReason": "STOP"
    }
  ],
  "usageMetadata": {
    "candidatesTokenCount": 1315,
    "totalTokenCount": 1331
  }
}

{
  "error": {
    "message": "error_message",
    "type": "error_type",
    "param": "request_id",
    "code": "error_code"
  }
}

curl -s -X POST \
  "https://hk.hyucloud.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
  -H "x-goog-api-key: $hyucloud_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"parts": [{"text": "一只坐着的橙色小猫，正面，纯色背景"}]}],
    "generationConfig": {
      "responseModalities": ["IMAGE"],
      "imageConfig": {"aspectRatio": "1:1", "imageSize": "512"}
    }
  }'

curl -X POST "https://hk.hyucloud.com/v1/flux-2-pro" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $hyucloud_API_KEY" \
  -d '{
     "prompt" : "A photograph of a red fox in an autumn forest",
     "width": 1024,
     "height": 1024
    }' | jq -r '.data[0].b64_json' | base64 --decode > flux-2-pro.png

import requests
import os

url = "https://hk.hyucloud.com/v1/flux-2-pro"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.getenv('hyucloud_API_KEY')}"
}
payload = {
    "prompt": "A photograph of a red fox in an autumn forest",
    "width": 1024,
    "height": 1024
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result)

curl -X POST "https://hk.hyucloud.com/v1/flux-kontext-pro" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $hyucloud_API_KEY" \
  -d '{
     "prompt" : "A photograph of a red fox in an autumn forest"
    }' | jq -r '.data[0].b64_json' | base64 --decode > flux-pro-1.1.png

import requests
import os

url = "https://hk.hyucloud.com/v1/flux-kontext-pro"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.getenv('hyucloud_API_KEY')}"
}
payload = {
    "prompt": "A photograph of a red fox in an autumn forest"
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result)

curl -X POST "https://hk.hyucloud.com/v1/flux-pro-1.1" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $hyucloud_API_KEY" \
  -d '{
     "prompt" : "A photograph of a red fox in an autumn forest",
     "width": 1024,
     "height": 1024
    }' | jq -r '.data[0].b64_json' | base64 --decode > flux-pro-1.1.png

import requests
import os

url = "https://hk.hyucloud.com/v1/flux-pro-1.1"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {os.getenv('hyucloud_API_KEY')}"
}
payload = {
    "prompt": "A photograph of a red fox in an autumn forest",
    "width": 1024,
    "height": 1024,
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result)

curl --location 'https://hk.hyucloud.com/v1/images/generations' \
--header "Authorization: Bearer $hyucloud_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "stepfun-ai/step1x-edit",
    "prompt": "Convert to quick pencil sketch",
    "image": "data:image/png;base64,{image_base64_string}",
    "negative_prompt": "blurry, low quality"
}'

import os
from openai import OpenAI

client = OpenAI(
    base_url=os.getenv("BASE_URL", "https://hk.hyucloud.com/v1"),
    api_key=os.getenv("API_KEY", "$hyucloud_API_KEY")
)

response = client.images.generate(
    model="stepfun-ai/step1x-edit",
    prompt="Convert to quick pencil sketch",
    extra_body={
        "image": "data:image/png;base64,{image_base64_string}",
        "negative_prompt": "blurry, low quality"
    }
)

print(response.data[0].url)

{
  "created": 1750667997,
  "data": [
    {
      "url": "https://xxxxx/xxxx.png",
      "b64_json": "data:image/png;base64,{image_base64_string}"
    }
  ],
  "usage": {
    "input_tokens_details": {}
  }
}

{
  "error": {
    "message": "error_message",
    "type": "error_type",
    "param": "request_id",
    "code": "error_code"
  }
}

---

# Qwen/Qwen-Image-Edit API

本文介绍 `Qwen/Qwen-Image-Edit` 模型调用 API 的输入输出参数，供您使用接口时查阅字段含义。

---

## 请求参数

### 请求体

| 字段名 | 类型   | 是否必须 | 默认值 | 描述                                                                                             |
| ------ | ------ | -------- | ------ | ------------------------------------------------------------------------------------------------ |
| prompt | string | 必须     | -      | 提示词                                                                                           |
| model  | string | 必须     | -      | 本次请求使用的模型名称，此处为 `Qwen/Qwen-Image-Edit`。                                          |
| image  | string | 必须     | -      | base64 数据或图片链接 `http://xxx`                                                               |
| seed   | int    | 可选     | -1     | 随机数种子，用于控制模型生成内容的随机性。如果希望生成内容保持一致，可以使用相同的 seed 参数值。 |
| size   | string | 可选     | `1664x928` | 生成图像的尺寸（宽x高）。默认 `1664x928`，可选值及对应比例：`1664x928`（16:9）、`1472x1104`（4:3）、`1328x1328`（1:1）、`1104x1472`（3:4）、`928x1664`（9:16） |

## 响应参数

| 字段名        | 类型      | 描述                                                                                                                                                                                                                                                    |
| ------------- | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| created       | `integer` | 本次请求创建时间的 Unix 时间戳（秒）。                                                                                                                                                                                                                  |
| data          | `array`   | 输出图像的信息，包括图像下载的 URL 或 Base64。<br/>• 当指定返回生成图像的格式为 url 时，则相应参数的子字段为 url；<br/>• 当指定返回生成图像的格式为 b64_json 时，则相应参数的子字段为 b64_json。<br/>注意：链接将在生成后 7 天内失效，请务必及时保存图像。 |
| error         | `Object`  | 错误信息对象                                                                                                                                                                                                                                            |
| error.code    | `string`  | 错误码                                                                                                                                                                                                                                                  |
| error.message | `string`  | 错误提示信息                                                                                                                                                                                                                                            |
| error.param   | `string`  | 请求 id                                                                                                                                                                                                                                                 |

## 示例

### OPENAI 兼容接口

`POST https://hk.hyucloud.com/v1/images/generations`

#### 同步请求

### 响应

{/*
TODO:异步请求
### 异步请求

curl --location 'https://hk.hyucloud.com/v1/images/generations' \
--header "Authorization: Bearer $hyucloud_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "Qwen/Qwen-Image",
    "prompt": "Convert to quick pencil sketch",
    "size": "1024x1024"
}'

import os
from openai import OpenAI

client = OpenAI(
    base_url=os.getenv("BASE_URL", "https://hk.hyucloud.com/v1"),
    api_key=os.getenv("API_KEY", "$hyucloud_API_KEY")
)

response = client.images.generate(
    model="Qwen/Qwen-Image",
    prompt="Convert to quick pencil sketch",
    size="1024x1024"
)

print(response.data[0].url)

{
  "created": 1750667997,
  "data": [
    {
      "url": "https://xxxxx/xxxx.png",
      "b64_json": "data:image/png;base64,{image_base64_string}"
    }
  ],
  "usage": {
    "input_tokens_details": {}
  }
}

{
  "error": {
    "message": "error_message",
    "type": "error_type",
    "param": "request_id",
    "code": "error_code"
  }
}

---

# gpt-image-1 API

本文介绍 `gpt-image-1` 模型调用 API 的输入输出参数，供您使用接口时查阅字段含义。

---

## 请求参数

### 请求体

| 字段名              | 类型   | 是否必须 | 默认值        | 描述                                                                                                                       |
| ------------------- | ------ | -------- | ------------- | -------------------------------------------------------------------------------------------------------------------------- |
| prompt              | string | 必须     | -             | 提示词                                                                                                                     |
| model               | string | 必须     | -             | 本次请求使用的模型名称，此处为 `gpt-image-1`。                                                                             |
| n                   | int    | 可选     | 1             | 生成图片数量，取值范围为 1~4                                                                                               |
| size                | string | 可选     | "1024x1024"   | 分辨率。GPT‑image‑1 支持 `1024x1024`、`1024x1536`、`1536x1024`。                                                             |
| quality             | string | 可选     | -             | 图片质量，支持 `low`、`medium`、`high`；质量越高耗时越长。                                                                  |
| output_format       | string | 可选     | "png"         | 输出图片格式，支持 `png`、`jpeg`。                                                                                          |
| output_compression  | int    | 可选     | 100           | 图片压缩强度，取值 0~100；`0` 为不压缩，`100` 为最大压缩。                                                                  |

## 响应参数

| 字段名        | 类型      | 描述                                                                                                                                                                                                                                                    |
| ------------- | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| created       | `integer` | 本次请求创建时间的 Unix 时间戳（秒）。                                                                                                                                                                                                                  |
| data          | `array`   | 输出图像的信息，包括图像下载的 URL 或 Base64，gpt-image-1模型返回的是base64数据。<br/>• 当指定返回生成图像的格式为 url 时，则相应参数的子字段为 url；<br/>• 当指定返回生成图像的格式为 b64_json 时，则相应参数的子字段为 b64_json。<br/>注意：链接将在生成后 7 天内失效，请务必及时保存图像。 |
| error         | `Object`  | 错误信息对象                                                                                                                                                                                                                                            |
| error.code    | `string`  | 错误码                                                                                                                                                                                                                                                  |
| error.message | `string`  | 错误提示信息                                                                                                                                                                                                                                            |
| error.param   | `string`  | 请求 id                                                                                                                                                                                                                                                 |

## 示例

### OPENAI 兼容接口

`POST https://hk.hyucloud.com/v1/images/generations`

#### 同步请求

#### curl

#### python

### 图片编辑

`POST https://hk.hyucloud.com/v1/images/edits`

使用 multipart/form-data 传参，至少包含待编辑图片 `image`（可选 `mask`）、以及 `model`、`prompt` 等字段；其余参数如 `size`、`quality`、`output_format`、`output_compression` 与生成接口一致。

#### curl

#### python

### 响应

{/*
TODO:异步请求
### 异步请求

curl --location 'https://hk.hyucloud.com/v1/images/generations' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "gpt-image-1-mini",
    "prompt": "a beautiful flower",
    "size": "1024x1024",
    "quality": "high",
    "output_format": "png",
    "output_compression": 100
  }'

import os, base64
from openai import OpenAI

client = OpenAI(
    base_url="https://hk.hyucloud.com/v1",
    api_key=os.getenv("hyucloud_API_KEY", "YOUR_API_KEY")
)

res = client.images.generate(
    model="gpt-image-1-mini",
    prompt="a beautiful flower",
    size="1024x1024",
    quality="high",
)

# gpt-image-1-mini 返回 base64 数据
image_b64 = res.data[0].b64_json
raw = image_b64.split(",")[-1] if image_b64.startswith("data:") else image_b64
with open("image.png", "wb") as f:
    f.write(base64.b64decode(raw))
print("Saved to image.png")

curl --location 'https://hk.hyucloud.com/v1/images/edits' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  -F "image=@/path/to/your/image.png" \
  -F "mask=@/path/to/your/mask.png" \
  -F "model=gpt-image-1-mini" \
  -F "prompt=Add a beach ball in the center" \
  -F "size=1024x1024" \
  -F "n=1" \
  -F "quality=high" \
  -F "output_format=png" \
  -F "output_compression=100"

import os, base64, requests

url = "https://hk.hyucloud.com/v1/images/edits"
headers = {"Authorization": f"Bearer {os.getenv('hyucloud_API_KEY', '$hyucloud_API_KEY')}"}

files = {
    "image": ("beach.png", open("beach.png", "rb"), "image/png"),
    # 可选：提供 mask 来限定编辑区域
    # "mask": ("mask.png", open("mask.png", "rb"), "image/png"),
}

data = {
    "model": "gpt-image-1-mini",
    "prompt": "Add a beach ball in the center",
    "size": "1024x1024",
    "n": "1",
    "quality": "high",
    "output_format": "png",
    "output_compression": "100",
}

r = requests.post(url, headers=headers, files=files, data=data)
r.raise_for_status()
resp = r.json()

image_b64 = resp["data"][0]["b64_json"]
raw = image_b64.split(",")[-1] if image_b64.startswith("data:") else image_b64
with open("edit.png", "wb") as f:
    f.write(base64.b64decode(raw))
print("Saved to edit.png")

{
  "created": 1748484857,
  "background": "opaque",
  "output_format": "png",
  "quality": "low",
  "size": "1024x1024",
  "data": [
    {
      "b64_json": "{image_base64_string}"
    }
  ],
  "usage": {
    "total_tokens": 280,
    "input_tokens": 8,
    "output_tokens": 272,
    "input_tokens_details": {
      "image_tokens": 0,
      "text_tokens": 8
    },
    "output_tokens_details": {
      "image_tokens": 272,
      "text_tokens": 0
    }
  }
}

{
  "error": {
    "message": "error_message",
    "type": "error_type",
    "param": "request_id",
    "code": "error_code"
  }
}

curl --location 'https://hk.hyucloud.com/v1/images/generations' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "gpt-image-1.5",
    "prompt": "a beautiful flower",
    "size": "1024x1024",
    "quality": "high",
    "output_format": "png",
    "output_compression": 100
  }'

import os, base64
from openai import OpenAI

client = OpenAI(
    base_url="https://hk.hyucloud.com/v1",
    api_key=os.getenv("hyucloud_API_KEY", "YOUR_API_KEY")
)

res = client.images.generate(
    model="gpt-image-1.5",
    prompt="a beautiful flower",
    size="1024x1024",
    quality="high",
)

# gpt-image-1.5 返回 base64 数据
image_b64 = res.data[0].b64_json
raw = image_b64.split(",")[-1] if image_b64.startswith("data:") else image_b64
with open("image.png", "wb") as f:
    f.write(base64.b64decode(raw))
print("Saved to image.png")

curl --location 'https://hk.hyucloud.com/v1/images/edits' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  -F "image=@/path/to/your/image.png" \
  -F "mask=@/path/to/your/mask.png" \
  -F "model=gpt-image-1.5" \
  -F "prompt=Add a beach ball in the center" \
  -F "size=1024x1024" \
  -F "n=1" \
  -F "quality=high" \
  -F "output_format=png" \
  -F "output_compression=100"

import os, base64, requests

url = "https://hk.hyucloud.com/v1/images/edits"
headers = {"Authorization": f"Bearer {os.getenv('hyucloud_API_KEY', '$hyucloud_API_KEY')}"}

files = {
    "image": ("beach.png", open("beach.png", "rb"), "image/png"),
    # 可选：提供 mask 来限定编辑区域
    # "mask": ("mask.png", open("mask.png", "rb"), "image/png"),
}

data = {
    "model": "gpt-image-1.5",
    "prompt": "Add a beach ball in the center",
    "size": "1024x1024",
    "n": "1",
    "quality": "high",
    "output_format": "png",
    "output_compression": "100",
}

r = requests.post(url, headers=headers, files=files, data=data)
r.raise_for_status()
resp = r.json()

image_b64 = resp["data"][0]["b64_json"]
raw = image_b64.split(",")[-1] if image_b64.startswith("data:") else image_b64
with open("edit.png", "wb") as f:
    f.write(base64.b64decode(raw))
print("Saved to edit.png")

{
  "created": 1750667997,
  "data":
  [
    {
      "b64_json": "{image_base64_string}"
    }
  ],
  "usage":
  {
    "total_tokens": 4169,
    "input_tokens": 9,
    "output_tokens": 4160,
    "input_tokens_details":
    {
      "text_tokens": 9
    }
  }
}

{
  "error": {
    "message": "error_message",
    "type": "error_type",
    "param": "request_id",
    "code": "error_code"
  }
}

curl --location 'https://hk.hyucloud.com/v1/images/generations' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "gpt-image-2",
    "prompt": "a beautiful flower",
    "size": "1024x1024",
    "quality": "high",
    "output_format": "png",
    "output_compression": 100
  }'

import os, base64
from openai import OpenAI

client = OpenAI(
    base_url="https://hk.hyucloud.com/v1",
    api_key=os.getenv("hyucloud_API_KEY", "YOUR_API_KEY")
)

res = client.images.generate(
    model="gpt-image-2",
    prompt="a beautiful flower",
    size="1024x1024",
    quality="high",
)

# gpt-image-2 返回 base64 数据
image_b64 = res.data[0].b64_json
raw = image_b64.split(",")[-1] if image_b64.startswith("data:") else image_b64
with open("image.png", "wb") as f:
    f.write(base64.b64decode(raw))
print("Saved to image.png")

curl --location 'https://hk.hyucloud.com/v1/images/edits' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  -F "image=@/path/to/your/image.png" \
  -F "mask=@/path/to/your/mask.png" \
  -F "model=gpt-image-2" \
  -F "prompt=Add a beach ball in the center" \
  -F "size=1024x1024" \
  -F "n=1" \
  -F "quality=low" \
  -F "output_format=png" \
  -F "output_compression=100"

import os, base64, requests

url = "https://hk.hyucloud.com/v1/images/edits"
headers = {"Authorization": f"Bearer {os.getenv('hyucloud_API_KEY', '$hyucloud_API_KEY')}"}

files = {
    "image": ("beach.png", open("beach.png", "rb"), "image/png"),
    # 可选：提供 mask 来限定编辑区域
    # "mask": ("mask.png", open("mask.png", "rb"), "image/png"),
}

data = {
    "model": "gpt-image-2",
    "prompt": "Add a beach ball in the center",
    "size": "1024x1024",
    "n": "1",
    "quality": "high",
    "output_format": "png",
    "output_compression": "100",
}

r = requests.post(url, headers=headers, files=files, data=data)
r.raise_for_status()
resp = r.json()

image_b64 = resp["data"][0]["b64_json"]
raw = image_b64.split(",")[-1] if image_b64.startswith("data:") else image_b64
with open("edit.png", "wb") as f:
    f.write(base64.b64decode(raw))
print("Saved to edit.png")

curl --location 'https://hk.hyucloud.com/v1/images/edits' \
  --header "Authorization: Bearer $hyucloud_API_KEY" \
  -F "model=gpt-image-2" \
  -F "image[]=@./body-lotion.png" \
  -F "image[]=@./bath-bomb.png" \
  -F "image[]=@./incense-kit.png" \
  -F "image[]=@./soap.png" \
  -F "prompt=a beautiful gift basket containing all these products arranged elegantly"

import os, base64, requests

url = "https://hk.hyucloud.com/v1/images/edits"
headers = {"Authorization": f"Bearer {os.getenv('hyucloud_API_KEY', '$hyucloud_API_KEY')}"}

image_paths = [
    "body-lotion.png",
    "bath-bomb.png",
    "incense-kit.png",
    "soap.png",
]

# image[] 重复字段需用列表形式传入
files = [("image[]", (p, open(p, "rb"), "image/png")) for p in image_paths]

data = {
    "model": "gpt-image-2",
    "prompt": "a beautiful gift basket containing all these products arranged elegantly",
}

r = requests.post(url, headers=headers, files=files, data=data)
r.raise_for_status()
resp = r.json()

image_b64 = resp["data"][0]["b64_json"]
with open("output.png", "wb") as f:
    f.write(base64.b64decode(image_b64))
print("Saved to output.png")

{
  "created": 1750667997,
  "data":
  [
    {
      "b64_json": "{image_base64_string}"
    }
  ],
  "usage":
  {
    "total_tokens": 4169,
    "input_tokens": 9,
    "output_tokens": 4160,
    "input_tokens_details":
    {
      "text_tokens": 9
    }
  }
}

{
  "error": {
    "message": "error_message",
    "type": "error_type",
    "param": "request_id",
    "code": "error_code"
  }
}