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Use this endpoint to send a conversation history and receive an AI-generated reply in the standard OpenAI Chat Completions format. Because Qhaigc is fully OpenAI-compatible, you can use the official OpenAI SDK without any code changes — just swap the base_url. Endpoint: POST https://api.qhaigc.net/v1/chat/completions

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Request Parameters

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model
string
必填
The model to use for completion. Examples: gpt-4o, gpt-4o-mini, gpt-5-mini. Call GET /v1/models to retrieve the full list of available models.
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messages
array
必填
The conversation history as an array of message objects. Each object must contain role and content.
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stream
boolean
默认值:"false"
When true, the response is delivered as a stream of server-sent events (SSE). Each event contains a partial delta until the [DONE] sentinel is sent.
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stream_options
object
Additional options for streaming responses. Pass {"include_usage": true} to receive token usage data in the final stream chunk.
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max_tokens
integer
The maximum number of tokens to generate. Set this to control costs and prevent unexpectedly long responses.
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temperature
number
Sampling temperature between 0 and 2. Higher values produce more varied output; lower values make responses more focused and deterministic. Defaults to 1.

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Response Fields

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id
string
A unique identifier for this completion, prefixed with chatcmpl-.
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object
string
Always "chat.completion" for non-streaming responses.
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created
integer
Unix timestamp (seconds) of when the completion was generated.
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model
string
The model that produced the completion.
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choices
array
Array of completion choices. For most requests this contains a single element.
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usage
object
Token counts for this request.

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Code Examples

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-api-key-here",
    base_url="https://api.qhaigc.net/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain RAG in three sentences."}
    ],
    max_tokens=256,
    temperature=0.7
)

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

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Streaming Example

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-api-key-here",
    base_url="https://api.qhaigc.net/v1"
)

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a short poem about the sea."}],
    stream=True,
    stream_options={"include_usage": True}
)

for chunk in stream:
    delta = chunk.choices[0].delta if chunk.choices else None
    if delta and delta.content:
        print(delta.content, end="", flush=True)

print()  # newline after stream ends

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Multimodal Input

To send an image alongside your text, replace the content string with an array of content parts. Each part is either {"type": "text", "text": "..."} or {"type": "image_url", "image_url": {"url": "https://..."}}. Only models that support vision (such as gpt-4o) will process the image.
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}}
            ]
        }
    ]
)