cloud-sandbox

xiaozhanAI can write code and run commands in a cloud Linux sandbox (an OpenAI-hosted container with Python 3, Node.js, LibreOffice, python-docx / python-pptx / openpyxl and CJK fonts) and hand you the resulting Word, PowerPoint, Excel or PDF files. Usage is exactly the same as OpenAI's Responses API:

1. Call POST /v1/responses with the code_interpreter or shell tool;
2. The container_file_citation annotation in the answer tells you which container (container_id) and which file (file_id) holds the result;
3. Download it with GET /v1/containers/{container_id}/files/{file_id}/content.

The model decides from your prompt whether to produce a file: ask it to "make a deck" and it will; ask a question and it just answers in text.

Only xiaozhanAI supports the cloud sandbox, and only on /v1/responses (/v1/chat/completions does not support these two tools — same as OpenAI).

Quick start#

1. Ask the model to make a file#

curl https://api.dflop.top/v1/responses \
  -H "Authorization: Bearer $GPUSHARE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "xiaozhanAI",
    "tools": [{ "type": "code_interpreter", "container": { "type": "auto" } }],
    "input": "Use python-pptx to build a 5-slide \"Q3 review\" deck, save it to /mnt/data/q3-review.pptx and give me the file link."
  }'

The message in output carries the file reference (excerpt):

{
  "type": "output_text",
  "text": "Done: [Download q3-review.pptx](sandbox:/mnt/data/q3-review.pptx)",
  "annotations": [
    {
      "type": "container_file_citation",
      "container_id": "cntr_6acf7f72fff48198b31b607db0a7a003020f215d49c216d5",
      "file_id": "cfile_6acf7f757e848191962c004f076016de",
      "filename": "q3-review.pptx",
      "start_index": 6,
      "end_index": 58
    }
  ]
}

The sandbox:/mnt/data/… link in the text is a path inside the container and can't be opened directly — download it with the endpoint below.

2. Download the file#

curl https://api.dflop.top/v1/containers/$CONTAINER_ID/files/$FILE_ID/content \
  -H "Authorization: Bearer $GPUSHARE_API_KEY" \
  -o q3-review.pptx

Python (OpenAI SDK)#

from openai import OpenAI

client = OpenAI(base_url="https://api.dflop.top/v1", api_key="sk-gpushare-...")

resp = client.responses.create(
    model="xiaozhanAI",
    tools=[{"type": "code_interpreter", "container": {"type": "auto"}}],
    input="Use python-docx to write a one-page event plan, save it to /mnt/data/event-plan.docx and give me the file link.",
)

for item in resp.output:
    if item.type != "message":
        continue
    for part in item.content:
        for ann in getattr(part, "annotations", None) or []:
            if ann.type == "container_file_citation":
                data = client.containers.files.content.retrieve(ann.file_id, container_id=ann.container_id)
                data.write_to_file(ann.filename)
                print("saved", ann.filename)

Making a file usually takes tens of seconds to a few minutes. Use stream=True ("stream": true) so data keeps flowing and no client or proxy times out.

Using the shell tool#

code_interpreter runs Python only; shell gives the model a full Linux shell (LibreOffice for PDF conversion, Node.js PptxGenJS for decks, rendering previews, …). Same shape as OpenAI:

{
  "model": "xiaozhanAI",
  "stream": true,
  "tools": [{ "type": "shell", "environment": { "type": "container_auto" } }],
  "input": "Make an 8-slide product-launch deck with a clean palette, save it to /mnt/data/launch.pptx and give me the link."
}

File references and downloads work the same way.

Giving the model your own files#

Create a container, upload files into it, then let the model work in that container:

# ① create a container
curl https://api.dflop.top/v1/containers \
  -H "Authorization: Bearer $GPUSHARE_API_KEY" -H "Content-Type: application/json" \
  -d '{"name": "q3-report"}'
# → {"id": "cntr_…", "object": "container", "status": "running", "memory_limit": "4g", …}

# ② upload (multipart, field name `file`)
curl https://api.dflop.top/v1/containers/$CONTAINER_ID/files \
  -H "Authorization: Bearer $GPUSHARE_API_KEY" \
  -F "[email protected]"
# → {"id": "cfile_…", "path": "/mnt/data/…sales.xlsx", "source": "user", …}

# ③ let the model use that container
curl https://api.dflop.top/v1/responses \
  -H "Authorization: Bearer $GPUSHARE_API_KEY" -H "Content-Type: application/json" \
  -d '{
    "model": "xiaozhanAI",
    "tools": [{ "type": "shell", "environment": { "type": "container_reference", "container_id": "'$CONTAINER_ID'" } }],
    "input": "Read sales.xlsx in /mnt/data and write a Word sales report with charts to /mnt/data/sales-report.docx."
  }'

With code_interpreter, reference an existing container as "container": "cntr_…". The real path of an uploaded file is the path returned by the upload (it carries a random prefix); you can tell the model to ls /mnt/data first.

In Python: client.containers.create(name=…) and client.containers.files.create(container_id, file=open("sales.xlsx", "rb")).

Endpoints#

Request and response fields match OpenAI's Containers API. These endpoints keep working for viewing and downloading when your balance runs out (creating a container does not).

Billing#

• Tokens, web search, image generation: billed at the normal xiaozhanAI prices (see Models & pricing).
• Container fee: 194.112 points per container per 20 minutes. The first 20-minute window starts when a container is first used (or created with POST /v1/containers); using it again within those 20 minutes costs nothing extra; still using it after 20 minutes starts the next window.
• The container fee is a separate row in your usage log: unit_type is container, unit_count is how many 20-minute windows were billed.

To save on container fees, keep related work in one container (reuse it with container_reference / "container": "cntr_…") instead of letting each request create a new one.

Limits#

• Containers always have 4 GB of memory (any other memory_limit is treated as 4 GB).
• Containers have no internet access: network_policy only accepts {"type": "disabled"}.
• OpenAI Files file_ids are not supported (not on container creation, auto containers or uploads) — upload your inputs with the upload endpoint.
• background: true is not supported; use stream: true for long tasks.
• A container is destroyed after 20 minutes of inactivity, together with its files — download promptly.
• Containers and their files are only accessible with the API key that created them; any other key (including other keys of the same account) gets a 404.

Common errors#

More error codes: Errors.