AI Chat v2 API Integration Instructions

The AI Chat v2 API (/aichat2/conversations) is the next generation dialogue interface, a comprehensive upgrade of the AI Chat API. It expands on the simplicity and multi-turn dialogue hosting of v1 by adding:

Multimodal User Input: Directly transmit text + images + file blocks through the structured message field, without needing to first attach via references.
Agent-like Tool Invocation: Built-in tools for web search, web scraping, file reading, etc., and can mount user-authorized MCP servers (Google Drive, Notion, Slack, GitHub, etc.), allowing the model to autonomously call tools multiple times in a single request to complete complex tasks.
Structured Streaming Events: By using accept: text/event-stream or application/x-ndjson, you can receive token-by-token events such as text_delta, tool_use, tool_result, thinking, citation, card, artifact, etc., making it easier to render them separately in the frontend by type.
Interruptible / Resumable: The model will emit an ask_user_question event and pause when it needs additional information from the user; the next call can continue by filling in the answer through tool_results.
New CRUD Actions: Complete retrieve / retrieve_batch / update / delete actions on the same endpoint using the action field, eliminating the need for additional session management APIs.
Continuously Updated Model List: By default, it connects to contemporary models such as GPT-5.4, Claude Opus 4.8, Claude Sonnet 4.6, Gemini 3.1 Pro, GLM 5.1, DeepSeek V4, Kimi K3, etc.

At the same time, it is fully backward compatible with v1 at the request body level: simply pass model + question (+ optional stateful / id / references / preset) to receive an equivalent {answer, id} JSON response as in v1, so migrating from /aichat/conversations does not require rewriting the client; just change the path to /aichat2/conversations.

If you are currently using /aichat/conversations, the old interface will still be available for service, allowing you to migrate at your own pace.

Application Process

To use the AI Chat v2 API, first obtain your API Token from the Ace Data Cloud Console for backup.

If you are not logged in or registered, you will be automatically redirected to the login page to invite you to register and log in, and will return to the current page automatically after completion. One API Token can call all services on the platform, no need to apply separately for each service. The first application will grant a free quota for a trial experience; when the quota is insufficient, you can recharge the general balance in the console.

📘 Complete documentation: AI Chat v2 API →

Basic Usage

The simplest usage is identical to v1: pass model + question to get {answer, id}. CURL Example:

curl -X POST 'https://api.acedata.cloud/aichat2/conversations' \
  -H 'accept: application/json' \
  -H 'authorization: Bearer {token}' \
  -H 'content-type: application/json' \
  -d '{
    "model": "gpt-5.4",
    "question": "Introduce AceDataCloud in one sentence."
  }'

Response:

{
  "answer": "AceDataCloud is a unified API platform that aggregates mainstream AI models and multimodal services, allowing developers to access services like GPT, Claude, Gemini, Midjourney, Suno, Veo, etc., with a single key.",
  "id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44"
}

Python Example:

import requests

url = "https://api.acedata.cloud/aichat2/conversations"

headers = {
    "accept": "application/json",
    "authorization": "Bearer {token}",
    "content-type": "application/json",
}

payload = {
    "model": "gpt-5.4",
    "question": "Introduce AceDataCloud in one sentence.",
}

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

Available model values can be seen directly in the dropdown on the right side of the Try panel, with common categories including:

OpenAI: gpt-5.4-mini, gpt-5.4-nano, gpt-5.2-pro, gpt-5.1-all, gpt-5-all, gpt-4.1, gpt-4o, gpt-4o-image, o3, o4-mini, etc.
Anthropic: claude-opus-4-8, claude-opus-4-7, claude-opus-4-6, claude-opus-4-5-20251101, claude-sonnet-4-6, claude-sonnet-4-5-20250929, claude-haiku-4-5-20251001, etc.
Google: gemini-3.1-pro, gemini-3.1-pro-preview, gemini-3.1-flash-image-preview, gemini-3-pro-preview, gemini-2.5-flash-lite, etc.
xAI: grok-4, etc.
DeepSeek: deepseek-v4-flash, deepseek-v3.2-exp, deepseek-r1-0528, etc.
Moonshot: kimi-k3, kimi-k2.6, kimi-k2.5, etc.
Zhipu: glm-5.1, glm-5, glm-5-turbo, glm-4.7, glm-4.5v, etc.

Specific billing rules can be found on the service page’s Pricing card.

Multi-Turn Dialogue

Like v1, pass stateful: true to enable session saving, and the API will return an id; subsequent requests can continue the conversation by bringing back the id, without needing to maintain message history yourself. First Request:

curl -X POST 'https://api.acedata.cloud/aichat2/conversations' \
  -H 'accept: application/json' \
  -H 'authorization: Bearer {token}' \
  -H 'content-type: application/json' \
  -d '{
    "model": "gpt-5.4",
    "stateful": true,
    "question": "Remember a number: 42."
  }'

Response:

{
  "answer": "Okay, I have remembered 42. What would you like me to do with it?",
  "id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44"
}

Second Request, bring the same id:

curl -X POST 'https://api.acedata.cloud/aichat2/conversations' \
  -H 'accept: application/json' \
  -H 'authorization: Bearer {token}' \
  -H 'content-type: application/json' \
  -d '{
    "model": "gpt-5.4",
    "stateful": true,
    "id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44",
    "question": "What number did I just ask you to remember?"
  }'

{
  "answer": "The number you asked me to remember is 42.",
  "id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44"
}

stateful defaults to true, omitting it is equivalent to explicitly passing true. If you do not want the server to save this round of conversation, you can explicitly set stateful: false.

Streaming Response

v2 supports two streaming formats, selected by the accept header:

Scenario	`accept`	Data Format
Web Frontend / EventSource	`text/event-stream`	`data: {json}\n\n`, last line `data: [DONE]\n\n`
Server / CLI / Node Streaming Parsing	`application/x-ndjson`	One JSON object per line
No Streaming	`application/json` (default)	Returns `{answer, id}` in one go

NDJSON Example

import json
import requests

url = "https://api.acedata.cloud/aichat2/conversations"

headers = {
    "accept": "application/x-ndjson",
    "authorization": "Bearer {token}",
    "content-type": "application/json",
}

payload = {
    "model": "gpt-5.4",
    "stateful": True,
    "question": "Introduce Hangzhou in three sentences.",
}

with requests.post(url, json=payload, headers=headers, stream=True) as resp:
    answer = ""
    for line in resp.iter_lines():
        if not line:
            continue
        event = json.loads(line)
        if event.get("type") == "text_delta":
            # Compatible with v1: Incremental fragments are also provided through delta_answer field
            answer += event["content"]
            print(event["delta_answer"], end="", flush=True)
        elif event.get("type") == "done":
            print()
            print("usage =", event.get("usage"))

Each line of NDJSON is a structured event, the most common being text_delta:

{"type":"text_delta","content":"杭","delta_answer":"杭","id":"f2f4b3e8-..."}
{"type":"text_delta","content":"州","delta_answer":"州","id":"f2f4b3e8-..."}
{"type":"text_delta","content":"是","delta_answer":"是","id":"f2f4b3e8-..."}
...
{"type":"done","conversation_id":"f2f4b3e8-...","usage":{"prompt_tokens":21,"completion_tokens":58,"total_tokens":79},"terminal_reason":"natural_stop"}

SSE Example

The browser side using EventSource does not support custom request bodies, it is recommended to use fetch + manually slice parsing by \n\n:

const resp = await fetch("https://api.acedata.cloud/aichat2/conversations", {
  method: "POST",
  headers: {
    accept: "text/event-stream",
    authorization: "Bearer {token}",
    "content-type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-5.4",
    stateful: true,
    question: "Introduce Hangzhou in three sentences.",
  }),
});

const reader = resp.body.getReader();
const decoder = new TextDecoder();
let buffer = "";
while (true) {
  const { value, done } = await reader.read();
  if (done) break;
  buffer += decoder.decode(value, { stream: true });
  const blocks = buffer.split("\n\n");
  buffer = blocks.pop() ?? "";
  for (const block of blocks) {
    const dataLine = block.split("\n").find((l) => l.startsWith("data: "));
    if (!dataLine) continue;
    const payload = dataLine.slice(6);
    if (payload === "[DONE]") return;
    const event = JSON.parse(payload);
    if (event.type === "text_delta") process.stdout.write(event.content);
  }
}

Streaming Event Types

`type`	Meaning
`text_delta`	Incremental text fragments of the assistant’s response. `content` is the new content; for compatibility with v1, the same event also carries `delta_answer` (equal to `content`) and `id`.
`thinking`	The model’s thinking process (only appears when the selected model exposes reasoning).
`tool_use`	The model decides to call a tool, the event carries `tool_id`, `tool_name`, and `input`.
`tool_result`	The result of the tool execution, paired with the previous `tool_use` by `tool_id`, `is_error` indicates whether it failed.
`card`	Structured cards produced by the tool (such as images, link previews), suitable for direct rendering.
`citation`	Used to supplement the source URL for the corresponding text fragment.
`ask_user_question`	The model issues a request for additional information from the user, entering the `awaiting_user_input` state, see below Resume Paused Conversation.
`artifact`	Independent products generated by the model (such as code blocks, documents), which can be saved or downloaded.
`system_message`	System prompt information (not user and assistant content), used only for UI prompts.
`compact`	Events where internal context is compressed, no special handling required.
`error`	An error occurred in this round, `message` describes the error content.
`done`	The streaming response ends, carrying `usage` (including `prompt_tokens` / `completion_tokens` / `total_tokens`) and `terminal_reason`.

For clients that only care about the final answer, concatenating all text_delta content is equivalent to the answer in application/json mode.

Multimodal Input

If the user input contains images or files, pass message (array) instead of question. Each array element is a content block:

{
  "model": "gpt-5.4",
  "stateful": true,
  "message": [
    { "type": "text", "text": "How many cats are in this picture?" },
    { "type": "image_url", "image_url": { "url": "https://cdn.acedata.cloud/cats.jpg" } }
  ]
}

Supported block types:

text — Plain text, the text field is required.
image_url — Image, the image_url.url is required.
file_url — File (PDF, CSV, TXT, etc.), the file_url.url is required.

Relationship with v1 `references`

To maintain compatibility with older clients, v2 still recognizes the references: ["https://...", ...] field:

The URL suffix is jpg / jpeg / png / gif / bmp / webp / svg / heic / heif, automatically converted to image_url block;
Other extensions are converted to file_url block;
If a question is also provided, it will be placed as a text block in front.

Therefore, if you only want to migrate from v1 without changing the request body, just change the path to /aichat2/conversations, and the original references usage will work as usual. For more precise control (such as placing multiple images between texts, or if the order is very important), use the message array directly.

Tool Invocation and MCP

The core enhancement of v2 is that the model can autonomously call tools to complete multi-step tasks, this is enabled by default, and the client does not need to make any additional configurations in the request. Common scenarios:

The user asks, “Help me find out what new exhibitions are in Shanghai recently” → the model calls the built-in web search → organizes the results into an answer.
The user asks, “Read this PDF and then write a summary” → the model calls file_read → writes a summary.
The user has authorized Google Drive / GitHub / Notion, etc., in Connections → the model can call the corresponding MCP tools to read and write their data.

In NDJSON / SSE streams, tool invocation is presented through tool_use and tool_result event types, for example:

{"type":"tool_use","tool_id":"toolu_01ABCDEF","tool_name":"web_search","input":{"query":"上海 2026 春季展览"},"id":"f2f4b3e8-..."}
{"type":"tool_result","tool_id":"toolu_01ABCDEF","output":"...","is_error":false,"id":"f2f4b3e8-..."}
{"type":"text_delta","content":"目前","delta_answer":"目前","id":"f2f4b3e8-..."}
{"type":"text_delta","content":"上海","delta_answer":"上海","id":"f2f4b3e8-..."}
...

If you do not want to display the details of tool invocation on the front end, you can ignore the tool_use / tool_result / card / citation event types, and the model’s final output will still flow out through text_delta. max_turns can limit how many rounds the model can self-invoke tools in this request, with the default limit determined by the platform. Setting it low (for example, max_turns: 1) can enforce a single response without allowing any tool invocation.

Asynchronous Execution and Unattended Authorization

If your invocation comes from an alert Webhook, CI/CD, monitoring system, or other background tasks, you can set async: true to let the interface immediately return the task ID, while the background continues to execute:

{
  "model": "gpt-5.5",
  "async": true,
  "question": "我的服务报警了，用个人微信通知微信群「AceDataCloud团队」……"
}

Return example:

{
  "task_id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44",
  "conversation_id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44",
  "id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44",
  "status": "queued"
}

You can then use action: retrieve + id to query the session result; you can also provide a callback_url, and after the task is completed, the platform will POST { status, answer, usage, error } to your callback address. The callback_url must use http / https, and cannot directly specify localhost or private IP literal addresses. Background tasks usually do not have anyone to click to confirm. If you want certain Skills or MCP Servers to perform actions like sending, publishing, writing, etc., in unattended mode, please explicitly pass the pre-authorization list in the request body:

{
  "model": "gpt-5.5",
  "async": true,
  "allowed_skills": ["acedatacloud/personal-wechat"],
  "allowed_mcp_servers": [],
  "question": "我的服务报警了，用个人微信通知微信群「AceDataCloud团队」……"
}

The values in allowed_skills are the slugs of the connected Skills; the values in allowed_mcp_servers are the slugs of the connected MCP Servers. Skills / MCP Servers not listed in the pre-authorization will still only be able to preview, dry-run, or refuse to execute write operations in unattended mode. For finer control, you can also use the equivalent unattended_policy object:

{
  "unattended_policy": {
    "allowed_skills": ["acedatacloud/personal-wechat"],
    "allowed_mcp_servers": [],
    "expires_at": 1790000000
  }
}

Pre-authorization is just these two lists themselves: an empty list means no capabilities are authorized, and no additional switch fields are needed. Note: Pre-authorization only represents “this request allows these capabilities to skip manual confirmation in unattended mode.” Specific Skills must still support --unattended-confirm or corresponding security mechanisms; otherwise, it will continue to dry-run and not directly execute write operations.

Resuming Paused Conversations

Some tools will make the model “ask the user,” at which point the model will emit an ask_user_question event, and the conversation will be frozen in the awaiting_user_input state:

{
  "type": "ask_user_question",
  "tool_id": "toolu_01XYZW",
  "tool_name": "ask_user_question",
  "question": "你希望生成的报告是中文还是英文？",
  "options": ["中文", "英文"],
  "id": "f2f4b3e8-..."
}

Render this event as a card on the front end for the user to select an answer, and then initiate the next request with the same id, filling in the answer through tool_results:

curl -X POST 'https://api.acedata.cloud/aichat2/conversations' \
  -H 'accept: text/event-stream' \
  -H 'authorization: Bearer {token}' \
  -H 'content-type: application/json' \
  -d '{
    "model": "gpt-5.4",
    "stateful": true,
    "id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44",
    "tool_results": [
      {
        "tool_use_id": "toolu_01XYZW",
        "output": "中文"
      }
    ]
  }'

The tool_use_id in the request body must exactly match the tool_id from when it was paused; otherwise, it will return 400. When tool_results are present in the request, question / message / references will all be ignored. If the user decides to abandon this question, simply pass a new question / message, and the platform will automatically mark the paused tool invocation as “user skipped.”

Session Management (CRUD)

v2 provides lightweight session management through the action field on the same endpoint, without needing to open another API.

`action: retrieve` — Pull a session

curl -X POST 'https://api.acedata.cloud/aichat2/conversations' \
  -H 'accept: application/json' \
  -H 'authorization: Bearer {token}' \
  -H 'content-type: application/json' \
  -d '{
    "action": "retrieve",
    "id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44"
  }'

Return the complete conversation document (including messages history, model, title, tools_used, etc.).

`action: retrieve_batch` —— List conversation summaries

{
  "action": "retrieve_batch",
  "model_group": "chatgpt",
  "limit": 20,
  "offset": 0
}

Return { items: [...], total }. Summaries do not include messages, suitable for sidebar lists; if the user clicks on a conversation, use action: retrieve to fetch its complete messages separately. Optional filter parameters: user_id, application_id, model_group, model.

`action: update` —— Change title or rewrite history

{
  "action": "update",
  "id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44",
  "title": "Hangzhou Travel Plan"
}

messages can also be passed, but the server will perform strict schema validation (must be in the collapsed ToolUseContent form), and non-compliance will return 400. Generally, it is only recommended to change the title.

`action: delete` —— Delete a conversation

{
  "action": "delete",
  "id": "f2f4b3e8-0c0a-4d3a-aaa2-7ff80c0a1c44"
}

Return { id, success: true }. Once deleted, it cannot be recovered, please confirm before calling.

Smooth migration from v1

If you are already using /aichat/conversations, migrating to v2 requires almost no code changes:

Change the URL from https://api.acedata.cloud/aichat/conversations to https://api.acedata.cloud/aichat2/conversations.
If you previously passed v1 model names (such as gpt-3.5, gpt-4-browsing, etc.), it is recommended to upgrade to contemporary models (such as gpt-5.4, claude-opus-4-8, gemini-3.1-pro, etc.) when switching to v2.
The fields of the NDJSON stream remain backward compatible: each text_delta event still carries delta_answer and id, so clients that originally parsed delta_answer by line do not need to change.

After migration, you can enable new capabilities of v2 as needed (multimodal message, SSE, tool calls, action CRUD), and proceed at your own pace.

Error handling

Error responses are unified as:

{
  "error": {
    "code": "chat_error",
    "message": "upstream LLM returned an error"
  },
  "trace_id": "2cf86e86-22a4-46e1-ac2f-032c0f2a4e89"
}

Common errors:

400 bad_request: Missing required fields, tool_use_id mismatch, illegal messages schema, etc.
401 invalid_token: Incorrect authorization header.
404 not_found: The session corresponding to id does not exist during action: retrieve / update / delete.
429 too_many_requests: Rate limit triggered.
500 chat_error: Upstream LLM error or this round completion_tokens=0 (treated as unconsumed, no charge will be incurred).

In streaming responses, errors are sent as {"type":"error","message":"..."} events, followed immediately by the end of the stream.

Conclusion

The AI Chat v2 API maintains backward compatibility with v1 while upgrading conversations from “single-turn/multi-turn Q&A” to “agent-based observable dialogues”: multimodal input, tool calls, pause/resume capabilities, streaming structured events, built-in CRUD. It is recommended for new integrations to use v2 directly; existing v1 integrations can migrate smoothly in phases. If you have any questions, please feel free to contact our technical support team.

Getting Started

Core Concepts

AI Chat

AI Image

AI Video

AI Audio

Web & Data

CAPTCHA

Identity

Proxy

Advanced

AI Chat v2 API Integration Instructions

Application Process

Basic Usage

Multi-Turn Dialogue

Streaming Response

NDJSON Example

SSE Example

Streaming Event Types

Multimodal Input

Relationship with v1 `references`

Tool Invocation and MCP

Asynchronous Execution and Unattended Authorization

Resuming Paused Conversations

Session Management (CRUD)

`action: retrieve` — Pull a session

`action: retrieve_batch` —— List conversation summaries

`action: update` —— Change title or rewrite history

`action: delete` —— Delete a conversation

Smooth migration from v1

Error handling

Conclusion

​Application Process

​Basic Usage

​Multi-Turn Dialogue

​Streaming Response

​NDJSON Example

​SSE Example

​Streaming Event Types

​Multimodal Input

​Relationship with v1 references

​Tool Invocation and MCP

​Asynchronous Execution and Unattended Authorization

​Resuming Paused Conversations

​Session Management (CRUD)

​action: retrieve — Pull a session

​action: retrieve_batch —— List conversation summaries

​action: update —— Change title or rewrite history

​action: delete —— Delete a conversation

​Smooth migration from v1

​Error handling

​Conclusion

Application Process

Basic Usage

Multi-Turn Dialogue

Streaming Response

NDJSON Example

SSE Example

Streaming Event Types

Multimodal Input

Relationship with v1 `references`

Tool Invocation and MCP

Asynchronous Execution and Unattended Authorization

Resuming Paused Conversations

Session Management (CRUD)

`action: retrieve` — Pull a session

`action: retrieve_batch` —— List conversation summaries

`action: update` —— Change title or rewrite history

`action: delete` —— Delete a conversation

Smooth migration from v1

Error handling

Conclusion