如何通过 API 向 Grok 提问

curl --request POST \
  --url https://api.twexapi.io/v3/twitter/grok/ask \
  --header "Authorization: Bearer $TWEXAPI_BEARER_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{
    "prompt": "把下面五条 X 帖子总结成三个风险和三个后续动作，回答保持简洁。Source posts: ...",
    "model": "grok-3-latest",
    "cookie": "auth_token=...; ct0=...; twid=..."
  }'

1{
2  "code": 200,
3  "msg": "success",
4  "data": {
5    "conversation_id": "2076320454898278729",
6    "user_chat_item_id": "2076320454898278730",
7    "agent_chat_item_id": "2076320454898278731",
8    "message": "主要风险是...",
9    "reasoning": "Thinking about your request",
10    "soft_stop": false,
11    "follow_up_suggestions": [
12      {
13        "label": "Turn this into an action plan",
14        "properties": null,
15        "tools_overrides": null
16      }
17    ]
18  }
19}

1Task:
2Summarize the following X posts into customer objections and sales follow-up actions.
3
4Source records:
5- tweet_id=...
6  author=...
7  created_at=...
8  text=...
9  metrics=...
10
11Output:
121. Top objections
132. Evidence from source posts
143. Follow-up actions
154. Questions that need human review

1import os
2from datetime import datetime, timezone
3from typing import Any
4
5import requests
6
7API_URL = "https://api.twexapi.io/v3/twitter/grok/ask"
8BEARER_TOKEN = os.environ["TWEXAPI_BEARER_TOKEN"]
9X_COOKIE = os.environ["TWEXAPI_X_COOKIE"]
10GROK_MODEL = os.environ.get("TWEXAPI_GROK_MODEL", "grok-3-latest")
11PROXY = os.environ.get("TWEXAPI_PROXY")
12
13def build_prompt(source_records: list[dict[str, Any]]) -> str:
14    lines = [
15        "Task:",
16        "Summarize these X posts into customer objections and follow-up actions.",
17        "",
18        "Source records:",
19    ]
20    for item in source_records:
21        lines.extend(
22            [
23                f"- tweet_id={item['tweet_id']}",
24                f"  author={item.get('author', 'unknown')}",
25                f"  created_at={item.get('created_at', 'unknown')}",
26                f"  text={item.get('text', '')}",
27                f"  metrics={item.get('metrics', {})}",
28            ]
29        )
30    lines.extend(
31        [
32            "",
33            "Output:",
34            "1. Top objections",
35            "2. Evidence from source posts",
36            "3. Follow-up actions",
37            "4. Questions that need human review",
38        ]
39    )
40    return "\n".join(lines)
41
42def ask_grok(
43    prompt: str,
44    *,
45    conversation_id: str | None = None,
46) -> dict[str, Any]:
47    payload: dict[str, Any] = {
48        "prompt": prompt,
49        "cookie": X_COOKIE,
50        "model": GROK_MODEL,
51    }
52    if conversation_id:
53        payload["conversation_id"] = conversation_id
54    if PROXY:
55        payload["proxy"] = PROXY
56
57    response = requests.post(
58        API_URL,
59        headers={
60            "Authorization": f"Bearer {BEARER_TOKEN}",
61            "Content-Type": "application/json",
62            "Accept": "application/json",
63        },
64        json=payload,
65        timeout=120,
66    )
67    response.raise_for_status()
68    data = response.json()
69
70    if data.get("code") != 200:
71        raise RuntimeError(data.get("msg", "TwexAPI returned an error"))
72
73    return data["data"]
74
75def normalize_grok_result(
76    *,
77    source_job_id: str,
78    prompt: str,
79    result: dict[str, Any],
80) -> dict[str, Any]:
81    return {
82        "source_job_id": source_job_id,
83        "prompt": prompt,
84        "grok_conversation_id": result["conversation_id"],
85        "user_chat_item_id": result.get("user_chat_item_id"),
86        "agent_chat_item_id": result.get("agent_chat_item_id"),
87        "message": result["message"],
88        "reasoning": result.get("reasoning"),
89        "soft_stop": result.get("soft_stop"),
90        "follow_up_suggestions": result.get("follow_up_suggestions") or [],
91        "model": GROK_MODEL,
92        "asked_at": datetime.now(timezone.utc).isoformat(),
93        "raw": result,
94    }
95
96if __name__ == "__main__":
97    source_records = [
98        {
99            "tweet_id": "1234567890123456789",
100            "author": "example_user",
101            "created_at": "2026-07-16T09:00:00Z",
102            "text": "导出功能正常，但 cursor 过期时需要更清晰的错误提示。",
103            "metrics": {"likes": 12, "replies": 3},
104        }
105    ]
106    prompt = build_prompt(source_records)
107    result = ask_grok(prompt)
108    print(
109        normalize_grok_result(
110            source_job_id="weekly-objection-review-2026-07-16",
111            prompt=prompt,
112            result=result,
113        )
114    )

follow_up = ask_grok(
    "Turn the previous answer into a support team checklist.",
    conversation_id=saved_grok_conversation_id,
)