You look for a baking topic each morning and find several popular bread notes. Were they published today, or did you simply discover them today? Resolve that distinction before calling the collection a daily review.

Define the date boundary

Choose a timezone and record the start, end and subject. Use documented search filters such as time_filter, then verify timing in available details. Mark entries with unconfirmed publication times pending instead of inferring recency from search position.

Screen the content as well as the date

Retrieve image or video details appropriately. Separate recipe instruction, finished-item displays, failures and promotion. An attractive result image is not necessarily a useful teaching reference. Preserve lookup time and actual sample size; search does not cover every note published that day.

Account for different accumulation periods

Morning posts and just-published posts have had different time to accumulate interactions. Record only available metrics with the lookup time. Current author information is context, not historical status at publication. Your table can include source, timing, reader problem, observed response and useful editorial choice.

Report traceable findings

Choose a few relevant examples and explain their value without calling them platform-wide winners. AI may shorten verified notes but cannot promise that adopting a topic will produce a hit. Report insufficient coverage rather than filling the day with old posts. The output is a bounded topic review that guides what to investigate next.

Requests used in this project

Request URL and parameters

GET https://api.trmesh.com/openapi/api/v1/xiaohongshu/app_v2/search_notes

Parameter Type Required Meaning
keyword string Yes Search keyword
page integer No Page number, start from 1
sort_type string No Sort type
note_type string No Note type: 不限, 视频笔记, 普通笔记, 直播笔记
time_filter string No Time filter: 不限, 一天内, 一周内, 半年内
search_id string No Search ID for pagination
search_session_id string No Search session ID for pagination
source string No Source
ai_mode integer No AI mode: 0=off, 1=on

Run the Python example

Install requests and set TRMESH_API_TOKEN in your environment. Replace the sample identifiers with the public object you intend to inspect. Example IDs may no longer be available.

import os
import requests

# Read your own developer key from the server environment.
url = "https://api.trmesh.com/openapi/api/v1/xiaohongshu/app_v2/search_notes"
params = {
    "keyword": "美食推荐",
    "page": "1",
    "sort_type": "general",
    "note_type": "不限",
    "time_filter": "不限",
    "source": "explore_feed",
    "ai_mode": "0"
}
response = requests.get(
    url, params=params, timeout=30,
    headers={"Authorization": f"Bearer {os.environ['TRMESH_API_TOKEN']}"},
)
response.raise_for_status()
payload = response.json()
print(payload)  # Inspect the actual response before mapping fields

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