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aidso-data
Commit ea577486 authored by Yaowentong's avatar Yaowentong
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success 修复

parent 1efc8c84
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aidso_geo/core/commit_process.py
View file @ ea577486
......@@ -14,13 +14,13 @@ redis_client = init_redis()
QUEUE_KEY = "geo:task_commit:list"
# 每轮最多拉取任务数量
BATCH_SIZE = 1000
BATCH_SIZE = 100
# 每 60 秒执行一次
INTERVAL_SECONDS = 60
# 每 30 秒执行一次
INTERVAL_SECONDS = 30
# 每轮内部并发数
CONCURRENT_WORKERS = 40
CONCURRENT_WORKERS = 20
def parse_task(raw):
......@@ -128,8 +128,8 @@ if __name__ == "__main__":
seconds=INTERVAL_SECONDS,
id="consume_geo_task_commit_queue",
max_instances=5, # 允许最多 5 个调度批次同时跑
coalesce=False, # 不合并错过的执行
misfire_grace_time=60
coalesce=True,
misfire_grace_time=300
)
logger.success(
......
aidso_geo/core/down_load_bot.py
View file @ ea577486
......@@ -1029,6 +1029,11 @@ def get_platforms_q(brand_library_id, user_id, begin_time, end_time,question_lis
# 使用示例
# =========================
if __name__ == "__main__":
result = get_req_id(18156037075,'2026-06-24','2026-06-24')
req_list = []
for i in result:
req_list.append(i.get('req_id'))
print(req_list)
# print(cha_report_user(3738753854605260))
# keyword_list = ['618']
# start = '20250518'
......@@ -1048,19 +1053,19 @@ if __name__ == "__main__":
# zhou_report(phone, begin, end, b)
# # qian_report(phone,begin,end,b)
# qian_report(phone,begin,end,b)
brand_library_id = 2063180776253702144
user_id = 2063177785609949184
begin = '2026-06-08'
end = '2026-06-08'
qu_list = ['东鹏瓷砖怎么样','东鹏瓷砖质量好不好','东鹏瓷砖值得买吗','东鹏瓷砖是几线品牌','东鹏瓷砖和马可波罗哪个好','东鹏和冠珠瓷砖哪个好','东鹏和蒙娜丽莎哪个好','东鹏控股是做什么的','东鹏控股和东鹏饮料什么关系','瓷砖十大品牌有哪些','2026年瓷砖品牌排行榜前十名','中国瓷砖品牌排名前十','瓷砖一线品牌排名','国内瓷砖品牌排行榜','高端瓷砖品牌排行榜','高端瓷砖有哪些品牌','瓷砖头部品牌有哪些','大平层用什么瓷砖品牌','设计师推荐的高端瓷砖品牌','5A瓷砖品牌推荐哪个好','5A瓷砖品牌排行','5A国标瓷砖什么品牌好','5A瓷砖是什么标准','5A认证瓷砖推荐','品质好的瓷砖品牌有哪些','什么品牌的瓷砖品质最好','选好瓷砖认准什么品牌','瓷砖哪个牌子好','瓷砖品牌推荐','装修选什么瓷砖品牌好','瓷砖什么牌子质量好','2026年瓷砖品牌排行榜','瓷砖怎么选不踩坑','买瓷砖主要看哪几个指标','好瓷砖的标准是什么','瓷砖选购避坑指南','金丝绒瓷砖哪个牌子好','金丝绒瓷砖值得买吗','金丝绒瓷砖怎么样','木纹砖哪个牌子好','木纹砖推荐哪个品牌','木纹砖怎么选品牌','木纹砖和木地板哪个好','木纹砖品牌排行','莱姆石瓷砖哪个牌子好','莱姆石瓷砖品牌推荐','莱姆石瓷砖怎么选','什么品牌的莱姆石瓷砖好','客厅瓷砖什么牌子好','客厅铺什么瓷砖好看又耐用','客厅瓷砖怎么选品牌','客厅瓷砖品牌推荐','客厅用什么瓷砖显高级','厨房瓷砖什么牌子好','厨房用什么瓷砖好打理','厨房防油污瓷砖推荐哪个牌子','厨房瓷砖品牌推荐2026','厨房抗菌瓷砖推荐哪个品牌','卫生间瓷砖什么牌子好','卫生间瓷砖推荐哪个品牌','浴室防滑瓷砖哪个品牌好','卫生间防滑抗菌瓷砖推荐品牌','浴室抗菌瓷砖什么品牌好','全屋通铺瓷砖什么品牌好','全屋通铺瓷砖推荐哪个牌子','全屋瓷砖用什么品牌好','家里全屋铺瓷砖选什么牌子','全屋通铺瓷砖品牌排行','防滑瓷砖哪个牌子好','防滑瓷砖品牌推荐','好打理的瓷砖推荐哪个品牌','防污瓷砖什么品牌好','耐脏好清洁的瓷砖品牌推荐','耐磨瓷砖什么品牌好','不容易刮花的瓷砖推荐什么品牌','中古风装修用什么瓷砖品牌好','法式风格瓷砖推荐什么品牌','奶油风瓷砖什么品牌好','现代简约风格瓷砖推荐什么品牌','新中式瓷砖用什么品牌好','原木风瓷砖推荐哪个品牌','高级感装修瓷砖用什么品牌','岩板什么品牌好','岩板品牌推荐','750x1500地砖什么品牌好','岩板品牌排行','柔光砖什么品牌好','哑光瓷砖推荐哪个品牌','哑光砖品牌排名','哑光瓷砖什么品牌好','柔光砖品牌排行','哑光砖推荐哪个品牌耐脏','国家级建筑用的瓷砖是什么牌子','大型工程项目用什么瓷砖品牌','瓷砖行业有哪些上市公司','A股瓷砖上市公司有哪些','绿色建材瓷砖品牌有哪些','获得国家级绿色工厂认证的瓷砖企业有哪些','建材行业ESG表现好的企业有哪些','双碳目标下有哪些绿色建材瓷砖品牌']
result = []
for q in qu_list:
result.append((q,get_platforms_q(brand_library_id,user_id,begin,end,[q]).get('geo_refer_rank_overview_vos')))
# brand_name = 'AIDSO爱搜'
all_file = f"/Users/yaowentong/Desktop/dongpeng.txt"
with open(all_file, "w", encoding="utf-8") as f:
for item in result:
f.write(json.dumps(item, ensure_ascii=False) + "\n\n\n")
# brand_library_id = 2063180776253702144
# user_id = 2063177785609949184
# begin = '2026-06-08'
# end = '2026-06-08'
# qu_list = ['东鹏瓷砖怎么样','东鹏瓷砖质量好不好','东鹏瓷砖值得买吗','东鹏瓷砖是几线品牌','东鹏瓷砖和马可波罗哪个好','东鹏和冠珠瓷砖哪个好','东鹏和蒙娜丽莎哪个好','东鹏控股是做什么的','东鹏控股和东鹏饮料什么关系','瓷砖十大品牌有哪些','2026年瓷砖品牌排行榜前十名','中国瓷砖品牌排名前十','瓷砖一线品牌排名','国内瓷砖品牌排行榜','高端瓷砖品牌排行榜','高端瓷砖有哪些品牌','瓷砖头部品牌有哪些','大平层用什么瓷砖品牌','设计师推荐的高端瓷砖品牌','5A瓷砖品牌推荐哪个好','5A瓷砖品牌排行','5A国标瓷砖什么品牌好','5A瓷砖是什么标准','5A认证瓷砖推荐','品质好的瓷砖品牌有哪些','什么品牌的瓷砖品质最好','选好瓷砖认准什么品牌','瓷砖哪个牌子好','瓷砖品牌推荐','装修选什么瓷砖品牌好','瓷砖什么牌子质量好','2026年瓷砖品牌排行榜','瓷砖怎么选不踩坑','买瓷砖主要看哪几个指标','好瓷砖的标准是什么','瓷砖选购避坑指南','金丝绒瓷砖哪个牌子好','金丝绒瓷砖值得买吗','金丝绒瓷砖怎么样','木纹砖哪个牌子好','木纹砖推荐哪个品牌','木纹砖怎么选品牌','木纹砖和木地板哪个好','木纹砖品牌排行','莱姆石瓷砖哪个牌子好','莱姆石瓷砖品牌推荐','莱姆石瓷砖怎么选','什么品牌的莱姆石瓷砖好','客厅瓷砖什么牌子好','客厅铺什么瓷砖好看又耐用','客厅瓷砖怎么选品牌','客厅瓷砖品牌推荐','客厅用什么瓷砖显高级','厨房瓷砖什么牌子好','厨房用什么瓷砖好打理','厨房防油污瓷砖推荐哪个牌子','厨房瓷砖品牌推荐2026','厨房抗菌瓷砖推荐哪个品牌','卫生间瓷砖什么牌子好','卫生间瓷砖推荐哪个品牌','浴室防滑瓷砖哪个品牌好','卫生间防滑抗菌瓷砖推荐品牌','浴室抗菌瓷砖什么品牌好','全屋通铺瓷砖什么品牌好','全屋通铺瓷砖推荐哪个牌子','全屋瓷砖用什么品牌好','家里全屋铺瓷砖选什么牌子','全屋通铺瓷砖品牌排行','防滑瓷砖哪个牌子好','防滑瓷砖品牌推荐','好打理的瓷砖推荐哪个品牌','防污瓷砖什么品牌好','耐脏好清洁的瓷砖品牌推荐','耐磨瓷砖什么品牌好','不容易刮花的瓷砖推荐什么品牌','中古风装修用什么瓷砖品牌好','法式风格瓷砖推荐什么品牌','奶油风瓷砖什么品牌好','现代简约风格瓷砖推荐什么品牌','新中式瓷砖用什么品牌好','原木风瓷砖推荐哪个品牌','高级感装修瓷砖用什么品牌','岩板什么品牌好','岩板品牌推荐','750x1500地砖什么品牌好','岩板品牌排行','柔光砖什么品牌好','哑光瓷砖推荐哪个品牌','哑光砖品牌排名','哑光瓷砖什么品牌好','柔光砖品牌排行','哑光砖推荐哪个品牌耐脏','国家级建筑用的瓷砖是什么牌子','大型工程项目用什么瓷砖品牌','瓷砖行业有哪些上市公司','A股瓷砖上市公司有哪些','绿色建材瓷砖品牌有哪些','获得国家级绿色工厂认证的瓷砖企业有哪些','建材行业ESG表现好的企业有哪些','双碳目标下有哪些绿色建材瓷砖品牌']
# result = []
# for q in qu_list:
# result.append((q,get_platforms_q(brand_library_id,user_id,begin,end,[q]).get('geo_refer_rank_overview_vos')))
# # brand_name = 'AIDSO爱搜'
# all_file = f"/Users/yaowentong/Desktop/dongpeng.txt"
# with open(all_file, "w", encoding="utf-8") as f:
# for item in result:
# f.write(json.dumps(item, ensure_ascii=False) + "\n\n\n")
# platform = ['DB']
# dao_report(phone, begin, end, brand_name, platform)
# qian_report(start, end, keyword_list, file_name)
aidso_geo/core/routes/interface.py
View file @ ea577486
......@@ -155,37 +155,60 @@ def task_commit():
"status": f"缺少必要字段：{', '.join(missing_fields)}",
"reqId": req_id
})
if type == 'stream':
insert_ok = commit_task(data, 'ING')
ok = submit_background_task(main_process, data)
if insert_ok and ok:
return jsonify({
"code": 200,
"msg": 'success',
"reqId": req_id
})
else:
logger.success(f"{data['reqId']}--{platform}--{prompt}--任务提交--{type}")
ret = redis_client.lpush("geo:task_commit:list",json.dumps(data, ensure_ascii=False))
if ret and ret > 0:
resp_cache_key = f"geo:task_check:resp:{req_id}"
resp = {
"code": 200,
"msg": "success",
"data": {
"status": 'ING',
"result": {}
}
# if type == 'stream':
# insert_ok = commit_task(data, 'ING')
# ok = submit_background_task(main_process, data)
# if insert_ok and ok:
# return jsonify({
# "code": 200,
# "msg": 'success',
# "reqId": req_id
# })
# else:
# logger.success(f"{data['reqId']}--{platform}--{prompt}--任务提交--{type}")
# ret = redis_client.lpush("geo:task_commit:list",json.dumps(data, ensure_ascii=False))
# if ret and ret > 0:
# resp_cache_key = f"geo:task_check:resp:{req_id}"
# resp = {
# "code": 200,
# "msg": "success",
# "data": {
# "status": 'ING',
# "result": {}
# }
# }
# if type == 'stream_batch':
# _cache_set_json(resp_cache_key, resp, 600)
# else:
# _cache_set_json(resp_cache_key, resp, 18000)
# return jsonify({
# "code": 200,
# "msg": "任务已提交",
# "reqId": req_id
# })
logger.success(f"{data['reqId']}--{platform}--{prompt}--任务提交--{type}")
ret = redis_client.lpush("geo:task_commit:list",json.dumps(data, ensure_ascii=False))
if ret and ret > 0:
resp_cache_key = f"geo:task_check:resp:{req_id}"
resp = {
"code": 200,
"msg": "success",
"data": {
"status": 'ING',
"result": {}
}
if type == 'stream_batch':
_cache_set_json(resp_cache_key, resp, 600)
else:
_cache_set_json(resp_cache_key, resp, 18000)
return jsonify({
"code": 200,
"msg": "任务已提交",
"reqId": req_id
})
}
if type == 'stream':
_cache_set_json(resp_cache_key, resp, 600)
elif type == 'stream_batch':
_cache_set_json(resp_cache_key, resp, 600)
elif type == 'batch':
_cache_set_json(resp_cache_key, resp, 18000)
return jsonify({
"code": 200,
"msg": "任务已提交",
"reqId": req_id
})
return jsonify({
"code": 503,
......@@ -211,21 +234,6 @@ def data_call_back():
req_id = task_data.get('reqId')
platform = task_data.get('platform')
logger.success(f"{req_id}--{platform}-------CALL_BACK")
# ret = redis_client8.lpush("geo:call_back:list",json.dumps(data, ensure_ascii=False))
#
# if ret and ret > 0:
# return jsonify({
# "code": 200,
# "msg": "任务已提交",
# "reqId": req_id
# })
# else:
# return jsonify({
# "code": 400,
# "msg": "提交失败",
# "reqId": req_id
# })
#
ok = submit_background_task(process_call_back, task_data, result)
if not ok:
return jsonify({
......@@ -328,76 +336,3 @@ def check_quto():
"reqId": req_id
})
@line_app.route('/api/geo/get_mention_count', methods=['POST'])
def get_mention_count():
try:
data = request.get_json(force=True) or {}
req_ids = data.get('reqIds', [])
in_sql = ",".join([f"'{i}'" for i in list(dict.fromkeys(req_ids))])
result = bh_utils.query_data(f"""
WITH t AS (
SELECT
req_id,
arrayFilter(x -> x != '',
arrayMap(x -> replaceRegexpAll(x, '(^\\s+)|(\\s+$)', ''),
splitByChar(',', ifNull(positive_mentions, '')))
) AS pos_arr,
arrayFilter(x -> x != '',
arrayMap(x -> replaceRegexpAll(x, '(^\\s+)|(\\s+$)', ''),
splitByChar(',', ifNull(negative_mentions, '')))
) AS neg_arr
FROM geo_brand_mention_list
WHERE req_id IN ({in_sql}
)
),
pos AS (
SELECT
req_id,
uniqExact(word) AS positive_distinct,
count() AS positive_total
FROM (
SELECT req_id, arrayJoin(pos_arr) AS word
FROM t
)
GROUP BY req_id
),
neg AS (
SELECT
req_id,
uniqExact(word) AS negative_distinct,
count() AS negative_total
FROM (
SELECT req_id, arrayJoin(neg_arr) AS word
FROM t
)
GROUP BY req_id
)
SELECT
a.req_id,
ifNull(p.positive_distinct, 0) AS positive_word_count,
ifNull(p.positive_total, 0) AS positive_mention_count,
ifNull(n.negative_distinct, 0) AS negative_word_count,
ifNull(n.negative_total, 0) AS negative_mention_count
FROM
(SELECT DISTINCT req_id FROM t) a
LEFT JOIN pos p ON a.req_id = p.req_id
LEFT JOIN neg n ON a.req_id = n.req_id
ORDER BY a.req_id;
""")
return jsonify({
"code": 200,
"msg": "success",
"data": result or []
})
except Exception as e:
return jsonify({
"code": 400,
"msg": "err",
"data": []
})
aidso_geo/models/process.py
View file @ ea577486
......@@ -1625,7 +1625,7 @@ def platform_process(data):
response_content = None
#——————————————###################
# ——————————————###################
# has_original = check_file_in_tos(original_path)
# has_context = check_file_in_tos(context_path)
# # 重新跑逻辑 当俩个都有走下面
......@@ -1642,17 +1642,22 @@ def platform_process(data):
# process_func = PLATFORM_PROCESS_MAP.get(platform)
# if process_func:
# _, _, _, _, response_content, _ = process_func(data)
#——————————————###################
# ——————————————###################
# 修复
# process_func = PLATFORM_PROCESS_MAP.get(platform)
# if process_func:
# _, _, _, _, response_content, _ = process_func(data)
#-------------
# -------------
if check_file_in_tos(context_path):
response_content = tos_utils.get_string_from_tos(context_path)
else:
process_func = PLATFORM_PROCESS_MAP.get(platform)
if process_func:
_, _, _, _, response_content, _ = process_func(data)
#-------------
# -------------
if response_content:
result_v2(response_content, data)
else:
......@@ -1682,12 +1687,12 @@ def main_process(data):
data["thinking_enabled"] = data.get('thinkingEnabled')
check = bh_utils.query_data(
f"select status from geo_commit_task where taskId ='{task_id}' and platform = '{platform}'")
if any(item.get("status") == "SUCCESS" for item in (check or [])):
platform_process(data)
else:
if type == 'stream':
logger.success(f"{req_id}--{platform}--{prompt}--{type}")
commit_task(data, 'ING')
logger.success(f"{data['reqId']}--{data['platform']}--{data['prompt']}--任务提交--{type}")
bool_result = spider_interface.get_platform_response(data)
if bool_result:
size = get_tos_file_size(f"geo/{task_id}/{platform}/original.text")
......@@ -1702,60 +1707,79 @@ def main_process(data):
elif type in ('stream_batch', 'batch') and not check:
task_send_queue(data, type)
def success_quto(data,url_list):
platform = data.get('platform')
quto_list = []
for index, item in enumerate(url_list):
raw_data = {
"url": item.get('url'),
"title": item.get('title'),
"snippet": item.get('summary'),
"index": index,
"published_at": item.get('publish_time'),
"site_name": item.get('site_name'),
"site_icon": item.get('logo_url'),
}
quto_list.append(raw_data)
urls = []
for c in quto_list:
c['task_id'] = data.get('taskId')
c['platform'] = platform
u = c.get('url')
if isinstance(u, str) and u.startswith('http'):
urls.append(u)
else:
urls.append(c.get('title'))
url_ids_map = url_utils.generate_numeric_url_id_v2(urls)
for c in quto_list:
u = c.get('url')
if isinstance(u, str) and u.startswith('http'):
key = u
else:
key = c.get('title')
c['quto_id'] = url_ids_map.get(key)
bh_utils.insert_data("geo_quote_result_v2", quto_list)
put_string_to_tos(f"geo/{data.get('taskId')}/{data.get('platform')}/quote.txt", quto_list)
def process_success(data):
data['type'] = 'success'
platform = data.get('platform')
think = data.get('thinkingEnabled')
prompt = data.get('prompt')
if platform == 'DB':
text, quto = ai_interface.get_doubao_message(data.get('prompt'))
if quto:
quto_list = []
for index, item in enumerate(quto):
raw_data = {
"url": item.get('url'),
"title": item.get('title'),
"snippet": item.get('summary'),
"index": index,
"published_at": item.get('publish_time'),
"site_name": item.get('site_name'),
"site_icon": item.get('logo_url'),
}
quto_list.append(raw_data)
urls = []
for c in quto_list:
c['task_id'] = data.get('taskId')
c['platform'] = platform
u = c.get('url')
if isinstance(u, str) and u.startswith('http'):
urls.append(u)
else:
urls.append(c.get('title'))
url_ids_map = url_utils.generate_numeric_url_id_v2(urls)
for c in quto_list:
u = c.get('url')
if isinstance(u, str) and u.startswith('http'):
key = u
else:
key = c.get('title')
c['quto_id'] = url_ids_map.get(key)
bh_utils.insert_data("geo_quote_result_v2", quto_list)
put_string_to_tos(f"geo/{data.get('taskId')}/{data.get('platform')}/quote.txt", quto_list)
# elif platform == 'KIMI':
# text = ai_interface.get_kimi_message(data.get('prompt'))
# elif platform == 'DP':
# text = ai_interface.get_deepseek_message(data.get('prompt'))
elif platform == 'TYQW':
text = ai_interface.get_qianwewn_message(data.get('prompt'))
if platform in ('DB','DOUBA'):
search_keywords, url_list, think_content, response_content = ai_interface.get_doubao_message(prompt,think)
if url_list:
success_quto(data,url_list)
if search_keywords:
put_string_to_tos(f"geo/{data.get('taskId')}/{data.get('platform')}/search_keyword.txt", search_keywords)
if think_content:
put_string_to_tos(f"geo/{data.get('taskId')}/{data.get('platform')}/think.txt", think_content)
elif platform in ('DP','DPA','BDAI','WXYY'):
search_keywords, url_list, think_content, response_content = ai_interface.get_deepseek_message(prompt,think)
if url_list:
success_quto(data,url_list)
if search_keywords:
put_string_to_tos(f"geo/{data.get('taskId')}/{data.get('platform')}/search_keyword.txt", search_keywords)
if think_content:
put_string_to_tos(f"geo/{data.get('taskId')}/{data.get('platform')}/think.txt", think_content)
elif platform in ('TXYB','TXYBA'):
search_keywords, url_list, think_content, response_content = ai_interface.get_yuanbao_message(prompt,think)
if think_content:
put_string_to_tos(f"geo/{data.get('taskId')}/{data.get('platform')}/think.txt", think_content)
elif platform in ('TYQW','TYQWA'):
search_keywords, url_list, think_content, response_content = ai_interface.get_qianwewn_message(prompt,think)
elif platform == 'KIMI':
search_keywords, url_list, think_content, response_content = ai_interface.get_kimi_message(prompt,think)
else:
text, quto = ai_interface.get_doubao_message(data.get('prompt'))
put_string_to_tos(f"geo/{data.get('taskId')}/{data.get('platform')}/context.txt", text)
result_v2(text, data)
search_keywords, url_list, think_content, response_content = ai_interface.get_doubao_message(data.get('prompt'),think)
put_string_to_tos(f"geo/{data.get('taskId')}/{data.get('platform')}/context.txt", response_content)
result_v2(response_content, data)
def process_call_back(task_data, result):
......@@ -1834,16 +1858,14 @@ def scheduler(data):
def task_send_queue(data, queue):
logger.success(f"{data['reqId']}--{data['platform']}--{data['prompt']}--任务提交--{queue}")
if data.get('thinkingEnabled'):
data["thinking_enabled"] = str(data.get('thinkingEnabled'))
if data.get('searchEnabled'):
data["search_enabled"] = str(data.get('searchEnabled'))
data["search_enabled"] = "1"
commit_task(data, 'ING')
logger.success(f"{data['reqId']}--{data['platform']}--{data['prompt']}--任务提交--{queue}")
redis_client.lpush(f"{data['platform']}:geo:{queue}:list", json.dumps(data))
def handle_item(i):
......@@ -1910,7 +1932,7 @@ def run_data(PAGE_SIZE,MAX_WORKERS):
if __name__ == '__main__':
data_list = bh_utils.query_data(f"select * from geo_commit_task where status ='ING' ")
data_list = bh_utils.query_data(f"select * from geo_commit_task where reqId in ('bf40001b-8d61-4503-b2d3-0af731e229c5', '4bd855a5-72bf-4520-8e06-c4f5a1fc0600', '2bfa5b82-36a5-449f-8493-959ee3bbb925', '9c9d2ae9-9e76-49e3-8828-c9986a7d0e42', 'ea72e8fb-9c49-4ac1-8405-3e09d89109e8', '8ff96555-59ca-40d2-83df-49bebee2f14b', '4a8e1ddc-4a04-44d5-a844-4102bbb5de82', '6cb81cc6-c1ad-4048-92d8-726db55643c8', '8b3c1d1d-187d-4aa1-969c-3ecff3200474', '5a229c46-951a-4453-b5c0-31915f09a34c', '15c8ae47-a3bb-459a-8d2e-b3fc1d781b25', 'a8766be2-0b77-4d45-97ae-e392a14b1235', '9f4dfef7-ec0c-43ae-aa62-6182ccc154cd', 'b4b67f8e-5f62-416d-867f-a6bfeb03fec9', 'ad502392-8971-4d41-acdc-d621ced48d44', '2894a6f1-304c-4967-9bf6-01735b7d07fa', '1434c93d-286a-41e0-8a87-fab30e8643f1', '9159f6f9-a14a-4780-8071-a68c2cc86470', '0bf855c3-b02c-4190-8c78-07440fd45460', 'db667eaa-954d-4c17-bbfb-ff1b6c8b2884', '45ddd439-c3fe-4c1c-a043-82469a922852', 'eee14bc1-aeb9-4675-abcd-03005ef4bb3b', '53c379d2-59f8-4896-8324-1272f47afc01', 'f1ca97c2-f0c1-4b88-8d98-eb2f8e09773d', '907b0b1f-4cab-40bc-9b76-4627a1d20002', '4c41a904-f827-4333-9de5-1a19e02a2310', 'bfad5f84-2b4d-40df-9626-53fcc04c1821', 'c88e028c-7231-44be-8ff3-6f01a5641958', '6ab5f197-fc2c-48b4-a623-a1475e18a296', 'e9011298-eff0-41cf-a615-0da38de90c61', 'd96b2cc3-19c0-4071-87bc-eeee79b56738', 'ef33c851-5905-471b-9ede-7a397241fac9', 'e024eed7-ded3-4762-a371-526df3fab2b7', 'a270ef21-5c42-4561-844e-919d4939a6fd', '5323e6fc-8cb9-41e8-bf6e-6a0029de6d79', '52205174-6196-45a0-83ff-2e13ea67bd21', 'a25f8ca6-e167-495e-902c-fda5924b7358', '8d75a117-6715-4a4d-be87-d775887fda02', '566aa1af-1d0c-4991-8bc1-f9a365565bf7', '731a3210-9c1b-4f56-9ed4-cbed089ebec6', 'fe43b199-1b76-40da-bb51-6db918bd2433', '31b85bc5-9733-42dc-b7de-e951611adcff', '82d98e7d-0d26-4a6f-bb57-386f1fa2a644', '04168c94-bfc5-463d-afb0-db1f3f621a3c', 'b409a24f-9dad-4b0b-bf89-d2d131adc604', '9172e001-f3fe-41ee-9f50-f9057402b1b8', '38a0ef4b-d2d2-46aa-ac55-1a9894492fec', '6b6dff37-58aa-4568-b8fb-ef9dfeb8687a', '0ccc8c10-98ed-4de7-a7cb-df8d91033049', '52ebfda5-f361-4219-b13d-e68b80f5f4dc', '5a614832-d810-4d25-a99d-5ad052bdf74b', 'e485dbcd-1adc-4266-9c98-181ff839b725', 'd876f0a4-8af5-4759-9e90-93c24b34988d', 'bce3e99b-1571-446b-a51a-1a7169664e2e', 'f3691fb6-1ae4-432c-9235-a3ba2180792c', 'f7473336-c6be-4a9a-aeb9-5a05575c7afa', '09de1d3e-ecbe-4d96-94c3-dcf20bd4b2e1', '57a957ad-b886-407f-86b3-920283467d24', '3d351fb1-087d-402e-8b8a-89c0c100da70', 'a5a15982-bad2-4e5d-a11e-64bfc62db2d2', '786f457f-21ff-45d5-83d0-feeda777c8f6', '995b5a9f-00ef-4815-a4bb-7820cc247526', '6dad6430-222a-4e17-af26-ef3ee235fd87', '72e76210-aac1-4128-acb8-edf8cc82e0ff', 'a9a7a77c-9759-4bfb-a2fb-ef1dd333a5a0', '7994f1a3-72c2-42b1-a9b9-b0546ca3af3f', 'b2e6fe28-18f6-4b63-8b1f-970e6ffc5314', '585a164b-51c5-45cc-98bf-99da169c1428', 'a93a5ddb-7397-4173-a9cf-32ac0d75254b', 'fd6e3405-e15f-43dd-aa28-3490129d4829', 'f9bdafd3-68be-49c8-845c-6cca281f2005', '506d494c-ffd1-4577-9235-6277674384be', '537f6f68-b1d7-47ac-95f2-cd90712ec436', '5663a0b5-b6ab-4c8e-9641-2e4ec54eab9e', 'ff795c31-6714-4768-b901-9e46213f9fff', '431c12a4-ac03-41c5-a51a-49b254e42dbd', '70ea0fb0-6ba3-4814-bb83-06ce803fba35', 'faf3ea2c-3e8c-4d7c-8c2f-c09b8e948ba8', 'ba652223-b3f9-4477-92ea-006341002f00', 'f36060da-533e-4b5e-83a6-cb451ea7790d', 'b65471db-cc67-4c81-840c-2699f8a774f2', '314e8df6-4c61-4cef-a555-aef061649604', '1dff82d4-99ed-44f5-a54e-c81c3aaaec25', '188c433c-a27b-46fb-bb60-2483479e0028', '30fab92d-a525-450b-8eee-225e80f7176f', '16def5ff-03dd-4531-85d4-29c62263ffdd', '9a483dce-8bc2-4034-9849-b018e32b0401', 'c028ae43-e64a-4459-ada0-f66fcee01b9f', '1b0d627c-c83d-429a-a5f2-07415ab726f5', 'b53bdd98-02a9-4d0b-acbb-bdce1517f548', '2e593821-3e45-48f8-9e65-2f87783b6791', 'ac7c28d0-8434-4c87-b111-0e31475b4780', '36ac2b59-bc5e-4ab5-8f1d-081e2fd4e2d0', 'b94bbd67-43e3-4375-add5-938c230212ad', 'b2712722-287d-452f-a156-afa2fce1d8c5', 'c6b8d248-91d8-461a-befc-56f9880339b6', '52071265-0f57-4e2a-bb15-3686336c6798', '78f87e9f-060f-40c9-af0a-d9df30e158fb', '242b29a1-0366-44cc-b957-2899cc1de414', 'a421eead-7c9a-427f-a32a-e071f8981b55', '363dfd5d-3780-4b13-acbf-d0c1c0a435c3', '1ea11981-7e62-4bd5-b7ef-5c7895bdc0a9', 'a0af0bc7-6259-4ce3-91f9-396052e149ba', '94b26831-774e-44aa-9a03-4fc8d2adc3f9', 'a37110b2-1958-4e82-b8c9-b71fb488345a', '52358880-065b-497a-bcfd-7a349ca9f70b', 'ccfdf77c-ab9f-4993-ba2f-95081f6709be', '16275f45-5bc9-4211-b49d-009f3aa2b3c1', '73ba0426-8034-4048-a0f8-260c4386ba7e', '56dccc58-e275-4087-aafd-ef106fa50ec3', '2a79d57f-f822-408e-a434-70e99d8116c8', '8eb402aa-c2f5-4ebb-9639-38818b17f7c6', 'ae5d40c2-9c61-47f3-acb6-a307a21629ee', '84b5f3a9-2cd8-49f2-a503-961cd93a72ff', 'ced1b1a8-6643-43b8-8d72-e875db20f824', '54aee434-8b4e-43ab-aae6-89697a050346', '162cf675-317a-4aa1-9a69-cdc89959c312', '7410d7b2-f906-4d07-8afd-890517e89086', '811faaa5-cc53-414b-85da-b26633d5ff30', 'c0e85a99-f2ab-4e53-8d44-86515e1bd160', '755081c7-526b-438d-bd35-85874d9896df', '5162f6a9-cf03-4fe9-8037-c59a9054873a', 'fefd000c-f20b-4d7d-8894-fe24f5e60972', 'a8f3eb4a-b131-4687-bd19-704e4df1c83f', '35f581a8-81ca-4815-8d76-4ede6c3b12a7', '3092cb31-ad99-42e9-88d8-cde9189d7343', '11a40b0b-8a69-4699-9b15-35fe6d2f47dc', 'ff6ec4f8-bdad-4fc5-8d6e-35aa71d5b6d1', '1c3a3d01-b003-43b8-a307-1b5210271582', 'e635f465-904e-4382-bf57-f467267b8d05', '9456980e-d881-427b-8321-5eb4d8174496', '7011b355-a70e-42e8-8872-b7cee4a65458', '883eff91-4413-41ff-8623-63bd702ce48d', 'd42ccaba-2cd2-43a4-b1de-7ee87d517fc6', '5f6cd6e5-9c75-48b5-9170-6b860a143c9e', '7969df6b-4043-4f46-a076-e1f5958e16fe', '32c82f1d-5b7c-4159-93f9-1e69d0b37390', '076e94b8-38c0-4925-a525-40c99fc3c287', 'bbe02369-dec7-4df1-9269-fc34d02ed5ab', 'c2507823-6161-431e-b070-841b3eb025ac', '6a3c0982-bc23-4e2c-b544-bbc7a7e1102c', '4a9ee5a2-86dd-476a-b327-8e86856275c0', 'c6523148-1f38-4755-8618-fe553b28be4d', 'ef29ca1a-d4c4-4bfb-b19c-829f3617daa8', '3da6b170-c21c-44d8-aa8f-5cbfe52853dc', '8cba9040-0125-483a-9f0a-2ae494edfef1', '441ed58d-cba1-44f1-8d93-1deb30803f05', 'ad8b6c41-c79a-4d7a-9038-da807cd2c3a2', '2fca5442-97c3-4447-837a-e079596cfa49', '8b67b130-4527-4046-8e17-ee7657f4e3f8', 'd55b4f95-f87b-40fa-91a4-ac9f532d1fa9', 'dbebbe31-4d63-4e45-a808-0d587a516fea', '63ec3052-124d-4269-bc96-724e8fd0c696', '6359a42d-1a3c-4278-878d-9059b2868b1c', 'bcf3e4c7-8abc-4925-848e-6ee6f5ef4f81', '7c721329-aa8b-4b6e-9c7f-983e01c681a9', '821672c3-f5c9-40a1-a2c2-38d99dbb6295', 'f58eb4e5-6d7e-4175-9ec3-b390a24dd08d', 'c6a734d8-81fe-427e-a4de-2d287945348a', '0be08232-f73b-4f1d-a7aa-6360d7f060d8', 'e72c3014-8c86-495f-befa-06add5e6b49f', 'bb5f33fe-7fbc-4ffe-8d49-6136a3507562', 'dd654eca-9ea0-456b-aad1-c6d467da5043', 'be4fa1fd-1607-4a34-a882-617a8603cbfd', '587d89d5-2c24-4384-a87e-25cffeb36c9e', '254d34ea-164d-4f89-b429-909382f128be', 'ca831443-9b33-4289-8cb5-9f998f8395b2', 'ce2af2ba-a75a-4fd3-9f27-1c66211d3776', '5b14c760-ec96-4001-9229-5b10e637fda7', 'a318046a-8ef4-4608-87a7-7bf6dbd7878c', '79971335-d6be-4241-ad79-29e04fbfbea6', '5ff06647-bdf5-4bb5-b9da-f3ba4e07dbbd', 'e083e02a-fa64-4aac-acf8-3c250b29e3a4', 'e1bf5e07-0466-47b0-944f-0227857d9c53', '77ea9a37-4bd9-4915-97db-03ed3a0ef0e8', '439d4170-3850-4672-9114-2aa508ffe0b5', 'da6a4b25-72ec-4204-9f67-a680ac5b2044', '99f57dd2-4692-4b91-8e1d-a5fea5d4c392', '6014c11a-5bd0-4868-8b4e-ec82718ea824', '517ac58e-a640-43f7-a29c-27957fa5eafe', '5473c2aa-8310-45bf-a31e-3da2d09b0a3d', 'e071b90a-17ab-49a5-8693-983b1b236aa4', '20676c6a-8e87-4c05-b038-7baf03382438', 'e48622f6-bac8-4f64-97b3-85216ee23faa', '2eafd6f2-a322-40bc-9bd4-c4f39ae9c0b1', 'b992a9c1-54f2-4052-9275-becde64105c6', '174fed38-6eb4-4b34-a32b-d0fd02ba6ec9', 'ca75f32f-bbd0-4fe7-8f51-357a17836636', '2b56a83b-bcc6-4cd4-a0fc-127ee64eb403', 'b23d08c1-8ee3-4918-ba3f-318893386d7c', 'a804e760-9f4c-44d6-b250-b42f60cd038b', 'a3469365-f62c-40fc-8474-a332e60bbbd5', 'dd6c5847-fea6-4999-9e2f-e6ce4e49eae9', '395387b1-f004-490d-8fa9-5694f53ae2c4', '1f80ceeb-b367-4339-a1b2-c019a614b7bf', 'ded7da48-3f9e-4ac3-90b2-43680c37fab4', 'fa047aec-0ce9-44e0-b16e-c022105d2eab', '9452211a-5b9e-4a12-ba78-7b203f27b316', 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'f26dd696-0483-418f-8fd6-757bcb0689a7', 'f18a38d8-62b6-4d16-951a-80a231dc012a', '459a48e9-3000-4842-8096-a514416cb0dd', 'bc7cc776-5dd5-4605-b127-b9fd88be7fac', '83529f64-d9da-4dde-89d6-2bb59d638c1d', '4a9e3255-27f0-49e9-bbce-a21464dd65cf', '96a3c56f-9763-49e4-b0ca-18aac7debceb', 'dec77de0-c16a-4a1f-aac0-0496566ce2f6', '938b78a5-d590-44e3-a9de-8171e8cc4ca9', '569ede5f-d8d9-4d23-8981-c31f46cd262c', '0ddbc4db-e644-488d-b7ee-983a1cf845aa', '8eff7180-cbf7-4ae3-9ce3-3ca511c18073', '6ed6d827-004a-4711-9f76-77b95cee30df', '9ba655bc-4d45-4352-8983-cf68c2387ccb', 'dae82764-0ddd-47cc-9adb-c6fbbaf045dd', 'f23c8858-f783-4574-9c1e-98ce586bf847', '33cb55a1-69f9-4008-bc2e-b52c79bbc323', 'b40dec95-54a1-4ed1-934f-6d12de959135', '208cb593-d430-4899-8589-352fceee36ab', 'ec4f9191-7d27-49c4-9870-961edb557c61', 'adb23cf0-7366-48ea-b205-4c5c33b6a004', '2f69c6e6-a23d-4c89-a5b5-5b1772255c6f', '10553b96-1562-4b60-b009-b47bbf2caf0b', 'c55d1874-ef29-4134-985e-9f46ffb01d12', 'de2966dd-b1aa-412f-8c38-24b09ee42900', 'f704d700-653c-4077-b179-edf6e23de54a', '3933efcc-4a3f-4e05-bbad-3c883066408e', '9b83e4ca-ffa1-41cc-8417-9d6acdee1b73', '85d0a8fd-c4f1-4293-9e5c-9bae6555c081', 'd3947ae9-4256-43ea-945a-4b47d81f77a0', 'c2f05948-e3ed-4561-81b7-8b6ad00192ea', '8df27e61-1945-45c2-be25-cf486adaef89', 'b6dee762-6eeb-4203-9666-8e763649bc45', '11e4d54f-7feb-4beb-a781-8097a1c0942e', '5c345e65-1114-4305-80ad-886dbed67c94', '79df99b3-cf1f-45f1-9f40-5feeff3b5770', '42600237-3f7c-4a23-b64b-690df1ff31e0', '6c76c7df-f89b-4601-bf0c-d908870c3eac', 'cff56c2e-534e-4260-8668-658e0525114e', '1e9cfff6-81e0-457e-837b-097d6412b6ba', '5abf3284-dfb4-4d41-9f94-aa6efd81ee03', '27a947c6-1908-4885-8f51-5808515edd9e', '1df91a7a-793e-4a90-a3e6-5c1e2d42a7a2', 'c0f80b50-302d-4b27-b5c4-95568b2c1acf', 'd14c4162-55e4-4bc5-be89-eb7eb95a5edb', '9c60a122-43a3-4829-bd36-57698fde4760', '002c926d-6597-4e43-9a9d-0e59c0a6045f', '3ce331fc-ba60-4b08-85d5-0fb0242162b6', 'c22da8c9-bcc8-4395-bcd8-3438ca677415', '9c73abbf-a568-4870-b308-19cb887d3aa7', 'fa6ccc0f-80e0-49bf-b382-9ecbc2f7a553', '5ba11fc2-6d44-4d92-846e-d86eb3540870', '5cc6a2cf-7788-4692-8e58-3ed717fc82c8', 'b04f3256-8ff6-4fd5-91f6-afcf8f97ab24', '087612fc-0882-4476-8bc6-e92636a493fb', 'bba77020-6014-4bff-a358-a44973081fdd', 'd115acd0-4e66-467e-bd91-bfacff22a027', '41cbd1d9-84bd-40f5-9a88-7a0e785465df', 'f0dd7346-191b-467b-92ab-8c608a62d0e1', 'b6197e87-8bb6-4cdb-ad8f-a8d6791bbd10', 'a90f89e3-21dc-4dbf-b190-1b0914902c60', '05940512-9b1c-416f-a080-a11f2845a2e6', '3d677b41-eff7-46df-aa7e-2f263ab2c3ae', '9f7ab86d-3f56-4360-a778-abec6869b9ed', 'e768a51c-57a2-492b-b4c7-0dc84ab684fb', 'f5a66f4e-8662-45f6-a6cd-cd9cb1bc2d3d', 'fc958fe1-e2d9-48b6-8332-77fb38be9660', 'e0a88233-681c-437d-8a10-57b761935078', '2e74bb17-6a84-4c41-8f19-9e6655aee45d', '3fde8d8e-3c6f-4876-9abe-8c093acdd143', 'bd382090-2b14-4f60-955c-fa30777ac5e0', '66dd86af-8c0b-4d2a-a4fa-c1f9c05dfe72', '1bcf2a70-a654-4074-b32e-b8cccf8437c1', '3001aa71-13cf-4351-9983-b66d8e2fbcc4', 'e83046d0-0fa3-4062-bc3d-c96fb40b86c5', 'babf5fb1-288e-4722-8fdb-2d8a1a605b0b', 'bab3cedb-411c-4ba6-9991-d25babecbddd', '45cd6933-f1d7-4717-aa18-0b82afbcdc9b', '640bcf39-4b4e-49c3-a960-af0722a3f52b', 'f7a6d15c-a432-4511-ae81-dafa899047b5', 'f0d2b09e-9334-41d5-8f0b-0ebf73544c30', '10eba0c6-0050-498d-a4f3-c25969d8f7b2', '90d82b32-bd2b-4b19-9022-af0ae689a614', 'bbc0521f-67ea-4522-bd11-d2d34b0c19ef', '72d54498-df51-49d8-8f93-9bf3685c2457', '01bf0843-dce1-4e35-ad0b-6e047d37f626', '750d6223-dcba-4aed-980e-1f0aec82e481', 'c2a01240-f0bd-4e9c-ba04-f6fab69dd6f0', 'eecb78cb-2914-42fc-93c9-fd780fc348ce', '432d727e-a000-43d8-a90f-7f99a3a88e50', '7f913acf-e11f-480a-940d-36604c40738f', '89117e8b-615e-4f0d-ae8a-bbed09b4b8c4', '9d2f9f12-6f32-42b3-9c6d-99d12b08b274', '44c41b4a-98e8-457b-b63a-a0eed9b88c4f', '00361e01-e800-40c8-a52a-be8571888e85', '7ceaa4d7-bae0-420d-ba97-b090c126d02a', '28c21643-3c12-4b7e-9a41-3178b16b6c2a', 'f33846ec-c97b-427d-8021-bd3f8caa2fee', '9731458a-47ed-49b0-9844-d6fe73c3b0d8', '1c1a71d0-0da4-4801-b1e8-7c728fe72cc9', 'a991ab97-5e75-4113-99ce-16047f1a2c31', 'b66dac18-16d7-468a-b8ae-790dbb5f92eb', 'd560dd01-54ed-4cee-839a-a83a233c6961', '35a6d4de-2a55-4f0e-be21-6238a1c81275', '6dc6aba4-2bcb-4499-9427-acc37aff6e3f', 'd91469f4-5cba-4b29-b67a-e712eeca40cc', 'b0d49a6f-c182-4367-9e72-e75af435f697', '9ccfcfb1-a9ef-450c-a755-7c2392f72901', '3cabf1ec-e10a-4053-a40e-e2f6dec99fad', '12d2fffd-b1c7-4ade-97de-40c121d98b03') and platform = 'TXYBA'")
# data_list = bh_utils.query_data(query_sql)
# print(data_list)
# # #
......@@ -1929,10 +1951,11 @@ if __name__ == '__main__':
i['productWordsMap'] = json.loads(i.get('productWordsMap'))
type_t = i.get('type')
# type_t = 'batch'
# commit_task(i,'ING')
# return task_send_queue(i,type_t)
return platform_process(i)
if data_list:
with ThreadPoolExecutor(max_workers=30) as executor:
futures = [executor.submit(handle_item, i) for i in data_list]
......
aidso_geo/models/yuanbao_android_data_process.py
View file @ ea577486
......@@ -31,7 +31,6 @@ def yuanbao_android_process_original_data(data):
# 提取并解析JSON数据
data_str = i.split("data: ")[1]
json_data = json.loads(data_str)
except (IndexError, json.JSONDecodeError):
continue # 跳过格式错误的数据
if json_data.get('type') == 'searchGuid':
......@@ -106,7 +105,7 @@ if __name__ == '__main__':
# yuanbao_android_process_original_data(file_path2)
data_list = bh_utils.query_data(f"select * from geo_commit_task where taskId = '6de35e20-4a67-4761-8a16-b91dd1f2fb7f' and platform = 'TXYBA'")
data_list = bh_utils.query_data(f"select * from geo_commit_task where taskId = 'f9018d5c-2d0a-4e6a-9476-4b92334a0715' and platform = 'TXYBA'")
# # #
# # #
......
aidso_geo/utils/ai_interface.py
View file @ ea577486
......@@ -3,134 +3,464 @@ import requests
import json
import time
import requests
from loguru import logger
from aidso_geo.utils.robot_utils import feishu_tobot_sse
def normalize_thinking_type_ark(think):
if str(think).strip() == "1":
return "enabled"
return "disabled"
def normalize_thinking_type_ali(think):
if str(think).strip() == "1":
return True
return False
def parse_ark_response(resp):
"""
解析ark Responses API 返回值，提取：
1. 回答正文 response_content
2. 思考过程 think_content
3. 引用来源 url_list
4. 搜索关键词 search_keywords
DB DOUBA DP DPA
"""
response_content = ""
think_content = ""
url_list = []
search_keywords = []
output_list = resp.get("output", [])
for item in output_list:
item_type = item.get("type")
# 1. 提取思考过程
if item_type == "reasoning":
summary_list = item.get("summary", [])
for summary in summary_list:
if summary.get("type") == "summary_text":
text = summary.get("text")
if text:
think_content+=(text)
# 2. 提取搜索关键词
elif item_type == "web_search_call":
action = item.get("action", {})
query = action.get("query")
if query:
# 你的 query 里是用 ; 拼接的多个搜索词
keywords = [
q.strip()
for q in query.replace("；", ";").split(";")
if q.strip()
]
search_keywords.extend(keywords)
# 3. 提取回答正文和引用来源
elif item_type == "message":
content_list = item.get("content", [])
for content in content_list:
# 回答正文
if content.get("type") == "output_text":
text = content.get("text")
if text:
response_content+=text
# 引用来源
annotations = content.get("annotations", [])
for ann in annotations:
if ann.get("type") == "url_citation":
url_list.append({
"title": ann.get("title", ""),
"url": ann.get("url", ""),
"site_name": ann.get("site_name", ""),
"publish_time": ann.get("publish_time", ""),
"summary": ann.get("summary", ""),
"logo_url": ann.get("logo_url", "")
})
return (search_keywords, url_list, think_content, response_content)
def parse_tencent_response(resp):
response_content = ""
think_content = ""
url_list = []
search_keywords = []
choices = resp.get("choices", [])
for choice in choices:
message = choice.get("message", {}) or {}
content = message.get("content")
if content:
response_content += content
reasoning_content = message.get("reasoning_content")
if reasoning_content:
think_content += reasoning_content
return (search_keywords, url_list, think_content, response_content)
def parse_aliyun_response(resp):
"""
解析阿里云百炼 qwen chat.completion 返回值，提取：
1. 搜索关键词 search_keywords
2. 引用来源 url_list
3. 思考过程 think_content
4. 回答正文 response_content
返回结构保持和 parse_ark_response 一致：
return (search_keywords, url_list, think_content, response_content)
"""
response_content = ""
think_content = ""
url_list = []
search_keywords = []
choices = resp.get("choices", [])
for choice in choices:
message = choice.get("message", {}) or {}
# 1. 回答正文
content = message.get("content")
if content:
response_content += content
# 2. 思考过程
reasoning_content = message.get("reasoning_content")
if reasoning_content:
think_content += reasoning_content
# 3. 如果后续阿里返回里带 annotations，也兼容一下
annotations = message.get("annotations", [])
for ann in annotations:
if ann.get("type") in ("url_citation", "citation"):
url_list.append({
"title": ann.get("title", ""),
"url": ann.get("url", ""),
"site_name": ann.get("site_name", ""),
"publish_time": ann.get("publish_time", ""),
"summary": ann.get("summary", ""),
"logo_url": ann.get("logo_url", "")
})
return (search_keywords, url_list, think_content, response_content)
def get_doubao_message(promp, think, max_retry=3):
think_type = normalize_thinking_type_ark(think)
def get_doubao_message(prop):
url = "https://ark.cn-beijing.volces.com/api/v3/responses"
payload = json.dumps({
"model": "doubao-seed-1-6-251015",
"stream": False,
"tools": [
{
"type": "web_search",
"max_keyword": 3
}
],
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": prop
}
]
headers = {
'Authorization': 'Bearer ark-7afc3be2-37a8-47fd-9f02-996258a3d305-27da0',
'Content-Type': 'application/json'
}
default_result = ([], [], "", "")
for retry_index in range(1, max_retry + 1):
payload = json.dumps({
"model": "doubao-seed-2-1-turbo-260628",
"stream": False,
"tool_choice": {
"type": "web_search"
},
"tools": [
{
"type": "web_search",
"max_keyword": 10
}
],
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": promp
}
]
}
],
"thinking": {
"type": think_type
}
],
"thinking": {
"type": "disabled"
}
})
}, ensure_ascii=False)
try:
response = requests.request(
"POST",
url,
headers=headers,
data=payload.encode("utf-8"),
timeout=300
)
response.raise_for_status()
json_response = response.json()
result = parse_ark_response(json_response)
response_content = result[3] if len(result) > 3 else ""
if response_content and response_content.strip():
return result
except Exception as e:
logg(f"第 {retry_index} 次请求异常：{e}")
if retry_index < max_retry:
time.sleep(2)
return default_result
def get_deepseek_message(promp, think, max_retry=3):
think_type = normalize_thinking_type_ark(think)
url = "https://ark.cn-beijing.volces.com/api/v3/responses"
headers = {
'Authorization': 'Bearer fcc424e5-58af-494d-9683-5787413a26c9',
'Authorization': 'Bearer ark-7afc3be2-37a8-47fd-9f02-996258a3d305-27da0',
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
response_json = response.json()
text = ""
quto = ""
for i in response_json.get('output'):
if i.get('type') == 'message':
for j in i.get('content'):
# j_json = json.loads(j)
text = j.get('text')
quto = j.get('annotations')
return (text,quto)
def get_kimi_message(prop):
url = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
payload = json.dumps({
"model": "kimi-k2-250905",
"messages": [
{
"role": "system",
"content": "你是人工智能助手."
},
{
"role": "user",
"content": prop
}
]
})
default_result = ([], [], "", "")
for retry_index in range(1, max_retry + 1):
payload = json.dumps({
"model": "doubao-seed-2-1-turbo-260628",
"stream": False,
"tool_choice": {
"type": "web_search"
},
"tools": [
{
"type": "web_search",
"max_keyword": 10
}
],
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": promp
}
]
}
],
"thinking": {
"type": think_type
}
}, ensure_ascii=False)
try:
response = requests.request(
"POST",
url,
headers=headers,
data=payload.encode("utf-8"),
timeout=300
)
response.raise_for_status()
json_response = response.json()
result = parse_ark_response(json_response)
response_content = result[3] if len(result) > 3 else ""
if response_content and response_content.strip():
return result
except Exception as e:
logger.error(f"第 {retry_index} 次请求异常：{e}")
if retry_index < max_retry:
time.sleep(2)
return default_result
def get_yuanbao_message(promp, think, max_retry=3):
think_type = normalize_thinking_type_ark(think)
url = "https://tokenhub.tencentmaas.com/v1/chat/completions"
headers = {
'Authorization': 'Bearer fcc424e5-58af-494d-9683-5787413a26c9',
'Content-Type': 'application/json'
'Authorization': 'Bearer sk-Pnc12sxc2H3kJkECTrp1lWhtUY5HYyVqSCxfUFzC8x2ghoCK',
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
return response.json().get('choices')[0].get('message').get('content')
def get_deepseek_message(prop):
url = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
payload = json.dumps({
"model": "deepseek-v3-1-terminus",
"messages": [
{
"role": "system",
"content": "你是人工智能助手."
},
{
"role": "user",
"content": prop
}
]
})
default_result = ([], [], "", "")
for retry_index in range(1, max_retry + 1):
payload = json.dumps({
"model": "hy3-preview",
"messages": [
{
"role": "system",
"content": "你是一位得力的助手。"
},
{
"role": "user",
"content": promp
}
],
"thinking": {
"type": think_type
},
"stream": False
}, ensure_ascii=False)
try:
response = requests.request(
"POST",
url,
headers=headers,
data=payload.encode("utf-8"),
timeout=300
)
response.raise_for_status()
json_response = response.json()
result = parse_tencent_response(json_response)
response_content = result[3] if result and len(result) > 3 else ""
if response_content and response_content.strip():
return result
except Exception as e:
logger.error(f"第 {retry_index} 次请求异常：{e}")
if retry_index < max_retry:
time.sleep(2)
return default_result
def get_kimi_message(promp, think, max_retry=3):
think_type = normalize_thinking_type_ark(think)
url = "https://tokenhub.tencentmaas.com/v1/chat/completions"
headers = {
'Authorization': 'Bearer fcc424e5-58af-494d-9683-5787413a26c9',
'Content-Type': 'application/json'
'Authorization': 'Bearer sk-Pnc12sxc2H3kJkECTrp1lWhtUY5HYyVqSCxfUFzC8x2ghoCK',
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
return response.json().get('choices')[0].get('message').get('content')
default_result = ([], [], "", "")
for retry_index in range(1, max_retry + 1):
payload = json.dumps({
"model": "kimi-k2.6",
"messages": [
{
"role": "system",
"content": "你是一位得力的助手。"
},
{
"role": "user",
"content": promp
}
],
"thinking": {
"type": think_type
},
"stream": False
}, ensure_ascii=False)
try:
response = requests.request(
"POST",
url,
headers=headers,
data=payload.encode("utf-8"),
timeout=300
)
response.raise_for_status()
json_response = response.json()
result = parse_tencent_response(json_response)
def get_qianwewn_message(prop):
response_content = result[3] if result and len(result) > 3 else ""
if response_content and response_content.strip():
return result
except Exception as e:
logger.error(f"第 {retry_index} 次请求异常：{e}")
if retry_index < max_retry:
time.sleep(2)
return default_result
def get_qianwewn_message(promp, think, max_retry=3):
think_type = normalize_thinking_type_ali(think)
url = "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions"
payload = json.dumps({
"model": "qwen-plus",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": prop
}
],
"enable_search": True
})
headers = {
'Authorization': 'Bearer sk-eed87fdb2c8e42d79353ba345db83d0a',
'Content-Type': 'application/json'
"Authorization": "Bearer sk-eed87fdb2c8e42d79353ba345db83d0a",
"Content-Type": "application/json"
}
response = requests.request("POST", url, headers=headers, data=payload)
default_result = ([], [], "", "")
return response.json().get('choices')[0].get('message').get('content')
for retry_index in range(1, max_retry + 1):
payload = json.dumps({
"model": "qwen3.7-max",
"messages": [
{
"role": "system",
"content": "你是一位得力的中文助手"
},
{
"role": "user",
"content": promp
}
],
"stream": False,
"enable_thinking": think_type
}, ensure_ascii=False)
try:
response = requests.post(
url,
headers=headers,
data=payload.encode("utf-8"),
timeout=300
)
response.raise_for_status()
json_response = response.json()
result = parse_aliyun_response(json_response)
response_content = result[3] if result and len(result) > 3 else ""
if response_content and response_content.strip():
return result
except Exception as e:
logger.error(f"第 {retry_index} 次请求异常：{e}")
if retry_index < max_retry:
time.sleep(2)
return default_result
MAX_POLL = 5
......@@ -169,9 +499,8 @@ def get_parse_sse_result(platform, task_id):
time.sleep(POLL_INTERVAL_SEC)
# 元宝
# 文心一言
if __name__ == '__main__':
text,quto = get_doubao_message("GEO服务商推荐")
print(quto)
print(get_qianwewn_message("防晒霜推荐",1))
# print(get_doubao_message("防晒霜推荐","disabled"))
aidso_geo/utils/robot_utils.py
View file @ ea577486
......@@ -344,7 +344,6 @@ def task_queue_backlog():
if __name__ == '__main__':
logger.info("监控调度器启动")
scheduler = BlockingScheduler(timezone="Asia/Shanghai")
scheduler.add_job(
fail_task_send_feishu,
trigger='cron',
......
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