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如何在网站上做qq群链接,移动宽带续费多少钱,大网站的二级域名,博物馆网站建设依据1，项目简介 WebThinker 是一个深度研究智能体，使 LRMs 能够在推理过程中自主搜索网络、导航网页，并撰写研究报告。这种技术的目标是革命性的：让用户通过简单的查询就能在互联网的海量信息中进行深度搜索、挖掘和整合，从…

1，项目简介

WebThinker 是一个深度研究智能体，使 LRMs 能够在推理过程中自主搜索网络、导航网页，并撰写研究报告。这种技术的目标是革命性的：让用户通过简单的查询就能在互联网的海量信息中进行深度搜索、挖掘和整合，从而为知识密集型领域（如金融、科学、工程）的研究人员大幅降低信息收集的时间和成本。

项目地址：https://github.com/RUC-NLPIR/WebThinker

评价：很垃圾，需要接入微软宙斯的东西，Bing搜索。这些都需要替换成国产的，而且质量一般，不兼容图片等等等，远远不如字节的 DeerFlow 强大。

现有的开源深度搜索智能体通常采用检索增强生成（Retrieval-Augmented Generation, RAG）技术，依循预定义的工作流程，这限制了 LRM 探索更深层次网页信息的能力，也阻碍了 LRM 与搜索引擎之间的紧密交互。

  • 传统 RAG：仅进行浅层搜索，缺乏思考深度和连贯性
  • 进阶 RAG：使用预定义工作流，包括查询拆解、多轮 RAG 等，但仍缺乏灵活性
  • WebThinker：在连续深思考过程中自主调用工具，实现端到端任务执行

WebThinker 使 LRM 能够在单次生成中自主执行操作，无需遵循预设的工作流程，从而实现真正的端到端任务执行。

2，替换原LLM

【替换Bing的检索】run_web_thinker.py & run_web_thinker_report.py

async def generate_response(client: AsyncOpenAI,prompt: str,semaphore: asyncio.Semaphore,generate_mode: str = "chat",temperature: float = 0.0,top_p: float = 1.0,max_tokens: int = 32768,repetition_penalty: float = 1.0,top_k: int = 1,min_p: float = 0.0,model_name: str = "QwQ-32B",stop: List[str] = [END_SEARCH_QUERY],retry_limit: int = 3,bad_words: List[str] = [f"{END_SEARCH_RESULT}\n\n{tokenizer.eos_token}"],
) -> Tuple[str, str]:"""Generate a single response with retry logic"""for attempt in range(retry_limit):try:async with semaphore:if generate_mode == "chat":messages = [{"role": "user", "content": prompt}]if 'qwq' in model_name.lower() or 'deepseek' in model_name.lower() or 'r1' in model_name.lower():formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)else:formatted_prompt = aux_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)if ('deepseek' in model_name.lower() or 'r1' in model_name.lower()) and "<think>\n" not in formatted_prompt:formatted_prompt = formatted_prompt + "<think>\n"else:formatted_prompt = promptresponse = await client.completions.create(model=model_name,prompt=formatted_prompt,temperature=temperature,top_p=top_p,max_tokens=max_tokens,stop=stop,extra_body={'top_k': top_k,'include_stop_str_in_output': True,'repetition_penalty': repetition_penalty,# 'bad_words': bad_words,# 'min_p': min_p},timeout=3600,)return formatted_prompt, response.choices[0].textexcept Exception as e:print(f"Generate Response Error occurred: {e}, Starting retry attempt {attempt + 1}")# print(prompt)if "maximum context length" in str(e).lower():# If length exceeds limit, reduce max_tokens by halfmax_tokens = max_tokens // 2print(f"Reducing max_tokens to {max_tokens}")if attempt == retry_limit - 1:print(f"Failed after {retry_limit} attempts: {e}")return "", ""await asyncio.sleep(1 * (attempt + 1))return "", ""

替换为：

async def generate_response(client: AsyncOpenAI,prompt: str,semaphore: asyncio.Semaphore,generate_mode: str = "chat",temperature: float = 0.0,top_p: float = 1.0,max_tokens: int = 32768,repetition_penalty: float = 1.0,top_k: int = 1,min_p: float = 0.0,model_name: str = "QwQ-32B",stop: List[str] = [END_SEARCH_QUERY],retry_limit: int = 3,bad_words: List[str] = [f"{END_SEARCH_RESULT}\n\n{tokenizer.eos_token}"],
) -> Tuple[str, str]:"""Generate a single response with retry logic"""for attempt in range(retry_limit):try:async with semaphore:if generate_mode == "chat":messages = [{"role": "user", "content": prompt}]if 'qwq' in model_name.lower() or 'deepseek' in model_name.lower() or 'r1' in model_name.lower():formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)else:formatted_prompt = aux_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)if ('deepseek' in model_name.lower() or 'r1' in model_name.lower()) and "<think>\n" not in formatted_prompt:formatted_prompt = formatted_prompt + "<think>\n"else:formatted_prompt = promptclient = OpenAI(api_key="sk-*****",base_url="https://api.deepseek.com/beta")response = client.chat.completions.create(model="deepseek-chat",temperature=temperature,messages=[{"role": "system", "content": formatted_prompt},],stream=False)return formatted_prompt, response.choices[0].message.contentexcept Exception as e:print(f"Generate Response Error occurred: {e}, Starting retry attempt {attempt + 1}")# print(prompt)if "maximum context length" in str(e).lower():# If length exceeds limit, reduce max_tokens by halfmax_tokens = max_tokens // 2print(f"Reducing max_tokens to {max_tokens}")if attempt == retry_limit - 1:print(f"Failed after {retry_limit} attempts: {e}")return "", ""await asyncio.sleep(1 * (attempt + 1))return "", ""
def extract_relevant_info(search_results):"""Extract relevant information from Bing search results.Args:search_results (dict): JSON response from the Bing Web Search API.Returns:list: A list of dictionaries containing the extracted information."""useful_info = []if 'webPages' in search_results and 'value' in search_results['webPages']:for id, result in enumerate(search_results['webPages']['value']):info = {'id': id + 1,  # Increment id for easier subsequent operations'title': result.get('name', ''),'url': result.get('url', ''),'site_name': result.get('siteName', ''),'date': result.get('datePublished', '').split('T')[0],'snippet': result.get('snippet', ''),  # Remove HTML tags# Add context content to the information'context': ''  # Reserved field to be filled later}useful_info.append(info)return useful_info

替换为：

def extract_relevant_info(search_results):"""Extract relevant information from search results.Compatible with custom search result JSON (not Bing).Args:search_results (dict): JSON response containing search results.Returns:list: A list of dictionaries containing the extracted information."""useful_info = []if 'results' in search_results and isinstance(search_results['results'], list):for id, result in enumerate(search_results['results']):info = {'id': id + 1,'title': result.get('title', ''),'url': result.get('url', ''),'site_name': '',  # 该结构中无 site_name 字段'date': '',       # 该结构中无 datePublished 字段'snippet': result.get('content', ''),  # 使用 content 字段'context': ''}useful_info.append(info)return useful_info

3，替换Bing搜索

目前Bing关闭的API，原方式无法继续使用。修改 bing_search.py：

async def bing_web_search_async(query, subscription_key, endpoint, market='en-US', language='en', timeout=20):"""Perform an asynchronous search using the Bing Web Search API.Args:query (str): Search query.subscription_key (str): Subscription key for the Bing Search API.endpoint (str): Endpoint for the Bing Search API.market (str): Market, e.g., "en-US" or "zh-CN".language (str): Language of the results, e.g., "en".timeout (int): Request timeout in seconds.Returns:dict: JSON response of the search results. Returns empty dict if all retries fail."""headers = {"Ocp-Apim-Subscription-Key": subscription_key}params = {"q": query,"mkt": market,"setLang": language,"textDecorations": True,"textFormat": "HTML"}max_retries = 5retry_count = 0while retry_count < max_retries:try:response = session.get(endpoint, headers=headers, params=params, timeout=timeout)response.raise_for_status()search_results = response.json()return search_resultsexcept Exception as e:retry_count += 1if retry_count == max_retries:print(f"Bing Web Search Request Error occurred: {e} after {max_retries} retries")return {}print(f"Bing Web Search Request Error occurred, retrying ({retry_count}/{max_retries})...")time.sleep(1)  # Wait 1 second between retries

替换为：

async def bing_web_search_async(query, subscription_key, endpoint, market='en-US', language='en', timeout=20):client = TavilyClient("tvly-dev-*****")response = client.search(query=query)response.raise_for_status()search_results = response.json()print("=========",search_results)return search_results

4，命令行启动

 【问题解决模式】

python scripts/run_web_thinker.py \--single_question "What is OpenAI Deep Research?" \--bing_subscription_key "YOUR_BING_SUBSCRIPTION_KEY" \  # 用于调用 Bing 搜索API实现搜索增强（如 RAG）--api_base_url "YOUR_API_BASE_URL" \                    # 主模型的 API 接口基础地址，如 http://localhost:8000/v1--model_name "QwQ-32B" \                                # 主模型名称，可为本地模型或远程托管模型--tokenizer_path "PATH_TO_YOUR_TOKENIZER" \             # 主模型使用的分词器路径，如 ./tokenizers/qwq32b/--aux_api_base_url "YOUR_AUX_API_BASE_URL" \            # 辅助模型 API 接口地址--aux_model_name "Qwen2.5-32B-Instruct" \               # 辅助模型名称，用于多模型结果对比、增强或检验--aux_tokenizer_path "PATH_TO_YOUR_AUX_TOKENIZER"       # 辅助模型的分词器路径

【报告生成模式】

python scripts/run_web_thinker_report.py \--single_question "What are the models of OpenAI and what are the differences?" \--bing_subscription_key "YOUR_BING_SUBSCRIPTION_KEY" \--api_base_url "YOUR_API_BASE_URL" \--model_name "QwQ-32B" \--aux_api_base_url "YOUR_AUX_API_BASE_URL" \--aux_model_name "Qwen2.5-32B-Instruct" \--tokenizer_path "PATH_TO_YOUR_TOKENIZER" \--aux_tokenizer_path "PATH_TO_YOUR_AUX_TOKENIZER"

5，web 启动

cd demo
streamlit run run_demo.py

【报错】There is no current event loop in thread 'ScriptRunner.scriptThread'.

【解决】修改 bin_search.py 457 行：关闭加锁。

self.lock = asyncio.Lock()
👇
self.lock = None

【报错】Generate Response Error occurred: Connection error

【解决】修改 settings.py _load_client()

   def _load_client(self, api_base_url, aux_api_base_url):self.client = AsyncOpenAI(api_key="empty",base_url=api_base_url,)self.aux_client = AsyncOpenAI(api_key="empty",base_url=aux_api_base_url,)   👇 def _load_client(self, api_base_url, aux_api_base_url):client = OpenAI(api_key="sk-***",base_url="https://api.deepseek.com/beta")self.client = clientself.aux_client = client
# bing_search.py
use_model_name='QwQ-32B',
aux_model_name='Qwen2.5-72B-Instruct',
👇
use_model_name='deepseek-chat',
aux_model_name='deepseek-chat',

【报错】Invalid max_tokens value, the valid range of max_tokens is [1, 8192]

【解决】修改 bing_search.py

response = await client.completions.create(model=env.aux_model_name,max_tokens=4096,prompt=prompt,timeout=3600,)
👇
response = client.chat.completions.create(model=env.aux_model_name,messages=[{"role": "system", "content": prompt},],)

【报错】Generate Response Error occurred: Error code: 400 - {'error': {'message': 'Invalid max_tokens value, the valid range of max_tokens is [1, 8192]', 'type': 'invalid_request_error', 'param': None, 'code': 'invalid_request_error'}}, Starting retry attempt 1

【解决】修改 run_login.py generate_response()

async def generate_response(client: AsyncOpenAI,prompt: str,temperature: float = 0.0,top_p: float = 1.0,max_tokens: int = 4096,repetition_penalty: float = 1.0,top_k: int = 1,min_p: float = 0.0,model_name: str = "QwQ-32B",stop: List[str] = ["<|end_search_query|>"],retry_limit: int = 3,
):"""Generate a streaming response with retry logic"""for attempt in range(retry_limit):try:client = OpenAI(api_key="sk-****",base_url="https://api.deepseek.com/beta")response = client.chat.completions.create(model="deepseek-chat",temperature=temperature,messages=[{"role": "system", "content": prompt},],stream=True)async for chunk in response:if chunk.choices[0].message.content:yield chunk.choices[0].message.contentreturn  except Exception as e:print(f"Generate Response Error occurred: {e}, Starting retry attempt {attempt + 1}")if attempt == retry_limit - 1:print(f"Failed after {retry_limit} attempts: {e}")await asyncio.sleep(0.5 * (attempt + 1))yield ""

【报错】Generate Response Error occurred: 'async for' requires an object with __aiter__ method, got Stream, Starting

【解决】DeepSeek的输出不是标准的迭代格式，修改 run_logit.py generate_response()

async for chunk in response:if chunk.choices[0].message.content:yield chunk.choices[0].message.contentreturn  
👇
yield response.choices[0].message.content


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