binary-husky/gpt_academic · error · Exception

未收到第 {i + 1} 个响应

Error message

未收到第 {i + 1} 个响应

What it means

Raised when the collected responses list is shorter than expected: the loop expects a response at index i*2+1 for every prompt i (multiplexed prompt/response interleaving), and the else-branch fires when that index is out of range. It means at least one of the parallel LLM calls returned nothing — the list has fewer than 2*len(prompts) entries.

Source

Thrown at crazy_functions/paper_fns/auto_git/query_analyzer.py:226

                sys_prompt_array=sys_prompts,
                max_workers=3
            )

            # 从收集的响应中提取我们需要的内容
            extracted_responses = []
            for i in range(len(prompts)):
                if (i * 2 + 1) < len(responses):
                    response = responses[i * 2 + 1]
                    if response is None:
                        raise Exception(f"Response {i} is None")
                    if not isinstance(response, str):
                        try:
                            response = str(response)
                        except:
                            raise Exception(f"Cannot convert response {i} to string")
                    extracted_responses.append(response)
                else:
                    raise Exception(f"未收到第 {i + 1} 个响应")

            # 解析基本信息
            query_type = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "query_type")
            if not query_type:
                print(
                    f"Debug - Failed to extract query_type. Response was: {extracted_responses[self.BASIC_QUERY_INDEX]}")
                raise Exception("无法提取query_type标签内容")
            query_type = query_type.lower()

            main_topic = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "main_topic")
            if not main_topic:
                print(f"Debug - Failed to extract main_topic. Using query as fallback.")
                main_topic = query

            query_type = self._normalize_query_type(query_type, query)

            # 提取子主题
            sub_topics = []

View on GitHub (pinned to d6bde0fa54)

Solutions

1. Log len(prompts) and len(responses) right before the loop to see how many responses are missing
2. Check the multiplexed request call above for silently swallowed exceptions (failed calls should either raise or insert a placeholder)
3. Retry the whole analysis call — transient network/API failures often cause a short response list
4. If failures are persistent, reduce parallelism of the LLM requests to avoid rate-limit drops

if len(responses) < 2 * len(prompts):
    # resend only the missing prompts instead of failing the whole analysis
    missing = [p for i, p in enumerate(prompts) if (i*2+1) >= len(responses)]
    responses = resend_and_merge(responses, missing)

try:
    analyzer.analyze(query)
except Exception as e:
    if "未收到第" in str(e):
        time.sleep(2)
        analyzer.analyze(query)  # one retry; transient drops are the usual cause

When it happens

Trigger: One or more parallel LLM requests fail silently or the multiplexer drops failed requests from the result list; mismatch between the number of prompts sent and responses collected (prompts changed but responses came from an earlier batch).

Common situations: API rate limiting causing some parallel calls to be dropped; timeout on one of the requests; a refactor that changed the prompt count without updating response handling; upstream returning early on first error.

Related errors

• Response {i} is None
• 未收到第 {i + 1} 个响应
• Cannot convert response {i} to string
• 无法提取query_type标签内容
• 分析查询失败: {str(e)}

AI-assisted analysis of binary-husky/gpt_academic@d6bde0fa54 (2026-08-14). Data as JSON: /api/errors/0c36a18209b6b43e. Report an issue: GitHub.