fix: robust LLM JSON parsing + sanitize report sections + chat history dedup
Closes #624, #622, #601, #599, #577 ## LLM JSON parsing (#624 / #622 / #601) - New `_parse_llm_json()` in llm_client.py with 5-stage fallback: 1. Strip markdown fences (existing) 2. Strict json.loads (fast path) 3. json.JSONDecoder.raw_decode (handles trailing prose after JSON) 4. Balanced-brace extraction (leading prose + embedded JSON) 5. Strip control chars + retry - Replaces strict json.loads in chat_json() that was failing on any LLM appending text after the JSON (common with qwen-plus, ollama, gemma even with response_format=json_object). - Logs which fallback was used so problematic LLMs are visible. - 8 unit-test cases covering each strategy. ## Report section tool_call leak (#599) - New `_sanitize_section_content()` in report_agent.py detects when a section's "final answer" is actually an unexecuted tool_call JSON (e.g. `{"name":"quick_search","parameters":{...}}`) and replaces it with a clear fallback message instead of writing the raw artifact to the report. - Applied at all 3 places where final_answer is returned in write_section(): the Final Answer path, the no-prefix fallback, and the force-finalize path. ## Chat history duplicate user message (#577) - In report_agent.py chat(), defensively dedupe chat_history: - Only keep {role, content} from history items - Skip entries that match the current message exactly - This prevents LLM from seeing a duplicate trailing user message and echoing back the previous answer. - Added debug log of constructed messages array for diagnostics.
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@ -1064,6 +1064,42 @@ class ReportAgent:
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# 合法的工具名称集合，用于裸 JSON 兜底解析时校验
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VALID_TOOL_NAMES = {"insight_forge", "panorama_search", "quick_search", "interview_agents"}
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def _sanitize_section_content(self, content: str, section_title: str = "") -> str:
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"""
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Bereinigt einen LLM-Output, der als Section-Content geschrieben werden soll.
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Wenn der Content NUR aus einem unausgeführten tool_call besteht
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(z.B. `{"name":"quick_search","parameters":{...}}`), wäre das nutzlos im
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finalen Report — siehe github.com/666ghj/MiroFish#599.
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Returns: bereinigter Content. Bei reinem tool_call-Leak: Fallback-Hinweis.
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"""
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if not content:
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return content
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cleaned = re.sub(r'<tool_call>.*?</tool_call>', '', content, flags=re.DOTALL)
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cleaned = re.sub(r'\[TOOL_CALL\].*?\)', '', cleaned)
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cleaned = cleaned.strip()
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if not cleaned:
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return f"_(Keine Inhalte verfügbar für: {section_title})_" if section_title else "_(Keine Inhalte)_"
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# Detect: ist der ganze Content ein einzelnes tool_call JSON?
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try:
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parsed = json.loads(cleaned)
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if isinstance(parsed, dict):
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tool_name = parsed.get("name") or parsed.get("tool")
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if tool_name and tool_name in self.VALID_TOOL_NAMES:
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logger.warning(
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"Section '%s' content is raw tool_call (tool=%s) — replaced with fallback",
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section_title, tool_name
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)
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return (
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f"_(Hinweis: Für diesen Abschnitt konnte das Tool `{tool_name}` "
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f"innerhalb der Iterations-Limits nicht ausgeführt werden. "
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f"Bitte Report neu generieren oder Modell-Konfiguration prüfen.)_"
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)
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except (json.JSONDecodeError, TypeError):
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pass
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return cleaned
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def _parse_tool_calls(self, response: str) -> List[Dict[str, Any]]:
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"""
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从LLM响应中解析工具调用
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@ -1390,6 +1426,7 @@ class ReportAgent:
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# 正常结束
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final_answer = response.split("Final Answer:")[-1].strip()
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final_answer = self._sanitize_section_content(final_answer, section.title)
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logger.info(t('report.sectionGenDone', title=section.title, count=tool_calls_count))
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if self.report_logger:
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@ -1488,7 +1525,7 @@ class ReportAgent:
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# 工具调用已足够，LLM 输出了内容但没带 "Final Answer:" 前缀
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# 直接将这段内容作为最终答案，不再空转
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logger.info(t('report.sectionNoPrefix', title=section.title, count=tool_calls_count))
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final_answer = response.strip()
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final_answer = self._sanitize_section_content(response.strip(), section.title)
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if self.report_logger:
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self.report_logger.log_section_content(
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@ -1517,6 +1554,8 @@ class ReportAgent:
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final_answer = response.split("Final Answer:")[-1].strip()
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else:
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final_answer = response
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# Sanitize: kein raw tool_call JSON als Content schreiben (Bug #599)
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final_answer = self._sanitize_section_content(final_answer, section.title)
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# 记录章节内容生成完成日志
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if self.report_logger:
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@ -1809,16 +1848,33 @@ class ReportAgent:
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# 构建消息
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messages = [{"role": "system", "content": system_prompt}]
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# 添加历史对话
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for h in chat_history[-10:]: # 限制历史长度
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messages.append(h)
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# 添加历史对话— defensiv: nur {role, content}, keine Duplikate der aktuellen Frage
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# Fix für github.com/666ghj/MiroFish#577 (Chat wiederholt erste Antwort):
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# Wenn das Frontend versehentlich die aktuelle User-Nachricht im chat_history mitschickt,
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# würde der LLM die Frage als "schon gestellt" sehen und die alte Antwort wiederholen.
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for h in chat_history[-10:]:
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if not isinstance(h, dict):
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continue
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role = h.get("role")
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content = h.get("content")
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if role not in ("user", "assistant") or not content:
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continue
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# Skip falls dies bereits die aktuelle User-Frage ist
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if role == "user" and content.strip() == message.strip():
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continue
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messages.append({"role": role, "content": content})
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# 添加用户消息
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messages.append({
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"role": "user",
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"role": "user",
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"content": message
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})
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logger.debug(
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"report_agent.chat: total_messages=%d history_len=%d current_msg_len=%d",
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len(messages), len(chat_history), len(message)
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)
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# ReACT循环（简化版）
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tool_calls_made = []
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@ -5,11 +5,120 @@ LLM客户端封装
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import json
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import re
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import logging
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from typing import Optional, Dict, Any, List
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from openai import OpenAI
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from ..config import Config
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logger = logging.getLogger(__name__)
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def _parse_llm_json(response: str) -> Dict[str, Any]:
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"""
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Robuster JSON-Parser für LLM-Outputs.
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LLMs (besonders qwen, gemma, ollama-Modelle) hängen oft Trailing-Text
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nach dem JSON an, auch mit response_format=json_object. Außerdem werden
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JSON-Blöcke häufig in ```json ... ``` Markdown-Fences gewrappt.
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Strategie:
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1. Markdown-Fences entfernen
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2. json.loads (strict, schnellster Weg)
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3. raw_decode (parsed Prefix, ignoriert Trailing-Text)
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4. Balanced-Brace-Extraktion (sucht erste vollständige {...} Struktur)
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5. Strip Control-Chars + Retry
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Bei allen Fehlern: ValueError mit hilfreichem Snippet.
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Fixes:
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- github.com/666ghj/MiroFish#624 ("Unexpected non-whitespace character after JSON at position N")
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- github.com/666ghj/MiroFish#622 (duplikat)
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- github.com/666ghj/MiroFish#601 (500 error on ontology/generate mit qwen-plus/ollama)
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"""
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if not response or not response.strip():
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raise ValueError("LLM lieferte leere Antwort")
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# 1. Strip Markdown-Fences
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cleaned = response.strip()
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cleaned = re.sub(r'^```(?:json|JSON)?\s*\n?', '', cleaned)
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cleaned = re.sub(r'\n?```\s*$', '', cleaned)
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cleaned = cleaned.strip()
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# 2. Schneller Pfad: vollständiges JSON
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try:
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return json.loads(cleaned)
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except json.JSONDecodeError as e_strict:
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first_error = e_strict
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# 3. raw_decode — parsed JSON-Prefix, ignoriert Trailing-Text
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try:
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decoder = json.JSONDecoder()
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obj, end_idx = decoder.raw_decode(cleaned)
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trailing = cleaned[end_idx:].strip()
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if trailing:
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logger.warning(
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"LLM appended trailing text after JSON (%d chars), ignored. Preview: %s",
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len(trailing), trailing[:120]
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)
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if isinstance(obj, dict):
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return obj
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if isinstance(obj, list):
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# Wrap in dict für Konsistenz mit chat_json-Erwartung
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return {"items": obj}
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except json.JSONDecodeError:
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pass
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# 4. Balanced-Brace-Extraktion: find first complete {...}
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start = cleaned.find('{')
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if start >= 0:
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depth = 0
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in_string = False
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escape = False
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for i in range(start, len(cleaned)):
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ch = cleaned[i]
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if escape:
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escape = False
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continue
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if ch == '\\' and in_string:
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escape = True
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continue
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if ch == '"':
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in_string = not in_string
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continue
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if in_string:
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continue
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if ch == '{':
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depth += 1
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elif ch == '}':
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depth -= 1
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if depth == 0:
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candidate = cleaned[start:i + 1]
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try:
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result = json.loads(candidate)
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logger.warning(
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"Extracted JSON from messy LLM output (%d chars before, %d after)",
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start, len(cleaned) - (i + 1)
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)
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return result
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except json.JSONDecodeError:
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break
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# 5. Letzter Versuch: control chars entfernen + retry
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sanitized = re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f]', '', cleaned)
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if sanitized != cleaned:
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try:
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return json.loads(sanitized)
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except json.JSONDecodeError:
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pass
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# Alle Strategien fehlgeschlagen — sprechende Fehlermeldung
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snippet = cleaned[:200] + ('...' if len(cleaned) > 200 else '')
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raise ValueError(
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f"LLM返回的JSON格式无效 (alle Parse-Strategien fehlgeschlagen): "
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f"first_error={first_error.msg} at pos {first_error.pos}. "
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f"Response-Preview: {snippet}"
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)
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class LLMClient:
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"""LLM客户端"""
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@ -90,14 +199,5 @@ class LLMClient:
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max_tokens=max_tokens,
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response_format={"type": "json_object"}
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)
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# 清理markdown代码块标记
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cleaned_response = response.strip()
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cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE)
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cleaned_response = re.sub(r'\n?```\s*$', '', cleaned_response)
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cleaned_response = cleaned_response.strip()
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try:
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return json.loads(cleaned_response)
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except json.JSONDecodeError:
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raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")
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return _parse_llm_json(response)
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