Merge pull request 'fix(srt): add disable_reasoning parameter for ASR correction and include title in archive filename' (#39) from fix/crawler-proxy-config into main

Reviewed-on: http://192.168.1.22:8418/starmen1265/kb-skills/pulls/39

.planning/backlog/semantic-segmentation.md

@@ -1,60 +0,0 @@
-# Semantic Segmentation for SRT Processing
-
-**Status:** Backlog
-**Priority:** Medium
-**Source:** User feedback during v1.5 testing
-
-## Problem
-
-Current `SemanticSegmenter` uses simple heuristic rules:
-- Time gap > 3 seconds
-- Paragraph length > 500 characters
-
-This approach cannot identify **semantic topic transitions**.
-
-### Example Case
-
-Video: BV1MiPSz2ErY (科学透镜 - 5 topics in one video)
-1. 银河系中心的超大黑洞人马座A*其实不是黑洞
-2. 银河系和仙女座星系等构成的本星系群陷入巨大物质薄片
-3. 科学家为世界上97%的建筑生成3D模型
-4. 雷暴的形成
-5. 闪电的形成
-
-Current segmentation mixes these topics together.
-
-## Potential Solutions
-
-### Option 1: Transition Keyword Detection (Simple)
-- Detect common transition phrases: "接下来", "另外", "第二个", etc.
-- **Pros:** Fast, no API calls
-- **Cons:** Low accuracy, many videos lack explicit transitions
-
-### Option 2: LLM-Based Segmentation (Accurate)
-- Ask LLM to analyze entire subtitle and identify topic boundaries
-- **Pros:** Highest accuracy, understands semantics
-- **Cons:** Extra API cost, token limits for long videos
-
-### Option 3: Two-Stage Approach (Balanced)
-- Stage 1: Simple rule-based segmentation (current)
-- Stage 2: LLM checks paragraph beginnings for new topics
-- **Pros:** Most cases use simple rules, LLM only for key decisions
-- **Cons:** More complex implementation
-
-### Option 4: Embedding-Based Similarity
-- Calculate semantic similarity between adjacent paragraphs
-- Low similarity = topic boundary
-- **Pros:** No LLM API calls, runs locally
-- **Cons:** Requires embedding model, implementation complexity
-
-## Recommendation
-
-**Phase 1 (Quick Win):** Transition keyword detection + improve prompt
-
-**Phase 2 (Full Solution):** Two-stage approach with optional LLM semantic segmentation
-
-## Success Criteria
-
-1. Topics are separated correctly
-2. Each paragraph covers a single coherent topic
-3. No false splits within a topic

kb_workflow/deep_analyzer/__init__.py

@@ -116,14 +116,6 @@ class SimpleAIClient:
         data = response.json()
         message = data.get('choices', [{}])[0].get('message', {})
 
-        # 调试：打印完整的 API 响应结构
-        print(f"[DEBUG] disable_reasoning={disable_reasoning}", file=sys.stderr)
-        print(f"[DEBUG] message keys: {list(message.keys())}", file=sys.stderr)
-        if 'content' in message:
-            print(f"[DEBUG] content 长度: {len(message['content'])}, 前200字符: {message['content'][:200]}", file=sys.stderr)
-        if 'reasoning_content' in message:
-            print(f"[DEBUG] reasoning_content 长度: {len(message['reasoning_content'])}, 前200字符: {message['reasoning_content'][:200]}", file=sys.stderr)
-
         # 返回 content 字段
         # 当禁用思考模式时，content 包含直接答案
         # 当启用思考模式时，content 可能包含思考过程和分析

kb_workflow/srt/corrector.py

@@ -26,11 +26,7 @@ class AIClientProtocol(Protocol):
 
 # 纠错 Prompt 模板
 CORRECTION_PROMPT = """你是一个文本校对专家。以下是语音识别生成的字幕，可能包含识别错误。
-
-请执行以下任务：
-1. 纠正明显的错误（如同音字、漏字、错字）
-2. 添加适当的标点符号（逗号、句号、顿号等）
-3. 不要改变原意和风格
+请只纠正明显的错误（如同音字、漏字、错字），不要改变原意和风格。
 
 原文：
 {text}