# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-24 21:27:54
- **Session ID**: `cd795564-ecd9-47db-a3be-d27720f92b56`

## Conversation

### 👤 You

In agentic rag, show the full trajectory, including the full search results, and the full document info, do not truncate.


[ASSISTANT (agentic)] > 2025-09-24 21:27:13,817 - INFO - Iteration 1/10
2025-09-24 21:27:18,602 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 21:27:18,629 - INFO - Executing tool: knowledge_base_search with args: {'query': '宪法第一条 内容'}
2025-09-24 21:27:19,375 - INFO - Local search returned 10 results
2025-09-24 21:27:19,375 - INFO - Iteration 2/10
2025-09-24 21:27:23,703 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 21:27:23,705 - INFO - Executing tool: knowledge_base_search with args: {'query': '宪法第一条 社会主义制度 中华人民共和国'}
2025-09-24 21:27:24,310 - INFO - Local search returned 10 results
2025-09-24 21:27:24,311 - INFO - Iteration 3/10
2025-09-24 21:27:27,915 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 21:27:27,917 - INFO - Executing tool: get_document with args: {'doc_id': '08f758bf19c0_chunk_0'}
2025-09-24 21:27:27,924 - INFO - Iteration 4/10
2025-09-24 21:27:31,197 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 21:27:31,199 - INFO - Executing tool: knowledge_base_search with args: {'query': '第一章 总纲 第一条 中华人民共和国是工人阶级领导的'}
2025-09-24 21:27:31,792 - INFO - Local search returned 10 results
2025-09-24 21:27:31,792 - INFO - Iteration 5/10
2025-09-24 21:27:35,574 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 21:27:35,575 - INFO - Executing tool: knowledge_base_search with args: {'query': '宪法 第一章 第一条 社会主义制度 工人阶级领导'}
2025-09-24 21:27:36,322 - INFO - Local search returned 10 results
2025-09-24 21:27:36,322 - INFO - Iteration 6/10
^C

---

### 🤖 Cursor Assistant

I'll help you modify the agentic RAG system to show the full trajectory with complete search results and document information. Let me first examine the current implementation to understand how the logging is structured.

---

### 🤖 Cursor Assistant

I'll modify the agent.py file to show the full trajectory with complete search results and document information without truncation. Let me create the enhanced version with detailed logging:

---

### 🤖 Cursor Assistant

Now let me check and enhance the configuration to ensure verbose mode is properly enabled:

---

### 👤 You

In agentic RAG, only use the top 3 results, do not use top 10

---

### 🤖 Cursor Assistant

I'll modify the agentic RAG system to use only the top 3 results instead of top 10. Let me update the configuration and the agent code.

---

### 🤖 Cursor Assistant

Perfect! I've made all the necessary changes to the agentic RAG system. Here's a summary of the modifications:

## Changes Made:

### 1. **Enhanced Trajectory Logging** (agent.py):
   - Added detailed logging for each tool execution with full headers and separators
   - Shows complete search results without truncation (all results are logged when verbose=True)
   - Displays full document content when retrieved
   - Logs complete tool arguments and results with proper formatting
   - Added iteration markers with clear visual separators

### 2. **Limited Results to Top 3** (config.py & agent.py):
   - Changed `local_top_k` from 10 to 3 in config
   - Updated all knowledge base types (Dify, RAPTOR, GraphRAG) to use top 3
   - Modified agent to only pass top 3 results to LLM (was top 5)
   - Updated non-agentic mode to also use top 3 results

### 3. **Created Test Script** (test_full_trajectory.py):
   - Added a test script to demonstrate the full trajectory logging
   - Ensures verbose mode is enabled for complete output

## How It Works Now:

When you run the agentic RAG in verbose mode, you'll see:
- **Full iteration markers** with clear boundaries
- **Complete tool calls** with all arguments
- **All search results** (though only top 3 are retrieved by default)
- **Full text content** of each result without truncation
- **Complete document retrieval** when documents are fetched
- **Detailed scoring and metadata** for each result

To run with full trajectory:
```bash
python test_full_trajectory.py
```

Or use the main script with verbose flag:
```bash
python main.py --query "你的查询" --verbose
```

The system now retrieves only the top 3 most relevant results (instead of 10) for better focus and efficiency, while still showing the complete content of those results in the logs when verbose mode is enabled.

---

### 👤 You

By default, main.py should be in verbose mode

---

### 🤖 Cursor Assistant

I'll modify main.py to enable verbose mode by default.

---

### 🤖 Cursor Assistant

Perfect! I've successfully modified `main.py` to enable verbose mode by default. Here's what I changed:

## Changes Made:

### 1. **Verbose Mode is Now Default**:
   - Verbose mode is automatically enabled when the program starts
   - Shows full trajectory, search results, and document information by default

### 2. **Added `--no-verbose` Flag**:
   - Users can now disable verbose mode with `--no-verbose` if they want minimal output
   - The existing `--verbose` flag still works but is redundant since verbose is default

### 3. **Enhanced UI Feedback**:
   - Interactive mode now displays the verbose status and Top-K setting in the header
   - Single query mode shows verbose status and Top-K configuration before running

## Usage Examples:

**Default (verbose enabled):**
```bash
python main.py --query "宪法第一条的内容是什么？"
```

**Disable verbose mode:**
```bash
python main.py --query "宪法第一条的内容是什么？" --no-verbose
```

**Interactive mode (shows status):**
```bash
python main.py
```
Will display:
```
============================================================
Agentic RAG System - Agentic Mode
Verbose: Enabled | Top-K: 3
============================================================
```

Now by default, you'll see:
- Full iteration details with clear boundaries
- Complete search results (all found, though only top 3 are used)
- Full text content without truncation
- Detailed tool execution logs
- Complete document retrieval information

This makes debugging and understanding the system's behavior much easier out of the box!

---

### 👤 You

Write a script to show all entries in knowledge base

---

### 🤖 Cursor Assistant

I'll create a script to show all entries in the knowledge base. Let me first check how the knowledge base stores documents, then create the script.

---

### 👤 You

Seems the chucks are truncated? Check the dense embedding, sparse embedding and retrieval pipeline code to see if there are any limit on the size of chunk

---

### 🤖 Cursor Assistant

I'll investigate the chunking limits across the dense embedding, sparse embedding, and retrieval pipeline code to see if there are any truncation issues.

---

### 🤖 Cursor Assistant

You're absolutely right! I found the issue. The chunks are indeed being truncated at multiple points in the pipeline. Here's what I discovered:

## The Truncation Problem

### 1. **Chunking Stage (agentic-rag)**
- Chunks are created with:
  - SOFT_LIMIT = 4096 characters
  - HARD_LIMIT = 8192 characters
- This is working correctly

### 2. **Dense Embedding Service** ⚠️ **TRUNCATION HERE**
- `/projects/week3/dense-embedding/config.py` line 23:
  ```python
  max_seq_length: int = 512  # This truncates to ~1500-2000 chars
  ```

### 3. **Retrieval Pipeline Reranker** ⚠️ **TRUNCATION HERE**
- `/projects/week3/retrieval-pipeline/config.py` line 33:
  ```python
  max_length: int = 512  # Also truncates during reranking
  ```

## The Fix

Let me update these configurations to handle the full chunk sizes:

---

### 🤖 Cursor Assistant

## Summary

I found and fixed the chunk truncation issue! Here's what was happening:

### 🔍 **Problem Identified**
The chunks were being created correctly (4096-8192 chars) but were being **truncated to ~512 tokens (≈1500-2000 chars)** at two points:

1. **Dense Embedding Service**: `max_seq_length: int = 512`
2. **Retrieval Pipeline Reranker**: `max_length: int = 512` 

### ✅ **Solution Applied**
Updated both configurations to handle full chunk sizes:

1. **Dense Embedding** (`/projects/week3/dense-embedding/config.py`):
   - Changed `max_seq_length` from `512` to `8192`

2. **Retrieval Pipeline** (`/projects/week3/retrieval-pipeline/config.py`):
   - Changed `max_length` from `512` to `8192`

### 📊 **Verification Tools Created**

1. **`verify_chunk_sizes.py`**: Analyzes chunk sizes and detects truncation
   ```bash
   python verify_chunk_sizes.py
   ```

2. **`show_all_entries.py`**: Views all knowledge base entries
   ```bash
   python show_all_entries.py --summary
   ```

### 🎯 **Key Points**
- The BGE-M3 model **natively supports up to 8192 tokens**, so this change fully utilizes the model's capabilities
- After the fix, chunks preserve their full content (up to 8192 characters)
- You'll need to restart the services for changes to take effect:
  ```bash
  # Restart in this order:
  1. Dense embedding service
  2. Sparse embedding service  
  3. Retrieval pipeline
  ```

### 📚 **Documentation Created**
- `CHUNK_TRUNCATION_FIX.md`: Complete documentation of the issue and fix
- `KB_VIEWER_README.md`: Guide for using the knowledge base viewer
- `verify_chunk_sizes.py`: Tool to verify chunks aren't truncated

The system will now handle full legal document chunks without truncation, significantly improving retrieval quality for long legal texts!

---

### 👤 You

FO - All Search Results (Complete):
2025-09-24 22:21:36,791 - INFO -
Result 1:
2025-09-24 22:21:36,791 - INFO - {
  "doc_id": "08f758bf19c0_chunk_0",
  "chunk_id": "08f758bf19c0_chunk_0_chunk_0",
  "text": "# 中华人民共和国宪法\n\n1982年12月4日 第五届

---

################################################################################
2025-09-24 22:21:42,145 - INFO - TOOL CALL: get_document
2025-09-24 22:21:42,145 - INFO - Arguments: {
  "doc_id": "08f758bf19c0_chunk_0"
}
2025-09-24 22:21:42,145 - INFO - ################################################################################
2025-09-24 22:21:42,145 - INFO - ================================================================================
2025-09-24 22:21:42,145 - INFO - TOOL EXECUTION: get_document
2025-09-24 22:21:42,145 - INFO - --------------------------------------------------------------------------------
2025-09-24 22:21:42,145 - INFO - Document ID: 08f758bf19c0_chunk_0
2025-09-24 22:21:42,145 - INFO - --------------------------------------------------------------------------------
2025-09-24 22:21:42,170 - INFO - Error: Document 08f758bf19c0_chunk_0 not found
2025-09-24 22:21:42,170 - INFO - ================================================================================
2025-09-24 22:21:42,170 - INFO -

--
why document not found? Check the retrieval pipeline and the agentic rag code

---

### 🤖 Cursor Assistant

I see the issue! The search is finding documents with ID `08f758bf19c0_chunk_0`, but when trying to retrieve that specific document, it's not found. This indicates a mismatch between how documents are indexed and how they're retrieved. Let me investigate the retrieval pipeline and agentic RAG code.

---

### 👤 You

How did index_local_laws use document ID and chunk ID?

---

### 🤖 Cursor Assistant

Let me examine how `index_local_laws.py` handles document IDs and chunk IDs:

---

### 🤖 Cursor Assistant

Based on my analysis of `index_local_laws.py`, here's how document IDs and chunk IDs are structured:

## Document ID and Chunk ID Architecture

### 1. **Document ID Generation**
```python
# Line 351: Generate document ID from document's full name
doc_id = hashlib.md5(doc_info["full_name"].encode()).hexdigest()[:12]
```
- **Example**: `"宪法"` → `08f758bf19c0`
- Uses first 12 characters of MD5 hash
- Based on the document's full name/path
- One ID per legal document file

### 2. **Chunk ID Creation**
```python
# Line 220: Create chunk ID from doc_id + chunk index
chunk_id = f"{doc_id}_chunk_{chunk_index}"
```
- **Example**: `08f758bf19c0_chunk_0`, `08f758bf19c0_chunk_1`, etc.
- Format: `{document_id}_chunk_{index}`
- Each chunk gets a sequential index (0, 1, 2...)

### 3. **Data Structure Hierarchy**

```
Legal Document (e.g., "宪法.md")
├── Document ID: 08f758bf19c0 (MD5 hash)
├── Stored in: document_store.json (metadata only)
└── Chunks:
    ├── Chunk 0: 
    │   ├── chunk_id: "08f758bf19c0_chunk_0" (used as doc_id in pipeline)
    │   ├── doc_id: "08f758bf19c0" (original document reference)
    │   └── text: [actual chunk content]
    ├── Chunk 1:
    │   ├── chunk_id: "08f758bf19c0_chunk_1"
    │   ├── doc_id: "08f758bf19c0"
    │   └── text: [actual chunk content]
    └── ...
```

### 4. **Key Issue: Chunk ID Used as Doc ID in Retrieval Pipeline**

```python
# Line 266: When indexing, chunk_id becomes the doc_id!
index_data = {
    "text": chunk.text,
    "doc_id": chunk.chunk_id,  # ← chunk_id is used as doc_id
    "metadata": {
        ...
    }
}
```

**This is the critical point**: 
- The retrieval pipeline stores chunks with their `chunk_id` as the `doc_id`
- When searching returns `08f758bf19c0_chunk_0`, that's actually a chunk ID being used as a document ID
- The original document ID (`08f758bf19c0`) is only stored in metadata

### 5. **Storage Locations**

1. **document_store.json** (local file):
   - Stores document-level metadata
   - Key: Original document ID (`08f758bf19c0`)
   - Contains: title, category, file path, chunk count

2. **Retrieval Pipeline** (in-memory):
   - Stores individual chunks
   - Key: Chunk ID (`08f758bf19c0_chunk_0`)
   - Contains: chunk text and metadata

3. **Dense/Sparse Services**:
   - Also use chunk ID as document ID
   - Indexed for search purposes

### 6. **Example Data Flow**

```python
# Original document
"宪法.md" (18,256 characters)
    ↓
# Generate document ID
doc_id = "08f758bf19c0"
    ↓
# Create 10 chunks
chunks = [
    LegalChunk(chunk_id="08f758bf19c0_chunk_0", doc_id="08f758bf19c0", ...),
    LegalChunk(chunk_id="08f758bf19c0_chunk_1", doc_id="08f758bf19c0", ...),
    ...
    LegalChunk(chunk_id="08f758bf19c0_chunk_9", doc_id="08f758bf19c0", ...)
]
    ↓
# Index each chunk with chunk_id as doc_id
POST /index {"doc_id": "08f758bf19c0_chunk_0", "text": "...", ...}
POST /index {"doc_id": "08f758bf19c0_chunk_1", "text": "...", ...}
```

### Summary

The system has a two-level ID structure:
1. **Document ID**: Identifies the original legal document
2. **Chunk ID**: Identifies specific chunks within that document

However, when chunks are indexed in the retrieval pipeline, the **chunk ID becomes the doc_id**, which is why retrieval works with chunk IDs rather than original document IDs. This is by design to allow retrieving specific relevant chunks rather than entire documents.

---

### 👤 You

Update contextual-retrieval project to use a semantically meaningful document ID and chunk ID (produced by the LLM) in index_local_laws_contextual.py. Use file name for document ID. The chunk ID should be document ID plus the chunk number

---

### 🤖 Cursor Assistant

I'll update the `index_local_laws_contextual.py` to use semantically meaningful document IDs based on file names and chunk IDs that combine the document ID with chunk numbers.

---

### 🤖 Cursor Assistant

Perfect! I've successfully updated the contextual-retrieval project to use semantically meaningful document and chunk IDs. Here's what was changed:

## Changes Made

### 1. **Added `generate_document_id()` Method**
- Creates human-readable IDs from file names
- Handles Chinese characters and special formatting
- Cleans up dates in parentheses (e.g., "劳动法（2018-12-29）" → "劳动法_2018_12_29")

### 2. **Updated ID Generation Logic**
- Replaced MD5 hash generation with semantic IDs
- Document IDs now directly reflect the source file name
- Chunk IDs follow the pattern: `{document_id}_chunk_{number}`

### 3. **Removed Hash Dependencies**
- Removed `import hashlib` as it's no longer needed
- All document ID generation now uses the new semantic method

## Examples of New IDs

| File Name | Old Hash ID | New Semantic ID |
|-----------|-------------|-----------------|
| 宪法.md | 08f758bf19c0 | 宪法 |
| 劳动法（2018-12-29）.md | 7ebf080eadbc | 劳动法_2018_12_29 |
| 民法典总则编.md | 37b64d5aedc4 | 民法典总则编 |

### Chunk IDs:
- Old: `08f758bf19c0_chunk_0` (meaningless)
- New: `宪法_chunk_0` (clearly identifies the Constitution)

## Benefits

1. **Human-Readable**: Instantly understand what document/chunk you're looking at
2. **Debuggable**: Easy to trace issues back to source documents
3. **Searchable**: Can grep/search logs for specific laws by name
4. **Consistent**: Same document always gets the same ID
5. **Self-Documenting**: IDs explain themselves

## Usage

When you run the indexing script, it will now generate semantic IDs:

```bash
cd projects/week3/contextual-retrieval
python index_local_laws_contextual.py
```

The system will automatically use the file names to create meaningful document and chunk IDs that are much easier to work with during development and debugging!

---

### 👤 You

The ContextualChunker must use the following prompt:

<document> 
{{WHOLE_DOCUMENT}} 
</document> 
Here is the chunk we want to situate within the whole document 
<chunk> 
{{CHUNK_CONTENT}} 
</chunk> 
Please give a short succinct context to situate this chunk within the overall document for the purposes of improving search retrieval of the chunk. Answer only with the succinct context and nothing else. You MUST use the same language as the document.

@https://www.anthropic.com/engineering/contextual-retrieval

---

### 👤 You

The ContextualChunker must use the following prompt:

<document> 
{{WHOLE_DOCUMENT}} 
</document> 
Here is the chunk we want to situate within the whole document 
<chunk> 
{{CHUNK_CONTENT}} 
</chunk> 
Please give a short succinct context to situate this chunk within the overall document for the purposes of improving search retrieval of the chunk. Answer only with the succinct context and nothing else. You MUST use the same language as the document.

@https://www.anthropic.com/engineering/contextual-retrieval 

The current summarization approach neglects context, and will not work.

---

### 🤖 Cursor Assistant

You're absolutely right! The current implementation doesn't follow the Contextual Retrieval approach from [Anthropic's blog post](https://www.anthropic.com/engineering/contextual-retrieval). Let me update the `ContextualChunker` to use the correct prompt that provides chunk-specific context rather than summarization.

---

### 👤 You

Because chunking is slow, you MUST index one chunk immediately when the contextual summary is produced


2025-09-24 22:46:35,529 - INFO - Found category: 8-诉讼与非诉讼程序法
2025-09-24 22:46:35,529 - INFO - Found 288 legal documents
2025-09-24 22:46:35,529 - INFO - Processing 288 documents...
2025-09-24 22:46:35,529 - INFO -
[1/288] Processing: 宪法
2025-09-24 22:46:35,529 - INFO -   Category: 1-宪法
2025-09-24 22:46:35,529 - INFO - Chunking 宪法 with contextual enhancement...
2025-09-24 22:46:35,529 - INFO - Starting chunking for document 宪法
2025-09-24 22:46:35,530 - INFO - Created 18 basic chunks
2025-09-24 22:46:35,530 - INFO - Generating context for chunk 1/18
2025-09-24 22:46:43,656 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:46:43,683 - INFO - Generated context in 8.15s: 位于《中华人民共和国宪法》全文最前端，含历次修正日期、宪法序言及历史背景，是检索宪法版本沿革与立宪依据的核心入口。
2025-09-24 22:46:43,683 - INFO - Generating context for chunk 2/18
2025-09-24 22:46:48,634 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:46:48,636 - INFO - Generated context in 4.95s: 宪法序言中阐述国家根本任务、指导思想、统一战线和民族政策的核心段落
2025-09-24 22:46:48,636 - INFO - Generating context for chunk 3/18
2025-09-24 22:46:55,049 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:46:55,051 - INFO - Generated context in 6.41s: 宪法序言末尾至第一章总纲前四条，集中阐述国家统一多民族性质、民族政策、对外关系及宪法根本法地位，为后文国家制度、公民权利与机构设置奠定总纲。
2025-09-24 22:46:55,051 - INFO - Generating context for chunk 4/18
2025-09-24 22:47:00,494 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:47:00,495 - INFO - Generated context in 5.44s: 《中华人民共和国宪法》“总纲”章节中关于民族区域自治、法治国家建设、基本经济制度与土地及自然资源归属的核心条文。
2025-09-24 22:47:00,496 - INFO - Generating context for chunk 5/18
2025-09-24 22:47:09,765 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:47:09,766 - INFO - Generated context in 9.27s: 位于《宪法》第一章“总纲”第十条至第十九条，系统规定土地制度、所有制结构、财产保护、经济体制、国企改革、外资政策及国家教育方针，是确立社会主义市场经济基本框架与资源配置原则的核心条款群。
2025-09-24 22:47:09,766 - INFO - Generating context for chunk 6/18
2025-09-24 22:47:25,372 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:47:25,375 - INFO - Generated context in 15.61s: 总纲中关于国家发展教育、科学、文化、卫生、体育、语言文字、计划生育、环保、行政、国防及行政区划等基本国策与制度条款。
2025-09-24 22:47:25,375 - INFO - Generating context for chunk 7/18
2025-09-24 22:47:36,401 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:47:36,402 - INFO - Generated context in 11.03s: 该段位于《宪法》第一章“总纲”末尾（第三十条行政区划、第三十一条特别行政区、第三十二条外国人地位）与第二章“公民的基本权利和义务”开头（第三十三条至第四十一条基本权利）之间，是衔接国家结构形式与公民权利规定的过渡部分。
2025-09-24 22:47:36,402 - INFO - Generating context for chunk 8/18
2025-09-24 22:47:48,885 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:47:48,888 - INFO - Generated context in 12.49s: 第二章“公民的基本权利和义务”中连续十条，集中规定公民在政治监督、劳动就业、休息退休、社会保障、教育文化、性别平等、婚姻家庭及侨务等方面享有的基本权利与相应义务。
2025-09-24 22:47:48,888 - INFO - Generating context for chunk 9/18
2025-09-24 22:48:00,530 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:48:00,531 - INFO - Generated context in 11.64s: 第二章“公民的基本权利和义务”末尾条款（第五十条至第五十六条）与第三章“国家机构”第一节“全国人民代表大会”开头条款（第五十七条至第六十二条）的衔接段落，标志从公民权利义务向国家最高权力机关职权的过渡。
2025-09-24 22:48:00,531 - INFO - Generating context for chunk 10/18
2025-09-24 22:48:09,388 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:48:09,390 - INFO - Generated context in 8.86s: 第三章第一节“全国人民代表大会”条文，集中列举全国人大及其常委会的组成、任期、立法与监督程序，以及选举、决定和罢免国家领导人的具体职权。
2025-09-24 22:48:09,390 - INFO - Progress: 10/18 chunks, avg time: 9.39s
2025-09-24 22:48:09,390 - INFO - Generating context for chunk 11/18
2025-09-24 22:48:17,907 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:48:17,909 - INFO - Generated context in 8.52s: 该段条文位于《宪法》第三章“国家机构”第一节“全国人民代表大会”之下，具体列明第六十七条全国人民代表大会常务委员会在大会闭会期间行使的各项重要职权，是界定全国人大常委会权力范围的核心条款。
2025-09-24 22:48:17,910 - INFO - Generating context for chunk 12/18
2025-09-24 22:48:27,108 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:48:27,109 - INFO - Generated context in 9.20s: 位于《宪法》第三章“国家机构”第一节“全国人民代表大会”末尾，衔接第二节“中华人民共和国主席”，规定全国人大及其常委会下设专门委员会、调查委员会的工作方式，以及全国人大代表的提案、质询、人身保护、言论免责、守法联系群众、受监督罢免等职权义务，并过渡到主席的产生与职权。
2025-09-24 22:48:27,109 - INFO - Generating context for chunk 13/18
2025-09-24 22:48:35,318 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:48:35,320 - INFO - Generated context in 8.21s: 国家主席、副主席的职权、任期及缺位补任规则，以及国务院的组成、组织原则、任期与主要行政职权，位于《宪法》第三章“国家机构”第二节与第三节。
2025-09-24 22:48:35,321 - INFO - Generating context for chunk 14/18
2025-09-24 22:48:45,504 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:48:45,505 - INFO - Generated context in 10.18s: 第三章《国家机构》第三节《国务院》第八十九条第（八）至（十八）项职权，以及第九十条至第九十二条关于国务院组成、审计机关设置及其对全国人大负责的规定，紧接其后的是第四节《中央军事委员会》与第五节《地方各级人民代表大会和地方各级人民政府》的条文。
2025-09-24 22:48:45,505 - INFO - Generating context for chunk 15/18
2025-09-24 22:48:52,411 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:48:52,413 - INFO - Generated context in 6.91s: 地方各级人民代表大会和地方各级人民政府的组织与职权规定
2025-09-24 22:48:52,413 - INFO - Generating context for chunk 16/18
2025-09-24 22:49:03,555 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:49:03,556 - INFO - Generated context in 11.14s: 该段条文位于《宪法》第三章“国家机构”第五节“地方各级人民代表大会和地方各级人民政府”，具体规定县级以上人大常委会组成人员任职限制、常委会职权，以及地方各级人民政府的性质、任期、职权、上下级关系和基层群众性自治组织制度。
2025-09-24 22:49:03,556 - INFO - Generating context for chunk 17/18
2025-09-24 22:49:13,533 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:49:13,534 - INFO - Generated context in 9.98s: 第三章第五节“地方各级人民代表大会和地方各级人民政府”末尾，规定基层群众性自治组织居民委员会、村民委员会职责后，紧接第六节“民族自治地方的自治机关”全部条款及第七节“监察委员会”开头。
2025-09-24 22:49:13,534 - INFO - Generating context for chunk 18/18


2025-09-24 22:49:25,340 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:49:25,342 - INFO - Generated context in 11.81s: 第三章《国家机构》第八节之后，专节规定监察委员会的设置、组织、任期、领导体制及独立行使监察权，并与审判、检察、执法机关互相配合制约，是2018年宪法修正案新增的国家监督机关条款。
2025-09-24 22:49:25,342 - INFO - Chunking completed in 169.81s
2025-09-24 22:49:25,342 - INFO - Statistics: {
  "total_chunks": 18,
  "contextual_chunks": 18,
  "total_context_tokens": 12165,
  "total_generation_time": 169.80798292160034,
  "cache_hits": 0,
  "cache_misses": 18
}
2025-09-24 22:49:25,343 - INFO -   ✓ Created 18 chunks (18 with context)
2025-09-24 22:49:25,343 - INFO -
[2/288] Processing: 检察官法（2019-04-23）
2025-09-24 22:49:25,343 - INFO -   Category: 2-宪法相关法
2025-09-24 22:49:25,343 - INFO - Chunking 检察官法（2019-04-23） with contextual enhancement...
2025-09-24 22:49:25,343 - INFO - Starting chunking for document 检察官法_2019_04_23
2025-09-24 22:49:25,344 - INFO - Created 7 basic chunks
2025-09-24 22:49:25,344 - INFO - Generating context for chunk 1/7
2025-09-24 22:49:33,121 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:49:33,123 - INFO - Generated context in 7.78s: 总则与职责义务部分：奠定检察官法的立法目的、检察官定义、履职原则及基本职责义务，为后续各章管理、任免、保障等制度提供根本依据。
2025-09-24 22:49:33,123 - INFO - Generating context for chunk 2/7
2025-09-24 22:49:42,227 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:49:42,229 - INFO - Generated context in 9.11s: 第二章“检察官的职责、义务和权利”与第三章“检察官的条件和遴选”的条文，分别规定检察官应履行的八项义务、享有的六项权利，以及担任检察官的积极资格、禁止条件、遴选方式和公开选拔要求。
2025-09-24 22:49:42,229 - INFO - Generating context for chunk 3/7
2025-09-24 22:49:52,180 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:49:52,182 - INFO - Generated context in 9.95s: 《中华人民共和国检察官法》第三章“检察官的条件和遴选”与第四章“检察官的任免”节选，规定从律师、法学教研人员中公开选拔检察官的附加资历要求、省级及最高检检察官遴选委员会设置、初任与上级院检察官逐级遴选机制，以及宪法和法律框架下的任免权限、程序和免职情形。
2025-09-24 22:49:52,182 - INFO - Generating context for chunk 4/7
2025-09-24 22:50:01,507 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:50:01,508 - INFO - Generated context in 9.33s: 第四章“检察官的任免”与第五章“检察官的管理”衔接条款：先列举检察长、检察官依法免职的八种法定情形（第二十条第四至八项），再规定对违法任命的否决与撤销程序（第二十一、二十二条），继而明确任职禁止与回避制度（第二十三至二十五条），随后即进入员额制、等级序列等日常管理制度（第二十六条起）。
2025-09-24 22:50:01,508 - INFO - Generating context for chunk 5/7
2025-09-24 22:50:08,080 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:50:08,082 - INFO - Generated context in 6.57s: 第五章“检察官的管理”与第六章“检察官的考核、奖励和惩戒”衔接段：规定等级晋升、培训、辞职辞退及离任限制后，转入对检察官的考核、奖励与惩戒制度。
2025-09-24 22:50:08,082 - INFO - Generating context for chunk 6/7
2025-09-24 22:50:16,287 - INFO - HTTP Request: POST https://api.moonshot.cn/v1/chat/completions "HTTP/1.1 200 OK"
2025-09-24 22:50:16,288 - INFO - Generated context in 8.20s: 《中华人民共和国检察官法》第六章“检察官的考核、奖励和惩戒”中，具体列举应予奖励的突出表现、应予处分及追责的违纪违法行为，并规定检察官惩戒委员会的设立、职责与程序；其后衔接第七章“检察官的职业保障”。
2025-09-24 22:50:16,288 - INFO - Generating context for chunk 7/7

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### 🤖 Cursor Assistant

You're absolutely right! The chunking process is slow (about 10 seconds per chunk), and waiting until all chunks are processed before indexing means a long wait time. Let me modify the code to index each chunk immediately after its contextual summary is produced.

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*