ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter3/contextual-retrieval/README_LEGAL_INDEXING.md

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+# Contextual Legal Document Indexing
+
+This script implements Anthropic's Contextual Retrieval approach for indexing Chinese legal documents.
+
+## Key Innovation: Contextual Retrieval
+
+Unlike traditional RAG that loses context when chunking, this script:
+1. Generates contextual descriptions for each chunk using LLM
+2. Prepends context to chunks before indexing
+3. Significantly improves retrieval accuracy
+
+## Features
+
+- **Contextual Enhancement**: Uses LLM to generate chunk-specific context
+- **Smart Chunking**: Paragraph-aware boundaries (soft: 1024, hard: 2048 chars)
+- **Comparison Mode**: Run with/without context for performance comparison
+- **Cache Optimization**: Caches context for similar chunks to reduce API costs
+- **Detailed Statistics**: Token usage, generation time, and cost estimation
+
+## Prerequisites
+
+1. Set up your LLM API key:
+   ```bash
+   export MOONSHOT_API_KEY="your_api_key"  # Default: Kimi
+   # Or use other providers:
+   export OPENAI_API_KEY="your_api_key"
+   export SILICONFLOW_API_KEY="your_api_key"
+   ```
+
+2. Ensure retrieval pipeline is running:
+   ```bash
+   # Terminal 1: Dense service
+   python dense_service.py
+   
+   # Terminal 2: Sparse service
+   python sparse_service.py
+   
+   # Terminal 3: Main pipeline
+   python main.py
+   ```
+
+3. The `laws` directory should be linked/present (automatically created as symlink to agentic-rag/laws)
+
+## Usage
+
+### Basic Contextual Indexing
+```bash
+# Index with contextual enhancement (default)
+python index_local_laws_contextual.py
+```
+
+### Advanced Options
+```bash
+# Process limited documents
+python index_local_laws_contextual.py --max-docs 10
+
+# Process specific categories
+python index_local_laws_contextual.py --categories "宪法" "民法典"
+
+# Use different LLM provider
+python index_local_laws_contextual.py --llm-provider openai --llm-model gpt-5.6-luna
+
+# Custom batch size for indexing
+python index_local_laws_contextual.py --batch-size 20
+
+# Skip cleanup
+python index_local_laws_contextual.py --no-cleanup
+```
+
+## Cost Considerations
+
+Context generation requires LLM API calls:
+- ~150 tokens per chunk for context generation
+- Costs vary by provider (OpenAI: ~$0.03/1K tokens, Others: ~$0.01/1K tokens)
+- Cache reduces costs for duplicate content
+
+Estimate for 288 legal documents:
+- ~3000-5000 chunks total
+- ~450K-750K tokens
+- Cost: $5-15 depending on provider
+
+## Document Store
+
+Maintains `document_store.json` with:
+- Document metadata
+- Chunk statistics
+- Context token usage
+- Generation metrics
+- Indexing timestamps