Files
Andy Lee 8899734952 refactor: Unify examples interface with BaseRAGExample (#12)
* refactor: Unify examples interface with BaseRAGExample

- Create BaseRAGExample base class for all RAG examples
- Refactor 4 examples to use unified interface:
  - document_rag.py (replaces main_cli_example.py)
  - email_rag.py (replaces mail_reader_leann.py)
  - browser_rag.py (replaces google_history_reader_leann.py)
  - wechat_rag.py (replaces wechat_history_reader_leann.py)
- Maintain 100% parameter compatibility with original files
- Add interactive mode support for all examples
- Unify parameter names (--max-items replaces --max-emails/--max-entries)
- Update README.md with new examples usage
- Add PARAMETER_CONSISTENCY.md documenting all parameter mappings
- Keep main_cli_example.py for backward compatibility with migration notice

All default values, LeannBuilder parameters, and chunking settings
remain identical to ensure full compatibility with existing indexes.

* fix: Update CI tests for new unified examples interface

- Rename test_main_cli.py to test_document_rag.py
- Update all references from main_cli_example.py to document_rag.py
- Update tests/README.md documentation

The tests now properly test the new unified interface while maintaining
the same test coverage and functionality.

* fix: Fix pre-commit issues and update tests

- Fix import sorting and unused imports
- Update type annotations to use built-in types (list, dict) instead of typing.List/Dict
- Fix trailing whitespace and end-of-file issues
- Fix Chinese fullwidth comma to regular comma
- Update test_main_cli.py to test_document_rag.py
- Add backward compatibility test for main_cli_example.py
- Pass all pre-commit hooks (ruff, ruff-format, etc.)

* refactor: Remove old example scripts and migration references

- Delete old example scripts (mail_reader_leann.py, google_history_reader_leann.py, etc.)
- Remove migration hints and backward compatibility
- Update tests to use new unified examples directly
- Clean up all references to old script names
- Users now only see the new unified interface

* fix: Restore embedding-mode parameter to all examples

- All examples now have --embedding-mode parameter (unified interface benefit)
- Default is 'sentence-transformers' (consistent with original behavior)
- Users can now use OpenAI or MLX embeddings with any data source
- Maintains functional equivalence with original scripts

* docs: Improve parameter categorization in README

- Clearly separate core (shared) vs specific parameters
- Move LLM and embedding examples to 'Example Commands' section
- Add descriptive comments for all specific parameters
- Keep only truly data-source-specific parameters in specific sections

* docs: Make example commands more representative

- Add default values to parameter descriptions
- Replace generic examples with real-world use cases
- Focus on data-source-specific features in examples
- Remove redundant demonstrations of common parameters

* docs: Reorganize parameter documentation structure

- Move common parameters to a dedicated section before all examples
- Rename sections to 'X-Specific Arguments' for clarity
- Remove duplicate common parameters from individual examples
- Better information architecture for users

* docs: polish applications

* docs: Add CLI installation instructions

- Add two installation options: venv and global uv tool
- Clearly explain when to use each option
- Make CLI more accessible for daily use

* docs: Clarify CLI global installation process

- Explain the transition from venv to global installation
- Add upgrade command for global installation
- Make it clear that global install allows usage without venv activation

* docs: Add collapsible section for CLI installation

- Wrap CLI installation instructions in details/summary tags
- Keep consistent with other collapsible sections in README
- Improve document readability and navigation

* style: format

* docs: Fix collapsible sections

- Make Common Parameters collapsible (as it's lengthy reference material)
- Keep CLI Installation visible (important for users to see immediately)
- Better information hierarchy

* docs: Add introduction for Common Parameters section

- Add 'Flexible Configuration' heading with descriptive sentence
- Create parallel structure with 'Generation Model Setup' section
- Improve document flow and readability

* docs: nit

* fix: Fix issues in unified examples

- Add smart path detection for data directory
- Fix add_texts -> add_text method call
- Handle both running from project root and examples directory

* fix: Fix async/await and add_text issues in unified examples

- Remove incorrect await from chat.ask() calls (not async)
- Fix add_texts -> add_text method calls
- Verify search-complexity correctly maps to efSearch parameter
- All examples now run successfully

* feat: Address review comments

- Add complexity parameter to LeannChat initialization (default: search_complexity)
- Fix chunk-size default in README documentation (256, not 2048)
- Add more index building parameters as CLI arguments:
  - --backend-name (hnsw/diskann)
  - --graph-degree (default: 32)
  - --build-complexity (default: 64)
  - --no-compact (disable compact storage)
  - --no-recompute (disable embedding recomputation)
- Update README to document all new parameters

* feat: Add chunk-size parameters and improve file type filtering

- Add --chunk-size and --chunk-overlap parameters to all RAG examples
- Preserve original default values for each data source:
  - Document: 256/128 (optimized for general documents)
  - Email: 256/25 (smaller overlap for email threads)
  - Browser: 256/128 (standard for web content)
  - WeChat: 192/64 (smaller chunks for chat messages)
- Make --file-types optional filter instead of restriction in document_rag
- Update README to clarify interactive mode and parameter usage
- Fix LLM default model documentation (gpt-4o, not gpt-4o-mini)

* feat: Update documentation based on review feedback

- Add MLX embedding example to README
- Clarify examples/data content description (two papers, Pride and Prejudice, Chinese README)
- Move chunk parameters to common parameters section
- Remove duplicate chunk parameters from document-specific section

* docs: Emphasize diverse data sources in examples/data description

* fix: update default embedding models for better performance

- Change WeChat, Browser, and Email RAG examples to use all-MiniLM-L6-v2
- Previous Qwen/Qwen3-Embedding-0.6B was too slow for these use cases
- all-MiniLM-L6-v2 is a fast 384-dim model, ideal for large-scale personal data

* add response highlight

* change rebuild logic

* fix some example

* feat: check if k is larger than #docs

* fix: WeChat history reader bugs and refactor wechat_rag to use unified architecture

* fix email wrong -1 to process all file

* refactor: reorgnize all examples/ and test/

* refactor: reorganize examples and add link checker

* fix: add init.py

* fix: handle certificate errors in link checker

* fix wechat

* merge

* docs: update README to use proper module imports for apps

- Change from 'python apps/xxx.py' to 'python -m apps.xxx'
- More professional and pythonic module calling
- Ensures proper module resolution and imports
- Better separation between apps/ (production tools) and examples/ (demos)

---------

Co-authored-by: yichuan520030910320 <yichuan_wang@berkeley.edu>
2025-08-03 23:06:24 -07:00

157 lines
5.3 KiB
Python

"""
Email RAG example using the unified interface.
Supports Apple Mail on macOS.
"""
import sys
from pathlib import Path
# Add parent directory to path for imports
sys.path.insert(0, str(Path(__file__).parent))
from base_rag_example import BaseRAGExample, create_text_chunks
from .email_data.LEANN_email_reader import EmlxReader
class EmailRAG(BaseRAGExample):
"""RAG example for Apple Mail processing."""
def __init__(self):
# Set default values BEFORE calling super().__init__
self.max_items_default = -1 # Process all emails by default
self.embedding_model_default = (
"sentence-transformers/all-MiniLM-L6-v2" # Fast 384-dim model
)
super().__init__(
name="Email",
description="Process and query Apple Mail emails with LEANN",
default_index_name="mail_index",
)
def _add_specific_arguments(self, parser):
"""Add email-specific arguments."""
email_group = parser.add_argument_group("Email Parameters")
email_group.add_argument(
"--mail-path",
type=str,
default=None,
help="Path to Apple Mail directory (auto-detected if not specified)",
)
email_group.add_argument(
"--include-html", action="store_true", help="Include HTML content in email processing"
)
email_group.add_argument(
"--chunk-size", type=int, default=256, help="Text chunk size (default: 256)"
)
email_group.add_argument(
"--chunk-overlap", type=int, default=25, help="Text chunk overlap (default: 25)"
)
def _find_mail_directories(self) -> list[Path]:
"""Auto-detect all Apple Mail directories."""
mail_base = Path.home() / "Library" / "Mail"
if not mail_base.exists():
return []
# Find all Messages directories
messages_dirs = []
for item in mail_base.rglob("Messages"):
if item.is_dir():
messages_dirs.append(item)
return messages_dirs
async def load_data(self, args) -> list[str]:
"""Load emails and convert to text chunks."""
# Determine mail directories
if args.mail_path:
messages_dirs = [Path(args.mail_path)]
else:
print("Auto-detecting Apple Mail directories...")
messages_dirs = self._find_mail_directories()
if not messages_dirs:
print("No Apple Mail directories found!")
print("Please specify --mail-path manually")
return []
print(f"Found {len(messages_dirs)} mail directories")
# Create reader
reader = EmlxReader(include_html=args.include_html)
# Process each directory
all_documents = []
total_processed = 0
for i, messages_dir in enumerate(messages_dirs):
print(f"\nProcessing directory {i + 1}/{len(messages_dirs)}: {messages_dir}")
try:
# Count emlx files
emlx_files = list(messages_dir.glob("*.emlx"))
print(f"Found {len(emlx_files)} email files")
# Apply max_items limit per directory
max_per_dir = -1 # Default to process all
if args.max_items > 0:
remaining = args.max_items - total_processed
if remaining <= 0:
break
max_per_dir = remaining
# If args.max_items == -1, max_per_dir stays -1 (process all)
# Load emails - fix the parameter passing
documents = reader.load_data(
input_dir=str(messages_dir),
max_count=max_per_dir,
)
if documents:
all_documents.extend(documents)
total_processed += len(documents)
print(f"Processed {len(documents)} emails from this directory")
except Exception as e:
print(f"Error processing {messages_dir}: {e}")
continue
if not all_documents:
print("No emails found to process!")
return []
print(f"\nTotal emails processed: {len(all_documents)}")
print("now starting to split into text chunks ... take some time")
# Convert to text chunks
# Email reader uses chunk_overlap=25 as in original
all_texts = create_text_chunks(
all_documents, chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap
)
return all_texts
if __name__ == "__main__":
import asyncio
# Check platform
if sys.platform != "darwin":
print("\n⚠️ Warning: This example is designed for macOS (Apple Mail)")
print(" Windows/Linux support coming soon!\n")
# Example queries for email RAG
print("\n📧 Email RAG Example")
print("=" * 50)
print("\nExample queries you can try:")
print("- 'What did my boss say about deadlines?'")
print("- 'Find emails about travel expenses'")
print("- 'Show me emails from last month about the project'")
print("- 'What food did I order from DoorDash?'")
print("\nNote: You may need to grant Full Disk Access to your terminal\n")
rag = EmailRAG()
asyncio.run(rag.run())