Files
LEANN/apps/email_data/LEANN_email_reader.py
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

168 lines
7.0 KiB
Python

import email
import os
from pathlib import Path
from typing import Any
from llama_index.core import Document
from llama_index.core.readers.base import BaseReader
def find_all_messages_directories(root: str | None = None) -> list[Path]:
"""
Recursively find all 'Messages' directories under the given root.
Returns a list of Path objects.
"""
if root is None:
# Auto-detect user's mail path
home_dir = os.path.expanduser("~")
root = os.path.join(home_dir, "Library", "Mail")
messages_dirs = []
for dirpath, _dirnames, _filenames in os.walk(root):
if os.path.basename(dirpath) == "Messages":
messages_dirs.append(Path(dirpath))
return messages_dirs
class EmlxReader(BaseReader):
"""
Apple Mail .emlx file reader with embedded metadata.
Reads individual .emlx files from Apple Mail's storage format.
"""
def __init__(self, include_html: bool = False) -> None:
"""
Initialize.
Args:
include_html: Whether to include HTML content in the email body (default: False)
"""
self.include_html = include_html
def load_data(self, input_dir: str, **load_kwargs: Any) -> list[Document]:
"""
Load data from the input directory containing .emlx files.
Args:
input_dir: Directory containing .emlx files
**load_kwargs:
max_count (int): Maximum amount of messages to read.
"""
docs: list[Document] = []
max_count = load_kwargs.get("max_count", 1000)
count = 0
total_files = 0
successful_files = 0
failed_files = 0
print(f"Starting to process directory: {input_dir}")
# Walk through the directory recursively
for dirpath, dirnames, filenames in os.walk(input_dir):
# Skip hidden directories
dirnames[:] = [d for d in dirnames if not d.startswith(".")]
for filename in filenames:
# Check if we've reached the max count (skip if max_count == -1)
if max_count > 0 and count >= max_count:
break
if filename.endswith(".emlx"):
total_files += 1
filepath = os.path.join(dirpath, filename)
try:
# Read the .emlx file
with open(filepath, encoding="utf-8", errors="ignore") as f:
content = f.read()
# .emlx files have a length prefix followed by the email content
# The first line contains the length, followed by the email
lines = content.split("\n", 1)
if len(lines) >= 2:
email_content = lines[1]
# Parse the email using Python's email module
try:
msg = email.message_from_string(email_content)
# Extract email metadata
subject = msg.get("Subject", "No Subject")
from_addr = msg.get("From", "Unknown")
to_addr = msg.get("To", "Unknown")
date = msg.get("Date", "Unknown")
# Extract email body
body = ""
if msg.is_multipart():
for part in msg.walk():
if (
part.get_content_type() == "text/plain"
or part.get_content_type() == "text/html"
):
if (
part.get_content_type() == "text/html"
and not self.include_html
):
continue
try:
payload = part.get_payload(decode=True)
if payload:
body += payload.decode("utf-8", errors="ignore")
except Exception as e:
print(f"Error decoding payload: {e}")
continue
else:
try:
payload = msg.get_payload(decode=True)
if payload:
body = payload.decode("utf-8", errors="ignore")
except Exception as e:
print(f"Error decoding single part payload: {e}")
body = ""
# Only create document if we have some content
if body.strip() or subject != "No Subject":
# Create document content with metadata embedded in text
doc_content = f"""
[File]: {filename}
[From]: {from_addr}
[To]: {to_addr}
[Subject]: {subject}
[Date]: {date}
[EMAIL BODY Start]:
{body}
"""
# No separate metadata - everything is in the text
doc = Document(text=doc_content, metadata={})
docs.append(doc)
count += 1
successful_files += 1
# Print first few successful files for debugging
if successful_files <= 3:
print(
f"Successfully loaded: {filename} - Subject: {subject[:50]}..."
)
except Exception as e:
failed_files += 1
if failed_files <= 5: # Only print first few errors
print(f"Error parsing email from {filepath}: {e}")
continue
except Exception as e:
failed_files += 1
if failed_files <= 5: # Only print first few errors
print(f"Error reading file {filepath}: {e}")
continue
print("Processing summary:")
print(f" Total .emlx files found: {total_files}")
print(f" Successfully loaded: {successful_files}")
print(f" Failed to load: {failed_files}")
print(f" Final documents: {len(docs)}")
return docs