LEANN/test/mail_reader_small_chunks.py

import email
import os
from typing import Any

from llama_index.core import Document, StorageContext, VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.readers.base import BaseReader


class EmlxReader(BaseReader):
    """
    Apple Mail .emlx file reader with reduced metadata.

    Reads individual .emlx files from Apple Mail's storage format.
    """

    def __init__(self) -> None:
        """Initialize."""
        pass

    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

        # 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:
                if count >= max_count:
                    break

                if filename.endswith(".emlx"):
                    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":
                                            body = part.get_payload(decode=True).decode(
                                                "utf-8", errors="ignore"
                                            )
                                            break
                                else:
                                    body = msg.get_payload(decode=True).decode(
                                        "utf-8", errors="ignore"
                                    )

                                # Create document content with metadata embedded in text
                                doc_content = f"""
From: {from_addr}
To: {to_addr}
Subject: {subject}
Date: {date}

{body}
"""

                                # Create minimal metadata (only essential info)
                                metadata = {
                                    "subject": subject[:50],  # Truncate subject
                                    "from": from_addr[:30],  # Truncate from
                                    "date": date[:20],  # Truncate date
                                    "filename": filename,  # Keep filename
                                }

                                doc = Document(text=doc_content, metadata=metadata)
                                docs.append(doc)
                                count += 1

                            except Exception as e:
                                print(f"Error parsing email from {filepath}: {e}")
                                continue

                    except Exception as e:
                        print(f"Error reading file {filepath}: {e}")
                        continue

        print(f"Loaded {len(docs)} email documents")
        return docs


def create_and_save_index(
    mail_path: str, save_dir: str = "mail_index_small", max_count: int = 1000
):
    """
    Create the index from mail data and save it to disk.

    Args:
        mail_path: Path to the mail directory
        save_dir: Directory to save the index
        max_count: Maximum number of emails to process
    """
    print("Creating index from mail data with small chunks...")

    # Load documents
    documents = EmlxReader().load_data(mail_path, max_count=max_count)

    if not documents:
        print("No documents loaded. Exiting.")
        return None

    # Create text splitter with small chunk size
    text_splitter = SentenceSplitter(chunk_size=512, chunk_overlap=50)

    # Create index
    index = VectorStoreIndex.from_documents(documents, transformations=[text_splitter])

    # Save the index
    os.makedirs(save_dir, exist_ok=True)
    index.storage_context.persist(persist_dir=save_dir)
    print(f"Index saved to {save_dir}")

    return index


def load_index(save_dir: str = "mail_index_small"):
    """
    Load the saved index from disk.

    Args:
        save_dir: Directory where the index is saved

    Returns:
        Loaded index or None if loading fails
    """
    try:
        # Load storage context
        storage_context = StorageContext.from_defaults(persist_dir=save_dir)

        # Load index
        index = VectorStoreIndex.from_vector_store(
            storage_context.vector_store, storage_context=storage_context
        )

        print(f"Index loaded from {save_dir}")
        return index

    except Exception as e:
        print(f"Error loading index: {e}")
        return None


def query_index(index, query: str):
    """
    Query the loaded index.

    Args:
        index: The loaded index
        query: The query string
    """
    if index is None:
        print("No index available for querying.")
        return

    query_engine = index.as_query_engine()
    response = query_engine.query(query)
    print(f"Query: {query}")
    print(f"Response: {response}")


def main():
    mail_path = "/Users/yichuan/Library/Mail/V10/0FCA0879-FD8C-4B7E-83BF-FDDA930791C5/[Gmail].mbox/All Mail.mbox/78BA5BE1-8819-4F9A-9613-EB63772F1DD0/Data/9/Messages"
    save_dir = "mail_index_small"

    # Check if index already exists
    if os.path.exists(save_dir) and os.path.exists(os.path.join(save_dir, "vector_store.json")):
        print("Loading existing index...")
        index = load_index(save_dir)
    else:
        print("Creating new index...")
        index = create_and_save_index(mail_path, save_dir, max_count=1000)

    if index:
        # Example queries
        queries = [
            "Hows Berkeley Graduate Student Instructor",
            "What emails mention GSR appointments?",
            "Find emails about deadlines",
        ]

        for query in queries:
            print("\n" + "=" * 50)
            query_index(index, query)


if __name__ == "__main__":
    main()