add email test code
This commit is contained in:
yichuan520030910320
147
test/mail_reader_llamaindex.py
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147
test/mail_reader_llamaindex.py
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import os
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import email
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from pathlib import Path
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from typing import List, Any
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from llama_index.core import VectorStoreIndex, Document
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from llama_index.core.readers.base import BaseReader
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class EmlxReader(BaseReader):
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"""
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Apple Mail .emlx file reader.
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Reads individual .emlx files from Apple Mail's storage format.
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"""
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def __init__(self) -> None:
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"""Initialize."""
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pass
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def load_data(self, input_dir: str, **load_kwargs: Any) -> List[Document]:
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"""
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Load data from the input directory containing .emlx files.
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Args:
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input_dir: Directory containing .emlx files
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**load_kwargs:
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max_count (int): Maximum amount of messages to read.
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"""
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docs: List[Document] = []
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max_count = load_kwargs.get('max_count', 1000)
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count = 0
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# Walk through the directory recursively
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for dirpath, dirnames, filenames in os.walk(input_dir):
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# Skip hidden directories
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dirnames[:] = [d for d in dirnames if not d.startswith(".")]
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for filename in filenames:
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if count >= max_count:
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break
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if filename.endswith(".emlx"):
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filepath = os.path.join(dirpath, filename)
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try:
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# Read the .emlx file
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with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
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content = f.read()
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# .emlx files have a length prefix followed by the email content
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# The first line contains the length, followed by the email
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lines = content.split('\n', 1)
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if len(lines) >= 2:
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email_content = lines[1]
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# Parse the email using Python's email module
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try:
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msg = email.message_from_string(email_content)
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# Extract email metadata
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subject = msg.get('Subject', 'No Subject')
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from_addr = msg.get('From', 'Unknown')
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to_addr = msg.get('To', 'Unknown')
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date = msg.get('Date', 'Unknown')
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# Extract email body
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body = ""
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if msg.is_multipart():
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for part in msg.walk():
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if part.get_content_type() == "text/plain" or part.get_content_type() == "text/html":
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body += part.get_payload(decode=True).decode('utf-8', errors='ignore')
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# break
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else:
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body = msg.get_payload(decode=True).decode('utf-8', errors='ignore')
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# Create document content
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doc_content = f"""
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From: {from_addr}
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To: {to_addr}
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Subject: {subject}
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Date: {date}
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{body}
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"""
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# Create metadata
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metadata = {
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'file_path': filepath,
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'subject': subject,
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'from': from_addr,
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'to': to_addr,
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'date': date,
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'filename': filename
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}
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if count == 0:
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print("--------------------------------")
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print('dir path', dirpath)
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print(metadata)
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print(doc_content)
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print("--------------------------------")
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body=[]
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if msg.is_multipart():
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for part in msg.walk():
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print("-------------------------------- get content type -------------------------------")
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print(part.get_content_type())
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print(part)
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# body.append(part.get_payload(decode=True).decode('utf-8', errors='ignore'))
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print("-------------------------------- get content type -------------------------------")
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else:
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body = msg.get_payload(decode=True).decode('utf-8', errors='ignore')
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print(body)
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print(body)
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print("--------------------------------")
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doc = Document(text=doc_content, metadata=metadata)
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docs.append(doc)
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count += 1
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except Exception as e:
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print(f"!!!!!!! Error parsing email from {filepath}: {e} !!!!!!!!")
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continue
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except Exception as e:
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print(f"!!!!!!! Error reading file !!!!!!!! {filepath}: {e}")
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continue
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print(f"Loaded {len(docs)} email documents")
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return docs
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# Use the custom EmlxReader instead of MboxReader
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documents = EmlxReader().load_data(
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"/Users/yichuan/Library/Mail/V10/0FCA0879-FD8C-4B7E-83BF-FDDA930791C5/[Gmail].mbox/All Mail.mbox/78BA5BE1-8819-4F9A-9613-EB63772F1DD0/Data/9/Messages",
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max_count=1000
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) # Returns list of documents
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# Configure the index with larger chunk size to handle long metadata
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from llama_index.core.node_parser import SentenceSplitter
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# Create a custom text splitter with larger chunk size
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text_splitter = SentenceSplitter(chunk_size=2048, chunk_overlap=200)
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index = VectorStoreIndex.from_documents(
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documents,
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transformations=[text_splitter]
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) # Initialize index with documents
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query_engine = index.as_query_engine()
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res = query_engine.query("Hows Berkeley Graduate Student Instructor")
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print(res)
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