add leann and llamaindex email infra, and need to align the results

examples/mail_reader_leann.py

@@ -0,0 +1,121 @@
+import os
+import asyncio
+import dotenv
+from pathlib import Path
+from typing import List, Any
+from leann.api import LeannBuilder, LeannSearcher, LeannChat
+from mail_reader_llamaindex import EmlxReader
+from llama_index.core.node_parser import SentenceSplitter
+
+dotenv.load_dotenv()
+
+def create_leann_index(mail_path: str, index_path: str = "mail_index.leann", max_count: int = 1000):
+    """
+    Create LEANN index from mail data.
+    
+    Args:
+        mail_path: Path to the mail directory
+        index_path: Path to save the LEANN index
+        max_count: Maximum number of emails to process
+    """
+    print("Creating LEANN index from mail data...")
+    
+    # Load documents using EmlxReader from mail_reader_llamaindex
+    reader = EmlxReader()
+    documents = reader.load_data(mail_path, max_count=max_count)
+    
+    if not documents:
+        print("No documents loaded. Exiting.")
+        return None
+    
+    print(f"Loaded {len(documents)} email documents")
+    
+    # Create text splitter with 256 chunk size
+    text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25)
+    
+    # Convert Documents to text strings and chunk them
+    all_texts = []
+    for doc in documents:
+        # Split the document into chunks
+        nodes = text_splitter.get_nodes_from_documents([doc])
+        for node in nodes:
+            all_texts.append(node.get_content())
+    
+    print(f"Created {len(all_texts)} text chunks from {len(documents)} documents")
+    
+    # Create LEANN index directory
+    INDEX_DIR = Path(index_path).parent
+    
+    if not INDEX_DIR.exists():
+        print(f"--- Index directory not found, building new index ---")
+        INDEX_DIR.mkdir(exist_ok=True)
+
+        print(f"--- Building new LEANN index ---")
+        
+        print(f"\n[PHASE 1] Building Leann index...")
+
+        # Use HNSW backend for better macOS compatibility
+        builder = LeannBuilder(
+            backend_name="hnsw",
+            embedding_model="facebook/contriever",
+            graph_degree=32, 
+            complexity=64,
+            is_compact=True,
+            is_recompute=True,
+            num_threads=1  # Force single-threaded mode
+        )
+
+        print(f"Adding {len(all_texts)} email chunks to index...")
+        for chunk_text in all_texts:
+            builder.add_text(chunk_text)
+            
+        builder.build_index(index_path)
+        print(f"\nLEANN index built at {index_path}!")
+    else:
+        print(f"--- Using existing index at {INDEX_DIR} ---")
+    
+    return index_path
+
+async def query_leann_index(index_path: str, query: str):
+    """
+    Query the LEANN index.
+    
+    Args:
+        index_path: Path to the LEANN index
+        query: The query string
+    """
+    print(f"\n[PHASE 2] Starting Leann chat session...")
+    chat = LeannChat(index_path=index_path)
+    
+    print(f"You: {query}")
+    chat_response = chat.ask(
+        query, 
+        top_k=5, 
+        recompute_beighbor_embeddings=True,
+        complexity=32,
+        beam_width=1
+    )
+    print(f"Leann: {chat_response}")
+
+async 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"
+    
+    INDEX_DIR = Path("./mail_index_leann")
+    INDEX_PATH = str(INDEX_DIR / "mail_documents.leann")
+    
+    # Create or load the LEANN index
+    index_path = create_leann_index(mail_path, INDEX_PATH, max_count=1000)
+    
+    if index_path:
+        # Example queries
+        queries = [
+            "Hows Berkeley Graduate Student Instructor",
+            "how's the icloud related advertisement saying"
+        ]
+        
+        for query in queries:
+            print("\n" + "="*60)
+            await query_leann_index(index_path, query)
+
+if __name__ == "__main__":
+    asyncio.run(main()) 

examples/mail_reader_llamaindex.py

@@ -122,10 +122,15 @@ def create_and_save_index(mail_path: str, save_dir: str = "mail_index_embedded",
     # Create text splitter with small chunk size (no metadata constraints)
     text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25)
     
-    # Create index
+    # Create index with Facebook Contriever embedding model
+    from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+    
+    embed_model = HuggingFaceEmbedding(model_name="facebook/contriever")
+    
     index = VectorStoreIndex.from_documents(
         documents,
-        transformations=[text_splitter]
+        transformations=[text_splitter],
+        embed_model=embed_model
     )
     
     # Save the index