Merge remote-tracking branch 'origin/main' into datastore-reproduce

.gitignore

@@ -8,11 +8,15 @@ demo/indices/
 *pycache*
 outputs/
 *.pkl
+*.pdf
 .history/
 scripts/
 lm_eval.egg-info/
 demo/experiment_results/**/*.json
 *.jsonl
+*.eml
+*.emlx
+*.json
 *.sh
 *.txt
 !CMakeLists.txt
@@ -42,6 +46,7 @@ embedding_comparison_results/
 *.ivecs
 *.index
 *.bin
+*.old
 
 read_graph
 analyze_diskann_graph

.gitmodules

@@ -4,3 +4,13 @@
 [submodule "packages/leann-backend-hnsw/third_party/faiss"]
 	path = packages/leann-backend-hnsw/third_party/faiss
 	url = https://github.com/yichuan520030910320/faiss.git
+[submodule "packages/leann-backend-hnsw/third_party/msgpack-c"]
+	path = packages/leann-backend-hnsw/third_party/msgpack-c
+	url = https://github.com/msgpack/msgpack-c.git
+	branch = cpp_master
+[submodule "packages/leann-backend-hnsw/third_party/cppzmq"]
+	path = packages/leann-backend-hnsw/third_party/cppzmq
+	url = https://github.com/zeromq/cppzmq.git
+[submodule "packages/leann-backend-hnsw/third_party/libzmq"]
+	path = packages/leann-backend-hnsw/third_party/libzmq
+	url = https://github.com/zeromq/libzmq.git

README.md

@@ -28,13 +28,15 @@
 ### 🎯 Why Leann?
 
 Traditional RAG systems face a fundamental trade-off:
+
 - **💾 Storage**: Storing embeddings for millions of documents requires massive disk space
 - **🔄 Freshness**: Pre-computed embeddings become stale when documents change
 - **💰 Cost**: Vector databases are expensive to scale
 
 **Leann solves this by:**
+
 - ✅ **Zero embedding storage** - Only graph structure is persisted
-- ✅ **Real-time computation** - Embeddings computed on-demand with ms latency  
+- ✅ **Real-time computation** - Embeddings computed on-demand with ms latency
 - ✅ **Memory efficient** - Runs on consumer hardware (8GB RAM)
 - ✅ **Always fresh** - No stale embeddings, ever
 
@@ -46,6 +48,19 @@ Traditional RAG systems face a fundamental trade-off:
 git clone git@github.com:yichuan520030910320/LEANN-RAG.git leann
 cd leann
 git submodule update --init --recursive
+```
+
+**macOS:**
+```bash
+brew install llvm libomp boost protobuf
+export CC=$(brew --prefix llvm)/bin/clang
+export CXX=$(brew --prefix llvm)/bin/clang++
+uv sync
+```
+
+**Linux (Ubuntu/Debian):**
+```bash
+sudo apt-get install libomp-dev libboost-all-dev protobuf-compiler libabsl-dev libmkl-full-dev libaio-dev
 uv sync
 ```
 
@@ -75,19 +90,39 @@ for result in results:
 uv run examples/document_search.py
 ```
 
+or you want to use python 
+
+```bash
+source .venv/bin/activate
+python ./examples/main_cli_example.py
+```
 **PDF RAG Demo (using LlamaIndex for document parsing and Leann for indexing/search)**
 
 This demo showcases how to build a RAG system for PDF documents using Leann.
-1.  Place your PDF files (and other supported formats like .docx, .pptx, .xlsx) into the `examples/data/` directory.
+
-2.  Ensure you have an `OPENAI_API_KEY` set in your environment variables or in a `.env` file for the LLM to function.
+1. Place your PDF files (and other supported formats like .docx, .pptx, .xlsx) into the `examples/data/` directory.
+2. Ensure you have an `OPENAI_API_KEY` set in your environment variables or in a `.env` file for the LLM to function.
 
 ```bash
 uv run examples/main_cli_example.py
 ```
 
+### Regenerating Protobuf Files
+
+If you modify any `.proto` files (such as `embedding.proto`), or if you see errors about protobuf version mismatch, **regenerate the C++ protobuf files** to match your installed version:
+
+```bash
+cd packages/leann-backend-diskann
+protoc --cpp_out=third_party/DiskANN/include --proto_path=third_party embedding.proto
+protoc --cpp_out=third_party/DiskANN/src --proto_path=third_party embedding.proto
+```
+
+This ensures the generated files are compatible with your system's protobuf library.
+
 ## ✨ Features
 
 ### 🔥 Core Features
+
 - **📊 Multiple Distance Functions**: L2, Cosine, MIPS (Maximum Inner Product Search)
 - **🏗️ Pluggable Backends**: DiskANN, HNSW/FAISS with unified API
 - **🔄 Real-time Embeddings**: Dynamic computation using optimized ZMQ servers
@@ -95,6 +130,7 @@ uv run examples/main_cli_example.py
 - **🎯 Graph Pruning**: Advanced techniques for memory-efficient search
 
 ### 🛠️ Technical Highlights
+
 - **Zero-copy operations** for maximum performance
 - **SIMD-optimized** distance computations (AVX2/AVX512)
 - **Async embedding pipeline** with batched processing
@@ -102,6 +138,7 @@ uv run examples/main_cli_example.py
 - **Recompute mode** for highest accuracy scenarios
 
 ### 🎨 Developer Experience
+
 - **Simple Python API** - Get started in minutes
 - **Extensible backend system** - Easy to add new algorithms
 - **Comprehensive examples** - From basic usage to production deployment
@@ -111,19 +148,19 @@ uv run examples/main_cli_example.py
 
 ### Memory Usage Comparison
 
-| System | 1M Documents | 10M Documents | 100M Documents |
+| System                | 1M Documents     | 10M Documents    | 100M Documents   |
-|--------|-------------|---------------|----------------|
+| --------------------- | ---------------- | ---------------- | ---------------- |
-| Traditional Vector DB | 3.1 GB | 31 GB | 310 GB |
+| Traditional Vector DB | 3.1 GB           | 31 GB            | 310 GB           |
-| **Leann** | **180 MB** | **1.2 GB** | **8.4 GB** |
+| **Leann**       | **180 MB** | **1.2 GB** | **8.4 GB** |
-| **Reduction** | **94.2%** | **96.1%** | **97.3%** |
+| **Reduction**   | **94.2%**  | **96.1%**  | **97.3%**  |
 
 ### Query Performance
 
-| Backend | Index Size | Query Time | Recall@10 |
+| Backend             | Index Size | Query Time | Recall@10 |
-|---------|------------|------------|-----------|
+| ------------------- | ---------- | ---------- | --------- |
-| DiskANN | 1M docs | 12ms | 0.95 |
+| DiskANN             | 1M docs    | 12ms       | 0.95      |
-| DiskANN + Recompute | 1M docs | 145ms | 0.98 |
+| DiskANN + Recompute | 1M docs    | 145ms      | 0.98      |
-| HNSW | 1M docs | 8ms | 0.93 |
+| HNSW                | 1M docs    | 8ms        | 0.93      |
 
 *Benchmarks run on AMD Ryzen 7 with 32GB RAM*
 
@@ -145,26 +182,29 @@ uv run examples/main_cli_example.py
 ### Key Components
 
 1. **🧠 Embedding Engine**: Real-time transformer inference with caching
-2. **📊 Graph Index**: Memory-efficient navigation structures  
+2. **📊 Graph Index**: Memory-efficient navigation structures
 3. **🔄 Search Coordinator**: Orchestrates embedding + graph search
 4. **⚡ Backend Adapters**: Pluggable algorithm implementations
 
 ## 🎓 Supported Models & Backends
 
 ### 🤖 Embedding Models
+
 - **sentence-transformers/all-mpnet-base-v2** (default)
 - **sentence-transformers/all-MiniLM-L6-v2** (lightweight)
 - Any HuggingFace sentence-transformer model
 - Custom model support via API
 
-### 🔧 Search Backends  
+### 🔧 Search Backends
+
 - **DiskANN**: Microsoft's billion-scale ANN algorithm
 - **HNSW**: Hierarchical Navigable Small World graphs
 - **Coming soon**: ScaNN, Faiss-IVF, NGT
 
 ### 📏 Distance Functions
+
 - **L2**: Euclidean distance for precise similarity
-- **Cosine**: Angular similarity for normalized vectors  
+- **Cosine**: Angular similarity for normalized vectors
 - **MIPS**: Maximum Inner Product Search for recommendation systems
 
 ## 🔬 Paper
@@ -188,6 +228,7 @@ If you find Leann useful, please cite:
 ## 🌍 Use Cases
 
 ### 💼 Enterprise RAG
+
 ```python
 # Handle millions of documents with limited resources
 builder = LeannBuilder(
@@ -198,7 +239,8 @@ builder = LeannBuilder(
 )
 ```
 
-### 🔬 Research & Experimentation  
+### 🔬 Research & Experimentation
+
 ```python
 # Quick prototyping with different algorithms
 for backend in ["diskann", "hnsw"]:
@@ -207,6 +249,7 @@ for backend in ["diskann", "hnsw"]:
 ```
 
 ### 🚀 Real-time Applications
+
 ```python
 # Sub-second response times
 chat = LeannChat("knowledge.leann")
@@ -219,6 +262,7 @@ response = chat.ask("What is quantum computing?")
 We welcome contributions! Leann is built by the community, for the community.
 
 ### Ways to Contribute
+
 - 🐛 **Bug Reports**: Found an issue? Let us know!
 - 💡 **Feature Requests**: Have an idea? We'd love to hear it!
 - 🔧 **Code Contributions**: PRs welcome for all skill levels
@@ -226,14 +270,17 @@ We welcome contributions! Leann is built by the community, for the community.
 - 🧪 **Benchmarks**: Share your performance results
 
 ### Development Setup
+
 ```bash
-git clone https://github.com/yourname/leann
+git clone git@github.com:yichuan520030910320/LEANN-RAG.git leann
 cd leann
+git submodule update --init --recursive
 uv sync --dev
 uv run pytest tests/
 ```
 
 ### Quick Tests
+
 ```bash
 # Sanity check all distance functions
 uv run python tests/sanity_checks/test_distance_functions.py
@@ -241,17 +288,21 @@ uv run python tests/sanity_checks/test_distance_functions.py
 # Verify L2 implementation
 uv run python tests/sanity_checks/test_l2_verification.py
 ```
+
 ## ❓ FAQ
 
 ### Common Issues
 
 #### NCCL Topology Error
+
 **Problem**: You encounter `ncclTopoComputePaths` error during document processing:
+
 ```
 ncclTopoComputePaths (system=<optimized out>, comm=comm@entry=0x5555a82fa3c0) at graph/paths.cc:688
 ```
 
 **Solution**: Set these environment variables before running your script:
+
 ```bash
 export NCCL_TOPO_DUMP_FILE=/tmp/nccl_topo.xml
 export NCCL_DEBUG=INFO
@@ -259,23 +310,26 @@ export NCCL_DEBUG_SUBSYS=INIT,GRAPH
 export NCCL_IB_DISABLE=1
 export NCCL_NET_PLUGIN=none
 export NCCL_SOCKET_IFNAME=ens5
-
+```
 
 ## 📈 Roadmap
 
 ### 🎯 Q1 2024
-- [x] DiskANN backend with MIPS/L2/Cosine support
+
-- [x] HNSW backend integration  
+- [X] DiskANN backend with MIPS/L2/Cosine support
-- [x] Real-time embedding pipeline
+- [X] HNSW backend integration
-- [x] Memory-efficient graph pruning
+- [X] Real-time embedding pipeline
+- [X] Memory-efficient graph pruning
 
 ### 🚀 Q2 2024
+
 - [ ] Distributed search across multiple nodes
 - [ ] ScaNN backend support
 - [ ] Advanced caching strategies
 - [ ] Kubernetes deployment guides
 
 ### 🌟 Q3 2024
+
 - [ ] GPU-accelerated embedding computation
 - [ ] Approximate distance functions
 - [ ] Integration with LangChain/LlamaIndex
@@ -297,7 +351,7 @@ MIT License - see [LICENSE](LICENSE) for details.
 ## 🙏 Acknowledgments
 
 - **Microsoft Research** for the DiskANN algorithm
-- **Meta AI** for FAISS and optimization insights  
+- **Meta AI** for FAISS and optimization insights
 - **HuggingFace** for the transformer ecosystem
 - **Our amazing contributors** who make this possible
 
@@ -309,4 +363,5 @@ MIT License - see [LICENSE](LICENSE) for details.
 
 <p align="center">
   Made with ❤️ by the Leann team
-</p>
+</p>
+

examples/LEANN_email_reader.py

@@ -0,0 +1,130 @@
+import os
+import email
+from pathlib import Path
+from typing import List, Any
+from llama_index.core import Document
+from llama_index.core.readers.base import BaseReader
+
+class EmlxReader(BaseReader):
+    """
+    Apple Mail .emlx file reader with embedded 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, 'r', 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":
+                                            #     continue
+                                            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"""
+[EMAIL METADATA]
+File: {filename}
+From: {from_addr}
+To: {to_addr}
+Subject: {subject}
+Date: {date}
+[END METADATA]
+
+{body}
+"""
+                                
+                                # No separate metadata - everything is in the text
+                                doc = Document(text=doc_content, 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
+
+    @staticmethod
+    def find_all_messages_directories(base_path: str) -> List[Path]:
+        """
+        Find all Messages directories under the given base path.
+        
+        Args:
+            base_path: Base path to search for Messages directories
+            
+        Returns:
+            List of Path objects pointing to Messages directories
+        """
+        base_path_obj = Path(base_path)
+        messages_dirs = []
+        
+        if not base_path_obj.exists():
+            print(f"Base path {base_path} does not exist")
+            return messages_dirs
+        
+        # Find all Messages directories recursively
+        for messages_dir in base_path_obj.rglob("Messages"):
+            if messages_dir.is_dir():
+                messages_dirs.append(messages_dir)
+                print(f"Found Messages directory: {messages_dir}")
+        
+        print(f"Found {len(messages_dirs)} Messages directories")
+        return messages_dirs 

examples/email_data/email.py

@@ -0,0 +1,192 @@
+"""
+Mbox parser.
+
+Contains simple parser for mbox files.
+
+"""
+
+import logging
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+from fsspec import AbstractFileSystem
+
+from llama_index.core.readers.base import BaseReader
+from llama_index.core.schema import Document
+
+logger = logging.getLogger(__name__)
+
+
+class MboxReader(BaseReader):
+    """
+    Mbox parser.
+
+    Extract messages from mailbox files.
+    Returns string including date, subject, sender, receiver and
+    content for each message.
+
+    """
+
+    DEFAULT_MESSAGE_FORMAT: str = (
+        "Date: {_date}\n"
+        "From: {_from}\n"
+        "To: {_to}\n"
+        "Subject: {_subject}\n"
+        "Content: {_content}"
+    )
+
+    def __init__(
+        self,
+        *args: Any,
+        max_count: int = 0,
+        message_format: str = DEFAULT_MESSAGE_FORMAT,
+        **kwargs: Any,
+    ) -> None:
+        """Init params."""
+        try:
+            from bs4 import BeautifulSoup  # noqa
+        except ImportError:
+            raise ImportError(
+                "`beautifulsoup4` package not found: `pip install beautifulsoup4`"
+            )
+
+        super().__init__(*args, **kwargs)
+        self.max_count = max_count
+        self.message_format = message_format
+
+    def load_data(
+        self,
+        file: Path,
+        extra_info: Optional[Dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
+    ) -> List[Document]:
+        """Parse file into string."""
+        # Import required libraries
+        import mailbox
+        from email.parser import BytesParser
+        from email.policy import default
+
+        from bs4 import BeautifulSoup
+
+        if fs:
+            logger.warning(
+                "fs was specified but MboxReader doesn't support loading "
+                "from fsspec filesystems. Will load from local filesystem instead."
+            )
+
+        i = 0
+        results: List[str] = []
+        # Load file using mailbox
+        bytes_parser = BytesParser(policy=default).parse
+        mbox = mailbox.mbox(file, factory=bytes_parser)  # type: ignore
+
+        # Iterate through all messages
+        for _, _msg in enumerate(mbox):
+            try:
+                msg: mailbox.mboxMessage = _msg
+                # Parse multipart messages
+                if msg.is_multipart():
+                    for part in msg.walk():
+                        ctype = part.get_content_type()
+                        cdispo = str(part.get("Content-Disposition"))
+                        if "attachment" in cdispo:
+                            print(f"Attachment found: {part.get_filename()}")
+                        if ctype == "text/plain" and "attachment" not in cdispo:
+                            content = part.get_payload(decode=True)  # decode
+                            break
+                # Get plain message payload for non-multipart messages
+                else:
+                    content = msg.get_payload(decode=True)
+
+                # Parse message HTML content and remove unneeded whitespace
+                soup = BeautifulSoup(content)
+                stripped_content = " ".join(soup.get_text().split())
+                # Format message to include date, sender, receiver and subject
+                msg_string = self.message_format.format(
+                    _date=msg["date"],
+                    _from=msg["from"],
+                    _to=msg["to"],
+                    _subject=msg["subject"],
+                    _content=stripped_content,
+                )
+                # Add message string to results
+                results.append(msg_string)
+            except Exception as e:
+                logger.warning(f"Failed to parse message:\n{_msg}\n with exception {e}")
+
+            # Increment counter and return if max count is met
+            i += 1
+            if self.max_count > 0 and i >= self.max_count:
+                break
+
+        return [Document(text=result, metadata=extra_info or {}) for result in results]
+
+
+class EmlxMboxReader(MboxReader):
+    """
+    EmlxMboxReader - Modified MboxReader that handles directories of .emlx files.
+    
+    Extends MboxReader to work with Apple Mail's .emlx format by:
+    1. Reading .emlx files from a directory
+    2. Converting them to mbox format in memory
+    3. Using the parent MboxReader's parsing logic
+    """
+
+    def load_data(
+        self,
+        directory: Path,
+        extra_info: Optional[Dict] = None,
+        fs: Optional[AbstractFileSystem] = None,
+    ) -> List[Document]:
+        """Parse .emlx files from directory into strings using MboxReader logic."""
+        import tempfile
+        import os
+        
+        if fs:
+            logger.warning(
+                "fs was specified but EmlxMboxReader doesn't support loading "
+                "from fsspec filesystems. Will load from local filesystem instead."
+            )
+
+        # Find all .emlx files in the directory
+        emlx_files = list(directory.glob("*.emlx"))
+        logger.info(f"Found {len(emlx_files)} .emlx files in {directory}")
+        
+        if not emlx_files:
+            logger.warning(f"No .emlx files found in {directory}")
+            return []
+
+        # Create a temporary mbox file
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.mbox', delete=False) as temp_mbox:
+            temp_mbox_path = temp_mbox.name
+            
+            # Convert .emlx files to mbox format
+            for emlx_file in emlx_files:
+                try:
+                    # Read the .emlx file
+                    with open(emlx_file, 'r', encoding='utf-8', errors='ignore') as f:
+                        content = f.read()
+                    
+                    # .emlx format: first line is length, rest is email content
+                    lines = content.split('\n', 1)
+                    if len(lines) >= 2:
+                        email_content = lines[1]  # Skip the length line
+                        
+                        # Write to mbox format (each message starts with "From " and ends with blank line)
+                        temp_mbox.write(f"From {emlx_file.name} {email_content}\n\n")
+                    
+                except Exception as e:
+                    logger.warning(f"Failed to process {emlx_file}: {e}")
+                    continue
+            
+            # Close the temporary file so MboxReader can read it
+            temp_mbox.close()
+            
+            try:
+                # Use the parent MboxReader's logic to parse the mbox file
+                return super().load_data(Path(temp_mbox_path), extra_info, fs)
+            finally:
+                # Clean up temporary file
+                try:
+                    os.unlink(temp_mbox_path)
+                except:
+                    pass

examples/mail_reader_leann.py

@@ -0,0 +1,229 @@
+import os
+import asyncio
+import dotenv
+from pathlib import Path
+from typing import List, Any
+from leann.api import LeannBuilder, LeannSearcher, LeannChat
+from llama_index.core.node_parser import SentenceSplitter
+
+dotenv.load_dotenv()
+
+def create_leann_index_from_multiple_sources(messages_dirs: List[Path], index_path: str = "mail_index.leann", max_count: int = -1):
+    """
+    Create LEANN index from multiple mail data sources.
+    
+    Args:
+        messages_dirs: List of Path objects pointing to Messages directories
+        index_path: Path to save the LEANN index
+        max_count: Maximum number of emails to process per directory
+    """
+    print("Creating LEANN index from multiple mail data sources...")
+    
+    # Load documents using EmlxReader from LEANN_email_reader
+    from LEANN_email_reader import EmlxReader
+    reader = EmlxReader()
+    # from email_data.email import EmlxMboxReader
+    # from pathlib import Path
+    # reader = EmlxMboxReader()
+    
+    all_documents = []
+    total_processed = 0
+    
+    # Process each Messages directory
+    for i, messages_dir in enumerate(messages_dirs):
+        print(f"\nProcessing Messages directory {i+1}/{len(messages_dirs)}: {messages_dir}")
+        
+        try:
+            documents = reader.load_data(messages_dir)
+            if documents:
+                print(f"Loaded {len(documents)} email documents from {messages_dir}")
+                all_documents.extend(documents)
+                total_processed += len(documents)
+                
+                # Check if we've reached the max count
+                if max_count > 0 and total_processed >= max_count:
+                    print(f"Reached max count of {max_count} documents")
+                    break
+            else:
+                print(f"No documents loaded from {messages_dir}")
+        except Exception as e:
+            print(f"Error processing {messages_dir}: {e}")
+            continue
+    
+    if not all_documents:
+        print("No documents loaded from any source. Exiting.")
+        return None
+    
+    print(f"\nTotal loaded {len(all_documents)} email documents from {len(messages_dirs)} directories")
+    
+    # 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 all_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(all_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
+
+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 LEANN_email_reader
+    from LEANN_email_reader import EmlxReader
+    reader = EmlxReader()
+    # from email_data.email import EmlxMboxReader
+    # from pathlib import Path
+    # reader = EmlxMboxReader()
+    documents = reader.load_data(Path(mail_path))
+    
+    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():
+    # Base path to the mail data directory
+    base_mail_path = "/Users/yichuan/Library/Mail/V10/0FCA0879-FD8C-4B7E-83BF-FDDA930791C5/[Gmail].mbox/All Mail.mbox/78BA5BE1-8819-4F9A-9613-EB63772F1DD0/Data"
+    
+    INDEX_DIR = Path("./mail_index_leann_raw_text_all")
+    INDEX_PATH = str(INDEX_DIR / "mail_documents.leann")
+    
+    # Find all Messages directories
+    from LEANN_email_reader import EmlxReader
+    messages_dirs = EmlxReader.find_all_messages_directories(base_mail_path)
+    
+    if not messages_dirs:
+        print("No Messages directories found. Exiting.")
+        return
+    
+    # Create or load the LEANN index from all sources
+    index_path = create_leann_index_from_multiple_sources(messages_dirs, INDEX_PATH)
+    
+    if index_path:
+        # Example queries
+        queries = [
+            "Hows Berkeley Graduate Student Instructor",
+            "how's the icloud related advertisement saying",
+            "Whats the number of class recommend to take per semester for incoming EECS students"
+
+        ]
+        
+        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

@@ -0,0 +1,86 @@
+import os
+from pathlib import Path
+from typing import List, Any
+from llama_index.core import VectorStoreIndex, StorageContext
+from llama_index.core.node_parser import SentenceSplitter
+
+# --- EMBEDDING MODEL ---
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+import torch
+
+# --- END EMBEDDING MODEL ---
+
+# Import EmlxReader from the new module
+from LEANN_email_reader import EmlxReader
+
+def create_and_save_index(mail_path: str, save_dir: str = "mail_index_embedded", max_count: int = 1000):
+    print("Creating index from mail data with embedded metadata...")
+    documents = EmlxReader().load_data(mail_path, max_count=max_count)
+    if not documents:
+        print("No documents loaded. Exiting.")
+        return None
+    text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=25)
+    # Use facebook/contriever as the embedder
+    embed_model = HuggingFaceEmbedding(model_name="facebook/contriever")
+    # set on device
+    import torch
+    if torch.cuda.is_available():
+        embed_model._model.to("cuda")
+    # set mps
+    elif torch.backends.mps.is_available():
+        embed_model._model.to("mps")
+    else:
+        embed_model._model.to("cpu")
+    index = VectorStoreIndex.from_documents(
+        documents,
+        transformations=[text_splitter],
+        embed_model=embed_model
+    )
+    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_embedded"):
+    try:
+        storage_context = StorageContext.from_defaults(persist_dir=save_dir)
+        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):
+    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_embedded"
+    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=10000)
+    if index:
+        queries = [
+            "Hows Berkeley Graduate Student Instructor",
+            "how's the icloud related advertisement saying"
+            "Whats the number of class recommend to take per semester for incoming EECS students"
+        ]
+        for query in queries:
+            print("\n" + "="*50)
+            query_index(index, query)
+
+if __name__ == "__main__":
+    main() 

examples/main_cli_example.py

@@ -8,7 +8,6 @@ from llama_index.node_parser.docling import DoclingNodeParser
 from llama_index.readers.docling import DoclingReader
 from docling_core.transforms.chunker.hybrid_chunker import HybridChunker
 import asyncio
-import os
 import dotenv
 from leann.api import LeannBuilder, LeannSearcher, LeannChat
 import shutil
@@ -22,9 +21,11 @@ file_extractor: dict[str, BaseReader] = {
     ".pptx": reader, 
     ".pdf": reader,
     ".xlsx": reader,
+    ".txt": reader,
+    ".md": reader,
 }
 node_parser = DoclingNodeParser(
-    chunker=HybridChunker(tokenizer="Qwen/Qwen3-Embedding-4B", max_tokens=64)
+    chunker=HybridChunker(tokenizer="facebook/contriever", max_tokens=128)
 )
 print("Loading documents...")
 documents = SimpleDirectoryReader(
@@ -32,7 +33,7 @@ documents = SimpleDirectoryReader(
     recursive=True, 
     file_extractor=file_extractor,
     encoding="utf-8",
-    required_exts=[".pdf", ".docx", ".pptx", ".xlsx"]
+    required_exts=[".pdf", ".docx", ".pptx", ".xlsx", ".txt", ".md"]
 ).load_data(show_progress=True)
 print("Documents loaded.")
 all_texts = []
@@ -41,7 +42,7 @@ for doc in documents:
     for node in nodes:
         all_texts.append(node.get_content())
 
-INDEX_DIR = Path("./test_pdf_index")
+INDEX_DIR = Path("./test_pdf_index_pangu_test")
 INDEX_PATH = str(INDEX_DIR / "pdf_documents.leann")
 
 if not INDEX_DIR.exists():
@@ -49,14 +50,15 @@ if not INDEX_DIR.exists():
     
     print(f"\n[PHASE 1] Building Leann index...")
 
-    # CSR compact mode with recompute
+    # 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
+        is_recompute=True,
+        num_threads=1  # Force single-threaded mode
     )
 
     print(f"Loaded {len(all_texts)} text chunks from documents.")
@@ -80,14 +82,17 @@ async def main(args):
     chat = LeannChat(index_path=INDEX_PATH, llm_config=llm_config)
     
     query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
+    query = "What is the main idea of RL and give me 5 exapmle of classic RL algorithms?"
+    query = "什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面，任务令一般在什么城市颁发"
+
     print(f"You: {query}")
-    chat_response = chat.ask(query, top_k=3, recompute_beighbor_embeddings=True)
+    chat_response = chat.ask(query, top_k=20, recompute_beighbor_embeddings=True, complexity=32, beam_width=1)
     print(f"Leann: {chat_response}")
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run Leann Chat with various LLM backends.")
-    parser.add_argument("--llm", type=str, default="hf", choices=["simulated", "ollama", "hf"], help="The LLM backend to use.")
+    parser.add_argument("--llm", type=str, default="hf", choices=["simulated", "ollama", "hf", "openai"], help="The LLM backend to use.")
-    parser.add_argument("--model", type=str, default='meta-llama/Llama-3.2-3B-Instruct', help="The model name to use (e.g., 'llama3:8b' for ollama, 'deepseek-ai/deepseek-llm-7b-chat' for hf).")
+    parser.add_argument("--model", type=str, default='meta-llama/Llama-3.2-3B-Instruct', help="The model name to use (e.g., 'llama3:8b' for ollama, 'deepseek-ai/deepseek-llm-7b-chat' for hf, 'gpt-4o' for openai).")
     parser.add_argument("--host", type=str, default="http://localhost:11434", help="The host for the Ollama API.")
     args = parser.parse_args()
 

examples/multi_vector_aggregator.py

@@ -0,0 +1,319 @@
+#!/usr/bin/env python3
+"""
+Multi-Vector Aggregator for Fat Embeddings
+==========================================
+
+This module implements aggregation strategies for multi-vector embeddings,
+similar to ColPali's approach where multiple patch vectors represent a single document.
+
+Key features:
+- MaxSim aggregation (take maximum similarity across patches)
+- Voting-based aggregation (count patch matches)
+- Weighted aggregation (attention-score weighted)
+- Spatial clustering of matching patches
+- Document-level result consolidation
+"""
+
+import numpy as np
+from typing import List, Dict, Any, Tuple, Optional
+from dataclasses import dataclass
+from collections import defaultdict
+import json
+
+@dataclass
+class PatchResult:
+    """Represents a single patch search result."""
+    patch_id: int
+    image_name: str
+    image_path: str
+    coordinates: Tuple[int, int, int, int]  # (x1, y1, x2, y2)
+    score: float
+    attention_score: float
+    scale: float
+    metadata: Dict[str, Any]
+
+@dataclass
+class AggregatedResult:
+    """Represents an aggregated document-level result."""
+    image_name: str
+    image_path: str
+    doc_score: float
+    patch_count: int
+    best_patch: PatchResult
+    all_patches: List[PatchResult]
+    aggregation_method: str
+    spatial_clusters: Optional[List[List[PatchResult]]] = None
+
+class MultiVectorAggregator:
+    """
+    Aggregates multiple patch-level results into document-level results.
+    """
+    
+    def __init__(self, 
+                 aggregation_method: str = "maxsim",
+                 spatial_clustering: bool = True,
+                 cluster_distance_threshold: float = 100.0):
+        """
+        Initialize the aggregator.
+        
+        Args:
+            aggregation_method: "maxsim", "voting", "weighted", or "mean"
+            spatial_clustering: Whether to cluster spatially close patches
+            cluster_distance_threshold: Distance threshold for spatial clustering
+        """
+        self.aggregation_method = aggregation_method
+        self.spatial_clustering = spatial_clustering
+        self.cluster_distance_threshold = cluster_distance_threshold
+    
+    def aggregate_results(self, 
+                         search_results: List[Dict[str, Any]], 
+                         top_k: int = 10) -> List[AggregatedResult]:
+        """
+        Aggregate patch-level search results into document-level results.
+        
+        Args:
+            search_results: List of search results from LeannSearcher
+            top_k: Number of top documents to return
+            
+        Returns:
+            List of aggregated document results
+        """
+        # Group results by image
+        image_groups = defaultdict(list)
+        
+        for result in search_results:
+            metadata = result.metadata
+            if "image_name" in metadata and "patch_id" in metadata:
+                patch_result = PatchResult(
+                    patch_id=metadata["patch_id"],
+                    image_name=metadata["image_name"],
+                    image_path=metadata["image_path"],
+                    coordinates=tuple(metadata["coordinates"]),
+                    score=result.score,
+                    attention_score=metadata.get("attention_score", 0.0),
+                    scale=metadata.get("scale", 1.0),
+                    metadata=metadata
+                )
+                image_groups[metadata["image_name"]].append(patch_result)
+        
+        # Aggregate each image group
+        aggregated_results = []
+        for image_name, patches in image_groups.items():
+            if len(patches) == 0:
+                continue
+                
+            agg_result = self._aggregate_image_patches(image_name, patches)
+            aggregated_results.append(agg_result)
+        
+        # Sort by aggregated score and return top-k
+        aggregated_results.sort(key=lambda x: x.doc_score, reverse=True)
+        return aggregated_results[:top_k]
+    
+    def _aggregate_image_patches(self, image_name: str, patches: List[PatchResult]) -> AggregatedResult:
+        """Aggregate patches for a single image."""
+        
+        if self.aggregation_method == "maxsim":
+            doc_score = max(patch.score for patch in patches)
+            best_patch = max(patches, key=lambda p: p.score)
+            
+        elif self.aggregation_method == "voting":
+            # Count patches above threshold
+            threshold = np.percentile([p.score for p in patches], 75)
+            doc_score = sum(1 for patch in patches if patch.score >= threshold)
+            best_patch = max(patches, key=lambda p: p.score)
+            
+        elif self.aggregation_method == "weighted":
+            # Weight by attention scores
+            total_weighted_score = sum(p.score * p.attention_score for p in patches)
+            total_weights = sum(p.attention_score for p in patches)
+            doc_score = total_weighted_score / max(total_weights, 1e-8)
+            best_patch = max(patches, key=lambda p: p.score * p.attention_score)
+            
+        elif self.aggregation_method == "mean":
+            doc_score = np.mean([patch.score for patch in patches])
+            best_patch = max(patches, key=lambda p: p.score)
+            
+        else:
+            raise ValueError(f"Unknown aggregation method: {self.aggregation_method}")
+        
+        # Spatial clustering if enabled
+        spatial_clusters = None
+        if self.spatial_clustering:
+            spatial_clusters = self._cluster_patches_spatially(patches)
+        
+        return AggregatedResult(
+            image_name=image_name,
+            image_path=patches[0].image_path,
+            doc_score=float(doc_score),
+            patch_count=len(patches),
+            best_patch=best_patch,
+            all_patches=sorted(patches, key=lambda p: p.score, reverse=True),
+            aggregation_method=self.aggregation_method,
+            spatial_clusters=spatial_clusters
+        )
+    
+    def _cluster_patches_spatially(self, patches: List[PatchResult]) -> List[List[PatchResult]]:
+        """Cluster patches that are spatially close to each other."""
+        if len(patches) <= 1:
+            return [patches]
+        
+        clusters = []
+        remaining_patches = patches.copy()
+        
+        while remaining_patches:
+            # Start new cluster with highest scoring remaining patch
+            seed_patch = max(remaining_patches, key=lambda p: p.score)
+            current_cluster = [seed_patch]
+            remaining_patches.remove(seed_patch)
+            
+            # Add nearby patches to cluster
+            added_to_cluster = True
+            while added_to_cluster:
+                added_to_cluster = False
+                for patch in remaining_patches.copy():
+                    if self._is_patch_nearby(patch, current_cluster):
+                        current_cluster.append(patch)
+                        remaining_patches.remove(patch)
+                        added_to_cluster = True
+            
+            clusters.append(current_cluster)
+        
+        return sorted(clusters, key=lambda cluster: max(p.score for p in cluster), reverse=True)
+    
+    def _is_patch_nearby(self, patch: PatchResult, cluster: List[PatchResult]) -> bool:
+        """Check if a patch is spatially close to any patch in the cluster."""
+        patch_center = self._get_patch_center(patch.coordinates)
+        
+        for cluster_patch in cluster:
+            cluster_center = self._get_patch_center(cluster_patch.coordinates)
+            distance = np.sqrt((patch_center[0] - cluster_center[0])**2 + 
+                             (patch_center[1] - cluster_center[1])**2)
+            
+            if distance <= self.cluster_distance_threshold:
+                return True
+        
+        return False
+    
+    def _get_patch_center(self, coordinates: Tuple[int, int, int, int]) -> Tuple[float, float]:
+        """Get center point of a patch."""
+        x1, y1, x2, y2 = coordinates
+        return ((x1 + x2) / 2, (y1 + y2) / 2)
+    
+    def print_aggregated_results(self, results: List[AggregatedResult], max_patches_per_doc: int = 3):
+        """Pretty print aggregated results."""
+        print(f"\n🔍 Aggregated Results (method: {self.aggregation_method})")
+        print("=" * 80)
+        
+        for i, result in enumerate(results):
+            print(f"\n{i+1}. {result.image_name}")
+            print(f"   Doc Score: {result.doc_score:.4f} | Patches: {result.patch_count}")
+            print(f"   Path: {result.image_path}")
+            
+            # Show best patch
+            best = result.best_patch
+            print(f"   🌟 Best Patch: #{best.patch_id} at {best.coordinates} (score: {best.score:.4f})")
+            
+            # Show top patches
+            print(f"   📍 Top Patches:")
+            for j, patch in enumerate(result.all_patches[:max_patches_per_doc]):
+                print(f"      {j+1}. Patch #{patch.patch_id}: {patch.score:.4f} at {patch.coordinates}")
+            
+            # Show spatial clusters if available
+            if result.spatial_clusters and len(result.spatial_clusters) > 1:
+                print(f"   🗂️ Spatial Clusters: {len(result.spatial_clusters)}")
+                for j, cluster in enumerate(result.spatial_clusters[:2]):  # Show top 2 clusters
+                    cluster_score = max(p.score for p in cluster)
+                    print(f"      Cluster {j+1}: {len(cluster)} patches (best: {cluster_score:.4f})")
+
+def demo_aggregation():
+    """Demonstrate the multi-vector aggregation functionality."""
+    print("=== Multi-Vector Aggregation Demo ===")
+    
+    # Simulate some patch-level search results
+    # In real usage, these would come from LeannSearcher.search()
+    
+    class MockResult:
+        def __init__(self, score, metadata):
+            self.score = score
+            self.metadata = metadata
+    
+    # Simulate results for 2 images with multiple patches each
+    mock_results = [
+        # Image 1: cats_and_kitchen.jpg - 4 patches
+        MockResult(0.85, {
+            "image_name": "cats_and_kitchen.jpg",
+            "image_path": "/path/to/cats_and_kitchen.jpg",
+            "patch_id": 3,
+            "coordinates": [100, 50, 224, 174],  # Kitchen area
+            "attention_score": 0.92,
+            "scale": 1.0
+        }),
+        MockResult(0.78, {
+            "image_name": "cats_and_kitchen.jpg", 
+            "image_path": "/path/to/cats_and_kitchen.jpg",
+            "patch_id": 7,
+            "coordinates": [200, 300, 324, 424],  # Cat area
+            "attention_score": 0.88,
+            "scale": 1.0
+        }),
+        MockResult(0.72, {
+            "image_name": "cats_and_kitchen.jpg",
+            "image_path": "/path/to/cats_and_kitchen.jpg", 
+            "patch_id": 12,
+            "coordinates": [150, 100, 274, 224],  # Appliances
+            "attention_score": 0.75,
+            "scale": 1.0
+        }),
+        MockResult(0.65, {
+            "image_name": "cats_and_kitchen.jpg",
+            "image_path": "/path/to/cats_and_kitchen.jpg",
+            "patch_id": 15,
+            "coordinates": [50, 250, 174, 374],  # Furniture
+            "attention_score": 0.70,
+            "scale": 1.0
+        }),
+        
+        # Image 2: city_street.jpg - 3 patches  
+        MockResult(0.68, {
+            "image_name": "city_street.jpg",
+            "image_path": "/path/to/city_street.jpg",
+            "patch_id": 2,
+            "coordinates": [300, 100, 424, 224],  # Buildings
+            "attention_score": 0.80,
+            "scale": 1.0
+        }),
+        MockResult(0.62, {
+            "image_name": "city_street.jpg",
+            "image_path": "/path/to/city_street.jpg",
+            "patch_id": 8,
+            "coordinates": [100, 350, 224, 474],  # Street level
+            "attention_score": 0.75,
+            "scale": 1.0
+        }),
+        MockResult(0.55, {
+            "image_name": "city_street.jpg", 
+            "image_path": "/path/to/city_street.jpg",
+            "patch_id": 11,
+            "coordinates": [400, 200, 524, 324],  # Sky area
+            "attention_score": 0.60,
+            "scale": 1.0
+        }),
+    ]
+    
+    # Test different aggregation methods
+    methods = ["maxsim", "voting", "weighted", "mean"]
+    
+    for method in methods:
+        print(f"\n{'='*20} {method.upper()} AGGREGATION {'='*20}")
+        
+        aggregator = MultiVectorAggregator(
+            aggregation_method=method,
+            spatial_clustering=True,
+            cluster_distance_threshold=100.0
+        )
+        
+        aggregated = aggregator.aggregate_results(mock_results, top_k=5)
+        aggregator.print_aggregated_results(aggregated)
+
+if __name__ == "__main__":
+    demo_aggregation()

examples/resue_index.py

@@ -0,0 +1,18 @@
+import asyncio
+from leann.api import LeannChat
+from pathlib import Path
+
+INDEX_DIR = Path("./test_pdf_index_huawei")
+INDEX_PATH = str(INDEX_DIR / "pdf_documents.leann")
+
+async def main():
+    print(f"\n[PHASE 2] Starting Leann chat session...")
+    chat = LeannChat(index_path=INDEX_PATH)
+    query = "What is the main idea of RL and give me 5 exapmle of classic RL algorithms?"
+    query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
+    # query = "什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面，任务令一般在什么城市颁发"
+    response = chat.ask(query,top_k=20,recompute_beighbor_embeddings=True,complexity=32,beam_width=1)
+    print(f"\n[PHASE 2] Response: {response}")
+
+if __name__ == "__main__":
+    asyncio.run(main())

knowledge.leann.meta.json

@@ -1,32 +0,0 @@
-{
-  "version": "0.1.0",
-  "backend_name": "diskann",
-  "embedding_model": "sentence-transformers/all-mpnet-base-v2",
-  "num_chunks": 6,
-  "chunks": [
-    {
-      "text": "Python is a powerful programming language",
-      "metadata": {}
-    },
-    {
-      "text": "Machine learning transforms industries",
-      "metadata": {}
-    },
-    {
-      "text": "Neural networks process complex data",
-      "metadata": {}
-    },
-    {
-      "text": "Java is a powerful programming language",
-      "metadata": {}
-    },
-    {
-      "text": "C++ is a powerful programming language",
-      "metadata": {}
-    },
-    {
-      "text": "C# is a powerful programming language",
-      "metadata": {}
-    }
-  ]
-}

packages/__init__.py

@@ -0,0 +1 @@
+

packages/leann-backend-diskann/__init__.py

@@ -0,0 +1 @@
+# This file makes the directory a Python package 

packages/leann-backend-diskann/leann_backend_diskann/embedding_server.py

@@ -15,6 +15,8 @@ import os
 from contextlib import contextmanager
 import zmq
 import numpy as np
+from pathlib import Path
+import pickle
 
 RED = "\033[91m"
 RESET = "\033[0m"
@@ -109,8 +111,6 @@ def load_passages_from_file(passages_file: str) -> SimplePassageLoader:
     Load passages from a JSONL file with label map support
     Expected format: {"id": "passage_id", "text": "passage_text", "metadata": {...}} (one per line)
     """
-    from pathlib import Path
-    import pickle
     
     if not os.path.exists(passages_file):
         raise FileNotFoundError(f"Passages file {passages_file} not found.")
@@ -210,7 +210,6 @@ def create_embedding_server_thread(
                 passages = load_passages_from_metadata(passages_file)
             else:
                 # Try to find metadata file in same directory
-                from pathlib import Path
                 passages_dir = Path(passages_file).parent
                 meta_files = list(passages_dir.glob("*.meta.json"))
                 if meta_files:

packages/leann-backend-hnsw/CMakeLists.txt

@@ -2,6 +2,33 @@
 cmake_minimum_required(VERSION 3.24)
 project(leann_backend_hnsw_wrapper)
 
+# Set OpenMP path for macOS
+if(APPLE)
+    set(OpenMP_C_FLAGS "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include")
+    set(OpenMP_CXX_FLAGS "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include")
+    set(OpenMP_C_LIB_NAMES "omp")
+    set(OpenMP_CXX_LIB_NAMES "omp")
+    set(OpenMP_omp_LIBRARY "/opt/homebrew/opt/libomp/lib/libomp.dylib")
+endif()
+
+# Build ZeroMQ from source
+set(ZMQ_BUILD_TESTS OFF CACHE BOOL "" FORCE)
+set(ENABLE_DRAFTS OFF CACHE BOOL "" FORCE)
+set(ENABLE_PRECOMPILED OFF CACHE BOOL "" FORCE)
+set(WITH_PERF_TOOL OFF CACHE BOOL "" FORCE)
+set(WITH_DOCS OFF CACHE BOOL "" FORCE)
+set(BUILD_SHARED OFF CACHE BOOL "" FORCE)
+set(BUILD_STATIC ON CACHE BOOL "" FORCE)
+add_subdirectory(third_party/libzmq)
+
+# Add cppzmq headers
+include_directories(third_party/cppzmq)
+
+# Configure msgpack-c - disable boost dependency
+set(MSGPACK_USE_BOOST OFF CACHE BOOL "" FORCE)
+add_compile_definitions(MSGPACK_NO_BOOST)
+include_directories(third_party/msgpack-c/include)
+
 set(FAISS_ENABLE_PYTHON ON CACHE BOOL "" FORCE)
 set(FAISS_ENABLE_GPU OFF CACHE BOOL "" FORCE)
 set(FAISS_ENABLE_EXTRAS OFF CACHE BOOL "" FORCE)

packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py

@@ -4,6 +4,7 @@ import json
 from pathlib import Path
 from typing import Dict, Any, List
 import pickle
+import shutil
 
 from leann.searcher_base import BaseSearcher
 from .convert_to_csr import convert_hnsw_graph_to_csr
@@ -77,17 +78,29 @@ class HNSWBuilder(LeannBackendBuilderInterface):
             self._convert_to_csr(index_file)
 
     def _convert_to_csr(self, index_file: Path):
+        """Convert built index to CSR format"""
+        mode_str = "CSR-pruned" if self.is_recompute else "CSR-standard"
+        print(f"INFO: Converting HNSW index to {mode_str} format...")
+
         csr_temp_file = index_file.with_suffix(".csr.tmp")
+
         success = convert_hnsw_graph_to_csr(
-            str(index_file), str(csr_temp_file), prune_embeddings=self.is_recompute
+            str(index_file), 
+            str(csr_temp_file),
+            prune_embeddings=self.is_recompute
         )
+
         if success:
-            import shutil
+            print("✅ CSR conversion successful.")
+            index_file_old = index_file.with_suffix(".old")
+            shutil.move(str(index_file), str(index_file_old))
             shutil.move(str(csr_temp_file), str(index_file))
+            print(f"INFO: Replaced original index with {mode_str} version at '{index_file}'")
         else:
+            # Clean up and fail fast
             if csr_temp_file.exists():
                 os.remove(csr_temp_file)
-            raise RuntimeError("CSR conversion failed")
+            raise RuntimeError("CSR conversion failed - cannot proceed with compact format")
 
 class HNSWSearcher(BaseSearcher):
     def __init__(self, index_path: str, **kwargs):
@@ -99,7 +112,10 @@ class HNSWSearcher(BaseSearcher):
         if metric_enum is None:
             raise ValueError(f"Unsupported distance_metric '{self.distance_metric}'.")
 
-        self.is_compact, self.is_pruned = self._get_index_storage_status_from_meta()
+        self.is_compact, self.is_pruned = (
+            self.meta.get('is_compact', True),
+            self.meta.get('is_pruned', True)
+        )
 
         index_file = self.index_dir / f"{self.index_path.stem}.index"
         if not index_file.exists():
@@ -114,11 +130,6 @@ class HNSWSearcher(BaseSearcher):
 
         self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
 
-    def _get_index_storage_status_from_meta(self) -> tuple[bool, bool]:
-        is_compact = self.meta.get('is_compact', True)
-        is_pruned = self.meta.get('is_pruned', True)
-        return is_compact, is_pruned
-
     def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, Any]:
         from . import faiss
 

packages/leann-core/src/leann/__init__.py

@@ -1,4 +1,14 @@
 # packages/leann-core/src/leann/__init__.py
+import os
+import platform
+
+# Fix OpenMP threading issues on macOS ARM64
+if platform.system() == "Darwin":
+    os.environ["OMP_NUM_THREADS"] = "1"
+    os.environ["MKL_NUM_THREADS"] = "1"
+    os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+    os.environ["KMP_BLOCKTIME"] = "0"
+
 from .api import LeannBuilder, LeannChat, LeannSearcher
 from .registry import BACKEND_REGISTRY, autodiscover_backends
 

packages/leann-core/src/leann/api.py

@@ -1,4 +1,3 @@
-#!/usr/bin/env python3
 """
 This file contains the core API for the LEANN project, now definitively updated
 with the correct, original embedding logic from the user's reference code.
@@ -11,6 +10,7 @@ from pathlib import Path
 from typing import List, Dict, Any, Optional
 from dataclasses import dataclass, field
 import uuid
+import torch
 
 from .registry import BACKEND_REGISTRY
 from .interface import LeannBackendFactoryInterface
@@ -25,13 +25,22 @@ def compute_embeddings(chunks: List[str], model_name: str) -> np.ndarray:
         raise RuntimeError(
             f"sentence-transformers not available. Install with: pip install sentence-transformers"
         ) from e
-    
+
     # Load model using sentence-transformers
     model = SentenceTransformer(model_name)
-    
+
+    model = model.half()
+    print(f"INFO: Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}'...")
+    # use acclerater GPU or MAC GPU
+
+    if torch.cuda.is_available():
+        model = model.to("cuda")
+    elif torch.backends.mps.is_available():
+        model = model.to("mps")
+
     # Generate embeddings
     embeddings = model.encode(chunks, convert_to_numpy=True, show_progress_bar=True, batch_size=64)
-    
+
     return embeddings
 
 # --- Core API Classes (Restored and Unchanged) ---
@@ -181,5 +190,25 @@ class LeannChat:
     def ask(self, question: str, top_k=5, **kwargs):
         results = self.searcher.search(question, top_k=top_k, **kwargs)
         context = "\n\n".join([r.text for r in results])
-        prompt = f"Context:\n{context}\n\nQuestion: {question}\n\nAnswer:"
+        prompt = (
-        return self.llm.ask(prompt, **kwargs.get("llm_kwargs", {}))
+            "Here is some retrieved context that might help answer your question:\n\n"
+            f"{context}\n\n"
+            f"Question: {question}\n\n"
+            "Please provide the best answer you can based on this context and your knowledge."
+        )
+        return self.llm.ask(prompt, **kwargs.get("llm_kwargs", {}))
+
+    def start_interactive(self):
+        print("\nLeann Chat started (type 'quit' to exit)")
+        while True:
+            try:
+                user_input = input("You: ").strip()
+                if user_input.lower() in ['quit', 'exit']:
+                    break
+                if not user_input:
+                    continue
+                response = self.ask(user_input)
+                print(f"Leann: {response}")
+            except (KeyboardInterrupt, EOFError):
+                print("\nGoodbye!")
+                break

packages/leann-core/src/leann/chat.py

@@ -7,6 +7,7 @@ supporting different backends like Ollama, Hugging Face Transformers, and a simu
 from abc import ABC, abstractmethod
 from typing import Dict, Any, Optional
 import logging
+import os
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -95,7 +96,57 @@ class HFChat(LLMInterface):
         }
         logger.info(f"Generating text with Hugging Face model with params: {params}")
         results = self.pipeline(prompt, **params)
-        return results[0]['generated_text']
+        
+        # Handle different response formats from transformers
+        if isinstance(results, list) and len(results) > 0:
+            generated_text = results[0].get('generated_text', '') if isinstance(results[0], dict) else str(results[0])
+        else:
+            generated_text = str(results)
+        
+        # Extract only the newly generated portion by removing the original prompt
+        if isinstance(generated_text, str) and generated_text.startswith(prompt):
+            response = generated_text[len(prompt):].strip()
+        else:
+            # Fallback: return the full response if prompt removal fails
+            response = str(generated_text)
+            
+        return response
+
+class OpenAIChat(LLMInterface):
+    """LLM interface for OpenAI models."""
+    def __init__(self, model: str = "gpt-4o", api_key: Optional[str] = None):
+        self.model = model
+        self.api_key = api_key or os.getenv("OPENAI_API_KEY")
+        
+        if not self.api_key:
+            raise ValueError("OpenAI API key is required. Set OPENAI_API_KEY environment variable or pass api_key parameter.")
+        
+        logger.info(f"Initializing OpenAI Chat with model='{model}'")
+        
+        try:
+            import openai
+            self.client = openai.OpenAI(api_key=self.api_key)
+        except ImportError:
+            raise ImportError("The 'openai' library is required for OpenAI models. Please install it with 'pip install openai'.")
+
+    def ask(self, prompt: str, **kwargs) -> str:
+        # Default parameters for OpenAI
+        params = {
+            "model": self.model,
+            "messages": [{"role": "user", "content": prompt}],
+            "max_tokens": kwargs.get("max_tokens", 1000),
+            "temperature": kwargs.get("temperature", 0.7),
+            **{k: v for k, v in kwargs.items() if k not in ["max_tokens", "temperature"]}
+        }
+        
+        logger.info(f"Sending request to OpenAI with model {self.model}")
+        
+        try:
+            response = self.client.chat.completions.create(**params)
+            return response.choices[0].message.content.strip()
+        except Exception as e:
+            logger.error(f"Error communicating with OpenAI: {e}")
+            return f"Error: Could not get a response from OpenAI. Details: {e}"
 
 class SimulatedChat(LLMInterface):
     """A simple simulated chat for testing and development."""
@@ -127,9 +178,11 @@ def get_llm(llm_config: Optional[Dict[str, Any]] = None) -> LLMInterface:
     logger.info(f"Attempting to create LLM of type='{llm_type}' with model='{model}'")
 
     if llm_type == "ollama":
-        return OllamaChat(model=model, host=llm_config.get("host"))
+        return OllamaChat(model=model or "llama3:8b", host=llm_config.get("host", "http://localhost:11434"))
     elif llm_type == "hf":
-        return HFChat(model_name=model)
+        return HFChat(model_name=model or "deepseek-ai/deepseek-llm-7b-chat")
+    elif llm_type == "openai":
+        return OpenAIChat(model=model or "gpt-4o", api_key=llm_config.get("api_key"))
     elif llm_type == "simulated":
         return SimulatedChat()
     else:

pyproject.toml

@@ -21,7 +21,7 @@ dependencies = [
     "colorama",
     "boto3",
     "protobuf==4.25.3",
-    "sglang[all]",
+    "sglang",
     "ollama",
     "requests>=2.25.0",
     "sentence-transformers>=2.2.0",

test/mail_reader_llamaindex.py

@@ -0,0 +1,147 @@
+import os
+import email
+from pathlib import Path
+from typing import List, Any
+from llama_index.core import VectorStoreIndex, Document
+from llama_index.core.readers.base import BaseReader
+
+class EmlxReader(BaseReader):
+    """
+    Apple Mail .emlx file reader.
+    
+    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, 'r', 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":
+                                            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
+                                doc_content = f"""
+From: {from_addr}
+To: {to_addr}
+Subject: {subject}
+Date: {date}
+
+{body}
+"""
+                                
+                                # Create metadata
+                                metadata = {
+                                    'file_path': filepath,
+                                    'subject': subject,
+                                    'from': from_addr,
+                                    'to': to_addr,
+                                    'date': date,
+                                    'filename': filename
+                                }
+                                if count == 0:
+                                    print("--------------------------------")
+                                    print('dir path', dirpath)
+                                    print(metadata)
+                                    print(doc_content)
+                                    print("--------------------------------")
+                                    body=[]
+                                    if msg.is_multipart():
+                                        for part in msg.walk():
+                                            print("--------------------------------  get content type -------------------------------")
+                                            print(part.get_content_type())
+                                            print(part)
+                                            # body.append(part.get_payload(decode=True).decode('utf-8', errors='ignore'))
+                                            print("--------------------------------  get content type -------------------------------")
+                                    else:
+                                        body = msg.get_payload(decode=True).decode('utf-8', errors='ignore')
+                                        print(body)
+
+                                    print(body)
+                                    print("--------------------------------")
+                                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
+
+# Use the custom EmlxReader instead of MboxReader
+documents = EmlxReader().load_data(
+    "/Users/yichuan/Library/Mail/V10/0FCA0879-FD8C-4B7E-83BF-FDDA930791C5/[Gmail].mbox/All Mail.mbox/78BA5BE1-8819-4F9A-9613-EB63772F1DD0/Data/9/Messages", 
+    max_count=1000
+)  # Returns list of documents
+
+# Configure the index with larger chunk size to handle long metadata
+from llama_index.core.node_parser import SentenceSplitter
+
+# Create a custom text splitter with larger chunk size
+text_splitter = SentenceSplitter(chunk_size=2048, chunk_overlap=200)
+
+index = VectorStoreIndex.from_documents(
+    documents,
+    transformations=[text_splitter]
+)  # Initialize index with documents
+
+query_engine = index.as_query_engine()
+res = query_engine.query("Hows Berkeley Graduate Student Instructor")
+print(res)

test/mail_reader_save_load.py

@@ -0,0 +1,213 @@
+import os
+import email
+from pathlib import Path
+from typing import List, Any
+from llama_index.core import VectorStoreIndex, Document, StorageContext
+from llama_index.core.readers.base import BaseReader
+from llama_index.core.node_parser import SentenceSplitter
+
+class EmlxReader(BaseReader):
+    """
+    Apple Mail .emlx file reader.
+    
+    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, 'r', 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
+                                doc_content = f"""
+From: {from_addr}
+To: {to_addr}
+Subject: {subject}
+Date: {date}
+
+{body}
+"""
+                                
+                                # Create metadata
+                                metadata = {
+                                    'file_path': filepath,
+                                    'subject': subject,
+                                    'from': from_addr,
+                                    'to': to_addr,
+                                    'date': date,
+                                    'filename': 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", 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...")
+    
+    # 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
+    text_splitter = SentenceSplitter(chunk_size=256, chunk_overlap=0)
+    
+    # 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"):
+    """
+    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"
+    
+    # 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() 

test/mail_reader_small_chunks.py

@@ -0,0 +1,211 @@
+import os
+import email
+from pathlib import Path
+from typing import List, Any
+from llama_index.core import VectorStoreIndex, Document, StorageContext
+from llama_index.core.readers.base import BaseReader
+from llama_index.core.node_parser import SentenceSplitter
+
+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, 'r', 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() 

test/mail_reader_test.py

@@ -0,0 +1,147 @@
+import os
+import email
+from pathlib import Path
+from typing import List, Any
+from llama_index.core import VectorStoreIndex, Document
+from llama_index.core.readers.base import BaseReader
+
+class EmlxReader(BaseReader):
+    """
+    Apple Mail .emlx file reader.
+    
+    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
+        
+        # Check if directory exists and is accessible
+        if not os.path.exists(input_dir):
+            print(f"Error: Directory '{input_dir}' does not exist")
+            return docs
+        
+        if not os.access(input_dir, os.R_OK):
+            print(f"Error: Directory '{input_dir}' is not accessible (permission denied)")
+            print("This is likely due to macOS security restrictions on Mail app data")
+            return docs
+        
+        print(f"Scanning 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:
+                if count >= max_count:
+                    break
+                    
+                if filename.endswith(".emlx"):
+                    filepath = os.path.join(dirpath, filename)
+                    print(f"Found .emlx file: {filepath}")
+                    try:
+                        # Read the .emlx file
+                        with open(filepath, 'r', 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
+                                doc_content = f"""
+From: {from_addr}
+To: {to_addr}
+Subject: {subject}
+Date: {date}
+
+{body}
+"""
+                                
+                                # Create metadata
+                                metadata = {
+                                    'file_path': filepath,
+                                    'subject': subject,
+                                    'from': from_addr,
+                                    'to': to_addr,
+                                    'date': date,
+                                    'filename': 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 main():
+    # Use the current directory where the sample.emlx file is located
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    
+    print("Testing EmlxReader with sample .emlx file...")
+    print(f"Scanning directory: {current_dir}")
+    
+    # Use the custom EmlxReader
+    documents = EmlxReader().load_data(current_dir, max_count=1000)
+    
+    if not documents:
+        print("No documents loaded. Make sure sample.emlx exists in the examples directory.")
+        return
+    
+    print(f"\nSuccessfully loaded {len(documents)} document(s)")
+    
+    # Initialize index with documents
+    index = VectorStoreIndex.from_documents(documents)
+    query_engine = index.as_query_engine()
+    
+    print("\nTesting query: 'Hows Berkeley Graduate Student Instructor'")
+    res = query_engine.query("Hows Berkeley Graduate Student Instructor")
+    print(f"Response: {res}")
+
+if __name__ == "__main__":
+    main() 

test/query_saved_index.py

@@ -0,0 +1,99 @@
+import os
+from llama_index.core import VectorStoreIndex, StorageContext
+
+def load_index(save_dir: str = "mail_index"):
+    """
+    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"\nQuery: {query}")
+    print(f"Response: {response}")
+
+def main():
+    save_dir = "mail_index"
+    
+    # Check if index exists
+    if not os.path.exists(save_dir) or not os.path.exists(os.path.join(save_dir, "vector_store.json")):
+        print(f"Index not found in {save_dir}")
+        print("Please run mail_reader_save_load.py first to create the index.")
+        return
+    
+    # Load the index
+    index = load_index(save_dir)
+    
+    if not index:
+        print("Failed to load index.")
+        return
+    
+    print("\n" + "="*60)
+    print("Email Query Interface")
+    print("="*60)
+    print("Type 'quit' to exit")
+    print("Type 'help' for example queries")
+    print("="*60)
+    
+    # Interactive query loop
+    while True:
+        try:
+            query = input("\nEnter your query: ").strip()
+            
+            if query.lower() == 'quit':
+                print("Goodbye!")
+                break
+            elif query.lower() == 'help':
+                print("\nExample queries:")
+                print("- Hows Berkeley Graduate Student Instructor")
+                print("- What emails mention GSR appointments?")
+                print("- Find emails about deadlines")
+                print("- Search for emails from specific sender")
+                print("- Find emails about meetings")
+                continue
+            elif not query:
+                continue
+            
+            query_index(index, query)
+            
+        except KeyboardInterrupt:
+            print("\nGoodbye!")
+            break
+        except Exception as e:
+            print(f"Error processing query: {e}")
+
+if __name__ == "__main__":
+    main()