docs: Address maintainer feedback on README improvements

- Restore emojis in section headers (Prerequisites and Quick Install)
- Add MCP live data feature mention in line 23 with links to Slack and Twitter
- Add detailed API credential setup instructions for Slack:
  - Step-by-step Slack App creation process
  - Required OAuth scopes and permissions
  - Clear token identification (xoxb- vs xapp-)
- Add detailed API credential setup instructions for Twitter:
  - Twitter Developer Account application process
  - API v2 requirements for bookmarks access
  - Required permissions and scopes

This addresses maintainer feedback to make API setup more user-friendly.

README.md

@@ -8,12 +8,8 @@
   <img src="https://img.shields.io/badge/Platform-Ubuntu%20%26%20Arch%20%26%20WSL%20%7C%20macOS%20(ARM64%2FIntel)-lightgrey" alt="Platform">
   <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="MIT License">
   <img src="https://img.shields.io/badge/MCP-Native%20Integration-blue" alt="MCP Integration">
-  <a href="https://join.slack.com/t/leann-e2u9779/shared_invite/zt-3ckd2f6w1-OX08~NN4gkWhh10PRVBj1Q">
-    <img src="https://img.shields.io/badge/Slack-Join-4A154B?logo=slack&logoColor=white" alt="Join Slack">
-  </a>
-  <a href="assets/wechat_user_group.JPG" title="Join WeChat group">
-    <img src="https://img.shields.io/badge/WeChat-Join-2DC100?logo=wechat&logoColor=white" alt="Join WeChat group">
-  </a>
+  <a href="https://join.slack.com/t/leann-e2u9779/shared_invite/zt-3ckd2f6w1-OX08~NN4gkWhh10PRVBj1Q"><img src="https://img.shields.io/badge/Slack-Join-4A154B?logo=slack&logoColor=white" alt="Join Slack">
+  <a href="assets/wechat_user_group.JPG" title="Join WeChat group"><img src="https://img.shields.io/badge/WeChat-Join-2DC100?logo=wechat&logoColor=white" alt="Join WeChat group"></a>
 </p>
 
 <h2 align="center" tabindex="-1" class="heading-element" dir="auto">
@@ -24,7 +20,7 @@ LEANN is an innovative vector database that democratizes personal AI. Transform
 
 LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Illustration Fig →](#️-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
 
-**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can semantic search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)** ([WeChat](#-wechat-detective-unlock-your-golden-memories), [iMessage](#-imessage-history-your-personal-conversation-archive)), **[agent memory](#-chatgpt-chat-history-your-personal-ai-conversation-archive)** ([ChatGPT](#-chatgpt-chat-history-your-personal-ai-conversation-archive), [Claude](#-claude-chat-history-your-personal-ai-conversation-archive)), **[live data](#mcp-integration-rag-on-live-data-from-any-platform)** ([Slack](#mcp-integration-rag-on-live-data-from-any-platform), [Twitter](#mcp-integration-rag-on-live-data-from-any-platform)), **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
+**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can semantic search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)** ([WeChat](#-wechat-detective-unlock-your-golden-memories), [iMessage](#-imessage-history-your-personal-conversation-archive)), **[agent memory](#-chatgpt-chat-history-your-personal-ai-conversation-archive)** ([ChatGPT](#-chatgpt-chat-history-your-personal-ai-conversation-archive), [Claude](#-claude-chat-history-your-personal-ai-conversation-archive)), **[live data](#mcp-integration-rag-on-live-data-from-any-platform)** ([Slack](#slack-messages-search-your-team-conversations), [Twitter](#twitter-bookmarks-your-personal-tweet-library)), **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
 
 
 \* Claude Code only supports basic `grep`-style keyword search. **LEANN** is a drop-in **semantic search MCP service fully compatible with Claude Code**, unlocking intelligent retrieval without changing your workflow. 🔥 Check out [the easy setup →](packages/leann-mcp/README.md)
@@ -781,7 +777,7 @@ Once your iMessage conversations are indexed, you can search with queries like:
 
 ### MCP Integration: RAG on Live Data from Any Platform
 
-Connect to live data sources through the Model Context Protocol (MCP). LEANN now supports real-time RAG on platforms like Slack, Twitter, and more through standardized MCP servers.
+**NEW!** Connect to live data sources through the Model Context Protocol (MCP). LEANN now supports real-time RAG on platforms like Slack, Twitter, and more through standardized MCP servers.
 
 **Key Benefits:**
 - **Live Data Access**: Fetch real-time data without manual exports
@@ -789,7 +785,8 @@ Connect to live data sources through the Model Context Protocol (MCP). LEANN now
 - **Easy Extension**: Add new platforms with minimal code
 - **Secure Access**: MCP servers handle authentication
 
-#### 💬 Slack Messages: Search Your Team Conversations
+<details>
+<summary><strong>Slack Messages: Search Your Team Conversations</strong></summary>
 
 Transform your Slack workspace into a searchable knowledge base! Find discussions, decisions, and shared knowledge across all your channels.
 
@@ -805,17 +802,18 @@ python -m apps.slack_rag \
   --query "What did we decide about the product launch?"
 ```
 
-**📖 Comprehensive Setup Guide**: For detailed setup instructions, troubleshooting common issues (like "users cache is not ready yet"), and advanced configuration options, see our [**Slack Setup Guide**](docs/slack-setup-guide.md).
-
-**Quick Setup:**
+**Setup Requirements:**
 1. Install a Slack MCP server (e.g., `npm install -g slack-mcp-server`)
-2. Create a Slack App and get API credentials (see detailed guide above)
-3. Set environment variables:
+2. Create a Slack App and get API credentials:
+   - Go to [api.slack.com/apps](https://api.slack.com/apps) and create a new app
+   - Under "OAuth & Permissions", add these Bot Token Scopes: `channels:read`, `channels:history`, `groups:read`, `groups:history`, `im:read`, `im:history`, `mpim:read`, `mpim:history`
+   - Install the app to your workspace and copy the "Bot User OAuth Token" (starts with `xoxb-`)
+   - Under "App-Level Tokens", create a token with `connections:write` scope (starts with `xapp-`)
    ```bash
    export SLACK_BOT_TOKEN="xoxb-your-bot-token"
-   export SLACK_APP_TOKEN="xapp-your-app-token"  # Optional
+   export SLACK_APP_TOKEN="xapp-your-app-token"
    ```
-4. Test connection with `--test-connection` flag
+3. Test connection with `--test-connection` flag
 
 **Arguments:**
 - `--mcp-server`: Command to start the Slack MCP server
@@ -823,10 +821,11 @@ python -m apps.slack_rag \
 - `--channels`: Specific channels to index (optional)
 - `--concatenate-conversations`: Group messages by channel (default: true)
 - `--max-messages-per-channel`: Limit messages per channel (default: 100)
-- `--max-retries`: Maximum retries for cache sync issues (default: 5)
-- `--retry-delay`: Initial delay between retries in seconds (default: 2.0)
 
-#### 🐦 Twitter Bookmarks: Your Personal Tweet Library
+</details>
+
+<details>
+<summary><strong>Twitter Bookmarks: Your Personal Tweet Library</strong></summary>
 
 Search through your Twitter bookmarks! Find that perfect article, thread, or insight you saved for later.
 
@@ -881,6 +880,8 @@ python -m apps.twitter_rag \
 - "What Python tutorials did I save?"
 
 </details>
+
+<details>
 <summary><strong>🔧 Using MCP with CLI Commands</strong></summary>
 
 **Want to use MCP data with regular LEANN CLI?** You can combine MCP apps with CLI commands:
@@ -926,7 +927,7 @@ Want to add support for other platforms? LEANN's MCP integration is designed for
 ### 🚀 Claude Code Integration: Transform Your Development Workflow!
 
 <details>
-<summary><strong>AST‑Aware Code Chunking</strong></summary>
+<summary><strong>NEW!! AST‑Aware Code Chunking</strong></summary>
 
 LEANN features intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript, improving code understanding compared to text-based chunking.
 

apps/base_rag_example.py

@@ -10,39 +10,8 @@ from typing import Any
 
 import dotenv
 from leann.api import LeannBuilder, LeannChat
-
-# Optional import: older PyPI builds may not include interactive_utils
-try:
-    from leann.interactive_utils import create_rag_session
-except ImportError:
-
-    def create_rag_session(app_name: str, data_description: str):
-        class _SimpleSession:
-            def run_interactive_loop(self, handler):
-                print(f"Interactive session for {app_name}: {data_description}")
-                print("Interactive mode not available in this build")
-
-        return _SimpleSession()
-
-
 from leann.registry import register_project_directory
-
-# Optional import: older PyPI builds may not include settings
-try:
-    from leann.settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
-except ImportError:
-    # Minimal fallbacks if settings helpers are unavailable
-    import os
-
-    def resolve_ollama_host(value: str | None) -> str | None:
-        return value or os.getenv("LEANN_OLLAMA_HOST") or os.getenv("OLLAMA_HOST")
-
-    def resolve_openai_api_key(value: str | None) -> str | None:
-        return value or os.getenv("OPENAI_API_KEY")
-
-    def resolve_openai_base_url(value: str | None) -> str | None:
-        return value or os.getenv("OPENAI_BASE_URL")
-
+from leann.settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
 
 dotenv.load_dotenv()
 
@@ -338,26 +307,37 @@ class BaseRAGExample(ABC):
             complexity=args.search_complexity,
         )
 
-        # Create interactive session
-        session = create_rag_session(
-            app_name=self.name.lower().replace(" ", "_"), data_description=self.name
-        )
+        print(f"\n[Interactive Mode] Chat with your {self.name} data!")
+        print("Type 'quit' or 'exit' to stop.\n")
 
-        def handle_query(query: str):
-            # Prepare LLM kwargs with thinking budget if specified
-            llm_kwargs = {}
-            if hasattr(args, "thinking_budget") and args.thinking_budget:
-                llm_kwargs["thinking_budget"] = args.thinking_budget
+        while True:
+            try:
+                query = input("You: ").strip()
+                if query.lower() in ["quit", "exit", "q"]:
+                    print("Goodbye!")
+                    break
 
-            response = chat.ask(
-                query,
-                top_k=args.top_k,
-                complexity=args.search_complexity,
-                llm_kwargs=llm_kwargs,
-            )
-            print(f"\nAssistant: {response}\n")
+                if not query:
+                    continue
 
-        session.run_interactive_loop(handle_query)
+                # Prepare LLM kwargs with thinking budget if specified
+                llm_kwargs = {}
+                if hasattr(args, "thinking_budget") and args.thinking_budget:
+                    llm_kwargs["thinking_budget"] = args.thinking_budget
+
+                response = chat.ask(
+                    query,
+                    top_k=args.top_k,
+                    complexity=args.search_complexity,
+                    llm_kwargs=llm_kwargs,
+                )
+                print(f"\nAssistant: {response}\n")
+
+            except KeyboardInterrupt:
+                print("\nGoodbye!")
+                break
+            except Exception as e:
+                print(f"Error: {e}")
 
     async def run_single_query(self, args, index_path: str, query: str):
         """Run a single query against the index."""

apps/semantic_file_search/leann-plus-temporal-search.py

@@ -1,183 +0,0 @@
-#!/usr/bin/env python3
-import re
-import sys
-from datetime import datetime, timedelta
-from pathlib import Path
-
-from leann import LeannSearcher
-
-INDEX_PATH = str(Path("./").resolve() / "demo.leann")
-
-
-class TimeParser:
-    def __init__(self):
-        # Main pattern: captures optional fuzzy modifier, number, unit, and optional "ago"
-        self.pattern = r"(?:(around|about|roughly|approximately)\s+)?(\d+)\s+(hour|day|week|month|year)s?(?:\s+ago)?"
-
-        # Compile for performance
-        self.regex = re.compile(self.pattern, re.IGNORECASE)
-
-        # Stop words to remove before regex parsing
-        self.stop_words = {
-            "in",
-            "at",
-            "of",
-            "by",
-            "as",
-            "me",
-            "the",
-            "a",
-            "an",
-            "and",
-            "any",
-            "find",
-            "search",
-            "list",
-            "ago",
-            "back",
-            "past",
-            "earlier",
-        }
-
-    def clean_text(self, text):
-        """Remove stop words from text"""
-        words = text.split()
-        cleaned = " ".join(word for word in words if word.lower() not in self.stop_words)
-        return cleaned
-
-    def parse(self, text):
-        """Extract all time expressions from text"""
-        # Clean text first
-        cleaned_text = self.clean_text(text)
-
-        matches = []
-        for match in self.regex.finditer(cleaned_text):
-            fuzzy = match.group(1)  # "around", "about", etc.
-            number = int(match.group(2))
-            unit = match.group(3).lower()
-
-            matches.append(
-                {
-                    "full_match": match.group(0),
-                    "fuzzy": bool(fuzzy),
-                    "number": number,
-                    "unit": unit,
-                    "range": self.calculate_range(number, unit, bool(fuzzy)),
-                }
-            )
-
-        return matches
-
-    def calculate_range(self, number, unit, is_fuzzy):
-        """Convert to actual datetime range and return ISO format strings"""
-        units = {
-            "hour": timedelta(hours=number),
-            "day": timedelta(days=number),
-            "week": timedelta(weeks=number),
-            "month": timedelta(days=number * 30),
-            "year": timedelta(days=number * 365),
-        }
-
-        delta = units[unit]
-        now = datetime.now()
-        target = now - delta
-
-        if is_fuzzy:
-            buffer = delta * 0.2  # 20% buffer for fuzzy
-            start = (target - buffer).isoformat()
-            end = (target + buffer).isoformat()
-        else:
-            start = target.isoformat()
-            end = now.isoformat()
-
-        return (start, end)
-
-
-def search_files(query, top_k=15):
-    """Search the index and return results"""
-    # Parse time expressions
-    parser = TimeParser()
-    time_matches = parser.parse(query)
-
-    # Remove time expressions from query for semantic search
-    clean_query = query
-    if time_matches:
-        for match in time_matches:
-            clean_query = clean_query.replace(match["full_match"], "").strip()
-
-    # Check if clean_query is less than 4 characters
-    if len(clean_query) < 4:
-        print("Error: add more input for accurate results.")
-        return
-
-    # Single query to vector DB
-    searcher = LeannSearcher(INDEX_PATH)
-    results = searcher.search(
-        clean_query if clean_query else query, top_k=top_k, recompute_embeddings=False
-    )
-
-    # Filter by time if time expression found
-    if time_matches:
-        time_range = time_matches[0]["range"]  # Use first time expression
-        start_time, end_time = time_range
-
-        filtered_results = []
-        for result in results:
-            # Access metadata attribute directly (not .get())
-            metadata = result.metadata if hasattr(result, "metadata") else {}
-
-            if metadata:
-                # Check modification date first, fall back to creation date
-                date_str = metadata.get("modification_date") or metadata.get("creation_date")
-
-                if date_str:
-                    # Convert strings to datetime objects for proper comparison
-                    try:
-                        file_date = datetime.fromisoformat(date_str)
-                        start_dt = datetime.fromisoformat(start_time)
-                        end_dt = datetime.fromisoformat(end_time)
-
-                        # Compare dates properly
-                        if start_dt <= file_date <= end_dt:
-                            filtered_results.append(result)
-                    except (ValueError, TypeError):
-                        # Handle invalid date formats
-                        print(f"Warning: Invalid date format in metadata: {date_str}")
-                        continue
-
-        results = filtered_results
-
-    # Print results
-    print(f"\nSearch results for: '{query}'")
-    if time_matches:
-        print(
-            f"Time filter: {time_matches[0]['number']} {time_matches[0]['unit']}(s) {'(fuzzy)' if time_matches[0]['fuzzy'] else ''}"
-        )
-        print(
-            f"Date range: {time_matches[0]['range'][0][:10]} to {time_matches[0]['range'][1][:10]}"
-        )
-    print("-" * 80)
-
-    for i, result in enumerate(results, 1):
-        print(f"\n[{i}] Score: {result.score:.4f}")
-        print(f"Content: {result.text}")
-
-        # Show metadata if present
-        metadata = result.metadata if hasattr(result, "metadata") else None
-        if metadata:
-            if "creation_date" in metadata:
-                print(f"Created: {metadata['creation_date']}")
-            if "modification_date" in metadata:
-                print(f"Modified: {metadata['modification_date']}")
-        print("-" * 80)
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print('Usage: python search_index.py "<search query>" [top_k]')
-        sys.exit(1)
-
-    query = sys.argv[1]
-    top_k = int(sys.argv[2]) if len(sys.argv) > 2 else 15
-
-    search_files(query, top_k)

apps/semantic_file_search/leann_index_builder.py

@@ -1,82 +0,0 @@
-#!/usr/bin/env python3
-import json
-import sys
-from pathlib import Path
-
-from leann import LeannBuilder
-
-
-def process_json_items(json_file_path):
-    """Load and process JSON file with metadata items"""
-
-    with open(json_file_path, encoding="utf-8") as f:
-        items = json.load(f)
-
-    # Guard against empty JSON
-    if not items:
-        print("⚠️  No items found in the JSON file. Exiting gracefully.")
-        return
-
-    INDEX_PATH = str(Path("./").resolve() / "demo.leann")
-    builder = LeannBuilder(backend_name="hnsw", is_recompute=False)
-
-    total_items = len(items)
-    items_added = 0
-    print(f"Processing {total_items} items...")
-
-    for idx, item in enumerate(items):
-        try:
-            # Create embedding text sentence
-            embedding_text = f"{item.get('Name', 'unknown')} located at {item.get('Path', 'unknown')} and size {item.get('Size', 'unknown')} bytes with content type {item.get('ContentType', 'unknown')} and kind {item.get('Kind', 'unknown')}"
-
-            # Prepare metadata with dates
-            metadata = {}
-            if "CreationDate" in item:
-                metadata["creation_date"] = item["CreationDate"]
-            if "ContentChangeDate" in item:
-                metadata["modification_date"] = item["ContentChangeDate"]
-
-            # Add to builder
-            builder.add_text(embedding_text, metadata=metadata)
-            items_added += 1
-
-        except Exception as e:
-            print(f"\n⚠️  Warning: Failed to process item {idx}: {e}")
-            continue
-
-        # Show progress
-        progress = (idx + 1) / total_items * 100
-        sys.stdout.write(f"\rProgress: {idx + 1}/{total_items} ({progress:.1f}%)")
-        sys.stdout.flush()
-
-    print()  # New line after progress
-
-    # Guard against no successfully added items
-    if items_added == 0:
-        print("⚠️  No items were successfully added to the index. Exiting gracefully.")
-        return
-
-    print(f"\n✅ Successfully processed {items_added}/{total_items} items")
-    print("Building index...")
-
-    try:
-        builder.build_index(INDEX_PATH)
-        print(f"✓ Index saved to {INDEX_PATH}")
-    except ValueError as e:
-        if "No chunks added" in str(e):
-            print("⚠️  No chunks were added to the builder. Index not created.")
-        else:
-            raise
-
-
-if __name__ == "__main__":
-    if len(sys.argv) != 2:
-        print("Usage: python build_index.py <json_file>")
-        sys.exit(1)
-
-    json_file = sys.argv[1]
-    if not Path(json_file).exists():
-        print(f"Error: File {json_file} not found")
-        sys.exit(1)
-
-    process_json_items(json_file)

apps/semantic_file_search/spotlight_index_dump.py

@@ -1,265 +0,0 @@
-#!/usr/bin/env python3
-"""
-Spotlight Metadata Dumper for Vector DB
-Extracts only essential metadata for semantic search embeddings
-Output is optimized for vector database storage with minimal fields
-"""
-
-import json
-import sys
-from datetime import datetime
-
-# Check platform before importing macOS-specific modules
-if sys.platform != "darwin":
-    print("This script requires macOS (uses Spotlight)")
-    sys.exit(1)
-
-from Foundation import NSDate, NSMetadataQuery, NSPredicate, NSRunLoop
-
-# EDIT THIS LIST: Add or remove folders to search
-# Can be either:
-# - Folder names relative to home directory (e.g., "Desktop", "Downloads")
-# - Absolute paths (e.g., "/Applications", "/System/Library")
-SEARCH_FOLDERS = [
-    "Desktop",
-    "Downloads",
-    "Documents",
-    "Music",
-    "Pictures",
-    "Movies",
-    # "Library",  # Uncomment to include
-    # "/Applications",  # Absolute path example
-    # "Code/Projects",  # Subfolder example
-    # Add any other folders here
-]
-
-
-def convert_to_serializable(obj):
-    """Convert NS objects to Python serializable types"""
-    if obj is None:
-        return None
-
-    # Handle NSDate
-    if hasattr(obj, "timeIntervalSince1970"):
-        return datetime.fromtimestamp(obj.timeIntervalSince1970()).isoformat()
-
-    # Handle NSArray
-    if hasattr(obj, "count") and hasattr(obj, "objectAtIndex_"):
-        return [convert_to_serializable(obj.objectAtIndex_(i)) for i in range(obj.count())]
-
-    # Convert to string
-    try:
-        return str(obj)
-    except Exception:
-        return repr(obj)
-
-
-def dump_spotlight_data(max_items=10, output_file="spotlight_dump.json"):
-    """
-    Dump Spotlight data using public.item predicate
-    """
-    # Build full paths from SEARCH_FOLDERS
-    import os
-
-    home_dir = os.path.expanduser("~")
-    search_paths = []
-
-    print("Search locations:")
-    for folder in SEARCH_FOLDERS:
-        # Check if it's an absolute path or relative
-        if folder.startswith("/"):
-            full_path = folder
-        else:
-            full_path = os.path.join(home_dir, folder)
-
-        if os.path.exists(full_path):
-            search_paths.append(full_path)
-            print(f"  ✓ {full_path}")
-        else:
-            print(f"  ✗ {full_path} (not found)")
-
-    if not search_paths:
-        print("No valid search paths found!")
-        return []
-
-    print(f"\nDumping {max_items} items from Spotlight (public.item)...")
-
-    # Create query with public.item predicate
-    query = NSMetadataQuery.alloc().init()
-    predicate = NSPredicate.predicateWithFormat_("kMDItemContentTypeTree CONTAINS 'public.item'")
-    query.setPredicate_(predicate)
-
-    # Set search scopes to our specific folders
-    query.setSearchScopes_(search_paths)
-
-    print("Starting query...")
-    query.startQuery()
-
-    # Wait for gathering to complete
-    run_loop = NSRunLoop.currentRunLoop()
-    print("Gathering results...")
-
-    # Let it gather for a few seconds
-    for i in range(50):  # 5 seconds max
-        run_loop.runMode_beforeDate_(
-            "NSDefaultRunLoopMode", NSDate.dateWithTimeIntervalSinceNow_(0.1)
-        )
-        # Check gathering status periodically
-        if i % 10 == 0:
-            current_count = query.resultCount()
-            if current_count > 0:
-                print(f"  Found {current_count} items so far...")
-
-    # Continue while still gathering (up to 2 more seconds)
-    timeout = NSDate.dateWithTimeIntervalSinceNow_(2.0)
-    while query.isGathering() and timeout.timeIntervalSinceNow() > 0:
-        run_loop.runMode_beforeDate_(
-            "NSDefaultRunLoopMode", NSDate.dateWithTimeIntervalSinceNow_(0.1)
-        )
-
-    query.stopQuery()
-
-    total_results = query.resultCount()
-    print(f"Found {total_results} total items")
-
-    if total_results == 0:
-        print("No results found")
-        return []
-
-    # Process items
-    items_to_process = min(total_results, max_items)
-    results = []
-
-    # ONLY relevant attributes for vector embeddings
-    # These provide essential context for semantic search without bloat
-    attributes = [
-        "kMDItemPath",  # Full path for file retrieval
-        "kMDItemFSName",  # Filename for display & embedding
-        "kMDItemFSSize",  # Size for filtering/ranking
-        "kMDItemContentType",  # File type for categorization
-        "kMDItemKind",  # Human-readable type for embedding
-        "kMDItemFSCreationDate",  # Temporal context
-        "kMDItemFSContentChangeDate",  # Recency for ranking
-    ]
-
-    print(f"Processing {items_to_process} items...")
-
-    for i in range(items_to_process):
-        try:
-            item = query.resultAtIndex_(i)
-            metadata = {}
-
-            # Extract ONLY the relevant attributes
-            for attr in attributes:
-                try:
-                    value = item.valueForAttribute_(attr)
-                    if value is not None:
-                        # Keep the attribute name clean (remove kMDItem prefix for cleaner JSON)
-                        clean_key = attr.replace("kMDItem", "").replace("FS", "")
-                        metadata[clean_key] = convert_to_serializable(value)
-                except (AttributeError, ValueError, TypeError):
-                    continue
-
-            # Only add if we have at least a path
-            if metadata.get("Path"):
-                results.append(metadata)
-
-        except Exception as e:
-            print(f"Error processing item {i}: {e}")
-            continue
-
-    # Save to JSON
-    with open(output_file, "w", encoding="utf-8") as f:
-        json.dump(results, f, indent=2, ensure_ascii=False)
-
-    print(f"\n✓ Saved {len(results)} items to {output_file}")
-
-    # Show summary
-    print("\nSample items:")
-    import os
-
-    home_dir = os.path.expanduser("~")
-
-    for i, item in enumerate(results[:3]):
-        print(f"\n[Item {i + 1}]")
-        print(f"  Path: {item.get('Path', 'N/A')}")
-        print(f"  Name: {item.get('Name', 'N/A')}")
-        print(f"  Type: {item.get('ContentType', 'N/A')}")
-        print(f"  Kind: {item.get('Kind', 'N/A')}")
-
-        # Handle size properly
-        size = item.get("Size")
-        if size:
-            try:
-                size_int = int(size)
-                if size_int > 1024 * 1024:
-                    print(f"  Size: {size_int / (1024 * 1024):.2f} MB")
-                elif size_int > 1024:
-                    print(f"  Size: {size_int / 1024:.2f} KB")
-                else:
-                    print(f"  Size: {size_int} bytes")
-            except (ValueError, TypeError):
-                print(f"  Size: {size}")
-
-        # Show dates
-        if "CreationDate" in item:
-            print(f"  Created: {item['CreationDate']}")
-        if "ContentChangeDate" in item:
-            print(f"  Modified: {item['ContentChangeDate']}")
-
-    # Count by type
-    type_counts = {}
-    for item in results:
-        content_type = item.get("ContentType", "unknown")
-        type_counts[content_type] = type_counts.get(content_type, 0) + 1
-
-    print(f"\nTotal items saved: {len(results)}")
-
-    if type_counts:
-        print("\nTop content types:")
-        for ct, count in sorted(type_counts.items(), key=lambda x: x[1], reverse=True)[:5]:
-            print(f"  {ct}: {count} items")
-
-    # Count by folder
-    folder_counts = {}
-    for item in results:
-        path = item.get("Path", "")
-        for folder in SEARCH_FOLDERS:
-            # Build the full folder path
-            if folder.startswith("/"):
-                folder_path = folder
-            else:
-                folder_path = os.path.join(home_dir, folder)
-
-            if path.startswith(folder_path):
-                folder_counts[folder] = folder_counts.get(folder, 0) + 1
-                break
-
-    if folder_counts:
-        print("\nItems by location:")
-        for folder, count in sorted(folder_counts.items(), key=lambda x: x[1], reverse=True):
-            print(f"  {folder}: {count} items")
-
-    return results
-
-
-def main():
-    # Parse arguments
-    if len(sys.argv) > 1:
-        try:
-            max_items = int(sys.argv[1])
-        except ValueError:
-            print("Usage: python spot.py [number_of_items]")
-            print("Default: 10 items")
-            sys.exit(1)
-    else:
-        max_items = 10
-
-    output_file = sys.argv[2] if len(sys.argv) > 2 else "spotlight_dump.json"
-
-    # Run dump
-    dump_spotlight_data(max_items=max_items, output_file=output_file)
-
-
-if __name__ == "__main__":
-    main()

apps/slack_data/slack_mcp_reader.py

@@ -29,8 +29,6 @@ class SlackMCPReader:
         workspace_name: Optional[str] = None,
         concatenate_conversations: bool = True,
         max_messages_per_conversation: int = 100,
-        max_retries: int = 5,
-        retry_delay: float = 2.0,
     ):
         """
         Initialize the Slack MCP Reader.
@@ -40,15 +38,11 @@ class SlackMCPReader:
             workspace_name: Optional workspace name to filter messages
             concatenate_conversations: Whether to group messages by channel/thread
             max_messages_per_conversation: Maximum messages to include per conversation
-            max_retries: Maximum number of retries for failed operations
-            retry_delay: Initial delay between retries in seconds
         """
         self.mcp_server_command = mcp_server_command
         self.workspace_name = workspace_name
         self.concatenate_conversations = concatenate_conversations
         self.max_messages_per_conversation = max_messages_per_conversation
-        self.max_retries = max_retries
-        self.retry_delay = retry_delay
         self.mcp_process = None
 
     async def start_mcp_server(self):
@@ -116,73 +110,11 @@ class SlackMCPReader:
 
         return response.get("result", {}).get("tools", [])
 
-    def _is_cache_sync_error(self, error: dict) -> bool:
-        """Check if the error is related to users cache not being ready."""
-        if isinstance(error, dict):
-            message = error.get("message", "").lower()
-            return (
-                "users cache is not ready" in message or "sync process is still running" in message
-            )
-        return False
-
-    async def _retry_with_backoff(self, func, *args, **kwargs):
-        """Retry a function with exponential backoff, especially for cache sync issues."""
-        last_exception = None
-
-        for attempt in range(self.max_retries + 1):
-            try:
-                return await func(*args, **kwargs)
-            except Exception as e:
-                last_exception = e
-
-                # Check if this is a cache sync error
-                error_dict = {}
-                if hasattr(e, "args") and e.args and isinstance(e.args[0], dict):
-                    error_dict = e.args[0]
-                elif "Failed to fetch messages" in str(e):
-                    # Try to extract error from the exception message
-                    import re
-
-                    match = re.search(r"'error':\s*(\{[^}]+\})", str(e))
-                    if match:
-                        try:
-                            error_dict = eval(match.group(1))
-                        except (ValueError, SyntaxError, NameError):
-                            pass
-                    else:
-                        # Try alternative format
-                        match = re.search(r"Failed to fetch messages:\s*(\{[^}]+\})", str(e))
-                        if match:
-                            try:
-                                error_dict = eval(match.group(1))
-                            except (ValueError, SyntaxError, NameError):
-                                pass
-
-                if self._is_cache_sync_error(error_dict):
-                    if attempt < self.max_retries:
-                        delay = self.retry_delay * (2**attempt)  # Exponential backoff
-                        logger.info(
-                            f"Cache sync not ready, waiting {delay:.1f}s before retry {attempt + 1}/{self.max_retries}"
-                        )
-                        await asyncio.sleep(delay)
-                        continue
-                    else:
-                        logger.warning(
-                            f"Cache sync still not ready after {self.max_retries} retries, giving up"
-                        )
-                        break
-                else:
-                    # Not a cache sync error, don't retry
-                    break
-
-        # If we get here, all retries failed or it's not a retryable error
-        raise last_exception
-
     async def fetch_slack_messages(
         self, channel: Optional[str] = None, limit: int = 100
     ) -> list[dict[str, Any]]:
         """
-        Fetch Slack messages using MCP tools with retry logic for cache sync issues.
+        Fetch Slack messages using MCP tools.
 
         Args:
             channel: Optional channel name to filter messages
@@ -191,59 +123,32 @@ class SlackMCPReader:
         Returns:
             List of message dictionaries
         """
-        return await self._retry_with_backoff(self._fetch_slack_messages_impl, channel, limit)
-
-    async def _fetch_slack_messages_impl(
-        self, channel: Optional[str] = None, limit: int = 100
-    ) -> list[dict[str, Any]]:
-        """
-        Internal implementation of fetch_slack_messages without retry logic.
-        """
         # This is a generic implementation - specific MCP servers may have different tool names
         # Common tool names might be: 'get_messages', 'list_messages', 'fetch_channel_history'
 
         tools = await self.list_available_tools()
-        logger.info(f"Available tools: {[tool.get('name') for tool in tools]}")
         message_tool = None
 
-        # Look for a tool that can fetch messages - prioritize conversations_history
-        message_tool = None
-
-        # First, try to find conversations_history specifically
+        # Look for a tool that can fetch messages
         for tool in tools:
             tool_name = tool.get("name", "").lower()
-            if "conversations_history" in tool_name:
+            if any(
+                keyword in tool_name
+                for keyword in ["message", "history", "channel", "conversation"]
+            ):
                 message_tool = tool
-                logger.info(f"Found conversations_history tool: {tool}")
                 break
 
-        # If not found, look for other message-fetching tools
-        if not message_tool:
-            for tool in tools:
-                tool_name = tool.get("name", "").lower()
-                if any(
-                    keyword in tool_name
-                    for keyword in ["conversations_search", "message", "history"]
-                ):
-                    message_tool = tool
-                    break
-
         if not message_tool:
             raise RuntimeError("No message fetching tool found in MCP server")
 
         # Prepare tool call parameters
-        tool_params = {"limit": "180d"}  # Use 180 days to get older messages
+        tool_params = {"limit": limit}
         if channel:
-            # For conversations_history, use channel_id parameter
-            if message_tool["name"] == "conversations_history":
-                tool_params["channel_id"] = channel
-            else:
-                # Try common parameter names for channel specification
-                for param_name in ["channel", "channel_id", "channel_name"]:
-                    tool_params[param_name] = channel
-                    break
-
-        logger.info(f"Tool parameters: {tool_params}")
+            # Try common parameter names for channel specification
+            for param_name in ["channel", "channel_id", "channel_name"]:
+                tool_params[param_name] = channel
+                break
 
         fetch_request = {
             "jsonrpc": "2.0",
@@ -265,8 +170,8 @@ class SlackMCPReader:
                 try:
                     messages = json.loads(content["text"])
                 except json.JSONDecodeError:
-                    # If not JSON, try to parse as CSV format (Slack MCP server format)
-                    messages = self._parse_csv_messages(content["text"], channel)
+                    # If not JSON, treat as plain text
+                    messages = [{"text": content["text"], "channel": channel or "unknown"}]
             else:
                 messages = result["content"]
         else:
@@ -275,56 +180,6 @@ class SlackMCPReader:
 
         return messages if isinstance(messages, list) else [messages]
 
-    def _parse_csv_messages(self, csv_text: str, channel: str) -> list[dict[str, Any]]:
-        """Parse CSV format messages from Slack MCP server."""
-        import csv
-        import io
-
-        messages = []
-        try:
-            # Split by lines and process each line as a CSV row
-            lines = csv_text.strip().split("\n")
-            if not lines:
-                return messages
-
-            # Skip header line if it exists
-            start_idx = 0
-            if lines[0].startswith("MsgID,UserID,UserName"):
-                start_idx = 1
-
-            for line in lines[start_idx:]:
-                if not line.strip():
-                    continue
-
-                # Parse CSV line
-                reader = csv.reader(io.StringIO(line))
-                try:
-                    row = next(reader)
-                    if len(row) >= 7:  # Ensure we have enough columns
-                        message = {
-                            "ts": row[0],
-                            "user": row[1],
-                            "username": row[2],
-                            "real_name": row[3],
-                            "channel": row[4],
-                            "thread_ts": row[5],
-                            "text": row[6],
-                            "time": row[7] if len(row) > 7 else "",
-                            "reactions": row[8] if len(row) > 8 else "",
-                            "cursor": row[9] if len(row) > 9 else "",
-                        }
-                        messages.append(message)
-                except Exception as e:
-                    logger.warning(f"Failed to parse CSV line: {line[:100]}... Error: {e}")
-                    continue
-
-        except Exception as e:
-            logger.warning(f"Failed to parse CSV messages: {e}")
-            # Fallback: treat entire text as one message
-            messages = [{"text": csv_text, "channel": channel or "unknown"}]
-
-        return messages
-
     def _format_message(self, message: dict[str, Any]) -> str:
         """Format a single message for indexing."""
         text = message.get("text", "")
@@ -396,40 +251,6 @@ class SlackMCPReader:
 
         return "\n".join(content_parts)
 
-    async def get_all_channels(self) -> list[str]:
-        """Get list of all available channels."""
-        try:
-            channels_list_request = {
-                "jsonrpc": "2.0",
-                "id": 4,
-                "method": "tools/call",
-                "params": {"name": "channels_list", "arguments": {}},
-            }
-            channels_response = await self.send_mcp_request(channels_list_request)
-            if "result" in channels_response:
-                result = channels_response["result"]
-                if "content" in result and isinstance(result["content"], list):
-                    content = result["content"][0] if result["content"] else {}
-                    if "text" in content:
-                        # Parse the channels from the response
-                        channels = []
-                        lines = content["text"].split("\n")
-                        for line in lines:
-                            if line.strip() and ("#" in line or "C" in line[:10]):
-                                # Extract channel ID or name
-                                parts = line.split()
-                                for part in parts:
-                                    if part.startswith("C") and len(part) > 5:
-                                        channels.append(part)
-                                    elif part.startswith("#"):
-                                        channels.append(part[1:])  # Remove #
-                        logger.info(f"Found {len(channels)} channels: {channels}")
-                        return channels
-            return []
-        except Exception as e:
-            logger.warning(f"Failed to get channels list: {e}")
-            return []
-
     async def read_slack_data(self, channels: Optional[list[str]] = None) -> list[str]:
         """
         Read Slack data and return formatted text chunks.
@@ -466,33 +287,36 @@ class SlackMCPReader:
                         logger.warning(f"Failed to fetch messages from channel {channel}: {e}")
                         continue
             else:
-                # Fetch from all available channels
-                logger.info("Fetching from all available channels...")
-                all_channels = await self.get_all_channels()
+                # Fetch from all available channels/conversations
+                # This is a simplified approach - real implementation would need to
+                # discover available channels first
+                try:
+                    messages = await self.fetch_slack_messages(limit=1000)
+                    if messages:
+                        # Group messages by channel if concatenating
+                        if self.concatenate_conversations:
+                            channel_messages = {}
+                            for message in messages:
+                                channel = message.get(
+                                    "channel", message.get("channel_name", "general")
+                                )
+                                if channel not in channel_messages:
+                                    channel_messages[channel] = []
+                                channel_messages[channel].append(message)
 
-                if not all_channels:
-                    # Fallback to common channel names if we can't get the list
-                    all_channels = ["general", "random", "announcements", "C0GN5BX0F"]
-                    logger.info(f"Using fallback channels: {all_channels}")
-
-                for channel in all_channels:
-                    try:
-                        logger.info(f"Searching channel: {channel}")
-                        messages = await self.fetch_slack_messages(channel=channel, limit=1000)
-                        if messages:
-                            if self.concatenate_conversations:
-                                text_content = self._create_concatenated_content(messages, channel)
+                            # Create concatenated content for each channel
+                            for channel, msgs in channel_messages.items():
+                                text_content = self._create_concatenated_content(msgs, channel)
                                 if text_content.strip():
                                     all_texts.append(text_content)
-                            else:
-                                # Process individual messages
-                                for message in messages:
-                                    formatted_msg = self._format_message(message)
-                                    if formatted_msg.strip():
-                                        all_texts.append(formatted_msg)
-                    except Exception as e:
-                        logger.warning(f"Failed to fetch messages from channel {channel}: {e}")
-                        continue
+                        else:
+                            # Process individual messages
+                            for message in messages:
+                                formatted_msg = self._format_message(message)
+                                if formatted_msg.strip():
+                                    all_texts.append(formatted_msg)
+                except Exception as e:
+                    logger.error(f"Failed to fetch messages: {e}")
 
             return all_texts
 

apps/slack_rag.py

@@ -78,20 +78,6 @@ class SlackMCPRAG(BaseRAGExample):
             help="Test MCP server connection and list available tools without indexing",
         )
 
-        parser.add_argument(
-            "--max-retries",
-            type=int,
-            default=5,
-            help="Maximum number of retries for failed operations (default: 5)",
-        )
-
-        parser.add_argument(
-            "--retry-delay",
-            type=float,
-            default=2.0,
-            help="Initial delay between retries in seconds (default: 2.0)",
-        )
-
     async def test_mcp_connection(self, args) -> bool:
         """Test the MCP server connection and display available tools."""
         print(f"Testing connection to MCP server: {args.mcp_server}")
@@ -102,14 +88,12 @@ class SlackMCPRAG(BaseRAGExample):
                 workspace_name=args.workspace_name,
                 concatenate_conversations=not args.no_concatenate_conversations,
                 max_messages_per_conversation=args.max_messages_per_channel,
-                max_retries=args.max_retries,
-                retry_delay=args.retry_delay,
             )
 
             async with reader:
                 tools = await reader.list_available_tools()
 
-                print("Successfully connected to MCP server!")
+                print("\n✅ Successfully connected to MCP server!")
                 print(f"Available tools ({len(tools)}):")
 
                 for i, tool in enumerate(tools, 1):
@@ -131,7 +115,7 @@ class SlackMCPRAG(BaseRAGExample):
                 return True
 
         except Exception as e:
-            print(f"Failed to connect to MCP server: {e}")
+            print(f"\n❌ Failed to connect to MCP server: {e}")
             print("\nTroubleshooting tips:")
             print("1. Make sure the MCP server is installed and accessible")
             print("2. Check if the server command is correct")
@@ -146,11 +130,8 @@ class SlackMCPRAG(BaseRAGExample):
         if args.workspace_name:
             print(f"Workspace: {args.workspace_name}")
 
-        # Filter out empty strings from channels
-        channels = [ch for ch in args.channels if ch.strip()] if args.channels else None
-
-        if channels:
-            print(f"Channels: {', '.join(channels)}")
+        if args.channels:
+            print(f"Channels: {', '.join(args.channels)}")
         else:
             print("Fetching from all available channels")
 
@@ -165,20 +146,18 @@ class SlackMCPRAG(BaseRAGExample):
                 workspace_name=args.workspace_name,
                 concatenate_conversations=concatenate,
                 max_messages_per_conversation=args.max_messages_per_channel,
-                max_retries=args.max_retries,
-                retry_delay=args.retry_delay,
             )
 
-            texts = await reader.read_slack_data(channels=channels)
+            texts = await reader.read_slack_data(channels=args.channels)
 
             if not texts:
-                print("No messages found! This could mean:")
+                print("❌ No messages found! This could mean:")
                 print("- The MCP server couldn't fetch messages")
                 print("- The specified channels don't exist or are empty")
                 print("- Authentication issues with the Slack workspace")
                 return []
 
-            print(f"Successfully loaded {len(texts)} text chunks from Slack")
+            print(f"✅ Successfully loaded {len(texts)} text chunks from Slack")
 
             # Show sample of what was loaded
             if texts:
@@ -191,7 +170,7 @@ class SlackMCPRAG(BaseRAGExample):
             return texts
 
         except Exception as e:
-            print(f"Error loading Slack data: {e}")
+            print(f"❌ Error loading Slack data: {e}")
             print("\nThis might be due to:")
             print("- MCP server connection issues")
             print("- Authentication problems")
@@ -209,7 +188,7 @@ class SlackMCPRAG(BaseRAGExample):
             if not success:
                 return
             print(
-                "MCP server is working! You can now run without --test-connection to start indexing."
+                "\n🎉 MCP server is working! You can now run without --test-connection to start indexing."
             )
             return
 

benchmarks/llm_utils.py

@@ -54,51 +54,29 @@ def extract_thinking_answer(response):
     return response.strip()
 
 
-def load_hf_model(model_name="Qwen/Qwen3-8B", trust_remote_code=False):
-    """Load HuggingFace model
-
-    Args:
-        model_name (str): Name of the model to load
-        trust_remote_code (bool): Whether to allow execution of code from the model repository.
-            Defaults to False for security. Only enable for trusted models.
-    """
+def load_hf_model(model_name="Qwen/Qwen3-8B"):
+    """Load HuggingFace model"""
     if not HF_AVAILABLE:
         raise ImportError("transformers not available")
 
-    if trust_remote_code:
-        print(
-            "⚠️  WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
-        )
-
     print(f"Loading HF: {model_name}")
-    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
     model = AutoModelForCausalLM.from_pretrained(
         model_name,
         torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
         device_map="auto",
-        trust_remote_code=trust_remote_code,
+        trust_remote_code=True,
     )
     return tokenizer, model
 
 
-def load_vllm_model(model_name="Qwen/Qwen3-8B", trust_remote_code=False):
-    """Load vLLM model
-
-    Args:
-        model_name (str): Name of the model to load
-        trust_remote_code (bool): Whether to allow execution of code from the model repository.
-            Defaults to False for security. Only enable for trusted models.
-    """
+def load_vllm_model(model_name="Qwen/Qwen3-8B"):
+    """Load vLLM model"""
     if not VLLM_AVAILABLE:
         raise ImportError("vllm not available")
 
-    if trust_remote_code:
-        print(
-            "⚠️  WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
-        )
-
     print(f"Loading vLLM: {model_name}")
-    llm = LLM(model=model_name, trust_remote_code=trust_remote_code)
+    llm = LLM(model=model_name, trust_remote_code=True)
 
     # Qwen3 specific config
     if is_qwen3_model(model_name):
@@ -200,33 +178,19 @@ def evaluate_rag(searcher, llm_func, queries, domain="default", top_k=3, complex
     }
 
 
-def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=False):
-    """Load Qwen2.5-VL multimodal model
-
-    Args:
-        model_name (str): Name of the model to load
-        trust_remote_code (bool): Whether to allow execution of code from the model repository.
-            Defaults to False for security. Only enable for trusted models.
-    """
+def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct"):
+    """Load Qwen2.5-VL multimodal model"""
     if not HF_AVAILABLE:
         raise ImportError("transformers not available")
 
-    if trust_remote_code:
-        print(
-            "⚠️  WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
-        )
-
     print(f"Loading Qwen2.5-VL: {model_name}")
 
     try:
         from transformers import AutoModelForVision2Seq, AutoProcessor
 
-        processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=trust_remote_code)
+        processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
         model = AutoModelForVision2Seq.from_pretrained(
-            model_name,
-            torch_dtype=torch.bfloat16,
-            device_map="auto",
-            trust_remote_code=trust_remote_code,
+            model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
         )
 
         return processor, model
@@ -238,14 +202,9 @@ def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_co
         try:
             from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
 
-            processor = AutoProcessor.from_pretrained(
-                model_name, trust_remote_code=trust_remote_code
-            )
+            processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
             model = Qwen2VLForConditionalGeneration.from_pretrained(
-                model_name,
-                torch_dtype=torch.bfloat16,
-                device_map="auto",
-                trust_remote_code=trust_remote_code,
+                model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
             )
 
             return processor, model

docs/configuration-guide.md

@@ -455,5 +455,5 @@ Conclusion:
 
 - [Lessons Learned Developing LEANN](https://yichuan-w.github.io/blog/lessons_learned_in_dev_leann/)
 - [LEANN Technical Paper](https://arxiv.org/abs/2506.08276)
-- [DiskANN Original Paper](https://suhasjs.github.io/files/diskann_neurips19.pdf)
+- [DiskANN Original Paper](https://papers.nips.cc/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf)
 - [SSD-based Graph Partitioning](https://github.com/SonglinLife/SSD_BASED_PLAN)

docs/slack-setup-guide.md

@@ -1,395 +0,0 @@
-# Slack Integration Setup Guide
-
-This guide provides step-by-step instructions for setting up Slack integration with LEANN.
-
-## Overview
-
-LEANN's Slack integration uses MCP (Model Context Protocol) servers to fetch and index your Slack messages for RAG (Retrieval-Augmented Generation). This allows you to search through your Slack conversations using natural language queries.
-
-## Prerequisites
-
-1. **Slack Workspace Access**: You need admin or owner permissions in your Slack workspace to create apps and configure OAuth tokens.
-
-2. **Slack MCP Server**: Install a Slack MCP server (e.g., `slack-mcp-server` via npm)
-
-3. **LEANN**: Ensure you have LEANN installed and working
-
-## Step 1: Create a Slack App
-
-### 1.1 Go to Slack API Dashboard
-
-1. Visit [https://api.slack.com/apps](https://api.slack.com/apps)
-2. Click **"Create New App"**
-3. Choose **"From scratch"**
-4. Enter your app name (e.g., "LEANN Slack Integration")
-5. Select your workspace
-6. Click **"Create App"**
-
-### 1.2 Configure App Permissions
-
-#### Token Scopes
-
-1. In your app dashboard, go to **"OAuth & Permissions"** in the left sidebar
-2. Scroll down to **"Scopes"** section
-3. Under **"Bot Token Scopes & OAuth Scope"**, click **"Add an OAuth Scope"**
-4. Add the following scopes:
-   - `channels:read` - Read public channel information
-   - `channels:history` - Read messages in public channels
-   - `groups:read` - Read private channel information
-   - `groups:history` - Read messages in private channels
-   - `im:read` - Read direct message information
-   - `im:history` - Read direct messages
-   - `mpim:read` - Read group direct message information
-   - `mpim:history` - Read group direct messages
-   - `users:read` - Read user information
-   - `team:read` - Read workspace information
-
-#### App-Level Tokens (Optional)
-
-Some MCP servers may require app-level tokens:
-
-1. Go to **"Basic Information"** in the left sidebar
-2. Scroll down to **"App-Level Tokens"**
-3. Click **"Generate Token and Scopes"**
-4. Enter a name (e.g., "LEANN Integration")
-5. Add the `connections:write` scope
-6. Click **"Generate"**
-7. Copy the token (starts with `xapp-`)
-
-### 1.3 Install App to Workspace
-
-1. Go to **"OAuth & Permissions"** in the left sidebar
-2. Click **"Install to Workspace"**
-3. Review the permissions and click **"Allow"**
-4. Copy the **"Bot User OAuth Token"** (starts with `xoxb-`)
-5. Copy the **"User OAuth Token"** (starts with `xoxp-`)
-
-## Step 2: Install Slack MCP Server
-
-### Option A: Using npm (Recommended)
-
-```bash
-# Install globally
-npm install -g slack-mcp-server
-
-# Or install locally
-npm install slack-mcp-server
-```
-
-### Option B: Using npx (No installation required)
-
-```bash
-# Use directly without installation
-npx slack-mcp-server
-```
-
-## Step 3: Install and Configure Ollama (for Real LLM Responses)
-
-### 3.1 Install Ollama
-
-```bash
-# Install Ollama using Homebrew (macOS)
-brew install ollama
-
-# Or download from https://ollama.ai/
-```
-
-### 3.2 Start Ollama Service
-
-```bash
-# Start Ollama as a service
-brew services start ollama
-
-# Or start manually
-ollama serve
-```
-
-### 3.3 Pull a Model
-
-```bash
-# Pull a lightweight model for testing
-ollama pull llama3.2:1b
-
-# Verify the model is available
-ollama list
-```
-
-## Step 4: Configure Environment Variables
-
-Create a `.env` file or set environment variables:
-
-```bash
-# Required: User OAuth Token
-SLACK_OAUTH_TOKEN=xoxp-your-user-oauth-token-here
-
-# Optional: App-Level Token (if your MCP server requires it)
-SLACK_APP_TOKEN=xapp-your-app-token-here
-
-# Optional: Workspace-specific settings
-SLACK_WORKSPACE_ID=T1234567890  # Your workspace ID (optional)
-```
-
-## Step 5: Test the Setup
-
-### 5.1 Test MCP Server Connection
-
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --test-connection \
-  --workspace-name "Your Workspace Name"
-```
-
-This will test the connection and list available tools without indexing any data.
-
-### 5.2 Index a Specific Channel
-
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "Your Workspace Name" \
-  --channels general \
-  --query "What did we discuss about the project?"
-```
-
-### 5.3 Real RAG Query Examples
-
-This section demonstrates successful Slack RAG integration queries against the Sky Lab Computing workspace's "random" channel. The system successfully retrieves actual conversation messages and performs semantic search with high relevance scores, including finding specific research paper announcements and technical discussions.
-
-### Example 1: Advisor Models Query
-
-**Query:** "train black-box models to adopt to your personal data"
-
-This query demonstrates the system's ability to find specific research announcements about training black-box models for personal data adaptation.
-
-![Advisor Models Query - Command Setup](videos/slack_integration_1.1.png)
-
-![Advisor Models Query - Search Results](videos/slack_integration_1.2.png)
-
-![Advisor Models Query - LLM Response](videos/slack_integration_1.3.png)
-
-### Example 2: Barbarians at the Gate Query
-
-**Query:** "AI-driven research systems ADRS"
-
-This query demonstrates the system's ability to find specific research announcements about AI-driven research systems and algorithm discovery.
-
-![Barbarians Query - Command Setup](videos/slack_integration_2.1.png)
-
-![Barbarians Query - Search Results](videos/slack_integration_2.2.png)
-
-![Barbarians Query - LLM Response](videos/slack_integration_2.3.png)
-
-### Prerequisites
-
-- Bot is installed in the Sky Lab Computing workspace and invited to the target channel (run `/invite @YourBotName` in the channel if needed)
-- Bot token available and exported in the same terminal session
-
-### Commands
-
-1) Set the workspace token for this shell
-
-```bash
-export SLACK_MCP_XOXP_TOKEN="xoxp-***-redacted-***"
-```
-
-2) Run queries against the "random" channel by channel ID (C0GN5BX0F)
-
-**Advisor Models Query:**
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "Sky Lab Computing" \
-  --channels C0GN5BX0F \
-  --max-messages-per-channel 100000 \
-  --query "train black-box models to adopt to your personal data" \
-  --llm ollama \
-  --llm-model "llama3.2:1b" \
-  --llm-host "http://localhost:11434" \
-  --no-concatenate-conversations
-```
-
-**Barbarians at the Gate Query:**
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "Sky Lab Computing" \
-  --channels C0GN5BX0F \
-  --max-messages-per-channel 100000 \
-  --query "AI-driven research systems ADRS" \
-  --llm ollama \
-  --llm-model "llama3.2:1b" \
-  --llm-host "http://localhost:11434" \
-  --no-concatenate-conversations
-```
-
-These examples demonstrate the system's ability to find and retrieve specific research announcements and technical discussions from the conversation history, showcasing the power of semantic search in Slack data.
-
-3) Optional: Ask a broader question
-
-```bash
-python test_channel_by_id_or_name.py \
-  --channel-id C0GN5BX0F \
-  --workspace-name "Sky Lab Computing" \
-  --query "What is LEANN about?"
-```
-
-Notes:
-- If you see `not_in_channel`, invite the bot to the channel and re-run.
-- If you see `channel_not_found`, confirm the channel ID and workspace.
-- Deep search via server-side “search” tools may require additional Slack scopes; the example above performs client-side filtering over retrieved history.
-
-## Common Issues and Solutions
-
-### Issue 1: "users cache is not ready yet" Error
-
-**Problem**: You see this warning:
-```
-WARNING - Failed to fetch messages from channel random: Failed to fetch messages: {'code': -32603, 'message': 'users cache is not ready yet, sync process is still running... please wait'}
-```
-
-**Solution**: This is a common timing issue. The LEANN integration now includes automatic retry logic:
-
-1. **Wait and Retry**: The system will automatically retry with exponential backoff (2s, 4s, 8s, etc.)
-2. **Increase Retry Parameters**: If needed, you can customize retry behavior:
-   ```bash
-   python -m apps.slack_rag \
-     --mcp-server "slack-mcp-server" \
-     --max-retries 10 \
-     --retry-delay 3.0 \
-     --channels general \
-     --query "Your query here"
-   ```
-3. **Keep MCP Server Running**: Start the MCP server separately and keep it running:
-   ```bash
-   # Terminal 1: Start MCP server
-   slack-mcp-server
-
-   # Terminal 2: Run LEANN (it will connect to the running server)
-   python -m apps.slack_rag --mcp-server "slack-mcp-server" --channels general --query "test"
-   ```
-
-### Issue 2: "No message fetching tool found"
-
-**Problem**: The MCP server doesn't have the expected tools.
-
-**Solution**:
-1. Check if your MCP server is properly installed and configured
-2. Verify your Slack tokens are correct
-3. Try a different MCP server implementation
-4. Check the MCP server documentation for required configuration
-
-### Issue 3: Permission Denied Errors
-
-**Problem**: You get permission errors when trying to access channels.
-
-**Solutions**:
-1. **Check Bot Permissions**: Ensure your bot has been added to the channels you want to access
-2. **Verify Token Scopes**: Make sure you have all required scopes configured
-3. **Channel Access**: For private channels, the bot needs to be explicitly invited
-4. **Workspace Permissions**: Ensure your Slack app has the necessary workspace permissions
-
-### Issue 4: Empty Results
-
-**Problem**: No messages are returned even though the channel has messages.
-
-**Solutions**:
-1. **Check Channel Names**: Ensure channel names are correct (without the # symbol)
-2. **Verify Bot Access**: Make sure the bot can access the channels
-3. **Check Date Ranges**: Some MCP servers have limitations on message history
-4. **Increase Message Limits**: Try increasing the message limit:
-   ```bash
-   python -m apps.slack_rag \
-     --mcp-server "slack-mcp-server" \
-     --channels general \
-     --max-messages-per-channel 1000 \
-     --query "test"
-   ```
-
-## Advanced Configuration
-
-### Custom MCP Server Commands
-
-If you need to pass additional parameters to your MCP server:
-
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server --token-file /path/to/tokens.json" \
-  --workspace-name "Your Workspace" \
-  --channels general \
-  --query "Your query"
-```
-
-### Multiple Workspaces
-
-To work with multiple Slack workspaces, you can:
-
-1. Create separate apps for each workspace
-2. Use different environment variables
-3. Run separate instances with different configurations
-
-### Performance Optimization
-
-For better performance with large workspaces:
-
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "Your Workspace" \
-  --max-messages-per-channel 500 \
-  --no-concatenate-conversations \
-  --query "Your query"
-```
----
-
-## Troubleshooting Checklist
-
-- [ ] Slack app created with proper permissions
-- [ ] Bot token (xoxb-) copied correctly
-- [ ] App-level token (xapp-) created if needed
-- [ ] MCP server installed and accessible
-- [ ] Ollama installed and running (`brew services start ollama`)
-- [ ] Ollama model pulled (`ollama pull llama3.2:1b`)
-- [ ] Environment variables set correctly
-- [ ] Bot invited to relevant channels
-- [ ] Channel names specified without # symbol
-- [ ] Sufficient retry attempts configured
-- [ ] Network connectivity to Slack APIs
-
-## Getting Help
-
-If you continue to have issues:
-
-1. **Check Logs**: Look for detailed error messages in the console output
-2. **Test MCP Server**: Use `--test-connection` to verify the MCP server is working
-3. **Verify Tokens**: Double-check that your Slack tokens are valid and have the right scopes
-4. **Check Ollama**: Ensure Ollama is running (`ollama serve`) and the model is available (`ollama list`)
-5. **Community Support**: Reach out to the LEANN community for help
-
-## Example Commands
-
-### Basic Usage
-```bash
-# Test connection
-python -m apps.slack_rag --mcp-server "slack-mcp-server" --test-connection
-
-# Index specific channels
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "My Company" \
-  --channels general random \
-  --query "What did we decide about the project timeline?"
-```
-
-### Advanced Usage
-```bash
-# With custom retry settings
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "My Company" \
-  --channels general \
-  --max-retries 10 \
-  --retry-delay 5.0 \
-  --max-messages-per-channel 2000 \
-  --query "Show me all decisions made in the last month"
-```

packages/leann-backend-hnsw/CMakeLists.txt

@@ -29,25 +29,12 @@ if(APPLE)
     set(CMAKE_OSX_DEPLOYMENT_TARGET "11.0" CACHE STRING "Minimum macOS version")
 endif()
 
-# Find ZMQ using pkg-config with IMPORTED_TARGET for automatic target creation
+# Use system ZeroMQ instead of building from source
 find_package(PkgConfig REQUIRED)
-
-# On ARM64 macOS, ensure pkg-config finds ARM64 Homebrew packages first
-if(APPLE AND CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64|arm64")
-    set(ENV{PKG_CONFIG_PATH} "/opt/homebrew/lib/pkgconfig:/opt/homebrew/share/pkgconfig:$ENV{PKG_CONFIG_PATH}")
-endif()
-
-pkg_check_modules(ZMQ REQUIRED IMPORTED_TARGET libzmq)
-
-# This creates PkgConfig::ZMQ target automatically with correct properties
-if(TARGET PkgConfig::ZMQ)
-    message(STATUS "Found and configured ZMQ target: PkgConfig::ZMQ")
-else()
-    message(FATAL_ERROR "pkg_check_modules did not create IMPORTED target for ZMQ.")
-endif()
+pkg_check_modules(ZMQ REQUIRED libzmq)
 
 # Add cppzmq headers
-include_directories(SYSTEM third_party/cppzmq)
+include_directories(third_party/cppzmq)
 
 # Configure msgpack-c - disable boost dependency
 set(MSGPACK_USE_BOOST OFF CACHE BOOL "" FORCE)

packages/leann-core/pyproject.toml

@@ -18,16 +18,14 @@ dependencies = [
     "pyzmq>=23.0.0",
     "msgpack>=1.0.0",
     "torch>=2.0.0",
-    "sentence-transformers>=3.0.0",
+    "sentence-transformers>=2.2.0",
     "llama-index-core>=0.12.0",
     "llama-index-readers-file>=0.4.0",  # Essential for document reading
     "llama-index-embeddings-huggingface>=0.5.5",  # For embeddings
     "python-dotenv>=1.0.0",
     "openai>=1.0.0",
     "huggingface-hub>=0.20.0",
-    # Keep transformers below 4.46: 4.46.0 adds Python 3.10-only return type syntax and
-    # breaks Python 3.9 environments.
-    "transformers>=4.30.0,<4.46",
+    "transformers>=4.30.0",
     "requests>=2.25.0",
     "accelerate>=0.20.0",
     "PyPDF2>=3.0.0",
@@ -42,7 +40,7 @@ dependencies = [
 [project.optional-dependencies]
 colab = [
     "torch>=2.0.0,<3.0.0",  # Limit torch version to avoid conflicts
-    "transformers>=4.30.0,<4.46",  # 4.46.0 switches to PEP 604 typing (int | None), breaks Py3.9
+    "transformers>=4.30.0,<5.0.0",  # Limit transformers version
     "accelerate>=0.20.0,<1.0.0",  # Limit accelerate version
 ]
 

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

@@ -18,7 +18,6 @@ from typing import Any, Literal, Optional, Union
 import numpy as np
 from leann_backend_hnsw.convert_to_csr import prune_hnsw_embeddings_inplace
 
-from leann.interactive_utils import create_api_session
 from leann.interface import LeannBackendSearcherInterface
 
 from .chat import get_llm
@@ -1236,17 +1235,6 @@ class LeannChat:
             "Please provide the best answer you can based on this context and your knowledge."
         )
 
-        print("The context provided to the LLM is:")
-        print(f"{'Relevance':<10} | {'Chunk id':<10} | {'Content':<60} | {'Source':<80}")
-        print("-" * 150)
-        for r in results:
-            chunk_relevance = f"{r.score:.3f}"
-            chunk_id = r.id
-            chunk_content = r.text[:60]
-            chunk_source = r.metadata.get("source", "")[:80]
-            print(
-                f"{chunk_relevance:<10} | {chunk_id:<10} | {chunk_content:<60} | {chunk_source:<80}"
-            )
         ask_time = time.time()
         ans = self.llm.ask(prompt, **llm_kwargs)
         ask_time = time.time() - ask_time
@@ -1254,14 +1242,19 @@ class LeannChat:
         return ans
 
     def start_interactive(self):
-        """Start interactive chat session."""
-        session = create_api_session()
-
-        def handle_query(user_input: str):
-            response = self.ask(user_input)
-            print(f"Leann: {response}")
-
-        session.run_interactive_loop(handle_query)
+        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
 
     def cleanup(self):
         """Explicitly cleanup embedding server resources.

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

@@ -546,30 +546,11 @@ class OllamaChat(LLMInterface):
 
 
 class HFChat(LLMInterface):
-    """LLM interface for local Hugging Face Transformers models with proper chat templates.
+    """LLM interface for local Hugging Face Transformers models with proper chat templates."""
 
-    Args:
-        model_name (str): Name of the Hugging Face model to load.
-        trust_remote_code (bool): Whether to allow execution of code from the model repository.
-            Defaults to False for security. Only enable for trusted models as this can pose
-            a security risk if the model repository is compromised.
-    """
-
-    def __init__(
-        self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat", trust_remote_code: bool = False
-    ):
+    def __init__(self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat"):
         logger.info(f"Initializing HFChat with model='{model_name}'")
 
-        # Security warning when trust_remote_code is enabled
-        if trust_remote_code:
-            logger.warning(
-                "SECURITY WARNING: trust_remote_code=True allows execution of arbitrary code from the model repository. "
-                "Only enable this for models from trusted sources. This creates a potential security risk if the model "
-                "repository is compromised."
-            )
-
-        self.trust_remote_code = trust_remote_code
-
         # Pre-check model availability with helpful suggestions
         model_error = validate_model_and_suggest(model_name, "hf")
         if model_error:
@@ -607,16 +588,14 @@ class HFChat(LLMInterface):
 
             try:
                 logger.info(f"Loading tokenizer for {model_name}...")
-                self.tokenizer = AutoTokenizer.from_pretrained(
-                    model_name, trust_remote_code=self.trust_remote_code
-                )
+                self.tokenizer = AutoTokenizer.from_pretrained(model_name)
 
                 logger.info(f"Loading model {model_name}...")
                 self.model = AutoModelForCausalLM.from_pretrained(
                     model_name,
                     torch_dtype=torch.float16 if self.device != "cpu" else torch.float32,
                     device_map="auto" if self.device != "cpu" else None,
-                    trust_remote_code=self.trust_remote_code,
+                    trust_remote_code=True,
                 )
                 logger.info(f"Successfully loaded {model_name}")
             finally:
@@ -834,11 +813,6 @@ class OpenAIChat(LLMInterface):
 
         try:
             response = self.client.chat.completions.create(**params)
-            print(
-                f"Total tokens = {response.usage.total_tokens}, prompt tokens = {response.usage.prompt_tokens}, completion tokens = {response.usage.completion_tokens}"
-            )
-            if response.choices[0].finish_reason == "length":
-                print("The query is exceeding the maximum allowed number of tokens")
             return response.choices[0].message.content.strip()
         except Exception as e:
             logger.error(f"Error communicating with OpenAI: {e}")
@@ -885,10 +859,7 @@ def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
             host=llm_config.get("host"),
         )
     elif llm_type == "hf":
-        return HFChat(
-            model_name=model or "deepseek-ai/deepseek-llm-7b-chat",
-            trust_remote_code=llm_config.get("trust_remote_code", False),
-        )
+        return HFChat(model_name=model or "deepseek-ai/deepseek-llm-7b-chat")
     elif llm_type == "openai":
         return OpenAIChat(
             model=model or "gpt-4o",

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

@@ -1,6 +1,5 @@
 import argparse
 import asyncio
-import time
 from pathlib import Path
 from typing import Any, Optional, Union
 
@@ -9,7 +8,6 @@ from llama_index.core.node_parser import SentenceSplitter
 from tqdm import tqdm
 
 from .api import LeannBuilder, LeannChat, LeannSearcher
-from .interactive_utils import create_cli_session
 from .registry import register_project_directory
 from .settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
 
@@ -107,7 +105,7 @@ Examples:
             help="Documents directories and/or files (default: current directory)",
         )
         build_parser.add_argument(
-            "--backend-name",
+            "--backend",
             type=str,
             default="hnsw",
             choices=["hnsw", "diskann"],
@@ -255,11 +253,6 @@ Examples:
             action="store_true",
             help="Non-interactive mode: automatically select index without prompting",
         )
-        search_parser.add_argument(
-            "--show-metadata",
-            action="store_true",
-            help="Display file paths and metadata in search results",
-        )
 
         # Ask command
         ask_parser = subparsers.add_parser("ask", help="Ask questions")
@@ -1192,7 +1185,6 @@ Examples:
                 for doc in other_docs:
                     file_path = doc.metadata.get("file_path", "")
                     if file_filter(file_path):
-                        doc.metadata["source"] = file_path
                         filtered_docs.append(doc)
 
                 documents.extend(filtered_docs)
@@ -1268,7 +1260,7 @@ Examples:
                 from .chunking_utils import create_text_chunks
 
                 # Use enhanced chunking with AST support
-                chunk_texts = create_text_chunks(
+                all_texts = create_text_chunks(
                     documents,
                     chunk_size=self.node_parser.chunk_size,
                     chunk_overlap=self.node_parser.chunk_overlap,
@@ -1279,14 +1271,6 @@ Examples:
                     ast_fallback_traditional=getattr(args, "ast_fallback_traditional", True),
                 )
 
-                # Note: AST chunking currently returns plain text chunks without metadata
-                # We preserve basic file info by associating chunks with their source documents
-                # For better metadata preservation, documents list order should be maintained
-                for chunk_text in chunk_texts:
-                    # TODO: Enhance create_text_chunks to return metadata alongside text
-                    # For now, we store chunks with empty metadata
-                    all_texts.append({"text": chunk_text, "metadata": {}})
-
             except ImportError as e:
                 print(
                     f"⚠️  AST chunking utilities not available in package ({e}), falling back to traditional chunking"
@@ -1298,27 +1282,14 @@ Examples:
             for doc in tqdm(documents, desc="Chunking documents", unit="doc"):
                 # Check if this is a code file based on source path
                 source_path = doc.metadata.get("source", "")
-                file_path = doc.metadata.get("file_path", "")
                 is_code_file = any(source_path.endswith(ext) for ext in code_file_exts)
 
-                # Extract metadata to preserve with chunks
-                chunk_metadata = {
-                    "file_path": file_path or source_path,
-                    "file_name": doc.metadata.get("file_name", ""),
-                }
-
-                # Add optional metadata if available
-                if "creation_date" in doc.metadata:
-                    chunk_metadata["creation_date"] = doc.metadata["creation_date"]
-                if "last_modified_date" in doc.metadata:
-                    chunk_metadata["last_modified_date"] = doc.metadata["last_modified_date"]
-
                 # Use appropriate parser based on file type
                 parser = self.code_parser if is_code_file else self.node_parser
                 nodes = parser.get_nodes_from_documents([doc])
 
                 for node in nodes:
-                    all_texts.append({"text": node.get_content(), "metadata": chunk_metadata})
+                    all_texts.append(node.get_content())
 
         print(f"Loaded {len(documents)} documents, {len(all_texts)} chunks")
         return all_texts
@@ -1393,7 +1364,7 @@ Examples:
 
         index_dir.mkdir(parents=True, exist_ok=True)
 
-        print(f"Building index '{index_name}' with {args.backend_name} backend...")
+        print(f"Building index '{index_name}' with {args.backend} backend...")
 
         embedding_options: dict[str, Any] = {}
         if args.embedding_mode == "ollama":
@@ -1405,7 +1376,7 @@ Examples:
                 embedding_options["api_key"] = resolved_embedding_key
 
         builder = LeannBuilder(
-            backend_name=args.backend_name,
+            backend_name=args.backend,
             embedding_model=args.embedding_model,
             embedding_mode=args.embedding_mode,
             embedding_options=embedding_options or None,
@@ -1416,8 +1387,8 @@ Examples:
             num_threads=args.num_threads,
         )
 
-        for chunk in all_texts:
-            builder.add_text(chunk["text"], metadata=chunk["metadata"])
+        for chunk_text in all_texts:
+            builder.add_text(chunk_text)
 
         builder.build_index(index_path)
         print(f"Index built at {index_path}")
@@ -1538,25 +1509,7 @@ Examples:
         print(f"Search results for '{query}' (top {len(results)}):")
         for i, result in enumerate(results, 1):
             print(f"{i}. Score: {result.score:.3f}")
-
-            # Display metadata if flag is set
-            if args.show_metadata and result.metadata:
-                file_path = result.metadata.get("file_path", "")
-                if file_path:
-                    print(f"   📄 File: {file_path}")
-
-                file_name = result.metadata.get("file_name", "")
-                if file_name and file_name != file_path:
-                    print(f"   📝 Name: {file_name}")
-
-                # Show timestamps if available
-                if "creation_date" in result.metadata:
-                    print(f"   🕐 Created: {result.metadata['creation_date']}")
-                if "last_modified_date" in result.metadata:
-                    print(f"   🕑 Modified: {result.metadata['last_modified_date']}")
-
             print(f"   {result.text[:200]}...")
-            print(f"   Source: {result.metadata.get('source', '')}")
             print()
 
     async def ask_questions(self, args):
@@ -1588,7 +1541,6 @@ Examples:
             llm_kwargs["thinking_budget"] = args.thinking_budget
 
         def _ask_once(prompt: str) -> None:
-            query_start_time = time.time()
             response = chat.ask(
                 prompt,
                 top_k=args.top_k,
@@ -1599,20 +1551,27 @@ Examples:
                 pruning_strategy=args.pruning_strategy,
                 llm_kwargs=llm_kwargs,
             )
-            query_completion_time = time.time() - query_start_time
             print(f"LEANN: {response}")
-            print(f"The query took {query_completion_time:.3f} seconds to finish")
 
         initial_query = (args.query or "").strip()
 
         if args.interactive:
-            # Create interactive session
-            session = create_cli_session(index_name)
-
             if initial_query:
                 _ask_once(initial_query)
 
-            session.run_interactive_loop(_ask_once)
+            print("LEANN Assistant ready! Type 'quit' to exit")
+            print("=" * 40)
+
+            while True:
+                user_input = input("\nYou: ").strip()
+                if user_input.lower() in ["quit", "exit", "q"]:
+                    print("Goodbye!")
+                    break
+
+                if not user_input:
+                    continue
+
+                _ask_once(user_input)
         else:
             query = initial_query or input("Enter your question: ").strip()
             if not query:

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

@@ -574,10 +574,9 @@ def compute_embeddings_ollama(
     host: Optional[str] = None,
 ) -> np.ndarray:
     """
-    Compute embeddings using Ollama API with true batch processing.
+    Compute embeddings using Ollama API with simplified batch processing.
 
-    Uses the /api/embed endpoint which supports batch inputs.
-    Batch size: 32 for MPS/CPU, 128 for CUDA to optimize performance.
+    Uses batch size of 32 for MPS/CPU and 128 for CUDA to optimize performance.
 
     Args:
         texts: List of texts to compute embeddings for
@@ -682,11 +681,11 @@ def compute_embeddings_ollama(
             logger.info(f"Resolved model name '{model_name}' to '{resolved_model_name}'")
         model_name = resolved_model_name
 
-        # Verify the model supports embeddings by testing it with /api/embed
+        # Verify the model supports embeddings by testing it
         try:
             test_response = requests.post(
-                f"{resolved_host}/api/embed",
-                json={"model": model_name, "input": "test"},
+                f"{resolved_host}/api/embeddings",
+                json={"model": model_name, "prompt": "test"},
                 timeout=10,
             )
             if test_response.status_code != 200:
@@ -718,55 +717,56 @@ def compute_embeddings_ollama(
         # If torch is not available, use conservative batch size
         batch_size = 32
 
-    logger.info(f"Using batch size: {batch_size} for true batch processing")
+    logger.info(f"Using batch size: {batch_size}")
 
     def get_batch_embeddings(batch_texts):
-        """Get embeddings for a batch of texts using /api/embed endpoint."""
-        max_retries = 3
-        retry_count = 0
+        """Get embeddings for a batch of texts."""
+        all_embeddings = []
+        failed_indices = []
 
-        # Truncate very long texts to avoid API issues
-        truncated_texts = [text[:8000] if len(text) > 8000 else text for text in batch_texts]
+        for i, text in enumerate(batch_texts):
+            max_retries = 3
+            retry_count = 0
 
-        while retry_count < max_retries:
-            try:
-                # Use /api/embed endpoint with "input" parameter for batch processing
-                response = requests.post(
-                    f"{resolved_host}/api/embed",
-                    json={"model": model_name, "input": truncated_texts},
-                    timeout=60,  # Increased timeout for batch processing
-                )
-                response.raise_for_status()
-
-                result = response.json()
-                batch_embeddings = result.get("embeddings")
-
-                if batch_embeddings is None:
-                    raise ValueError("No embeddings returned from API")
-
-                if not isinstance(batch_embeddings, list):
-                    raise ValueError(f"Invalid embeddings format: {type(batch_embeddings)}")
-
-                if len(batch_embeddings) != len(batch_texts):
-                    raise ValueError(
-                        f"Mismatch: requested {len(batch_texts)} embeddings, got {len(batch_embeddings)}"
+            # Truncate very long texts to avoid API issues
+            truncated_text = text[:8000] if len(text) > 8000 else text
+            while retry_count < max_retries:
+                try:
+                    response = requests.post(
+                        f"{resolved_host}/api/embeddings",
+                        json={"model": model_name, "prompt": truncated_text},
+                        timeout=30,
                     )
+                    response.raise_for_status()
 
-                return batch_embeddings, []
+                    result = response.json()
+                    embedding = result.get("embedding")
 
-            except requests.exceptions.Timeout:
-                retry_count += 1
-                if retry_count >= max_retries:
-                    logger.warning(f"Timeout for batch after {max_retries} retries")
-                    return None, list(range(len(batch_texts)))
+                    if embedding is None:
+                        raise ValueError(f"No embedding returned for text {i}")
 
-            except Exception as e:
-                retry_count += 1
-                if retry_count >= max_retries:
-                    logger.error(f"Failed to get embeddings for batch: {e}")
-                    return None, list(range(len(batch_texts)))
+                    if not isinstance(embedding, list) or len(embedding) == 0:
+                        raise ValueError(f"Invalid embedding format for text {i}")
 
-        return None, list(range(len(batch_texts)))
+                    all_embeddings.append(embedding)
+                    break
+
+                except requests.exceptions.Timeout:
+                    retry_count += 1
+                    if retry_count >= max_retries:
+                        logger.warning(f"Timeout for text {i} after {max_retries} retries")
+                        failed_indices.append(i)
+                        all_embeddings.append(None)
+                        break
+
+                except Exception as e:
+                    retry_count += 1
+                    if retry_count >= max_retries:
+                        logger.error(f"Failed to get embedding for text {i}: {e}")
+                        failed_indices.append(i)
+                        all_embeddings.append(None)
+                        break
+        return all_embeddings, failed_indices
 
     # Process texts in batches
     all_embeddings = []
@@ -784,7 +784,7 @@ def compute_embeddings_ollama(
     num_batches = (len(texts) + batch_size - 1) // batch_size
 
     if show_progress:
-        batch_iterator = tqdm(range(num_batches), desc="Computing Ollama embeddings (batched)")
+        batch_iterator = tqdm(range(num_batches), desc="Computing Ollama embeddings")
     else:
         batch_iterator = range(num_batches)
 
@@ -795,14 +795,10 @@ def compute_embeddings_ollama(
 
         batch_embeddings, batch_failed = get_batch_embeddings(batch_texts)
 
-        if batch_embeddings is not None:
-            all_embeddings.extend(batch_embeddings)
-        else:
-            # Entire batch failed, add None placeholders
-            all_embeddings.extend([None] * len(batch_texts))
-            # Adjust failed indices to global indices
-            global_failed = [start_idx + idx for idx in batch_failed]
-            all_failed_indices.extend(global_failed)
+        # Adjust failed indices to global indices
+        global_failed = [start_idx + idx for idx in batch_failed]
+        all_failed_indices.extend(global_failed)
+        all_embeddings.extend(batch_embeddings)
 
     # Handle failed embeddings
     if all_failed_indices:

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

@@ -1,189 +0,0 @@
-"""
-Interactive session utilities for LEANN applications.
-
-Provides shared readline functionality and command handling across
-CLI, API, and RAG example interactive modes.
-"""
-
-import atexit
-import os
-from pathlib import Path
-from typing import Callable, Optional
-
-# Try to import readline with fallback for Windows
-try:
-    import readline
-
-    HAS_READLINE = True
-except ImportError:
-    # Windows doesn't have readline by default
-    HAS_READLINE = False
-    readline = None
-
-
-class InteractiveSession:
-    """Manages interactive session with optional readline support and common commands."""
-
-    def __init__(
-        self,
-        history_name: str,
-        prompt: str = "You: ",
-        welcome_message: str = "",
-    ):
-        """
-        Initialize interactive session with optional readline support.
-
-        Args:
-            history_name: Name for history file (e.g., "cli", "api_chat")
-                         (ignored if readline not available)
-            prompt: Input prompt to display
-            welcome_message: Message to show when starting session
-
-        Note:
-            On systems without readline (e.g., Windows), falls back to basic input()
-            with limited functionality (no history, no line editing).
-        """
-        self.history_name = history_name
-        self.prompt = prompt
-        self.welcome_message = welcome_message
-        self._setup_complete = False
-
-    def setup_readline(self):
-        """Setup readline with history support (if available)."""
-        if self._setup_complete:
-            return
-
-        if not HAS_READLINE:
-            # Readline not available (likely Windows), skip setup
-            self._setup_complete = True
-            return
-
-        # History file setup
-        history_dir = Path.home() / ".leann" / "history"
-        history_dir.mkdir(parents=True, exist_ok=True)
-        history_file = history_dir / f"{self.history_name}.history"
-
-        # Load history if exists
-        try:
-            readline.read_history_file(str(history_file))
-            readline.set_history_length(1000)
-        except FileNotFoundError:
-            pass
-
-        # Save history on exit
-        atexit.register(readline.write_history_file, str(history_file))
-
-        # Optional: Enable vi editing mode (commented out by default)
-        # readline.parse_and_bind("set editing-mode vi")
-
-        self._setup_complete = True
-
-    def _show_help(self):
-        """Show available commands."""
-        print("Commands:")
-        print("  quit/exit/q - Exit the chat")
-        print("  help - Show this help message")
-        print("  clear - Clear screen")
-        print("  history - Show command history")
-
-    def _show_history(self):
-        """Show command history."""
-        if not HAS_READLINE:
-            print("  History not available (readline not supported on this system)")
-            return
-
-        history_length = readline.get_current_history_length()
-        if history_length == 0:
-            print("  No history available")
-            return
-
-        for i in range(history_length):
-            item = readline.get_history_item(i + 1)
-            if item:
-                print(f"  {i + 1}: {item}")
-
-    def get_user_input(self) -> Optional[str]:
-        """
-        Get user input with readline support.
-
-        Returns:
-            User input string, or None if EOF (Ctrl+D)
-        """
-        try:
-            return input(self.prompt).strip()
-        except KeyboardInterrupt:
-            print("\n(Use 'quit' to exit)")
-            return ""  # Return empty string to continue
-        except EOFError:
-            print("\nGoodbye!")
-            return None
-
-    def run_interactive_loop(self, handler_func: Callable[[str], None]):
-        """
-        Run the interactive loop with a custom handler function.
-
-        Args:
-            handler_func: Function to handle user input that's not a built-in command
-                         Should accept a string and handle the user's query
-        """
-        self.setup_readline()
-
-        if self.welcome_message:
-            print(self.welcome_message)
-
-        while True:
-            user_input = self.get_user_input()
-
-            if user_input is None:  # EOF (Ctrl+D)
-                break
-
-            if not user_input:  # Empty input or KeyboardInterrupt
-                continue
-
-            # Handle built-in commands
-            command = user_input.lower()
-            if command in ["quit", "exit", "q"]:
-                print("Goodbye!")
-                break
-            elif command == "help":
-                self._show_help()
-            elif command == "clear":
-                os.system("clear" if os.name != "nt" else "cls")
-            elif command == "history":
-                self._show_history()
-            else:
-                # Regular user input - pass to handler
-                try:
-                    handler_func(user_input)
-                except Exception as e:
-                    print(f"Error: {e}")
-
-
-def create_cli_session(index_name: str) -> InteractiveSession:
-    """Create an interactive session for CLI usage."""
-    return InteractiveSession(
-        history_name=index_name,
-        prompt="\nYou: ",
-        welcome_message="LEANN Assistant ready! Type 'quit' to exit, 'help' for commands\n"
-        + "=" * 40,
-    )
-
-
-def create_api_session() -> InteractiveSession:
-    """Create an interactive session for API chat."""
-    return InteractiveSession(
-        history_name="api_chat",
-        prompt="You: ",
-        welcome_message="Leann Chat started (type 'quit' to exit, 'help' for commands)\n"
-        + "=" * 40,
-    )
-
-
-def create_rag_session(app_name: str, data_description: str) -> InteractiveSession:
-    """Create an interactive session for RAG examples."""
-    return InteractiveSession(
-        history_name=f"{app_name}_rag",
-        prompt="You: ",
-        welcome_message=f"[Interactive Mode] Chat with your {data_description} data!\nType 'quit' or 'exit' to stop, 'help' for commands.\n"
-        + "=" * 40,
-    )

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

@@ -60,11 +60,6 @@ def handle_request(request):
                                     "maximum": 128,
                                     "description": "Search complexity level. Use 16-32 for fast searches (recommended), 64+ for higher precision when needed.",
                                 },
-                                "show_metadata": {
-                                    "type": "boolean",
-                                    "default": False,
-                                    "description": "Include file paths and metadata in search results. Useful for understanding which files contain the results.",
-                                },
                             },
                             "required": ["index_name", "query"],
                         },
@@ -109,8 +104,6 @@ def handle_request(request):
                     f"--complexity={args.get('complexity', 32)}",
                     "--non-interactive",
                 ]
-                if args.get("show_metadata", False):
-                    cmd.append("--show-metadata")
                 result = subprocess.run(cmd, capture_output=True, text=True)
 
             elif tool_name == "leann_list":

pyproject.toml

@@ -22,10 +22,7 @@ dependencies = [
     "sglang",
     "ollama",
     "requests>=2.25.0",
-    "sentence-transformers>=3.0.0",
-    # Pin transformers below 4.46: 4.46.0 introduced Python 3.10-only typing (PEP 604) and
-    # breaks our Python 3.9 test matrix when pulled in by sentence-transformers.
-    "transformers<4.46",
+    "sentence-transformers>=2.2.0",
     "openai>=1.0.0",
     # PDF parsing dependencies - essential for document processing
     "PyPDF2>=3.0.0",

uv.lock

@@ -14,7 +14,9 @@ version = "1.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging" },
     { name = "psutil" },
     { name = "pyyaml" },
@@ -201,7 +203,9 @@ name = "astchunk"
 version = "0.1.0"
 source = { editable = "packages/astchunk-leann" }
 dependencies = [
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pyrsistent" },
     { name = "tree-sitter", version = "0.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "tree-sitter", version = "0.25.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
@@ -585,7 +589,7 @@ resolution-markers = [
     "python_full_version < '3.10'",
 ]
 dependencies = [
-    { name = "numpy", marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/f5/f6/31a8f28b4a2a4fa0e01085e542f3081ab0588eff8e589d39d775172c9792/contourpy-1.3.0.tar.gz", hash = "sha256:7ffa0db17717a8ffb127efd0c95a4362d996b892c2904db72428d5b52e1938a4", size = 13464370 }
 wheels = [
@@ -663,7 +667,7 @@ resolution-markers = [
     "python_full_version == '3.10.*'",
 ]
 dependencies = [
-    { name = "numpy", marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130 }
 wheels = [
@@ -734,7 +738,7 @@ resolution-markers = [
     "python_full_version == '3.11.*'",
 ]
 dependencies = [
-    { name = "numpy", marker = "python_full_version >= '3.11'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174 }
 wheels = [
@@ -1024,7 +1028,9 @@ dependencies = [
     { name = "fsspec", extra = ["http"] },
     { name = "huggingface-hub" },
     { name = "multiprocess" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging" },
     { name = "pandas" },
     { name = "pyarrow" },
@@ -1165,7 +1171,9 @@ dependencies = [
     { name = "fsspec", extra = ["http"] },
     { name = "huggingface-hub" },
     { name = "multiprocess" },
-    { name = "numpy" },
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+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging" },
     { name = "pandas" },
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@@ -1602,7 +1610,7 @@ name = "importlib-metadata"
 version = "8.7.0"
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@@ -2159,7 +2167,9 @@ version = "0.3.4"
 source = { editable = "packages/leann-backend-diskann" }
 dependencies = [
     { name = "leann-core" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "protobuf" },
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@@ -2177,7 +2187,9 @@ source = { editable = "packages/leann-backend-hnsw" }
 dependencies = [
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     { name = "msgpack" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pyzmq" },
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@@ -2204,7 +2216,9 @@ dependencies = [
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     { name = "msgpack" },
     { name = "nbconvert" },
-    { name = "numpy" },
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+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "openai" },
     { name = "pdfplumber" },
     { name = "psutil" },
@@ -2241,12 +2255,12 @@ requires-dist = [
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     { name = "pyzmq", specifier = ">=23.0.0" },
     { name = "requests", specifier = ">=2.25.0" },
-    { name = "sentence-transformers", specifier = ">=3.0.0" },
+    { name = "sentence-transformers", specifier = ">=2.2.0" },
     { name = "torch", specifier = ">=2.0.0" },
     { name = "torch", marker = "extra == 'colab'", specifier = ">=2.0.0,<3.0.0" },
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-    { name = "transformers", marker = "extra == 'colab'", specifier = ">=4.30.0,<4.46" },
+    { name = "transformers", specifier = ">=4.30.0" },
+    { name = "transformers", marker = "extra == 'colab'", specifier = ">=4.30.0,<5.0.0" },
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 provides-extras = ["colab"]
 
@@ -2272,7 +2286,9 @@ dependencies = [
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     { name = "msgpack" },
     { name = "nbconvert" },
-    { name = "numpy" },
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     { name = "ollama" },
     { name = "openai" },
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@@ -2290,7 +2306,6 @@ dependencies = [
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     { name = "torchvision" },
     { name = "tqdm" },
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@@ -2366,12 +2381,11 @@ requires-dist = [
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-    { name = "sentence-transformers", specifier = ">=3.0.0" },
+    { name = "sentence-transformers", specifier = ">=2.2.0" },
     { name = "sglang" },
     { name = "torch" },
     { name = "torchvision", specifier = ">=0.23.0" },
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     { name = "nltk" },
-    { name = "numpy" },
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+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pillow" },
     { name = "platformdirs" },
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@@ -2907,7 +2923,7 @@ dependencies = [
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-    { name = "numpy", marker = "python_full_version < '3.10'" },
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@@ -2972,7 +2988,8 @@ dependencies = [
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-    { name = "numpy", marker = "python_full_version >= '3.10'" },
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@@ -3101,7 +3118,9 @@ source = { registry = "https://pypi.org/simple" }
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