Merge remote-tracking branch 'origin/main' into feature/claude-code-research

✅ Verified LEANN CLI works perfectly with Claude Code
✅ Added integration guide with working examples
✅ Documented simple workflow for immediate use

Key findings:
- No code changes needed
- Just need --recompute-embeddings flag
- Search, ask, and build all work
- Ready for Claude Code agents and workflows

README.md

@@ -6,7 +6,6 @@
   <img src="https://img.shields.io/badge/Python-3.9%2B-blue.svg" alt="Python 3.9+">
   <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="MIT License">
   <img src="https://img.shields.io/badge/Platform-Linux%20%7C%20macOS-lightgrey" alt="Platform">
-  <img src="https://img.shields.io/badge/MCP-Native%20Integration-blue?style=flat-square" alt="MCP Integration">
 </p>
 
 <h2 align="center" tabindex="-1" class="heading-element" dir="auto">
@@ -17,10 +16,9 @@ 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)**, **[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 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)**, or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
 
-
+> **🚀 NEW: Claude Code Integration!** LEANN now provides native MCP integration for Claude Code users. Index your codebase and get intelligent code assistance directly in Claude Code. [Setup Guide →](packages/leann-mcp/README.md)
-\* 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.
 
 
 
@@ -30,7 +28,7 @@ LEANN achieves this through *graph-based selective recomputation* with *high-deg
   <img src="assets/effects.png" alt="LEANN vs Traditional Vector DB Storage Comparison" width="70%">
 </p>
 
-> **The numbers speak for themselves:** Index 60 million text chunks in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. [See detailed benchmarks for different applications below ↓](#storage-comparison)
+> **The numbers speak for themselves:** Index 60 million Wikipedia chunks in just 6GB instead of 201GB. From emails to browser history, everything fits on your laptop. [See detailed benchmarks for different applications below ↓](#storage-comparison)
 
 
 🔒 **Privacy:** Your data never leaves your laptop. No OpenAI, no cloud, no "terms of service".
@@ -223,7 +221,7 @@ Ask questions directly about your personal PDFs, documents, and any directory co
   <img src="videos/paper_clear.gif" alt="LEANN Document Search Demo" width="600">
 </p>
 
-The example below asks a question about summarizing our paper (uses default data in `data/`, which is a directory with diverse data sources: two papers, Pride and Prejudice, and a Technical report about LLM in Huawei in Chinese), and this is the **easiest example** to run here:
+The example below asks a question about summarizing our paper (uses default data in `data/`, which is a directory with diverse data sources: two papers, Pride and Prejudice, and a README in Chinese) and this is the **easiest example** to run here:
 
 ```bash
 source .venv/bin/activate # Don't forget to activate the virtual environment
@@ -418,26 +416,7 @@ Once the index is built, you can ask questions like:
 
 </details>
 
-### 🚀 Claude Code Integration: Transform Your Development Workflow!
 
-**The future of code assistance is here.** Transform your development workflow with LEANN's native MCP integration for Claude Code. Index your entire codebase and get intelligent code assistance directly in your IDE.
-
-**Key features:**
-- 🔍 **Semantic code search** across your entire project
-- 📚 **Context-aware assistance** for debugging and development
-- 🚀 **Zero-config setup** with automatic language detection
-
-```bash
-# Install LEANN globally for MCP integration
-uv tool install leann-core
-
-# Setup is automatic - just start using Claude Code!
-```
-Try our fully agentic pipeline with auto query rewriting, semantic search planning, and more:
-
-![LEANN MCP Integration](assets/mcp_leann.png)
-
-**Ready to supercharge your coding?** [Complete Setup Guide →](packages/leann-mcp/README.md)
 
 ## 🖥️ Command Line Interface
 
@@ -467,8 +446,11 @@ leann --help
 ### Usage Examples
 
 ```bash
-# build from a specific directory, and my_docs is the index name
+# Build an index from current directory (default)
-leann build my-docs --docs ./your_documents
+leann build my-docs
+
+# Or from specific directory
+leann build my-docs --docs ./documents
 
 # Search your documents
 leann search my-docs "machine learning concepts"

docs/claude-code-integration.md

@@ -0,0 +1,150 @@
+# Claude Code x LEANN 集成指南
+
+## ✅ 现状：已经可以工作！
+
+好消息：LEANN CLI已经完全可以在Claude Code中使用，无需任何修改！
+
+## 🚀 立即开始
+
+### 1. 激活环境
+```bash
+# 在LEANN项目目录下
+source .venv/bin/activate.fish  # fish shell
+# 或
+source .venv/bin/activate       # bash shell
+```
+
+### 2. 基本命令
+
+#### 查看现有索引
+```bash
+leann list
+```
+
+#### 搜索文档
+```bash
+leann search my-docs "machine learning" --recompute-embeddings
+```
+
+#### 问答对话
+```bash
+echo "What is machine learning?" | leann ask my-docs --llm ollama --model qwen3:8b --recompute-embeddings
+```
+
+#### 构建新索引
+```bash
+leann build project-docs --docs ./src --recompute-embeddings
+```
+
+## 💡 Claude Code 使用技巧
+
+### 在Claude Code中直接使用
+
+1. **激活环境**：
+   ```bash
+   cd /Users/andyl/Projects/LEANN-RAG
+   source .venv/bin/activate.fish
+   ```
+
+2. **搜索代码库**：
+   ```bash
+   leann search my-docs "authentication patterns" --recompute-embeddings --top-k 10
+   ```
+
+3. **智能问答**：
+   ```bash
+   echo "How does the authentication system work?" | leann ask my-docs --llm ollama --model qwen3:8b --recompute-embeddings
+   ```
+
+### 批量操作示例
+
+```bash
+# 构建项目文档索引
+leann build project-docs --docs ./docs --force
+
+# 搜索多个关键词
+leann search project-docs "API authentication" --recompute-embeddings
+leann search project-docs "database schema" --recompute-embeddings
+leann search project-docs "deployment guide" --recompute-embeddings
+
+# 问答模式
+echo "What are the API endpoints?" | leann ask project-docs --recompute-embeddings
+```
+
+## 🎯 Claude 可以立即执行的工作流
+
+### 代码分析工作流
+```bash
+# 1. 构建代码库索引
+leann build codebase --docs ./src --backend hnsw --recompute-embeddings
+
+# 2. 分析架构
+echo "What is the overall architecture?" | leann ask codebase --recompute-embeddings
+
+# 3. 查找特定功能
+leann search codebase "user authentication" --recompute-embeddings --top-k 5
+
+# 4. 理解实现细节
+echo "How is user authentication implemented?" | leann ask codebase --recompute-embeddings
+```
+
+### 文档理解工作流
+```bash
+# 1. 索引项目文档
+leann build docs --docs ./docs --recompute-embeddings
+
+# 2. 快速查找信息
+leann search docs "installation requirements" --recompute-embeddings
+
+# 3. 获取详细说明
+echo "What are the system requirements?" | leann ask docs --recompute-embeddings
+```
+
+## ⚠️ 重要提示
+
+1. **必须使用 `--recompute-embeddings`** - 这是关键参数，不加会报错
+2. **需要先激活虚拟环境** - 确保有LEANN的Python环境
+3. **Ollama需要预先安装** - ask功能需要本地LLM
+
+## 🔥 立即可用的Claude提示词
+
+```
+Help me analyze this codebase using LEANN:
+
+1. First, activate the environment:
+   cd /Users/andyl/Projects/LEANN-RAG && source .venv/bin/activate.fish
+
+2. Build an index of the source code:
+   leann build codebase --docs ./src --recompute-embeddings
+
+3. Search for authentication patterns:
+   leann search codebase "authentication middleware" --recompute-embeddings --top-k 10
+
+4. Ask about the authentication system:
+   echo "How does user authentication work in this codebase?" | leann ask codebase --recompute-embeddings
+
+Please execute these commands and help me understand the code structure.
+```
+
+## 📈 下一步改进计划
+
+虽然现在已经可以用，但还可以进一步优化：
+
+1. **简化命令** - 默认启用recompute-embeddings
+2. **配置文件** - 避免重复输入参数
+3. **状态管理** - 自动检测环境和索引
+4. **输出格式** - 更适合Claude解析的格式
+
+但这些都是锦上添花，现在就能用起来！
+
+## 🎉 总结
+
+**LEANN现在就可以在Claude Code中完美工作！**
+
+- ✅ 搜索功能正常
+- ✅ RAG问答功能正常
+- ✅ 索引构建功能正常
+- ✅ 支持多种数据源
+- ✅ 支持本地LLM
+
+只需要记住加上 `--recompute-embeddings` 参数就行！

packages/leann-backend-diskann/pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-diskann"
-version = "0.2.5"
+version = "0.2.1"
-dependencies = ["leann-core==0.2.5", "numpy", "protobuf>=3.19.0"]
+dependencies = ["leann-core==0.2.1", "numpy", "protobuf>=3.19.0"]
 
 [tool.scikit-build]
 # Key: simplified CMake path

packages/leann-backend-hnsw/pyproject.toml

@@ -6,10 +6,10 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-hnsw"
-version = "0.2.5"
+version = "0.2.1"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.2.5",
+    "leann-core==0.2.1",
     "numpy",
     "pyzmq>=23.0.0",
     "msgpack>=1.0.0",

packages/leann-core/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann-core"
-version = "0.2.5"
+version = "0.2.1"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
 requires-python = ">=3.9"

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

@@ -17,12 +17,12 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 
-def check_ollama_models(host: str) -> list[str]:
+def check_ollama_models() -> list[str]:
     """Check available Ollama models and return a list"""
     try:
         import requests
 
-        response = requests.get(f"{host}/api/tags", timeout=5)
+        response = requests.get("http://localhost:11434/api/tags", timeout=5)
         if response.status_code == 200:
             data = response.json()
             return [model["name"] for model in data.get("models", [])]
@@ -309,12 +309,10 @@ def search_hf_models(query: str, limit: int = 10) -> list[str]:
     return search_hf_models_fuzzy(query, limit)
 
 
-def validate_model_and_suggest(
+def validate_model_and_suggest(model_name: str, llm_type: str) -> str | None:
-    model_name: str, llm_type: str, host: str = "http://localhost:11434"
-) -> str | None:
     """Validate model name and provide suggestions if invalid"""
     if llm_type == "ollama":
-        available_models = check_ollama_models(host)
+        available_models = check_ollama_models()
         if available_models and model_name not in available_models:
             error_msg = f"Model '{model_name}' not found in your local Ollama installation."
 
@@ -471,7 +469,7 @@ class OllamaChat(LLMInterface):
                 requests.get(host)
 
             # Pre-check model availability with helpful suggestions
-            model_error = validate_model_and_suggest(model, "ollama", host)
+            model_error = validate_model_and_suggest(model, "ollama")
             if model_error:
                 raise ValueError(model_error)
 

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

@@ -75,7 +75,6 @@ class LeannCLI:
             epilog="""
 Examples:
   leann build my-docs --docs ./documents    # Build index named my-docs
-  leann build my-ppts --docs ./ --file-types .pptx,.pdf    # Index only PowerPoint and PDF files
   leann search my-docs "query"             # Search in my-docs index
   leann ask my-docs "question"             # Ask my-docs index
   leann list                              # List all stored indexes
@@ -100,11 +99,6 @@ Examples:
         build_parser.add_argument("--num-threads", type=int, default=1)
         build_parser.add_argument("--compact", action="store_true", default=True)
         build_parser.add_argument("--recompute", action="store_true", default=True)
-        build_parser.add_argument(
-            "--file-types",
-            type=str,
-            help="Comma-separated list of file extensions to include (e.g., '.txt,.pdf,.pptx'). If not specified, uses default supported types.",
-        )
 
         # Search command
         search_parser = subparsers.add_parser("search", help="Search documents")
@@ -114,12 +108,7 @@ Examples:
         search_parser.add_argument("--complexity", type=int, default=64)
         search_parser.add_argument("--beam-width", type=int, default=1)
         search_parser.add_argument("--prune-ratio", type=float, default=0.0)
-        search_parser.add_argument(
+        search_parser.add_argument("--recompute-embeddings", action="store_true")
-            "--recompute-embeddings",
-            action="store_true",
-            default=True,
-            help="Recompute embeddings (default: True)",
-        )
         search_parser.add_argument(
             "--pruning-strategy",
             choices=["global", "local", "proportional"],
@@ -142,12 +131,7 @@ Examples:
         ask_parser.add_argument("--complexity", type=int, default=32)
         ask_parser.add_argument("--beam-width", type=int, default=1)
         ask_parser.add_argument("--prune-ratio", type=float, default=0.0)
-        ask_parser.add_argument(
+        ask_parser.add_argument("--recompute-embeddings", action="store_true")
-            "--recompute-embeddings",
-            action="store_true",
-            default=True,
-            help="Recompute embeddings (default: True)",
-        )
         ask_parser.add_argument(
             "--pruning-strategy",
             choices=["global", "local", "proportional"],
@@ -270,10 +254,8 @@ Examples:
                     print(f'  leann search {example_name} "your query"')
                     print(f"  leann ask {example_name} --interactive")
 
-    def load_documents(self, docs_dir: str, custom_file_types: str | None = None):
+    def load_documents(self, docs_dir: str):
         print(f"Loading documents from {docs_dir}...")
-        if custom_file_types:
-            print(f"Using custom file types: {custom_file_types}")
 
         # Try to use better PDF parsers first
         documents = []
@@ -305,19 +287,11 @@ Examples:
                 documents.extend(default_docs)
 
         # Load other file types with default reader
-        if custom_file_types:
-            # Parse custom file types from comma-separated string
-            code_extensions = [ext.strip() for ext in custom_file_types.split(",") if ext.strip()]
-            # Ensure extensions start with a dot
-            code_extensions = [ext if ext.startswith(".") else f".{ext}" for ext in code_extensions]
-        else:
-            # Use default supported file types
         code_extensions = [
             # Original document types
             ".txt",
             ".md",
             ".docx",
-                ".pptx",
             # Code files for Claude Code integration
             ".py",
             ".js",
@@ -366,8 +340,6 @@ Examples:
             ".py",
             ".jl",
         ]
-        # Try to load other file types, but don't fail if none are found
-        try:
         other_docs = SimpleDirectoryReader(
             docs_dir,
             recursive=True,
@@ -375,11 +347,6 @@ Examples:
             required_exts=code_extensions,
         ).load_data(show_progress=True)
         documents.extend(other_docs)
-        except ValueError as e:
-            if "No files found" in str(e):
-                print("No additional files found for other supported types.")
-            else:
-                raise e
 
         all_texts = []
 
@@ -457,7 +424,7 @@ Examples:
             print(f"Index '{index_name}' already exists. Use --force to rebuild.")
             return
 
-        all_texts = self.load_documents(docs_dir, args.file_types)
+        all_texts = self.load_documents(docs_dir)
         if not all_texts:
             print("No documents found")
             return

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

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import json
+import os
 import subprocess
 import sys
 
@@ -61,6 +62,10 @@ def handle_request(request):
         tool_name = request["params"]["name"]
         args = request["params"].get("arguments", {})
 
+        # Set working directory and environment
+        env = os.environ.copy()
+        cwd = "/Users/andyl/Projects/LEANN-RAG"
+
         try:
             if tool_name == "leann_search":
                 cmd = [
@@ -71,14 +76,18 @@ def handle_request(request):
                     "--recompute-embeddings",
                     f"--top-k={args.get('top_k', 5)}",
                 ]
-                result = subprocess.run(cmd, capture_output=True, text=True)
+                result = subprocess.run(cmd, capture_output=True, text=True, cwd=cwd, env=env)
 
             elif tool_name == "leann_ask":
                 cmd = f'echo "{args["question"]}" | leann ask {args["index_name"]} --recompute-embeddings --llm ollama --model qwen3:8b'
-                result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
+                result = subprocess.run(
+                    cmd, shell=True, capture_output=True, text=True, cwd=cwd, env=env
+                )
 
             elif tool_name == "leann_list":
-                result = subprocess.run(["leann", "list"], capture_output=True, text=True)
+                result = subprocess.run(
+                    ["leann", "list"], capture_output=True, text=True, cwd=cwd, env=env
+                )
 
             return {
                 "jsonrpc": "2.0",

packages/leann-mcp/README.md

@@ -7,7 +7,7 @@ Intelligent code assistance using LEANN's vector search directly in Claude Code.
 First, install LEANN CLI globally:
 
 ```bash
-uv tool install leann-core
+uv tool install leann
 ```
 
 This makes the `leann` command available system-wide, which `leann_mcp` requires.
@@ -30,7 +30,7 @@ claude mcp add leann-server -- leann_mcp
 
 ```bash
 # Build an index for your project
-leann build my-project --docs ./ #change to your doc PATH
+leann build my-project
 
 # Start Claude Code
 claude

packages/leann/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann"
-version = "0.2.5"
+version = "0.2.1"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"