docs: Specify core contributors and welcome new contributors

README.md

@@ -18,8 +18,6 @@ LEANN achieves this through *graph-based selective recomputation* with *high-deg
 
 **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)
-
 
 
 ## Why LEANN?
@@ -168,7 +166,7 @@ ollama pull llama3.2:1b
 
 </details>
 
-### ⭐ Flexible Configuration
+### Flexible Configuration
 
 LEANN provides flexible parameters for embedding models, search strategies, and data processing to fit your specific needs.
 
@@ -193,7 +191,6 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
 # LLM Parameters (Text generation models)
 --llm TYPE              # LLM backend: openai, ollama, or hf (default: openai)
 --llm-model MODEL       # Model name (default: gpt-4o) e.g., gpt-4o-mini, llama3.2:1b, Qwen/Qwen2.5-1.5B-Instruct
---thinking-budget LEVEL # Thinking budget for reasoning models: low/medium/high (supported by o3, o3-mini, GPT-Oss:20b, and other reasoning models)
 
 # Search Parameters
 --top-k N               # Number of results to retrieve (default: 20)
@@ -430,7 +427,7 @@ source .venv/bin/activate
 leann --help
 ```
 
-**To make it globally available:**
+**To make it globally available (recommended for daily use):**
 ```bash
 # Install the LEANN CLI globally using uv tool
 uv tool install leann
@@ -439,17 +436,12 @@ uv tool install leann
 leann --help
 ```
 
-> **Note**: Global installation is required for Claude Code integration. The `leann_mcp` server depends on the globally available `leann` command.
-
 
 
 ### Usage Examples
 
 ```bash
-# Build an index from current directory (default)
+# Build an index from documents
-leann build my-docs
-
-# Or from specific directory
 leann build my-docs --docs ./documents
 
 # Search your documents

apps/base_rag_example.py

@@ -100,13 +100,6 @@ class BaseRAGExample(ABC):
             default="http://localhost:11434",
             help="Host for Ollama API (default: http://localhost:11434)",
         )
-        llm_group.add_argument(
-            "--thinking-budget",
-            type=str,
-            choices=["low", "medium", "high"],
-            default=None,
-            help="Thinking budget for reasoning models (low/medium/high). Supported by GPT-Oss:20b and other reasoning models.",
-        )
 
         # Search parameters
         search_group = parser.add_argument_group("Search Parameters")
@@ -235,17 +228,7 @@ class BaseRAGExample(ABC):
                 if not query:
                     continue
 
-                # Prepare LLM kwargs with thinking budget if specified
+                response = chat.ask(query, top_k=args.top_k, complexity=args.search_complexity)
-                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:
@@ -264,15 +247,7 @@ class BaseRAGExample(ABC):
         )
 
         print(f"\n[Query]: \033[36m{query}\033[0m")
-
+        response = chat.ask(query, top_k=args.top_k, complexity=args.search_complexity)
-        # 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"\n[Response]: \033[36m{response}\033[0m")
 
     async def run(self):

docs/THINKING_BUDGET_FEATURE.md

@@ -1,123 +0,0 @@
-# Thinking Budget Feature Implementation
-
-## Overview
-
-This document describes the implementation of the **thinking budget** feature for LEANN, which allows users to control the computational effort for reasoning models like GPT-Oss:20b.
-
-## Feature Description
-
-The thinking budget feature provides three levels of computational effort for reasoning models:
-- **`low`**: Fast responses, basic reasoning (default for simple queries)
-- **`medium`**: Balanced speed and reasoning depth
-- **`high`**: Maximum reasoning effort, best for complex analytical questions
-
-## Implementation Details
-
-### 1. Command Line Interface
-
-Added `--thinking-budget` parameter to both CLI and RAG examples:
-
-```bash
-# LEANN CLI
-leann ask my-index --llm ollama --model gpt-oss:20b --thinking-budget high
-
-# RAG Examples
-python apps/email_rag.py --llm ollama --llm-model gpt-oss:20b --thinking-budget high
-python apps/document_rag.py --llm openai --llm-model o3 --thinking-budget medium
-```
-
-### 2. LLM Backend Support
-
-#### Ollama Backend (`packages/leann-core/src/leann/chat.py`)
-
-```python
-def ask(self, prompt: str, **kwargs) -> str:
-    # Handle thinking budget for reasoning models
-    options = kwargs.copy()
-    thinking_budget = kwargs.get("thinking_budget")
-    if thinking_budget:
-        options.pop("thinking_budget", None)
-        if thinking_budget in ["low", "medium", "high"]:
-            options["reasoning"] = {"effort": thinking_budget, "exclude": False}
-```
-
-**API Format**: Uses Ollama's `reasoning` parameter with `effort` and `exclude` fields.
-
-#### OpenAI Backend (`packages/leann-core/src/leann/chat.py`)
-
-```python
-def ask(self, prompt: str, **kwargs) -> str:
-    # Handle thinking budget for reasoning models
-    thinking_budget = kwargs.get("thinking_budget")
-    if thinking_budget and thinking_budget in ["low", "medium", "high"]:
-        # Check if this is an o-series model
-        o_series_models = ["o3", "o3-mini", "o4-mini", "o1", "o3-pro", "o3-deep-research"]
-        if any(model in self.model for model in o_series_models):
-            params["reasoning_effort"] = thinking_budget
-```
-
-**API Format**: Uses OpenAI's `reasoning_effort` parameter for o-series models.
-
-### 3. Parameter Propagation
-
-The thinking budget parameter is properly propagated through the LEANN architecture:
-
-1. **CLI** (`packages/leann-core/src/leann/cli.py`): Captures `--thinking-budget` argument
-2. **Base RAG** (`apps/base_rag_example.py`): Adds parameter to argument parser
-3. **LeannChat** (`packages/leann-core/src/leann/api.py`): Passes `llm_kwargs` to LLM
-4. **LLM Interface**: Handles the parameter in backend-specific implementations
-
-## Files Modified
-
-### Core Implementation
-- `packages/leann-core/src/leann/chat.py`: Added thinking budget support to OllamaChat and OpenAIChat
-- `packages/leann-core/src/leann/cli.py`: Added `--thinking-budget` argument
-- `apps/base_rag_example.py`: Added thinking budget parameter to RAG examples
-
-### Documentation
-- `README.md`: Added thinking budget parameter to usage examples
-- `docs/configuration-guide.md`: Added detailed documentation and usage guidelines
-
-### Examples
-- `examples/thinking_budget_demo.py`: Comprehensive demo script with usage examples
-
-## Usage Examples
-
-### Basic Usage
-```bash
-# High reasoning effort for complex questions
-leann ask my-index --llm ollama --model gpt-oss:20b --thinking-budget high
-
-# Medium reasoning for balanced performance
-leann ask my-index --llm openai --model gpt-4o --thinking-budget medium
-
-# Low reasoning for fast responses
-leann ask my-index --llm ollama --model gpt-oss:20b --thinking-budget low
-```
-
-### RAG Examples
-```bash
-# Email RAG with high reasoning
-python apps/email_rag.py --llm ollama --llm-model gpt-oss:20b --thinking-budget high
-
-# Document RAG with medium reasoning
-python apps/document_rag.py --llm openai --llm-model gpt-4o --thinking-budget medium
-```
-
-## Supported Models
-
-### Ollama Models
-- **GPT-Oss:20b**: Primary target model with reasoning capabilities
-- **Other reasoning models**: Any Ollama model that supports the `reasoning` parameter
-
-### OpenAI Models
-- **o3, o3-mini, o4-mini, o1**: o-series reasoning models with `reasoning_effort` parameter
-- **GPT-OSS models**: Models that support reasoning capabilities
-
-## Testing
-
-The implementation includes comprehensive testing:
-- Parameter handling verification
-- Backend-specific API format validation
-- CLI argument parsing tests
-- Integration with existing LEANN architecture

docs/claude-code-integration.md

@@ -1,150 +0,0 @@
-# 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！

docs/configuration-guide.md

@@ -103,15 +103,13 @@ For immediate testing without local model downloads:
 **OpenAI** (`--llm openai`)
 - **Pros**: Best quality, consistent performance, no local resources needed
 - **Cons**: Costs money ($0.15-2.5 per million tokens), requires internet, data privacy concerns
-- **Models**: `gpt-4o-mini` (fast, cheap), `gpt-4o` (best quality), `o3` (reasoning), `o3-mini` (reasoning, cheaper)
+- **Models**: `gpt-4o-mini` (fast, cheap), `gpt-4o` (best quality), `o3-mini` (reasoning, not so expensive)
-- **Thinking Budget**: Use `--thinking-budget low/medium/high` for o-series reasoning models (o3, o3-mini, o4-mini)
 - **Note**: Our current default, but we recommend switching to Ollama for most use cases
 
 **Ollama** (`--llm ollama`)
 - **Pros**: Fully local, free, privacy-preserving, good model variety
 - **Cons**: Requires local GPU/CPU resources, slower than cloud APIs, need to install extra [ollama app](https://github.com/ollama/ollama?tab=readme-ov-file#ollama) and pre-download models by `ollama pull`
 - **Models**: `qwen3:0.6b` (ultra-fast), `qwen3:1.7b` (balanced), `qwen3:4b` (good quality), `qwen3:7b` (high quality), `deepseek-r1:1.5b` (reasoning)
-- **Thinking Budget**: Use `--thinking-budget low/medium/high` for reasoning models like GPT-Oss:20b
 
 **HuggingFace** (`--llm hf`)
 - **Pros**: Free tier available, huge model selection, direct model loading (vs Ollama's server-based approach)
@@ -153,36 +151,6 @@ For immediate testing without local model downloads:
 - LLM processing time ∝ top_k × chunk_size
 - Total context = top_k × chunk_size tokens
 
-### Thinking Budget for Reasoning Models
-
-**`--thinking-budget`** (reasoning effort level)
-- Controls the computational effort for reasoning models
-- Options: `low`, `medium`, `high`
-- Guidelines:
-  - `low`: Fast responses, basic reasoning (default for simple queries)
-  - `medium`: Balanced speed and reasoning depth
-  - `high`: Maximum reasoning effort, best for complex analytical questions
-- **Supported Models**:
-  - **Ollama**: `gpt-oss:20b`, `gpt-oss:120b`
-  - **OpenAI**: `o3`, `o3-mini`, `o4-mini`, `o1` (o-series reasoning models)
-- **Note**: Models without reasoning support will show a warning and proceed without reasoning parameters
-- **Example**: `--thinking-budget high` for complex analytical questions
-
-**📖 For detailed usage examples and implementation details, check out [Thinking Budget Documentation](THINKING_BUDGET_FEATURE.md)**
-
-**💡 Quick Examples:**
-```bash
-# OpenAI o-series reasoning model
-python apps/document_rag.py --query "What are the main techniques LEANN explores?" \
-  --index-dir hnswbuild --backend hnsw \
-  --llm openai --llm-model o3 --thinking-budget medium
-
-# Ollama reasoning model
-python apps/document_rag.py --query "What are the main techniques LEANN explores?" \
-  --index-dir hnswbuild --backend hnsw \
-  --llm ollama --llm-model gpt-oss:20b --thinking-budget high
-```
-
 ### Graph Degree (HNSW/DiskANN)
 
 **`--graph-degree`**

packages/leann-core/pyproject.toml

@@ -44,7 +44,6 @@ colab = [
 
 [project.scripts]
 leann = "leann.cli:main"
-leann_mcp = "leann.mcp:main"
 
 [tool.setuptools.packages.find]
 where = ["src"]

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

@@ -489,35 +489,11 @@ class OllamaChat(LLMInterface):
         import requests
 
         full_url = f"{self.host}/api/generate"
-
-        # Handle thinking budget for reasoning models
-        options = kwargs.copy()
-        thinking_budget = kwargs.get("thinking_budget")
-        if thinking_budget:
-            # Remove thinking_budget from options as it's not a standard Ollama option
-            options.pop("thinking_budget", None)
-            # Only apply reasoning parameters to models that support it
-            reasoning_supported_models = [
-                "gpt-oss:20b",
-                "gpt-oss:120b",
-                "deepseek-r1",
-                "deepseek-coder",
-            ]
-
-            if thinking_budget in ["low", "medium", "high"]:
-                if any(model in self.model.lower() for model in reasoning_supported_models):
-                    options["reasoning"] = {"effort": thinking_budget, "exclude": False}
-                    logger.info(f"Applied reasoning effort={thinking_budget} to model {self.model}")
-                else:
-                    logger.warning(
-                        f"Thinking budget '{thinking_budget}' requested but model '{self.model}' may not support reasoning parameters. Proceeding without reasoning."
-                    )
-
         payload = {
             "model": self.model,
             "prompt": prompt,
             "stream": False,  # Keep it simple for now
-            "options": options,
+            "options": kwargs,
         }
         logger.debug(f"Sending request to Ollama: {payload}")
         try:
@@ -708,38 +684,11 @@ class OpenAIChat(LLMInterface):
         params = {
             "model": self.model,
             "messages": [{"role": "user", "content": prompt}],
+            "max_tokens": kwargs.get("max_tokens", 1000),
             "temperature": kwargs.get("temperature", 0.7),
+            **{k: v for k, v in kwargs.items() if k not in ["max_tokens", "temperature"]},
         }
 
-        # Handle max_tokens vs max_completion_tokens based on model
-        max_tokens = kwargs.get("max_tokens", 1000)
-        if "o3" in self.model or "o4" in self.model or "o1" in self.model:
-            # o-series models use max_completion_tokens
-            params["max_completion_tokens"] = max_tokens
-            params["temperature"] = 1.0
-        else:
-            # Other models use max_tokens
-            params["max_tokens"] = max_tokens
-
-        # Handle thinking budget for reasoning models
-        thinking_budget = kwargs.get("thinking_budget")
-        if thinking_budget and thinking_budget in ["low", "medium", "high"]:
-            # Check if this is an o-series model (partial match for model names)
-            o_series_models = ["o3", "o3-mini", "o4-mini", "o1", "o3-pro", "o3-deep-research"]
-            if any(model in self.model for model in o_series_models):
-                # Use the correct OpenAI reasoning parameter format
-                params["reasoning_effort"] = thinking_budget
-                logger.info(f"Applied reasoning_effort={thinking_budget} to model {self.model}")
-            else:
-                logger.warning(
-                    f"Thinking budget '{thinking_budget}' requested but model '{self.model}' may not support reasoning parameters. Proceeding without reasoning."
-                )
-
-        # Add other kwargs (excluding thinking_budget as it's handled above)
-        for k, v in kwargs.items():
-            if k not in ["max_tokens", "temperature", "thinking_budget"]:
-                params[k] = v
-
         logger.info(f"Sending request to OpenAI with model {self.model}")
 
         try:

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

@@ -41,23 +41,13 @@ def extract_pdf_text_with_pdfplumber(file_path: str) -> str:
 
 class LeannCLI:
     def __init__(self):
-        # Always use project-local .leann directory (like .git)
+        self.indexes_dir = Path.home() / ".leann" / "indexes"
-        self.indexes_dir = Path.cwd() / ".leann" / "indexes"
         self.indexes_dir.mkdir(parents=True, exist_ok=True)
 
-        # Default parser for documents
         self.node_parser = SentenceSplitter(
             chunk_size=256, chunk_overlap=128, separator=" ", paragraph_separator="\n\n"
         )
 
-        # Code-optimized parser
-        self.code_parser = SentenceSplitter(
-            chunk_size=512,  # Larger chunks for code context
-            chunk_overlap=50,  # Less overlap to preserve function boundaries
-            separator="\n",  # Split by lines for code
-            paragraph_separator="\n\n",  # Preserve logical code blocks
-        )
-
     def get_index_path(self, index_name: str) -> str:
         index_dir = self.indexes_dir / index_name
         return str(index_dir / "documents.leann")
@@ -86,9 +76,7 @@ Examples:
         # Build command
         build_parser = subparsers.add_parser("build", help="Build document index")
         build_parser.add_argument("index_name", help="Index name")
-        build_parser.add_argument(
+        build_parser.add_argument("--docs", type=str, required=True, help="Documents directory")
-            "--docs", type=str, default=".", help="Documents directory (default: current directory)"
-        )
         build_parser.add_argument(
             "--backend", type=str, default="hnsw", choices=["hnsw", "diskann"]
         )
@@ -137,122 +125,43 @@ Examples:
             choices=["global", "local", "proportional"],
             default="global",
         )
-        ask_parser.add_argument(
-            "--thinking-budget",
-            type=str,
-            choices=["low", "medium", "high"],
-            default=None,
-            help="Thinking budget for reasoning models (low/medium/high). Supported by GPT-Oss:20b and other reasoning models.",
-        )
 
         # List command
         subparsers.add_parser("list", help="List all indexes")
 
         return parser
 
-    def register_project_dir(self):
-        """Register current project directory in global registry"""
-        global_registry = Path.home() / ".leann" / "projects.json"
-        global_registry.parent.mkdir(exist_ok=True)
-
-        current_dir = str(Path.cwd())
-
-        # Load existing registry
-        projects = []
-        if global_registry.exists():
-            try:
-                import json
-
-                with open(global_registry) as f:
-                    projects = json.load(f)
-            except Exception:
-                projects = []
-
-        # Add current directory if not already present
-        if current_dir not in projects:
-            projects.append(current_dir)
-
-        # Save registry
-        import json
-
-        with open(global_registry, "w") as f:
-            json.dump(projects, f, indent=2)
-
     def list_indexes(self):
         print("Stored LEANN indexes:")
 
-        # Get all project directories with .leann
+        if not self.indexes_dir.exists():
-        global_registry = Path.home() / ".leann" / "projects.json"
-        all_projects = []
-
-        if global_registry.exists():
-            try:
-                import json
-
-                with open(global_registry) as f:
-                    all_projects = json.load(f)
-            except Exception:
-                pass
-
-        # Filter to only existing directories with .leann
-        valid_projects = []
-        for project_dir in all_projects:
-            project_path = Path(project_dir)
-            if project_path.exists() and (project_path / ".leann" / "indexes").exists():
-                valid_projects.append(project_path)
-
-        # Add current project if it has .leann but not in registry
-        current_path = Path.cwd()
-        if (current_path / ".leann" / "indexes").exists() and current_path not in valid_projects:
-            valid_projects.append(current_path)
-
-        if not valid_projects:
             print("No indexes found. Use 'leann build <name> --docs <dir>' to create one.")
             return
 
-        total_indexes = 0
+        index_dirs = [d for d in self.indexes_dir.iterdir() if d.is_dir()]
-        current_dir = Path.cwd()
 
-        for project_path in valid_projects:
+        if not index_dirs:
-            indexes_dir = project_path / ".leann" / "indexes"
+            print("No indexes found. Use 'leann build <name> --docs <dir>' to create one.")
-            if not indexes_dir.exists():
+            return
-                continue
 
-            index_dirs = [d for d in indexes_dir.iterdir() if d.is_dir()]
+        print(f"Found {len(index_dirs)} indexes:")
-            if not index_dirs:
+        for i, index_dir in enumerate(index_dirs, 1):
-                continue
+            index_name = index_dir.name
+            status = "✓" if self.index_exists(index_name) else "✗"
 
-            # Show project header
+            print(f"  {i}. {index_name} [{status}]")
-            if project_path == current_dir:
+            if self.index_exists(index_name):
-                print(f"\n📁 Current project ({project_path}):")
+                index_dir / "documents.leann.meta.json"
-            else:
+                size_mb = sum(f.stat().st_size for f in index_dir.iterdir() if f.is_file()) / (
-                print(f"\n📂 {project_path}:")
+                    1024 * 1024
+                )
+                print(f"     Size: {size_mb:.1f} MB")
 
-            for index_dir in index_dirs:
+        if index_dirs:
-                total_indexes += 1
+            example_name = index_dirs[0].name
-                index_name = index_dir.name
+            print("\nUsage:")
-                meta_file = index_dir / "documents.leann.meta.json"
+            print(f'  leann search {example_name} "your query"')
-                status = "✓" if meta_file.exists() else "✗"
+            print(f"  leann ask {example_name} --interactive")
-
-                print(f"  {total_indexes}. {index_name} [{status}]")
-                if status == "✓":
-                    size_mb = sum(f.stat().st_size for f in index_dir.iterdir() if f.is_file()) / (
-                        1024 * 1024
-                    )
-                    print(f"     Size: {size_mb:.1f} MB")
-
-        if total_indexes > 0:
-            print(f"\nTotal: {total_indexes} indexes across {len(valid_projects)} projects")
-            print("\nUsage (current project only):")
-
-            # Show example from current project
-            current_indexes_dir = current_dir / ".leann" / "indexes"
-            if current_indexes_dir.exists():
-                current_index_dirs = [d for d in current_indexes_dir.iterdir() if d.is_dir()]
-                if current_index_dirs:
-                    example_name = current_index_dirs[0].name
-                    print(f'  leann search {example_name} "your query"')
-                    print(f"  leann ask {example_name} --interactive")
 
     def load_documents(self, docs_dir: str):
         print(f"Loading documents from {docs_dir}...")
@@ -287,125 +196,17 @@ Examples:
                 documents.extend(default_docs)
 
         # Load other file types with default reader
-        code_extensions = [
-            # Original document types
-            ".txt",
-            ".md",
-            ".docx",
-            # Code files for Claude Code integration
-            ".py",
-            ".js",
-            ".ts",
-            ".jsx",
-            ".tsx",
-            ".java",
-            ".cpp",
-            ".c",
-            ".h",
-            ".hpp",
-            ".cs",
-            ".go",
-            ".rs",
-            ".rb",
-            ".php",
-            ".swift",
-            ".kt",
-            ".scala",
-            ".r",
-            ".sql",
-            ".sh",
-            ".bash",
-            ".zsh",
-            ".fish",
-            ".ps1",
-            ".bat",
-            # Config and markup files
-            ".json",
-            ".yaml",
-            ".yml",
-            ".xml",
-            ".toml",
-            ".ini",
-            ".cfg",
-            ".conf",
-            ".html",
-            ".css",
-            ".scss",
-            ".less",
-            ".vue",
-            ".svelte",
-            # Data science
-            ".ipynb",
-            ".R",
-            ".py",
-            ".jl",
-        ]
         other_docs = SimpleDirectoryReader(
             docs_dir,
             recursive=True,
             encoding="utf-8",
-            required_exts=code_extensions,
+            required_exts=[".txt", ".md", ".docx"],
         ).load_data(show_progress=True)
         documents.extend(other_docs)
 
         all_texts = []
-
-        # Define code file extensions for intelligent chunking
-        code_file_exts = {
-            ".py",
-            ".js",
-            ".ts",
-            ".jsx",
-            ".tsx",
-            ".java",
-            ".cpp",
-            ".c",
-            ".h",
-            ".hpp",
-            ".cs",
-            ".go",
-            ".rs",
-            ".rb",
-            ".php",
-            ".swift",
-            ".kt",
-            ".scala",
-            ".r",
-            ".sql",
-            ".sh",
-            ".bash",
-            ".zsh",
-            ".fish",
-            ".ps1",
-            ".bat",
-            ".json",
-            ".yaml",
-            ".yml",
-            ".xml",
-            ".toml",
-            ".ini",
-            ".cfg",
-            ".conf",
-            ".html",
-            ".css",
-            ".scss",
-            ".less",
-            ".vue",
-            ".svelte",
-            ".ipynb",
-            ".R",
-            ".jl",
-        }
-
         for doc in documents:
-            # Check if this is a code file based on source path
+            nodes = self.node_parser.get_nodes_from_documents([doc])
-            source_path = doc.metadata.get("source", "")
-            is_code_file = any(source_path.endswith(ext) for ext in code_file_exts)
-
-            # 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(node.get_content())
 
@@ -418,8 +219,6 @@ Examples:
         index_dir = self.indexes_dir / index_name
         index_path = self.get_index_path(index_name)
 
-        print(f"📂 Indexing: {Path(docs_dir).resolve()}")
-
         if index_dir.exists() and not args.force:
             print(f"Index '{index_name}' already exists. Use --force to rebuild.")
             return
@@ -449,9 +248,6 @@ Examples:
         builder.build_index(index_path)
         print(f"Index built at {index_path}")
 
-        # Register this project directory in global registry
-        self.register_project_dir()
-
     async def search_documents(self, args):
         index_name = args.index_name
         query = args.query
@@ -512,11 +308,6 @@ Examples:
                 if not user_input:
                     continue
 
-                # Prepare LLM kwargs with thinking budget if specified
-                llm_kwargs = {}
-                if args.thinking_budget:
-                    llm_kwargs["thinking_budget"] = args.thinking_budget
-
                 response = chat.ask(
                     user_input,
                     top_k=args.top_k,
@@ -525,17 +316,11 @@ Examples:
                     prune_ratio=args.prune_ratio,
                     recompute_embeddings=args.recompute_embeddings,
                     pruning_strategy=args.pruning_strategy,
-                    llm_kwargs=llm_kwargs,
                 )
                 print(f"LEANN: {response}")
         else:
             query = input("Enter your question: ").strip()
             if query:
-                # Prepare LLM kwargs with thinking budget if specified
-                llm_kwargs = {}
-                if args.thinking_budget:
-                    llm_kwargs["thinking_budget"] = args.thinking_budget
-
                 response = chat.ask(
                     query,
                     top_k=args.top_k,
@@ -544,7 +329,6 @@ Examples:
                     prune_ratio=args.prune_ratio,
                     recompute_embeddings=args.recompute_embeddings,
                     pruning_strategy=args.pruning_strategy,
-                    llm_kwargs=llm_kwargs,
                 )
                 print(f"LEANN: {response}")
 

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

@@ -1,134 +0,0 @@
-#!/usr/bin/env python3
-
-import json
-import os
-import subprocess
-import sys
-
-
-def handle_request(request):
-    if request.get("method") == "initialize":
-        return {
-            "jsonrpc": "2.0",
-            "id": request.get("id"),
-            "result": {
-                "capabilities": {"tools": {}},
-                "protocolVersion": "2024-11-05",
-                "serverInfo": {"name": "leann-mcp", "version": "1.0.0"},
-            },
-        }
-
-    elif request.get("method") == "tools/list":
-        return {
-            "jsonrpc": "2.0",
-            "id": request.get("id"),
-            "result": {
-                "tools": [
-                    {
-                        "name": "leann_search",
-                        "description": "Search LEANN index",
-                        "inputSchema": {
-                            "type": "object",
-                            "properties": {
-                                "index_name": {"type": "string"},
-                                "query": {"type": "string"},
-                                "top_k": {"type": "integer", "default": 5},
-                            },
-                            "required": ["index_name", "query"],
-                        },
-                    },
-                    {
-                        "name": "leann_ask",
-                        "description": "Ask question using LEANN RAG",
-                        "inputSchema": {
-                            "type": "object",
-                            "properties": {
-                                "index_name": {"type": "string"},
-                                "question": {"type": "string"},
-                            },
-                            "required": ["index_name", "question"],
-                        },
-                    },
-                    {
-                        "name": "leann_list",
-                        "description": "List all LEANN indexes",
-                        "inputSchema": {"type": "object", "properties": {}},
-                    },
-                ]
-            },
-        }
-
-    elif request.get("method") == "tools/call":
-        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 = [
-                    "leann",
-                    "search",
-                    args["index_name"],
-                    args["query"],
-                    "--recompute-embeddings",
-                    f"--top-k={args.get('top_k', 5)}",
-                ]
-                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, cwd=cwd, env=env
-                )
-
-            elif tool_name == "leann_list":
-                result = subprocess.run(
-                    ["leann", "list"], capture_output=True, text=True, cwd=cwd, env=env
-                )
-
-            return {
-                "jsonrpc": "2.0",
-                "id": request.get("id"),
-                "result": {
-                    "content": [
-                        {
-                            "type": "text",
-                            "text": result.stdout
-                            if result.returncode == 0
-                            else f"Error: {result.stderr}",
-                        }
-                    ]
-                },
-            }
-
-        except Exception as e:
-            return {
-                "jsonrpc": "2.0",
-                "id": request.get("id"),
-                "error": {"code": -1, "message": str(e)},
-            }
-
-
-def main():
-    for line in sys.stdin:
-        try:
-            request = json.loads(line.strip())
-            response = handle_request(request)
-            if response:
-                print(json.dumps(response))
-                sys.stdout.flush()
-        except Exception as e:
-            error_response = {
-                "jsonrpc": "2.0",
-                "id": None,
-                "error": {"code": -1, "message": str(e)},
-            }
-            print(json.dumps(error_response))
-            sys.stdout.flush()
-
-
-if __name__ == "__main__":
-    main()

packages/leann-mcp/README.md

@@ -1,69 +0,0 @@
-# LEANN Claude Code Integration
-
-Intelligent code assistance using LEANN's vector search directly in Claude Code.
-
-## Prerequisites
-
-First, install LEANN CLI globally:
-
-```bash
-uv tool install leann
-```
-
-This makes the `leann` command available system-wide, which `leann_mcp` requires.
-
-## Quick Setup
-
-Add the LEANN MCP server to Claude Code:
-
-```bash
-claude mcp add leann-server -- leann_mcp
-```
-
-## Available Tools
-
-- **`leann_list`** - List available indexes across all projects
-- **`leann_search`** - Search code and documents with semantic queries
-- **`leann_ask`** - Ask questions and get AI-powered answers from your codebase
-
-## Quick Start
-
-```bash
-# Build an index for your project
-leann build my-project
-
-# Start Claude Code
-claude
-```
-
-Then in Claude Code:
-```
-Help me understand this codebase. List available indexes and search for authentication patterns.
-```
-
-<p align="center">
-  <img src="../../assets/claude_code_leann.png" alt="LEANN in Claude Code" width="80%">
-</p>
-
-
-## How It Works
-
-- **`leann`** - Core CLI tool for indexing and searching (installed globally)
-- **`leann_mcp`** - MCP server that wraps `leann` commands for Claude Code integration
-- Claude Code calls `leann_mcp`, which executes `leann` commands and returns results
-
-## File Support
-
-Python, JavaScript, TypeScript, Java, Go, Rust, SQL, YAML, JSON, and 30+ more file types.
-
-## Storage
-
-- Project indexes in `.leann/` directory (like `.git`)
-- Global project registry at `~/.leann/projects.json`
-- Multi-project support built-in
-
-## Removing
-
-```bash
-claude mcp remove leann-server
-```

uv.lock

@@ -2155,7 +2155,7 @@ wheels = [
 
 [[package]]
 name = "leann-backend-diskann"
-version = "0.2.1"
+version = "0.2.0"
 source = { editable = "packages/leann-backend-diskann" }
 dependencies = [
     { name = "leann-core" },
@@ -2167,14 +2167,14 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.2.1" },
+    { name = "leann-core", specifier = "==0.2.0" },
     { name = "numpy" },
     { name = "protobuf", specifier = ">=3.19.0" },
 ]
 
 [[package]]
 name = "leann-backend-hnsw"
-version = "0.2.1"
+version = "0.2.0"
 source = { editable = "packages/leann-backend-hnsw" }
 dependencies = [
     { name = "leann-core" },
@@ -2187,7 +2187,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "leann-core", specifier = "==0.2.1" },
+    { name = "leann-core", specifier = "==0.2.0" },
     { name = "msgpack", specifier = ">=1.0.0" },
     { name = "numpy" },
     { name = "pyzmq", specifier = ">=23.0.0" },
@@ -2195,7 +2195,7 @@ requires-dist = [
 
 [[package]]
 name = "leann-core"
-version = "0.2.1"
+version = "0.2.0"
 source = { editable = "packages/leann-core" }
 dependencies = [
     { name = "accelerate" },