chore: release v0.2.5

* Propigate hosts argument for ollama through chat.py

* Apply suggestions from code review

Good AI slop suggestions.

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

README.md

@@ -6,6 +6,7 @@
   <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">
@@ -16,9 +17,10 @@ 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 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.
+**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.
 
-> **🚀 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.
 
 
 
@@ -28,7 +30,7 @@ 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 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)
+> **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)
 
 
 🔒 **Privacy:** Your data never leaves your laptop. No OpenAI, no cloud, no "terms of service".
@@ -221,7 +223,7 @@ 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 README 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 Technical report about LLM in Huawei in Chinese), and this is the **easiest example** to run here:
 
 ```bash
 source .venv/bin/activate # Don't forget to activate the virtual environment
@@ -416,7 +418,26 @@ 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
 
@@ -446,11 +467,8 @@ leann --help
 ### Usage Examples
 
 ```bash
-# Build an index from current directory (default)
+# build from a specific directory, and my_docs is the index name
-leann build my-docs
+leann build my-docs --docs ./your_documents
-
-# 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

@@ -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！

packages/leann-backend-diskann/pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-diskann"
-version = "0.2.2"
+version = "0.2.5"
-dependencies = ["leann-core==0.2.2", "numpy", "protobuf>=3.19.0"]
+dependencies = ["leann-core==0.2.5", "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 = "0.2.2"
+version = "0.2.5"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.2.2",
+    "leann-core==0.2.5",
     "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 = "0.2.2"
+version = "0.2.5"
 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() -> list[str]:
+def check_ollama_models(host: str) -> list[str]:
     """Check available Ollama models and return a list"""
     try:
         import requests
 
-        response = requests.get("http://localhost:11434/api/tags", timeout=5)
+        response = requests.get(f"{host}/api/tags", timeout=5)
         if response.status_code == 200:
             data = response.json()
             return [model["name"] for model in data.get("models", [])]
@@ -309,10 +309,12 @@ def search_hf_models(query: str, limit: int = 10) -> list[str]:
     return search_hf_models_fuzzy(query, limit)
 
 
-def validate_model_and_suggest(model_name: str, llm_type: str) -> str | None:
+def validate_model_and_suggest(
+    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()
+        available_models = check_ollama_models(host)
         if available_models and model_name not in available_models:
             error_msg = f"Model '{model_name}' not found in your local Ollama installation."
 
@@ -469,7 +471,7 @@ class OllamaChat(LLMInterface):
                 requests.get(host)
 
             # Pre-check model availability with helpful suggestions
-            model_error = validate_model_and_suggest(model, "ollama")
+            model_error = validate_model_and_suggest(model, "ollama", host)
             if model_error:
                 raise ValueError(model_error)
 

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

@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
 
 import json
-import os
 import subprocess
 import sys
 
@@ -62,10 +61,6 @@ 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 = [
@@ -76,18 +71,14 @@ def handle_request(request):
                     "--recompute-embeddings",
                     f"--top-k={args.get('top_k', 5)}",
                 ]
-                result = subprocess.run(cmd, capture_output=True, text=True, cwd=cwd, env=env)
+                result = subprocess.run(cmd, capture_output=True, text=True)
 
             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(
+                result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
-                    cmd, shell=True, capture_output=True, text=True, cwd=cwd, env=env
-                )
 
             elif tool_name == "leann_list":
-                result = subprocess.run(
+                result = subprocess.run(["leann", "list"], capture_output=True, text=True)
-                    ["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
+uv tool install leann-core
 ```
 
 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
+leann build my-project --docs ./ #change to your doc PATH
 
 # Start Claude Code
 claude

packages/leann/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann"
-version = "0.2.2"
+version = "0.2.5"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"