docs: Specify core contributors and welcome new contributors

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 +16,7 @@ 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)**, **[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.
-
-
-\* 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)
 
 
 
@@ -30,7 +26,7 @@ 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".
@@ -170,7 +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.
 
@@ -189,13 +185,12 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
 --force-rebuild         # Force rebuild index even if it exists
 
 # Embedding Parameters
---embedding-model MODEL  # e.g., facebook/contriever, text-embedding-3-small, nomic-embed-text, mlx-community/Qwen3-Embedding-0.6B-8bit or nomic-embed-text
+--embedding-model MODEL  # e.g., facebook/contriever, text-embedding-3-small or mlx-community/multilingual-e5-base-mlx
---embedding-mode MODE    # sentence-transformers, openai, mlx, or ollama
+--embedding-mode MODE    # sentence-transformers, openai, or mlx
 
 # 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)
@@ -223,7 +218,7 @@ 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 +413,7 @@ 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
 
@@ -451,7 +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
@@ -460,15 +436,13 @@ 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 from a specific directory, and my_docs is the index name
+# Build an index from documents
-leann build my-docs --docs ./your_documents
+leann build my-docs --docs ./documents
 
 # Search your documents
 leann search my-docs "machine learning concepts"

apps/base_rag_example.py

@@ -75,7 +75,7 @@ class BaseRAGExample(ABC):
             "--embedding-mode",
             type=str,
             default="sentence-transformers",
-            choices=["sentence-transformers", "openai", "mlx", "ollama"],
+            choices=["sentence-transformers", "openai", "mlx"],
             help="Embedding backend mode (default: sentence-transformers)",
         )
 
@@ -85,7 +85,7 @@ class BaseRAGExample(ABC):
             "--llm",
             type=str,
             default="openai",
-            choices=["openai", "ollama", "hf", "simulated"],
+            choices=["openai", "ollama", "hf"],
             help="LLM backend to use (default: openai)",
         )
         llm_group.add_argument(
@@ -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/configuration-guide.md

@@ -49,25 +49,14 @@ Based on our experience developing LEANN, embedding models fall into three categ
 - **Cons**: Slower inference, longer index build times
 - **Use when**: Quality is paramount and you have sufficient compute resources. **Highly recommended** for production use
 
-### Quick Start: Cloud and Local Embedding Options
+### Quick Start: OpenAI Embeddings (Fastest Setup)
 
-**OpenAI Embeddings (Fastest Setup)**
 For immediate testing without local model downloads:
 ```bash
 # Set OpenAI embeddings (requires OPENAI_API_KEY)
 --embedding-mode openai --embedding-model text-embedding-3-small
 ```
 
-**Ollama Embeddings (Privacy-Focused)**
-For local embeddings with complete privacy:
-```bash
-# First, pull an embedding model
-ollama pull nomic-embed-text
-
-# Use Ollama embeddings
---embedding-mode ollama --embedding-model nomic-embed-text
-```
-
 <details>
 <summary><strong>Cloud vs Local Trade-offs</strong></summary>
 
@@ -114,15 +103,13 @@ ollama pull nomic-embed-text
 **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)
@@ -164,36 +151,6 @@ ollama pull nomic-embed-text
 - 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`**
@@ -222,15 +179,9 @@ python apps/document_rag.py --query "What are the main techniques LEANN explores
 
 3. **Use MLX on Apple Silicon** (optional optimization):
    ```bash
-   --embedding-mode mlx --embedding-model mlx-community/Qwen3-Embedding-0.6B-8bit
+   --embedding-mode mlx --embedding-model mlx-community/multilingual-e5-base-mlx
    ```
-    MLX might not be the best choice, as we tested and found that it only offers 1.3x acceleration compared to HF, so maybe using ollama is a better choice for embedding generation
 
-4. **Use Ollama**
-   ```bash
-   --embedding-mode ollama --embedding-model nomic-embed-text
-   ```
-   To discover additional embedding models in ollama, check out https://ollama.com/search?c=embedding or read more about embedding models at https://ollama.com/blog/embedding-models, please do check the model size that works best for you
 ### If Search Quality is Poor
 
 1. **Increase retrieval count**:

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

@@ -261,7 +261,7 @@ if __name__ == "__main__":
         "--embedding-mode",
         type=str,
         default="sentence-transformers",
-        choices=["sentence-transformers", "openai", "mlx", "ollama"],
+        choices=["sentence-transformers", "openai", "mlx"],
         help="Embedding backend mode",
     )
     parser.add_argument(

packages/leann-backend-diskann/pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-diskann"
-version = "0.2.6"
+version = "0.2.1"
-dependencies = ["leann-core==0.2.6", "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/leann_backend_hnsw/hnsw_embedding_server.py

@@ -295,7 +295,7 @@ if __name__ == "__main__":
         "--embedding-mode",
         type=str,
         default="sentence-transformers",
-        choices=["sentence-transformers", "openai", "mlx", "ollama"],
+        choices=["sentence-transformers", "openai", "mlx"],
         help="Embedding backend mode",
     )
 

packages/leann-backend-hnsw/pyproject.toml

@@ -6,10 +6,10 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-hnsw"
-version = "0.2.6"
+version = "0.2.1"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.2.6",
+    "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 = "0.2.6"
+version = "0.2.1"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
 requires-python = ">=3.9"
@@ -31,8 +31,6 @@ dependencies = [
     "PyPDF2>=3.0.0",
     "pymupdf>=1.23.0",
     "pdfplumber>=0.10.0",
-    "nbconvert>=7.0.0",  # For .ipynb file support
-    "gitignore-parser>=0.1.12",  # For proper .gitignore handling
     "mlx>=0.26.3; sys_platform == 'darwin'",
     "mlx-lm>=0.26.0; sys_platform == 'darwin'",
 ]
@@ -46,7 +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

@@ -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)
 
@@ -491,35 +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:
@@ -710,38 +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")
@@ -74,11 +64,10 @@ class LeannCLI:
             formatter_class=argparse.RawDescriptionHelpFormatter,
             epilog="""
 Examples:
-  leann build my-docs --docs ./documents                    # Build index named my-docs
+  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 search my-docs "query"                             # Search in my-docs index
+  leann ask my-docs "question"             # Ask my-docs index
-  leann ask my-docs "question"                             # Ask my-docs index
+  leann list                              # List all stored indexes
-  leann list                                              # List all stored indexes
             """,
         )
 
@@ -86,34 +75,18 @@ Examples:
 
         # Build command
         build_parser = subparsers.add_parser("build", help="Build document index")
-        build_parser.add_argument(
+        build_parser.add_argument("index_name", help="Index name")
-            "index_name", nargs="?", help="Index name (default: current directory name)"
+        build_parser.add_argument("--docs", type=str, required=True, help="Documents directory")
-        )
-        build_parser.add_argument(
-            "--docs", type=str, default=".", help="Documents directory (default: current directory)"
-        )
         build_parser.add_argument(
             "--backend", type=str, default="hnsw", choices=["hnsw", "diskann"]
         )
         build_parser.add_argument("--embedding-model", type=str, default="facebook/contriever")
-        build_parser.add_argument(
-            "--embedding-mode",
-            type=str,
-            default="sentence-transformers",
-            choices=["sentence-transformers", "openai", "mlx", "ollama"],
-            help="Embedding backend mode (default: sentence-transformers)",
-        )
         build_parser.add_argument("--force", "-f", action="store_true", help="Force rebuild")
         build_parser.add_argument("--graph-degree", type=int, default=32)
         build_parser.add_argument("--complexity", type=int, default=64)
         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")
@@ -123,12 +96,7 @@ 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"],
@@ -151,370 +119,94 @@ 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"],
             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 _build_gitignore_parser(self, docs_dir: str):
-        """Build gitignore parser using gitignore-parser library."""
-        from gitignore_parser import parse_gitignore
-
-        # Try to parse the root .gitignore
-        gitignore_path = Path(docs_dir) / ".gitignore"
-
-        if gitignore_path.exists():
-            try:
-                # gitignore-parser automatically handles all subdirectory .gitignore files!
-                matches = parse_gitignore(str(gitignore_path))
-                print(f"📋 Loaded .gitignore from {docs_dir} (includes all subdirectories)")
-                return matches
-            except Exception as e:
-                print(f"Warning: Could not parse .gitignore: {e}")
-        else:
-            print("📋 No .gitignore found")
-
-        # Fallback: basic pattern matching for essential files
-        essential_patterns = {".git", ".DS_Store", "__pycache__", "node_modules", ".venv", "venv"}
-
-        def basic_matches(file_path):
-            path_parts = Path(file_path).parts
-            return any(part in essential_patterns for part in path_parts)
-
-        return basic_matches
-
-    def _should_exclude_file(self, relative_path: Path, gitignore_matches) -> bool:
-        """Check if a file should be excluded using gitignore parser."""
-        return gitignore_matches(str(relative_path))
-
     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}]")
+    def load_documents(self, docs_dir: str):
-                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, custom_file_types: str | None = None):
         print(f"Loading documents from {docs_dir}...")
-        if custom_file_types:
-            print(f"Using custom file types: {custom_file_types}")
 
-        # Build gitignore parser
+        # Try to use better PDF parsers first
-        gitignore_matches = self._build_gitignore_parser(docs_dir)
-
-        # Try to use better PDF parsers first, but only if PDFs are requested
         documents = []
         docs_path = Path(docs_dir)
 
-        # Check if we should process PDFs
+        for file_path in docs_path.rglob("*.pdf"):
-        should_process_pdfs = custom_file_types is None or ".pdf" in custom_file_types
+            print(f"Processing PDF: {file_path}")
 
-        if should_process_pdfs:
+            # Try PyMuPDF first (best quality)
-            for file_path in docs_path.rglob("*.pdf"):
+            text = extract_pdf_text_with_pymupdf(str(file_path))
-                # Check if file matches any exclude pattern
+            if text is None:
-                relative_path = file_path.relative_to(docs_path)
+                # Try pdfplumber
-                if self._should_exclude_file(relative_path, gitignore_matches):
+                text = extract_pdf_text_with_pdfplumber(str(file_path))
-                    continue
 
-                print(f"Processing PDF: {file_path}")
+            if text:
+                # Create a simple document structure
+                from llama_index.core import Document
 
-                # Try PyMuPDF first (best quality)
+                doc = Document(text=text, metadata={"source": str(file_path)})
-                text = extract_pdf_text_with_pymupdf(str(file_path))
+                documents.append(doc)
-                if text is None:
+            else:
-                    # Try pdfplumber
+                # Fallback to default reader
-                    text = extract_pdf_text_with_pdfplumber(str(file_path))
+                print(f"Using default reader for {file_path}")
-
+                default_docs = SimpleDirectoryReader(
-                if text:
+                    str(file_path.parent),
-                    # Create a simple document structure
+                    filename_as_id=True,
-                    from llama_index.core import Document
+                    required_exts=[file_path.suffix],
-
+                ).load_data()
-                    doc = Document(text=text, metadata={"source": str(file_path)})
+                documents.extend(default_docs)
-                    documents.append(doc)
-                else:
-                    # Fallback to default reader
-                    print(f"Using default reader for {file_path}")
-                    try:
-                        default_docs = SimpleDirectoryReader(
-                            str(file_path.parent),
-                            filename_as_id=True,
-                            required_exts=[file_path.suffix],
-                        ).load_data()
-                        documents.extend(default_docs)
-                    except Exception as e:
-                        print(f"Warning: Could not process {file_path}: {e}")
 
         # Load other file types with default reader
-        if custom_file_types:
+        other_docs = SimpleDirectoryReader(
-            # Parse custom file types from comma-separated string
+            docs_dir,
-            code_extensions = [ext.strip() for ext in custom_file_types.split(",") if ext.strip()]
+            recursive=True,
-            # Ensure extensions start with a dot
+            encoding="utf-8",
-            code_extensions = [ext if ext.startswith(".") else f".{ext}" for ext in code_extensions]
+            required_exts=[".txt", ".md", ".docx"],
-        else:
+        ).load_data(show_progress=True)
-            # Use default supported file types
+        documents.extend(other_docs)
-            code_extensions = [
-                # Original document types
-                ".txt",
-                ".md",
-                ".docx",
-                ".pptx",
-                # 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",
-            ]
-        # Try to load other file types, but don't fail if none are found
-        try:
-            # Create a custom file filter function using our PathSpec
-            def file_filter(file_path: str) -> bool:
-                """Return True if file should be included (not excluded)"""
-                try:
-                    docs_path_obj = Path(docs_dir)
-                    file_path_obj = Path(file_path)
-                    relative_path = file_path_obj.relative_to(docs_path_obj)
-                    return not self._should_exclude_file(relative_path, gitignore_matches)
-                except (ValueError, OSError):
-                    return True  # Include files that can't be processed
-
-            other_docs = SimpleDirectoryReader(
-                docs_dir,
-                recursive=True,
-                encoding="utf-8",
-                required_exts=code_extensions,
-                file_extractor={},  # Use default extractors
-                filename_as_id=True,
-            ).load_data(show_progress=True)
-
-            # Filter documents after loading based on gitignore rules
-            filtered_docs = []
-            for doc in other_docs:
-                file_path = doc.metadata.get("file_path", "")
-                if file_filter(file_path):
-                    filtered_docs.append(doc)
-
-            documents.extend(filtered_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 = []
-
-        # 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())
 
@@ -523,23 +215,15 @@ Examples:
 
     async def build_index(self, args):
         docs_dir = args.docs
-        # Use current directory name if index_name not provided
+        index_name = args.index_name
-        if args.index_name:
-            index_name = args.index_name
-        else:
-            index_name = Path.cwd().name
-            print(f"Using current directory name as index: '{index_name}'")
-
         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
 
-        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
@@ -551,7 +235,6 @@ Examples:
         builder = LeannBuilder(
             backend_name=args.backend,
             embedding_model=args.embedding_model,
-            embedding_mode=args.embedding_mode,
             graph_degree=args.graph_degree,
             complexity=args.complexity,
             is_compact=args.compact,
@@ -565,9 +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
@@ -628,11 +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,
@@ -641,17 +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,
@@ -660,7 +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/embedding_compute.py

@@ -6,7 +6,6 @@ Preserves all optimization parameters to ensure performance
 
 import logging
 import os
-from concurrent.futures import ThreadPoolExecutor, as_completed
 from typing import Any
 
 import numpy as np
@@ -36,7 +35,7 @@ def compute_embeddings(
     Args:
         texts: List of texts to compute embeddings for
         model_name: Model name
-        mode: Computation mode ('sentence-transformers', 'openai', 'mlx', 'ollama')
+        mode: Computation mode ('sentence-transformers', 'openai', 'mlx')
         is_build: Whether this is a build operation (shows progress bar)
         batch_size: Batch size for processing
         adaptive_optimization: Whether to use adaptive optimization based on batch size
@@ -56,8 +55,6 @@ def compute_embeddings(
         return compute_embeddings_openai(texts, model_name)
     elif mode == "mlx":
         return compute_embeddings_mlx(texts, model_name)
-    elif mode == "ollama":
-        return compute_embeddings_ollama(texts, model_name, is_build=is_build)
     else:
         raise ValueError(f"Unsupported embedding mode: {mode}")
 
@@ -368,262 +365,3 @@ def compute_embeddings_mlx(chunks: list[str], model_name: str, batch_size: int =
 
     # Stack numpy arrays
     return np.stack(all_embeddings)
-
-
-def compute_embeddings_ollama(
-    texts: list[str], model_name: str, is_build: bool = False, host: str = "http://localhost:11434"
-) -> np.ndarray:
-    """
-    Compute embeddings using Ollama API.
-
-    Args:
-        texts: List of texts to compute embeddings for
-        model_name: Ollama model name (e.g., "nomic-embed-text", "mxbai-embed-large")
-        is_build: Whether this is a build operation (shows progress bar)
-        host: Ollama host URL (default: http://localhost:11434)
-
-    Returns:
-        Normalized embeddings array, shape: (len(texts), embedding_dim)
-    """
-    try:
-        import requests
-    except ImportError:
-        raise ImportError(
-            "The 'requests' library is required for Ollama embeddings. Install with: uv pip install requests"
-        )
-
-    if not texts:
-        raise ValueError("Cannot compute embeddings for empty text list")
-
-    logger.info(
-        f"Computing embeddings for {len(texts)} texts using Ollama API, model: '{model_name}'"
-    )
-
-    # Check if Ollama is running
-    try:
-        response = requests.get(f"{host}/api/version", timeout=5)
-        response.raise_for_status()
-    except requests.exceptions.ConnectionError:
-        error_msg = (
-            f"❌ Could not connect to Ollama at {host}.\n\n"
-            "Please ensure Ollama is running:\n"
-            "  • macOS/Linux: ollama serve\n"
-            "  • Windows: Make sure Ollama is running in the system tray\n\n"
-            "Installation: https://ollama.com/download"
-        )
-        raise RuntimeError(error_msg)
-    except Exception as e:
-        raise RuntimeError(f"Unexpected error connecting to Ollama: {e}")
-
-    # Check if model exists and provide helpful suggestions
-    try:
-        response = requests.get(f"{host}/api/tags", timeout=5)
-        response.raise_for_status()
-        models = response.json()
-        model_names = [model["name"] for model in models.get("models", [])]
-
-        # Filter for embedding models (models that support embeddings)
-        embedding_models = []
-        suggested_embedding_models = [
-            "nomic-embed-text",
-            "mxbai-embed-large",
-            "bge-m3",
-            "all-minilm",
-            "snowflake-arctic-embed",
-        ]
-
-        for model in model_names:
-            # Check if it's an embedding model (by name patterns or known models)
-            base_name = model.split(":")[0]
-            if any(emb in base_name for emb in ["embed", "bge", "minilm", "e5"]):
-                embedding_models.append(model)
-
-        # Check if model exists (handle versioned names)
-        model_found = any(
-            model_name == name.split(":")[0] or model_name == name for name in model_names
-        )
-
-        if not model_found:
-            error_msg = f"❌ Model '{model_name}' not found in local Ollama.\n\n"
-
-            # Suggest pulling the model
-            error_msg += "📦 To install this embedding model:\n"
-            error_msg += f"   ollama pull {model_name}\n\n"
-
-            # Show available embedding models
-            if embedding_models:
-                error_msg += "✅ Available embedding models:\n"
-                for model in embedding_models[:5]:
-                    error_msg += f"   • {model}\n"
-                if len(embedding_models) > 5:
-                    error_msg += f"   ... and {len(embedding_models) - 5} more\n"
-            else:
-                error_msg += "💡 Popular embedding models to install:\n"
-                for model in suggested_embedding_models[:3]:
-                    error_msg += f"   • ollama pull {model}\n"
-
-            error_msg += "\n📚 Browse more: https://ollama.com/library"
-            raise ValueError(error_msg)
-
-        # Verify the model supports embeddings by testing it
-        try:
-            test_response = requests.post(
-                f"{host}/api/embeddings", json={"model": model_name, "prompt": "test"}, timeout=10
-            )
-            if test_response.status_code != 200:
-                error_msg = (
-                    f"⚠️ Model '{model_name}' exists but may not support embeddings.\n\n"
-                    f"Please use an embedding model like:\n"
-                )
-                for model in suggested_embedding_models[:3]:
-                    error_msg += f"   • {model}\n"
-                raise ValueError(error_msg)
-        except requests.exceptions.RequestException:
-            # If test fails, continue anyway - model might still work
-            pass
-
-    except requests.exceptions.RequestException as e:
-        logger.warning(f"Could not verify model existence: {e}")
-
-    # Process embeddings with optimized concurrent processing
-    import requests
-
-    def get_single_embedding(text_idx_tuple):
-        """Helper function to get embedding for a single text."""
-        text, idx = text_idx_tuple
-        max_retries = 3
-        retry_count = 0
-
-        # 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"{host}/api/embeddings",
-                    json={"model": model_name, "prompt": truncated_text},
-                    timeout=30,
-                )
-                response.raise_for_status()
-
-                result = response.json()
-                embedding = result.get("embedding")
-
-                if embedding is None:
-                    raise ValueError(f"No embedding returned for text {idx}")
-
-                return idx, embedding
-
-            except requests.exceptions.Timeout:
-                retry_count += 1
-                if retry_count >= max_retries:
-                    logger.warning(f"Timeout for text {idx} after {max_retries} retries")
-                    return idx, None
-
-            except Exception as e:
-                if retry_count >= max_retries - 1:
-                    logger.error(f"Failed to get embedding for text {idx}: {e}")
-                    return idx, None
-                retry_count += 1
-
-        return idx, None
-
-    # Determine if we should use concurrent processing
-    use_concurrent = (
-        len(texts) > 5 and not is_build
-    )  # Don't use concurrent in build mode to avoid overwhelming
-    max_workers = min(4, len(texts))  # Limit concurrent requests to avoid overwhelming Ollama
-
-    all_embeddings = [None] * len(texts)  # Pre-allocate list to maintain order
-    failed_indices = []
-
-    if use_concurrent:
-        logger.info(
-            f"Using concurrent processing with {max_workers} workers for {len(texts)} texts"
-        )
-
-        with ThreadPoolExecutor(max_workers=max_workers) as executor:
-            # Submit all tasks
-            future_to_idx = {
-                executor.submit(get_single_embedding, (text, idx)): idx
-                for idx, text in enumerate(texts)
-            }
-
-            # Add progress bar for concurrent processing
-            try:
-                if is_build or len(texts) > 10:
-                    from tqdm import tqdm
-
-                    futures_iterator = tqdm(
-                        as_completed(future_to_idx),
-                        total=len(texts),
-                        desc="Computing Ollama embeddings",
-                    )
-                else:
-                    futures_iterator = as_completed(future_to_idx)
-            except ImportError:
-                futures_iterator = as_completed(future_to_idx)
-
-            # Collect results as they complete
-            for future in futures_iterator:
-                try:
-                    idx, embedding = future.result()
-                    if embedding is not None:
-                        all_embeddings[idx] = embedding
-                    else:
-                        failed_indices.append(idx)
-                except Exception as e:
-                    idx = future_to_idx[future]
-                    logger.error(f"Exception for text {idx}: {e}")
-                    failed_indices.append(idx)
-
-    else:
-        # Sequential processing with progress bar
-        show_progress = is_build or len(texts) > 10
-
-        try:
-            if show_progress:
-                from tqdm import tqdm
-
-                iterator = tqdm(
-                    enumerate(texts), total=len(texts), desc="Computing Ollama embeddings"
-                )
-            else:
-                iterator = enumerate(texts)
-        except ImportError:
-            iterator = enumerate(texts)
-
-        for idx, text in iterator:
-            result_idx, embedding = get_single_embedding((text, idx))
-            if embedding is not None:
-                all_embeddings[idx] = embedding
-            else:
-                failed_indices.append(idx)
-
-    # Handle failed embeddings
-    if failed_indices:
-        if len(failed_indices) == len(texts):
-            raise RuntimeError("Failed to compute any embeddings")
-
-        logger.warning(f"Failed to compute embeddings for {len(failed_indices)}/{len(texts)} texts")
-
-        # Use zero embeddings as fallback for failed ones
-        valid_embedding = next((e for e in all_embeddings if e is not None), None)
-        if valid_embedding:
-            embedding_dim = len(valid_embedding)
-            for idx in failed_indices:
-                all_embeddings[idx] = [0.0] * embedding_dim
-
-    # Remove None values and convert to numpy array
-    all_embeddings = [e for e in all_embeddings if e is not None]
-
-    # Convert to numpy array and normalize
-    embeddings = np.array(all_embeddings, dtype=np.float32)
-
-    # Normalize embeddings (L2 normalization)
-    norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
-    embeddings = embeddings / (norms + 1e-8)  # Add small epsilon to avoid division by zero
-
-    logger.info(f"Generated {len(embeddings)} embeddings, dimension: {embeddings.shape[1]}")
-
-    return embeddings

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

@@ -1,176 +0,0 @@
-#!/usr/bin/env python3
-
-import json
-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 code using natural language - like having a coding assistant who knows your entire codebase!
-
-🎯 **Perfect for**:
-- "How does authentication work?" → finds auth-related code
-- "Error handling patterns" → locates try-catch blocks and error logic
-- "Database connection setup" → finds DB initialization code
-- "API endpoint definitions" → locates route handlers
-- "Configuration management" → finds config files and usage
-
-💡 **Pro tip**: Use this before making any changes to understand existing patterns and conventions.""",
-                        "inputSchema": {
-                            "type": "object",
-                            "properties": {
-                                "index_name": {
-                                    "type": "string",
-                                    "description": "Name of the LEANN index to search. Use 'leann_list' first to see available indexes.",
-                                },
-                                "query": {
-                                    "type": "string",
-                                    "description": "Search query - can be natural language (e.g., 'how to handle errors') or technical terms (e.g., 'async function definition')",
-                                },
-                                "top_k": {
-                                    "type": "integer",
-                                    "default": 5,
-                                    "minimum": 1,
-                                    "maximum": 20,
-                                    "description": "Number of search results to return. Use 5-10 for focused results, 15-20 for comprehensive exploration.",
-                                },
-                                "complexity": {
-                                    "type": "integer",
-                                    "default": 32,
-                                    "minimum": 16,
-                                    "maximum": 128,
-                                    "description": "Search complexity level. Use 16-32 for fast searches (recommended), 64+ for higher precision when needed.",
-                                },
-                            },
-                            "required": ["index_name", "query"],
-                        },
-                    },
-                    {
-                        "name": "leann_status",
-                        "description": "📊 Check the health and stats of your code indexes - like a medical checkup for your codebase knowledge!",
-                        "inputSchema": {
-                            "type": "object",
-                            "properties": {
-                                "index_name": {
-                                    "type": "string",
-                                    "description": "Optional: Name of specific index to check. If not provided, shows status of all indexes.",
-                                }
-                            },
-                        },
-                    },
-                    {
-                        "name": "leann_list",
-                        "description": "📋 Show all your indexed codebases - your personal code library! Use this to see what's available for search.",
-                        "inputSchema": {"type": "object", "properties": {}},
-                    },
-                ]
-            },
-        }
-
-    elif request.get("method") == "tools/call":
-        tool_name = request["params"]["name"]
-        args = request["params"].get("arguments", {})
-
-        try:
-            if tool_name == "leann_search":
-                # Validate required parameters
-                if not args.get("index_name") or not args.get("query"):
-                    return {
-                        "jsonrpc": "2.0",
-                        "id": request.get("id"),
-                        "result": {
-                            "content": [
-                                {
-                                    "type": "text",
-                                    "text": "Error: Both index_name and query are required",
-                                }
-                            ]
-                        },
-                    }
-
-                # Build simplified command
-                cmd = [
-                    "leann",
-                    "search",
-                    args["index_name"],
-                    args["query"],
-                    f"--top-k={args.get('top_k', 5)}",
-                    f"--complexity={args.get('complexity', 32)}",
-                ]
-
-                result = subprocess.run(cmd, capture_output=True, text=True)
-
-            elif tool_name == "leann_status":
-                if args.get("index_name"):
-                    # Check specific index status - for now, we'll use leann list and filter
-                    result = subprocess.run(["leann", "list"], capture_output=True, text=True)
-                    # We could enhance this to show more detailed status per index
-                else:
-                    # Show all indexes status
-                    result = subprocess.run(["leann", "list"], capture_output=True, text=True)
-
-            elif tool_name == "leann_list":
-                result = subprocess.run(["leann", "list"], capture_output=True, text=True)
-
-            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

@@ -1,91 +0,0 @@
-# 🔥 LEANN Claude Code Integration
-
-Transform your development workflow with intelligent code assistance using LEANN's semantic search directly in Claude Code.
-
-## Prerequisites
-
-**Step 1:** First, complete the basic LEANN installation following the [📦 Installation guide](../../README.md#installation) in the root README:
-
-```bash
-uv venv
-source .venv/bin/activate
-uv pip install leann
-```
-
-**Step 2:** Install LEANN globally for MCP integration:
-```bash
-uv tool install leann-core
-```
-
-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
-
-Once connected, you'll have access to these powerful semantic search tools in Claude Code:
-
-- **`leann_list`** - List all available indexes across your projects
-- **`leann_search`** - Perform semantic searches across code and documents
-- **`leann_ask`** - Ask natural language questions and get AI-powered answers from your codebase
-
-## 🎯 Quick Start Example
-
-```bash
-# Build an index for your project (change to your actual path)
-leann build my-project --docs ./
-
-# Start Claude Code
-claude
-```
-
-**Try this 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
-
-The integration consists of three key components working seamlessly together:
-
-- **`leann`** - Core CLI tool for indexing and searching (installed globally via `uv tool install`)
-- **`leann_mcp`** - MCP server that wraps `leann` commands for Claude Code integration
-- **Claude Code** - Calls `leann_mcp`, which executes `leann` commands and returns intelligent results
-
-## 📁 File Support
-
-LEANN understands **30+ file types** including:
-- **Programming**: Python, JavaScript, TypeScript, Java, Go, Rust, C++, C#
-- **Data**: SQL, YAML, JSON, CSV, XML
-- **Documentation**: Markdown, TXT, PDF
-- **And many more!**
-
-## 💾 Storage & Organization
-
-- **Project indexes**: Stored in `.leann/` directory (just like `.git`)
-- **Global registry**: Project tracking at `~/.leann/projects.json`
-- **Multi-project support**: Switch between different codebases seamlessly
-- **Portable**: Transfer indexes between machines with minimal overhead
-
-## 🗑️ Uninstalling
-
-To remove the LEANN MCP server from Claude Code:
-
-```bash
-claude mcp remove leann-server
-```
-To remove LEANN
-```
-uv pip uninstall leann leann-backend-hnsw leann-core
-```

packages/leann/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann"
-version = "0.2.6"
+version = "0.2.1"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"

pyproject.toml

@@ -32,7 +32,7 @@ dependencies = [
     "pypdfium2>=4.30.0",
     # LlamaIndex core and readers - updated versions
     "llama-index>=0.12.44",
-    "llama-index-readers-file>=0.4.0", # Essential for PDF parsing
+    "llama-index-readers-file>=0.4.0",  # Essential for PDF parsing
     # "llama-index-readers-docling",  # Requires Python >= 3.10
     # "llama-index-node-parser-docling",  # Requires Python >= 3.10
     "llama-index-vector-stores-faiss>=0.4.0",
@@ -43,9 +43,6 @@ dependencies = [
     "mlx>=0.26.3; sys_platform == 'darwin'",
     "mlx-lm>=0.26.0; sys_platform == 'darwin'",
     "psutil>=5.8.0",
-    "pathspec>=0.12.1",
-    "nbconvert>=7.16.6",
-    "gitignore-parser>=0.1.12",
 ]
 
 [project.optional-dependencies]