Fix linting errors in colqwen_rag.py and test_colqwen_reproduction.py

- Add noqa comments for E402 errors (imports after sys.path modifications)
- Remove unused variable assignment in colqwen_rag.py
- Use importlib.util.find_spec for dependency checks instead of unused imports
- Fix import ordering in test_colqwen_reproduction.py

README.md

@@ -24,7 +24,7 @@ LEANN is an innovative vector database that democratizes personal AI. Transform
 
 LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Illustration Fig →](#️-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
 
-**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can semantic search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)** ([WeChat](#-wechat-detective-unlock-your-golden-memories), [iMessage](#-imessage-history-your-personal-conversation-archive)), **[agent memory](#-chatgpt-chat-history-your-personal-ai-conversation-archive)** ([ChatGPT](#-chatgpt-chat-history-your-personal-ai-conversation-archive), [Claude](#-claude-chat-history-your-personal-ai-conversation-archive)), **[live data](#mcp-integration-rag-on-live-data-from-any-platform)** ([Slack](#mcp-integration-rag-on-live-data-from-any-platform), [Twitter](#mcp-integration-rag-on-live-data-from-any-platform)), **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
+**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can semantic search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)** ([WeChat](#-wechat-detective-unlock-your-golden-memories), [iMessage](#-imessage-history-your-personal-conversation-archive)), **[agent memory](#-chatgpt-chat-history-your-personal-ai-conversation-archive)** ([ChatGPT](#-chatgpt-chat-history-your-personal-ai-conversation-archive), [Claude](#-claude-chat-history-your-personal-ai-conversation-archive)), **[live data](#mcp-integration-rag-on-live-data-from-any-platform)** ([Slack](#slack-messages-search-your-team-conversations), [Twitter](#-twitter-bookmarks-your-personal-tweet-library)), **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
 
 
 \* Claude Code only supports basic `grep`-style keyword search. **LEANN** is a drop-in **semantic search MCP service fully compatible with Claude Code**, unlocking intelligent retrieval without changing your workflow. 🔥 Check out [the easy setup →](packages/leann-mcp/README.md)

apps/colqwen_rag.py

@@ -23,17 +23,17 @@ if str(_leann_core_src) not in sys.path:
 if str(_leann_hnsw_pkg) not in sys.path:
     sys.path.append(str(_leann_hnsw_pkg))
 
-import torch
-from colpali_engine import ColPali, ColPaliProcessor, ColQwen2, ColQwen2Processor
-from colpali_engine.utils.torch_utils import ListDataset
-from pdf2image import convert_from_path
-from PIL import Image
-from torch.utils.data import DataLoader
-from tqdm import tqdm
+import torch  # noqa: E402
+from colpali_engine import ColPali, ColPaliProcessor, ColQwen2, ColQwen2Processor  # noqa: E402
+from colpali_engine.utils.torch_utils import ListDataset  # noqa: E402
+from pdf2image import convert_from_path  # noqa: E402
+from PIL import Image  # noqa: E402
+from torch.utils.data import DataLoader  # noqa: E402
+from tqdm import tqdm  # noqa: E402
 
 # Import the existing multi-vector implementation
 sys.path.append(str(_repo_root / "apps" / "multimodal" / "vision-based-pdf-multi-vector"))
-from leann_multi_vector import LeannMultiVector
+from leann_multi_vector import LeannMultiVector  # noqa: E402
 
 
 class ColQwenRAG:
@@ -259,7 +259,7 @@ class ColQwenRAG:
                     elif not query:
                         continue
 
-                    results = self.search(index_name, query, top_k=3)
+                    self.search(index_name, query, top_k=3)
 
                     # TODO: Add answer generation with Qwen-VL
                     print("\n💡 For detailed answers, we can integrate Qwen-VL here!")

test_colqwen_reproduction.py

@@ -11,6 +11,7 @@ This script demonstrates the ColQwen workflow:
 5. Generate similarity maps
 """
 
+import importlib.util
 import os
 from pathlib import Path
 
@@ -31,9 +32,14 @@ def main():
     # Step 1: Check dependencies
     print("\n📦 Checking dependencies...")
     try:
-        import pdf2image
         import torch
-        from colpali_engine.models import ColQwen2
+
+        # Check if pdf2image is available
+        if importlib.util.find_spec("pdf2image") is None:
+            raise ImportError("pdf2image not found")
+        # Check if colpali_engine is available
+        if importlib.util.find_spec("colpali_engine") is None:
+            raise ImportError("colpali_engine not found")
 
         print("✅ Core dependencies available")
         print(f"   - PyTorch: {torch.__version__}")