fix security vulnerability: replace eval()

COLQWEN_GUIDE.md

@@ -60,6 +60,20 @@ python -m apps.colqwen_rag ask my_index --interactive
 - `help`: Show available commands
 - `quit`/`exit`/`q`: Exit interactive mode
 
+## 🧪 Test & Reproduce Results
+
+Run the reproduction test for issue #119:
+```bash
+python test_colqwen_reproduction.py
+```
+
+This will:
+1. ✅ Check dependencies
+2. 📥 Download sample PDF (Attention Is All You Need paper)
+3. 🏗️ Build test index
+4. 🔍 Run sample queries
+5. 📊 Show how to generate similarity maps
+
 ## 🎨 Advanced: Similarity Maps
 
 For visual similarity analysis, use the existing advanced script:

README.md

@@ -379,54 +379,6 @@ python -m apps.code_rag --repo-dir "./my_codebase" --query "How does authenticat
 
 </details>
 
-### 🎨 ColQwen: Multimodal PDF Retrieval with Vision-Language Models
-
-Search through PDFs using both text and visual understanding with ColQwen2/ColPali models. Perfect for research papers, technical documents, and any PDFs with complex layouts, figures, or diagrams.
-
-> **🍎 Mac Users**: ColQwen is optimized for Apple Silicon with MPS acceleration for faster inference!
-
-```bash
-# Build index from PDFs
-python -m apps.colqwen_rag build --pdfs ./my_papers/ --index research_papers
-
-# Search with text queries
-python -m apps.colqwen_rag search research_papers "How does attention mechanism work?"
-
-# Interactive Q&A
-python -m apps.colqwen_rag ask research_papers --interactive
-```
-
-<details>
-<summary><strong>📋 Click to expand: ColQwen Setup & Usage</strong></summary>
-
-#### Prerequisites
-```bash
-# Install dependencies
-uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
-brew install poppler  # macOS only, for PDF processing
-```
-
-#### Build Index
-```bash
-python -m apps.colqwen_rag build \
-  --pdfs ./pdf_directory/ \
-  --index my_index \
-  --model colqwen2  # or colpali
-```
-
-#### Search
-```bash
-python -m apps.colqwen_rag search my_index "your question here" --top-k 5
-```
-
-#### Models
-- **ColQwen2** (`colqwen2`): Latest vision-language model with improved performance
-- **ColPali** (`colpali`): Proven multimodal retriever
-
-For detailed usage, see the [ColQwen Guide](docs/COLQWEN_GUIDE.md).
-
-</details>
-
 ### 📧 Your Personal Email Secretary: RAG on Apple Mail!
 
 > **Note:** The examples below currently support macOS only. Windows support coming soon.

apps/image_rag.py

@@ -1,218 +0,0 @@
-#!/usr/bin/env python3
-"""
-CLIP Image RAG Application
-
-This application enables RAG (Retrieval-Augmented Generation) on images using CLIP embeddings.
-You can index a directory of images and search them using text queries.
-
-Usage:
-    python -m apps.image_rag --image-dir ./my_images/ --query "a sunset over mountains"
-    python -m apps.image_rag --image-dir ./my_images/ --interactive
-"""
-
-import argparse
-import pickle
-import tempfile
-from pathlib import Path
-
-import numpy as np
-from PIL import Image
-from sentence_transformers import SentenceTransformer
-from tqdm import tqdm
-
-from apps.base_rag_example import BaseRAGExample
-
-
-class ImageRAG(BaseRAGExample):
-    """
-    RAG application for images using CLIP embeddings.
-
-    This class provides a complete RAG pipeline for image data, including
-    CLIP embedding generation, indexing, and text-based image search.
-    """
-
-    def __init__(self):
-        super().__init__(
-            name="Image RAG",
-            description="RAG application for images using CLIP embeddings",
-            default_index_name="image_index",
-        )
-        # Override default embedding model to use CLIP
-        self.embedding_model_default = "clip-ViT-L-14"
-        self.embedding_mode_default = "sentence-transformers"
-        self._image_data: list[dict] = []
-
-    def _add_specific_arguments(self, parser: argparse.ArgumentParser):
-        """Add image-specific arguments."""
-        image_group = parser.add_argument_group("Image Parameters")
-        image_group.add_argument(
-            "--image-dir",
-            type=str,
-            required=True,
-            help="Directory containing images to index",
-        )
-        image_group.add_argument(
-            "--image-extensions",
-            type=str,
-            nargs="+",
-            default=[".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp"],
-            help="Image file extensions to process (default: .jpg .jpeg .png .gif .bmp .webp)",
-        )
-        image_group.add_argument(
-            "--batch-size",
-            type=int,
-            default=32,
-            help="Batch size for CLIP embedding generation (default: 32)",
-        )
-
-    async def load_data(self, args) -> list[str]:
-        """Load images, generate CLIP embeddings, and return text descriptions."""
-        self._image_data = self._load_images_and_embeddings(args)
-        return [entry["text"] for entry in self._image_data]
-
-    def _load_images_and_embeddings(self, args) -> list[dict]:
-        """Helper to process images and produce embeddings/metadata."""
-        image_dir = Path(args.image_dir)
-        if not image_dir.exists():
-            raise ValueError(f"Image directory does not exist: {image_dir}")
-
-        print(f"📸 Loading images from {image_dir}...")
-
-        # Find all image files
-        image_files = []
-        for ext in args.image_extensions:
-            image_files.extend(image_dir.rglob(f"*{ext}"))
-            image_files.extend(image_dir.rglob(f"*{ext.upper()}"))
-
-        if not image_files:
-            raise ValueError(
-                f"No images found in {image_dir} with extensions {args.image_extensions}"
-            )
-
-        print(f"✅ Found {len(image_files)} images")
-
-        # Limit if max_items is set
-        if args.max_items > 0:
-            image_files = image_files[: args.max_items]
-            print(f"📊 Processing {len(image_files)} images (limited by --max-items)")
-
-        # Load CLIP model
-        print("🔍 Loading CLIP model...")
-        model = SentenceTransformer(self.embedding_model_default)
-
-        # Process images and generate embeddings
-        print("🖼️  Processing images and generating embeddings...")
-        image_data = []
-        batch_images = []
-        batch_paths = []
-
-        for image_path in tqdm(image_files, desc="Processing images"):
-            try:
-                image = Image.open(image_path).convert("RGB")
-                batch_images.append(image)
-                batch_paths.append(image_path)
-
-                # Process in batches
-                if len(batch_images) >= args.batch_size:
-                    embeddings = model.encode(
-                        batch_images,
-                        convert_to_numpy=True,
-                        normalize_embeddings=True,
-                        batch_size=args.batch_size,
-                        show_progress_bar=False,
-                    )
-
-                    for img_path, embedding in zip(batch_paths, embeddings):
-                        image_data.append(
-                            {
-                                "text": f"Image: {img_path.name}\nPath: {img_path}",
-                                "metadata": {
-                                    "image_path": str(img_path),
-                                    "image_name": img_path.name,
-                                    "image_dir": str(image_dir),
-                                },
-                                "embedding": embedding.astype(np.float32),
-                            }
-                        )
-
-                    batch_images = []
-                    batch_paths = []
-
-            except Exception as e:
-                print(f"⚠️  Failed to process {image_path}: {e}")
-                continue
-
-        # Process remaining images
-        if batch_images:
-            embeddings = model.encode(
-                batch_images,
-                convert_to_numpy=True,
-                normalize_embeddings=True,
-                batch_size=len(batch_images),
-                show_progress_bar=False,
-            )
-
-            for img_path, embedding in zip(batch_paths, embeddings):
-                image_data.append(
-                    {
-                        "text": f"Image: {img_path.name}\nPath: {img_path}",
-                        "metadata": {
-                            "image_path": str(img_path),
-                            "image_name": img_path.name,
-                            "image_dir": str(image_dir),
-                        },
-                        "embedding": embedding.astype(np.float32),
-                    }
-                )
-
-        print(f"✅ Processed {len(image_data)} images")
-        return image_data
-
-    async def build_index(self, args, texts: list[str]) -> str:
-        """Build index using pre-computed CLIP embeddings."""
-        from leann.api import LeannBuilder
-
-        if not self._image_data or len(self._image_data) != len(texts):
-            raise RuntimeError("No image data found. Make sure load_data() ran successfully.")
-
-        print("🔨 Building LEANN index with CLIP embeddings...")
-        builder = LeannBuilder(
-            backend_name=args.backend_name,
-            embedding_model=self.embedding_model_default,
-            embedding_mode=self.embedding_mode_default,
-            is_recompute=False,
-            distance_metric="cosine",
-            graph_degree=args.graph_degree,
-            build_complexity=args.build_complexity,
-            is_compact=not args.no_compact,
-        )
-
-        for text, data in zip(texts, self._image_data):
-            builder.add_text(text=text, metadata=data["metadata"])
-
-        ids = [str(i) for i in range(len(self._image_data))]
-        embeddings = np.array([data["embedding"] for data in self._image_data], dtype=np.float32)
-
-        with tempfile.NamedTemporaryFile(mode="wb", suffix=".pkl", delete=False) as f:
-            pickle.dump((ids, embeddings), f)
-            pkl_path = f.name
-
-        try:
-            index_path = str(Path(args.index_dir) / f"{self.default_index_name}.leann")
-            builder.build_index_from_embeddings(index_path, pkl_path)
-            print(f"✅ Index built successfully at {index_path}")
-            return index_path
-        finally:
-            Path(pkl_path).unlink()
-
-
-def main():
-    """Main entry point for the image RAG application."""
-    import asyncio
-
-    app = ImageRAG()
-    asyncio.run(app.run())
-
-
-if __name__ == "__main__":
-    main()

apps/slack_data/slack_mcp_reader.py

@@ -7,6 +7,7 @@ for indexing in LEANN. It supports various Slack MCP server implementations and
 flexible message processing options.
 """
 
+import ast
 import asyncio
 import json
 import logging
@@ -146,16 +147,16 @@ class SlackMCPReader:
                     match = re.search(r"'error':\s*(\{[^}]+\})", str(e))
                     if match:
                         try:
-                            error_dict = eval(match.group(1))
-                        except (ValueError, SyntaxError, NameError):
+                            error_dict = ast.literal_eval(match.group(1))
+                        except (ValueError, SyntaxError):
                             pass
                     else:
                         # Try alternative format
                         match = re.search(r"Failed to fetch messages:\s*(\{[^}]+\})", str(e))
                         if match:
                             try:
-                                error_dict = eval(match.group(1))
-                            except (ValueError, SyntaxError, NameError):
+                                error_dict = ast.literal_eval(match.group(1))
+                            except (ValueError, SyntaxError):
                                 pass
 
                 if self._is_cache_sync_error(error_dict):

test_colqwen_reproduction.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+"""
+Test script to reproduce ColQwen results from issue #119
+https://github.com/yichuan-w/LEANN/issues/119
+
+This script demonstrates the ColQwen workflow:
+1. Download sample PDF
+2. Convert to images
+3. Build multimodal index
+4. Run test queries
+5. Generate similarity maps
+"""
+
+import importlib.util
+import os
+from pathlib import Path
+
+
+def main():
+    print("🧪 ColQwen Reproduction Test - Issue #119")
+    print("=" * 50)
+
+    # Check if we're in the right directory
+    repo_root = Path.cwd()
+    if not (repo_root / "apps" / "colqwen_rag.py").exists():
+        print("❌ Please run this script from the LEANN repository root")
+        print("   cd /path/to/LEANN && python test_colqwen_reproduction.py")
+        return
+
+    print("✅ Repository structure looks good")
+
+    # Step 1: Check dependencies
+    print("\n📦 Checking dependencies...")
+    try:
+        import torch
+
+        # 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__}")
+        print(f"   - CUDA available: {torch.cuda.is_available()}")
+        print(
+            f"   - MPS available: {hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()}"
+        )
+    except ImportError as e:
+        print(f"❌ Missing dependency: {e}")
+        print("\n📥 Install missing dependencies:")
+        print(
+            "   uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn"
+        )
+        return
+
+    # Step 2: Download sample PDF
+    print("\n📄 Setting up sample PDF...")
+    pdf_dir = repo_root / "test_pdfs"
+    pdf_dir.mkdir(exist_ok=True)
+    sample_pdf = pdf_dir / "attention_paper.pdf"
+
+    if not sample_pdf.exists():
+        print("📥 Downloading sample paper (Attention Is All You Need)...")
+        import urllib.request
+
+        try:
+            urllib.request.urlretrieve("https://arxiv.org/pdf/1706.03762.pdf", sample_pdf)
+            print(f"✅ Downloaded: {sample_pdf}")
+        except Exception as e:
+            print(f"❌ Download failed: {e}")
+            print("   Please manually download a PDF to test_pdfs/attention_paper.pdf")
+            return
+    else:
+        print(f"✅ Using existing PDF: {sample_pdf}")
+
+    # Step 3: Test ColQwen RAG
+    print("\n🚀 Testing ColQwen RAG...")
+
+    # Build index
+    print("\n1️⃣ Building multimodal index...")
+    build_cmd = f"python -m apps.colqwen_rag build --pdfs {pdf_dir} --index test_attention --model colqwen2 --pages-dir test_pages"
+    print(f"   Command: {build_cmd}")
+
+    try:
+        result = os.system(build_cmd)
+        if result == 0:
+            print("✅ Index built successfully!")
+        else:
+            print("❌ Index building failed")
+            return
+    except Exception as e:
+        print(f"❌ Error building index: {e}")
+        return
+
+    # Test search
+    print("\n2️⃣ Testing search...")
+    test_queries = [
+        "How does attention mechanism work?",
+        "What is the transformer architecture?",
+        "How do you compute self-attention?",
+    ]
+
+    for query in test_queries:
+        print(f"\n🔍 Query: '{query}'")
+        search_cmd = f'python -m apps.colqwen_rag search test_attention "{query}" --top-k 3'
+        print(f"   Command: {search_cmd}")
+
+        try:
+            result = os.system(search_cmd)
+            if result == 0:
+                print("✅ Search completed")
+            else:
+                print("❌ Search failed")
+        except Exception as e:
+            print(f"❌ Search error: {e}")
+
+    # Test interactive mode (briefly)
+    print("\n3️⃣ Testing interactive mode...")
+    print("   You can test interactive mode with:")
+    print("   python -m apps.colqwen_rag ask test_attention --interactive")
+
+    # Step 4: Test similarity maps (using existing script)
+    print("\n4️⃣ Testing similarity maps...")
+    similarity_script = (
+        repo_root
+        / "apps"
+        / "multimodal"
+        / "vision-based-pdf-multi-vector"
+        / "multi-vector-leann-similarity-map.py"
+    )
+
+    if similarity_script.exists():
+        print("   You can generate similarity maps with:")
+        print(f"   cd {similarity_script.parent}")
+        print("   python multi-vector-leann-similarity-map.py")
+        print("   (Edit the script to use your local PDF)")
+
+    print("\n🎉 ColQwen reproduction test completed!")
+    print("\n📋 Summary:")
+    print("   ✅ Dependencies checked")
+    print("   ✅ Sample PDF prepared")
+    print("   ✅ Index building tested")
+    print("   ✅ Search functionality tested")
+    print("   ✅ Interactive mode available")
+    print("   ✅ Similarity maps available")
+
+    print("\n🔗 Related repositories to check:")
+    print("   - https://github.com/lightonai/fast-plaid")
+    print("   - https://github.com/lightonai/pylate")
+    print("   - https://github.com/stanford-futuredata/ColBERT")
+
+    print("\n📝 Next steps:")
+    print("   1. Test with your own PDFs")
+    print("   2. Experiment with different queries")
+    print("   3. Generate similarity maps for visual analysis")
+    print("   4. Compare ColQwen2 vs ColPali performance")
+
+
+if __name__ == "__main__":
+    main()