add gif

README.md

@@ -133,6 +133,10 @@ LEANN supports RAG on various data sources including documents (.pdf, .txt, .md)
 
 Ask questions directly about your personal PDFs, documents, and any directory containing your files!
 
+<p align="center">
+  <img src="videos/paper_clear.gif" alt="LEANN Document Search Demo" width="600">
+</p>
+
 The example below asks a question about summarizing two papers (uses default data in `examples/data`):
 
 ```bash
@@ -150,6 +154,10 @@ python ./examples/main_cli_example.py
 
 ### 📧 Your Personal Email Secretary: RAG on Apple Mail!
 
+<p align="center">
+  <img src="videos/mail_clear.gif" alt="LEANN Email Search Demo" width="600">
+</p>
+
 **Note:** You need to grant full disk access to your terminal/VS Code in System Preferences → Privacy & Security → Full Disk Access.
 ```bash
 python examples/mail_reader_leann.py --query "What's the food I ordered by doordash or Uber eat mostly?"
@@ -188,6 +196,11 @@ Once the index is built, you can ask questions like:
 </details>
 
 ### 🔍 Time Machine for the Web: RAG Your Entire Google Browser History!
+
+<p align="center">
+  <img src="videos/google_clear.gif" alt="LEANN Browser History Search Demo" width="600">
+</p>
+
 ```bash
 python examples/google_history_reader_leann.py --query "Tell me my browser history about machine learning?"
 ```
@@ -242,6 +255,10 @@ Once the index is built, you can ask questions like:
 
 ### 💬 WeChat Detective: Unlock Your Golden Memories!
 
+<p align="center">
+  <img src="videos/wechat_clear.gif" alt="LEANN WeChat Search Demo" width="600">
+</p>
+
 ```bash
 python examples/wechat_history_reader_leann.py --query "Show me all group chats about weekend plans"
 ```

examples/google_history_reader_leann.py

@@ -222,14 +222,15 @@ async def query_leann_index(index_path: str, query: str):
             "max_tokens": 1000
         }
     )
-    print(f"Leann: {chat_response}")
+
+    print(f"Leann chat response: \033[36m{chat_response}\033[0m")
 
 async def main():
     # Parse command line arguments
     parser = argparse.ArgumentParser(description='LEANN Chrome History Reader - Create and query browser history index')
     parser.add_argument('--chrome-profile', type=str, default=DEFAULT_CHROME_PROFILE,
                        help=f'Path to Chrome profile directory (default: {DEFAULT_CHROME_PROFILE}), usually you dont need to change this')
-    parser.add_argument('--index-dir', type=str, default="./all_google_new",
+    parser.add_argument('--index-dir', type=str, default="./google_history_index",
                        help='Directory to store the LEANN index (default: ./chrome_history_index_leann_test)')
     parser.add_argument('--max-entries', type=int, default=1000,
                        help='Maximum number of history entries to process (default: 1000)')

examples/mail_reader_leann.py

@@ -224,15 +224,16 @@ async def query_leann_index(index_path: str, query: str):
         beam_width=1,
     )
     end_time = time.time()
-    print(f"Time taken: {end_time - start_time} seconds")
+    # print(f"Time taken: {end_time - start_time} seconds")
-    print(f"Leann: {chat_response}")
+    # highlight the answer
+    print(f"Leann chat response: \033[36m{chat_response}\033[0m")
 
 async def main():
     # Parse command line arguments
     parser = argparse.ArgumentParser(description='LEANN Mail Reader - Create and query email index')
     # Remove --mail-path argument and auto-detect all Messages directories
     # Remove DEFAULT_MAIL_PATH
-    parser.add_argument('--index-dir', type=str, default="./mail_index_index_file",
+    parser.add_argument('--index-dir', type=str, default="./mail_index",
                        help='Directory to store the LEANN index (default: ./mail_index_leann_raw_text_all_dicts)')
     parser.add_argument('--max-emails', type=int, default=1000,
                        help='Maximum number of emails to process (-1 means all)')

examples/main_cli_example.py

@@ -63,16 +63,14 @@ async def main(args):
     llm_config = {"type": "openai", "model": "gpt-4o"}
 
     chat = LeannChat(index_path=INDEX_PATH, llm_config=llm_config)
-
-    query = "Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?"
-
     # query = (
     #     "什么是盘古大模型以及盘古开发过程中遇到了什么阴暗面，任务令一般在什么城市颁发"
     # )
+    query = args.query
 
     print(f"You: {query}")
     chat_response = chat.ask(query, top_k=20, recompute_embeddings=True, complexity=32)
-    print(f"Leann: {chat_response}")
+    print(f"Leann chat response: \033[36m{chat_response}\033[0m")
 
 
 if __name__ == "__main__":
@@ -110,6 +108,12 @@ if __name__ == "__main__":
         default="examples/data",
         help="Directory containing documents to index (PDF, TXT, MD files).",
     )
+    parser.add_argument(
+        "--query",
+        type=str,
+        default="Based on the paper, what are the main techniques LEANN explores to reduce the storage overhead and DLPM explore to achieve Fairness and Efiiciency trade-off?",
+        help="The query to ask the Leann chat system.",
+    )
     args = parser.parse_args()
 
     asyncio.run(main(args))

examples/wechat_history_reader_leann.py

@@ -234,7 +234,7 @@ async def query_leann_index(index_path: str, query: str):
         },
         llm_kwargs={"temperature": 0.0, "max_tokens": 1000},
     )
-    print(f"Leann: {chat_response}")
+    print(f"Leann chat response: \033[36m{chat_response}\033[0m")
 
 
 async def main():

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

@@ -441,9 +441,9 @@ class LeannSearcher:
             use_server_if_available=recompute_embeddings,
             zmq_port=zmq_port,
         )
-        logger.info(f"  Generated embedding shape: {query_embedding.shape}")
+        # logger.info(f"  Generated embedding shape: {query_embedding.shape}")
         embedding_time = time.time() - start_time
-        logger.info(f"  Embedding time: {embedding_time} seconds")
+        # logger.info(f"  Embedding time: {embedding_time} seconds")
 
         start_time = time.time()
         results = self.backend_impl.search(
@@ -458,7 +458,7 @@ class LeannSearcher:
             **kwargs,
         )
         search_time = time.time() - start_time
-        logger.info(f"  Search time: {search_time} seconds")
+        # logger.info(f"  Search time: {search_time} seconds")
         logger.info(
             f"  Backend returned: labels={len(results.get('labels', [[]])[0])} results"
         )
@@ -479,15 +479,25 @@ class LeannSearcher:
                             metadata=passage_data.get("metadata", {}),
                         )
                     )
+                    
+                    # Color codes for better logging
+                    GREEN = "\033[92m"
+                    BLUE = "\033[94m"
+                    YELLOW = "\033[93m"
+                    RESET = "\033[0m"
+                    
+                    # Truncate text for display (first 100 chars)
+                    display_text = passage_data['text']
                     logger.info(
-                        f"    {i + 1}. passage_id='{string_id}' -> SUCCESS: {passage_data['text']}..."
+                        f"   {GREEN}✓{RESET} {BLUE}[{i + 1:2d}]{RESET} {YELLOW}ID:{RESET} '{string_id}' {YELLOW}Score:{RESET} {dist:.4f} {YELLOW}Text:{RESET} {display_text}"
                     )
                 except KeyError:
+                    RED = "\033[91m"
                     logger.error(
-                        f"    {i + 1}. passage_id='{string_id}' -> ERROR: Passage not found in PassageManager!"
+                        f"   {RED}✗{RESET} [{i + 1:2d}] ID: '{string_id}' -> {RED}ERROR: Passage not found!{RESET}"
                     )
 
-        logger.info(f"  Final enriched results: {len(enriched_results)} passages")
+        logger.info(f"  {GREEN}✓ Final enriched results: {len(enriched_results)} passages{RESET}")
         return enriched_results
 
 
@@ -517,7 +527,7 @@ class LeannChat:
     ):
         if llm_kwargs is None:
             llm_kwargs = {}
-
+        search_time = time.time()
         results = self.searcher.search(
             question,
             top_k=top_k,
@@ -529,6 +539,8 @@ class LeannChat:
             expected_zmq_port=expected_zmq_port,
             **search_kwargs,
         )
+        search_time = time.time() - search_time
+        # logger.info(f"  Search time: {search_time} seconds")
         context = "\n\n".join([r.text for r in results])
         prompt = (
             "Here is some retrieved context that might help answer your question:\n\n"