refactor: reorgnize all examples/ and test/

examples/basic_demo.py

@@ -0,0 +1,88 @@
+"""
+Simple demo showing basic leann usage
+Run: uv run python examples/basic_demo.py
+"""
+
+import argparse
+
+from leann import LeannBuilder, LeannChat, LeannSearcher
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Simple demo of Leann with selectable embedding models."
+    )
+    parser.add_argument(
+        "--embedding_model",
+        type=str,
+        default="sentence-transformers/all-mpnet-base-v2",
+        help="The embedding model to use, e.g., 'sentence-transformers/all-mpnet-base-v2' or 'text-embedding-ada-002'.",
+    )
+    args = parser.parse_args()
+
+    print(f"=== Leann Simple Demo with {args.embedding_model} ===")
+    print()
+
+    # Sample knowledge base
+    chunks = [
+        "Machine learning is a subset of artificial intelligence that enables computers to learn without being explicitly programmed.",
+        "Deep learning uses neural networks with multiple layers to process data and make decisions.",
+        "Natural language processing helps computers understand and generate human language.",
+        "Computer vision enables machines to interpret and understand visual information from images and videos.",
+        "Reinforcement learning teaches agents to make decisions by receiving rewards or penalties for their actions.",
+        "Data science combines statistics, programming, and domain expertise to extract insights from data.",
+        "Big data refers to extremely large datasets that require special tools and techniques to process.",
+        "Cloud computing provides on-demand access to computing resources over the internet.",
+    ]
+
+    print("1. Building index (no embeddings stored)...")
+    builder = LeannBuilder(
+        embedding_model=args.embedding_model,
+        backend_name="hnsw",
+    )
+    for chunk in chunks:
+        builder.add_text(chunk)
+    builder.build_index("demo_knowledge.leann")
+    print()
+
+    print("2. Searching with real-time embeddings...")
+    searcher = LeannSearcher("demo_knowledge.leann")
+
+    queries = [
+        "What is machine learning?",
+        "How does neural network work?",
+        "Tell me about data processing",
+    ]
+
+    for query in queries:
+        print(f"Query: {query}")
+        results = searcher.search(query, top_k=2)
+
+        for i, result in enumerate(results, 1):
+            print(f"  {i}. Score: {result.score:.3f}")
+            print(f"     Text: {result.text[:100]}...")
+        print()
+
+    print("3. Interactive chat demo:")
+    print("   (Note: Requires OpenAI API key for real responses)")
+
+    chat = LeannChat("demo_knowledge.leann")
+
+    # Demo questions
+    demo_questions: list[str] = [
+        "What is the difference between machine learning and deep learning?",
+        "How is data science related to big data?",
+    ]
+
+    for question in demo_questions:
+        print(f"   Q: {question}")
+        response = chat.ask(question)
+        print(f"   A: {response}")
+        print()
+
+    print("Demo completed! Try running:")
+    print("   uv run python apps/document_rag.py")
+
+
+if __name__ == "__main__":
+    main()