向量数据库

rag_indexer/example_parent_child.py

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+"""
+Example demonstrating ParentDocumentRetriever usage.
+
+This script shows how to:
+1. Build an index with parent-child chunking
+2. Search with child chunks (fast, precise)
+3. Search with parent context (large context)
+4. Access the retriever directly for advanced use cases
+"""
+
+import logging
+from pathlib import Path
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+
+from builder import IndexBuilder
+from splitters import SplitterType
+
+
+def main():
+    print("=" * 70)
+    print("ParentDocumentRetriever Example")
+    print("=" * 70)
+    
+    # Step 1: Create IndexBuilder with parent-child splitting
+    print("\n1. Creating IndexBuilder with parent-child splitting...")
+    builder = IndexBuilder(
+        collection_name="parent_child_demo",
+        splitter_type=SplitterType.PARENT_CHILD,
+        parent_chunk_size=1000,   # Parent chunks: larger context
+        child_chunk_size=200,     # Child chunks: smaller for precision
+        docstore_path="./my_parent_docs",  # Where to store parent chunks
+        search_k=5,  # Number of child chunks to retrieve
+    )
+    
+    print(f"   Parent splitter: chunk_size={builder.get_parent_splitter().chunk_size}")
+    print(f"   Child splitter: chunk_size={builder.get_child_splitter().chunk_size}")
+    print(f"   Docstore path: {builder.get_docstore_path()}")
+    print(f"   Search k: {builder.retriever.search_kwargs['k']}")
+    
+    # Step 2: Build index from a sample file
+    print("\n2. Building index from sample file...")
+    
+    # Create a test document
+    test_content = """
+    This is a test document for demonstrating ParentDocumentRetriever.
+    
+    Parent chunks contain larger portions of text (1000 characters),
+    while child chunks are smaller (200 characters) for precise retrieval.
+    
+    When you search with ParentDocumentRetriever:
+    - It first retrieves relevant child chunks
+    - Then replaces them with their corresponding parent chunks
+    - This gives you large context while maintaining precision
+    
+    Example search queries:
+    - "ParentDocumentRetriever" 
+    - "child chunks"
+    - "large context"
+    - "precise retrieval"
+    """
+    
+    test_file = Path("./test_document.txt")
+    test_file.write_text(test_content)
+    
+    chunk_count = builder.build_from_file(str(test_file))
+    print(f"   Indexed {chunk_count} documents")
+    
+    # Step 3: Search with child chunks (fast, precise)
+    print("\n3. Searching with child chunks (fast, precise)...")
+    child_results = builder.search("ParentDocumentRetriever", k=3)
+    print(f"   Found {len(child_results)} child chunks:")
+    for i, doc in enumerate(child_results, 1):
+        print(f"   [{i}] {doc.page_content[:100]}...")
+    
+    # Step 4: Search with parent context (large context)
+    print("\n4. Searching with parent context (large context)...")
+    parent_results = builder.search_with_parent_context("ParentDocumentRetriever", k=3)
+    print(f"   Found {len(parent_results)} parent chunks:")
+    for i, doc in enumerate(parent_results, 1):
+        print(f"   [{i}] {doc.page_content[:150]}...")
+    
+    # Step 5: Compare results
+    print("\n5. Comparing child vs parent results...")
+    print(f"   Child chunks total length: {sum(len(d.page_content) for d in child_results)}")
+    print(f"   Parent chunks total length: {sum(len(d.page_content) for d in parent_results)}")
+    print(f"   Ratio: parent/child = {sum(len(d.page_content) for d in parent_results) / max(sum(len(d.page_content) for d in child_results), 1):.2f}x larger")
+    
+    # Step 6: Access retriever directly
+    print("\n6. Accessing retriever directly...")
+    retriever = builder.get_retriever()
+    print(f"   Retriever type: {type(retriever).__name__}")
+    print(f"   Vectorstore: {retriever.vectorstore}")
+    print(f"   Docstore: {retriever.docstore}")
+    
+    # Step 7: Unified retrieval interface
+    print("\n7. Using unified retrieval interface...")
+    unified_results = builder.retrieve("ParentDocumentRetriever", return_parent=True)
+    print(f"   Retrieved {len(unified_results)} documents (with parent context)")
+    
+    # Step 8: Collection info
+    print("\n8. Collection info...")
+    info = builder.get_collection_info()
+    print(f"   Collection: {info['name']}")
+    print(f"   Vectors: {info['vectors_count']}")
+    print(f"   Vector size: {info['vector_size']}")
+    
+    # Cleanup
+    print("\n9. Cleaning up...")
+    builder.close()
+    
+    print("\n" + "=" * 70)
+    print("Example completed successfully!")
+    print("=" * 70)
+    
+    return builder
+
+
+if __name__ == "__main__":
+    builder = main()