Add LightRAG demo with PostgreSQL and Gemini integration

This script demonstrates the integration of LightRAG with PostgreSQL and Google Gemini for document indexing and querying.

examples/lightrag_postgres_demo.py

@@ -0,0 +1,180 @@
+"""
+LightRAG Demo with PostgreSQL + Google Gemini
+
+This example demonstrates how to use LightRAG with:
+- Google Gemini (LLM + Embeddings)
+- PostgreSQL-backed storages for:
+  - Vector storage
+  - Graph storage
+  - KV storage
+  - Document status storage
+
+Prerequisites:
+1. PostgreSQL database running and accessible
+2. Required tables will be auto-created by LightRAG
+3. Set environment variables (example .env):
+
+   POSTGRES_HOST=localhost
+   POSTGRES_PORT=5432
+   POSTGRES_USER=admin
+   POSTGRES_PASSWORD=admin
+   POSTGRES_DATABASE=ai
+
+   LIGHTRAG_KV_STORAGE=PGKVStorage
+   LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
+   LIGHTRAG_GRAPH_STORAGE=PGGraphStorage
+   LIGHTRAG_VECTOR_STORAGE=PGVectorStorage
+
+   GEMINI_API_KEY=your-api-key
+
+4. Prepare a text file to index (default: Data/book-small.txt)
+
+Usage:
+    python examples/lightrag_postgres_demo.py
+"""
+
+import os
+import asyncio
+import numpy as np
+
+from lightrag import LightRAG, QueryParam
+from lightrag.llm.gemini import gemini_model_complete, gemini_embed
+from lightrag.utils import setup_logger, wrap_embedding_func_with_attrs
+
+
+# --------------------------------------------------
+# Logger
+# --------------------------------------------------
+setup_logger("lightrag", level="INFO")
+
+
+# --------------------------------------------------
+# Config
+# --------------------------------------------------
+WORKING_DIR = "./rag_storage"
+BOOK_FILE = "Data/book.txt"
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+if not GEMINI_API_KEY:
+    raise ValueError("GEMINI_API_KEY environment variable is not set")
+
+
+# --------------------------------------------------
+# LLM function (Gemini)
+# --------------------------------------------------
+async def llm_model_func(
+    prompt,
+    system_prompt=None,
+    history_messages=[],
+    keyword_extraction=False,
+    **kwargs,
+) -> str:
+    return await gemini_model_complete(
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        api_key=GEMINI_API_KEY,
+        model_name="gemini-2.0-flash",
+        **kwargs,
+    )
+
+
+# --------------------------------------------------
+# Embedding function (Gemini)
+# --------------------------------------------------
+@wrap_embedding_func_with_attrs(
+    embedding_dim=768,
+    max_token_size=2048,
+    model_name="models/text-embedding-004",
+)
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await gemini_embed.func(
+        texts,
+        api_key=GEMINI_API_KEY,
+        model="models/text-embedding-004",
+    )
+
+
+# --------------------------------------------------
+# Initialize RAG with PostgreSQL storages
+# --------------------------------------------------
+async def initialize_rag() -> LightRAG:
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_name="gemini-2.0-flash",
+        llm_model_func=llm_model_func,
+        embedding_func=embedding_func,
+
+        # Performance tuning
+        embedding_func_max_async=4,
+        embedding_batch_num=8,
+        llm_model_max_async=2,
+
+        # Chunking
+        chunk_token_size=1200,
+        chunk_overlap_token_size=100,
+
+        # PostgreSQL-backed storages
+        graph_storage="PGGraphStorage",
+        vector_storage="PGVectorStorage",
+        doc_status_storage="PGDocStatusStorage",
+        kv_storage="PGKVStorage",
+    )
+
+    # REQUIRED: initialize all storage backends
+    await rag.initialize_storages()
+    return rag
+
+
+# --------------------------------------------------
+# Main
+# --------------------------------------------------
+async def main():
+    rag = None
+    try:
+        print("Initializing LightRAG with PostgreSQL + Gemini...")
+        rag = await initialize_rag()
+
+        if not os.path.exists(BOOK_FILE):
+            raise FileNotFoundError(
+                f"'{BOOK_FILE}' not found. Please provide a text file to index."
+            )
+
+        print(f"\nReading document: {BOOK_FILE}")
+        with open(BOOK_FILE, "r", encoding="utf-8") as f:
+            content = f.read()
+
+        print(f"Loaded document ({len(content)} characters)")
+
+        print("\nInserting document into LightRAG (this may take some time)...")
+        await rag.ainsert(content)
+        print("Document indexed successfully!")
+
+        print("\n" + "=" * 60)
+        print("Running sample queries")
+        print("=" * 60)
+
+        query = "What are the top themes in this document?"
+
+        for mode in ["naive", "local", "global", "hybrid"]:
+            print(f"\n[{mode.upper()} MODE]")
+            result = await rag.aquery(query, param=QueryParam(mode=mode))
+            print(result[:400] + "..." if len(result) > 400 else result)
+
+        print("\nRAG system is ready for use!")
+
+    except Exception as e:
+        print("An error occurred:", e)
+        import traceback
+        traceback.print_exc()
+
+    finally:
+        if rag is not None:
+            await rag.finalize_storages()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())