Update sample code for OpenAI and OpenAI compatible

examples/lightrag_openai_compatible_demo.py

@@ -1,13 +1,83 @@
 import os
 import asyncio
+import inspect
+import logging
+import logging.config
 from lightrag import LightRAG, QueryParam
-from lightrag.llm.openai import openai_complete_if_cache, openai_embed
+from lightrag.llm.openai import openai_complete_if_cache
-from lightrag.utils import EmbeddingFunc
+from lightrag.llm.ollama import ollama_embed
+from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug
 import numpy as np
 from lightrag.kg.shared_storage import initialize_pipeline_status
 
 WORKING_DIR = "./dickens"
 
+
+def configure_logging():
+    """Configure logging for the application"""
+
+    # Reset any existing handlers to ensure clean configuration
+    for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]:
+        logger_instance = logging.getLogger(logger_name)
+        logger_instance.handlers = []
+        logger_instance.filters = []
+
+    # Get log directory path from environment variable or use current directory
+    log_dir = os.getenv("LOG_DIR", os.getcwd())
+    log_file_path = os.path.abspath(
+        os.path.join(log_dir, "lightrag_compatible_demo.log")
+    )
+
+    print(f"\nLightRAG compatible demo log file: {log_file_path}\n")
+    os.makedirs(os.path.dirname(log_dir), exist_ok=True)
+
+    # Get log file max size and backup count from environment variables
+    log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760))  # Default 10MB
+    log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5))  # Default 5 backups
+
+    logging.config.dictConfig(
+        {
+            "version": 1,
+            "disable_existing_loggers": False,
+            "formatters": {
+                "default": {
+                    "format": "%(levelname)s: %(message)s",
+                },
+                "detailed": {
+                    "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+                },
+            },
+            "handlers": {
+                "console": {
+                    "formatter": "default",
+                    "class": "logging.StreamHandler",
+                    "stream": "ext://sys.stderr",
+                },
+                "file": {
+                    "formatter": "detailed",
+                    "class": "logging.handlers.RotatingFileHandler",
+                    "filename": log_file_path,
+                    "maxBytes": log_max_bytes,
+                    "backupCount": log_backup_count,
+                    "encoding": "utf-8",
+                },
+            },
+            "loggers": {
+                "lightrag": {
+                    "handlers": ["console", "file"],
+                    "level": "INFO",
+                    "propagate": False,
+                },
+            },
+        }
+    )
+
+    # Set the logger level to INFO
+    logger.setLevel(logging.INFO)
+    # Enable verbose debug if needed
+    set_verbose_debug(os.getenv("VERBOSE_DEBUG", "false").lower() == "true")
+
+
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
@@ -16,22 +86,21 @@ async def llm_model_func(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
 ) -> str:
     return await openai_complete_if_cache(
-        "solar-mini",
+        "deepseek-chat",
         prompt,
         system_prompt=system_prompt,
         history_messages=history_messages,
-        api_key=os.getenv("UPSTAGE_API_KEY"),
+        api_key=os.getenv("OPENAI_API_KEY"),
-        base_url="https://api.upstage.ai/v1/solar",
+        base_url="https://api.deepseek.com",
         **kwargs,
     )
 
 
 async def embedding_func(texts: list[str]) -> np.ndarray:
-    return await openai_embed(
+    return await ollama_embed(
-        texts,
+        texts=texts,
-        model="solar-embedding-1-large-query",
+        embed_model="bge-m3:latest",
-        api_key=os.getenv("UPSTAGE_API_KEY"),
+        host="http://m4.lan.znipower.com:11434",
-        base_url="https://api.upstage.ai/v1/solar",
     )
 
 
@@ -54,6 +123,12 @@ async def test_funcs():
 # asyncio.run(test_funcs())
 
 
+async def print_stream(stream):
+    async for chunk in stream:
+        if chunk:
+            print(chunk, end="", flush=True)
+
+
 async def initialize_rag():
     embedding_dimension = await get_embedding_dim()
     print(f"Detected embedding dimension: {embedding_dimension}")
@@ -83,37 +158,66 @@ async def main():
             await rag.ainsert(f.read())
 
         # Perform naive search
-        print(
+        print("\n=====================")
-            await rag.aquery(
+        print("Query mode: naive")
-                "What are the top themes in this story?", param=QueryParam(mode="naive")
+        print("=====================")
-            )
+        resp = await rag.aquery(
+            "What are the top themes in this story?",
+            param=QueryParam(mode="naive", stream=True),
         )
+        if inspect.isasyncgen(resp):
+            await print_stream(resp)
+        else:
+            print(resp)
 
         # Perform local search
-        print(
+        print("\n=====================")
-            await rag.aquery(
+        print("Query mode: local")
-                "What are the top themes in this story?", param=QueryParam(mode="local")
+        print("=====================")
-            )
+        resp = await rag.aquery(
+            "What are the top themes in this story?",
+            param=QueryParam(mode="local", stream=True),
         )
+        if inspect.isasyncgen(resp):
+            await print_stream(resp)
+        else:
+            print(resp)
 
         # Perform global search
-        print(
+        print("\n=====================")
-            await rag.aquery(
+        print("Query mode: global")
-                "What are the top themes in this story?",
+        print("=====================")
-                param=QueryParam(mode="global"),
+        resp = await rag.aquery(
-            )
+            "What are the top themes in this story?",
+            param=QueryParam(mode="global", stream=True),
         )
+        if inspect.isasyncgen(resp):
+            await print_stream(resp)
+        else:
+            print(resp)
 
         # Perform hybrid search
-        print(
+        print("\n=====================")
-            await rag.aquery(
+        print("Query mode: hybrid")
-                "What are the top themes in this story?",
+        print("=====================")
-                param=QueryParam(mode="hybrid"),
+        resp = await rag.aquery(
-            )
+            "What are the top themes in this story?",
+            param=QueryParam(mode="hybrid", stream=True),
         )
+        if inspect.isasyncgen(resp):
+            await print_stream(resp)
+        else:
+            print(resp)
+
     except Exception as e:
         print(f"An error occurred: {e}")
+    finally:
+        if rag:
+            await rag.finalize_storages()
 
 
 if __name__ == "__main__":
+    # Configure logging before running the main function
+    configure_logging()
     asyncio.run(main())
+    print("\nDone!")

examples/lightrag_openai_compatible_stream_demo.py

@@ -1,72 +0,0 @@
-import inspect
-import os
-import asyncio
-from lightrag import LightRAG
-from lightrag.llm import openai_complete, openai_embed
-from lightrag.utils import EmbeddingFunc, always_get_an_event_loop
-from lightrag import QueryParam
-from lightrag.kg.shared_storage import initialize_pipeline_status
-
-# WorkingDir
-ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-WORKING_DIR = os.path.join(ROOT_DIR, "dickens")
-if not os.path.exists(WORKING_DIR):
-    os.mkdir(WORKING_DIR)
-print(f"WorkingDir: {WORKING_DIR}")
-
-api_key = "empty"
-
-
-async def initialize_rag():
-    rag = LightRAG(
-        working_dir=WORKING_DIR,
-        llm_model_func=openai_complete,
-        llm_model_name="qwen2.5-14b-instruct@4bit",
-        llm_model_max_async=4,
-        llm_model_max_token_size=32768,
-        llm_model_kwargs={"base_url": "http://127.0.0.1:1234/v1", "api_key": api_key},
-        embedding_func=EmbeddingFunc(
-            embedding_dim=1024,
-            max_token_size=8192,
-            func=lambda texts: openai_embed(
-                texts=texts,
-                model="text-embedding-bge-m3",
-                base_url="http://127.0.0.1:1234/v1",
-                api_key=api_key,
-            ),
-        ),
-    )
-
-    await rag.initialize_storages()
-    await initialize_pipeline_status()
-
-    return rag
-
-
-async def print_stream(stream):
-    async for chunk in stream:
-        if chunk:
-            print(chunk, end="", flush=True)
-
-
-def main():
-    # Initialize RAG instance
-    rag = asyncio.run(initialize_rag())
-
-    with open("./book.txt", "r", encoding="utf-8") as f:
-        rag.insert(f.read())
-
-    resp = rag.query(
-        "What are the top themes in this story?",
-        param=QueryParam(mode="hybrid", stream=True),
-    )
-
-    loop = always_get_an_event_loop()
-    if inspect.isasyncgen(resp):
-        loop.run_until_complete(print_stream(resp))
-    else:
-        print(resp)
-
-
-if __name__ == "__main__":
-    main()

examples/lightrag_openai_demo.py

@@ -9,6 +9,7 @@ from lightrag.utils import logger, set_verbose_debug
 
 WORKING_DIR = "./dickens"
 
+
 def configure_logging():
     """Configure logging for the application"""
 
@@ -71,6 +72,7 @@ def configure_logging():
     # Enable verbose debug if needed
     set_verbose_debug(os.getenv("VERBOSE_DEBUG", "false").lower() == "true")
 
+
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
@@ -97,6 +99,9 @@ async def main():
         await rag.ainsert(f.read())
 
     # Perform naive search
+    print("\n=====================")
+    print("Query mode: naive")
+    print("=====================")
     print(
         await rag.aquery(
             "What are the top themes in this story?", param=QueryParam(mode="naive")
@@ -104,6 +109,9 @@ async def main():
     )
 
     # Perform local search
+    print("\n=====================")
+    print("Query mode: local")
+    print("=====================")
     print(
         await rag.aquery(
             "What are the top themes in this story?", param=QueryParam(mode="local")
@@ -111,6 +119,9 @@ async def main():
     )
 
     # Perform global search
+    print("\n=====================")
+    print("Query mode: global")
+    print("=====================")
     print(
         await rag.aquery(
             "What are the top themes in this story?", param=QueryParam(mode="global")
@@ -118,6 +129,9 @@ async def main():
     )
 
     # Perform hybrid search
+    print("\n=====================")
+    print("Query mode: hybrid")
+    print("=====================")
     print(
         await rag.aquery(
             "What are the top themes in this story?", param=QueryParam(mode="hybrid")