集中处理环境变量

examples/lightrag_api_openai_compatible_demo.py

@@ -18,6 +18,13 @@ app = FastAPI(title="LightRAG API", description="API for RAG operations")
 # Configure working directory
 WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}")
 print(f"WORKING_DIR: {WORKING_DIR}")
+LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4o-mini")
+print(f"LLM_MODEL: {LLM_MODEL}")
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
+print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
+EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
+print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
+
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
@@ -29,7 +36,7 @@ async def llm_model_func(
         prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
     return await openai_complete_if_cache(
-        os.environ.get("LLM_MODEL", "gpt-4o-mini"),
+        LLM_MODEL,
         prompt,
         system_prompt=system_prompt,
         history_messages=history_messages,
@@ -43,7 +50,7 @@ async def llm_model_func(
 async def embedding_func(texts: list[str]) -> np.ndarray:
     return await openai_embedding(
         texts,
-        model=os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large"),
+        model=EMBEDDING_MODEL,
     )
 
 
@@ -60,7 +67,7 @@ rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=llm_model_func,
     embedding_func=EmbeddingFunc(embedding_dim=asyncio.run(get_embedding_dim()),
-                                 max_token_size=os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192),
+                                 max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
                                  func=embedding_func),
 )