add lightrag_api_openai_compatible_demo_simplified.py

examples/lightrag_api_openai_compatible_demo_simplified.py

@@ -0,0 +1,102 @@
+import os
+from lightrag import LightRAG, QueryParam
+from lightrag.llm.openai import openai_complete_if_cache, openai_embed
+from lightrag.utils import EmbeddingFunc
+import numpy as np
+import asyncio
+import nest_asyncio
+
+# Apply nest_asyncio to solve event loop issues
+nest_asyncio.apply()
+
+DEFAULT_RAG_DIR = "index_default"
+
+# 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-small")
+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}")
+BASE_URL = int(os.environ.get("BASE_URL", "https://api.openai.com/v1"))
+print(f"BASE_URL: {BASE_URL}")
+API_KEY = int(os.environ.get("API_KEY", "xxxxxxxx"))
+print(f"API_KEY: {API_KEY}")
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+
+# LLM model function
+
+
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+) -> str:
+    return await openai_complete_if_cache(
+        model=LLM_MODEL,
+        prompt=prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        base_url=BASE_URL,
+        api_key=API_KEY,
+        **kwargs,
+    )
+
+
+# Embedding function
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await openai_embed(
+        texts=texts,
+        model=EMBEDDING_MODEL,
+        base_url=BASE_URL,
+        api_key=API_KEY,
+    )
+
+
+async def get_embedding_dim():
+    test_text = ["This is a test sentence."]
+    embedding = await embedding_func(test_text)
+    embedding_dim = embedding.shape[1]
+    print(f"{embedding_dim=}")
+    return embedding_dim
+
+
+# Initialize RAG instance
+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=EMBEDDING_MAX_TOKEN_SIZE,
+        func=embedding_func,
+    ),
+)
+
+with open("./book.txt", "r", encoding="utf-8") as f:
+    rag.insert(f.read())
+
+# Perform naive search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+)
+
+# Perform local search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
+)
+
+# Perform global search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
+)
+
+# Perform hybrid search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
+)
+