Added OpenAI compatible options and examples

examples/lightrag_openai_compatible_demo.py

@@ -0,0 +1,69 @@
+import os
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import openai_complete_if_cache, openai_embedding
+from lightrag.utils import EmbeddingFunc
+import numpy as np
+
+WORKING_DIR = "./dickens"
+    
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    return await openai_complete_if_cache(
+        "solar-mini",
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        api_key=os.getenv("UPSTAGE_API_KEY"),
+        base_url="https://api.upstage.ai/v1/solar",
+        **kwargs
+    )
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await openai_embedding(
+        texts,
+        model="solar-embedding-1-large-query",
+        api_key=os.getenv("UPSTAGE_API_KEY"),
+        base_url="https://api.upstage.ai/v1/solar"
+    )
+
+# function test
+async def test_funcs():
+    result = await llm_model_func("How are you?")
+    print("llm_model_func: ", result)
+
+    result = await embedding_func(["How are you?"])
+    print("embedding_func: ", result)
+
+asyncio.run(test_funcs())
+
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=llm_model_func,
+    embedding_func=EmbeddingFunc(
+        embedding_dim=4096,
+        max_token_size=8192,
+        func=embedding_func
+    )
+)
+
+
+with open("./book.txt") 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")))

lightrag/llm.py

@@ -19,9 +19,12 @@ os.environ["TOKENIZERS_PARALLELISM"] = "false"
     retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
 )
 async def openai_complete_if_cache(
-    model, prompt, system_prompt=None, history_messages=[], **kwargs
+    model, prompt, system_prompt=None, history_messages=[], base_url=None, api_key=None, **kwargs
 ) -> str:
-    openai_async_client = AsyncOpenAI()
+    if api_key:
+        os.environ["OPENAI_API_KEY"] = api_key
+
+    openai_async_client = AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url)
     hashing_kv: BaseKVStorage = kwargs.pop("hashing_kv", None)
     messages = []
     if system_prompt:
@@ -133,10 +136,13 @@ async def hf_model_complete(
     wait=wait_exponential(multiplier=1, min=4, max=10),
     retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
 )
-async def openai_embedding(texts: list[str]) -> np.ndarray:
+async def openai_embedding(texts: list[str], model: str = "text-embedding-3-small", base_url: str = None, api_key: str = None) -> np.ndarray:
-    openai_async_client = AsyncOpenAI()
+    if api_key:
+        os.environ["OPENAI_API_KEY"] = api_key
+
+    openai_async_client = AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url)
     response = await openai_async_client.embeddings.create(
-        model="text-embedding-3-small", input=texts, encoding_format="float"
+        model=model, input=texts, encoding_format="float"
     )
     return np.array([dp.embedding for dp in response.data])