Adding Tongyi OpenAI demo to use Qwen

qwen-turbo-latest (currently Qwen3) is supported by now

Signed-off-by: Ben Luo <bn0418@gmail.com>

examples/lightrag_tongyi_openai_demo.py

@@ -0,0 +1,136 @@
+import os
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.utils import EmbeddingFunc
+import numpy as np
+from dotenv import load_dotenv
+import logging
+from openai import OpenAI
+from lightrag.kg.shared_storage import initialize_pipeline_status
+
+logging.basicConfig(level=logging.INFO)
+
+load_dotenv()
+
+LLM_MODEL = os.environ.get("LLM_MODEL", "qwen-turbo-latest")
+LLM_BINDING_HOST = "https://dashscope.aliyuncs.com/compatible-mode/v1"
+LLM_BINDING_API_KEY = os.getenv("LLM_BINDING_API_KEY")
+
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-v3")
+EMBEDDING_BINDING_HOST = os.getenv("EMBEDDING_BINDING_HOST", LLM_BINDING_HOST)
+EMBEDDING_BINDING_API_KEY = os.getenv("EMBEDDING_BINDING_API_KEY", LLM_BINDING_API_KEY)
+EMBEDDING_DIM = int(os.environ.get("EMBEDDING_DIM", 1024))
+EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
+EMBEDDING_MAX_BATCH_SIZE = int(os.environ.get("EMBEDDING_MAX_BATCH_SIZE", 10))
+
+print(f"LLM_MODEL: {LLM_MODEL}")
+print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
+
+WORKING_DIR = "./dickens"
+
+if os.path.exists(WORKING_DIR):
+    import shutil
+
+    shutil.rmtree(WORKING_DIR)
+
+os.mkdir(WORKING_DIR)
+
+
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+) -> str:
+    client = OpenAI(
+        api_key=LLM_BINDING_API_KEY,
+        base_url=LLM_BINDING_HOST,
+    )
+
+    messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+    if history_messages:
+        messages.extend(history_messages)
+    messages.append({"role": "user", "content": prompt})
+
+    chat_completion = client.chat.completions.create(
+        model=LLM_MODEL,
+        messages=messages,
+        temperature=kwargs.get("temperature", 0),
+        top_p=kwargs.get("top_p", 1),
+        n=kwargs.get("n", 1),
+        extra_body={"enable_thinking": False},
+    )
+    return chat_completion.choices[0].message.content
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    client = OpenAI(
+        api_key=EMBEDDING_BINDING_API_KEY,
+        base_url=EMBEDDING_BINDING_HOST,
+    )
+
+    print("##### embedding: texts: %d #####" % len(texts))
+    max_batch_size = EMBEDDING_MAX_BATCH_SIZE
+    embeddings = []
+    for i in range(0, len(texts), max_batch_size):
+        batch = texts[i : i + max_batch_size]
+        embedding = client.embeddings.create(model=EMBEDDING_MODEL, input=batch)
+        embeddings += [item.embedding for item in embedding.data]
+
+    return np.array(embeddings)
+
+
+async def test_funcs():
+    result = await llm_model_func("How are you?")
+    print("Resposta do llm_model_func: ", result)
+
+    result = await embedding_func(["How are you?"])
+    print("Resultado do embedding_func: ", result.shape)
+    print("Dimensão da embedding: ", result.shape[1])
+
+
+asyncio.run(test_funcs())
+
+
+async def initialize_rag():
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=llm_model_func,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=EMBEDDING_DIM,
+            max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
+            func=embedding_func,
+        ),
+    )
+
+    await rag.initialize_storages()
+    await initialize_pipeline_status()
+
+    return rag
+
+
+def main():
+    rag = asyncio.run(initialize_rag())
+
+    with open("./book.txt", "r", encoding="utf-8") as f:
+        rag.insert(f.read())
+
+    query_text = "What are the main themes?"
+
+    print("Result (Naive):")
+    print(rag.query(query_text, param=QueryParam(mode="naive")))
+
+    print("\nResult (Local):")
+    print(rag.query(query_text, param=QueryParam(mode="local")))
+
+    print("\nResult (Global):")
+    print(rag.query(query_text, param=QueryParam(mode="global")))
+
+    print("\nResult (Hybrid):")
+    print(rag.query(query_text, param=QueryParam(mode="hybrid")))
+
+    print("\nResult (mix):")
+    print(rag.query(query_text, param=QueryParam(mode="mix")))
+
+
+if __name__ == "__main__":
+    main()