Merge pull request #96 from tpoisonooo/support-siliconcloud

feat(examples): support siliconcloud free API

examples/lightrag_siliconcloud_demo.py

@@ -0,0 +1,79 @@
+import os
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import openai_complete_if_cache, siliconcloud_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(
+        "Qwen/Qwen2.5-7B-Instruct",
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        api_key=os.getenv("UPSTAGE_API_KEY"),
+        base_url="https://api.siliconflow.cn/v1/",
+        **kwargs,
+    )
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await siliconcloud_embedding(
+        texts,
+        model="netease-youdao/bce-embedding-base_v1",
+        api_key=os.getenv("UPSTAGE_API_KEY"),
+        max_token_size=int(512 * 1.5)
+    )
+
+
+# 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=768, max_token_size=512, 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"))
+)