feat(examples): support siliconcloud free API

README.md

@@ -629,6 +629,7 @@ def extract_queries(file_path):
 │   ├── lightrag_ollama_demo.py
 │   ├── lightrag_openai_compatible_demo.py
 │   ├── lightrag_openai_demo.py
+│   ├── lightrag_siliconcloud_demo.py
 │   └── vram_management_demo.py
 ├── lightrag
 │   ├── __init__.py

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"))
+)

lightrag/llm.py

@@ -2,8 +2,11 @@ import os
 import copy
 import json
 import aioboto3
+import aiohttp
 import numpy as np
 import ollama
+import base64
+import struct
 from openai import AsyncOpenAI, APIConnectionError, RateLimitError, Timeout
 from tenacity import (
     retry,
@@ -312,7 +315,7 @@ async def ollama_model_complete(
 @wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
 @retry(
     stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=10),
+    wait=wait_exponential(multiplier=1, min=4, max=60),
     retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
 )
 async def openai_embedding(
@@ -332,6 +335,49 @@ async def openai_embedding(
     )
     return np.array([dp.embedding for dp in response.data])
 
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=60),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
+)
+async def siliconcloud_embedding(
+    texts: list[str],
+    model: str = "netease-youdao/bce-embedding-base_v1",
+    base_url: str = "https://api.siliconflow.cn/v1/embeddings",
+    max_token_size: int = 512,
+    api_key: str = None,
+) -> np.ndarray:
+    if api_key and not api_key.startswith('Bearer '):
+        api_key = 'Bearer ' + api_key
+
+    headers = {
+        "Authorization": api_key,
+        "Content-Type": "application/json"
+    }
+
+    truncate_texts = [text[0:max_token_size] for text in texts]
+
+    payload = {
+        "model": model,
+        "input": truncate_texts,
+        "encoding_format": "base64"
+    }
+
+    base64_strings = []
+    async with aiohttp.ClientSession() as session:
+        async with session.post(base_url, headers=headers, json=payload) as response:
+            content = await response.json()
+            if 'code' in content:
+                raise ValueError(content)
+            base64_strings = [item['embedding'] for item in content['data']]
+    
+    embeddings = []
+    for string in base64_strings:
+        decode_bytes = base64.b64decode(string)
+        n = len(decode_bytes) // 4
+        float_array = struct.unpack('<' + 'f' * n, decode_bytes)
+        embeddings.append(float_array)
+    return np.array(embeddings)
 
 # @wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
 # @retry(

requirements.txt

@@ -11,4 +11,5 @@ tiktoken
 torch
 transformers
 xxhash
-pyvis
+pyvis
+aiohttp