feat(examples): support siliconcloud free API

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(