feat: LLamaIndexEmbeddings，同时支持同步异步的embedding调用

app/utils/LLamaIndexEmbeddings.py

@@ -0,0 +1,49 @@
+import json
+
+import httpx
+import requests
+from llama_index.core.base.embeddings.base import BaseEmbedding, Embedding
+
+
+class LLamaIndexEmbeddings(BaseEmbedding):
+  base_url: str = ""
+  api_key: str = ""
+  model: str = ""
+  def __init__(self, base_url, api_key, model):
+    super().__init__()
+    self.base_url = base_url  # API基础URL
+    self.api_key = api_key  # API访问密钥
+    self.model = model  # 嵌入模型名称
+
+  def embed_documents(self, texts):
+    headers = {"Content-Type": "application/json","Authorization": f"Bearer {self.api_key}"}
+    payload = {"input": texts, "model": self.model, "encoding_format": "float"}
+    response = requests.post(f"{self.base_url}/embeddings",headers=headers,data=json.dumps(payload))
+    response.raise_for_status()
+    json_data = response.json()
+    return [item["embedding"] for item in json_data["data"]]
+
+  def embed_query(self, text):
+    return self.embed_documents([text])[0]
+
+  async def aembed_documents(self, texts):
+    headers = {"Content-Type": "application/json","Authorization": f"Bearer {self.api_key}"}
+    payload = {"input": texts, "model": self.model, "encoding_format": "float"}
+    async with httpx.AsyncClient() as client:
+      response = await client.post(url=f"{self.base_url}/embeddings",headers=headers,json=payload)
+      json_data = response.json()
+    return [item["embedding"] for item in json_data["data"]]
+
+  async def aembed_query(self, text):
+    return (await self.aembed_documents([text]))[0]
+
+
+  def _get_query_embedding(self, query: str) -> Embedding:
+    raise Exception("_get_query_embedding: LLamaIndexEmbeddings only support async event loop.")
+
+  async def _aget_query_embedding(self, query: str) -> Embedding:
+    return await self.aembed_query(query)
+
+  def _get_text_embedding(self, text: str) -> Embedding:
+    return self.embed_query(text)
+