diff --git a/rag/llm/embedding_model.py b/rag/llm/embedding_model.py
index 91c2e77ee..29ec5444f 100644
--- a/rag/llm/embedding_model.py
+++ b/rag/llm/embedding_model.py
@@ -26,6 +26,7 @@ import dashscope
 from openai import OpenAI
 from FlagEmbedding import FlagModel
 import torch
+import asyncio
 import numpy as np
 
 from api.utils.file_utils import get_home_cache_dir
@@ -304,4 +305,44 @@ class JinaEmbed(Base):
 
     def encode_queries(self, text):
         embds, cnt = self.encode([text])
-        return np.array(embds[0]), cnt
\ No newline at end of file
+        return np.array(embds[0]), cnt
+
+
+class InfinityEmbed(Base):
+    _model = None
+
+    def __init__(
+            self,
+            model_names: list[str] = ("BAAI/bge-small-en-v1.5",),
+            engine_kwargs: dict = {},
+            key = None,
+    ):
+        
+        from infinity_emb import EngineArgs
+        from infinity_emb.engine import AsyncEngineArray
+        
+        self._default_model = model_names[0]
+        self.engine_array = AsyncEngineArray.from_args([EngineArgs(model_name_or_path = model_name, **engine_kwargs) for model_name in model_names])  
+
+    async def _embed(self, sentences: list[str], model_name: str = ""):
+        if not model_name:
+            model_name = self._default_model
+        engine = self.engine_array[model_name]
+        was_already_running = engine.is_running
+        if not was_already_running:
+            await engine.astart()
+        embeddings, usage = await engine.embed(sentences=sentences)
+        if not was_already_running:
+            await engine.astop()
+        return embeddings, usage
+
+    def encode(self, texts: list[str], model_name: str = "") -> tuple[np.ndarray, int]:
+        # Using the internal tokenizer to encode the texts and get the total
+        # number of tokens
+        embeddings, usage = asyncio.run(self._embed(texts, model_name))
+        return np.array(embeddings), usage
+
+    def encode_queries(self, text: str) -> tuple[np.ndarray, int]:
+        # Using the internal tokenizer to encode the texts and get the total
+        # number of tokens
+        return self.encode([text])