add huggingface model (#2624)

### What problem does this PR solve?

#2469

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

rag/llm/__init__.py

@@ -18,7 +18,7 @@ from .chat_model import *
 from .cv_model import *
 from .rerank_model import *
 from .sequence2txt_model import *
-from .tts_model import * 
+from .tts_model import *
 
 EmbeddingModel = {
     "Ollama": OllamaEmbed,
@@ -46,7 +46,8 @@ EmbeddingModel = {
     "SILICONFLOW": SILICONFLOWEmbed,
     "Replicate": ReplicateEmbed,
     "BaiduYiyan": BaiduYiyanEmbed,
-    "Voyage AI": VoyageEmbed
+    "Voyage AI": VoyageEmbed,
+    "HuggingFace":HuggingFaceEmbed,
 }
 
 

rag/llm/chat_model.py

@@ -1414,3 +1414,4 @@ class GoogleChat(Base):
                 yield ans + "\n**ERROR**: " + str(e)
 
             yield response._chunks[-1].usage_metadata.total_token_count
+            

rag/llm/embedding_model.py

@@ -678,3 +678,40 @@ class VoyageEmbed(Base):
             texts=text, model=self.model_name, input_type="query"
             )
         return np.array(res.embeddings), res.total_tokens
+
+
+class HuggingFaceEmbed(Base):
+    def __init__(self, key, model_name, base_url=None):
+        if not model_name:
+            raise ValueError("Model name cannot be None")
+        self.key = key
+        self.model_name = model_name
+        self.base_url = base_url or "http://127.0.0.1:8080"
+
+    def encode(self, texts: list, batch_size=32):
+        embeddings = []
+        for text in texts:
+            response = requests.post(
+                f"{self.base_url}/embed",
+                json={"inputs": text},
+                headers={'Content-Type': 'application/json'}
+            )
+            if response.status_code == 200:
+                embedding = response.json()
+                embeddings.append(embedding[0])
+            else:
+                raise Exception(f"Error: {response.status_code} - {response.text}")
+        return np.array(embeddings), sum([num_tokens_from_string(text) for text in texts])
+
+    def encode_queries(self, text):
+        response = requests.post(
+            f"{self.base_url}/embed",
+            json={"inputs": text},
+            headers={'Content-Type': 'application/json'}
+        )
+        if response.status_code == 200:
+            embedding = response.json()
+            return np.array(embedding[0]), num_tokens_from_string(text)
+        else:
+            raise Exception(f"Error: {response.status_code} - {response.text}")
+