Feat/huggingface embedding support (#1211)

Co-authored-by: StyleZhang <jasonapring2015@outlook.com>

api/core/third_party/langchain/embeddings/huggingface_hub_embedding.py

@@ -0,0 +1,74 @@
+from typing import Any, Dict, List, Optional
+import json
+import numpy as np
+
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.embeddings.base import Embeddings
+from langchain.utils import get_from_dict_or_env
+from huggingface_hub import InferenceClient
+
+HOSTED_INFERENCE_API = 'hosted_inference_api'
+INFERENCE_ENDPOINTS = 'inference_endpoints'
+
+
+class HuggingfaceHubEmbeddings(BaseModel, Embeddings):
+    client: Any
+    model: str
+
+    huggingface_namespace: Optional[str] = None
+    task_type: Optional[str] = None
+    huggingfacehub_api_type: Optional[str] = None
+    huggingfacehub_api_token: Optional[str] = None
+    huggingfacehub_endpoint_url: Optional[str] = None
+
+    class Config:
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        values['huggingfacehub_api_token'] = get_from_dict_or_env(
+            values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
+        )
+
+        values['client'] = InferenceClient(token=values['huggingfacehub_api_token'])
+
+        return values
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        model = ''
+
+        if self.huggingfacehub_api_type == HOSTED_INFERENCE_API:
+            model = self.model
+        else:
+            model = self.huggingfacehub_endpoint_url
+
+        output = self.client.post(
+            json={
+                "inputs": texts,
+                "options": {
+                    "wait_for_model": False,
+                    "use_cache": False
+                }
+            }, model=model)
+        
+        embeddings =  json.loads(output.decode())
+        return self.mean_pooling(embeddings)
+
+    def embed_query(self, text: str) -> List[float]:
+        return self.embed_documents([text])[0]
+    
+    # https://huggingface.co/docs/api-inference/detailed_parameters#feature-extraction-task
+    # Returned values are a list of floats, or a list of list of floats 
+    # (depending on if you sent a string or a list of string, 
+    # and if the automatic reduction, usually mean_pooling for instance was applied for you or not. 
+    # This should be explained on the model's README.)
+    def mean_pooling(self, embeddings: List) -> List[float]:
+        # If automatic reduction by giving model, no need to mean_pooling.
+        # For example one: List[List[float]]
+        if not isinstance(embeddings[0][0], list):
+            return embeddings
+
+        # For example two: List[List[List[float]]], need to mean_pooling.
+        sentence_embeddings = [np.mean(embedding[0], axis=0).tolist() for embedding in embeddings]
+        return sentence_embeddings

api/core/third_party/langchain/llms/huggingface_hub_llm.py

@@ -16,7 +16,7 @@ class HuggingFaceHubLLM(HuggingFaceHub):
     environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
     it as a named parameter to the constructor.
 
-    Only supports `text-generation`, `text2text-generation` and `summarization` for now.
+    Only supports `text-generation`, `text2text-generation` for now.
 
     Example:
         .. code-block:: python