community[minor]: implement huggingface show_progress consistently (#22682)

- **Description:** This implements `show_progress` more consistently
(i.e. it is also added to the `HuggingFaceBgeEmbeddings` object).
- **Issue:** This implements `show_progress` more consistently in the
embeddings huggingface classes. Previously this could have been set via
`encode_kwargs`.
 - **Dependencies:** None
 - **Twitter handle:** @jonzeolla

libs/community/langchain_community/embeddings/huggingface.py

@@ -1,7 +1,8 @@
+import warnings
 from typing import Any, Dict, List, Optional
 
 import requests
-from langchain_core._api import deprecated
+from langchain_core._api import deprecated, warn_deprecated
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra, Field, SecretStr
 
@@ -154,6 +155,8 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
     """Instruction to use for embedding documents."""
     query_instruction: str = DEFAULT_QUERY_INSTRUCTION
     """Instruction to use for embedding query."""
+    show_progress: bool = False
+    """Whether to show a progress bar."""
 
     def __init__(self, **kwargs: Any):
         """Initialize the sentence_transformer."""
@@ -167,6 +170,20 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
         except ImportError as e:
             raise ImportError("Dependencies for InstructorEmbedding not found.") from e
 
+        if "show_progress_bar" in self.encode_kwargs:
+            warn_deprecated(
+                since="0.2.5",
+                removal="0.4.0",
+                name="encode_kwargs['show_progress_bar']",
+                alternative=f"the show_progress method on {self.__class__.__name__}",
+            )
+            if self.show_progress:
+                warnings.warn(
+                    "Both encode_kwargs['show_progress_bar'] and show_progress are set;"
+                    "encode_kwargs['show_progress_bar'] takes precedence"
+                )
+            self.show_progress = self.encode_kwargs.pop("show_progress_bar")
+
     class Config:
         """Configuration for this pydantic object."""
 
@@ -182,7 +199,11 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
             List of embeddings, one for each text.
         """
         instruction_pairs = [[self.embed_instruction, text] for text in texts]
-        embeddings = self.client.encode(instruction_pairs, **self.encode_kwargs)
+        embeddings = self.client.encode(
+            instruction_pairs,
+            show_progress_bar=self.show_progress,
+            **self.encode_kwargs,
+        )
         return embeddings.tolist()
 
     def embed_query(self, text: str) -> List[float]:
@@ -195,7 +216,11 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
             Embeddings for the text.
         """
         instruction_pair = [self.query_instruction, text]
-        embedding = self.client.encode([instruction_pair], **self.encode_kwargs)[0]
+        embedding = self.client.encode(
+            [instruction_pair],
+            show_progress_bar=self.show_progress,
+            **self.encode_kwargs,
+        )[0]
         return embedding.tolist()
 
 
@@ -252,6 +277,8 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
     """Instruction to use for embedding query."""
     embed_instruction: str = ""
     """Instruction to use for embedding document."""
+    show_progress: bool = False
+    """Whether to show a progress bar."""
 
     def __init__(self, **kwargs: Any):
         """Initialize the sentence_transformer."""
@@ -268,9 +295,24 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
         self.client = sentence_transformers.SentenceTransformer(
             self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
         )
+
         if "-zh" in self.model_name:
             self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH
 
+        if "show_progress_bar" in self.encode_kwargs:
+            warn_deprecated(
+                since="0.2.5",
+                removal="0.4.0",
+                name="encode_kwargs['show_progress_bar']",
+                alternative=f"the show_progress method on {self.__class__.__name__}",
+            )
+            if self.show_progress:
+                warnings.warn(
+                    "Both encode_kwargs['show_progress_bar'] and show_progress are set;"
+                    "encode_kwargs['show_progress_bar'] takes precedence"
+                )
+            self.show_progress = self.encode_kwargs.pop("show_progress_bar")
+
     class Config:
         """Configuration for this pydantic object."""
 
@@ -286,7 +328,9 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
             List of embeddings, one for each text.
         """
         texts = [self.embed_instruction + t.replace("\n", " ") for t in texts]
-        embeddings = self.client.encode(texts, **self.encode_kwargs)
+        embeddings = self.client.encode(
+            texts, show_progress_bar=self.show_progress, **self.encode_kwargs
+        )
         return embeddings.tolist()
 
     def embed_query(self, text: str) -> List[float]:
@@ -300,7 +344,9 @@ class HuggingFaceBgeEmbeddings(BaseModel, Embeddings):
         """
         text = text.replace("\n", " ")
         embedding = self.client.encode(
-            self.query_instruction + text, **self.encode_kwargs
+            self.query_instruction + text,
+            show_progress_bar=self.show_progress,
+            **self.encode_kwargs,
         )
         return embedding.tolist()
 
@@ -353,7 +399,9 @@ class HuggingFaceInferenceAPIEmbeddings(BaseModel, Embeddings):
         Example:
             .. code-block:: python
 
-                from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+                from langchain_community.embeddings import (
+                    HuggingFaceInferenceAPIEmbeddings,
+                )
 
                 hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
                     api_key="your_api_key",