diff --git a/libs/langchain/langchain/embeddings/ollama.py b/libs/langchain/langchain/embeddings/ollama.py
index 1938d916405..c1b910af4ee 100644
--- a/libs/langchain/langchain/embeddings/ollama.py
+++ b/libs/langchain/langchain/embeddings/ollama.py
@@ -1,9 +1,12 @@
+import logging
 from typing import Any, Dict, List, Mapping, Optional
 
 import requests
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra
 
+logger = logging.getLogger(__name__)
+
 
 class OllamaEmbeddings(BaseModel, Embeddings):
     """Ollama locally runs large language models.
@@ -99,6 +102,9 @@ class OllamaEmbeddings(BaseModel, Embeddings):
     to more diverse text, while a lower value (e.g., 0.5) will
     generate more focused and conservative text. (Default: 0.9)"""
 
+    show_progress: bool = False
+    """Whether to show a tqdm progress bar. Must have `tqdm` installed."""
+
     @property
     def _default_params(self) -> Dict[str, Any]:
         """Get the default parameters for calling Ollama."""
@@ -170,15 +176,23 @@ class OllamaEmbeddings(BaseModel, Embeddings):
             )
 
     def _embed(self, input: List[str]) -> List[List[float]]:
-        embeddings_list: List[List[float]] = []
-        for prompt in input:
-            embeddings = self._process_emb_response(prompt)
-            embeddings_list.append(embeddings)
+        if self.show_progress:
+            try:
+                from tqdm import tqdm
 
-        return embeddings_list
+                iter_ = tqdm(input, desc="OllamaEmbeddings")
+            except ImportError:
+                logger.warning(
+                    "Unable to show progress bar because tqdm could not be imported. "
+                    "Please install with `pip install tqdm`."
+                )
+                iter_ = input
+        else:
+            iter_ = input
+        return [self._process_emb_response(prompt) for prompt in iter_]
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Embed documents using a Ollama deployed embedding model.
+        """Embed documents using an Ollama deployed embedding model.
 
         Args:
             texts: The list of texts to embed.