diff --git a/docs/components/embedding-models.mdx b/docs/components/embedding-models.mdx
index 4e362fb3..af24b89c 100644
--- a/docs/components/embedding-models.mdx
+++ b/docs/components/embedding-models.mdx
@@ -15,6 +15,7 @@ Embedchain supports several embedding models from the following providers:
   <Card title="Vertex AI" href="#vertex-ai"></Card>
   <Card title="NVIDIA AI" href="#nvidia-ai"></Card>
   <Card title="Cohere" href="#cohere"></Card>
+  <Card title="Ollama" href="#ollama"></Card>
 </CardGroup>
 
 ## OpenAI
@@ -357,4 +358,31 @@ embedder:
     vector_dimension: 768
 ```
 
+</CodeGroup>
+
+## Ollama
+
+Ollama enables the use of embedding models, allowing you to generate high-quality embeddings directly on your local machine. Make sure to install [Ollama](https://ollama.com/download) and keep it running before using the embedding model.
+
+You can find the list of models at [Ollama Embedding Models](https://ollama.com/blog/embedding-models).
+
+Below is an example of how to use embedding model Ollama:
+
+<CodeGroup>
+
+```python main.py
+import os
+from embedchain import App
+
+# load embedding model configuration from config.yaml file
+app = App.from_config(config_path="config.yaml")
+```
+
+```yaml config.yaml
+embedder:
+  provider: ollama
+  config:
+    model: 'all-minilm:latest'
+```
+
 </CodeGroup>
\ No newline at end of file
diff --git a/embedchain/embedder/ollama.py b/embedchain/embedder/ollama.py
index 41001114..a70e402e 100644
--- a/embedchain/embedder/ollama.py
+++ b/embedchain/embedder/ollama.py
@@ -1,16 +1,28 @@
+import logging
 from typing import Optional
 
+try:
+    import ollama
+except ImportError:
+    raise ImportError("Ollama Embedder requires extra dependencies. Install with `pip install ollama`") from None
+
 from langchain_community.embeddings import OllamaEmbeddings
 
 from embedchain.config import OllamaEmbedderConfig
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.models import VectorDimensions
 
+logger = logging.getLogger(__name__)
+
 
 class OllamaEmbedder(BaseEmbedder):
     def __init__(self, config: Optional[OllamaEmbedderConfig] = None):
         super().__init__(config=config)
 
+        local_models = ollama.list()["models"]
+        if not any(model.get("name") == self.config.model for model in local_models):
+            logger.info(f"Pulling {self.config.model} from Ollama!")
+            ollama.pull(self.config.model)
         embeddings = OllamaEmbeddings(model=self.config.model, base_url=self.config.base_url)
         embedding_fn = BaseEmbedder._langchain_default_concept(embeddings)
         self.set_embedding_fn(embedding_fn=embedding_fn)