--- a
+++ b/aiagents4pharma/talk2knowledgegraphs/utils/embeddings/ollama.py
@@ -0,0 +1,81 @@
+"""
+Embedding class using Ollama model based on LangChain Embeddings class.
+"""
+
+import time
+from typing import List
+import subprocess
+import ollama
+from langchain_ollama import OllamaEmbeddings
+from .embeddings import Embeddings
+
+class EmbeddingWithOllama(Embeddings):
+    """
+    Embedding class using Ollama model based on LangChain Embeddings class.
+    """
+    def __init__(self, model_name: str):
+        """
+        Initialize the EmbeddingWithOllama class.
+
+        Args:
+            model_name: The name of the Ollama model to be used.
+        """
+        # Setup the Ollama server
+        self.__setup(model_name)
+
+        # Set parameters
+        self.model_name = model_name
+
+        # Prepare model
+        self.model = OllamaEmbeddings(model=self.model_name)
+
+    def __setup(self, model_name: str) -> None:
+        """
+        Check if the Ollama model is available and run the Ollama server if needed.
+
+        Args:
+            model_name: The name of the Ollama model to be used.
+        """
+        try:
+            models_list = ollama.list()["models"]
+            if model_name not in [m['model'].replace(":latest", "") for m in models_list]:
+                ollama.pull(model_name)
+                time.sleep(30)
+                raise ValueError(f"Pulled {model_name} model")
+        except Exception as e:
+            with subprocess.Popen(
+                "ollama serve", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE
+            ):
+                time.sleep(10)
+            raise ValueError(f"Error: {e} and restarted Ollama server.") from e
+
+    def embed_documents(self, texts: List[str]) -> List[float]:
+        """
+        Generate embedding for a list of input texts using Ollama model.
+
+        Args:
+            texts: The list of texts to be embedded.
+
+        Returns:
+            The list of embeddings for the given texts.
+        """
+
+        # Generate the embedding
+        embeddings = self.model.embed_documents(texts)
+
+        return embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """
+        Generate embeddings for an input text using Ollama model.
+
+        Args:
+            text: A query to be embedded.
+        Returns:
+            The embeddings for the given query.
+        """
+
+        # Generate the embedding
+        embeddings = self.model.embed_query(text)
+
+        return embeddings