diff --git a/trustgraph-flow/setup.py b/trustgraph-flow/setup.py
index bea1d496..f5a0bc3c 100644
--- a/trustgraph-flow/setup.py
+++ b/trustgraph-flow/setup.py
@@ -49,6 +49,7 @@ setuptools.setup(
         "langchain-core",
         "langchain-text-splitters",
         "neo4j",
+        "ollama",
         "openai",
         "pinecone[grpc]",
         "prometheus-client",
diff --git a/trustgraph-flow/trustgraph/embeddings/ollama/processor.py b/trustgraph-flow/trustgraph/embeddings/ollama/processor.py
index 6682a79f..fc54cbb8 100755
--- a/trustgraph-flow/trustgraph/embeddings/ollama/processor.py
+++ b/trustgraph-flow/trustgraph/embeddings/ollama/processor.py
@@ -3,12 +3,13 @@
 Embeddings service, applies an embeddings model selected from HuggingFace.
 Input is text, output is embeddings vector.
 """
-from langchain_community.embeddings import OllamaEmbeddings
 
 from ... schema import EmbeddingsRequest, EmbeddingsResponse
 from ... schema import embeddings_request_queue, embeddings_response_queue
 from ... log_level import LogLevel
 from ... base import ConsumerProducer
+from ollama import Client
+import os
 
 module = ".".join(__name__.split(".")[1:-1])
 
@@ -16,7 +17,7 @@ default_input_queue = embeddings_request_queue
 default_output_queue = embeddings_response_queue
 default_subscriber = module
 default_model="mxbai-embed-large"
-default_ollama = 'http://localhost:11434'
+default_ollama = os.getenv("OLLAMA_HOST", 'http://localhost:11434')
 
 class Processor(ConsumerProducer):
 
@@ -26,6 +27,9 @@ class Processor(ConsumerProducer):
         output_queue = params.get("output_queue", default_output_queue)
         subscriber = params.get("subscriber", default_subscriber)
 
+        ollama = params.get("ollama", default_ollama)
+        model = params.get("model", default_model)
+
         super(Processor, self).__init__(
             **params | {
                 "input_queue": input_queue,
@@ -33,10 +37,13 @@ class Processor(ConsumerProducer):
                 "subscriber": subscriber,
                 "input_schema": EmbeddingsRequest,
                 "output_schema": EmbeddingsResponse,
+                "ollama": ollama,
+                "model": model,
             }
         )
 
-        self.embeddings = OllamaEmbeddings(base_url=ollama, model=model)
+        self.client = Client(host=ollama)
+        self.model = model
 
     def handle(self, msg):
 
@@ -49,10 +56,16 @@ class Processor(ConsumerProducer):
         print(f"Handling input {id}...", flush=True)
 
         text = v.text
-        embeds = self.embeddings.embed_query([text])
+        embeds = self.client.embed(
+            model = self.model,
+            input = text
+        )
 
         print("Send response...", flush=True)
-        r = EmbeddingsResponse(vectors=[embeds])
+        r = EmbeddingsResponse(
+            vectors=embeds.embeddings,
+            error=None,
+        )
 
         self.producer.send(r, properties={"id": id})