Add ollama embedder example

integrations/ollama/example/embedder_example.py

@@ -0,0 +1,34 @@
+# In order to run this example, you will need to have an instance of Ollama running with the
+# nomic-embed-text model downloaded. Use the following commands to serve an nomic-embed-text model from Ollama
+#
+# docker run -d -p 11434:11434 --name ollama ollama/ollama:latest
+# docker exec ollama ollama pull nomic-embed-text
+
+from haystack import Document, Pipeline
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack_integrations.components.embedders.ollama.document_embedder import OllamaDocumentEmbedder
+from haystack_integrations.components.embedders.ollama.text_embedder import OllamaTextEmbedder
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+documents = [
+    Document(content="I saw a black horse running"),
+    Document(content="Germany has many big cities"),
+    Document(content="My name is Wolfgang and I live in Berlin"),
+]
+
+document_embedder = OllamaDocumentEmbedder()
+documents_with_embeddings = document_embedder.run(documents)["documents"]
+document_store.write_documents(documents_with_embeddings)
+
+query_pipeline = Pipeline()
+query_pipeline.add_component("text_embedder", OllamaTextEmbedder())
+query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
+query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+
+query = "Who lives in Berlin?"
+
+result = query_pipeline.run({"text_embedder": {"text": query}})
+
+print(result["retriever"]["documents"][0])