feat: add run_async support for OllamaDocumentEmbedder

integrations/ollama/src/haystack_integrations/components/embedders/ollama/document_embedder.py

@@ -1,9 +1,10 @@
+import asyncio
 from typing import Any, Dict, List, Optional
 
 from haystack import Document, component
 from tqdm import tqdm
 
-from ollama import Client
+from ollama import AsyncClient, Client
 
 
 @component
@@ -74,6 +75,20 @@ def __init__(
         self.prefix = prefix
 
         self._client = Client(host=self.url, timeout=self.timeout)
+        self._async_client = AsyncClient(host=self.url, timeout=self.timeout)
+
+    def _prepare_input(self, documents: List[Document]) -> List[Document]:
+        """
+        Prepares the list of documents to embed by appropriate validation.
+        """
+        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
+            msg = (
+                "OllamaDocumentEmbedder expects a list of Documents as input."
+                "In case you want to embed a list of strings, please use the OllamaTextEmbedder."
+            )
+            raise TypeError(msg)
+
+        return documents
 
     def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
         """
@@ -115,6 +130,35 @@ def _embed_batch(
 
         return all_embeddings
 
+    async def _embed_batch_async(
+        self, texts_to_embed: List[str], batch_size: int, generation_kwargs: Optional[Dict[str, Any]] = None
+    ):
+        """
+        Internal method to embed a batch of texts asynchronously.
+        """
+        all_embeddings = []
+
+        batches = [texts_to_embed[i : i + batch_size] for i in range(0, len(texts_to_embed), batch_size)]
+
+        tasks = [
+            self._async_client.embed(
+                model=self.model,
+                input=batch,
+                options=generation_kwargs,
+            )
+            for batch in batches
+        ]
+
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for idx, res in enumerate(results):
+            if isinstance(res, BaseException):
+                err_msg = f"Embedding batch {idx} raised an exception."
+                raise RuntimeError(err_msg)
+            all_embeddings.extend(res["embeddings"])
+
+        return all_embeddings
+
     @component.output_types(documents=List[Document], meta=Dict[str, Any])
     def run(self, documents: List[Document], generation_kwargs: Optional[Dict[str, Any]] = None):
         """
@@ -130,12 +174,11 @@ def run(self, documents: List[Document], generation_kwargs: Optional[Dict[str, A
             - `documents`: Documents with embedding information attached
             - `meta`: The metadata collected during the embedding process
         """
-        if not isinstance(documents, list) or (documents and not isinstance(documents[0], Document)):
-            msg = (
-                "OllamaDocumentEmbedder expects a list of Documents as input."
-                "In case you want to embed a list of strings, please use the OllamaTextEmbedder."
-            )
-            raise TypeError(msg)
+        documents = self._prepare_input(documents=documents)
+
+        if not documents:
+            # return early if we were passed an empty list
+            return {"documents": [], "meta": {}}
 
         generation_kwargs = generation_kwargs or self.generation_kwargs
 
@@ -148,3 +191,37 @@ def run(self, documents: List[Document], generation_kwargs: Optional[Dict[str, A
             doc.embedding = emb
 
         return {"documents": documents, "meta": {"model": self.model}}
+
+    @component.output_types(documents=List[Document], meta=Dict[str, Any])
+    async def run_async(self, documents: List[Document], generation_kwargs: Optional[Dict[str, Any]] = None):
+        """
+        Asynchronously run an Ollama Model to compute embeddings of the provided documents.
+
+        :param documents:
+            Documents to be converted to an embedding.
+        :param generation_kwargs:
+            Optional arguments to pass to the Ollama generation endpoint, such as temperature,
+            top_p, etc. See the
+            [Ollama docs](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
+        :returns: A dictionary with the following keys:
+            - `documents`: Documents with embedding information attached
+            - `meta`: The metadata collected during the embedding process
+        """
+
+        documents = self._prepare_input(documents=documents)
+
+        if not documents:
+            # return early if we were passed an empty list
+            return {"documents": [], "meta": {}}
+
+        generation_kwargs = generation_kwargs or self.generation_kwargs
+
+        texts_to_embed = self._prepare_texts_to_embed(documents=documents)
+        embeddings = await self._embed_batch_async(
+            texts_to_embed=texts_to_embed, batch_size=self.batch_size, generation_kwargs=generation_kwargs
+        )
+
+        for doc, emb in zip(documents, embeddings):
+            doc.embedding = emb
+
+        return {"documents": documents, "meta": {"model": self.model}}

integrations/ollama/tests/test_document_embedder.py

@@ -50,6 +50,23 @@ def test_run(self):
             Document(content="Llamas have been used as pack animals for centuries, especially in South America."),
         ]
         result = embedder.run(list_of_docs)
+
+        assert result["meta"]["model"] == "nomic-embed-text"
+        documents = result["documents"]
+        assert len(documents) == 3
+        assert all(isinstance(element, float) for document in documents for element in document.embedding)
+
+    @pytest.mark.asyncio
+    @pytest.mark.integration
+    async def test_run_async(self):
+        embedder = OllamaDocumentEmbedder(model="nomic-embed-text", batch_size=2)
+        list_of_docs = [
+            Document(content="Llamas are amazing animals known for their soft wool and gentle demeanor."),
+            Document(content="The Andes mountains are the natural habitat of many llamas."),
+            Document(content="Llamas have been used as pack animals for centuries, especially in South America."),
+        ]
+        result = await embedder.run_async(list_of_docs)
+
         assert result["meta"]["model"] == "nomic-embed-text"
         documents = result["documents"]
         assert len(documents) == 3