diff --git a/src/neo4j_graphrag/embeddings/ollama.py b/src/neo4j_graphrag/embeddings/ollama.py
index 88f85096..cfb43dd6 100644
--- a/src/neo4j_graphrag/embeddings/ollama.py
+++ b/src/neo4j_graphrag/embeddings/ollama.py
@@ -19,7 +19,7 @@
 
 from neo4j_graphrag.embeddings.base import Embedder
 from neo4j_graphrag.exceptions import EmbeddingsGenerationError
-from neo4j_graphrag.utils.rate_limit import RateLimitHandler, rate_limit_handler
+from neo4j_graphrag.utils.rate_limit import RateLimitHandler, rate_limit_handler, async_rate_limit_handler
 
 
 class OllamaEmbeddings(Embedder):
@@ -47,6 +47,7 @@ def __init__(
         super().__init__(rate_limit_handler)
         self.model = model
         self.client = ollama.Client(**kwargs)
+        self.async_client = ollama.AsyncClient(**kwargs)
 
     @rate_limit_handler
     def embed_query(self, text: str, **kwargs: Any) -> list[float]:
@@ -73,3 +74,29 @@ def embed_query(self, text: str, **kwargs: Any) -> list[float]:
             raise EmbeddingsGenerationError("Embedding is not a list of floats.")
 
         return embedding
+
+    @async_rate_limit_handler
+    async def async_embed_query(self, text: str, **kwargs: Any) -> list[float]:
+        """
+        Generate embeddings for a given query using an Ollama text embedding model.
+
+        Args:
+            text (str): The text to generate an embedding for.
+            **kwargs (Any): Additional keyword arguments to pass to the Ollama client.
+        """
+        embeddings_response = await self.async_client.embed(
+            model=self.model,
+            input=text,
+            **kwargs,
+        )
+
+        if embeddings_response is None or not embeddings_response.embeddings:
+            raise EmbeddingsGenerationError("Failed to retrieve embeddings.")
+
+        embeddings = embeddings_response.embeddings
+        # client always returns a sequence of sequences
+        embedding = embeddings[0]
+        if not isinstance(embedding, list):
+            raise EmbeddingsGenerationError("Embedding is not a list of floats.")
+
+        return embedding
\ No newline at end of file
diff --git a/src/neo4j_graphrag/experimental/components/embedder.py b/src/neo4j_graphrag/experimental/components/embedder.py
index f113ecde..cdf24d76 100644
--- a/src/neo4j_graphrag/experimental/components/embedder.py
+++ b/src/neo4j_graphrag/experimental/components/embedder.py
@@ -14,6 +14,9 @@
 #  limitations under the License.
 from pydantic import validate_call
 
+import asyncio
+from typing import Any, List, Optional, Union
+
 from neo4j_graphrag.embeddings.base import Embedder
 from neo4j_graphrag.experimental.components.types import TextChunk, TextChunks
 from neo4j_graphrag.experimental.pipeline.component import Component
@@ -24,6 +27,7 @@ class TextChunkEmbedder(Component):
 
     Args:
         embedder (Embedder): The embedder to use to create the embeddings.
+        max_concurrency (int): The maximum number of concurrent embedding requests. Defaults to 5.
 
     Example:
 
@@ -34,14 +38,21 @@ class TextChunkEmbedder(Component):
         from neo4j_graphrag.experimental.pipeline import Pipeline
 
         embedder = OpenAIEmbeddings(model="text-embedding-3-large")
-        chunk_embedder = TextChunkEmbedder(embedder)
+        chunk_embedder = TextChunkEmbedder(embedder=embedder, max_concurrency=10)
         pipeline = Pipeline()
         pipeline.add_component(chunk_embedder, "chunk_embedder")
 
     """
 
-    def __init__(self, embedder: Embedder):
+    def __init__(
+        self, 
+        *args: Any,
+        embedder: Embedder,
+        max_concurrency: int = 5,
+        **kwargs: Any,
+    ) -> None:
         self._embedder = embedder
+        self._max_concurrency = max_concurrency
 
     def _embed_chunk(self, text_chunk: TextChunk) -> TextChunk:
         """Embed a single text chunk.
@@ -62,9 +73,36 @@ def _embed_chunk(self, text_chunk: TextChunk) -> TextChunk:
             metadata=metadata,
             uid=text_chunk.uid,
         )
+        
+    async def _async_embed_chunk(
+        self, 
+        sem: asyncio.Semaphore,
+        text_chunk: TextChunk) -> TextChunk:
+        """Asynchronously embed a single text chunk.
+
+        Args:
+            text_chunk (TextChunk): The text chunk to embed.
+
+        Returns:
+            TextChunk: The text chunk with an added "embedding" key in its
+            metadata containing the embeddings of the text chunk's text.
+        """
+        async with sem:
+            embedding = await self._embedder.async_embed_query(text_chunk.text)
+            metadata = text_chunk.metadata if text_chunk.metadata else {}
+            metadata["embedding"] = embedding
+            return TextChunk(
+                text=text_chunk.text,
+                index=text_chunk.index,
+                metadata=metadata,
+                uid=text_chunk.uid,
+            )
 
     @validate_call
-    async def run(self, text_chunks: TextChunks) -> TextChunks:
+    async def run(
+        self, 
+        text_chunks: TextChunks
+    ) -> TextChunks:
         """Embed a list of text chunks.
 
         Args:
@@ -73,6 +111,13 @@ async def run(self, text_chunks: TextChunks) -> TextChunks:
         Returns:
             TextChunks: The input text chunks with each one having an added embedding.
         """
-        return TextChunks(
-            chunks=[self._embed_chunk(text_chunk) for text_chunk in text_chunks.chunks]
-        )
+        sem = asyncio.Semaphore(self._max_concurrency)
+        tasks = [
+            self._async_embed_chunk(
+                sem,
+                text_chunk,
+            )
+            for text_chunk in text_chunks.chunks
+        ]
+        text_chunks: TextChunks = list(await asyncio.gather(*tasks))
+        return TextChunks(chunks=text_chunks)
\ No newline at end of file