diff --git a/chromadb/utils/embedding_functions.py b/chromadb/utils/embedding_functions.py
index 21ca2eb78e2..23826ac4a6e 100644
--- a/chromadb/utils/embedding_functions.py
+++ b/chromadb/utils/embedding_functions.py
@@ -256,6 +256,64 @@ def __call__(self, input: Documents) -> Embeddings:
         ).json()
 
 
+class JinaEmbeddingFunction(EmbeddingFunction[Documents]):
+    """
+    This class is used to get embeddings for a list of texts using the Jina AI API.
+    It requires an API key and a model name. The default model name is "jina-embeddings-v2-base-en".
+    """
+
+    def __init__(
+            self, api_key: str, model_name: str = "jina-embeddings-v2-base-en"
+    ):
+        """
+        Initialize the JinaEmbeddingFunction.
+
+        Args:
+            api_key (str): Your API key for the Jina AI API.
+            model_name (str, optional): The name of the model to use for text embeddings. Defaults to "jina-embeddings-v2-base-en".
+        """
+        try:
+            import requests
+        except ImportError:
+            raise ValueError(
+                "The requests python package is not installed. Please install it with `pip install requests`"
+            )
+        self._model_name = model_name
+        self._api_url = 'https://api.jina.ai/v1/embeddings'
+        self._session = requests.Session()
+        self._session.headers.update({"Authorization": f"Bearer {api_key}", "Accept-Encoding": "identity"})
+
+    def __call__(self, input: Documents) -> Embeddings:
+        """
+        Get the embeddings for a list of texts.
+
+        Args:
+            texts (Documents): A list of texts to get embeddings for.
+
+        Returns:
+            Embeddings: The embeddings for the texts.
+
+        Example:
+            >>> jina_ai_fn = JinaEmbeddingFunction(api_key="your_api_key")
+            >>> input = ["Hello, world!", "How are you?"]
+            >>> embeddings = jina_ai_fn(input)
+        """
+        # Call Jina AI Embedding API
+        resp = self._session.post(  # type: ignore
+            self._api_url, json={"input": input, "model": self._model_name}
+        ).json()
+        if "data" not in resp:
+            raise RuntimeError(resp["detail"])
+
+        embeddings = resp["data"]
+
+        # Sort resulting embeddings by index
+        sorted_embeddings = sorted(embeddings, key=lambda e: e["index"])  # type: ignore
+
+        # Return just the embeddings
+        return [result["embedding"] for result in sorted_embeddings]
+
+
 class InstructorEmbeddingFunction(EmbeddingFunction[Documents]):
     # If you have a GPU with at least 6GB try model_name = "hkunlp/instructor-xl" and device = "cuda"
     # for a full list of options: https://github.com/HKUNLP/instructor-embedding#model-list