Add Nomic embedding integration

examples/README.md

@@ -150,6 +150,20 @@ paddlenlp = PaddleNLP()
 
 </details>
 
+<details>
+
+<summary> Nomic </summary>
+
+```python
+from gptcache.embedding import Nomic
+
+nm = Nomic(api_key='your-api-key')
+# nm.dimension
+# nm.to_embeddings
+```
+
+</details>
+
 ### Custom embedding
 
 The function has two parameters: the preprocessed string and parameters reserved for user customization. To acquire these parameters, a similar method to the one above is used: `kwargs.get("embedding_func", {})`.

gptcache/adapter/api.py

@@ -18,6 +18,7 @@
     Rwkv,
     PaddleNLP,
     UForm,
+    Nomic
 )
 from gptcache.embedding.base import BaseEmbedding
 from gptcache.manager import manager_factory
@@ -285,6 +286,8 @@ def _get_model(model_src, model_config=None):
         return PaddleNLP(**model_config)
     if model_src == "uform":
         return UForm(**model_config)
+    if model_src == "nomic":
+        return Nomic(**model_config)
 
 
 def _get_eval(strategy, kws=None):

gptcache/embedding/__init__.py

@@ -30,6 +30,14 @@
 rwkv = LazyImport("rwkv", globals(), "gptcache.embedding.rwkv")
 paddlenlp = LazyImport("paddlenlp", globals(), "gptcache.embedding.paddlenlp")
 uform = LazyImport("uform", globals(), "gptcache.embedding.uform")
+nomic = LazyImport("nomic", globals(), "gptcache.embedding.nomic")
+
+
+def Nomic(model: str = "nomic-embed-text-v1.5",
+          api_key: str = None,
+          task_type: str = "search_document",
+          dimensionality: int = None):
+    return nomic.Nomic(model, api_key, task_type, dimensionality)
 
 
 def Cohere(model="large", api_key=None):

gptcache/embedding/nomic.py

@@ -0,0 +1,82 @@
+"""Nomic embedding integration"""
+
+import numpy as np
+
+from gptcache.utils import import_nomic
+from gptcache.embedding.base import BaseEmbedding
+
+import_nomic()
+
+# import nomic # pylint: disable=C0413
+from nomic import cli # pylint: disable=C0413
+from nomic import embed # pylint: disable=C0413
+
+class Nomic(BaseEmbedding):
+    """Generate text embedding for given text using Cohere.
+    """
+    def __init__(self,
+                 model: str = "nomic-embed-text-v1.5",
+                 api_key: str = None,
+                 task_type: str = "search_document",
+                 dimensionality: int = None) -> None:
+        """Generate text embedding for given text using Nomic embed.
+
+        :param model: model name, defaults to 'nomic-embed-text-v1.5'.
+        :type model: str
+        :param api_key: Nomic API Key.
+        :type api_key: str
+        :param task_type: Task type in Nomic, defaults to 'search_document'.
+        :type task_type: str
+        :param dimensionality: Desired dimension of embeddings.
+        :type dimensionality: int
+
+        Example:
+        .. code-block:: python
+
+            import os
+            from gptcache.embedding import Nomic
+
+            test_sentence = 'Hey this is Nomic embedding integration to gaptcache.'
+            encoder = Nomic(model='nomic-embed-text-v1.5',
+                            api_key=os.getenv("NOMIC_API_KEY"),
+                            dimensionality=64)
+            embed = encoder.to_embeddings(test_sentence)
+        """
+        # Login to nomic
+        cli.login(token=api_key)
+
+        self._model = model
+        self._task_type = task_type
+        self._dimensionality = dimensionality
+
+    def to_embeddings(self, data, **_):
+        """Generate embedding given text input
+
+        :param data: text in string.
+        :type data: str
+
+        :return: a text embedding in shape of (self._dimensionality,).
+        """
+        if not isinstance(data, list):
+            data = [data]
+
+        # Response will be a dictionary with key 'embeddings'
+        # and value will be a list of lists
+        response = embed.text(
+            texts=data,
+            model=self._model,
+            task_type=self._task_type,
+            dimensionality=self._dimensionality)
+        embeddings = response["embeddings"]
+        return np.array(embeddings).astype("float32").squeeze(0)
+
+    @property
+    def dimension(self):
+        """Embedding dimension.
+
+        :return: embedding dimension
+        """
+        if not self._dimensionality:
+            foo_emb = self.to_embeddings("foo")
+            self._dimensionality = len(foo_emb)
+        return self._dimensionality

gptcache/utils/__init__.py

@@ -3,6 +3,7 @@
     "import_milvus_lite",
     "import_sbert",
     "import_cohere",
+    "import_nomic",
     "import_fasttext",
     "import_huggingface",
     "import_uform",
@@ -80,6 +81,10 @@ def import_cohere():
     _check_library("cohere")
 
 
+def import_nomic():
+    _check_library("nomic")
+
+
 def import_fasttext():
     _check_library("fasttext", package="fasttext==0.9.2")
 

tests/unit_tests/embedding/test_nomic.py

@@ -0,0 +1,17 @@
+import os
+import types
+from unittest.mock import patch
+from gptcache.utils import import_nomic
+from gptcache.embedding import Nomic
+from gptcache.adapter.api import _get_model
+
+import_nomic()
+
+def test_nomic():
+    t = Nomic(model='nomic-embed-text-v1.5', api_key=os.getenv("NOMIC_API_KEY"), dimensionality=64)
+    data = t.to_embeddings("foo")
+    assert len(data) == t.dimension, f"{len(data)}, {t.dimension}"
+
+    t = _get_model(model_src="nomic", model_config={"model": "nomic-embed-text-v1.5"})
+    data = t.to_embeddings("foo")
+    assert len(data) == t.dimension, f"{len(data)}, {t.dimension}"