Add cohere embedding function (#550)

python/lancedb/embeddings/__init__.py

@@ -12,6 +12,7 @@
 #  limitations under the License.
 
 
+from .cohere import CohereEmbeddingFunction
 from .functions import (
     EmbeddingFunction,
     EmbeddingFunctionConfig,

python/lancedb/embeddings/cohere.py

@@ -0,0 +1,86 @@
+#  Copyright (c) 2023. LanceDB Developers
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import os
+from typing import ClassVar, List, Union
+
+import numpy as np
+
+from .functions import TextEmbeddingFunction, register
+from .utils import api_key_not_found_help
+
+
+@register("cohere")
+class CohereEmbeddingFunction(TextEmbeddingFunction):
+    """
+    An embedding function that uses the Cohere API
+
+    https://docs.cohere.com/docs/multilingual-language-models
+
+    Parameters
+    ----------
+    name: str, default "embed-multilingual-v2.0"
+        The name of the model to use. See the Cohere documentation for a list of available models.
+
+    Examples
+    --------
+    import lancedb
+    from lancedb.pydantic import LanceModel, Vector
+    from lancedb.embeddings import EmbeddingFunctionRegistry
+
+    cohere = EmbeddingFunctionRegistry.get_instance().get("cohere").create(name="embed-multilingual-v2.0")
+
+    class TextModel(LanceModel):
+        text: str = cohere.SourceField()
+        vector: Vector(cohere.ndims()) =  cohere.VectorField()
+
+    data = [ { "text": "hello world" },
+            { "text": "goodbye world" }]
+
+    db = lancedb.connect("~/.lancedb")
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(data)
+
+    """
+
+    name: str = "embed-multilingual-v2.0"
+    client: ClassVar = None
+
+    def ndims(self):
+        # TODO: fix hardcoding
+        return 768
+
+    def generate_embeddings(
+        self, texts: Union[List[str], np.ndarray]
+    ) -> List[np.array]:
+        """
+        Get the embeddings for the given texts
+
+        Parameters
+        ----------
+        texts: list[str] or np.ndarray (of str)
+            The texts to embed
+        """
+        # TODO retry, rate limit, token limit
+        self._init_client()
+        rs = CohereEmbeddingFunction.client.embed(texts=texts, model=self.name)
+
+        return [emb for emb in rs.embeddings]
+
+    def _init_client(self):
+        cohere = self.safe_import("cohere")
+        if CohereEmbeddingFunction.client is None:
+            if os.environ.get("COHERE_API_KEY") is None:
+                api_key_not_found_help("cohere")
+            CohereEmbeddingFunction.client = cohere.Client(os.environ["COHERE_API_KEY"])

python/lancedb/embeddings/utils.py

@@ -21,6 +21,7 @@ from lance.vector import vec_to_table
 from retry import retry
 
 from ..util import safe_import_pandas
+from ..utils.general import LOGGER
 
 pd = safe_import_pandas()
 DATA = Union[pa.Table, "pd.DataFrame"]
@@ -152,3 +153,8 @@ class FunctionWrapper:
             yield from tqdm(_chunker(arr), total=math.ceil(length / self._batch_size))
         else:
             yield from _chunker(arr)
+
+
+def api_key_not_found_help(provider):
+    LOGGER.error(f"Could not find API key for {provider}.")
+    raise ValueError(f"Please set the {provider.upper()}_API_KEY environment variable.")