fix(cohere.py): support async cohere embedding calls

litellm/llms/cohere.py

@@ -4,12 +4,14 @@ import time
 import traceback
 import types
 from enum import Enum
-from typing import Callable, Optional
+from typing import Any, Callable, Optional, Union
 
 import httpx  # type: ignore
 import requests  # type: ignore
 
 import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 from litellm.utils import Choices, Message, ModelResponse, Usage
 
 
@@ -246,14 +248,98 @@ def completion(
         return model_response
 
 
+def _process_embedding_response(
+    embeddings: list,
+    model_response: litellm.EmbeddingResponse,
+    model: str,
+    encoding: Any,
+    input: list,
+) -> litellm.EmbeddingResponse:
+    output_data = []
+    for idx, embedding in enumerate(embeddings):
+        output_data.append(
+            {"object": "embedding", "index": idx, "embedding": embedding}
+        )
+    model_response.object = "list"
+    model_response.data = output_data
+    model_response.model = model
+    input_tokens = 0
+    for text in input:
+        input_tokens += len(encoding.encode(text))
+
+    setattr(
+        model_response,
+        "usage",
+        Usage(
+            prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
+        ),
+    )
+
+    return model_response
+
+
+async def async_embedding(
+    model: str,
+    data: dict,
+    input: list,
+    model_response: litellm.utils.EmbeddingResponse,
+    timeout: Union[float, httpx.Timeout],
+    logging_obj: LiteLLMLoggingObj,
+    optional_params: dict,
+    api_base: str,
+    api_key: Optional[str],
+    headers: dict,
+    encoding: Callable,
+    client: Optional[AsyncHTTPHandler] = None,
+):
+
+    ## LOGGING
+    logging_obj.pre_call(
+        input=input,
+        api_key=api_key,
+        additional_args={
+            "complete_input_dict": data,
+            "headers": headers,
+            "api_base": api_base,
+        },
+    )
+    ## COMPLETION CALL
+    if client is None:
+        client = AsyncHTTPHandler(concurrent_limit=1)
+
+    response = await client.post(api_base, headers=headers, data=json.dumps(data))
+
+    ## LOGGING
+    logging_obj.post_call(
+        input=input,
+        api_key=api_key,
+        additional_args={"complete_input_dict": data},
+        original_response=response,
+    )
+
+    embeddings = response.json()["embeddings"]
+
+    ## PROCESS RESPONSE ##
+    return _process_embedding_response(
+        embeddings=embeddings,
+        model_response=model_response,
+        model=model,
+        encoding=encoding,
+        input=input,
+    )
+
+
 def embedding(
     model: str,
     input: list,
     model_response: litellm.EmbeddingResponse,
+    logging_obj: LiteLLMLoggingObj,
+    optional_params: dict,
+    encoding: Any,
     api_key: Optional[str] = None,
-    logging_obj=None,
-    encoding=None,
-    optional_params=None,
+    aembedding: Optional[bool] = None,
+    timeout: Union[float, httpx.Timeout] = httpx.Timeout(None),
+    client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
 ):
     headers = validate_environment(api_key)
     embed_url = "https://api.cohere.ai/v1/embed"
@@ -270,8 +356,26 @@ def embedding(
         api_key=api_key,
         additional_args={"complete_input_dict": data},
     )
+
+    ## ROUTING
+    if aembedding is True:
+        return async_embedding(
+            model=model,
+            data=data,
+            input=input,
+            model_response=model_response,
+            timeout=timeout,
+            logging_obj=logging_obj,
+            optional_params=optional_params,
+            api_base=embed_url,
+            api_key=api_key,
+            headers=headers,
+            encoding=encoding,
+        )
     ## COMPLETION CALL
-    response = requests.post(embed_url, headers=headers, data=json.dumps(data))
+    if client is None or not isinstance(client, HTTPHandler):
+        client = HTTPHandler(concurrent_limit=1)
+    response = client.post(embed_url, headers=headers, data=json.dumps(data))
     ## LOGGING
     logging_obj.post_call(
         input=input,
@@ -293,23 +397,11 @@ def embedding(
     if response.status_code != 200:
         raise CohereError(message=response.text, status_code=response.status_code)
     embeddings = response.json()["embeddings"]
-    output_data = []
-    for idx, embedding in enumerate(embeddings):
-        output_data.append(
-            {"object": "embedding", "index": idx, "embedding": embedding}
-        )
-    model_response.object = "list"
-    model_response.data = output_data
-    model_response.model = model
-    input_tokens = 0
-    for text in input:
-        input_tokens += len(encoding.encode(text))
 
-    setattr(
-        model_response,
-        "usage",
-        Usage(
-            prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
-        ),
+    return _process_embedding_response(
+        embeddings=embeddings,
+        model_response=model_response,
+        model=model,
+        encoding=encoding,
+        input=input,
     )
-    return model_response

litellm/main.py

@@ -3114,6 +3114,7 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse:
             or custom_llm_provider == "vertex_ai"
             or custom_llm_provider == "databricks"
             or custom_llm_provider == "watsonx"
+            or custom_llm_provider == "cohere"
         ):  # currently implemented aiohttp calls for just azure and openai, soon all.
             # Await normally
             init_response = await loop.run_in_executor(None, func_with_context)
@@ -3440,9 +3441,12 @@ def embedding(
                 input=input,
                 optional_params=optional_params,
                 encoding=encoding,
-                api_key=cohere_key,
+                api_key=cohere_key,  # type: ignore
                 logging_obj=logging,
                 model_response=EmbeddingResponse(),
+                aembedding=aembedding,
+                timeout=float(timeout),
+                client=client,
             )
         elif custom_llm_provider == "huggingface":
             api_key = (

litellm/tests/test_embedding.py

@@ -257,14 +257,20 @@ def test_openai_azure_embedding_optional_arg(mocker):
 # test_openai_embedding()
 
 
-def test_cohere_embedding():
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_cohere_embedding(sync_mode):
     try:
         # litellm.set_verbose=True
-        response = embedding(
-            model="embed-english-v2.0",
-            input=["good morning from litellm", "this is another item"],
-            input_type="search_query",
-        )
+        data = {
+            "model": "embed-english-v2.0",
+            "input": ["good morning from litellm", "this is another item"],
+            "input_type": "search_query",
+        }
+        if sync_mode:
+            response = embedding(**data)
+        else:
+            response = await litellm.aembedding(**data)
         print(f"response:", response)
 
         assert isinstance(response.usage, litellm.Usage)