fix(bedrock.py): fix output format for cohere embeddings

litellm/llms/bedrock.py

@@ -552,8 +552,8 @@ def _embedding_func_single(
     ## FORMAT EMBEDDING INPUT ## 
     provider = model.split(".")[0]
     inference_params = copy.deepcopy(optional_params)
-    input = input.replace(os.linesep, " ")
     if provider == "amazon":
+        input = input.replace(os.linesep, " ")
         data = {"inputText": input, **inference_params}
         # data = json.dumps(data)
     elif provider == "cohere":
@@ -590,7 +590,10 @@ def _embedding_func_single(
                 original_response=response_body,
             )
         if provider == "cohere":
-            return response_body.get("embeddings")
+            response = response_body.get("embeddings")
+            # flatten list
+            response = [item for sublist in response for item in sublist]
+            return response
         elif provider == "amazon":
             return response_body.get("embedding")
     except Exception as e:

litellm/main.py

@@ -1775,16 +1775,20 @@ def embedding(
     rpm = kwargs.pop("rpm", None)
     tpm = kwargs.pop("tpm", None)
     aembedding = kwargs.pop("aembedding", None)
-
+    openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "response_format", "seed", "tools", "tool_choice", "max_retries", "encoding_format"]
+    litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name"]
+    default_params = openai_params + litellm_params
+    non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
     optional_params = {}
-    for param in kwargs:
+    for param in non_default_params:
-        if param != "metadata":                     # filter out metadata from optional_params
         optional_params[param] = kwargs[param]
     model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base, api_key=api_key)
+    
+
     try:
         response = None
         logging = litellm_logging_obj
-        logging.update_environment_variables(model=model, user="", optional_params={}, litellm_params={"timeout": timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn})
+        logging.update_environment_variables(model=model, user="", optional_params=optional_params, litellm_params={"timeout": timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn})
         if azure == True or custom_llm_provider == "azure":
             # azure configs
             api_type = get_secret("AZURE_API_TYPE") or "azure"
@@ -1903,7 +1907,7 @@ def embedding(
                 input=input,
                 encoding=encoding,
                 logging_obj=logging,
-                optional_params=kwargs,
+                optional_params=optional_params,
                 model_response= EmbeddingResponse()
             )
         elif custom_llm_provider == "sagemaker": 
@@ -1912,7 +1916,7 @@ def embedding(
                 input=input,
                 encoding=encoding,
                 logging_obj=logging,
-                optional_params=kwargs,
+                optional_params=optional_params,
                 model_response= EmbeddingResponse(),
                 print_verbose=print_verbose
             )

litellm/proxy/proxy_server.py

@@ -989,6 +989,7 @@ async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depen
         body = await request.body()
         data = orjson.loads(body)
 
+        
         data["user"] = user_api_key_dict.user_id
         data["model"] = (
             general_settings.get("embedding_model", None) # server default
@@ -1001,9 +1002,24 @@ async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depen
             data["metadata"]["user_api_key"] = user_api_key_dict.api_key
         else:
             data["metadata"] = {"user_api_key": user_api_key_dict.api_key}
+        router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else []
+        print(f"received data: {data['input']}")
+        if "input" in data and isinstance(data['input'], list) and isinstance(data['input'][0], list) and isinstance(data['input'][0][0], int): # check if array of tokens passed in
+            # check if non-openai/azure model called - e.g. for langchain integration
+            if data["model"] in router_model_names: 
+                for m in llm_model_list: 
+                    if m["model_name"] == data["model"] and (m["litellm_params"]["model"] in litellm.open_ai_embedding_models 
+                                                             or m["litellm_params"]["model"].startswith("azure/")):
+                        pass
+                    else: 
+                        # non-openai/azure embedding model called with token input
+                        input_list = []
+                        for i in data["input"]: 
+                            input_list.append(litellm.decode(model="gpt-3.5-turbo", tokens=i))
+                        data["input"] = input_list
+                        break
 
         ## ROUTE TO CORRECT ENDPOINT ##
-        router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else []
         if llm_router is not None and data["model"] in router_model_names: # model in router model list 
             response = await llm_router.aembedding(**data)
         elif llm_router is not None and data["model"] in llm_router.deployment_names: # model in router deployments, calling a specific deployment on the router

litellm/tests/test_embedding.py

@@ -161,20 +161,23 @@ def test_bedrock_embedding_titan():
         print(f"response:", response)
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
-test_bedrock_embedding_titan()
+# test_bedrock_embedding_titan()
 
 def test_bedrock_embedding_cohere():
     try:
-        # litellm.set_verbose=True
+        litellm.set_verbose=False
         response = embedding(
             model="cohere.embed-multilingual-v3", input=["good morning from litellm, attempting to embed data", "lets test a second string for good measure"],
             aws_region_name="os.environ/AWS_REGION_NAME_2"
         )
+        assert isinstance(response['data'][0]['embedding'], list), "Expected response to be a list"
+        print(f"type of first embedding:", type(response['data'][0]['embedding'][0]))
+        assert all(isinstance(x, float) for x in response['data'][0]['embedding']), "Expected response to be a list of floats"
         # print(f"response:", response)
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
-# test_bedrock_embedding_cohere()
+test_bedrock_embedding_cohere()
 
 # comment out hf tests - since hf endpoints are unstable
 def test_hf_embedding():
@@ -234,7 +237,7 @@ def test_sagemaker_embeddings():
         print(f"response: {response}")
     except Exception as e: 
         pytest.fail(f"Error occurred: {e}")
-test_sagemaker_embeddings()
+# test_sagemaker_embeddings()
 # def local_proxy_embeddings():
 #     litellm.set_verbose=True
 #     response = embedding(