adding support for nlp cloud

litellm/llms/nlp_cloud.py

@@ -0,0 +1,103 @@
+import os
+import json
+from enum import Enum
+import requests
+import time
+from typing import Callable
+from litellm.utils import ModelResponse
+
+class NLPCloudError(Exception):
+    def __init__(self, status_code, message):
+        self.status_code = status_code
+        self.message = message
+        super().__init__(
+            self.message
+        )  # Call the base class constructor with the parameters it needs
+
+def validate_environment(api_key):
+    headers = {
+        "accept": "application/json",
+        "content-type": "application/json",
+    }
+    if api_key:
+        headers["Authorization"] = f"Token {api_key}"
+    return headers
+
+def completion(
+    model: str,
+    messages: list,
+    model_response: ModelResponse,
+    print_verbose: Callable,
+    encoding,
+    api_key,
+    logging_obj,
+    optional_params=None,
+    litellm_params=None,
+    logger_fn=None,
+    default_max_tokens_to_sample=None,
+):
+    headers = validate_environment(api_key)
+    completion_url_fragment_1 = "https://api.nlpcloud.io/v1/gpu/"
+    completion_url_fragment_2 = "/generation"
+    model = model
+    text = " ".join(message["content"] for message in messages)
+
+    data = {
+        "text": text,
+        **optional_params,
+    }
+
+    completion_url = completion_url_fragment_1 + model + completion_url_fragment_2
+    ## LOGGING
+    logging_obj.pre_call(
+            input=text,
+            api_key=api_key,
+            additional_args={"complete_input_dict": data},
+        )
+    ## COMPLETION CALL
+    response = requests.post(
+        completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False
+    )
+    if "stream" in optional_params and optional_params["stream"] == True:
+        return response.iter_lines()
+    else:
+        ## LOGGING
+        logging_obj.post_call(
+                input=text,
+                api_key=api_key,
+                original_response=response.text,
+                additional_args={"complete_input_dict": data},
+            )
+        print_verbose(f"raw model_response: {response.text}")
+        ## RESPONSE OBJECT
+        try:
+            completion_response = response.json()
+        except:
+            raise NLPCloudError(message=response.text, status_code=response.status_code)
+        if "error" in completion_response:
+            raise NLPCloudError(
+                message=completion_response["error"],
+                status_code=response.status_code,
+            )
+        else:
+            try:
+                model_response["choices"][0]["message"]["content"] = completion_response["generated_text"]
+            except:
+                raise NLPCloudError(message=json.dumps(completion_response), status_code=response.status_code)
+
+        ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. 
+        prompt_tokens = completion_response["nb_input_tokens"]
+        completion_tokens = completion_response["nb_generated_tokens"]
+
+        model_response["created"] = time.time()
+        model_response["model"] = model
+        model_response["usage"] = {
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "total_tokens": prompt_tokens + completion_tokens,
+        }
+        return model_response
+
+def embedding():
+    # logic for parsing in - calling - parsing out model embedding calls
+    pass