adding support for aleph alpha

litellm/llms/aleph_alpha.py

@@ -0,0 +1,138 @@
+import os, json
+from enum import Enum
+import requests
+import time
+from typing import Callable
+from litellm.utils import ModelResponse
+
+class AlephAlphaError(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
+
+
+class AlephAlphaLLM:
+    def __init__(
+        self, encoding, default_max_tokens_to_sample, logging_obj, api_key=None
+    ):
+        self.encoding = encoding
+        self.default_max_tokens_to_sample = default_max_tokens_to_sample
+        self.completion_url = "https://api.aleph-alpha.com/complete"
+        self.api_key = api_key
+        self.logging_obj = logging_obj
+        self.validate_environment(api_key=api_key)
+
+    def validate_environment(
+        self, api_key
+    ):  # set up the environment required to run the model
+        # set the api key
+        if self.api_key == None:
+            raise ValueError(
+                "Missing Aleph Alpha API Key - A call is being made to Aleph Alpha but no key is set either in the environment variables or via params"
+            )
+        self.api_key = api_key
+        self.headers = {
+            "accept": "application/json",
+            "content-type": "application/json",
+            "Authorization": "Bearer " + self.api_key,
+        }
+
+    def completion(
+        self,
+        model: str,
+        messages: list,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        optional_params=None,
+        litellm_params=None,
+        logger_fn=None,
+    ):  # logic for parsing in - calling - parsing out model completion calls
+        model = model
+        prompt = ""
+        if "control" in model: # follow the ###Instruction / ###Response format
+            for idx, message in enumerate(messages):
+                if "role" in message:
+                    if idx == 0: # set first message as instruction (required), let later user messages be input 
+                        prompt += f"###Instruction: {message['content']}"
+                    else:
+                        if message["role"] == "system":
+                            prompt += (
+                                f"###Instruction: {message['content']}"
+                            )
+                        elif message["role"] == "user":
+                            prompt += (
+                                f"###Input: {message['content']}"
+                            )
+                        else:
+                            prompt += (
+                                f"###Response: {message['content']}"
+                            )
+                else:
+                    prompt += f"{message['content']}"
+        else:
+            prompt = " ".join(message["content"] for message in messages)
+        data = {
+            "model": model,
+            "prompt": prompt,
+            "maximum_tokens": optional_params["maximum_tokens"] if  "maximum_tokens" in optional_params else self.default_max_tokens_to_sample, # required input
+            **optional_params,
+        }
+
+        ## LOGGING
+        self.logging_obj.pre_call(
+            input=prompt,
+            api_key=self.api_key,
+            additional_args={"complete_input_dict": data},
+        )
+        ## COMPLETION CALL
+        response = requests.post(
+            self.completion_url, headers=self.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
+            self.logging_obj.post_call(
+                input=prompt,
+                api_key=self.api_key,
+                original_response=response.text,
+                additional_args={"complete_input_dict": data},
+            )
+            print_verbose(f"raw model_response: {response.text}")
+            ## RESPONSE OBJECT
+            completion_response = response.json()
+            if "error" in completion_response:
+                raise AlephAlphaError(
+                    message=completion_response["error"],
+                    status_code=response.status_code,
+                )
+            else:
+                try:
+                    model_response["choices"][0]["message"]["content"] = completion_response["completions"][0]["completion"]
+                except:
+                    raise AlephAlphaError(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 = len(
+                self.encoding.encode(prompt)
+            ) 
+            completion_tokens = len(
+                self.encoding.encode(model_response["choices"][0]["message"]["content"])
+            )
+
+            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(
+        self,
+    ):  # logic for parsing in - calling - parsing out model embedding calls
+        pass