add palm

litellm/llms/palm.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, get_secret
+import sys
+
+class PalmError(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 completion(
+    model: str,
+    messages: list,
+    model_response: ModelResponse,
+    api_key: str,
+    print_verbose: Callable,
+    encoding,
+    logging_obj,
+    optional_params=None,
+    litellm_params=None,
+    logger_fn=None,
+):
+
+    import google.generativeai as palm
+    palm.configure(api_key=api_key)
+
+    model = model
+    prompt = ""
+    for message in messages:
+        if "role" in message:
+            if message["role"] == "user":
+                prompt += (
+                    f"{message['content']}"
+                )
+            else:
+                prompt += (
+                    f"{message['content']}"
+                )
+        else:
+            prompt += f"{message['content']}"
+    
+    ## LOGGING
+    logging_obj.pre_call(
+            input=prompt,
+            api_key="",
+            additional_args={"complete_input_dict": {}},
+        )
+    ## COMPLETION CALL
+    response = palm.chat(messages=prompt)
+
+
+    if "stream" in optional_params and optional_params["stream"] == True:
+        return response.iter_lines()
+    else:
+        ## LOGGING
+        logging_obj.post_call(
+                input=prompt,
+                api_key="",
+                original_response=response,
+                additional_args={"complete_input_dict": {}},
+            )
+        print_verbose(f"raw model_response: {response}")
+        ## RESPONSE OBJECT
+        completion_response = response.last
+
+        if "error" in completion_response:
+            raise PalmError(
+                message=completion_response["error"],
+                status_code=response.status_code,
+            )
+        else:
+            try:
+                model_response["choices"][0]["message"]["content"] = completion_response
+            except:
+                raise PalmError(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(
+            encoding.encode(prompt)
+        ) 
+        completion_tokens = len(
+            encoding.encode(model_response["choices"][0]["message"]["content"])
+        )
+
+        model_response["created"] = time.time()
+        model_response["model"] = "palm/" + 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

litellm/main.py

@@ -44,6 +44,7 @@ from .llms import ollama
 from .llms import cohere
 from .llms import petals
 from .llms import oobabooga
+from .llms import palm
 import tiktoken
 from concurrent.futures import ThreadPoolExecutor
 from typing import Callable, List, Optional, Dict
@@ -792,6 +793,32 @@ def completion(
                 )
                 return response
             response = model_response
+        elif custom_llm_provider == "palm":
+            api_key = (
+                api_key
+                or get_secret("PALM_API_KEY")
+                or litellm.api_key
+            )
+            
+            model_response = palm.completion(
+                model=model,
+                messages=messages,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                encoding=encoding,
+                api_key=api_key,
+                logging_obj=logging
+            )
+            if "stream_tokens" in optional_params and optional_params["stream_tokens"] == True:
+                # don't try to access stream object,
+                response = CustomStreamWrapper(
+                    model_response, model, custom_llm_provider="palm", logging_obj=logging
+                )
+                return response
+            response = model_response
         elif model in litellm.vertex_chat_models or model in litellm.vertex_code_chat_models:
             try:
                 import vertexai