v0 bedrock support

litellm/__init__.py

@@ -190,6 +190,7 @@ provider_list = [
     "baseten",
     "azure",
     "sagemaker",
+    "bedrock",
 ]
 
 models_by_provider = {

litellm/llms/bedrock.py

@@ -0,0 +1,139 @@
+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 BedrockError(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
+
+"""
+BEDROCK AUTH Keys/Vars
+os.environ['AWS_ACCESS_KEY_ID'] = ""
+os.environ['AWS_SECRET_ACCESS_KEY'] = ""
+"""
+
+# set os.environ['AWS_REGION_NAME'] = <your-region_name>
+
+def completion(
+    model: str,
+    messages: list,
+    model_response: ModelResponse,
+    print_verbose: Callable,
+    encoding,
+    logging_obj,
+    optional_params=None,
+    litellm_params=None,
+    logger_fn=None,
+):
+    import sys
+    if 'boto3' not in sys.modules:
+        import boto3
+
+    region_name = (
+        get_secret("AWS_REGION_NAME") or
+        "us-west-2" # default to us-west-2
+    )
+
+    client = boto3.client(
+        service_name="bedrock",
+        region_name=region_name
+    )
+
+    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']}"
+
+
+    data = json.dumps({
+        "inputText": prompt, 
+        "textGenerationConfig":{
+            "maxTokenCount":4096,
+            "stopSequences":[],
+            "temperature":0,
+            "topP":0.9
+            }
+        }) 
+
+    ## LOGGING
+    logging_obj.pre_call(
+            input=prompt,
+            api_key="",
+            additional_args={"complete_input_dict": data},
+        )
+    
+    ## COMPLETION CALL
+    accept = 'application/json'
+    contentType = 'application/json'
+
+    response = client.invoke_model(
+        body=data, 
+        modelId=model, 
+        accept=accept, 
+        contentType=contentType
+    )
+    response_body = json.loads(response.get('body').read())
+    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": data},
+            )
+        print_verbose(f"raw model_response: {response}")
+        ## RESPONSE OBJECT
+        outputText = response_body.get('results')[0].get('outputText')
+        print(outputText)
+        if "error" in outputText:
+            raise BedrockError(
+                message=outputText["error"],
+                status_code=response.status_code,
+            )
+        else:
+            try:
+                model_response["choices"][0]["message"]["content"] = outputText
+            except:
+                raise BedrockError(message=json.dumps(outputText), 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"] = 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

@@ -23,6 +23,7 @@ from .llms import anthropic
 from .llms import together_ai
 from .llms import ai21
 from .llms import sagemaker
+from .llms import bedrock
 from .llms.huggingface_restapi import HuggingfaceRestAPILLM
 from .llms.baseten import BasetenLLM
 from .llms.aleph_alpha import AlephAlphaLLM
@@ -703,6 +704,30 @@ def completion(
             #     )
             #     return response
             
+            ## RESPONSE OBJECT
+            response = model_response
+        elif custom_llm_provider == "bedrock":
+            # boto3 reads keys from .env
+            model_response = bedrock.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,
+                logging_obj=logging
+            )
+            
+            # TODO: Add streaming for bedrock
+            # if "stream" in optional_params and optional_params["stream"] == True:
+            #     # don't try to access stream object,
+            #     response = CustomStreamWrapper(
+            #         model_response, model, custom_llm_provider="ai21", logging_obj=logging
+            #     )
+            #     return response
+            
             ## RESPONSE OBJECT
             response = model_response