feat(vertex_ai_llama.py): vertex ai llama3.1 api support

Initial working commit for vertex ai llama 3.1 api support

litellm/llms/vertex_ai_llama.py

@@ -0,0 +1,270 @@
+# What is this?
+## Handler for calling llama 3.1 API on Vertex AI
+import copy
+import json
+import os
+import time
+import types
+import uuid
+from enum import Enum
+from typing import Any, Callable, List, Optional, Tuple, Union
+
+import httpx  # type: ignore
+import requests  # type: ignore
+
+import litellm
+from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.types.llms.anthropic import (
+    AnthropicMessagesTool,
+    AnthropicMessagesToolChoice,
+)
+from litellm.types.llms.openai import (
+    ChatCompletionToolParam,
+    ChatCompletionToolParamFunctionChunk,
+)
+from litellm.types.utils import ResponseFormatChunk
+from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
+
+from .base import BaseLLM
+from .prompt_templates.factory import (
+    construct_tool_use_system_prompt,
+    contains_tag,
+    custom_prompt,
+    extract_between_tags,
+    parse_xml_params,
+    prompt_factory,
+    response_schema_prompt,
+)
+
+
+class VertexAIError(Exception):
+    def __init__(self, status_code, message):
+        self.status_code = status_code
+        self.message = message
+        self.request = httpx.Request(
+            method="POST", url=" https://cloud.google.com/vertex-ai/"
+        )
+        self.response = httpx.Response(status_code=status_code, request=self.request)
+        super().__init__(
+            self.message
+        )  # Call the base class constructor with the parameters it needs
+
+
+class VertexAILlama3Config:
+    """
+    Reference:https://docs.anthropic.com/claude/reference/messages_post
+
+    Note that the API for Claude on Vertex differs from the Anthropic API documentation in the following ways:
+
+    - `model` is not a valid parameter. The model is instead specified in the Google Cloud endpoint URL.
+    - `anthropic_version` is a required parameter and must be set to "vertex-2023-10-16".
+
+    The class `VertexAIAnthropicConfig` provides configuration for the VertexAI's Anthropic API interface. Below are the parameters:
+
+    - `max_tokens` Required (integer) max tokens,
+    - `anthropic_version` Required (string) version of anthropic for bedrock - e.g. "bedrock-2023-05-31"
+    - `system` Optional (string) the system prompt, conversion from openai format to this is handled in factory.py
+    - `temperature` Optional (float) The amount of randomness injected into the response
+    - `top_p` Optional (float) Use nucleus sampling.
+    - `top_k` Optional (int) Only sample from the top K options for each subsequent token
+    - `stop_sequences` Optional (List[str]) Custom text sequences that cause the model to stop generating
+
+    Note: Please make sure to modify the default parameters as required for your use case.
+    """
+
+    max_tokens: Optional[int] = (
+        4096  # anthropic max - setting this doesn't impact response, but is required by anthropic.
+    )
+    system: Optional[str] = None
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    top_k: Optional[int] = None
+    stop_sequences: Optional[List[str]] = None
+
+    def __init__(
+        self,
+        max_tokens: Optional[int] = None,
+        anthropic_version: Optional[str] = None,
+    ) -> None:
+        locals_ = locals()
+        for key, value in locals_.items():
+            if key == "max_tokens" and value is None:
+                value = self.max_tokens
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self):
+        return [
+            "max_tokens",
+            "tools",
+            "tool_choice",
+            "stream",
+            "stop",
+            "temperature",
+            "top_p",
+            "response_format",
+        ]
+
+    def map_openai_params(self, non_default_params: dict, optional_params: dict):
+        for param, value in non_default_params.items():
+            if param == "max_tokens":
+                optional_params["max_tokens"] = value
+            if param == "tools":
+                optional_params["tools"] = value
+            if param == "tool_choice":
+                _tool_choice: Optional[AnthropicMessagesToolChoice] = None
+                if value == "auto":
+                    _tool_choice = {"type": "auto"}
+                elif value == "required":
+                    _tool_choice = {"type": "any"}
+                elif isinstance(value, dict):
+                    _tool_choice = {"type": "tool", "name": value["function"]["name"]}
+
+                if _tool_choice is not None:
+                    optional_params["tool_choice"] = _tool_choice
+            if param == "stream":
+                optional_params["stream"] = value
+            if param == "stop":
+                optional_params["stop_sequences"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["top_p"] = value
+            if param == "response_format" and "response_schema" in value:
+                """
+                When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode
+                - You usually want to provide a single tool
+                - You should set tool_choice (see Forcing tool use) to instruct the model to explicitly use that tool
+                - Remember that the model will pass the input to the tool, so the name of the tool and description should be from the model’s perspective.
+                """
+                _tool_choice = None
+                _tool_choice = {"name": "json_tool_call", "type": "tool"}
+
+                _tool = AnthropicMessagesTool(
+                    name="json_tool_call",
+                    input_schema={
+                        "type": "object",
+                        "properties": {"values": value["response_schema"]},  # type: ignore
+                    },
+                )
+
+                optional_params["tools"] = [_tool]
+                optional_params["tool_choice"] = _tool_choice
+                optional_params["json_mode"] = True
+
+        return optional_params
+
+
+class VertexAILlama3(BaseLLM):
+    def __init__(self) -> None:
+        pass
+
+    def create_vertex_llama3_url(
+        self, vertex_location: str, vertex_project: str
+    ) -> str:
+        return f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}/endpoints/openapi"
+
+    def completion(
+        self,
+        model: str,
+        messages: list,
+        model_response: ModelResponse,
+        print_verbose: Callable,
+        encoding,
+        logging_obj,
+        optional_params: dict,
+        custom_prompt_dict: dict,
+        headers: Optional[dict],
+        timeout: Union[float, httpx.Timeout],
+        vertex_project=None,
+        vertex_location=None,
+        vertex_credentials=None,
+        litellm_params=None,
+        logger_fn=None,
+        acompletion: bool = False,
+        client=None,
+    ):
+        try:
+            import vertexai
+            from google.cloud import aiplatform
+
+            from litellm.llms.openai import OpenAIChatCompletion
+            from litellm.llms.vertex_httpx import VertexLLM
+        except Exception:
+
+            raise VertexAIError(
+                status_code=400,
+                message="""vertexai import failed please run `pip install -U "google-cloud-aiplatform>=1.38"`""",
+            )
+
+        if not (
+            hasattr(vertexai, "preview") or hasattr(vertexai.preview, "language_models")
+        ):
+            raise VertexAIError(
+                status_code=400,
+                message="""Upgrade vertex ai. Run `pip install "google-cloud-aiplatform>=1.38"`""",
+            )
+        try:
+
+            vertex_httpx_logic = VertexLLM()
+
+            access_token, project_id = vertex_httpx_logic._ensure_access_token(
+                credentials=vertex_credentials, project_id=vertex_project
+            )
+
+            openai_chat_completions = OpenAIChatCompletion()
+
+            ## Load Config
+            # config = litellm.VertexAILlama3.get_config()
+            # for k, v in config.items():
+            #     if k not in optional_params:
+            #         optional_params[k] = v
+
+            ## CONSTRUCT API BASE
+            stream: bool = optional_params.get("stream", False) or False
+
+            optional_params["stream"] = stream
+
+            api_base = self.create_vertex_llama3_url(
+                vertex_location=vertex_location or "us-central1",
+                vertex_project=vertex_project or project_id,
+            )
+
+            return openai_chat_completions.completion(
+                model=model,
+                messages=messages,
+                api_base=api_base,
+                api_key=access_token,
+                custom_prompt_dict=custom_prompt_dict,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                logging_obj=logging_obj,
+                optional_params=optional_params,
+                acompletion=acompletion,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                client=client,
+                timeout=timeout,
+            )
+
+        except Exception as e:
+            raise VertexAIError(status_code=500, message=str(e))

litellm/llms/vertex_httpx.py

@@ -1189,7 +1189,7 @@ class VertexLLM(BaseLLM):
             response.raise_for_status()
         except httpx.HTTPStatusError as err:
             error_code = err.response.status_code
-            raise VertexAIError(status_code=error_code, message=response.text)
+            raise VertexAIError(status_code=error_code, message=err.response.text)
         except httpx.TimeoutException:
             raise VertexAIError(status_code=408, message="Timeout error occurred.")
 

litellm/main.py

@@ -120,6 +120,7 @@ from .llms.prompt_templates.factory import (
 )
 from .llms.text_completion_codestral import CodestralTextCompletion
 from .llms.triton import TritonChatCompletion
+from .llms.vertex_ai_llama import VertexAILlama3
 from .llms.vertex_httpx import VertexLLM
 from .llms.watsonx import IBMWatsonXAI
 from .types.llms.openai import HttpxBinaryResponseContent
@@ -156,6 +157,7 @@ triton_chat_completions = TritonChatCompletion()
 bedrock_chat_completion = BedrockLLM()
 bedrock_converse_chat_completion = BedrockConverseLLM()
 vertex_chat_completion = VertexLLM()
+vertex_llama_chat_completion = VertexAILlama3()
 watsonxai = IBMWatsonXAI()
 ####### COMPLETION ENDPOINTS ################
 
@@ -2064,7 +2066,26 @@ def completion(
                     timeout=timeout,
                     client=client,
                 )
-
+            elif model.startswith("meta/"):
+                model_response = vertex_llama_chat_completion.completion(
+                    model=model,
+                    messages=messages,
+                    model_response=model_response,
+                    print_verbose=print_verbose,
+                    optional_params=new_params,
+                    litellm_params=litellm_params,
+                    logger_fn=logger_fn,
+                    encoding=encoding,
+                    vertex_location=vertex_ai_location,
+                    vertex_project=vertex_ai_project,
+                    vertex_credentials=vertex_credentials,
+                    logging_obj=logging,
+                    acompletion=acompletion,
+                    headers=headers,
+                    custom_prompt_dict=custom_prompt_dict,
+                    timeout=timeout,
+                    client=client,
+                )
             else:
                 model_response = vertex_ai.completion(
                     model=model,
@@ -2478,28 +2499,25 @@ def completion(
                 return generator
 
             response = generator
-        
+
         elif custom_llm_provider == "triton":
-            api_base = (
-                litellm.api_base  or api_base
-            )
+            api_base = litellm.api_base or api_base
             model_response = triton_chat_completions.completion(
-            api_base=api_base,
-            timeout=timeout, # type: ignore
-            model=model,
-            messages=messages,
-            model_response=model_response,
-            optional_params=optional_params,
-            logging_obj=logging,
-            stream=stream,
-            acompletion=acompletion
+                api_base=api_base,
+                timeout=timeout,  # type: ignore
+                model=model,
+                messages=messages,
+                model_response=model_response,
+                optional_params=optional_params,
+                logging_obj=logging,
+                stream=stream,
+                acompletion=acompletion,
             )
 
             ## RESPONSE OBJECT
             response = model_response
             return response
-        
-        
+
         elif custom_llm_provider == "cloudflare":
             api_key = (
                 api_key

litellm/tests/test_amazing_vertex_completion.py

@@ -895,6 +895,52 @@ async def test_gemini_pro_function_calling_httpx(model, sync_mode):
             pytest.fail("An unexpected exception occurred - {}".format(str(e)))
 
 
+from litellm.tests.test_completion import response_format_tests
+
+
+@pytest.mark.parametrize(
+    "model", ["vertex_ai/meta/llama3-405b-instruct-maas"]
+)  # "vertex_ai",
+@pytest.mark.parametrize("sync_mode", [True, False])  # "vertex_ai",
+@pytest.mark.asyncio
+async def test_llama_3_httpx(model, sync_mode):
+    try:
+        load_vertex_ai_credentials()
+        litellm.set_verbose = True
+
+        messages = [
+            {
+                "role": "system",
+                "content": "Your name is Litellm Bot, you are a helpful assistant",
+            },
+            # User asks for their name and weather in San Francisco
+            {
+                "role": "user",
+                "content": "Hello, what is your name and can you tell me the weather?",
+            },
+        ]
+
+        data = {
+            "model": model,
+            "messages": messages,
+        }
+        if sync_mode:
+            response = litellm.completion(**data)
+        else:
+            response = await litellm.acompletion(**data)
+
+        response_format_tests(response=response)
+
+        print(f"response: {response}")
+    except litellm.RateLimitError as e:
+        pass
+    except Exception as e:
+        if "429 Quota exceeded" in str(e):
+            pass
+        else:
+            pytest.fail("An unexpected exception occurred - {}".format(str(e)))
+
+
 def vertex_httpx_mock_reject_prompt_post(*args, **kwargs):
     mock_response = MagicMock()
     mock_response.status_code = 200

litellm/utils.py

@@ -5752,10 +5752,12 @@ def convert_to_model_response_object(
                 model_response_object.usage.total_tokens = response_object["usage"].get("total_tokens", 0)  # type: ignore
 
             if "created" in response_object:
-                model_response_object.created = response_object["created"]
+                model_response_object.created = response_object["created"] or int(
+                    time.time()
+                )
 
             if "id" in response_object:
-                model_response_object.id = response_object["id"]
+                model_response_object.id = response_object["id"] or str(uuid.uuid4())
 
             if "system_fingerprint" in response_object:
                 model_response_object.system_fingerprint = response_object[