Initial commit

src/Dockerfile

@@ -0,0 +1,9 @@
+FROM public.ecr.aws/lambda/python:3.12
+
+COPY ./api ./api
+
+COPY requirements.txt .
+
+RUN pip3 install -r requirements.txt -U --no-cache-dir
+
+CMD [ "api.app.handler" ]

src/api/app.py

@@ -0,0 +1,52 @@
+import logging
+
+import uvicorn
+from fastapi import FastAPI
+from fastapi.exceptions import RequestValidationError
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import PlainTextResponse
+from mangum import Mangum
+
+from api.routers import model, chat
+from api.setting import API_ROUTE_PREFIX, TITLE, DESCRIPTION, SUMMARY, VERSION
+
+config = {
+    "title": TITLE,
+    "description": DESCRIPTION,
+    "summary": SUMMARY,
+    "version": VERSION,
+}
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+)
+app = FastAPI(**config)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.include_router(model.router, prefix=API_ROUTE_PREFIX)
+app.include_router(chat.router, prefix=API_ROUTE_PREFIX)
+
+
+@app.get("/health")
+async def health():
+    """For health check if needed"""
+    return {"status": "OK"}
+
+
+@app.exception_handler(RequestValidationError)
+async def validation_exception_handler(request, exc):
+    return PlainTextResponse(str(exc), status_code=400)
+
+
+handler = Mangum(app)
+
+if __name__ == "__main__":
+    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)

src/api/auth.py

@@ -0,0 +1,28 @@
+import os
+from typing import Annotated
+
+import boto3
+from fastapi import Depends, HTTPException, status
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+
+from api.setting import DEFAULT_API_KEYS
+
+api_key_param = os.environ.get("API_KEY_PARAM_NAME")
+if api_key_param:
+    ssm = boto3.client("ssm")
+    api_key = ssm.get_parameter(Name=api_key_param, WithDecryption=True)["Parameter"][
+        "Value"
+    ]
+else:
+    api_key = DEFAULT_API_KEYS
+
+security = HTTPBearer()
+
+
+def api_key_auth(
+    credentials: Annotated[HTTPAuthorizationCredentials, Depends(security)]
+):
+    if credentials.credentials != api_key:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API Key"
+        )

src/api/models/__init__.py

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+from api.models.bedrock import ClaudeModel, SUPPORTED_BEDROCK_MODELS, get_model

src/api/models/bedrock.py

@@ -0,0 +1,391 @@
+import json
+import logging
+import uuid
+from abc import ABC, abstractmethod
+from typing import AsyncIterable
+
+import boto3
+
+from api.schema import (
+    ChatResponse,
+    ChatRequest,
+    ChatRequestMessage,
+    Choice,
+    ChatResponseMessage,
+    Usage,
+    ChatStreamResponse,
+    ChoiceDelta,
+)
+from api.setting import DEBUG, AWS_REGION
+
+logger = logging.getLogger(__name__)
+
+bedrock_runtime = boto3.client(
+    service_name="bedrock-runtime",
+    region_name=AWS_REGION,
+)
+
+SUPPORTED_BEDROCK_MODELS = {
+    "anthropic.claude-instant-v1": "Claude Instant",
+    "anthropic.claude-v2:1": "Claude",
+    "anthropic.claude-v2": "Claude",
+    "anthropic.claude-3-sonnet-20240229-v1:0": "Claude 3 Sonnet",
+    "anthropic.claude-3-haiku-20240307-v1:0": "Claude 3 Haiku",
+    "meta.llama2-13b-chat-v1": "Llama 2 Chat 13B",
+    "meta.llama2-70b-chat-v1": "Llama 2 Chat 70B",
+    "mistral.mistral-7b-instruct-v0:2": "Mistral 7B Instruct",
+    "mistral.mixtral-8x7b-instruct-v0:1": "Mixtral 8x7B Instruct",
+}
+
+
+class BaseChatModel(ABC):
+    """Represent a basic chat model
+
+    Currently, only Bedrock model is supported, but may be used for SageMaker models if needed.
+    """
+
+    @abstractmethod
+    def chat(self, chat_request: ChatRequest) -> ChatResponse:
+        """Handle a basic chat completion requests."""
+        pass
+
+    @abstractmethod
+    def chat_stream(self, chat_request: ChatRequest) -> AsyncIterable[bytes]:
+        """Handle a basic chat completion requests with stream response."""
+        pass
+
+    def _generate_message_id(self) -> str:
+        return "chatcmpl-" + str(uuid.uuid4())[:8]
+
+    def _stream_response_to_bytes(self, response: ChatStreamResponse) -> bytes:
+        return "data: {}\n\n".format(response.model_dump_json()).encode("utf-8")
+
+
+# https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html
+class BedrockModel(BaseChatModel):
+    accept = "application/json"
+    content_type = "application/json"
+
+    def _invoke_model(self, args: dict, model_id: str, with_stream: bool = False):
+        body = json.dumps(args)
+        if DEBUG:
+            logger.info("Invoke Bedrock Model: " + model_id)
+            logger.info("Bedrock request body: " + body)
+        if with_stream:
+            return bedrock_runtime.invoke_model_with_response_stream(
+                body=body,
+                modelId=model_id,
+                accept=self.accept,
+                contentType=self.content_type,
+            )
+        return bedrock_runtime.invoke_model(
+            body=body,
+            modelId=model_id,
+            accept=self.accept,
+            contentType=self.content_type,
+        )
+
+    def _create_response(
+        self,
+        model: str,
+        message: str,
+        message_id: str,
+        input_tokens: int = 0,
+        output_tokens: int = 0,
+    ) -> ChatResponse:
+        choice = Choice(
+            index=0,
+            message=ChatResponseMessage(
+                role="assistant",
+                content=message,
+            ),
+            finish_reason="stop",
+        )
+        response = ChatResponse(
+            id=message_id,
+            model=model,
+            choices=[choice],
+            usage=Usage(
+                prompt_tokens=input_tokens,
+                completion_tokens=output_tokens,
+                total_tokens=input_tokens + output_tokens,
+            ),
+        )
+        if DEBUG:
+            logger.info("Proxy response :" + response.model_dump_json())
+        return response
+
+    def _create_response_stream(
+        self, model: str, message_id: str, chunk_message: str, finish_reason: str | None
+    ) -> ChatStreamResponse:
+        choice = ChoiceDelta(
+            index=0,
+            delta=ChatResponseMessage(
+                role="assistant",
+                content=chunk_message,
+            ),
+            finish_reason=finish_reason,
+        )
+        response = ChatStreamResponse(
+            id=message_id,
+            model=model,
+            choices=[choice],
+        )
+        if DEBUG:
+            logger.info("Proxy response :" + response.model_dump_json())
+        return response
+
+
+def get_model(model_id: str) -> BedrockModel:
+    model_name = SUPPORTED_BEDROCK_MODELS.get(model_id, "")
+    if DEBUG:
+        logger.info("model name is " + model_name)
+    if model_name in ["Claude Instant", "Claude", "Claude 3 Sonnet", "Claude 3 Haiku"]:
+        return ClaudeModel()
+    elif model_name in ["Llama 2 Chat 13B", "Llama 2 Chat 70B"]:
+        return Llama2Model()
+    elif model_name in ["Mistral 7B Instruct", "Mixtral 8x7B Instruct"]:
+        return MistralModel()
+    else:
+        logger.error("Unsupported model id " + model_id)
+        raise ValueError("Invalid model ID")
+
+
+class ClaudeModel(BedrockModel):
+    anthropic_version = "bedrock-2023-05-31"
+
+    def _parse_args(self, chat_request: ChatRequest) -> dict:
+        args = {
+            "anthropic_version": self.anthropic_version,
+            "max_tokens": chat_request.max_tokens,
+            "top_p": chat_request.top_p,
+            "temperature": chat_request.temperature,
+        }
+        if chat_request.messages[0].role == "system":
+            args["system"] = chat_request.messages[0].content
+            args["messages"] = [
+                {"role": msg.role, "content": msg.content}
+                for msg in chat_request.messages[1:]
+            ]
+        else:
+            args["messages"] = [
+                {"role": msg.role, "content": msg.content}
+                for msg in chat_request.messages
+            ]
+
+        return args
+
+    def chat(self, chat_request: ChatRequest) -> ChatResponse:
+        response = self._invoke_model(
+            args=self._parse_args(chat_request), model_id=chat_request.model
+        )
+        response_body = json.loads(response.get("body").read())
+        if DEBUG:
+            logger.info("Bedrock response body: " + str(response_body))
+
+        return self._create_response(
+            model=chat_request.model,
+            message=response_body["content"][0]["text"],
+            message_id=response_body["id"],
+            input_tokens=response_body["usage"]["input_tokens"],
+            output_tokens=response_body["usage"]["output_tokens"],
+        )
+
+    def chat_stream(self, chat_request: ChatRequest) -> AsyncIterable[bytes]:
+        response = self._invoke_model(
+            args=self._parse_args(chat_request),
+            model_id=chat_request.model,
+            with_stream=True,
+        )
+        msg_id = ""
+        chunk_id = 0
+        for event in response.get("body"):
+            if DEBUG:
+                logger.info("Bedrock response chunk: " + str(event))
+            chunk = json.loads(event["chunk"]["bytes"])
+            chunk_id += 1
+            if chunk["type"] == "message_start":
+                msg_id = chunk["message"]["id"]
+                continue
+
+            if chunk["type"] == "message_delta":
+                chunk_message = ""
+                finish_reason = "stop"
+
+            elif chunk["type"] == "content_block_delta":
+                chunk_message = chunk["delta"]["text"]
+                finish_reason = None
+            else:
+                continue
+            response = self._create_response_stream(
+                model=chat_request.model,
+                message_id=msg_id,
+                chunk_message=chunk_message,
+                finish_reason=finish_reason,
+            )
+
+            yield self._stream_response_to_bytes(response)
+
+
+class Llama2Model(BedrockModel):
+
+    def _convert_prompt(self, messages: list[ChatRequestMessage]) -> str:
+        """Create a prompt message follow below example:
+
+        <s>[INST] <<SYS>>\n{your_system_message}\n<</SYS>>\n\n{user_message_1} [/INST] {model_reply_1}</s>
+        <s>[INST] {user_message_2} [/INST]
+        """
+        if DEBUG:
+            logger.info("Convert below messages to prompt for Llama 2: ")
+            for msg in messages:
+                logger.info(msg.model_dump_json())
+        bos_token = "<s>"
+        eos_token = "</s>"
+        prompt = bos_token + "[INST] "
+        start = 0
+        end_turn = False
+        if messages[0].role == "system":
+            prompt += "<<SYS>>\n" + messages[0].content + "\n<<SYS>>\n\n"
+            start = 1
+        # TODO: Add validation
+        for i in range(start, len(messages)):
+            msg = messages[i]
+            if msg.role == "user":
+                if end_turn:
+                    prompt += bos_token + "[INST] "
+                prompt += msg.content + " [/INST] "
+                end_turn = False
+            else:
+                prompt += msg.content + eos_token
+                end_turn = True
+        if DEBUG:
+            logger.info("Converted prompt: " + prompt.replace("\n", "\\n"))
+        return prompt
+
+    def _parse_args(self, chat_request: ChatRequest) -> dict:
+        prompt = self._convert_prompt(chat_request.messages)
+        return {
+            "prompt": prompt,
+            "max_gen_len": chat_request.max_tokens,
+            "temperature": chat_request.temperature,
+            "top_p": chat_request.top_p,
+        }
+
+    def chat(self, chat_request: ChatRequest) -> ChatResponse:
+        response = self._invoke_model(
+            args=self._parse_args(chat_request), model_id=chat_request.model
+        )
+        response_body = json.loads(response.get("body").read())
+        if DEBUG:
+            logger.info("Bedrock response body: " + str(response_body))
+        message_id = self._generate_message_id()
+
+        return self._create_response(
+            model=chat_request.model,
+            message=response_body["generation"],
+            message_id=message_id,
+            input_tokens=response_body["prompt_token_count"],
+            output_tokens=response_body["generation_token_count"],
+        )
+
+    def chat_stream(self, chat_request: ChatRequest) -> AsyncIterable[bytes]:
+        response = self._invoke_model(
+            args=self._parse_args(chat_request),
+            model_id=chat_request.model,
+            with_stream=True,
+        )
+        msg_id = ""
+        chunk_id = 0
+        for event in response.get("body"):
+            if DEBUG:
+                logger.info("Bedrock response chunk: " + str(event))
+            chunk = json.loads(event["chunk"]["bytes"])
+            chunk_id += 1
+            response = self._create_response_stream(
+                model=chat_request.model,
+                message_id=msg_id,
+                chunk_message=chunk["generation"],
+                finish_reason=chunk["stop_reason"],
+            )
+            yield self._stream_response_to_bytes(response)
+
+
+class MistralModel(BedrockModel):
+    def _convert_prompt(self, messages: list[ChatRequestMessage]) -> str:
+        """Create a prompt message follow below example:
+
+        <s>[INST] {your_system_message}\n{user_message_1} [/INST] {model_reply_1}</s>
+        <s>[INST] {user_message_2} [/INST]
+        """
+        if DEBUG:
+            logger.info("Convert below messages to prompt for Llama 2: ")
+            for msg in messages:
+                logger.info(msg.model_dump_json())
+        bos_token = "<s>"
+        eos_token = "</s>"
+        prompt = bos_token + "[INST] "
+        start = 0
+        end_turn = False
+        if messages[0].role == "system":
+            prompt += messages[0].content + "\n"
+            start = 1
+        # TODO: Add validation
+        for i in range(start, len(messages)):
+            msg = messages[i]
+            if msg.role == "user":
+                if end_turn:
+                    prompt += bos_token + "[INST] "
+                prompt += msg.content + " [/INST] "
+                end_turn = False
+            else:
+                prompt += msg.content + eos_token
+                end_turn = True
+        if DEBUG:
+            logger.info("Converted prompt: " + prompt.replace("\n", "\\n"))
+        return prompt
+
+    def _parse_args(self, chat_request: ChatRequest) -> dict:
+        prompt = self._convert_prompt(chat_request.messages)
+        return {
+            "prompt": prompt,
+            "max_tokens": chat_request.max_tokens,
+            "temperature": chat_request.temperature,
+            "top_p": chat_request.top_p,
+        }
+
+    def chat(self, chat_request: ChatRequest) -> ChatResponse:
+
+        response = self._invoke_model(
+            args=self._parse_args(chat_request), model_id=chat_request.model
+        )
+        response_body = json.loads(response.get("body").read())
+        if DEBUG:
+            logger.info("Bedrock response body: " + str(response_body))
+        message_id = self._generate_message_id()
+
+        return self._create_response(
+            model=chat_request.model,
+            message=response_body["outputs"][0]["text"],
+            message_id=message_id,
+        )
+
+    def chat_stream(self, chat_request: ChatRequest) -> AsyncIterable[bytes]:
+        response = self._invoke_model(
+            args=self._parse_args(chat_request),
+            model_id=chat_request.model,
+            with_stream=True,
+        )
+        msg_id = ""
+        chunk_id = 0
+        for event in response.get("body"):
+            if DEBUG:
+                logger.info("Bedrock response chunk: " + str(event))
+            chunk = json.loads(event["chunk"]["bytes"])
+            chunk_id += 1
+            response = self._create_response_stream(
+                model=chat_request.model,
+                message_id=msg_id,
+                chunk_message=chunk["outputs"][0]["text"],
+                finish_reason=chunk["outputs"][0]["stop_reason"],
+            )
+            yield self._stream_response_to_bytes(response)

src/api/routers/chat.py

@@ -0,0 +1,51 @@
+from typing import Annotated
+
+from fastapi import APIRouter, Depends, Body, HTTPException
+from fastapi.responses import StreamingResponse
+
+from api.auth import api_key_auth
+from api.models import get_model, SUPPORTED_BEDROCK_MODELS
+from api.schema import ChatRequest, ChatResponse, ChatStreamResponse
+from api.setting import DEFAULT_MODEL
+
+router = APIRouter()
+
+router = APIRouter(
+    prefix="/chat",
+    tags=["items"],
+    dependencies=[Depends(api_key_auth)],
+    # responses={404: {"description": "Not found"}},
+)
+
+
+@router.post("/completions", response_model=ChatResponse | ChatStreamResponse)
+async def chat_completions(
+        chat_request: Annotated[
+            ChatRequest,
+            Body(
+                examples=[
+                    {
+                        "model": "anthropic.claude-3-sonnet-20240229-v1:0",
+                        "messages": [
+                            {"role": "system", "content": "You are a helpful assistant."},
+                            {"role": "user", "content": "Hello!"},
+                        ],
+                    }
+                ],
+            ),
+        ]
+):
+    if chat_request.model.lower().startswith("gpt-"):
+        chat_request.model = DEFAULT_MODEL
+    if chat_request.model not in SUPPORTED_BEDROCK_MODELS.keys():
+        raise HTTPException(status_code=400, detail="Unsupported Model Id " + chat_request.model)
+    try:
+        model = get_model(chat_request.model)
+
+        if chat_request.stream:
+            return StreamingResponse(
+                content=model.chat_stream(chat_request), media_type="text/event-stream"
+            )
+        return model.chat(chat_request)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))

src/api/routers/model.py

@@ -0,0 +1,41 @@
+from typing import Annotated
+
+from fastapi import APIRouter, Depends, HTTPException, Path
+
+from api.auth import api_key_auth
+from api.models import SUPPORTED_BEDROCK_MODELS
+from api.schema import Models, Model
+
+router = APIRouter()
+
+router = APIRouter(
+    prefix="/models",
+    tags=["items"],
+    dependencies=[Depends(api_key_auth)],
+    # responses={404: {"description": "Not found"}},
+)
+
+
+async def validate_model_id(model_id: str):
+    if model_id not in SUPPORTED_BEDROCK_MODELS.keys():
+        raise HTTPException(status_code=400, detail="Unsupported Model Id")
+
+
+@router.get("/", response_model=Models)
+async def list_models():
+    model_list = [Model(id=model_id) for model_id in SUPPORTED_BEDROCK_MODELS.keys()]
+    return Models(data=model_list)
+
+
+@router.get(
+    "/{model_id}",
+    response_model=Model,
+)
+async def get_model(
+    model_id: Annotated[
+        str,
+        Path(description="Model ID", example="anthropic.claude-3-sonnet-20240229-v1:0"),
+    ]
+):
+    await validate_model_id(model_id)
+    return Model(id=model_id)

src/api/schema.py

@@ -0,0 +1,80 @@
+import time
+from typing import Literal
+
+from pydantic import BaseModel, Field
+
+
+class Model(BaseModel):
+    id: str
+    created: int = Field(default_factory=lambda: int(time.time()))
+    object: str | None = "model"
+    owned_by: str | None = "bedrock"
+
+
+class Models(BaseModel):
+    object: str | None = "list"
+    data: list[Model] = []
+
+
+class ChatRequestMessage(BaseModel):
+    name: str | None = None
+    role: Literal["user", "assistant", "system"]
+    content: str
+
+
+class ChatRequest(BaseModel):
+    messages: list[ChatRequestMessage]
+    model: str
+    frequency_penalty: float | None = Field(default=0.0, le=2.0, ge=-2.0)  # Not used
+    presence_penalty: float | None = Field(default=0.0, le=2.0, ge=-2.0)  # Not used
+    stream: bool | None = False
+    temperature: float | None = Field(default=1.0, le=2.0, ge=0.0)
+    top_p: float | None = Field(default=1.0, le=1.0, ge=0.0)
+    user: str | None = None  # Not used
+    max_tokens: int | None = 2048
+    n: int | None = 1  # Not used
+
+
+class Usage(BaseModel):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+
+class ChatResponseMessage(BaseModel):
+    # tool_calls
+    role: Literal["assistant"] | None = None
+    content: str | None = None
+
+
+class BaseChoice(BaseModel):
+    index: int
+    finish_reason: str | None
+    logprobs: dict | None = None
+
+
+class Choice(BaseChoice):
+    message: ChatResponseMessage
+
+
+class ChoiceDelta(BaseChoice):
+    delta: ChatResponseMessage
+
+
+class BaseChatResponse(BaseModel):
+    # id: str = Field(default_factory=lambda: "chatcmpl-" + str(uuid.uuid4())[:8])
+    id: str
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    system_fingerprint: str = "fp_e97c09dd4e26"
+
+
+class ChatResponse(BaseChatResponse):
+    choices: list[Choice]
+    object: Literal["chat.completion"] = "chat.completion"
+    usage: Usage
+
+
+class ChatStreamResponse(BaseChatResponse):
+    choices: list[ChoiceDelta]
+    object: Literal["chat.completion.chunk"] = "chat.completion.chunk"

src/api/setting.py

@@ -0,0 +1,27 @@
+import os
+
+DEFAULT_API_KEYS = "bedrock"
+
+API_ROUTE_PREFIX = "/api/v1"
+
+TITLE = "Amazon Bedrock Proxy APIs"
+SUMMARY = "OpenAI-Compatible RESTful APIs for Amazon Bedrock"
+VERSION = "0.1.0"
+DESCRIPTION = """
+Use OpenAI-Compatible RESTful APIs for Amazon Bedrock models.
+
+List of Amazon Bedrock models currently supported:
+- anthropic.claude-instant-v1
+- anthropic.claude-v2:1
+- anthropic.claude-v2
+- anthropic.claude-3-sonnet-20240229-v1:0
+- anthropic.claude-3-haiku-20240307-v1:0
+- meta.llama2-13b-chat-v1
+- meta.llama2-70b-chat-v1
+- mistral.mistral-7b-instruct-v0:2
+- mistral.mixtral-8x7b-instruct-v0:1
+"""
+
+DEBUG = os.environ.get("DEBUG", "false").lower() != "false"
+AWS_REGION = os.environ.get("AWS_REGION", "us-west-2")
+DEFAULT_MODEL = os.environ.get("DEFAULT_MODEL", "anthropic.claude-3-sonnet-20240229-v1:0")

src/requirements.txt

@@ -0,0 +1,4 @@
+fastapi==0.103.0
+pydantic==2.6.3
+uvicorn==0.27.0.post1
+mangum==0.17.0