Replace usage of env variables with application config in app state

aidial_adapter_openai/app.py

@@ -1,12 +1,13 @@
 from contextlib import asynccontextmanager
+from typing import Annotated
 
 import pydantic
 from aidial_sdk._errors import pydantic_validation_exception_handler
 from aidial_sdk.exceptions import HTTPException as DialException
 from aidial_sdk.exceptions import InvalidRequestError
 from aidial_sdk.telemetry.init import init_telemetry
 from aidial_sdk.telemetry.types import TelemetryConfig
-from fastapi import FastAPI, Request
+from fastapi import Depends, FastAPI, Request
 from fastapi.responses import Response
 from openai import (
     APIConnectionError,
@@ -16,6 +17,7 @@
     OpenAIError,
 )
 
+from aidial_adapter_openai.app_config import ApplicationConfig
 from aidial_adapter_openai.completions import chat_completion as completion
 from aidial_adapter_openai.dalle3 import (
     chat_completion as dalle3_chat_completion,
@@ -30,17 +32,6 @@
 from aidial_adapter_openai.embeddings.openai import (
     embeddings as openai_embeddings,
 )
-from aidial_adapter_openai.env import (
-    API_VERSIONS_MAPPING,
-    AZURE_AI_VISION_DEPLOYMENTS,
-    DALLE3_AZURE_API_VERSION,
-    DALLE3_DEPLOYMENTS,
-    DATABRICKS_DEPLOYMENTS,
-    GPT4_VISION_DEPLOYMENTS,
-    MISTRAL_DEPLOYMENTS,
-    MODEL_ALIASES,
-    NON_STREAMING_DEPLOYMENTS,
-)
 from aidial_adapter_openai.gpt import gpt_chat_completion
 from aidial_adapter_openai.gpt4_multi_modal.chat_completion import (
     gpt4_vision_chat_completion,
@@ -51,7 +42,10 @@
 )
 from aidial_adapter_openai.utils.auth import get_credentials
 from aidial_adapter_openai.utils.http_client import get_http_client
-from aidial_adapter_openai.utils.image_tokenizer import get_image_tokenizer
+from aidial_adapter_openai.utils.image_tokenizer import (
+    ImageTokenizer,
+    get_image_tokenizer,
+)
 from aidial_adapter_openai.utils.log_config import configure_loggers, logger
 from aidial_adapter_openai.utils.parsers import completions_parser, parse_body
 from aidial_adapter_openai.utils.streaming import create_server_response
@@ -68,43 +62,88 @@ async def lifespan(app: FastAPI):
     await get_http_client().aclose()
 
 
-app = FastAPI(lifespan=lifespan)
+def create_app(
+    app_config: ApplicationConfig | None = None,
+    to_init_telemetry: bool = True,
+    to_configure_loggers: bool = True,
+) -> FastAPI:
+    app = FastAPI(lifespan=lifespan)
+
+    if app_config is None:
+        app_config = ApplicationConfig.from_env()
+
+    app.state.app_config = app_config
+
+    if to_init_telemetry:
+        init_telemetry(app, TelemetryConfig())
 
+    if to_configure_loggers:
+        configure_loggers()
 
-init_telemetry(app, TelemetryConfig())
-configure_loggers()
+    return app
 
 
-def get_api_version(request: Request):
+def get_app_config(request: Request) -> ApplicationConfig:
+    return request.app.state.app_config
+
+
+def get_api_version(request: Request) -> str:
     api_version = request.query_params.get("api-version", "")
-    api_version = API_VERSIONS_MAPPING.get(api_version, api_version)
+    app_config = get_app_config(request)
+    api_version = app_config.API_VERSIONS_MAPPING.get(api_version, api_version)
 
     if api_version == "":
         raise InvalidRequestError("api-version is a required query parameter")
 
     return api_version
 
 
+def _get_image_tokenizer(
+    deployment_id: str, app_config: ApplicationConfig
+) -> ImageTokenizer:
+    image_tokenizer = get_image_tokenizer(deployment_id, app_config)
+    if not image_tokenizer:
+        raise RuntimeError(
+            f"No image tokenizer found for deployment {deployment_id}"
+        )
+    return image_tokenizer
+
+
+app = create_app()
+
+
 @app.post("/openai/deployments/{deployment_id:path}/chat/completions")
-async def chat_completion(deployment_id: str, request: Request):
+async def chat_completion(
+    deployment_id: str,
+    request: Request,
+    app_config: Annotated[ApplicationConfig, Depends(get_app_config)],
+):
 
     data = await parse_body(request)
 
     is_stream = bool(data.get("stream"))
 
-    emulate_streaming = deployment_id in NON_STREAMING_DEPLOYMENTS and is_stream
+    emulate_streaming = (
+        deployment_id in app_config.NON_STREAMING_DEPLOYMENTS and is_stream
+    )
 
     if emulate_streaming:
         data["stream"] = False
 
     return create_server_response(
         emulate_streaming,
-        await call_chat_completion(deployment_id, data, is_stream, request),
+        await call_chat_completion(
+            deployment_id, data, is_stream, request, app_config
+        ),
     )
 
 
 async def call_chat_completion(
-    deployment_id: str, data: dict, is_stream: bool, request: Request
+    deployment_id: str,
+    data: dict,
+    is_stream: bool,
+    request: Request,
+    app_config: ApplicationConfig,
 ):
 
     # Azure OpenAI deployments ignore "model" request field,
@@ -129,56 +168,62 @@ async def call_chat_completion(
             creds,
             api_version,
             deployment_id,
+            app_config,
         )
-
-    if deployment_id in DALLE3_DEPLOYMENTS:
+    if deployment_id in app_config.DALLE3_DEPLOYMENTS:
         storage = create_file_storage("images", request.headers)
         return await dalle3_chat_completion(
             data,
             upstream_endpoint,
             creds,
             is_stream,
             storage,
-            DALLE3_AZURE_API_VERSION,
+            app_config.DALLE3_AZURE_API_VERSION,
         )
 
-    if deployment_id in MISTRAL_DEPLOYMENTS:
+    if deployment_id in app_config.MISTRAL_DEPLOYMENTS:
         return await mistral_chat_completion(data, upstream_endpoint, creds)
 
-    if deployment_id in DATABRICKS_DEPLOYMENTS:
+    if deployment_id in app_config.DATABRICKS_DEPLOYMENTS:
         return await databricks_chat_completion(data, upstream_endpoint, creds)
 
-    text_tokenizer_model = MODEL_ALIASES.get(deployment_id, deployment_id)
+    text_tokenizer_model = app_config.MODEL_ALIASES.get(
+        deployment_id, deployment_id
+    )
 
-    if image_tokenizer := get_image_tokenizer(deployment_id):
-        storage = create_file_storage("images", request.headers)
+    if deployment_id in app_config.GPT4_VISION_DEPLOYMENTS:
+        tokenizer = MultiModalTokenizer(
+            "gpt-4", _get_image_tokenizer(deployment_id, app_config)
+        )
+        return await gpt4_vision_chat_completion(
+            data,
+            deployment_id,
+            upstream_endpoint,
+            creds,
+            is_stream,
+            create_file_storage("images", request.headers),
+            api_version,
+            tokenizer,
+        )
 
-        if deployment_id in GPT4_VISION_DEPLOYMENTS:
-            tokenizer = MultiModalTokenizer("gpt-4", image_tokenizer)
-            return await gpt4_vision_chat_completion(
-                data,
-                deployment_id,
-                upstream_endpoint,
-                creds,
-                is_stream,
-                storage,
-                api_version,
-                tokenizer,
-            )
-        else:
-            tokenizer = MultiModalTokenizer(
-                text_tokenizer_model, image_tokenizer
-            )
-            return await gpt4o_chat_completion(
-                data,
-                deployment_id,
-                upstream_endpoint,
-                creds,
-                is_stream,
-                storage,
-                api_version,
-                tokenizer,
-            )
+    if deployment_id in (
+        *app_config.GPT4O_DEPLOYMENTS,
+        *app_config.GPT4O_MINI_DEPLOYMENTS,
+    ):
+        tokenizer = MultiModalTokenizer(
+            text_tokenizer_model,
+            _get_image_tokenizer(deployment_id, app_config),
+        )
+        return await gpt4o_chat_completion(
+            data,
+            deployment_id,
+            upstream_endpoint,
+            creds,
+            is_stream,
+            create_file_storage("images", request.headers),
+            api_version,
+            tokenizer,
+        )
 
     tokenizer = PlainTextTokenizer(model=text_tokenizer_model)
     return await gpt_chat_completion(
@@ -192,7 +237,11 @@ async def call_chat_completion(
 
 
 @app.post("/openai/deployments/{deployment_id:path}/embeddings")
-async def embedding(deployment_id: str, request: Request):
+async def embedding(
+    deployment_id: str,
+    request: Request,
+    app_config: Annotated[ApplicationConfig, Depends(get_app_config)],
+):
     data = await parse_body(request)
 
     # See note for /chat/completions endpoint
@@ -202,7 +251,7 @@ async def embedding(deployment_id: str, request: Request):
     api_version = get_api_version(request)
     upstream_endpoint = request.headers["X-UPSTREAM-ENDPOINT"]
 
-    if deployment_id in AZURE_AI_VISION_DEPLOYMENTS:
+    if deployment_id in app_config.AZURE_AI_VISION_DEPLOYMENTS:
         storage = create_file_storage("images", request.headers)
         return await azure_ai_vision_embeddings(
             creds, deployment_id, upstream_endpoint, storage, data

aidial_adapter_openai/app_config.py

@@ -0,0 +1,39 @@
+from typing import Dict, List
+
+from pydantic import BaseModel, Field
+
+import aidial_adapter_openai.env as env
+
+
+class ApplicationConfig(BaseModel):
+    MODEL_ALIASES: Dict[str, str] = Field(default_factory=dict)
+    DALLE3_DEPLOYMENTS: List[str] = Field(default_factory=list)
+    GPT4_VISION_DEPLOYMENTS: List[str] = Field(default_factory=list)
+    MISTRAL_DEPLOYMENTS: List[str] = Field(default_factory=list)
+    DATABRICKS_DEPLOYMENTS: List[str] = Field(default_factory=list)
+    GPT4O_DEPLOYMENTS: List[str] = Field(default_factory=list)
+    GPT4O_MINI_DEPLOYMENTS: List[str] = Field(default_factory=list)
+    AZURE_AI_VISION_DEPLOYMENTS: List[str] = Field(default_factory=list)
+    API_VERSIONS_MAPPING: Dict[str, str] = Field(default_factory=dict)
+    COMPLETION_DEPLOYMENTS_PROMPT_TEMPLATES: Dict[str, str] = Field(
+        default_factory=dict
+    )
+    DALLE3_AZURE_API_VERSION: str = Field(default="2024-02-01")
+    NON_STREAMING_DEPLOYMENTS: List[str] = Field(default_factory=list)
+
+    @classmethod
+    def from_env(cls) -> "ApplicationConfig":
+        return cls(
+            MODEL_ALIASES=env.MODEL_ALIASES,
+            DALLE3_DEPLOYMENTS=env.DALLE3_DEPLOYMENTS,
+            GPT4_VISION_DEPLOYMENTS=env.GPT4_VISION_DEPLOYMENTS,
+            MISTRAL_DEPLOYMENTS=env.MISTRAL_DEPLOYMENTS,
+            DATABRICKS_DEPLOYMENTS=env.DATABRICKS_DEPLOYMENTS,
+            GPT4O_DEPLOYMENTS=env.GPT4O_DEPLOYMENTS,
+            GPT4O_MINI_DEPLOYMENTS=env.GPT4O_MINI_DEPLOYMENTS,
+            AZURE_AI_VISION_DEPLOYMENTS=env.AZURE_AI_VISION_DEPLOYMENTS,
+            API_VERSIONS_MAPPING=env.API_VERSIONS_MAPPING,
+            COMPLETION_DEPLOYMENTS_PROMPT_TEMPLATES=env.COMPLETION_DEPLOYMENTS_PROMPT_TEMPLATES,
+            DALLE3_AZURE_API_VERSION=env.DALLE3_AZURE_API_VERSION,
+            NON_STREAMING_DEPLOYMENTS=env.NON_STREAMING_DEPLOYMENTS,
+        )

aidial_adapter_openai/completions.py

@@ -4,7 +4,7 @@
 from openai import AsyncStream
 from openai.types import Completion
 
-from aidial_adapter_openai.env import COMPLETION_DEPLOYMENTS_PROMPT_TEMPLATES
+from aidial_adapter_openai.app_config import ApplicationConfig
 from aidial_adapter_openai.utils.auth import OpenAICreds
 from aidial_adapter_openai.utils.parsers import (
     AzureOpenAIEndpoint,
@@ -46,6 +46,7 @@ async def chat_completion(
     creds: OpenAICreds,
     api_version: str,
     deployment_id: str,
+    app_config: ApplicationConfig,
 ):
 
     if data.get("n") or 1 > 1:
@@ -60,7 +61,9 @@ async def chat_completion(
     prompt = messages[-1].get("content") or ""
 
     if (
-        template := COMPLETION_DEPLOYMENTS_PROMPT_TEMPLATES.get(deployment_id)
+        template := app_config.COMPLETION_DEPLOYMENTS_PROMPT_TEMPLATES.get(
+            deployment_id
+        )
     ) is not None:
         prompt = template.format(prompt=prompt)
 

aidial_adapter_openai/utils/image_tokenizer.py

@@ -8,11 +8,7 @@
 
 from pydantic import BaseModel
 
-from aidial_adapter_openai.env import (
-    GPT4_VISION_DEPLOYMENTS,
-    GPT4O_DEPLOYMENTS,
-    GPT4O_MINI_DEPLOYMENTS,
-)
+from aidial_adapter_openai.app_config import ApplicationConfig
 from aidial_adapter_openai.utils.image import ImageDetail, resolve_detail_level
 
 
@@ -58,14 +54,18 @@ def _compute_high_detail_tokens(self, width: int, height: int) -> int:
     low_detail_tokens=2833, tokens_per_tile=5667
 )
 
-_TOKENIZERS: List[Tuple[ImageTokenizer, List[str]]] = [
-    (GPT4O_IMAGE_TOKENIZER, GPT4O_DEPLOYMENTS),
-    (GPT4O_MINI_IMAGE_TOKENIZER, GPT4O_MINI_DEPLOYMENTS),
-    (GPT4_VISION_IMAGE_TOKENIZER, GPT4_VISION_DEPLOYMENTS),
-]
-
 
-def get_image_tokenizer(deployment_id: str) -> ImageTokenizer | None:
+def get_image_tokenizer(
+    deployment_id: str, app_config: ApplicationConfig
+) -> ImageTokenizer | None:
+    _TOKENIZERS: List[Tuple[ImageTokenizer, List[str]]] = [
+        (GPT4O_IMAGE_TOKENIZER, app_config.GPT4O_DEPLOYMENTS),
+        (GPT4O_MINI_IMAGE_TOKENIZER, app_config.GPT4O_MINI_DEPLOYMENTS),
+        (
+            GPT4_VISION_IMAGE_TOKENIZER,
+            app_config.GPT4_VISION_DEPLOYMENTS,
+        ),
+    ]
     for tokenizer, ids in _TOKENIZERS:
         if deployment_id in ids:
             return tokenizer