feat(ai-server): improved logging

opentrons-ai-server/Dockerfile

@@ -1,7 +1,8 @@
-FROM --platform=linux/amd64 python:3.12-slim
+ARG PLATFORM=linux/amd64
+FROM --platform=$PLATFORM python:3.12-slim
 
-ENV PYTHONUNBUFFERED True
-ENV DOCKER_RUNNING True
+ENV PYTHONUNBUFFERED=True
+ENV DOCKER_RUNNING=True
 
 WORKDIR /code
 
@@ -15,4 +16,4 @@ COPY ./api /code/api
 
 EXPOSE 8000
 
-CMD ["ddtrace-run", "uvicorn", "api.handler.fast:app", "--proxy-headers",  "--host", "0.0.0.0", "--port", "8000", "--timeout-keep-alive", "190", "--workers", "3"]
+CMD ["ddtrace-run", "uvicorn", "api.handler.fast:app", "--proxy-headers",  "--host", "0.0.0.0", "--port", "8000", "--timeout-keep-alive", "190", "--log-config", "/code/api/uvicorn_disable_logging.json", "--workers", "3"]

opentrons-ai-server/Makefile

@@ -115,7 +115,7 @@ run:
 	docker logs -f $(CONTAINER_NAME)
 
 .PHONY: clean
-clean:
+clean: gen-requirements
 	docker stop $(CONTAINER_NAME) || true
 	docker rm $(CONTAINER_NAME) || true
 

opentrons-ai-server/Pipfile

@@ -13,9 +13,10 @@ fastapi = "==0.111.0"
 ddtrace = "==2.9.2"
 pydantic-settings = "==2.3.4"
 pyjwt = {extras = ["crypto"], version = "*"}
-python-json-logger = "==2.0.7"
 beautifulsoup4 = "==4.12.3" 
 markdownify = "==0.13.1"
+structlog = "==24.4.0"
+asgi-correlation-id = "==4.3.3"
 
 [dev-packages]
 docker = "==7.1.0"

opentrons-ai-server/api/domain/openai_predict.py

@@ -1,7 +1,8 @@
-import logging
 from pathlib import Path
 from typing import List, Tuple
 
+import structlog
+from ddtrace import tracer
 from llama_index.core import Settings as li_settings
 from llama_index.core import StorageContext, load_index_from_storage
 from llama_index.embeddings.openai import OpenAIEmbedding
@@ -25,8 +26,8 @@
 from api.domain.utils import refine_characters
 from api.settings import Settings
 
-logger = logging.getLogger(__name__)
-
+settings: Settings = Settings()
+logger = structlog.stdlib.get_logger(settings.logger_name)
 ROOT_PATH: Path = Path(Path(__file__)).parent.parent.parent
 
 
@@ -38,6 +39,7 @@ def __init__(self, settings: Settings) -> None:
             model_name="text-embedding-3-large", api_key=self.settings.openai_api_key.get_secret_value()
         )
 
+    @tracer.wrap()
     def get_docs_all(self, query: str) -> Tuple[str, str, str]:
         commands = self.extract_atomic_description(query)
         logger.info("Commands", extra={"commands": commands})
@@ -85,6 +87,7 @@ def get_docs_all(self, query: str) -> Tuple[str, str, str]:
 
         return example_commands, docs + docs_ref, standard_api_names
 
+    @tracer.wrap()
     def extract_atomic_description(self, protocol_description: str) -> List[str]:
         class atomic_descr(BaseModel):
             """
@@ -106,6 +109,7 @@ class atomic_descr(BaseModel):
                 descriptions.append(x)
         return descriptions
 
+    @tracer.wrap()
     def refine_response(self, assistant_message: str) -> str:
         if assistant_message is None:
             return ""
@@ -129,6 +133,7 @@ def refine_response(self, assistant_message: str) -> str:
 
         return response.choices[0].message.content if response.choices[0].message.content is not None else ""
 
+    @tracer.wrap()
     def predict(self, prompt: str, chat_completion_message_params: List[ChatCompletionMessageParam] | None = None) -> None | str:
 
         prompt = refine_characters(prompt)

opentrons-ai-server/api/domain/prompts.py

@@ -1,29 +1,31 @@
 import json
-import logging
 import uuid
 from typing import Any, Dict, Iterable
 
 import requests
+import structlog
+from ddtrace import tracer
 from openai.types.chat import ChatCompletionToolParam
 
 from api.settings import Settings
 
 settings: Settings = Settings()
-logger = logging.getLogger(__name__)
+logger = structlog.stdlib.get_logger(settings.logger_name)
 
 
 def generate_unique_name() -> str:
     unique_name = str(uuid.uuid4()) + ".py"
     return unique_name
 
 
+@tracer.wrap()
 def send_post_request(payload: str) -> str:
     url = "https://Opentrons-simulator.hf.space/protocol"
     protocol_name: str = generate_unique_name()
     data = {"name": protocol_name, "content": payload}
     hf_token: str = settings.huggingface_api_key.get_secret_value()
     headers = {"Content-Type": "application/json", "Authorization": "Bearer {}".format(hf_token)}
-
+    logger.info("Sending POST request to the simulate API", extra={"url": url, "protocolName": data["name"]})
     response = requests.post(url, json=data, headers=headers)
 
     if response.status_code != 200:

opentrons-ai-server/api/handler/custom_logging.py

@@ -0,0 +1,133 @@
+# Taken directly from https://gist.github.com/nymous/f138c7f06062b7c43c060bf03759c29e
+import logging
+import sys
+
+import ddtrace
+import structlog
+from ddtrace import tracer
+from structlog.types import EventDict, Processor
+
+
+# https://github.com/hynek/structlog/issues/35#issuecomment-591321744
+def rename_event_key(_, __, event_dict: EventDict) -> EventDict:  # type: ignore[no-untyped-def]
+    """
+    Log entries keep the text message in the `event` field, but Datadog
+    uses the `message` field. This processor moves the value from one field to
+    the other.
+    See https://github.com/hynek/structlog/issues/35#issuecomment-591321744
+    """
+    event_dict["message"] = event_dict.pop("event")
+    return event_dict
+
+
+def drop_color_message_key(_, __, event_dict: EventDict) -> EventDict:  # type: ignore[no-untyped-def]
+    """
+    Uvicorn logs the message a second time in the extra `color_message`, but we don't
+    need it. This processor drops the key from the event dict if it exists.
+    """
+    event_dict.pop("color_message", None)
+    return event_dict
+
+
+def tracer_injection(_, __, event_dict: EventDict) -> EventDict:  # type: ignore[no-untyped-def]
+    # get correlation ids from current tracer context
+    span = tracer.current_span()
+    trace_id, span_id = (str((1 << 64) - 1 & span.trace_id), span.span_id) if span else (None, None)
+
+    # add ids to structlog event dictionary
+    event_dict["dd.trace_id"] = str(trace_id or 0)
+    event_dict["dd.span_id"] = str(span_id or 0)
+
+    # add the env, service, and version configured for the tracer
+    event_dict["dd.env"] = ddtrace.config.env or ""
+    event_dict["dd.service"] = ddtrace.config.service or ""
+    event_dict["dd.version"] = ddtrace.config.version or ""
+
+    return event_dict
+
+
+def setup_logging(json_logs: bool = False, log_level: str = "INFO") -> None:
+    timestamper = structlog.processors.TimeStamper(fmt="iso")
+
+    shared_processors: list[Processor] = [
+        structlog.contextvars.merge_contextvars,
+        structlog.stdlib.add_logger_name,
+        structlog.stdlib.add_log_level,
+        structlog.stdlib.PositionalArgumentsFormatter(),
+        structlog.stdlib.ExtraAdder(),
+        drop_color_message_key,
+        tracer_injection,
+        timestamper,
+        structlog.processors.StackInfoRenderer(),
+    ]
+
+    if json_logs:
+        # We rename the `event` key to `message` only in JSON logs, as Datadog looks for the
+        # `message` key but the pretty ConsoleRenderer looks for `event`
+        shared_processors.append(rename_event_key)
+        # Format the exception only for JSON logs, as we want to pretty-print them when
+        # using the ConsoleRenderer
+        shared_processors.append(structlog.processors.format_exc_info)
+
+    structlog.configure(
+        processors=shared_processors
+        + [
+            # Prepare event dict for `ProcessorFormatter`.
+            structlog.stdlib.ProcessorFormatter.wrap_for_formatter,
+        ],
+        logger_factory=structlog.stdlib.LoggerFactory(),
+        cache_logger_on_first_use=True,
+    )
+
+    log_renderer: structlog.types.Processor
+    if json_logs:
+        log_renderer = structlog.processors.JSONRenderer()
+    else:
+        log_renderer = structlog.dev.ConsoleRenderer()
+
+    formatter = structlog.stdlib.ProcessorFormatter(
+        # These run ONLY on `logging` entries that do NOT originate within
+        # structlog.
+        foreign_pre_chain=shared_processors,
+        # These run on ALL entries after the pre_chain is done.
+        processors=[
+            # Remove _record & _from_structlog.
+            structlog.stdlib.ProcessorFormatter.remove_processors_meta,
+            log_renderer,
+        ],
+    )
+
+    handler = logging.StreamHandler()
+    # Use OUR `ProcessorFormatter` to format all `logging` entries.
+    handler.setFormatter(formatter)
+    root_logger = logging.getLogger()
+    root_logger.addHandler(handler)
+    root_logger.setLevel(log_level.upper())
+
+    for _log in ["uvicorn", "uvicorn.error"]:
+        # Clear the log handlers for uvicorn loggers, and enable propagation
+        # so the messages are caught by our root logger and formatted correctly
+        # by structlog
+        logging.getLogger(_log).handlers.clear()
+        logging.getLogger(_log).propagate = True
+
+    # Since we re-create the access logs ourselves, to add all information
+    # in the structured log (see the `logging_middleware` in main.py), we clear
+    # the handlers and prevent the logs to propagate to a logger higher up in the
+    # hierarchy (effectively rendering them silent).
+    logging.getLogger("uvicorn.access").handlers.clear()
+    logging.getLogger("uvicorn.access").propagate = False
+
+    def handle_exception(exc_type, exc_value, exc_traceback):  # type: ignore[no-untyped-def]
+        """
+        Log any uncaught exception instead of letting it be printed by Python
+        (but leave KeyboardInterrupt untouched to allow users to Ctrl+C to stop)
+        See https://stackoverflow.com/a/16993115/3641865
+        """
+        if issubclass(exc_type, KeyboardInterrupt):
+            sys.__excepthook__(exc_type, exc_value, exc_traceback)
+            return
+
+        root_logger.error("Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback))
+
+    sys.excepthook = handle_exception

opentrons-ai-server/api/handler/fast.py

@@ -1,30 +1,37 @@
 import asyncio
 import os
+import time
 from typing import Any, Awaitable, Callable, List, Literal, Union
 
-import ddtrace
+import structlog
+from asgi_correlation_id import CorrelationIdMiddleware
+from asgi_correlation_id.context import correlation_id
 from ddtrace import tracer
+from ddtrace.contrib.asgi.middleware import TraceMiddleware
 from fastapi import FastAPI, HTTPException, Query, Request, Response, Security, status
 from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
 from fastapi.responses import HTMLResponse, JSONResponse
 from pydantic import BaseModel, Field, conint
 from starlette.middleware.base import BaseHTTPMiddleware
+from uvicorn.protocols.utils import get_path_with_query_string
 
 from api.domain.openai_predict import OpenAIPredict
-from api.handler.logging_config import get_logger, setup_logging
+from api.handler.custom_logging import setup_logging
 from api.integration.auth import VerifyToken
 from api.models.chat_request import ChatRequest
 from api.models.chat_response import ChatResponse
 from api.models.empty_request_error import EmptyRequestError
 from api.models.internal_server_error import InternalServerError
 from api.settings import Settings
 
-setup_logging()
-logger = get_logger(__name__)
-ddtrace.patch(logging=True)
 settings: Settings = Settings()
+setup_logging(json_logs=settings.json_logging, log_level=settings.log_level.upper())
+
+access_logger = structlog.stdlib.get_logger("api.access")
+logger = structlog.stdlib.get_logger(settings.logger_name)
+
 auth: VerifyToken = VerifyToken()
 openai: OpenAIPredict = OpenAIPredict(settings)
 
@@ -75,6 +82,61 @@ async def dispatch(self, request: Request, call_next: Any) -> JSONResponse | Any
 app.add_middleware(TimeoutMiddleware, timeout_s=178)
 
 
+@app.middleware("http")
+async def logging_middleware(request: Request, call_next) -> Response:  # type: ignore[no-untyped-def]
+    structlog.contextvars.clear_contextvars()
+    # These context vars will be added to all log entries emitted during the request
+    request_id = correlation_id.get()
+    structlog.contextvars.bind_contextvars(request_id=request_id)
+
+    start_time = time.perf_counter_ns()
+    # If the call_next raises an error, we still want to return our own 500 response,
+    # so we can add headers to it (process time, request ID...)
+    response = Response(status_code=500)
+    try:
+        response = await call_next(request)
+    except Exception:
+        structlog.stdlib.get_logger("api.error").exception("Uncaught exception")
+        raise
+    finally:
+        process_time = time.perf_counter_ns() - start_time
+        status_code = response.status_code
+        url = get_path_with_query_string(request.scope)  # type: ignore[arg-type]
+        client_host = request.client.host if request.client and request.client.host else "unknown"
+        client_port = request.client.port if request.client and request.client.port else "unknown"
+        http_method = request.method if request.method else "unknown"
+        http_version = request.scope["http_version"]
+        # Recreate the Uvicorn access log format, but add all parameters as structured information
+        access_logger.info(
+            f"""{client_host}:{client_port} - "{http_method} {url} HTTP/{http_version}" {status_code}""",
+            http={
+                "url": str(request.url),
+                "status_code": status_code,
+                "method": http_method,
+                "request_id": request_id,
+                "version": http_version,
+            },
+            network={"client": {"ip": client_host, "port": client_port}},
+            duration=process_time,
+        )
+        response.headers["X-Process-Time"] = str(process_time / 10**9)
+    return response
+
+
+# This middleware must be placed after the logging, to populate the context with the request ID
+# NOTE: Why last??
+# Answer: middlewares are applied in the reverse order of when they are added (you can verify this
+# by debugging `app.middleware_stack` and recursively drilling down the `app` property).
+app.add_middleware(CorrelationIdMiddleware)
+
+tracing_middleware = next((m for m in app.user_middleware if m.cls == TraceMiddleware), None)
+if tracing_middleware is not None:
+    app.user_middleware = [m for m in app.user_middleware if m.cls != TraceMiddleware]
+    structlog.stdlib.get_logger("api.datadog_patch").info("Patching Datadog tracing middleware to be the outermost middleware...")
+    app.user_middleware.insert(0, tracing_middleware)
+    app.middleware_stack = app.build_middleware_stack()
+
+
 # Models
 class Status(BaseModel):
     status: Literal["ok", "error"]
@@ -134,7 +196,7 @@ async def create_chat_completion(
         return ChatResponse(reply=response, fake=body.fake)
 
     except Exception as e:
-        logger.exception(e)
+        logger.exception("Error processing chat completion")
         raise HTTPException(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=InternalServerError(exception_object=e).model_dump()
         ) from e
@@ -143,7 +205,7 @@ async def create_chat_completion(
 @app.get(
     "/health",
     response_model=Status,
-    summary="LB Health Check",
+    summary="Load Balancer Health Check",
     description="Check the health and version of the API.",
     include_in_schema=False,
 )
@@ -154,10 +216,14 @@ async def get_health(request: Request) -> Status:
 
     - **returns**: A Status containing the version of the API.
     """
-    logger.debug(f"{request.method} {request.url.path}")
+    if request.url.path == "/health":
+        pass  # This is a health check from the load balancer
+    else:
+        logger.info(f"{request.method} {request.url.path}", extra={"requestMethod": request.method, "requestPath": request.url.path})
     return Status(status="ok", version=settings.dd_version)
 
 
+@tracer.wrap()
 @app.get("/api/timeout", response_model=TimeoutResponse)
 async def timeout_endpoint(request: Request, seconds: conint(ge=1, le=300) = Query(..., description="Number of seconds to wait")):  # type: ignore # noqa: B008
     """