Natuurlijk! Hier is een uitgebreide aanpak voor het ontwerpen van een OpenAI-integratie met robuuste foutafhandeling en herstelmechanismen, afgestemd op je technische omgeving (Python met Flask en Celery): --- **1. Soorten fouten en uitzonderingen om op te vangen** - **Time-outs**: Wanneer een API-call te lang duurt of geen antwoord krijgt. - **Netwerkfouten**: Bijvoorbeeld verbindingsproblemen, DNS-fouten, of verloren verbinding. - **API limiet overschrijdingen (rate limits)**: Wanneer je het toegestane aantal aanvragen per tijdseenheid overschrijdt. - **Fouten van de API zelf**: Bijvoorbeeld 4xx of 5xx statuscodes zoals 429 (Too Many Requests), 500 (Server Error), 503 (Service Unavailable). - **Onverwachte uitzonderingen**: Bijvoorbeeld Python-fouten in je code of JSON parsing errors. --- **2. Herstelstrategieën** - **Exponentiële retries**: Herhaal de API-aanroep met toenemende wachttijden bij tijdelijke fouten zoals time-outs of 5xx-fouten. - **Fallbacks**: Gebruik alternatieve data of services indien de OpenAI API niet beschikbaar is. - **Notificaties**: Stuur e-mail of log fouten voor latere analyse en monitoring. --- **3. Technische implementatie in Python (Flask + Celery)** **a. OpenAI API selecties** - Gebruik het meest betrouwbare model op dat moment, bijvoorbeeld: - `gpt-3.5-turbo` voor kosten-efficiëntie en goede betrouwbaarheid. - Of `gpt-4` voor meer complexiteit en precisie (mits je API-limieten dat toelaten). - API functies: - `chat/completions` voor conversaties. - `edits` of `images` indien nodig. **b. API call voorbeeld met foutafhandeling** ```python import openai import time import logging from smtplib import SMTP # Configuratie MAX_RETRIES = 5 INITIAL_BACKOFF = 1 # seconde def send_email_alert(subject, message): # Eenvoudige functie voor e-mail notificaties with SMTP('smtp.example.com') as smtp: smtp.login('user', 'password') smtp.sendmail('from@example.com', 'to@example.com', f"Subject: {subject}\n\n{message}") def call_openai_with_retries(prompt): retries = 0 backoff = INITIAL_BACKOFF while retries < MAX_RETRIES: try: response = openai.ChatCompletion.create( model='gpt-3.5-turbo', messages=[{"role": "user", "content": prompt}], timeout=10 # seconds ) return response except openai.error.Timeout as e: logging.warning(f"Timeout bij API call: {e}") except openai.error.APIError as e: logging.warning(f"API Error: {e}") if e.http_status == 429: # Rate limit overschrijding send_email_alert("API Rate Limit Overschreden", str(e)) time.sleep(backoff) backoff *= 2 retries += 1 continue elif e.http_status >= 500: # Serverfouten time.sleep(backoff) backoff *= 2 retries += 1 continue else: # Andere API fouten send_email_alert("OpenAI API fout", str(e)) break except (openai.error.APIConnectionError, openai.error.ServiceUnavailableError) as e: # Netwerkfouten logging.warning(f"Netwerkfout: {e}") time.sleep(backoff) backoff *= 2 retries += 1 continue except Exception as e: # Onverwachte fouten logging.error(f"Onverwachte fout: {e}") send_email_alert("Onverwachte fout in API integratie", str(e)) break # Na alle retries send_email_alert("API Call Fout", f"Na {MAX_RETRIES} retries kon de API niet worden aangeroepen.") return None ``` **c. Integratie in Celery task** ```python from celery import Celery app = Celery('tasks', broker='redis://localhost:6379/0') @app.task(bind=True, max_retries=MAX_RETRIES, default_retry_delay=INITIAL_BACKOFF) def process_prompt(self, prompt): response = call_openai_with_retries(prompt) if response: # Verwerk de response verder return response.choices[0].message.content else: # Fout afgehandeld, mogelijk fallback of error logging return None ``` --- **4. Aanbevolen modellen en API functies voor betrouwbaarheid** - **Modellen**: - `gpt-3.5-turbo`: Sneller en goedkoper, goede betrouwbaarheid. - `gpt-4`: Voor kritieke toepassingen waar hogere betrouwbaarheid en complexiteit nodig zijn (met de juiste API-limieten). - **API functies**: - `chat/completions` met korte, duidelijke prompts. - Gebruik `temperature=0` of laag voor meer consistente output. - Overweeg gebruik van `stream=True` voor asynchrone verwerking en tijdsbesparing. --- **5. Samenvatting** - Vang specifieke fouten zoals time-outs, netwerkfouten, rate limits. - Implementeer retries met exponential backoff. - Stuur notificaties voor kritieke fouten. - Gebruik de meest betrouwbare modellen volgens je use case. - Integreer alles in je Celery tasks voor asynchrone verwerking en schaalbaarheid. --- Heeft u nog aanvullende wensen of vragen over specifieke implementaties?

Of course. Here is a comprehensive guide to designing a robust OpenAI integration for your Python/Flask/Celery environment, focusing on advanced error handling and recovery. ### 1. Types of Errors and Exceptions to Handle You should categorize and handle the following types of errors: | Error Category | Specific Exception/HTTP Code | Description & Cause | | :--- | :--- | :--- | | **Network & Timeouts** | `requests.exceptions.Timeout` `requests.exceptions.ConnectionError` `openai.APITimeoutError` | Unstable network, DNS failure, server taking too long to respond. | | **OpenAI API Errors (4xx)** | `openai.BadRequestError` (400) | Invalid request (e.g., malformed parameters, model not found). | | | `openai.AuthenticationError` (401) | Invalid API key. | | | `openai.PermissionDeniedError` (403) | API key doesn't have permission for the resource. | | | `openai.NotFoundError` (404) | Requested resource (e.g., fine-tuned model) doesn't exist. | | | `openai.ConflictError` (409) | Conflict during resource creation. | | | `openai.UnprocessableEntityError` (422) | Semantic errors in request (e.g., content policy violation). | | **Rate Limits (429)** | `openai.RateLimitError` (429) | You have hit OpenAI's requests-per-minute or tokens-per-minute limit. | | **Server Errors (5xx)** | `openai.InternalServerError` (500+) | An error on OpenAI's servers. | | **API Status** | `openai.APIConnectionError` | Low-level network issue, often before the HTTP request is made. | ### 2. Recovery Strategies Your strategy should be layered, escalating in response to the type and persistence of the error. 1. **Exponential Backoff with Jitter for Retries:** This is the primary strategy for **transient errors** (Timeouts, Rate Limits, 5xx errors). It progressively increases the wait time between retries and adds randomness ("jitter") to prevent all failed tasks from retrying simultaneously (a "thundering herd"). * **Use for:** `Timeout`, `RateLimitError`, `APIConnectionError`, `InternalServerError`. * **Do not use for:** `AuthenticationError`, `PermissionDeniedError`, `BadRequestError` (retrying won't help). 2. **Fallback Responses:** For user-facing requests where a total failure is unacceptable, have a predefined, graceful fallback response. * **Use for:** All error types after retries are exhausted. * **Example:** "I'm experiencing high demand right now. Please try again in a moment. In the meantime, you might find this help article useful: [link]" 3. **Error Alerts & Notifications:** For **non-transient, critical errors** that require immediate developer attention. These should not be retried endlessly. * **Use for:** `AuthenticationError` (invalid API key), `PermissionDeniedError`. * **Implementation:** Send an email (e.g., via SendGrid, Mailgun) or a message to a monitoring platform (e.g., Sentry, Datadog) from within your exception handler. ### 3. Recommended OpenAI Models & API Features * **Models:** For optimal reliability, use the latest stable models (e.g., `gpt-4-turbo-preview`, `gpt-3.5-turbo`). They are hosted on OpenAI's most robust infrastructure. * **API Features:** * **`timeout` parameter:** **Crucially important.** Always set a reasonable timeout (e.g., 10-30 seconds) on your requests to prevent your application from hanging indefinitely. * **`max_retries` parameter:** You can set this in the OpenAI client, but for more advanced control (like exponential backoff), it's better to implement your own retry logic as shown below. ### 4. Implementation Example for Your Tech Stack This example combines Flask for the web layer and Celery for executing the async, retry-able task. We'll use the official `openai` Python library and the `tenacity` library for robust retry logic. #### Step 1: Install Dependencies ```bash pip install openai flask celery tenacity blinker python-dotemail ``` #### Step 2: Configure the OpenAI Client and Celery `app/config.py` or similar: ```python import os import openai from celery import Celery # Configure OpenAI openai.api_key = os.getenv('OPENAI_API_KEY') # Configure Celery (example using Redis as broker) celery = Celery( __name__, broker=os.getenv('REDIS_URL', 'redis://localhost:6379/0'), backend=os.getenv('REDIS_URL', 'redis://localhost:6379/0') ) ``` #### Step 3: Define the Robust Celery Task with Tenacity `app/tasks.py`: ```python from celery import shared_task from tenacity import ( retry, stop_after_attempt, wait_exponential_jitter, retry_if_exception_type, ) import openai from openai import OpenAIError, RateLimitError, APITimeoutError, APIConnectionError, InternalServerError from app import celery, mail # Assume 'mail' is a Flask-Mail instance from flask_mail import Message # Define which exceptions are worth retrying RETRYABLE_EXCEPTIONS = (RateLimitError, APITimeoutError, APIConnectionError, InternalServerError) @retry( # Stop after 5 attempts total stop=stop_after_attempt(5), # Wait: 2^1 * 1s, 2^2 * 1s, ... up to 10s max, plus random jitter wait=wait_exponential_jitter(initial=1, expiration=10, jitter=1), # Only retry on these specific exceptions retry=retry_if_exception_type(RETRYABLE_EXCEPTIONS), ) def _make_openai_api_call_with_retry(messages, model="gpt-3.5-turbo"): """Internal function with tenacity retry logic.""" client = openai.OpenAI() try: response = client.chat.completions.create( model=model, messages=messages, timeout=10.0, # Critical: Set a request timeout ) return response.choices[0].message.content except RETRYABLE_EXCEPTIONS as e: # This will be caught by tenacity and retried print(f"Retryable error: {e}. Retrying...") raise # Re-raise for tenacity to handle # Non-retryable errors are handled in the main task function @shared_task(bind=True, max_retries=None) # max_retries handled by tenacity def get_chat_completion_task(self, messages, user_email=None): """Main Celery task to get a completion with full error handling.""" try: result = _make_openai_api_call_with_retry(messages) return result except openai.AuthenticationError: # CRITICAL: Alert developers immediately. Do not retry. msg = Message("OpenAI Authentication Failed - ACTION REQUIRED", sender="admin@your-app.com", recipients=["dev-team@your-company.com"]) msg.body = "The OpenAI API key is invalid or has been revoked." mail.send(msg) # Abort the task completely return None except (openai.BadRequestError, openai.PermissionDeniedError) as e: # Client error. Log it for debugging but don't retry. print(f"Non-retryable client error: {e}") return None except Exception as e: # Catch any other unexpected errors. # You could trigger a final retry from Celery itself here if desired. print(f"Unexpected error in task {self.request.id}: {e}") # self.retry(exc=e, countdown=60) # Optional: one final retry after 60s return None ``` #### Step 4: Create the Flask Endpoint `app/routes.py`: ```python from flask import request, jsonify, current_app from app.tasks import get_chat_completion_task @ app.route('/chat', methods=['POST']) def chat(): user_message = request.json.get('message') user_email = request.json.get('email') # For potential notifications if not user_message: return jsonify({'error': 'No message provided'}), 400 # Build the prompt/messages messages = [{"role": "user", "content": user_message}] # Send the task to Celery for async processing task = get_chat_completion_task.apply_async( args=[messages], kwargs={'user_email': user_email} ) # Return immediately with the task ID return jsonify({'task_id': task.id}), 202 @ app.route('/task-status/<task_id>', methods=['GET']) def get_task_status(task_id): task = get_chat_completion_task.AsyncResult(task_id) response = {'task_id': task_id, 'status': task.status} if task.status == 'SUCCESS': response['result'] = task.result # If task.result is None, it means it failed and we should show a fallback if task.result is None: response['fallback'] = "Apologies, our AI service is temporarily unavailable. Please try your request again shortly." elif task.status == 'FAILURE': response['fallback'] = "We encountered an unexpected issue. Our team has been notified." return jsonify(response) ``` ### Summary of the Flow: 1. **User** sends a message to `/chat`. 2. **Flask** validates the input and queues the `get_chat_completion_task` in Celery, returning a `task_id`. 3. **Celery Worker** picks up the task. 4. **Tenacity** manages the retry logic for transient errors (`RateLimitError`, timeouts, etc.). 5. **On Success:** The result is stored, and the user can fetch it via `/task-status/<task_id>`. 6. **On Critical Failure (e.g., Auth Error):** An email alert is sent to the dev team, and the task aborts. 7. **On Ultimate Failure (after all retries):** The user receives a graceful fallback message when they check the task status. This design ensures your application remains responsive, resilient to temporary outages, and provides a good user experience even when backend services are failing.