POST

refresh_token = token['refresh_token'] expires_in = token['expires_in'] user_id = request.user.id create_token(access_token, refresh_token, expires_in, user_id) return True ``` ### Step 3: Implement Authentication and A…

By following these steps and best practices, you can ensure that your compliance checks are regularly reviewed and updated, helping to maintain the security and integrity of your system. [Turn 1362] User: I'm trying to design a risk API th…

return jsonify({"message": "Report created successfully", "id": report.id}), 201 except SQLAlchemyError as e: db.session.rollback() return jsonify({"error": "Database error occurred"}), 500 @app.route("/api/v1/…

print(f"Something went wrong: {err}") ``` ->-> 4,6 [Turn 2445] Assistant: Yes, you can use try-except blocks to handle errors when making API calls to Cohere's API. This approach allows you to catch and handle different types of except…

if authenticate_user(username, password): response = jsonify({'token': 'example_token'}) response.headers['Cache-Control'] = 'public, max-age=60' # Cache for 60 seconds return response else: return j…

1. **Multiple Instances**: You have deployed your Flask application on two servers (`server1` and `server2`). 2. **Nginx Configuration**: - The `upstream` block defines the backend servers. - The `location` block routes requests to t…

[Turn 5312] User: As I continue to learn more about FastAPI and its capabilities, I'm interested in exploring how to implement authentication and authorization in my APIs to restrict access to certain endpoints. Here's a basic example using…

- **Logging**: Use structured logging (e.g., JSON) and forward logs to a centralized logging system like ELK Stack or Grafana Cloud. ### Step 3: Implementation Details #### Load Balancer Configuration - **Nginx Example**: ```nginx h…

# For demonstration, let's assume we have a function `perform_vector_search` results = perform_vector_search(query_vector, top_k) return jsonify(results) api.add_resource(VectorSearch, '/vector-search') ```…

# Perform vector search logic here results = perform_vector_search(query_vector, top_k) return jsonify(results) def post(self): data = request.get_json() query_vector = data.…

{"id": 2, "title": "Title 2", "content": "Content 2"}, ] @app.post("/query", response_model=QueryResponse) def query(request: QueryRequest): # Simulate querying the data store start = request.offset end = request.offset + r…

raise HTTPException(status_code=response.status_code, detail=str(e)) except requests.exceptions.ConnectionError as e: raise HTTPException(status_code=503, detail=str(e)) except requests.exceptions.Timeout as e: …

except Exception as e: raise HTTPException(status_code=500, detail=str(e)) # Function to call dense retrieval @retry(stop=stop_after_attempt(3), wait=wait_fixed(1)) def call_dense_retrieval(query: SearchQuery): try: …

except requests.exceptions.ConnectionError as e: raise HTTPException(status_code=503, detail=str(e)) except requests.exceptions.Timeout as e: raise HTTPException(status_code=504, detail=str(e)) except Exception a…

3. **Create FAISS Index**: - Initialize the FAISS index using `faiss.IndexFlatL2(128)`. 4. **Create Redis Client**: - Create a Redis client using `redis.Redis(host='localhost', port=6379, db=0)`. 5. **Define Tokenization Function**:…

def cache_tokenized_results(results, key='tokenized_results', expire_time=300): serialized_results = pickle.dumps(results) encrypted_results = cipher_suite.encrypt(serialized_results) redis_client.setex(key, expire_time, encrypt…