An overview of popular techniques to confine LLMs' output to a predefined schema, covering API providers, prompting/reprompting strategies, and constrained decoding.
A list of 10 Python libraries that can improve your workflow and make coding easier.
1. **boltons:** Collection of useful Python utilities, filling gaps in the standard library.
2. **tenacity:** Elegant retrying mechanism for functions that might fail.
3. **diskcache:** Persistent caching system backed by a disk (SQLite).
4. **glom:** Toolkit for easily accessing nested data structures (like JSON).
5. **tqdm.contrib.concurrent:** Multi-threaded progress bars using `tqdm`.
6. **anyio:** Compatibility layer for asynchronous Python code across different libraries.
7. **deepdiff:** Detects differences between Python objects (dicts, lists, etc.).
8. **pyrsistent:** Immutable data structures for functional programming.
9. **structlog:** Library for creating structured, parseable logs.
10. **pyinstrument:** Python profiler that generates flamegraphs for performance analysis.
1. **boltons:** Collection of useful Python utilities, filling gaps in the standard library.
2. **tenacity:** Elegant retrying mechanism for functions that might fail.
3. **diskcache:** Persistent caching system backed by a disk (SQLite).
4. **glom:** Toolkit for easily accessing nested data structures (like JSON).
5. **tqdm.contrib.concurrent:** Multi-threaded progress bars using `tqdm`.
6. **anyio:** Compatibility layer for asynchronous Python code across different libraries.
7. **deepdiff:** Detects differences between Python objects (dicts, lists, etc.).
8. **pyrsistent:** Immutable data structures for functional programming.
9. **structlog:** Library for creating structured, parseable logs.
10. **pyinstrument:** Python profiler that generates flamegraphs for performance analysis.
Model Context Protocol server to run Python code in a sandbox using Pyodide in Deno, isolated from the operating system.
This article explores ten underrated Python libraries that can help automate tasks, debug faster, and improve coding efficiency.
- **Rich**: Terminal beautification
- **PyWhatKit**: Automation tasks
- **Pydantic**: Data validation
- **Black**: Code formatting
- **HTTPie**: API testing
- **Typer**: Building CLI applications
- **IceCream**: Debugging
- **Poetry**: Package management
- **Faker**: Generating fake data
- **Pyppeteer**: Browser automation
- **Rich**: Terminal beautification
- **PyWhatKit**: Automation tasks
- **Pydantic**: Data validation
- **Black**: Code formatting
- **HTTPie**: API testing
- **Typer**: Building CLI applications
- **IceCream**: Debugging
- **Poetry**: Package management
- **Faker**: Generating fake data
- **Pyppeteer**: Browser automation
An analysis showing that structured outputs can sometimes perform worse than unstructured ones in certain tasks for different LLM models, emphasizing the importance of testing both approaches.
# main.py
import json
from pydantic import BaseModel, EmailStr, ValidationError, validator
class Employee(BaseModel):
name: str
age: int
email: EmailStr
department: str
employee_id: str
@validator("employee_id")
def validate_employee_id(cls, v):
if not v.isalnum() or len(v) != 6:
raise ValueError("Employee ID must be exactly 6 alphanumeric characters")
return v
# Load and parse the JSON data
with open("employees.json", "r") as f:
data = json.load(f)
# Validate each employee record
for record in data:
try:
employee = Employee(**record)
print(f"Valid employee record: {employee.name}")
except ValidationError as e:
print(f"Invalid employee record: {record 'name' » }")
print(f"Errors: {e.errors()}"
import json
from pydantic import BaseModel, EmailStr, ValidationError, validator
class Employee(BaseModel):
name: str
age: int
email: EmailStr
department: str
employee_id: str
@validator("employee_id")
def validate_employee_id(cls, v):
if not v.isalnum() or len(v) != 6:
raise ValueError("Employee ID must be exactly 6 alphanumeric characters")
return v
# Load and parse the JSON data
with open("employees.json", "r") as f:
data = json.load(f)
# Validate each employee record
for record in data:
try:
employee = Employee(**record)
print(f"Valid employee record: {employee.name}")
except ValidationError as e:
print(f"Invalid employee record: {record 'name' » }")
print(f"Errors: {e.errors()}"
