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How to build a function calling schema dict in Python
Build an OpenAI-compatible function calling schema dictionary with a helper function that takes name, description, parameters, and required fields.
import json
from typing import Dict, Any, List, Optional
def build_function_schema(
name: str,
description: str,
parameters: Optional[Dict[str, Any]] = None,
required: Optional[List[str]] = None
) -> Dict[str, Any]:
"""Build an OpenAI-compatible function calling schema dictionary."""
schema: …How to cache embeddings with a Python dict to avoid recomputation
Caches embeddings computed from text in a dictionary keyed by SHA-256 hash, returning cached results for repeated calls.
import hashlib
import time
class EmbeddingCache:
def __init__(self):
self.cache = {}
def _hash_text(self, text):
return hashlib.sha256(text.encode()).hexdigest()
def get_embedding(self, text, compute_func):
key = self._hash_text(text)
if key not in self.cache:
…How to compute exact match metric in Python
Computes the exact match (EM) metric for LLM outputs by normalizing text and comparing predictions against references.
def compute_exact_match(predictions, references):
def normalize(text):
import re
text = text.lower().strip()
text = re.sub(r'\b(a|an|the)\b', ' ', text)
text = re.sub(r'[^a-z0-9\s]', '', text)
text = ' '.join(text.split())
return text
matches = sum(1 for pred, r…How to parallel map embeddings with a thread pool in Python
Run embedding computations in parallel using ThreadPoolExecutor, collect results into a dict keyed by the original item.
import threading
from concurrent.futures import ThreadPoolExecutor
import time
def compute_embedding(item: int) -> tuple[int, int]:
time.sleep(0.05) # Simulate embedding work
return item, item * 10
def parallel_map_embed(items, max_workers=3):
results = {}
with ThreadPoolExecutor(max_workers=max_w…How to parse JSON in Python: A Beginner's Guide with Code Examples
This guide shows you how to parse JSON data in Python step by step, with practical code examples and expected outputs.
import json
from typing import Any, Dict, List, Optional
class DataHelper:
"""Beginner-friendly helper for common AI/LLM data tasks."""
def __init__(self, data: Optional[Dict[str, Any]] = None):
self.data = data or {}
def to_prompt(self, template: str) -> str:
"""Format a prompt…How to randomly assign a prompt variant to each key in Python
Randomly pick one variant from a list for each prompt key, useful for A/B testing message variations.
import random
def assign_prompt_variant(prompts: dict[str, list[str]]) -> dict[str, str]:
"""Assign a random prompt variant to each prompt key."""
return {key: random.choice(variants) for key, variants in prompts.items()}
if __name__ == "__main__":
prompt_bank = {
"greeting": ["Hello!", "Hi there…Route Tool Call Name to Python Handler Dict
Routes a tool call name to the correct Python handler function using a dictionary lookup, returning an error for unknown tools.
def get_name():
return {"name": "Alice"}
def get_age():
return {"age": 30}
def get_email():
return {"email": "alice@example.com"}
handlers = {
"get_name": get_name,
"get_age": get_age,
"get_email": get_email,
}
def route(tool_call):
handler = handlers.get(tool_call["name"])
if handl…Aggregate Log Errors Count by Hour in Python
Counts ERROR log lines per hour using regex and Counter, returning a sorted dictionary of hourly totals.
import re
from collections import Counter
from datetime import datetime
def aggregate_errors_by_hour(log_lines):
pattern = re.compile(r'^(\d{4}-\d{2}-\d{2} \d{2}):\d{2}:\d{2}.*ERROR')
hourly_counts = Counter()
for line in log_lines:
match = pattern.match(line)
if match:
ho…Fetch weather API mock and write dashboard HTML in Python
This script fetches a mock weather API response as a Python dict, builds a simple HTML dashboard, writes it to a file, and prints both the file path and JSON payload.
from datetime import datetime
import json
import os
def fetch_weather_mock(city: str) -> dict:
"""Return a mock weather payload for a given city."""
return {
"city": city,
"temperature_c": 21.5,
"condition": "Partly Cloudy",
"humidity": 58,
"wind_kph": 12.3,
"u…Fill PDF Form Fields from a Mock Template in Python
Fills a PDF-style form template dictionary with user data, preserving template fields and formatting output as JSON.
import json
template = {
"first_name": "",
"last_name": "",
"email": "",
"phone": "",
"date_of_birth": "",
"address": "",
"city": "",
"state": "",
"zip_code": "",
"agree_to_terms": False
}
def fill_pdf_form(template: dict, data: dict) -> dict:
for key, value in data.items…Generate Random Fake User Data for Testing in Python
This code generates a list of fake user dictionaries with random names, emails, ages, and timestamps using the Python standard library for testing purposes.
import json
import random
import string
from datetime import datetime, timedelta
def generate_user_data(num_users=1):
first_names = ["Alice", "Bob", "Charlie", "Diana", "Eve"]
last_names = ["Smith", "Johnson", "Brown", "Taylor", "Wilson"]
domains = ["example.com", "test.org", "demo.net"]
users = …How to Import Users from CSV into LDAP-like Dicts in Python
Reads a CSV of user records and converts each row into an LDAP-style dictionary with standard attributes using Python's csv module.
import csv
import io
from pathlib import Path
def mock_ldap_import(csv_path):
"""
Reads a CSV file with user data and returns a list of LDAP-like user dicts.
Adds standard LDAP attributes that would come from directory schema.
"""
with open(csv_path, newline="", encoding="utf-8") as csvfile:
…How to Map Network Drive Paths to Local Paths in Python
Convert mock SMB network drive paths (like 'S:\reports\q1.xlsx') to local placeholder paths and back using a simple mapping dictionary in Python.
"""Map mock SMB network drive paths to local placeholder paths."""
from dataclasses import dataclass
@dataclass(frozen=True)
class NetworkDrive:
letter: str
remote_path: str
DRIVES = {
"S:": NetworkDrive("S", r"\\server01\shares\sales"),
"M:": NetworkDrive("M", r"\\server02\media\movies"),
"X:": …Attach Source File Metadata to Records in Python
Add a source filename field to each record in a list by merging a new key into every dictionary using a dict unpacking comprehension.
from pathlib import Path
import json
def attach_source_metadata(records, source_file):
"""Attach source filename metadata to each record."""
return [
{**record, "source": Path(source_file).name}
for record in records
]
if __name__ == "__main__":
source = "/data/raw/customers.csv"
…Count Records Processed per Category in Python
Use a Counter dictionary to track how many records of each type (ok, error, retry) were processed in a data pipeline.
from collections import Counter
import random
processed_counter = Counter()
def process_records(records):
for record in records:
processed_counter[record] += 1
return len(records)
if __name__ == "__main__":
sample_records = [random.choice(["ok", "error", "retry"]) for _ in range(10)]
print(f…Deduplicate events by ID within a window in Python
Deduplicate event streams by ID within sliding time windows, keeping the newest occurrence per window using heaps and sets.
import heapq
from collections import defaultdict
def deduplicate_events(events, window_size):
"""Return events deduplicated by id, keeping newest within each sliding window."""
# Index events by (timestamp, id) for deterministic ordering
events_by_id = defaultdict(list)
for ts, eid, *payload in events…ETL in Python: Extract CSV, Transform Dict, Load JSON
Build a simple ETL pipeline in Python that reads a CSV file, transforms each row (stripping whitespace and converting numeric fields), and writes the result to JSON.
import csv
import json
from pathlib import Path
def extract_csv(file_path):
"""Read CSV file and return list of row dictionaries."""
with Path(file_path).open('r', newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
return list(reader)
def transform_dicts(rows):
"""Transform ro…ETL in Python: Extract CSV, Transform Dicts, Load JSON
Build a simple ETL pipeline that reads a CSV, normalizes keys and converts price to float, then writes structured JSON.
import csv
import json
from pathlib import Path
def etl_csv_to_json(csv_path: str, json_path: str) -> None:
"""Extract CSV, transform rows to dicts, load to JSON."""
with open(csv_path, mode='r', newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
records = list(reader)
# Trans…Enrich Events with Geo IP Data in Python
Returns a copy of each event dictionary, enriched with a geo-location dict from a mock IP-to-geo lookup table, with a fallback for unknown IPs.
import ipaddress
GEO_IP_DB = {
"192.168.1.10": {"country": "US", "city": "New York", "lat": 40.7128, "lon": -74.0060},
"10.0.0.5": {"country": "DE", "city": "Berlin", "lat": 52.5200, "lon": 13.4050},
"172.16.0.8": {"country": "JP", "city": "Tokyo", "lat": 35.6762, "lon": 139.6503},
}
EVENTS = [
{"id…Enrich a stream with reference data by key lookup in Python
Uses streamz to join each incoming record to a reference dictionary by name, adding department and level fields or defaults.
from streamz import Stream
reference = {"alice": {"dept": "eng", "level": 3}, "bob": {"dept": "sales", "level": 5}}
def enrich(record):
name = record.get("name")
ref = reference.get(name)
joined = dict(record)
if ref:
joined.update(ref)
else:
joined["dept"] = "unknown"
joi…Fan Out Records to Multiple Sinks in Python
Distribute the same records across multiple target sinks (database, API, queue, etc.) using a defaultdict-based fan-out pattern.
import json
from collections import defaultdict
SINKS = ["database", "api", "message_queue", "data_lake", "monitoring"]
def fan_out(records, *sinks):
dist = defaultdict(list)
for record in records:
for sink in sinks:
dist[sink].append(record)
return dict(dist)
if __name__ == "__main_…Filter Records by Required Fields in Python
Filter a list of dictionaries, keeping only records where every required field is present and not None.
def filter_records(records, required_fields):
"""Return only records that have all required fields non-null."""
return [
record for record in records
if all(record.get(field) is not None for field in required_fields)
]
if __name__ == "__main__":
sample_records = [
{"name": "Al…How to Count Events by Minute with a Tumbling Window in Python
Group timestamps into fixed 60-second tumbling windows and count events per bucket using a dict.
from collections import defaultdict
from datetime import datetime, timedelta
def tumbling_window_count(events, window_seconds=60):
buckets = defaultdict(int)
for event in events:
ts = datetime.fromisoformat(event["timestamp"])
bucket_start = ts - timedelta(seconds=ts.second % window_seconds,
…How to Count JSON Records in Python
Read a JSON file and count the number of top-level records, handling both list and dictionary structures.
import json
from pathlib import Path
def count_records(json_file):
"""Count top-level records in a JSON file."""
with open(json_file, "r") as f:
data = json.load(f)
# Handle both list of records and dict of records
if isinstance(data, list):
return len(data)
elif isinstance(da…Browse by section
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