| .phony: all | |
| all: push | |
| # configuration setup | |
| PROJECT_FILE_NAME=project.yml | |
| CONFIG := python3 -c "import yaml; import json; from pathlib import Path; print(json.dumps(yaml.safe_load(Path(\"$(PROJECT_FILE_NAME)\").read_text())))" | |
| OS := $(shell python3 -c "import platform; print(platform.system())") | |
| # defaults |
| import pandas as pd | |
| def sample_dataframe(df, date_column, N): | |
| """ | |
| Samples a pandas DataFrame to achieve a balanced representation across different date groups. | |
| This function ensures that smaller date groups (those with counts less than an average size) | |
| are fully represented in the sample. Larger date groups are sampled uniformly to contribute | |
| towards a total sample size of N. If the initial sampling process results in a total sample size |
A Python utility that converts natural language time expressions into structured datetime objects using LLMs. Perfect for parsing user inputs like "next Tuesday", "in a few months", or "sometime next summer".
| import argparse | |
| import json | |
| import os | |
| import statistics | |
| import time | |
| from statistics import mean, stdev | |
| from typing import List, Dict | |
| import plotext as plt | |
| from litellm import completion |
Welcome to this guide on using LangChain with tools and chat history management. This guide will explain how to set up and use tools with LangChain, manage chat history, and handle tool interactions within your conversation flow.