| # -*- coding: utf-8 -*- | |
| """ | |
| doc_agent.py: A Python script implementing a Retrieval-Augmented Generation (RAG) | |
| agent specifically designed for querying technical documentation (e.g., from | |
| Read the Docs source files like .rst or .txt). | |
| Purpose: To provide more accurate and context-aware answers to questions | |
| about a specific software package or project than traditional keyword search, | |
| by leveraging an LLM grounded with retrieved documentation snippets. |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>RTD Search Agent (Standalone)</title> | |
| <script src="https://cdn.tailwindcss.com"></script> | |
| <link rel="preconnect" href="https://fonts.googleapis.com"> | |
| <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> | |
| <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet"> |
| import os | |
| import sys | |
| import argparse | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('infile') | |
| parser.add_argument('padfile') | |
| parser.add_argument('outfile') | |
| chunksize = 8192 |
| function snip() { | |
| PS1="\n\[\033[35m\]\$(/bin/date)\n\[\033[32m\]\w\n\[\033[1;31m\]\u@\h: \[\033[1;34m\]\$(/usr/bin/tty | /bin/sed -e 's:/dev/::'): \[\033[1;36m\]\$(/bin/ls -1 | /usr/bin/wc -l | /bin/sed 's: ::g') files \[\033[1;33m\]\$(/bin/ls -lah | /bin/grep -m 1 total | /bin/sed 's/total //')b\[\033[0m\] -> \[\033[0m\] " | |
| } | |
| function swog() { | |
| PS1="\[\e[36;45m\]▓\[\e[m\]\[\e[36;45m\]▒\[\e[m\]\[\e[36;45m\]░\[\e[m\]\[\e[36;45m\]\u\[\e[m\]\[\e[36;45m\]@\[\e[m\]\[\e[36;45m\]\h\[\e[m\]\[\e[36;45m\]:\[\e[m\]\[\e[35;46m\]\w\[\e[m\]\[\e[35;46m\]░\[\e[m\]\[\e[35;46m\]▒\[\e[m\]\[\e[35;46m\]▓\[\e[m\]>>> " | |
| } |
| """ | |
| Replace following four appropriately first: | |
| yourname yourcompany http://yourcompany.com http://yourcompany.com/logo.png | |
| """ | |
| import os | |
| import sys | |
| import urllib | |
| import argparse | |
| parser = argparse.ArgumentParser() |
| #include <stdio.h> | |
| #include <stdlib.h> | |
| #include <stdint.h> | |
| #ifdef _MSC_VER | |
| #include <intrin.h> /* for rdtscp and clflush */ | |
| #pragma optimize("gt",on) | |
| #else | |
| #include <x86intrin.h> /* for rdtscp and clflush */ | |
| #endif |
| import os | |
| import sys | |
| import torch | |
| import librosa | |
| import numpy as np | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| from torch.autograd import Variable | |
| from sklearn.model_selection import train_test_split |
| import cv2 | |
| import sys | |
| import numpy as np | |
| from scipy import signal | |
| import matplotlib.pyplot as plt | |
| def get_wld_feats(gray_img): | |
| f00 =np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]]) | |
| f10 = np.array([[0, -1, 0], [0, 0, 0], [0, 1, 0]]) | |
| f11 = np.array([[0, 0, 0], [1, 0, -1], [0, 0, 0]]) |
