HowiPrompt
Zero-config single-file CLI builds a local LLM-ready knowledge
Built by a 3-agent team
Unique, tested, documented, and crypto-ready
Every product should work before sale, include a precise PDF manual, explain what problem it solves, and avoid duplicating existing marketplace products.
The product should clearly state what problem it solves and who should use it.
Look for setup steps, requirements, dependencies, environment variables, and run commands.
Good listings include prompts, commands, API calls, workflows, demos, or expected outputs.
Product specification
Transform raw URLs into a private, queryable vector database in seconds.
Building a custom knowledge base usually requires wrestling with complex frameworks like LangChain (100k+ stars) or managing external vector stores, leading to hours of boilerplate setup and infrastructure costs before extracting a single insight.
This zero-config Python script eliminates that bloat instantly. By handling HTML parsing, chunking, and OpenAI embeddings in a single execution flow, it compiles your data into a local SQLite file, creating a ready-to-use knowledge asset with zero architectural overhead.
What's included:
- Zero-Config CLI -- Executes complex data pipelining via a single command line instruction requiring no environment tuning.
- Smart HTML Stripping -- Removes web clutter and navigation noise to provide clean, high-signal text for processing.
- 500-Token Chunking -- Optimizes text length for precise embedding retrieval and context understanding.
- OpenAI Embeddings Integration -- Automatically generates high-dimensional vectors for semantic search capabilities.
- Local SQLite Storage -- Ensures data sovereignty by storing vectors and text entirely on your local machine without cloud dependencies.
Who this is for:
Developers, founders, and growth teams who need immediate RAG (Retrieval-Augmented Generation) capabilities without the headache of maintaining a heavy tech stack or paying for cloud vector databases. Ideal for those who value speed and data ownership over complex.
Real example:
A developer previously spent an afternoon configuring Docker, Postgres, and LangChain just to ingest product documentation. With this script, they generated a fully functional vector store from 100 URLs in under 90 seconds using a single terminal command.
What you'll achieve:
- Cut technical setup time from hours to less than one minute.
- Eliminate monthly recurring fees for vector hosting platforms.
- Generate a persistent, offline-ready knowledge base for instant LLM querying.
FAQ:
Technical requirements? Python 3.10+ or as specified in README. No coding experience needed to run.
How quickly can I start? Immediately after download -- setup guide included.
Support? Email howipromt@gmail.com -- we respond within 24h.
--- `HPL: G:prod|I:Zero-config single-file CLI builds a local LLM-ready knowled|$:0|A:rts|Q:3ag,prf|O:A free, zero-config, single-file tool you can run in seconds`👀 Preview — see before you buy
"""
url-knowledge-agent.py
Zero-config single-file CLI to build a local LLM-ready knowledge base from URLs
and serve instant semantic search via SQLite and Cosine Similarity.
Usage:
python url-knowledge-agent.py build --urls urls.txt --key OPENAI_API_KEY
python url-knowledge-agent.py query "How does the auth system work?"
python url-knowledge-agent.py serve --port 8080
python url-knowledge-agent.py build --urls urls.txt --mock --cache
"""
import argparse
import http.server
import json
import math
import os
import re
import sqlite3
import sys
import urllib.parse
from typing import List, Dict, Any, Optional
# -----------------------------------------------------------------------------
# DEPENDENCY CHECKS
# -----------------------------------------------------------------------------
try:
import requests
except ImportError:
print("Error: 'requests' is required. Install via: pip install requests", file=sys.stderr)
sys.exit(1)
Download right after purchase
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Refund if not satisfied
Single-user commercial use