A full semantic search tutorial about: data mining with requests and beautifulsoup preprocessing in pandas chunking the document text in smaller paragraphs of the right size for the ML model creating embeddings for each chunk calculating the mean embedding for each document saving data as gzipped json (small file size & easy and fast to read in js with pako.js) creating a static web app based on transformers.js on GitHub Pages App here
Semantic search right in your browser! Calculates the embeddings and cosine similarity client-side without server-side inferencing, using transformers.js and a quantized version of sentence-transformers/all-MiniLM-L6-v2.
Create a fully working semantic search stack with only Qdrant as vector database with built-in API and transformers.js using any huggingface model as your frontend-only embedding generator. No additional inference server needed!
[Work in progress] Useful PyScript file handling examples for all combinations: local/remote files and import/export.
A simple PyScript-fueled Jupyter Notebook running entirely in your browser - no server needed!
Execute Python right in your Browser!
<!--Load PyScript, Pyodide, Micropip and Python packages-->
<script defer src="https://pyscript.net/alpha/pyscript.js"></script>
<py-env>
- pandas
</py-env>
<!-- Write your Python code! -->
<py-script>
import pandas as pd
from pyodide.http import open_url
df = pd.read_csv(open_url("/downloads/iris.csv"))
df
</py-script>Create sophisticated walking isochrones for Milan's metro stops and search for flats with immobiliare! Reproducible for all of Italy!
