Making and Deploying an AI Web App in 2023 (Part 2)

Make a Proof of Concept for an AI App

This is part of a multi-part blogpost about how to build an AI Web App. Please refer to Part 1 for more context.

This post uses txtai as the AI library.

Alternatives would be: transformers, sentence-transformers, PyTorch, Keras, scikit-learn, among many others.

The first step in any project is always to make a proof of concept. At this stage, we don’t care about performance, edge cases, or any other intricacies— we just want to confirm that the project is viable.

For the sake of example, we will take strong inspiration from this example from the txtai library. We’ll index a database of documents and then query for them with natural language. Basically we’ll be implementing our own little Google Search, which only returns results from the files we feed it.

But we won’t just search for keywords, like classical search engines. It’s 2023 and we have AI at our disposal! We will use text embeddings to search for the closest match. By searching for embeddings we’re not literally searching for the words we give it, but for the meaning of the whole query. This is called Semantic search.

So, let’s get thing going by creating a new directory for our little project:

mkdir ai-web-app
cd ai-web-app

We will need a database to index and query. For this example, we will use a test dataset that txtai made available. This data is a collection of research documents into COVID-19. The original version of this dataset can be found on Kaggle.

We download the dataset with the following shell commands:

gunzip tests.gz
mv tests articles.sqlite

and install the needed libraries with

pip install txtai sentence-transformers pandas jupyterlab

We can then start a notebook server with Jupyter Lab with

mkdir notebooks && cd notebooks && jupyter lab

From our database, we will need to create an index (a searchable database of documents), and a way to query (search) the index. The following code (mostly stolen from txtai’s example) creates an index with the COVID-19 dataset downloaded above:

import sqlite3

import regex as re

from txtai.embeddings import Embeddings
from txtai.pipeline import Tokenizer

def stream():
    # Connection to database file
    db = sqlite3.connect("../articles.sqlite")
    cur = db.cursor()

    # Select tagged sentences without a NLP label. NLP labels are set for non-informative sentences.
    cur.execute("SELECT Id, Name, Text FROM sections WHERE (labels is null or labels NOT IN ('FRAGMENT', 'QUESTION')) AND tags is not null")

    count = 0
    for row in cur:
        # Unpack row
        uid, name, text = row

        # Only process certain document sections
        if not name or not"background|(?<!.*?results.*?)discussion|introduction|reference", name.lower()):
            # Tokenize text
            tokens = Tokenizer.tokenize(text)

            document = (uid, tokens, None)

            count += 1
            if count % 1000 == 0:
                print("Streamed %d documents" % (count), end="\r")

            # Skip documents with no tokens parsed
            if tokens:
                yield document

    print("Iterated over %d total rows" % (count))

    # Free database resources

# BM25 + fastText vectors
embeddings = Embeddings({
    "method": "sentence-transformers",
    "path": "all-MiniLM-L6-v2",
    "scoring": "bm25"


In the code above, the text embeddings are generated using the all-MiniLM-L6-v2 model. We could also train our own model instead of using a pre-trained model, but that would require a lot of work. If we can avoid it, why not?

So at this point, we have a database and want to search it. In the following cell, we define a Python function to query the index for the closest results, and then query the original data for extra information about the results. The last thing we do in this cell is to search for "risk factors".

import pandas as pd

pd.set_option("display.max_colwidth", None)

def search(query: str, topn: int = 5) -> pd.DataFrame:
    db = sqlite3.connect("../articles.sqlite")
    cur = db.cursor()

    results = []
    for uid, score in, topn):
        cur.execute("SELECT article, text FROM sections WHERE id = ?", [uid])
        uid, text = cur.fetchone()

        cur.execute("SELECT Title, Published, Reference from articles where id = ?", [uid])
        results.append(cur.fetchone() + (text,))

    df = pd.DataFrame(results, columns=["Title", "Published", "Reference", "Match"])
    return df

search("risk factors")

The output of that cell will be a pandas dataframe with the results:


We can also try other queries:


As you can see, while the results maybe aren’t the best, they are good enough for our proof of concept. We could spend a lot more time making this part better, but in the spirit of shipping early and often, this is enough for now.

If you’ve been following these instructions, your code should look like this:

To continue this tutorial, go to Part 3.

For comments or questions, use the Reddit discussion or reach out to me directly via email.