Project: Build an Explainable TF-IDF Search Engine

Ship a Search Index You Can Test and Explain

• Define the document record, fitted index, query result and explanation fields as one reproducible contract.
• Build an index, run fixed test queries and preserve the terms that explain every top result.
• Measure recall at k, reciprocal rank and no-result behaviour against reviewed queries.
• Issue several queries and explain each ranking with the terms that contributed most to its score.

How the Index, Query Path and Evidence Fit Together

A reviewed query set defines expected relevant IDs. Report recall at k, mean reciprocal rank and no-result rate, then inspect individual failures.

The sample index is intentionally compact, which makes every document ID and contributing term auditable before you automate ingestion.

Audit One Result from Query to Document

Persist the model and metadata as one versioned artifact. Refuse to combine a matrix with a vocabulary from another fit.

Turn Ranking Choices into a Search Contract

Successful indexing proves only that artifacts were produced. A fixed query suite must still confirm retrieval quality and explanations.

For Project Build an Explainable TF-IDF Search Engine, change one setting at a time while the data split, comparison baseline and metric remain fixed.

What to record before scaling Project Build an Explainable TF-IDF Search Engine

Before a longer TF-IDF search engine Python run, write the data source, split rule, dependency versions and acceptance criteria.

Keep a fixed Project Build an Explainable TF-IDF Search Engine failure set and a short limitations note so later changes can be compared rather than guessed.

Build the Project Build an Explainable TF-IDF Search Engine example

Build the supplied corpus and preserve its expected result IDs first. Add new documents after the test queries are reproducible.

Keep the data and split fixed while you change the TF-IDF search engine Python decision.

Install:

python -m pip install scikit-learn numpy

import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
docs=[{'id':'a','text':'reset your account password'},{'id':'b','text':'export invoice PDF'},{'id':'c','text':'update account email'}]
vec=TfidfVectorizer(ngram_range=(1,2),sublinear_tf=True)
M=vec.fit_transform([d['text'] for d in docs])
def search(query,k=2):
    scores=(vec.transform([query]) @ M.T).toarray()[0]
    return [{'id':docs[i]['id'],'score':round(float(scores[i]),3)} for i in np.argsort(scores)[::-1][:k] if scores[i]>0]
print(search('forgot password'))

What the Search Engine Should Return

[{'id': 'a', 'score': a positive value}]
The result ID maps back to the password-reset source document.

Compare every returned ID, rank and explanation term with the documented query expectations.

If the Project Build an Explainable TF-IDF Search Engine result differs, print intermediate values and confirm the documented dependency versions first.

Diagnose failures in Project Build an Explainable TF-IDF Search Engine

Confirm artifact versions, row-to-document alignment and query vocabulary before investigating ranking parameters.

Check your Project Build an Explainable TF-IDF Search Engine reasoning

Complete the project questions before opening the explanations. They cover artifact alignment, fitted vectorizers and traceable search evidence.

Primary references for Project Build an Explainable TF-IDF Search Engine

• scikit-learn text feature extraction: official count and TF-IDF reference.
• scikit-learn model evaluation: official metric definitions.
• PostgreSQL full-text search controls: official parsing, query and ranking reference.

Re-run the fixed query suite when indexing, vectorizer or ranking dependencies change. Archive the artifact versions with the comparison.