Tfidf Search

TF-IDF Search Engine

Implement search engines using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and cosine similarity for ranking documents by relevance to a query.

When to Use This Skill

• Building search functionality for text datasets
• Finding similar documents or passages
• Ranking documents by relevance to a query
• Implementing information retrieval systems
• Analyzing song lyrics, articles, documents, or any text corpus

Core Concepts

Vector Space Model (VSM): Represents text as vectors where each dimension corresponds to a unique word in the corpus.

TF-IDF Score: Combines term frequency (how often a word appears in a document) with inverse document frequency (how unique the word is across all documents). Common words like "the" get lower scores; rare, distinctive words get higher scores.

Cosine Similarity: Measures the angle between two vectors to determine document similarity. Range: -1 to 1, where 1 means identical direction (most similar).

Implementation Workflow

Step 1: Install Required Packages

Check project instructions for package management. For uv-based projects:

uv add numpy pandas scikit-learn

For pip-based projects:

pip install numpy pandas scikit-learn

Step 2: Prepare Your Dataset

Your dataset should be in CSV format with at least one text column containing the documents to search.

Example structure:

song,artist,text
"Song Title","Artist Name","lyrics text here..."

Step 3: Use the Helper Script

Run the TF-IDF search implementation:

python .claude/skills/tfidf-search/scripts/tfidf_search.py <csv_file> <text_column> "<query>" [--top_k 10]

Parameters:

• csv_file: Path to your CSV dataset
• text_column: Name of the column containing text to search
• query: Search query string (in quotes)
• --top_k: Number of top results to return (default: 10)

Example:

python .claude/skills/tfidf-search/scripts/tfidf_search.py songdata.csv text "Take it easy with me, please" --top_k 10

Step 4: Custom Implementation

For custom implementations or integration into existing code:

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Load dataset
df = pd.read_csv('your_data.csv')

# Create and fit vectorizer
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(df['text_column'])

# Transform query
query = "your search query"
query_vec = vectorizer.transform([query])

# Calculate similarities
results = cosine_similarity(X, query_vec)

# Get top results
top_indices = results.argsort(axis=0)[-10:][::-1].flatten()
for idx in top_indices:
    print(f"Score: {results[idx][0]:.4f} - {df.iloc[idx]['title']}")

Key Implementation Details

Query as List: The transform() method expects a list of documents, even for a single query:

query_vec = vectorizer.transform([query])  # Note the brackets

Shape Verification: Use .shape to verify dimensions:

print(f"Corpus shape: {X.shape}")  # (n_documents, n_features)
print(f"Query shape: {query_vec.shape}")  # (1, n_features)

Sorting Results: Get top-k results using argsort:

# For single query (results is 2D array)
top_k = 10
top_indices = results.argsort(axis=0)[-top_k:][::-1].flatten()

Advanced Options

TfidfVectorizer Parameters

Customize the vectorizer for better results:

vectorizer = TfidfVectorizer(
    max_features=5000,        # Limit vocabulary size
    min_df=2,                 # Ignore terms appearing in < 2 docs
    max_df=0.8,               # Ignore terms appearing in > 80% of docs
    ngram_range=(1, 2),       # Include unigrams and bigrams
    stop_words='english'      # Remove common English words
)

Handling Large Datasets

For large datasets, consider:

1. Using sparse matrix operations (scikit-learn handles this automatically)
2. Limiting vocabulary with max_features
3. Processing in batches if memory is constrained

Improving Search Quality

Preprocessing: Clean text before vectorization:

df['text'] = df['text'].str.lower()  # Lowercase
df['text'] = df['text'].str.replace('[^a-zA-Z\s]', '', regex=True)  # Remove punctuation

N-grams: Include phrases, not just single words:

vectorizer = TfidfVectorizer(ngram_range=(1, 2))  # unigrams + bigrams

Stop Words: Remove common words that don't help distinguish documents:

vectorizer = TfidfVectorizer(stop_words='english')

Common Issues

Low Similarity Scores: Normal for TF-IDF. Scores of 0.1-0.3 can still indicate relevant matches. Focus on relative ranking, not absolute scores.

Out of Vocabulary: Query words not in the training corpus get zero weight. Preprocess queries the same way as documents.

Memory Errors: Reduce max_features or process smaller batches.

References

For implementation examples and variations, see examples.md.

Performance Considerations

• Vectorization: O(n × m) where n = documents, m = avg words per doc
• Query Processing: O(m) for single query transformation
• Similarity Calculation: O(n) for comparing query against n documents
• Memory: Sparse matrices keep memory usage manageable for large vocabularies