Wiki Search - Technical Documentation

Project Overview

Wiki Search is a semantic search engine for Wikipedia articles built on modern NLP embeddings and vector similarity search. It enables users to find relevant articles based on semantic meaning rather than keyword matching.

What It Does

The system ingests Wikipedia articles, breaks them into chunks, converts chunks into semantic embeddings (numerical representations), indexes them for fast retrieval, and provides an interactive search interface that ranks results by semantic similarity.

Why Semantic Search?

Traditional keyword search fails when:

• Users search with different vocabulary than article content
• Multiple concepts refer to the same thing (synonyms)
• Context matters more than exact wording

Semantic search solves this by understanding the meaning of text, not just keywords.

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Architecture

System Architecture Diagram

┌─────────────────────────────────────────────────────────────┐
│                    Wikipedia Dataset                         │
│  (10,000 article sample via Hugging Face Datasets)          │
└────────────────────────┬────────────────────────────────────┘
                         ↓
                  [load_articles.py]
                         ↓
         ┌───────────────────────────────┐
         │   SQLite Database (wiki.db)   │
         │  ┌─────────────┐ ┌──────────┐ │
         │  │  articles   │ │ chunks   │ │
         │  │  table      │ │ table    │ │
         │  └─────────────┘ └──────────┘ │
         └───────────────────────────────┘
                         ↓
                 [chunk_articles.py]
                         ↓
         ┌───────────────────────────────┐
         │   Chunked Articles in DB      │
         │   (50-250 words per chunk)    │
         └───────────────────────────────┘
                         ↓
                 [embed_chunks.py]
                         ↓
        ┌────────────────────────────────┐
        │  Embeddings Generated          │
        │  (384-dim vectors)             │
        │  - embeddings.npy              │
        │  - chunk_ids.npy               │
        └────────────────────────────────┘
                         ↓
                 [build_faiss.py]
                         ↓
        ┌────────────────────────────────┐
        │   FAISS Vector Index           │
        │   (faiss.index)                │
        │   IndexFlatIP L2 normalized    │
        └────────────────────────────────┘
                         ↓
                  [search.py]
                         ↓
        ┌────────────────────────────────┐
        │  Interactive Search Interface  │
        │  Returns ranked results by     │
        │  semantic similarity           │
        └────────────────────────────────┘

Pipeline Stages

Stage 1: Data Ingestion (load_articles.py)

Purpose: Load Wikipedia articles into persistent storage

Process:

1. Fetch Wikipedia dataset: wikipedia-20220301-en-sample-10k
2. Extract metadata: id, title, url, text
3. Insert into SQLite articles table
4. Enable fast lookups by article ID

Key Parameters:

• Dataset: Hugging Face kaitchup/wikipedia-20220301-en-sample-10k
• Size: 10,000 articles
• Database: SQLite (file-based, serverless)

Database Schema:

CREATE TABLE articles (
    id TEXT PRIMARY KEY,
    title TEXT,
    url TEXT,
    text TEXT
);

---

Stage 2: Chunking (chunk_articles.py)

Purpose: Break long articles into semantic chunks for embedding

Problem Solved:

• Full articles are too long (~5KB average) for effective embeddings
• Embeddings work best on sentences/paragraphs (50-250 words)
• Finer granularity = better search precision

Algorithm:

For each article:
  1. Normalize text:
     - Convert \r to \n (newline normalization)
     - Collapse multiple spaces/tabs to single space
  
  2. Smart splitting:
     - First try: split by paragraph breaks (\n)
     - If too few parts: split by sentence boundaries ([.!?])
  
  3. Chunk building:
     - Use sliding window approach
     - Constraints:
       * Max: 250 words per chunk
       * Min: 50 words per chunk
     - Skip chunks < 50 words

Configuration:

MAX_WORDS = 250      # Maximum chunk size
MIN_WORDS = 50       # Minimum chunk size (skip smaller)

Database Table Created:

CREATE TABLE chunks (
    chunk_id INTEGER PRIMARY KEY AUTOINCREMENT,
    article_id TEXT,
    content TEXT,  -- Fixed from "contect" typo
    FOREIGN KEY(article_id) REFERENCES articles(id)
);

Example Output:

• Input: 1 article (2000 words)
• Output: 12-15 chunks (average 150 words each)

---

Stage 3: Embedding (embed_chunks.py)

Purpose: Convert text chunks into dense vector representations

Model: sentence-transformers/all-MiniLM-L6-v2

Specifications:

• Architecture: MiniLM (lightweight BERT variant)
• Output Dimension: 384 dimensions
• Training: Fine-tuned on semantic similarity tasks
• Inference: Fast (~2000 chunks/minute on CPU)

Process:

For each chunk:
  1. Combine: "{article_title}. {chunk_content}"
     (Title context improves embeddings)
  
  2. Encode: Pass through sentence-transformer model
     Output: 384-dimensional vector
  
  3. Normalize: Apply L2 normalization
     (Makes vectors magnitude-invariant)
  
  4. Store:
     - embeddings.npy: numpy matrix (N × 384)
     - chunk_ids.npy: chunk ID mappings

Batch Processing:

BATCH_SIZE = 64  # Process 64 chunks at a time
# Speeds up GPU/multi-core processing
# Reduces memory overhead vs. processing one-by-one

Output Files:

• embeddings.npy: Shape (N_chunks, 384) - float32
• chunk_ids.npy: Shape (N_chunks,) - chunk identifiers

Performance:

• 10,000 articles → ~100,000 chunks
• Embeddings file: ~150 MB (100K × 384 × 4 bytes)

---

Stage 4: Indexing (build_faiss.py)

Purpose: Create fast similarity search index for embeddings

FAISS (Facebook AI Similarity Search):

• Production-grade vector database
• Optimized for billion-scale searches
• Multiple index types available

Index Configuration:

index = faiss.IndexFlatIP(dim)
# IndexFlatIP = Flat Inner Product (cosine similarity)
# Exact search, no quantization
# Best for datasets < 1M vectors

faiss.normalize_L2(embeddings)
# L2 normalization ensures:
# - Inner product = cosine similarity
# - Scores in range [0, 1] (easier to interpret)

Index Properties:

• Search Complexity: O(N) - linear scan (acceptable for 100K vectors)
• Memory: ~1.5 MB index file + embeddings in RAM
• Similarity Metric: Cosine similarity via dot product

Save/Load:

faiss.write_index(index, "data/db/faiss.index")
index = faiss.read_index("data/db/faiss.index")

---

Stage 5: Search (search.py)

Purpose: Interactive semantic search interface

Algorithm:

User Query
    ↓
1. Load FAISS index, embeddings, chunk_ids, database
2. Encode query using same model (all-MiniLM-L6-v2)
3. Normalize query vector (L2)
4. Search index for top-K nearest neighbors (K=8)
5. Filter: score >= MIN_SCORE (0.25)
6. Retrieve articles from database using chunk IDs
7. Deduplicate: show one result per article
8. Display results with scores and previews

Configuration:

TOP_K = 8          # Retrieve 8 nearest neighbors
MIN_SCORE = 0.25   # Minimum similarity (relevance threshold)

Result Deduplication:

seen_articles = set()
for title in results:
    if title not in seen_articles:
        seen_articles.add(title)
        display_result(title)

Ensures user doesn't see same article multiple times (from different chunks).

Output Format:

Article Title
Score: 0.543
Preview text (first 350 chars)...

---

Database Schema

articles Table

CREATE TABLE articles (
    id TEXT PRIMARY KEY,           -- Wikipedia article ID
    title TEXT,                    -- Article title
    url TEXT,                      -- Wikipedia URL
    text TEXT                      -- Full article text
);

Indexes: Primary key on id for fast lookups

chunks Table

CREATE TABLE chunks (
    chunk_id INTEGER PRIMARY KEY AUTOINCREMENT,  -- Unique identifier
    article_id TEXT,                            -- Foreign key
    content TEXT,                               -- Chunk text (50-250 words)
    FOREIGN KEY(article_id) REFERENCES articles(id)
);

Indexes:

• Implicit on chunk_id (primary key)
• Implicit on article_id (foreign key) for join performance

Sample Query:

SELECT a.title, c.content
FROM chunks c
JOIN articles a ON c.article_id = a.id
WHERE c.chunk_id = 42;

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Technologies & Dependencies

Core Libraries

Technology	Version	Purpose	Usage
Python	3.8+	Language	Runtime
SQLite3	Built-in	Database	Schema, CRUD operations
NumPy	Latest	Arrays	Matrix operations on embeddings
sentence-transformers	Latest	Embeddings	Generate semantic vectors
FAISS	faiss-cpu	Indexing	Vector similarity search
Hugging Face Datasets	Latest	Data source	Load Wikipedia data

Optional Dependencies

• faiss-gpu: GPU-accelerated FAISS (for large-scale indexing)

Installation

pip install sentence-transformers faiss-cpu numpy datasets

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File Descriptions

/data - Data Management Layer

File	Purpose	Key Operations
init_db.py	Initialize schema	CREATE TABLE articles, chunks
load_articles.py	Load data	INSERT INTO articles
check_db.py	Verify data	SELECT COUNT(*), sample rows
check_chunks.py	Verify chunking	Display sample chunks with titles
check_counts.py	Statistics	Show article and chunk counts

/indexing - Processing Pipeline

File	Purpose	Key Operations
chunk_articles.py	Create chunks	Read articles, split, INSERT chunks
embed_chunks.py	Generate embeddings	Load chunks, encode, save .npy files
build_faiss.py	Build index	Load embeddings, create FAISS, save

/search - Search Interface

File	Purpose	Key Operations
search.py	Interactive search	Load index, encode query, search, display results

/data/db - Artifacts

File	Purpose	Format	Size
wiki.db	SQLite database	SQLite3	~50-100 MB
faiss.index	Vector index	FAISS	~1.5 MB
embeddings.npy	Embedding matrix	NumPy (float32)	~150 MB
chunk_ids.npy	ID mapping	NumPy (int64)	~1 MB

---

Configuration & Parameters

Chunking Parameters

MAX_WORDS = 250      # Maximum words in a chunk
MIN_WORDS = 50       # Minimum words (skip smaller)

Effect: Tuning these affects:

• Higher MAX_WORDS → Fewer chunks, less granular search
• Lower MIN_WORDS → More chunks, higher search granularity
• Trade-off: More chunks = slower indexing but better precision

Embedding Parameters

BATCH_SIZE = 64      # Chunks processed per batch
MODEL = "all-MiniLM-L6-v2"  # Sentence-transformer model

Effect:

• Higher BATCH_SIZE → Faster but more memory
• Model choice → Different embedding quality/speed tradeoffs

Search Parameters

TOP_K = 8            # Number of results to retrieve
MIN_SCORE = 0.25     # Similarity threshold (0-1)

Effect:

• TOP_K: More results = more computation but better coverage
• MIN_SCORE: Higher = stricter filtering (fewer results)

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Execution Flow & Commands

Complete Pipeline

# Step 1: Initialize database
python data/init_db.py
# Output: "Database initialized at data/db/wiki.db"

# Step 2: Load Wikipedia articles
python data/load_articles.py
# Output: "Loading 10000 articles...", "Articles stored in sqlite"

# Step 3: Chunk articles
python indexing/chunk_articles.py
# Output: "Found X articles", "Processed X articles → Y chunks"

# Step 4: Generate embeddings
python indexing/embed_chunks.py
# Output: "Loading embedding model...", "Embedding X chunks...", progr... bar

# Step 5: Build index
python indexing/build_faiss.py
# Output: "Loading embeddings...", "Building FAISS index..."
#         "FAISS index built with X vectors"

# Step 6: Search interface
python search/search.py
# Interactive loop: prompts for queries, displays results

Verification Commands

# Check database setup
python data/check_db.py

# View sample chunks
python data/check_chunks.py

# Get statistics
python data/check_counts.py

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Performance Characteristics

Processing Speed

Stage	Input	Output	Time (approx)
Load Articles	Dataset API	10K articles	30-60 sec
Chunk Articles	10K articles	100K chunks	15-30 sec
Embed Chunks	100K chunks	Embeddings	10-20 min (CPU)
Build Index	Embeddings	FAISS index	5-10 sec

Hardware Assumptions: CPU-based (no GPU acceleration)

Search Speed

• Query encoding: ~1-2 ms
• FAISS search (K=8): ~5-10 ms
• Database lookups: ~10-20 ms
• Total per search: ~20-30 ms (interactive speed)

Memory Usage

• Model in memory: ~50 MB (all-MiniLM-L6-v2)
• FAISS index: ~200-300 MB (for 100K embeddings)
• Database file: ~100 MB
• Total: ~400-500 MB

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Design Decisions

1. Why SQLite?

• ✅ Serverless, file-based
• ✅ No external dependencies
• ✅ Fast enough for 10K articles
• ✅ Easy to inspect with any SQL tool
• ❌ Not suitable for > 1M chunks (would need PostgreSQL)

2. Why FAISS?

• ✅ Production-grade, battle-tested at Meta
• ✅ Fast exact search for our scale
• ✅ Supports GPU acceleration if needed
• ✅ Industry standard
• ❌ Overkill for tiny datasets, adds complexity

3. Why all-MiniLM-L6-v2?

• ✅ Lightweight (22M parameters)
• ✅ Fast inference (~2000 chunks/min CPU)
• ✅ Good quality embeddings
• ✅ Only 384 dimensions (vs. 768 for base models)
• ❌ Less accurate than larger models (e.g., all-mpnet-base-v2)

4. Why Chunking?

• ✅ Improves search precision (semantic coherence)
• ✅ Fits embedding model's context window
• ✅ Enables fine-grained search results
• ❌ Adds processing complexity

5. Why L2 Normalization?

• ✅ Converts dot product to cosine similarity
• ✅ Makes scores interpretable (0-1 range)
• ✅ Required by FAISS IndexFlatIP
• ❌ Slightly slower than plain dot product

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Potential Improvements

Short-term

1. Create requirements.txt for dependency pinning
2. Fix database schema typo (contect → content)
3. Add error handling for missing files/database
4. Implement config file (YAML/JSON) for parameters

Medium-term

1. Support larger datasets (1M+ articles → PostgreSQL + Milvus)
2. Add result filtering (by date, source, category)
3. Implement caching for popular queries
4. Add reranking (use BERT-large for final ranking)

Long-term

1. Web interface (FastAPI + React frontend)
2. Real-time indexing (stream new articles)
3. Multi-language support (multilingual embeddings)
4. Question-answering (RAG with LLM)
5. User feedback loop (learn from click-through)

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Troubleshooting

Database Issues

# If "contect" column error appears:
# Solution: Fix schema in init_db.py or recreate database

Missing Embeddings

# If "embeddings.npy not found":
# Re-run: python indexing/embed_chunks.py

FAISS Index Corrupted

# If index read fails:
# Re-run: python indexing/build_faiss.py

Out of Memory

# If OOM during embedding:
# Reduce BATCH_SIZE in embed_chunks.py
BATCH_SIZE = 32  # Instead of 64

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References

• FAISS Documentation
• Sentence-Transformers
• all-MiniLM-L6-v2 Model Card
• Wikipedia Dataset

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Author Notes

This project demonstrates end-to-end semantic search engineering. Key learning points:

1. Embeddings are just vectors - understanding their properties is crucial
2. Chunking strategy matters - affects search quality significantly
3. FAISS is powerful but simple - don't over-engineer for small scales
4. Semantic search > keyword search - for most real-world use cases

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Last Updated: February 2026 Project Status: Production-ready for up to 1M articles License: MIT (if published)