Semantic Search: Finding Meaning in Text

Semantic Search Overview

Semantic search finds documents by meaning, not just keywords. It uses embeddings to capture semantics. It understands query intent. It finds relevant documents regardless of exact word matches. It works better than keyword search for many tasks.

Semantic search converts queries and documents to embeddings. It compares embeddings using similarity. It ranks documents by semantic relevance. It returns most relevant results.

The diagram shows semantic search flow. Query converts to embedding. Documents convert to embeddings. Similarity search finds relevant documents.

Document Chunking Strategies

Document chunking splits documents into searchable pieces. Chunks must balance context and granularity. Too large chunks lose precision. Too small chunks lose context.

Chunking strategies include fixed-size, sentence-based, and semantic chunking. Fixed-size uses character or token limits. Sentence-based splits at sentence boundaries. Semantic chunking groups related content.

# Document Chunking
def chunk_documents(text, chunk_size=500, overlap=50):
    chunks = []
    start = 0
    
    while start < len(text):
        end = start + chunk_size
        chunk = text[start:end]
        chunks.append(chunk)
        start = end - overlap
    
    return chunks

# Sentence-based chunking
from nltk.tokenize import sent_tokenize

def chunk_by_sentences(text, max_chunk_size=500):
    sentences = sent_tokenize(text)
    chunks = []
    current_chunk = ""
    
    for sentence in sentences:
        if len(current_chunk) + len(sentence) <= max_chunk_size:
            current_chunk += " " + sentence
        else:
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = sentence
    
    if current_chunk:
        chunks.append(current_chunk.strip())
    
    return chunks

# Example
text = "Machine learning is a subset of AI. It enables computers to learn. Deep learning uses neural networks."
chunks = chunk_by_sentences(text, max_chunk_size=100)
print("Chunks: " + str(chunks))

-- NeuronDB: Document Chunking
CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    content TEXT
);

CREATE TABLE document_chunks AS
SELECT 
    id,
    generate_substring(content, 1, 500, 50) AS chunk,
    embedding vector(384)
FROM documents;

-- Create embeddings for chunks
UPDATE document_chunks
SET embedding = neurondb.embed(chunk, 'sentence-transformers/all-MiniLM-L6-v2');

Chunking affects search quality. Good chunking preserves context. It enables precise retrieval.

Detailed Chunking Strategies

Fixed-size chunking uses character or token limits. It is simple to implement. It works for uniform documents. It may split sentences or concepts. Overlap helps preserve context across boundaries.

Sentence-based chunking splits at sentence boundaries. It preserves sentence integrity. It works well for natural language. It may create variable-sized chunks. It requires sentence segmentation.

Semantic chunking groups related content. It uses embeddings to find boundaries. It creates coherent chunks. It requires more computation. It produces better quality chunks.

# Detailed Chunking Implementation
from sentence_transformers import SentenceTransformer
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class AdvancedChunking:
    def __init__(self):
        self.embedder = SentenceTransformer('all-MiniLM-L6-v2')
    
    def semantic_chunking(self, text, similarity_threshold=0.7, min_chunk_size=100, max_chunk_size=500):
        """Chunk based on semantic similarity"""
        sentences = self.split_sentences(text)
        if len(sentences) == 0:
            return [text]
        
        # Create embeddings
        embeddings = self.embedder.encode(sentences)
        
        chunks = []
        current_chunk = [sentences[0]]
        current_embeddings = [embeddings[0]]
        
        for i in range(1, len(sentences)):
            # Compute similarity with current chunk
            chunk_embedding = np.mean(current_embeddings, axis=0)
            similarity = cosine_similarity(
                chunk_embedding.reshape(1, -1),
                embeddings[i].reshape(1, -1)
            )[0][0]
            
            # Check if should start new chunk
            chunk_text = ' '.join(current_chunk)
            should_split = (
                similarity < similarity_threshold or
                len(chunk_text) + len(sentences[i]) > max_chunk_size
            )
            
            if should_split and len(chunk_text) >= min_chunk_size:
                chunks.append(' '.join(current_chunk))
                current_chunk = [sentences[i]]
                current_embeddings = [embeddings[i]]
            else:
                current_chunk.append(sentences[i])
                current_embeddings.append(embeddings[i])
        
        if current_chunk:
            chunks.append(' '.join(current_chunk))
        
        return chunks
    
    def recursive_chunking(self, text, max_chunk_size=500, chunk_overlap=50):
        """Recursively chunk large documents"""
        if len(text) <= max_chunk_size:
            return [text]
        
        # Try to split at paragraph boundaries
        paragraphs = text.split('\n\n')
        chunks = []
        current_chunk = ""
        
        for para in paragraphs:
            if len(current_chunk) + len(para) <= max_chunk_size:
                current_chunk += para + "\n\n"
            else:
                if current_chunk:
                    chunks.append(current_chunk.strip())
                # Recursively chunk large paragraphs
                if len(para) > max_chunk_size:
                    sub_chunks = self.recursive_chunking(para, max_chunk_size, chunk_overlap)
                    chunks.extend(sub_chunks)
                else:
                    current_chunk = para + "\n\n"
        
        if current_chunk:
            chunks.append(current_chunk.strip())
        
        return chunks
    
    def split_sentences(self, text):
        """Split text into sentences"""
        import re
        # Simple sentence splitting
        sentences = re.split(r'[.!?]+', text)
        return [s.strip() for s in sentences if s.strip()]

# Example
chunker = AdvancedChunking()
long_text = "Machine learning is a subset of AI. It enables computers to learn. Deep learning uses neural networks. Neural networks have multiple layers. Each layer processes information differently."

semantic_chunks = chunker.semantic_chunking(long_text)
print("Semantic chunks: " + str(semantic_chunks))

recursive_chunks = chunker.recursive_chunking(long_text * 10)
print("Recursive chunks count: " + str(len(recursive_chunks)))

Chunking Best Practices

Choose chunk size based on document type. Short documents need smaller chunks. Long documents can use larger chunks. Typical sizes are 200-500 tokens. Test different sizes for your use case.

Overlap helps preserve context. Typical overlap is 10-20% of chunk size. It ensures important information isn't split. It improves retrieval quality. It increases storage requirements.

Consider document structure. Respect paragraph boundaries when possible. Preserve section headers. Maintain list formatting. These improve chunk quality.

# Chunking Best Practices
class BestPracticeChunking:
    def __init__(self):
        self.chunk_sizes = {
            'short': 200,
            'medium': 500,
            'long': 1000
        }
    
    def adaptive_chunking(self, text, document_type='medium'):
        """Adapt chunk size to document type"""
        chunk_size = self.chunk_sizes.get(document_type, 500)
        
        # Detect document type
        if len(text) < 1000:
            chunk_size = self.chunk_sizes['short']
        elif len(text) > 10000:
            chunk_size = self.chunk_sizes['long']
        
        return self.chunk_with_overlap(text, chunk_size, overlap=int(chunk_size * 0.1))
    
    def chunk_with_overlap(self, text, chunk_size, overlap=50):
        """Chunk with overlap"""
        chunks = []
        start = 0
        
        while start < len(text):
            end = start + chunk_size
            chunk = text[start:end]
            chunks.append(chunk)
            start = end - overlap
        
        return chunks
    
    def preserve_structure(self, text):
        """Chunk while preserving document structure"""
        # Split by paragraphs first
        paragraphs = text.split('\n\n')
        chunks = []
        current_chunk = ""
        
        for para in paragraphs:
            if len(current_chunk) + len(para) <= 500:
                current_chunk += para + "\n\n"
            else:
                if current_chunk:
                    chunks.append(current_chunk.strip())
                current_chunk = para + "\n\n"
        
        if current_chunk:
            chunks.append(current_chunk.strip())
        
        return chunks

# Example
best_practice = BestPracticeChunking()
chunks = best_practice.adaptive_chunking(long_text, document_type='medium')
structured_chunks = best_practice.preserve_structure(long_text)
print("Adaptive chunks: " + str(len(chunks)))
print("Structured chunks: " + str(len(structured_chunks)))

The diagram shows chunking strategies. Fixed-size creates uniform chunks. Sentence-based preserves sentence boundaries. Semantic chunking groups related content.

Query Processing

Query processing prepares queries for search. It converts queries to embeddings. It handles query expansion. It normalizes queries. It improves search quality.

Query processing includes normalization, expansion, and embedding. Normalization standardizes text. Expansion adds related terms. Embedding converts to vectors.

# Query Processing
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')

def process_query(query):
    # Normalize
    query = query.lower().strip()
    
    # Convert to embedding
    embedding = model.encode(query)
    
    return embedding, query

# Example
query = "How does machine learning work?"
embedding, processed_query = process_query(query)
print("Query embedding shape: " + str(embedding.shape))

Query processing improves search. It handles variations. It captures intent.

The diagram shows embedding generation process. Text preprocessed and tokenized. Encoder processes tokens. Embedding vector generated. Normalized for similarity search.

Ranking Algorithms

Ranking algorithms order search results. They use similarity scores. They combine multiple signals. They improve result relevance.

Ranking methods include similarity-based, learning-to-rank, and hybrid approaches. Similarity-based uses embedding similarity. Learning-to-rank uses machine learning. Hybrid combines multiple signals.

# Ranking Algorithms
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

def rank_documents(query_embedding, document_embeddings, top_k=10):
    similarities = cosine_similarity([query_embedding], document_embeddings)[0]
    ranked_indices = np.argsort(similarities)[::-1][:top_k]
    return ranked_indices, similarities[ranked_indices]

# Example
query_emb = np.random.randn(384)
doc_embs = np.random.randn(1000, 384)
indices, scores = rank_documents(query_emb, doc_embs, top_k=10)
print("Top documents: " + str(indices))
print("Scores: " + str(scores))

Ranking improves result quality. It orders by relevance. It combines multiple signals.

Detailed Ranking Algorithms

Similarity-based ranking uses embedding similarity directly. It is simple and fast. It works well when embeddings are good. It may miss important signals.

Learning-to-rank uses machine learning models. Features include query-document similarity, document length, position, and query characteristics. Models learn optimal feature weights. They improve ranking quality. They require training data.

BM25 is probabilistic ranking function. It combines term frequency and inverse document frequency. It works well for keyword search. It can be combined with semantic scores.

# Detailed Ranking Implementation
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from rank_bm25 import BM25Okapi

class AdvancedRanking:
    def __init__(self):
        self.ltr_model = RandomForestRegressor(n_estimators=100)
        self.bm25 = None
    
    def similarity_ranking(self, query_emb, doc_embeddings, top_k=10):
        """Simple similarity-based ranking"""
        similarities = cosine_similarity([query_emb], doc_embeddings)[0]
        ranked_indices = np.argsort(similarities)[::-1][:top_k]
        return ranked_indices, similarities[ranked_indices]
    
    def bm25_ranking(self, query, documents, top_k=10):
        """BM25 ranking for keyword matching"""
        if self.bm25 is None:
            tokenized_docs = [doc.lower().split() for doc in documents]
            self.bm25 = BM25Okapi(tokenized_docs)
        
        tokenized_query = query.lower().split()
        scores = self.bm25.get_scores(tokenized_query)
        ranked_indices = np.argsort(scores)[::-1][:top_k]
        return ranked_indices, scores[ranked_indices]
    
    def learned_to_rank(self, query, documents, query_emb, doc_embeddings, top_k=10):
        """Learning-to-rank with multiple features"""
        # Extract features
        features = []
        similarities = cosine_similarity([query_emb], doc_embeddings)[0]
        
        for i, doc in enumerate(documents):
            feature_vector = [
                similarities[i],  # Semantic similarity
                len(doc),  # Document length
                i,  # Initial position
                len(query),  # Query length
                doc.count(' '),  # Word count
                len(set(doc.lower().split()) & set(query.lower().split()))  # Term overlap
            ]
            features.append(feature_vector)
        
        # Predict relevance scores
        relevance_scores = self.ltr_model.predict(features)
        
        # Rank by predicted relevance
        ranked_indices = np.argsort(relevance_scores)[::-1][:top_k]
        return ranked_indices, relevance_scores[ranked_indices]
    
    def train_ltr_model(self, queries, documents_list, query_embeddings, doc_embeddings_list, relevance_labels):
        """Train learning-to-rank model"""
        X_train = []
        y_train = []
        
        for queries_batch, docs_batch, q_embs, d_embs, labels_batch in zip(
            queries, documents_list, query_embeddings, doc_embeddings_list, relevance_labels
        ):
            for query, docs, q_emb, d_embs, labels in zip(queries_batch, docs_batch, q_embs, d_embs, labels_batch):
                similarities = cosine_similarity([q_emb], d_embs)[0]
                for i, (doc, sim, label) in enumerate(zip(docs, similarities, labels)):
                    features = [
                        sim, len(doc), i, len(query),
                        doc.count(' '), len(set(doc.lower().split()) & set(query.lower().split()))
                    ]
                    X_train.append(features)
                    y_train.append(label)
        
        self.ltr_model.fit(X_train, y_train)
        return self.ltr_model

# Example
ranker = AdvancedRanking()
query = "machine learning"
documents = ["ML tutorial", "Deep learning guide", "AI introduction"]
query_emb = np.random.randn(384)
doc_embs = np.random.randn(3, 384)

# Similarity ranking
sim_indices, sim_scores = ranker.similarity_ranking(query_emb, doc_embs)
print("Similarity ranking: " + str(sim_indices))

# BM25 ranking
bm25_indices, bm25_scores = ranker.bm25_ranking(query, documents)
print("BM25 ranking: " + str(bm25_indices))

The diagram shows ranking process. Similarity scores computed. Results ordered by score. Top results returned.

Keyword vs Semantic Search

Keyword search matches exact words. It is fast and simple. It misses synonyms and related concepts. Semantic search matches meaning. It finds related content. It works better for many queries.

Keyword search uses inverted indexes. It matches query terms. It ranks by term frequency. Semantic search uses embeddings. It matches meaning. It ranks by semantic similarity.

# Keyword vs Semantic Search
from sklearn.feature_extraction.text import TfidfVectorizer
from sentence_transformers import SentenceTransformer
import numpy as np

# Keyword search
tfidf = TfidfVectorizer()
documents = ["Machine learning tutorial", "Deep learning guide", "AI introduction"]
tfidf_matrix = tfidf.fit_transform(documents)

query = "neural networks"
query_vector = tfidf.transform([query])
keyword_scores = (query_vector * tfidf_matrix.T).toarray()[0]

# Semantic search
model = SentenceTransformer('all-MiniLM-L6-v2')
doc_embeddings = model.encode(documents)
query_embedding = model.encode([query])
semantic_scores = cosine_similarity(query_embedding, doc_embeddings)[0]

print("Keyword scores: " + str(keyword_scores))
print("Semantic scores: " + str(semantic_scores))

Semantic search works better for meaning-based queries. Keyword search works for exact matches. Hybrid approaches combine both.

The diagram compares search methods. Keyword matches terms. Semantic matches meaning.

Hybrid Approaches

Hybrid search combines keyword and semantic search. It uses both exact matches and meaning. It improves result quality. It handles diverse query types.

Hybrid methods include score fusion, reranking, and multi-stage retrieval. Score fusion combines similarity scores. Reranking uses semantic scores to rerank keyword results. Multi-stage uses keyword for recall, semantic for precision.

# Hybrid Search
def hybrid_search(query, documents, alpha=0.5):
    # Keyword scores
    keyword_scores = compute_keyword_scores(query, documents)
    
    # Semantic scores
    semantic_scores = compute_semantic_scores(query, documents)
    
    # Combine scores
    hybrid_scores = alpha * keyword_scores + (1 - alpha) * semantic_scores
    
    # Rank
    ranked_indices = np.argsort(hybrid_scores)[::-1]
    return ranked_indices

# Example
query = "machine learning tutorial"
documents = ["ML guide", "Deep learning intro", "AI basics"]
results = hybrid_search(query, documents, alpha=0.6)
print("Hybrid search results: " + str(results))

Hybrid search improves quality. It combines strengths of both methods. It handles diverse queries.

Query Expansion

Query expansion adds related terms to queries. It improves recall. It handles vocabulary mismatches. It finds more relevant documents.

Expansion methods include synonym expansion, embedding-based expansion, and feedback-based expansion. Synonym expansion adds synonyms. Embedding-based adds similar terms. Feedback-based uses user feedback.

# Query Expansion
def expand_query(query, word_embeddings, top_k=3):
    query_words = query.split()
    expanded_terms = set(query_words)
    
    for word in query_words:
        if word in word_embeddings:
            similar = word_embeddings.most_similar(word, topn=top_k)
            expanded_terms.update([w for w, _ in similar])
    
    expanded_query = " ".join(expanded_terms)
    return expanded_query

# Example
expanded = expand_query("machine learning", word_embeddings, top_k=2)
print("Expanded query: " + str(expanded))

Query expansion improves recall. It finds more relevant documents. It handles vocabulary variations.

The diagram shows query expansion methods. Synonym expansion uses word embeddings. LLM expansion generates related terms. Both methods improve retrieval coverage.

Summary

Semantic search finds documents by meaning. Document chunking splits documents appropriately. Query processing prepares queries. Ranking algorithms order results. Semantic search works better than keyword for many queries. Hybrid approaches combine both methods. Query expansion improves recall. Semantic search enables better information retrieval.

References

• NeuronDB Documentation
• Semantic Search
• Vector Search Best Practices
• Hybrid Search

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