deep-semantic-search 2.0.0

A library for embedding, indexing, and applying semantic search for text and image data

Deep Semantic Search

A Python library for embedding, indexing, and applying semantic search for text and image data.

Features

• Multi-modal Semantic Search

  • Embed and index text data using Sentence Transformers (paraphrase-multilingual-MiniLM-L12-v2)
  • Embed and index image data using CLIP
  • Search images by image or text queries
  • Search text by semantic similarity

• Clustering & Captioning

  • Cluster image embeddings using KMeans (scikit-learn)
  • Caption images using BLIP
  • Customizable LLM-powered topic labeling via callback

• Retrieval-Augmented Generation (RAG)

  • Answer questions based on text data
  • Pluggable LLM via callback pattern

Installation

pip install deep-semantic-search

Install with optional extras:

pip install deep-semantic-search[rag]         # RAG / question answering
pip install deep-semantic-search[clustering]  # Image clustering
pip install deep-semantic-search[viz]         # Plotting / visualization
pip install deep-semantic-search[all]         # Everything

For development:

pip install deep-semantic-search[dev]

Quick Start

Image Search

from deep_semantic_search import LoadImageData, ImageIndexer, ImageSearcher

# Load images
loader = LoadImageData()
image_paths = loader.from_folder(["path/to/images"])

# Index images
indexer = ImageIndexer(image_paths)
indexer.run_index()

# Search by text
searcher = ImageSearcher(indexer)
results = searcher.search_by_text("cat on a sofa", n=5)
for r in results:
    print(f"{r['score']:.3f}  {r['path']}")

# Search by image
results = searcher.search_by_image("query.jpg", n=5)

Text Search

from deep_semantic_search import LoadTextData, TextEmbedder, TextSearch

# Load text data
loader = LoadTextData()
corpus = loader.from_folder("path/to/text/files")

# Embed
embedder = TextEmbedder()
embedder.embed(corpus)

# Search
search = TextSearch(embedder)
results = search.find_similar("your search query", top_n=5)
for r in results:
    print(f"Score: {r['score']:.3f}  {r['path']}")

Image Clustering

from deep_semantic_search import ImageIndexer, ImageClusterer, ImageCaptioner

indexer = ImageIndexer(image_paths)
indexer.run_index()

# Optional: use captioner for topic labels
captioner = ImageCaptioner()
clusterer = ImageClusterer(indexer)
result = clusterer.cluster(n_clusters=5, captioner=captioner)

# Save organized clusters to disk
clusterer.save_clusters("./output/clusters")

RAG (Question Answering)

from deep_semantic_search import ask_question

texts = ["Document 1 content...", "Document 2 content..."]
answer = ask_question(texts, "What is the main topic?")
print(answer)

# With a custom LLM
answer = ask_question(texts, "Summarize this.", llm_fn=my_custom_llm)

Custom Data Paths

By default, metadata is stored in ~/.deep-semantic-search/. Override per instance:

indexer = ImageIndexer(image_paths, metadata_dir="./my_project/index")
embedder = TextEmbedder(metadata_dir="./my_project/text_index")

API Reference

Image Module

• LoadImageData — Load image paths from folders or CSV
• ImageIndexer — CLIP embedding + FAISS indexing
• ImageSearcher — Image/text similarity search
• ImageClusterer — KMeans clustering with topic labeling
• ImageCaptioner — BLIP image captioning

Text Module

• LoadTextData — Load text from folders (.txt/.html) or CSV
• TextEmbedder — Sentence Transformer embeddings
• TextSearch — Cosine similarity search

RAG

• ask_question() — RAG Q&A with pluggable LLM

Exceptions

• DeepSemanticSearchError — Base exception
• IndexNotFoundError, ModelLoadError, SearchError, EmbeddingError, ClusteringError

CLI Tool

The package includes dss, a command-line interface for all major features. After installing the package, the dss command is available globally.

General Usage

dss --help          # Show all commands
dss --version       # Show version
dss <command> --help  # Help for a specific command

Global flags: -v/--verbose for debug output, -q/--quiet to suppress progress.

Image Search

Search images by text query or by image similarity:

# Search by text
dss image-search --folder ./photos --query "sunset over the ocean" --top 5

# Search by image
dss image-search --folder ./photos --query ./photos/reference.jpg --top 10

# Multiple folders, JSON output
dss image-search -f ./photos -f ./vacation --query "mountains" --format json

# Force re-indexing
dss image-search -f ./photos --query "cat" --reindex

Text Search

Search text documents by semantic similarity:

dss text-search --folder ./documents "machine learning algorithms" --top 5

# CSV output
dss text-search -f ./docs "neural networks" --format csv

# Custom model
dss text-search -f ./docs "query" --model sentence-transformers/all-MiniLM-L6-v2

Image Clustering

Cluster images using KMeans on CLIP embeddings:

# Basic clustering
dss image-cluster --folder ./photos --clusters 5

# With BLIP captioning for topic labels
dss image-cluster -f ./photos -k 5 --caption

# Save clustered images into organized folders
dss image-cluster -f ./photos -k 8 --caption --save-dir ./output/clusters

# JSON output
dss image-cluster -f ./photos -k 3 --format json

RAG (Question Answering)

Ask questions over text documents using Retrieval-Augmented Generation:

dss ask --folder ./documents "What is the main conclusion?"

# Custom Ollama model
dss ask -f ./research "Summarize the findings" --model llama2:13b

# Adjust chunking
dss ask -f ./docs "question" --chunk-size 2000 --chunk-overlap 200

Configuration

The CLI respects environment variables:

• OLLAMA_LLM_MODEL — LLM model for RAG (default: gemma4:e4b)
• DEFAULT_SEARCH_FOLDER_PATH — Default folder path

All CLI flags override environment variables when provided.

Requirements

• Python >= 3.10
• PyTorch, Sentence Transformers, Transformers, FAISS, LangChain, and more (auto-installed)

License

MIT