deep-semantic-search 0.1.0

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:

  • Embedding and indexing text data using the nli-mpnet-base-v2 model
  • Embedding and indexing image data using the CLIP model
  • Semantic search for both text and image data
  • Search images by both image and text queries

• Clustering and Image Captioning:

  • Cluster image embeddings using PyTorch KMeans (with GPU support)
  • Caption images using the BLIP model

• Retrieval-Augmented Generation (RAG):

  • Answer questions based on search results
  • Summarize search results
  • Generate topics for image captions

Installation

pip install deep-semantic-search

Quick Start

Text Search

from deep_semantic_search import LoadTextData, TextEmbedder, TextSearch

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

# Embed the text data
embedder = TextEmbedder()
embedder.embed(corpus_dict)

# Search for similar texts
search = TextSearch()
results = search.find_similar("your search query", top_n=5)

for result in results:
    print(f"Score: {result['score']}, Text: {result['text'][:100]}...")

Image Search

from deep_semantic_search import LoadImageData, ImageSearch

# Load image data
loader = LoadImageData()
image_paths = loader.from_folder("path/to/images")

# Set up image search
searcher = ImageSearch(image_paths)

# Search for similar images to a text query
results = searcher.get_similar_images_to_text("cat on a sofa", number_of_images=5)

# Display results
for path, score in results.items():
    print(f"Score: {score}, Image: {path}")

RAG (Retrieval-Augmented Generation)

from deep_semantic_search import ask_question

# Ask a question based on provided text data
texts = ["Text document 1...", "Text document 2..."]
answer = ask_question(texts, "What is the main topic discussed?")
print(answer)

Requirements

• Python 3.8+
• PyTorch
• Sentence Transformers
• Hugging Face Transformers
• FAISS
• LangChain

License

MIT