This article discusses the growing importance of search functionality LLM applications. The author highlights the potential of search engines to handle complex queries, understand context, and deliver relevant results. The use of AI and machine learning in search is also explored, with examples of current and potential applications.
Google Photos is about to undergo a radical change with the integration of Gemini AI models. A new 'Ask Photos' feature will turn the platform into a visual database, enabling users to search for photos and videos using everyday language.
LangChain has many advanced retrieval methods to help address these challenges. (1) Multi representation indexing: Create a document representation (like a summary) that is well-suited for retrieval (read about this using the Multi Vector Retriever in a blog post from last week). (2) Query transformation: in this post, we'll review a few approaches to transform humans questions in order to improve retrieval. (3) Query construction: convert human question into a particular query syntax or language, which will be covered in a future post
Image Similarity Search
Reverse Image Search
Object Similarity Search
Robust OCR Document Search
Semantic Search
Cross-modal Retrieval
Probing Perceptual Similarity
Comparing Model Representations
Concept Interpolation
Concept Space Traversal
Image Similarity Search
Reverse Image Search
Object Similarity Search
Robust OCR Document Search
Semantic Search
Cross-modal Retrieval
Probing Perceptual Similarity
Comparing Model Representations
Concept Interpolation
Concept Space Traversal
Image Similarity Search
