Marqo

Search and retrieve results from a combined index of text documents and images using natural language queries or image inputs. The system converts both queries and indexed content into vector embeddings and finds semantically similar matches across modalities.

Automatically splits large documents and PDFs into semantically meaningful chunks and preprocesses them for indexing. Handles text extraction, formatting normalization, and optimal chunk sizing without manual configuration.

Automatically extracts text content from PDF files and indexes it for semantic search. Handles multi-page PDFs, preserves document structure, and makes PDF content searchable without manual conversion.

Provides tools to create, update, delete, and manage multiple search indexes. Supports index versioning and allows switching between different index versions for A/B testing or rollback scenarios.

Provides cloud-hosted vector database infrastructure that automatically scales with data volume and query load. Eliminates the need to self-host or manage vector database deployments, handling replication, backups, and performance optimization.

Ranks search results by semantic similarity to the query, providing relevance scores that indicate how closely each result matches the user's intent. Uses vector embeddings to measure semantic distance rather than keyword overlap.

Enables searching image indexes with text queries and text indexes with image queries. Bridges the gap between different content modalities by mapping them to a shared vector space.

Supports uploading and indexing large volumes of documents and images in batch operations. Processes multiple files simultaneously and adds them to the search index efficiently.

Allows filtering search results by document metadata fields and applying faceted search constraints. Combines semantic similarity with structured metadata filtering for more precise results.

Provides access to multiple pre-trained embedding models optimized for different use cases (text, images, multimodal). Allows selection of embedding models that best fit the application's domain and performance requirements.

Provides a RESTful API for all search and indexing operations, enabling integration with existing applications and workflows. Supports CRUD operations on indexes and search queries with configurable parameters.

Offers a free tier for prototyping and small-scale deployments with automatic scaling to paid tiers as usage grows. Removes friction for initial development and testing without upfront costs.