Context API Public Documentation

The Context API is designed to infer new metadata for ingested content, making it more structured, searchable, and enriched. Its primary goal is to analyze unstructured or semi-structured data—such as documents or images—and generate meaningful metadata that improves organization, discoverability, and usability of the content.

Key Functions of the Context API

Metadata Extraction & Enrichment

Automatically generates metadata fields (e.g., document type, keywords, topics, entities). Enhances content classification, making retrieval easier.

Example: Extracting "Contract Type: NDA" from a legal document.

Content Categorization & Structuring

Assigns content to relevant categories based on AI-driven analysis.

Example: Identifying that an uploaded file is a "Medical Report" vs. "Invoice".

Semantic Understanding & Contextual Analysis

Uses NLP and AI models to understand content meaning. Can detect sentiment, subject matter, or summary of text.

Example: Summarizing key points from a research paper.

Multi-Format Support

Processes text and images

Example: Extracting actionable insights, deadlines, etc. from a meeting transcript.

Integration with Search & Retrieval Systems

Enhances search capabilities by adding structured metadata.

Example: Enables users to find documents by searching for extracted topics.

Automated Tagging & Classification

Generates labels to improve content discovery.

Example: Tagging an image with "Office Environment" or "Legal Document".