Cognita is an open-source RAG framework that enables building modular AI assistants with document retrieval, vector search, and customizable pipelines.
Cognita offers a modular architecture for building RAG applications: ingest and index documents, select from OpenAI, TrueFoundry or third-party embeddings, and configure retrieval pipelines via YAML or Python DSL. Its integrated frontend UI lets you test queries, tune retrieval parameters, and visualize vector similarity. Once validated, Cognita provides deployment templates for Kubernetes and serverless environments, enabling you to scale knowledge-driven AI assistants in production with observability and security.
Cognita Core Features
Modular RAG pipeline definitions
Multi-provider embedding support
Vector store integration
Built-in frontend playground
YAML and Python DSL configs
Production deployment templates
Cognita Pro & Cons
The Cons
No clear open-source availability
Pricing details not explicitly shown on the main page
No direct mention of AI Agent capabilities or autonomous agents
No visible GitHub or app store links for deeper exploration
The Pros
Comprehensive AI platform integrating data, applications, and APIs
Facilitates scalable AI solution development and deployment
Works as a collaborative environment for AI and data workflows
Supports rapid building and management of AI-powered products
TGenAI utilizes advanced AI and large language models to transform the process of creating test cases. By analyzing web pages, it extracts relevant components and automatically generates comprehensive test scenarios. This not only reduces manual input but also minimizes errors, allowing teams to focus on higher-level testing strategies. Whether for user interfaces, APIs, or other web functionalities, TGenAI helps ensure that your applications are thoroughly validated, enabling faster releases with improved quality.
