Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM…
Retrieves implementation knowledge, code examples, and documentation references. Use to inform technical decision-making when the user requires specific library usage, framework patterns, or syntax details. Trigger on requests to 'search docs', 'find code examples', or 'check implementation details'.
Performs dynamic, reflective problem-solving through iterative thought chains. Use for complex planning requiring revision, branching, backtracking, or hypothesis verification. Ideal for multi-step analysis where context maintenance is required or the full scope isn't initially clear.
Generates technical implementation plans and architectural strategies that enforce the Project Constitution. Use when designing new features, starting implementation tasks, refactoring code, or ensuring compliance with critical standards like Testability-First Architecture, security mandates, testing strategies, and…