{"id":"leo-capvano-generative-ai-demos","name":"generative_ai_demos","af_score":26.5,"security_score":28.2,"reliability_score":25.0,"what_it_does":"A Python demo app for generative AI knowledge management using Retrieval-Augmented Generation (RAG). It provides (1) a Streamlit-based RAG storage admin panel backed by Postgres/pgvector via docker-compose, (2) legacy LLM interaction, (3) RAG-based LLM interaction that selects context via semantic search, and (4) an OpenAI-function/tool-agent runner with intermediate step visibility. It supports OpenAI, Azure OpenAI, and local Ollama (Mistral) via environment variable configuration.","best_when":"You want a learning/demo environment to understand RAG workflows and LangChain tool/agent patterns, either with OpenAI/Azure OpenAI or with local Ollama.","avoid_when":"You need a secure, multi-tenant service with robust access control, or a well-specified external API suitable for long-term automated integration.","last_evaluated":"2026-04-04T19:49:06.824377+00:00","has_mcp":false,"has_api":false,"auth_methods":["OpenAI API key via environment variables (implied)","Azure OpenAI credentials via environment variables (implied)","No explicit app-level user auth described; likely relies on local use/demo configuration"],"has_free_tier":false,"known_gotchas":["Demo-centric UI/flows (Streamlit) are not described as an agent-facing programmatic API; automation may require screen-scraping or code-level integration.","RAG content insertion/knowledge updates are tool-driven in the agent demo, but idempotency/duplicate handling is not documented.","Operational dependencies (Postgres/pgvector via docker-compose, env var configuration) can break the flow if not set correctly; missing diagnostics may slow agents down.","Agent intermediate steps are displayed in the UI, but no structured machine-readable traces are described."],"error_quality":0.0}