generative_ai_demos
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.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Security controls for the app itself are not described. The README suggests using a .env template for keys (better than hardcoding) but does not confirm safe secret handling practices (e.g., no logging) or transport guarantees. TLS enforcement for any externally exposed service is not specified; Streamlit default setups vary. Dependency hygiene and CVE status cannot be determined from the provided content.
⚡ Reliability
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.
Use Cases
- • Local/demo RAG ingestion, semantic search, and administration (insert/read/query).
- • Interactive Q&A over stored documents with semantic context selection.
- • LLM “legacy” chat without retrieval for general-purpose prompts.
- • Agentic tool use demos (tool calling, including inserting results back into the RAG knowledge store).
- • Hands-on evaluation of OpenAI vs Azure OpenAI vs Ollama/Mistral setups.
Not For
- • Production deployment without additional hardening (authz/authn, monitoring, and operational controls are not described).
- • Enterprise compliance needs (no compliance/security guarantees documented).
- • Use cases requiring a stable, versioned public API/SDK contract (the repo appears demo-oriented).
Interface
Authentication
README mentions an external OpenAI key and a dot_env_template for a .env file, and selection of LLM/embeddings via env vars (llm_impl, embeddings_model_impl). No documentation of API endpoints requiring auth or app-level authentication/authorization is provided.
Pricing
If using OpenAI/Azure OpenAI backends, costs depend on model usage. No cost estimates or quotas are documented. Local Ollama avoids API costs but has infrastructure/hardware requirements.
Agent Metadata
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.
Alternatives
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Scores are editorial opinions as of 2026-04-04.