.env.example

# Copy to `.env` and adjust. All values are optional; defaults run end-to-end
# with the TF-IDF retriever and no API keys.
#
# NOTE: Regulus automatically reuses OPENAI_* credentials from a sibling
# geometric_knowledge_network/.env if present — so if your Azure/OpenAI keys are
# already configured for GKN, embeddings and the LLM interpreter work with no
# extra setup here. Values in THIS file take precedence.

# Which frameworks to ingest (comma-separated ids; empty = all).
# Options today: eu_ai_act, nist_ai_rmf, nist_ai_600_1, oecd_ai, iso_42001
REGULUS_FRAMEWORKS=eu_ai_act,nist_ai_rmf,nist_ai_600_1,oecd_ai,iso_42001

# Retriever: "tfidf" (no deps) or "embedding" (uses GKN's embedding store).
REGULUS_RETRIEVER=auto   # auto|tfidf|embedding (auto uses embeddings if keys present)
REGULUS_TOP_K=5
REGULUS_CHUNK_SIZE=900
REGULUS_CHUNK_OVERLAP=150

# --- Embedding retriever (only if REGULUS_RETRIEVER=embedding) ---
# These are read by the underlying GKN embedding store:
#   EMBEDDING_CHOICE=small        # small|large (cloud) or local
#   EMBEDDING_FALLBACK_TO_LOCAL=true
#   OPENAI_API_KEY=...
#   OPENAI_BASE_URL=...            # Azure / gateway
#   OPENAI_API_VERSION=...         # for Azure
#   OPENAI_EMBEDDING_MODEL=text-embedding-3-small

# --- LLM interpretation layer (Section 6; needs `pip install openai`) ---
# Reuses OPENAI_API_KEY / OPENAI_BASE_URL / OPENAI_API_VERSION above, plus:
#   OPENAI_GENERATION_MODEL=gpt-4o-mini   # or your Azure deployment name
# Without these the interpret step runs in dry-run mode (shows the structured context).