.env.example

# CodeRadius environment overrides (all optional).
#
# `cr init` writes the primary configuration to ~/.coderadius (settings.json
# and credentials.json). Every variable below overrides that configuration or
# a built-in default; uncomment only what you need.

# ── Graph database ───────────────────────────────────────────────────────────

# Bolt endpoint of Memgraph (docker-compose exposes 7687 on localhost).
#MEMGRAPH_URI=bolt://localhost:7687
# Credentials; match the MEMGRAPH_USER/MEMGRAPH_PASSWORD of docker-compose.yml.
#MEMGRAPH_USER=coderadius
#MEMGRAPH_PASSWORD=coderadius
# Set to 1 to force TLS on plain bolt:// URIs (bolt+s:// implies it).
#MEMGRAPH_ENCRYPTED=1
# Read-only credentials for the policy runner sandbox (fall back to the above).
#MEMGRAPH_POLICY_USER=
#MEMGRAPH_POLICY_PASSWORD=

# ── LLM providers ────────────────────────────────────────────────────────────
# Configure the block matching the provider you selected in `cr init`.

# Google Vertex AI
# GCP project that hosts the Vertex AI API.
#GOOGLE_VERTEX_PROJECT=my-gcp-project
# Vertex location/region.
#GOOGLE_VERTEX_LOCATION=global
# Service-account JSON key; omit to use Application Default Credentials.
#GOOGLE_APPLICATION_CREDENTIALS=~/.coderadius/config/gcp-service-account.json
# Pre-issued OAuth access token; skips ADC and gcloud (useful in CI).
#GOOGLE_VERTEX_TOKEN=

# Google AI Studio (plain API key, simplest)
#GOOGLE_GENERATIVE_AI_API_KEY=

# OpenAI
#OPENAI_API_KEY=
# OpenAI-compatible endpoint override (proxies, gateways, local servers).
#OPENAI_BASE_URL=https://api.openai.com/v1

# Anthropic
#ANTHROPIC_API_KEY=

# AWS Bedrock (uses the standard AWS credential chain)
#AWS_REGION=us-east-1
# Named profile from ~/.aws/credentials.
#AWS_PROFILE=default
# Static credentials, if not using a profile or an instance role.
#AWS_ACCESS_KEY_ID=
#AWS_SECRET_ACCESS_KEY=

# Ollama (local, no API key)
#OLLAMA_BASE_URL=http://localhost:11434
#OLLAMA_COMPLETION_MODEL=qwen3.5:9b
#OLLAMA_EMBEDDING_MODEL=qwen2.5-coder:7b

# ── Model selection ──────────────────────────────────────────────────────────

# Override the provider from ~/.coderadius/config/settings.json.
#MODEL_PROVIDER=google-genai
# Override the model id for the selected provider.
#MODEL_NAME=gemini-2.5-flash
# Embedding model in provider/model format.
#EMBEDDING_MODEL=google-genai/gemini-embedding-001
# Explicit embedding vector size; otherwise derived from the model.
#EMBEDDING_DIMENSION=768
# Fallback model (provider/model) when the primary structured output fails.
#INGEST_FALLBACK_MODEL=google-genai/gemini-2.5-flash

# ── Tuning ───────────────────────────────────────────────────────────────────

# Max concurrent LLM calls.
#LLM_CONCURRENCY=3
# Requests-per-minute ceiling for LLM calls; 0 disables the rate limiter,
# unset means adaptive discovery (slow-start up to LLM_RATE_MAX).
#LLM_RATE_RPM=60
# Adaptive rate-limiter knobs (values shown are the defaults).
#LLM_RATE_INITIAL=60
#LLM_RATE_MIN=6
#LLM_RATE_MAX=6000
#LLM_RATE_INCREASE_RPM=12
#LLM_RATE_SUCCESS_STREAK=10
#LLM_RATE_COOLDOWN_MS=5000
#LLM_RATE_BURST_SECONDS=5
#LLM_RATE_DECREASE_FACTOR=0.5
# Max files processed in parallel by the ingestion pipeline.
#FILE_CONCURRENCY=500
# Parse worker pool size; defaults to CPU cores minus one.
#PARSE_CONCURRENCY=4
# Max concurrent Memgraph write transactions.
#MEMGRAPH_CONCURRENCY=10

# ── Debug ────────────────────────────────────────────────────────────────────

# Set to 'true' to record LLM traces (viewable in Mastra Studio).
#RADIUS_TRACE=true
# Log verbosity; set to 'debug' for verbose pipeline logs.
#LOG_LEVEL=debug
# Timeout for git network operations, in milliseconds.
#RADIUS_GIT_TIMEOUT_MS=15000