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# CHANDRA — Enterprise AI Cloud Operations Platform
# Environment Configuration (.env.example)
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# Copy this file to .env and fill in your values.
#   cp .env.example .env
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# ── REQUIRED: AWS Credentials ─────────────────────────────────────────────────
AWS_DEFAULT_REGION=us-east-1
AWS_ACCESS_KEY_ID=YOUR_AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY=YOUR_AWS_SECRET_ACCESS_KEY

# ── REQUIRED: Database ────────────────────────────────────────────────────────
# PostgreSQL connection string (Docker: docker compose up -d postgres)
# Default: postgresql+psycopg://chandra:chandra@localhost:5434/chandra
POSTGRES_URL=postgresql+psycopg://chandra:chandra@localhost:5434/chandra
DATABASE_URL=${POSTGRES_URL}

# ── REQUIRED: Jira Integration ────────────────────────────────────────────────
JIRA_SERVER=https://your-domain.atlassian.net
JIRA_EMAIL=your-email@example.com
JIRA_API_TOKEN=YOUR_JIRA_API_TOKEN

# ── OPTIONAL: Slack Integration ───────────────────────────────────────────────
# Create a Slack app at https://api.slack.com/apps, enable Events API
# Set Request URL to: https://your-host/webhooks/slack
# SLACK_WEBHOOK_URL=
# SLACK_BOT_TOKEN=

# ── OPTIONAL: Microsoft Teams Integration ─────────────────────────────────────
# Use Power Automate HTTP action, POST to: https://your-host/webhooks/teams
# TEAMS_WEBHOOK_URL=

# ── OPTIONAL: Email Agent ────────────────────────────────────────────────────
# POST to: https://your-host/webhooks/email
# EMAIL_WEBHOOK_URL=

# ── OPTIONAL: Webhook Security ────────────────────────────────────────────────
# CHANDRA_WEBHOOK_TOKEN=your-secret-token

# ── REQUIRED: AWS Resources ───────────────────────────────────────────────────
SYNTHETIC_ACCOUNT_ID=YOUR_SYNTHETIC_ACCOUNT_ID
SNS_TOPIC_ARN=arn:aws:sns:us-east-1:YOUR_ACCOUNT:chandra-escalation-critical

# ── REQUIRED: Frontend / API URL ──────────────────────────────────────────────
# For local development: http://localhost:6001
# For Docker: http://backend:6001
# For production: https://your-domain.com/api/backend
NEXT_PUBLIC_API_URL=http://localhost:6001
FRONTEND_URL=http://localhost:3000

# ── OPTIONAL: Auto-approve (development only) ─────────────────────────────────
# CHANDRA_AUTO_APPROVE=1

# ═══════════════════════════════════════════════════════════════════════════════
# LLM Provider Configuration
# ═══════════════════════════════════════════════════════════════════════════════

# LLM provider backend. One of:
#   bedrock  — Amazon Bedrock (default, production)
#   vllm     — Local vLLM server (development, air-gapped)
#   openai   — Any OpenAI-compatible endpoint (Together, Groq, etc.)
#   ollama   — Local Ollama daemon
LLM_PROVIDER=bedrock

# Model name for the active provider
#   Bedrock:  anthropic.claude-sonnet-4-5-20250929-v1:0
#   vLLM:     google/gemma-4-12B-it-qat-w4a16-ct
#   OpenAI:   Qwen/Qwen2.5-32B-Coder-Instruct
#   Ollama:   qwen2.5-coder:32b
LLM_MODEL=anthropic.claude-sonnet-4-5-20250929-v1:0

# Sampling temperature (0.0 = deterministic for code generation)
LLM_TEMPERATURE=0.0

# ── Bedrock (default) ────────────────────────────────────────────────────────
BEDROCK_MODEL_ID=anthropic.claude-sonnet-4-5-20250929-v1:0

# ── vLLM (local) — set LLM_PROVIDER=vllm to use ──────────────────────────────
# Start vLLM with:
#   vllm serve google/gemma-4-12B-it-qat-w4a16-ct \
#     --max-model-len 16384 \
#     --enable-prefix-caching \
#     --gpu-memory-utilization 0.90 \
#     --host 0.0.0.0 \
#     --port 8000
#
# The model google/gemma-4-12B-it-qat-w4a16-ct is already QAT 4-bit quantized.
# No additional quantization implementation is required — the model name suffix
# (qat-w4a16) indicates Quantization-Aware Training with 4-bit weights.
# vLLM serves the quantized weights directly from the HuggingFace checkpoint.
# VLLM_API_BASE=http://localhost:8000/v1
# VLLM_MODEL=google/gemma-4-12B-it-qat-w4a16-ct
# VLLM_API_KEY=not-needed

# ── OpenAI-compatible (fallback) ──────────────────────────────────────────────
# OPENAI_API_BASE=http://localhost:8000/v1
# OPENAI_API_KEY=not-needed
# OPENAI_MODEL_NAME=google/gemma-4-12B-it-qat-w4a16-ct

# ── Ollama ───────────────────────────────────────────────────────────────────
# OLLAMA_HOST=http://localhost:11434
# OLLAMA_MODEL=qwen2.5-coder:32b

# ═══════════════════════════════════════════════════════════════════════════════
# Execution Agent Settings
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# Enforce typed ExecutionPlan pipeline (validated plan + deterministic executor)
# CHANDRA_TYPED_EXECUTION=false

# Agent decoding knobs (deterministic by default)
CHANDRA_AGENT_TEMPERATURE=0.0
CHANDRA_AGENT_TOP_P=1.0

# Max output tokens. LEAVE UNSET for vLLM to use full context (context_len - prompt).
# CHANDRA_AGENT_MAX_TOKENS=

# Structured output method
#   json_schema       — vLLM guided decoding (works for any model, no --tool-call-parser needed)
#   function_calling  — Tool-use path (default for Bedrock/Claude)
#   json_mode         — JSON mode (OpenAI-compatible)
CHANDRA_STRUCTURED_OUTPUT_METHOD=json_schema

# ── Token Budget Caps (for local LLM with 16K context) ──────────────────────
# ~4 chars per token. Local models have limited context (16K), so cap sections.
# Bedrock is exempt (full context). Adjust when vLLM --max-model-len increases.
CHANDRA_TF_DOCS_MAX_CHARS=8000
CHANDRA_AWS_CTX_MAX_CHARS=6000
CHANDRA_MEMORY_MAX_CHARS=3000
CHANDRA_AGENT_MAX_INPUT_CHARS=30000

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# Observability & Tracing (OPTIONAL)
# ═══════════════════════════════════════════════════════════════════════════════

# LangSmith (LangChain tracing)
# LANGCHAIN_TRACING_V2=true
# LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
# LANGCHAIN_API_KEY=ls__your-key-here
# LANGCHAIN_PROJECT=chandra

# LangFuse (open-source LLM observability)
# LANGFUSE_PUBLIC_KEY=pk-lf-xxxxx
# LANGFUSE_SECRET_KEY=sk-lf-xxxxx
# LANGFUSE_HOST=https://cloud.langfuse.com

# AgentOps (AI agent monitoring)
# AGENTOPS_API_KEY=your-agentops-key

# ═══════════════════════════════════════════════════════════════════════════════
# Logging
# ═══════════════════════════════════════════════════════════════════════════════
# LOG_LEVEL=INFO       # DEBUG, INFO, WARNING, ERROR
# UVICORN_LOG_LEVEL=info