# Experiment 1 Cell C — Agent-as-Tool + MCP optimized.
#
# Same tools as Cell B (MCP baseline), with two layers of optimization:
#  - MCP transport: batch runner + connection reuse (Anonymous reviewer's #31)
#  - vLLM serving: prefix caching only (this config, Lane 2 / #30)
# (Cell B - Cell C) latency quantifies how much of the transport overhead is
# recoverable when both optimizations are stacked. See
# docs/experiment1_capture_plan.md for the full design and
# docs/lane2_int8_kv_status.md for the Lane 2 rationale.
#
# STATUS (2026-04-30): Both optimization layers now wired and captured:
#   - KV-cache: prefix caching only (fp8 deferred — see status doc §"#30 KV-Cache")
#   - MCP transport: batch runner + connection reuse (#31)
#   - parallel tool calls disabled: vLLM 0.19.0 + Llama-3.1-8B-Instruct
#     rejects parallel tool-call requests ("This model only supports single
#     tool-calls at once!"). Successful Cell C proof: Slurm job 9071639.
# INT8 deferred (see status doc).

EXPERIMENT_NAME="aat_mcp_optimized"
EXPERIMENT_CELL="C"
EXPERIMENT_FAMILY="exp1_mcp_overhead"
SCENARIO_SET_NAME="smartgrid_multi_domain"
SCENARIOS_GLOB="data/scenarios/multi_*.json"

MODEL_ID="openai/Llama-3.1-8B-Instruct"

ORCHESTRATION="agent_as_tool"
MCP_MODE="optimized"
TRIALS=3
ENABLE_SMARTGRID_SERVERS=1
CONTRIBUTING_EXPERIMENTS="exp1_mcp_overhead"
SCENARIO_DOMAIN_SCOPE="multi_domain"

MODEL_PROVIDER="vllm"
SERVING_STACK="insomnia_vllm"
QUANTIZATION_MODE="fp16"
# 32768 matches the repo's canonical benchmark-context lane. See #135.
MAX_MODEL_LEN=32768
TEMPERATURE=0.0
MAX_TOKENS=0

LAUNCH_VLLM=1
VLLM_MODEL_PATH="models/Llama-3.1-8B-Instruct"
VLLM_PORT=8000
VLLM_ENABLE_AUTO_TOOL_CHOICE=1
VLLM_TOOL_CALL_PARSER="llama3_json"

# Lane 2 optimization knobs (#30):
#   --enable-prefix-caching: AOB system prompt + tool catalog are identical
#       across every turn and every scenario. Prefix caching skips re-prefill
#       of those tokens after the first turn. Direct win for ReAct workloads.
#       Lane 2 smoke (job 8979532) measured 7.77s → 5.64s wall-clock on
#       multi_01_end_to_end_fault_response (-27%). See status doc.
# `--kv-cache-dtype fp8` was the original Lane 2 second pick but failed in
# the smoke: vLLM 0.19.0 FlashAttention-3 kernel rejects fp8 KV with FP16
# weights ("For FP8 input, output must have dtype BF16"). Switching the
# model to BF16 to enable fp8 KV would change inference precision and
# confound (B-A) and (B-C). Dropped from this config; documented in
# status doc.
# INT8 weight quantization is intentionally NOT enabled — see
# docs/lane2_int8_kv_status.md §"Why we're deferring INT8".
EXTRA_VLLM_ARGS="--enable-prefix-caching"

ENABLE_WANDB=1
WANDB_ENTITY="assetopsbench-smartgrid"
WANDB_PROJECT="assetopsbench-smartgrid"
WANDB_MODE="online"

# Cell C optimization knobs — wired into scripts/aat_runner.py --mcp-mode optimized.
# Keep parallel tool calls disabled for the canonical Insomnia/Llama-vLLM path;
# Cell C's measured optimization is connection reuse plus prefix caching.
AAT_MCP_SERVER_LAUNCH_MODE="python"
AAT_MCP_CLIENT_TIMEOUT_SECONDS=120
AAT_PARALLEL_TOOL_CALLS=false

# Torch profiler: captures one replay pass per run while vLLM is still live.
TORCH_PROFILE="${TORCH_PROFILE:-1}"