#!/bin/bash
# Example SLURM array script for flat dense retrieval (GTE 7B) with top-k=20.
#
# Usage:
#   1. Set SLURM_ACCOUNT, PARTITIONS, PROJECT_DIR, and MODEL_NAME below.
#   2. Make sure shards exist under $SHARD_ROOT/dataset/ and $SHARD_ROOT/ret_cache/
#      (see scripts/make_v5_shards.py and scripts/build_retrieval_cache.py).
#   3. sbatch scripts/slurm/example_dense_retrieval.slurm
#
#SBATCH -J dense_gte_topk20
#SBATCH -A ${SLURM_ACCOUNT:-your-account}
#SBATCH -p ${PARTITIONS:-cpu}
#SBATCH --nodes=1
#SBATCH --time=04:00:00
#SBATCH --array=0-7
#SBATCH --output=logs/dense_gte_topk20_%A_%a.log
#SBATCH --export=ALL,NV_API_KEY

set -euo pipefail

PROJECT_DIR="${PROJECT_DIR:-$(pwd)}"
cd "$PROJECT_DIR"

MODEL_NAME="${MODEL_NAME:-gpt-5.5}"     # any key from model_zoo.py
TOP_K="${TOP_K:-20}"
SHARD_ROOT="${SHARD_ROOT:-output/shards/v5_${MODEL_NAME//./_}_nchunks10}"

shard_id=$(printf "%02d" "$SLURM_ARRAY_TASK_ID")

export ret_cache="$SHARD_ROOT/ret_cache/shard_${shard_id}.jsonl"

python main.py \
    --in_file  "$SHARD_ROOT/dataset/shard_${shard_id}.json" \
    --out_file "$SHARD_ROOT/dense_gte_topk${TOP_K}/part_${shard_id}.jsonl" \
    --model_name "$MODEL_NAME" \
    --top_k "$TOP_K" \
    --n_chunks 10 \
    --nvidia \
    --all_sessions_file dataset/all_sessions.json \
    --no_semantic \
    --mode embed