Add collaborativeagents module and update gitignore

- Add collaborativeagents subproject with adapters, agents, and evaluation modules
- Update .gitignore to exclude large binary files (.whl, .tar), wandb logs, and results

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

1 files changed, 121 insertions, 0 deletions

diff --git a/collaborativeagents/scripts/scale_test_batch1.sbatch b/collaborativeagents/scripts/scale_test_batch1.sbatch
new file mode 100644
index 0000000..119be9b
--- /dev/null
+++ b/collaborativeagents/scripts/scale_test_batch1.sbatch
@@ -0,0 +1,121 @@
+#!/bin/bash
+#SBATCH --job-name=scale_b1
+#SBATCH --account=bfqt-delta-gpu
+#SBATCH --partition=gpuH200x8-interactive
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=32
+#SBATCH --gres=gpu:4
+#SBATCH --mem=200G
+#SBATCH --time=01:00:00
+#SBATCH --output=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/scale_b1-%j.out
+#SBATCH --error=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/scale_b1-%j.err
+
+# Scale Test Batch 1: Users 1-5, 15 sessions each, 3 methods
+# With CollaborativeAgents-style prompts
+
+cd /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents
+source /u/yurenh2/miniforge3/etc/profile.d/conda.sh
+conda activate eval
+export HF_HOME=/projects/bfqt/users/yurenh2/hf_cache/huggingface
+export PYTHONPATH="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/src:$PYTHONPATH"
+
+PROFILE_PATH="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents/data/complex_profiles_v2/profiles_200.jsonl"
+AGENT_MODEL="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct"
+USER_MODEL="meta-llama/Llama-3.1-70B-Instruct"
+MEMORY_STORE="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/data/corpora/empty_store"
+
+echo "=== Scale Test Batch 1: 5 users × 15 sessions × 3 methods ==="
+date
+nvidia-smi --query-gpu=index,name,memory.total --format=csv
+
+# Start vLLM servers
+CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
+    --model $USER_MODEL \
+    --port 8004 --tensor-parallel-size 2 --gpu-memory-utilization 0.90 \
+    --max-model-len 16384 --dtype bfloat16 --download-dir $HF_HOME &
+
+CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.openai.api_server \
+    --model $AGENT_MODEL \
+    --port 8003 --tensor-parallel-size 2 --gpu-memory-utilization 0.45 \
+    --max-model-len 16384 --dtype bfloat16 &
+
+echo "Waiting for vLLM servers..."
+for i in {1..200}; do
+    if curl -s http://localhost:8004/health > /dev/null 2>&1; then
+        echo "User simulator ready after $((i*5))s"
+        break
+    fi
+    sleep 5
+done
+for i in {1..60}; do
+    if curl -s http://localhost:8003/health > /dev/null 2>&1; then
+        echo "Agent ready after $((i*5))s"
+        break
+    fi
+    sleep 5
+done
+sleep 5
+
+OUTPUT_DIR="../results/scale_test_$(date +%Y%m%d_%H%M%S)"
+
+# Run each method with 5 profiles, 15 sessions
+for METHOD in vanilla rag rag_vector; do
+    echo ""
+    echo "============================================"
+    echo "Testing: $METHOD (5 users × 15 sessions)"
+    echo "============================================"
+
+    # Clear memory store before each method
+    > ${MEMORY_STORE}/memory_cards.jsonl
+    rm -f ${MEMORY_STORE}/memory_embeddings.npy
+
+    date
+    python scripts/run_experiments.py --methods $METHOD \
+        --datasets math-hard --n-profiles 5 --n-sessions 15 --max-turns 15 \
+        --use-vllm --no-batch-processing --parallel-profiles 1 \
+        --output-dir $OUTPUT_DIR --profile-path $PROFILE_PATH
+
+    echo "Method $METHOD completed"
+    if [ "$METHOD" != "vanilla" ]; then
+        echo "Final memory cards: $(wc -l < ${MEMORY_STORE}/memory_cards.jsonl 2>/dev/null || echo 0)"
+    fi
+done
+
+echo ""
+echo "=== Scale Test Batch 1 Complete ==="
+date
+
+# Generate comparison
+python3 << 'PYEOF'
+import json
+from pathlib import Path
+
+output_base = sorted(Path("../results").glob("scale_test_*"))[-1]
+print(f"\n=== Results Summary ===\nDir: {output_base}\n")
+
+methods = ["vanilla", "rag", "rag_vector"]
+results = {}
+
+for subdir in output_base.iterdir():
+    if subdir.is_dir():
+        for method in methods:
+            result_file = subdir / method / "results.json"
+            if result_file.exists() and method not in results:
+                with open(result_file) as f:
+                    results[method] = json.load(f)
+
+if results:
+    print(f"{'Method':<12} {'Success':<10} {'Turns':<10} {'Enforce':<10} {'Sessions':<10}")
+    print("-" * 55)
+    for method in methods:
+        if method in results:
+            data = results[method]
+            n = len(data)
+            succ = sum(r['metrics']['task_success'] for r in data) / n
+            turns = sum(r['metrics']['total_turns'] for r in data) / n
+            enf = sum(r['metrics']['enforcement_count'] for r in data) / n
+            print(f"{method:<12} {succ:<10.1%} {turns:<10.1f} {enf:<10.1f} {n:<10}")
+PYEOF
+
+pkill -f "vllm.entrypoints" 2>/dev/null || true