{
  "filename": "rollout-2026-01-31T07-51-36-019c1309-3467-7613-a009-e6e76be0c721.jsonl",
  "date": "2026-01-31",
  "session_id": "019c1309-3467-7613-a009-e6e76be0c721",
  "first_timestamp": "2026-01-31T07:51:36.296Z",
  "last_timestamp": "2026-01-31T07:56:37.616Z",
  "duration_seconds": 301.3,
  "model": "gpt-5.2-codex",
  "cli_version": "0.92.0",
  "approval_policy": "on-request",
  "effort": "high",
  "raw_event_count": 229,
  "user_messages": [
    {
      "index": 0,
      "text": "<permissions instructions>Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `read-only`: The sandbox only permits reading files. Network access is restricted. Approvals are your mechanism to get user consent to run shell commands without the sandbox. `approval_policy` is `on-request`: Commands will be run in the sandbox by default, and you can specify in your tool call if you want to escalate a command to run without sandboxing. If the completing the task requires escalated permissions, Do not let these settings or the sandbox deter you from attempting to accomplish the user's task.\n\nHere are scenarios where you'll need to request approval:\n- You need to run a command that writes to a directory that requires it (e.g. running tests that write to /var)\n- You need to run a GUI app (e.g., open/xdg-open/osascript) to open browsers or files.\n- You are running sandboxed and need to run a command that requires network access (e.g. installing packages)\n- If you run a command that is important to solving the user's query, but it fails because of sandboxing, rerun the command with approval. ALWAYS proceed to use the `sandbox_permissions` and `justification` parameters - do not message the user before requesting approval for the command.\n- You are about to take a potentially destructive action such as an `rm` or `git reset` that the user did not explicitly ask for.\n\nWhen requesting approval to execute a command that will require escalated privileges:\n  - Provide the `sandbox_permissions` parameter with the value `\"require_escalated\"`\n  - Include a short, 1 sentence explanation for why you need escalated permissions in the justification parameter</permissions instructions>",
      "timestamp": "2026-01-31T07:51:36.296Z"
    },
    {
      "index": 1,
      "text": "# AGENTS.md instructions for /home/ubuntu/nikhil/cold-ranker\n\n<INSTRUCTIONS>\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.\n### Available skills\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)\n### How to use skills\n- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.\n- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.\n- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.\n- How to use a skill (progressive disclosure):\n  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.\n  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.\n  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.\n  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.\n- Coordination and sequencing:\n  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.\n- Context hygiene:\n  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.\n  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.\n  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.\n- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.\n</INSTRUCTIONS>",
      "timestamp": "2026-01-31T07:51:36.297Z"
    },
    {
      "index": 2,
      "text": "<environment_context>\n  <cwd>/home/ubuntu/nikhil/cold-ranker</cwd>\n  <shell>bash</shell>\n</environment_context>",
      "timestamp": "2026-01-31T07:51:36.297Z"
    },
    {
      "index": 3,
      "text": "# Context from my IDE setup:\n\n## Open tabs:\n- en: data/en\n- dataset.py: col/src/model/dataset.py\n- mapping.py: col/src/data_prep/mapping.py\n- user_meta.py: col/src/data_prep/user_meta.py\n- interaction.py: col/src/data_prep/interaction.py\n\n## My request for Codex:\nthis is from another repo\n\nimport pandas as pd\nimport numpy as np\nfrom collections import OrderedDict\nfrom tqdm.auto import tqdm\nimport faiss\n\nimport time\nfrom ast import literal_eval\nimport gzip\nimport shutil\nimport s3fs, boto3\nfrom fastavro import writer, parse_schema\nfrom config import (\n    TQDM_DISABLE_MODE,\n    ALGO_NAME,\n    WIDGET_NAME,\n    PRODUCTION_BUCKET,\n    SCHEMA_PATH,\n)\nfrom utils.logger import logger\nfrom data_prep import get_pratilipi_meta\n\ns3_client = boto3.client(\"s3\")\ns3 = s3fs.S3FileSystem(anon=False)\n\n\ndef make_avro_format(\n    df: pd.DataFrame, category: list, algo_name: str, language: str\n) -> pd.DataFrame:\n    user_id_list = []\n    categories_list = []\n\n    for i, row in df.iterrows():\n        user_id_list.append(row[\"user_id\"])\n\n        cat_list = []\n        for cat in category:\n            uid_dict = {}\n            uid_dict[\"widgetName\"] = cat\n            uid_dict[\"algorithmName\"] = algo_name\n\n            contentList = []\n            for s, fp in zip(row[\"recommended_items\"], row[\"first_pratilipi\"]):\n                content_list_dict = {}\n                content_list_dict[\"contentId\"] = int(s)\n                content_list_dict[\"contentType\"] = \"SERIES\"\n                content_list_dict[\"firstPratilipiId\"] = int(fp)\n\n                contentList.append(content_list_dict)\n\n            uid_dict[\"contentList\"] = contentList\n\n            cat_list.append(uid_dict)\n\n        categories_list.append(cat_list)\n\n    df_avro = pd.DataFrame(\n        {\n            \"userId\": user_id_list,\n            \"data\": categories_list,\n        }\n    )\n\n    df_avro = df_avro.to_dict(orient=\"records\")\n    schema = parse_schema(\n        literal_eval(s3.open(SCHEMA_PATH, mode=\"rb\").read().decode(\"utf-8\"))\n    )\n    file_name = f\"{language}.avro\"\n    with open(file_name, \"ab+\") as out:\n        writer(out, schema, df_avro)\n\n    with open(file_name, \"rb\") as f_in:\n        with gzip.open(file_name + \".gz\", \"wb\") as f_out:\n            shutil.copyfileobj(f_in, f_out)\n\n    s3_client.upload_file(\n        Filename=f\"{file_name}.gz\",\n        Bucket=PRODUCTION_BUCKET,\n        Key=f\"production/{algo_name}/premium_home/{file_name}.gz\",\n    )\n    logger.i(\"Avro file uploaded\")\n    return df_avro\n\n\ndef recommend_with_retries(\n    language_key, user_vecs, faiss_index, id2user, id2item, user_history, streak\n):\n    \"\"\"\n    Run batched FAISS recommendations with automatic retries (doubling topk)\n    until min_required recs are satisfied or max_topk is reached.\n    \"\"\"\n\n    pratilipi_meta = get_pratilipi_meta(language_key)\n    pratilipi_meta = (\n        pratilipi_meta[[\"pratilipi_id\", \"series_id\", \"part\"]].dropna().astype(int)\n    )\n    pratilipi_meta[[\"pratilipi_id\", \"series_id\"]] = pratilipi_meta[\n        [\"pratilipi_id\", \"series_id\"]\n    ]\n\n    mapper_pratilipi_series = dict(\n        zip(pratilipi_meta[\"pratilipi_id\"], pratilipi_meta[\"series_id\"])\n    )\n\n    a = pratilipi_meta.loc[pratilipi_meta.groupby(\"series_id\")[\"part\"].idxmin()]\n    mapper_series_to_first_pratilipi = dict(zip(a[\"series_id\"], a[\"pratilipi_id\"]))\n\n    results = []\n    failed_users = np.arange(len(user_vecs))  # start with all users\n    current_topk = 2000\n    batch_size = 40000\n    flag = 0\n    for start in tqdm(\n        range(0, len(failed_users), batch_size), disable=TQDM_DISABLE_MODE\n    ):\n        batch_users = failed_users[start : start + batch_size]\n        user_emb = user_vecs[batch_users]\n\n        # Search\n        _, I = faiss_index.search(user_emb, current_topk)\n\n        for row_idx, u in enumerate(batch_users):\n            uid = id2user[u]\n            seen = user_history.get(uid, set())\n            candidates = [id2item[i] for i in I[row_idx]]\n            recs = [it for it in candidates if it not in seen]\n            item_tokens = [\n                mapper_pratilipi_series[j] for j in recs if j in mapper_pratilipi_series\n            ]\n            item_tokens = list(OrderedDict.fromkeys(item_tokens).keys())\n\n            if len(item_tokens) < 100:\n                flag = flag + 1\n                random_sample = np.random.choice(\n                    streak.series_id, size=100, replace=False, p=streak.prob\n                ).tolist()\n                item_tokens = list(\n                    OrderedDict.fromkeys(item_tokens + random_sample).keys()\n                )\n            else:\n                item_tokens = item_tokens[:100]\n            first_pratilipi_tokens = [\n                mapper_series_to_first_pratilipi[i] for i in item_tokens\n            ]\n            results.append(\n                {\n                    \"user_id\": uid,\n                    \"recommended_items\": item_tokens,\n                    \"first_pratilipi\": first_pratilipi_tokens,\n                }\n            )\n\n    logger.i(\n        f\"✅ Done. Generated recs for {len(results)} users. in which streak fallback count {flag}\"\n    )\n    results = pd.DataFrame(results)\n    row = results.iloc[0].copy()\n    row[\"user_id\"] = -1\n    results = pd.concat([pd.DataFrame([row]), results], ignore_index=True)\n    results.to_parquet(f\"{language_key}_recommendations.parquet\", index=False)\n    return results\n\n\ndef inference(\n    language, language_key, user_vecs, item_vecs, user_history, streak, id2user, id2item\n):\n    # -------------------------------------------------\n    # 2. Normalize embeddings for cosine similarity\n    # -------------------------------------------------\n    start_time = time.time()\n\n    faiss.normalize_L2(user_vecs)\n    faiss.normalize_L2(item_vecs)\n\n    dim = user_vecs.shape[1]\n    faiss_index = faiss.index_cpu_to_gpu(\n        faiss.StandardGpuResources(), 0, faiss.IndexFlatIP(dim)\n    )\n\n    for ptr in range(0, item_vecs.shape[0], 20_000):\n        faiss_index.add(item_vecs[ptr : ptr + 20_000])\n    logger.i(f\"✅ FAISS index built with {faiss_index.ntotal} items\")\n\n    results = recommend_with_retries(\n        language_key, user_vecs, faiss_index, id2user, id2item, user_history, streak\n    )\n\n    avro_df = make_avro_format(results, WIDGET_NAME, ALGO_NAME, language)\n\n    print(f\"{language} Done\")\n    t = (time.time() - start_time) / 3600\n    logger.i(f\"Total time spent in inferencing {language} - {t} hours\")\n    return results, avro_df\n\nsimilar to this i want to generate the avro file in the same from in the end by recommend.py not the jsonl. please do the needful\n",
      "timestamp": "2026-01-31T07:51:36.357Z"
    }
  ],
  "assistant_messages": [
    {
      "index": 0,
      "text": "Updated `col/src/model/recommend.py` to emit AVRO in the same structure as your other repo (and optional `.gz`), instead of JSONL by default. It now builds widget/algorithm/contentList records, maps `series_id -> firstPratilipiId`, and writes AVRO in batches.\n\nDetails / usage knobs:\n- Default output is now AVRO: `../../data/{LANGUAGE_KEY}/{LANGUAGE}.avro`.\n- Default widget/algorithm come from env vars `WIDGET_NAMES`/`WIDGET_NAME` and `ALGO_NAME` (fallbacks: `premium_home`, `cold-ranker`).\n- Series→first pratilipi mapping is loaded from `SERIES_PART_PATH` if set, otherwise `s3://datascience-commons/commons/series_v2/{LANGUAGE_KEY}-series.parquet`.\n- Optional schema override via `AVRO_SCHEMA_PATH`; otherwise a built‑in schema matching your sample.\n- Gzip output is enabled by default; disable with `gzip_output=False`.\n- The “userId = -1” default row is included; disable with `include_default_user=False`.\n- You can still get JSONL by calling `driver(output_format=\"jsonl\")`.\n\nFiles changed:\n- `col/src/model/recommend.py`\n\nIf you want, I can also wire these defaults into `config/config.py` or add an explicit CLI flag for output format.\n\nNext steps (pick one):\n1) Run your driver as-is to generate AVRO.\n2) Set `WIDGET_NAME`, `ALGO_NAME`, and optionally `AVRO_SCHEMA_PATH` / `SERIES_PART_PATH` before running.\n3) Call `recommend.driver(output_format=\"jsonl\")` if you still need JSONL for comparison.",
      "timestamp": "2026-01-31T07:56:37.615Z"
    }
  ],
  "tool_calls": [
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\"}",
      "output_summary": "Exit code: 101\nWall time: 0 seconds\nOutput:\n\nthread 'main' (24893) panicked at linux-sandbox/src/linux_run_main.rs:30:9:\nerror running landlock: Sandbox(LandlockRestrict)\nnote: run with `RUST_BACKTRAC..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to list repository files; sandbox blocked landlock.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol\ndata\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubunt..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg --files -g 'AGENTS.md'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to locate AGENTS.md instructions in repo.\"}",
      "output_summary": "Exit code: 1\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg --files -g 'recommend.py'\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to locate recommend.py in repo.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol/src/model/recommend.py\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: li..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,240p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need to inspect recommend.py to modify ou...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport json\nimport os\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom model.model import CTRModelWithCosineSignals\nfrom utils.utils import load_pic..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '240,520p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Continue inspecting recommend.py.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n                age_b,\n                total_interactions_b,\n                if_pref_b,\n                target_rating_count,\n            )\n            scores ..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"avro|SCHEMA_PATH|PRODUCTION_BUCKET|WIDGET_NAME|ALGO_NAME\\\" col/src -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Need t...",
      "output_summary": "Exit code: 1\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,220p' col/src/config/config.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect config for existing constants neede...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom openai import OpenAI\n\nLANGUAGE=\"hindi\"\nLANGUAGE_KEY=\"hi\"\n\nMODE='prod'\n\ndef _env_flag(name: str, default: bool = False) -> bool:\n    value = os...."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"get_pratilipi_meta\\\" -S col/src\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Look for get_pratilipi_meta implementation t...",
      "output_summary": "Exit code: 1\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg --files -g '*.avsc' -g '*.json' -g '*schema*' data col -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Search for avro schema ...",
      "output_summary": "Exit code: 1\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"boto3|s3fs|fastavro|avro\\\" col/src data -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check if avro or s3 utilities alr...",
      "output_summary": "Exit code: 1\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg --files -g 'requirements*.txt' -g 'pyproject.toml' -g 'environment*.yml' -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Find ...",
      "output_summary": "Exit code: 1\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls -la data\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect data directory for metadata files.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ntotal 12\ndrwxrwxr-x 3 ubuntu ubuntu 4096 Jan 31 07:44 .\ndrwxrwxr-x 4 ubuntu ubuntu 4096 Jan 31 02:03 ..\ndrwxrwxr-x 2 ubuntu ubuntu 4096 Jan 31 07:44 en\n/home/..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls -la data/en\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect data/en for meta files needed for avro mapping.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ntotal 8\ndrwxrwxr-x 2 ubuntu ubuntu 4096 Jan 31 07:44 .\ndrwxrwxr-x 3 ubuntu ubuntu 4096 Jan 31 07:44 ..\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"pratilipi|series_id|first_pratilipi\\\" col/src/data_prep -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Search data_prep ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol/src/data_prep/data_prep.py:22:    series_meta_df = series_meta_df.sort_values(\"target_series_id\").reset_index(drop=True)\ncol/src/data_prep/data_prep.py:25..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,240p' col/src/data_prep/series_meta.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect series_meta module for seri...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nfrom utils.utils import chunked\nfrom utils.athena import query_from_athena\nimport pandas as pd\nfrom config.config import BATCH_SIZE, get_openai_client\nfrom tq..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' col/src/data_prep/interaction.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect interaction data prep to se...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nfrom pandarallel import pandarallel\n\ndef get_user_series_interaction(language, language_key):\n    up_df = pd.read_parqu..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"driver\\\\(\\\" col/src -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Find how recommend.driver is used to ensure interface...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ncol/src/driver.py:26:    # data_prep.driver()\ncol/src/driver.py:28:    # training.driver()\ncol/src/driver.py:30:    recommend.driver()\ncol/src/model/training...."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,120p' col/src/driver.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect top-level driver to see recommendation out...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport os\nfrom datetime import datetime\nos.environ[\"AWS_DEFAULT_REGION\"] = \"ap-south-1\"\nos.environ[\"OPENAI_API_KEY\"] = \"sk-proj-qRoswT4wSHS3R4i7ZcIbP3FW5XT0xI..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"rg -n \\\"logger\\\" col/src/utils -S\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Check logger util for consistent logging.\"}",
      "output_summary": "Exit code: 1\nWall time: 0 seconds\nOutput:\n/home/ubuntu/.profile: line 28: /home/ubuntu/miniconda3/etc/profile.d/conda.sh: No such file or directory\n/home/ubuntu/.profile: line 29: /home/ubuntu/minicon..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"ls -la col/src/utils\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"List utils modules.\"}",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\ntotal 24\ndrwxrwxr-x 3 ubuntu ubuntu 4096 Jan 28 15:44 .\ndrwxrwxr-x 7 ubuntu ubuntu 4096 Jan 31 02:07 ..\ndrwxrwxr-x 2 ubuntu ubuntu 4096 Jan 30 16:55 __pycache..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,200p' col/src/data_prep/mapping.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect mapping module for any avro-rel...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport numpy as np\nfrom config.config import CATEGORICAL_COLUMNS, CONTINUOUS_COLUMNS, EMBEDDING_COLUMNS_TO_SIZE\n\nclass Normalizer:\n    def __init__(self, list..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,260p' col/src/data_prep/user_meta.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Inspect user_meta module for similar ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport pandas as pd\nimport numpy as np\nfrom utils.athena import query_from_athena\nfrom utils.utils import chunked\nimport mysql.connector\nfrom concurrent.futur..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '360,520p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"View driver function section to modify ...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n            target_rating_count = series_meta_floats_t[\n                start:end, series_meta_float_slices[\"target_rating_count\"]\n            ]\n            t..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '520,720p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"View driver function code for editing.\"...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n                target_prod2vec_emb_b,\n                target_average_rating_b,\n                age_b,\n                total_interactions_b,\n                i..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '1,220p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Review updated recommend.py for correctne...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\nimport json\nimport os\nimport gzip\nimport shutil\nfrom ast import literal_eval\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom model.model import CTR..."
    },
    {
      "name": "shell_command",
      "input_summary": "{\"command\":\"sed -n '520,760p' col/src/model/recommend.py\",\"workdir\":\"/home/ubuntu/nikhil/cold-ranker\",\"sandbox_permissions\":\"require_escalated\",\"justification\":\"Review updated driver section for corre...",
      "output_summary": "Exit code: 0\nWall time: 0 seconds\nOutput:\n                target_prod2vec_emb_b,\n                target_average_rating_b,\n                age_b,\n                total_interactions_b,\n                i..."
    }
  ],
  "turn_count": 4,
  "files_touched": [],
  "summary_from_context": "auto",
  "metrics": {
    "outcome": "resolved",
    "session_type": "bug-fix",
    "iteration_count": 4,
    "struggle_score": 2,
    "prompt_specificity": 5,
    "time_of_day": "morning"
  }
}