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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d520f6c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from datasets import load_dataset\n",
    "\n",
    "import boto3\n",
    "from botocore.config import Config\n",
    "from langchain_core.messages import SystemMessage, HumanMessage\n",
    "\n",
    "from src.utils.llm_factory import create_chat_model\n",
    "from src.config import settings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08b9060",
   "metadata": {},
   "source": [
    "### Create LLM isntance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81111a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = Config(\n",
    "    region_name=\"us-east-1\",\n",
    "    connect_timeout=10,     \n",
    "    read_timeout=600,        \n",
    ")\n",
    "\n",
    "client = boto3.client(\"bedrock-runtime\", config=config)\n",
    "\n",
    "llm = create_chat_model(\n",
    "    provider=\"bedrock\",\n",
    "    client=client,\n",
    "    model=\"global.anthropic.claude-sonnet-4-6\",\n",
    "    temperature=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045f8e81",
   "metadata": {},
   "source": [
    "### Load AVAP data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "07dea3fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(settings.proj_root / \"docs/LRM/avap.md\", \"r\") as f:\n",
    "    avap_docs = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adbbe8b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 33 AVAP samples\n"
     ]
    }
   ],
   "source": [
    "samples_dir = settings.proj_root / \"docs/samples\"\n",
    "avap_samples = []\n",
    "\n",
    "for avap_file in sorted(samples_dir.glob(\"*.avap\")):\n",
    "    with open(avap_file, \"r\") as f:\n",
    "        code = f.read()\n",
    "    \n",
    "    avap_samples.append({\n",
    "        \"file\": avap_file.name,\n",
    "        \"code\": code\n",
    "    })\n",
    "\n",
    "# Display as JSON\n",
    "avap_samples_json = json.dumps(avap_samples, indent=2, ensure_ascii=False)\n",
    "print(f\"Loaded {len(avap_samples)} AVAP samples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a15e09a",
   "metadata": {},
   "source": [
    "### Prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "895a170f",
   "metadata": {},
   "outputs": [],
   "source": [
    "GOLDEN_DATASET_PROMPT = SystemMessage(\n",
    "    content=f\"\"\"\n",
    "    You are an AI agent responsible for generating a golden dataset of queries for AVAP code retrieval and understanding.\n",
    "\n",
    "    You will receive a JSON array of AVAP code samples, each with a 'file' name and 'code' content.\n",
    "\n",
    "    Your task is to:\n",
    "    1. Analyze each AVAP code sample.\n",
    "    2. Generate 2-3 natural language queries that can be answered by examining that specific code.\n",
    "    3. Output a JSON array where each element has:\n",
    "       - \"query\": A natural language question about AVAP code implementation, best practices, or specific constructs.\n",
    "       - \"context\": The filename of the code sample that provides the context/answer for this query.\n",
    "\n",
    "    Requirements:\n",
    "    - Queries should be diverse: ask about functions, control flow, API operations, error handling, etc.\n",
    "    - Queries must be answerable using ONLY the provided code samples.\n",
    "    - Queries should be framed as natural developer questions (e.g., \"How do you handle errors in AVAP?\" or \"Show me an example of looping over a list\").\n",
    "    - Use natural English (or Spanish if context is Spanish-language code).\n",
    "    - Do not reference exact variable names unless necessary; focus on the patterns and constructs used.\n",
    "    - Output MUST be valid JSON array format.\n",
    "\n",
    "    AVAP Code Samples:\n",
    "    {avap_samples_json}\n",
    "\n",
    "    Output format (JSON array):\n",
    "    [\n",
    "      {{\"query\": \"...\", \"context\": \"filename.avap\"}},\n",
    "      {{\"query\": \"...\", \"context\": \"filename.avap\"}},\n",
    "      ...\n",
    "    ]\n",
    "    \"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3123199",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98c4f93c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "723352ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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