{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66cbbaf8",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c01c19dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "from typing import Dict, List, Union\n",
    "import numpy as np\n",
    "from datasets import load_dataset\n",
    "from langchain_ollama import OllamaEmbeddings\n",
    "from beir.datasets.data_loader import GenericDataLoader\n",
    "from beir.retrieval.search.dense import DenseRetrievalExactSearch\n",
    "from beir.retrieval.evaluation import EvaluateRetrieval\n",
    "from beir import util"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac011c1c",
   "metadata": {},
   "source": [
    "# Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b83e7900",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BEIROllamaEmbeddings:\n",
    "    \"\"\"\n",
    "    Adapter that makes LangChain's OllamaEmbeddings compatible with BEIR.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        base_url: str,\n",
    "        model: str,\n",
    "        batch_size: int = 64,\n",
    "    ) -> None:\n",
    "        self.batch_size = batch_size\n",
    "        self.embeddings = OllamaEmbeddings(\n",
    "            base_url=base_url,\n",
    "            model=model,\n",
    "        )\n",
    "\n",
    "    def _batch_embed(self, texts: List[str]) -> np.ndarray:\n",
    "        vectors = []\n",
    "\n",
    "        for i in range(0, len(texts), self.batch_size):\n",
    "            batch = texts[i : i + self.batch_size]\n",
    "            batch_vectors = self.embeddings.embed_documents(batch)\n",
    "            vectors.extend(batch_vectors)\n",
    "\n",
    "        return np.asarray(vectors, dtype=np.float32)\n",
    "\n",
    "    def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray:\n",
    "        \"\"\"\n",
    "        BEIR query encoder\n",
    "        \"\"\"\n",
    "        return self._batch_embed(queries)\n",
    "\n",
    "    def encode_corpus(\n",
    "        self,\n",
    "        corpus: Union[List[Dict[str, str]], Dict[str, Dict[str, str]]],\n",
    "        **kwargs,\n",
    "    ) -> np.ndarray:\n",
    "        \"\"\"\n",
    "        BEIR corpus encoder\n",
    "        \"\"\"\n",
    "        if isinstance(corpus, dict):\n",
    "            corpus = list(corpus.values())\n",
    "\n",
    "        texts = []\n",
    "        for doc in corpus:\n",
    "            title = (doc.get(\"title\") or \"\").strip()\n",
    "            text = (doc.get(\"text\") or \"\").strip()\n",
    "\n",
    "            if title:\n",
    "                texts.append(f\"{title}\\n{text}\")\n",
    "            else:\n",
    "                texts.append(text)\n",
    "\n",
    "        return self._batch_embed(texts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9528fb6",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "230aae25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descargando datos de Hugging Face...\n",
      "Cargando con BEIR GenericDataLoader...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e67479e959248f598db3415efbb13ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20604 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_name = \"cosqa\"\n",
    "data_path = f\"/home/acano/PycharmProjects/assistance-engine/data/external/{dataset_name}\"\n",
    "\n",
    "os.makedirs(f\"{data_path}/qrels\", exist_ok=True)\n",
    "\n",
    "# 1. Cargar desde Hugging Face con los nombres de configuración correctos\n",
    "print(\"Descargando datos de Hugging Face...\")\n",
    "hf_corpus = load_dataset(\"CoIR-Retrieval/cosqa\", \"corpus\", split=\"corpus\")\n",
    "hf_queries = load_dataset(\"CoIR-Retrieval/cosqa\", \"queries\", split=\"queries\")\n",
    "# Los qrels están en la config 'default'\n",
    "hf_qrels = load_dataset(\"CoIR-Retrieval/cosqa\", \"default\", split=\"test\")\n",
    "\n",
    "# 2. Guardar Corpus\n",
    "with open(f\"{data_path}/corpus.jsonl\", \"w\") as f:\n",
    "    for item in hf_corpus:\n",
    "        f.write(json.dumps({\"_id\": str(item[\"_id\"]), \"text\": item[\"text\"], \"title\": \"\"}) + \"\\n\")\n",
    "\n",
    "# 3. Guardar Queries\n",
    "with open(f\"{data_path}/queries.jsonl\", \"w\") as f:\n",
    "    for item in hf_queries:\n",
    "        f.write(json.dumps({\"_id\": str(item[\"_id\"]), \"text\": item[\"text\"]}) + \"\\n\")\n",
    "\n",
    "# 4. Guardar Qrels (Formato TSV para BEIR)\n",
    "with open(f\"{data_path}/qrels/test.tsv\", \"w\") as f:\n",
    "    f.write(\"query-id\\tcorpus-id\\tscore\\n\")\n",
    "    for item in hf_qrels:\n",
    "        # En la config 'default', los campos suelen ser 'query-id' y 'corpus-id'\n",
    "        f.write(f\"{item['query-id']}\\t{item['corpus-id']}\\t{item['score']}\\n\")\n",
    "\n",
    "print(\"Cargando con BEIR GenericDataLoader...\")\n",
    "corpus, queries, qrels = GenericDataLoader(data_path).load(split=\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13050d31",
   "metadata": {},
   "source": [
    "# Test qwen3-0.6B-emb:latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "514540af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NDCG: {'NDCG@1': 0.174, 'NDCG@3': 0.27374, 'NDCG@5': 0.33509, 'NDCG@10': 0.39086, 'NDCG@100': 0.45099}\n",
      "MAP: {'MAP@1': 0.174, 'MAP@3': 0.247, 'MAP@5': 0.2808, 'MAP@10': 0.30466, 'MAP@100': 0.31702}\n",
      "Recall: {'Recall@1': 0.174, 'Recall@3': 0.352, 'Recall@5': 0.502, 'Recall@10': 0.67, 'Recall@100': 0.952}\n",
      "Precision: {'P@1': 0.174, 'P@3': 0.11733, 'P@5': 0.1004, 'P@10': 0.067, 'P@100': 0.00952}\n"
     ]
    }
   ],
   "source": [
    "model = BEIROllamaEmbeddings(\n",
    "    base_url=\"http://localhost:11434\",\n",
    "    model=\"qwen3-0.6B-emb:latest\",\n",
    "    batch_size=64,\n",
    ")\n",
    "\n",
    "retriever = DenseRetrievalExactSearch(model, batch_size=64)\n",
    "evaluator = EvaluateRetrieval(retriever, score_function=\"cos_sim\")\n",
    "\n",
    "results = evaluator.retrieve(corpus, queries)\n",
    "ndcg, _map, recall, precision = evaluator.evaluate(\n",
    "    qrels, results, [1, 3, 5, 10, 100]\n",
    ")\n",
    "\n",
    "print(\"NDCG:\", ndcg)\n",
    "print(\"MAP:\", _map)\n",
    "print(\"Recall:\", recall)\n",
    "print(\"Precision:\", precision)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e643ca",
   "metadata": {},
   "source": [
    "# Test qwen2.5:1.5b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ced1c25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NDCG: {'NDCG@1': 0.0, 'NDCG@3': 0.0, 'NDCG@5': 0.0, 'NDCG@10': 0.0, 'NDCG@100': 0.0021}\n",
      "MAP: {'MAP@1': 0.0, 'MAP@3': 0.0, 'MAP@5': 0.0, 'MAP@10': 0.0, 'MAP@100': 0.00043}\n",
      "Recall: {'Recall@1': 0.0, 'Recall@3': 0.0, 'Recall@5': 0.0, 'Recall@10': 0.0, 'Recall@100': 0.01}\n",
      "Precision: {'P@1': 0.0, 'P@3': 0.0, 'P@5': 0.0, 'P@10': 0.0, 'P@100': 0.0001}\n"
     ]
    }
   ],
   "source": [
    "model_qwen2 = BEIROllamaEmbeddings(\n",
    "    base_url=\"http://localhost:11434\",\n",
    "    model=\"qwen2.5:1.5b\",\n",
    "    batch_size=64,\n",
    ")\n",
    "\n",
    "retriever_qwen_2 = DenseRetrievalExactSearch(model_qwen2, batch_size=64)\n",
    "evaluator_qwen_2 = EvaluateRetrieval(retriever_qwen_2, score_function=\"cos_sim\")\n",
    "\n",
    "results_qwen_2 = evaluator_qwen_2.retrieve(corpus, queries)\n",
    "ndcg_qwen_2, _map_qwen_2, recall_qwen_2, precision_qwen_2 = evaluator_qwen_2.evaluate(\n",
    "    qrels, results_qwen_2, [1, 3, 5, 10, 100]\n",
    ")\n",
    "\n",
    "print(\"NDCG:\", ndcg_qwen_2)\n",
    "print(\"MAP:\", _map_qwen_2)\n",
    "print(\"Recall:\", recall_qwen_2)\n",
    "print(\"Precision:\", precision_qwen_2)"
   ]
<<<<<<< HEAD
=======
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1db7d110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resultados guardados en /home/acano/PycharmProjects/assistance-engine/data/interim/beir_cosqa_results.json\n"
     ]
    }
   ],
   "source": [
    "results_data = {\n",
    "    \"qwen3-0.6B-emb:latest\": {\n",
    "        \"NDCG\": ndcg,\n",
    "        \"MAP\": _map,\n",
    "        \"Recall\": recall,\n",
    "        \"Precision\": precision,\n",
    "    },\n",
    "    \"qwen2.5:1.5b\": {\n",
    "        \"NDCG\": ndcg_qwen_2,\n",
    "        \"MAP\": _map_qwen_2,\n",
    "        \"Recall\": recall_qwen_2,\n",
    "        \"Precision\": precision_qwen_2,\n",
    "    }\n",
    "}\n",
    " \n",
    "output_file = \"/home/acano/PycharmProjects/assistance-engine/data/interim/beir_cosqa_results.json\"\n",
    "with open(output_file, \"w\") as f:\n",
    "    json.dump(results_data, f, indent=2)\n",
    " \n",
    "print(f\"Resultados guardados en {output_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4f8d78b",
   "metadata": {},
   "outputs": [],
   "source": []
>>>>>>> 4b5352d93cf89b7562895b550fb5bd62160586c5
  }
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