Add BEIR analysis notebook for CosQA and update dependencies

- Created a new Jupyter notebook for analyzing BEIR dataset with CosQA using Ollama embeddings.
- Implemented a custom embedding class to integrate LangChain's OllamaEmbeddings with BEIR.
- Added data loading and evaluation logic for the CosQA dataset.
- Updated `uv.lock` to remove unnecessary dependencies (`mteb` and `polars`) and incremented revision number.

pyproject.toml

@@ -28,7 +28,5 @@ dev = [
     "beir>=2.2.0",
     "jupyter>=1.1.1",
     "langfuse>=3.14.4",
-    "mteb>=2.8.8",
-    "polars>=1.38.1",
     "ruff>=0.15.1",
 ]

scratches/acano/es_ingestion.ipynb

@@ -235,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "c35182b8",
    "metadata": {},
    "outputs": [],
@@ -277,7 +277,7 @@
     "\n",
     "\n",
     "chunks = build_chunks_from_folder(\n",
-    "    folder_path=\"/home/acano/PycharmProjects/assistance-engine/data\",\n",
+    "    folder_path=\"/home/acano/PycharmProjects/assistance-engine/data/raw/avap_docs_web\",\n",
     "    chunking_strategy=\"fixed\",\n",
     "    chunk_size=500,\n",
     "    overlap=25,\n",

scratches/pseco/evaluation/embeddings/datasets/scifact/qrels/test.tsv

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scratches/pseco/evaluation/embeddings/datasets/scifact/qrels/train.tsv

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scratches/pseco/evaluation/embeddings/n00 Beir Analysis_cosqa.ipynb

@@ -0,0 +1,295 @@
+{
+ "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1db7d110",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "assistance-engine",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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