{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1f56ae3-a056-4b31-bcab-27c2c97c00f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from dotenv import load_dotenv\n",
    "import numpy as np\n",
    "\n",
    "load_dotenv()\n",
    "\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"rag\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')\n",
    "from rag.parser.pdf import parser\n",
    "from rag.db.embeddings import Embeddings\n",
    "from rag.llm.encoder import Encoder\n",
    "from rag.db.documents import Documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b5cb12e-df7e-4532-b78b-216e11ed6161",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path(\"/home/aktersnurra/projects/library/quant/math/a-signal-processing-perspective-on-financial-engineering.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1a57be8-21a2-48d3-b99f-d1bbf7b8780a",
   "metadata": {},
   "outputs": [],
   "source": [
    "chunks = parser(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14408e20-3dec-40b4-9dda-3397beb0c453",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = Encoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e94993c5-6b19-4bac-b7e8-c26a5200a626",
   "metadata": {},
   "outputs": [],
   "source": [
    "embs = encoder.encode_document(chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b845bb31-0909-42cb-9957-9a8b3bb0b5c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embs[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa279b1a-465e-4820-ab56-b25fc513c0a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_db = Embeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1dc655de-2359-42ce-b705-76ec06c5f72f",
   "metadata": {},
   "outputs": [],
   "source": [
    "emb_db.add(embs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "117d3416-e79f-436f-a33e-ffb45b972b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "q = \"the variance of the portfolio\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a6ef474-678c-4525-8dcb-ece67aa9c7ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "qe = encoder.query(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c8a16ba-6025-4a6e-95c2-bbba7a9a5de5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[122.61588, 127.4572 , 128.07301, 128.97739, 131.64783]],\n",
       "       dtype=float32),\n",
       " array([[149,  47, 224, 255, 254]]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s, i = emb_db.search(qe, 5)\n",
    "s,i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c90dd20-c640-48b5-88c0-4ba93b60c5e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "docs_db = Documents()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed69d8bf-93f1-4353-a4c2-c4aacbe25420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "docs_db.add_document(chunks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "40ebc825-2e2c-4110-93ff-ae6ec3dc1322",
   "metadata": {},
   "outputs": [],
   "source": [
    "docs_db.add_chunk(chunks[100].page_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4352b32d-c2e7-4fbf-aa05-fc46baf7c9f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}