{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_openai import ChatOpenAI\n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_chroma import Chroma\n",
    "\n",
    "from langchain.chains import create_retrieval_chain\n",
    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
    "\n",
    "from langchain_core.retrievers import BaseRetriever\n",
    "from langchain_core.language_models.llms import BaseLLM\n",
    "from langchain_core.output_parsers.base import BaseOutputParser\n",
    "from langchain_core.runnables import RunnablePassthrough, RunnableParallel\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.documents import Document\n",
    "\n",
    "# define prompt\n",
    "BASE_SYSTEM_PROMPT: str = (\n",
    "    \"You are an assistant for question-answering tasks over books. \"\n",
    "    \"Only use the following book extracts to answer the question. \"\n",
    "    \"If you don't know the answer, say that you don't know. \"\n",
    "    \"\\n\\n\"\n",
    "    \"{context}\"\n",
    ")\n",
    "\n",
    "BASE_QA_PROMPT: ChatPromptTemplate  = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\"system\", BASE_SYSTEM_PROMPT),\n",
    "        (\"human\", \"{question}\"),\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context'], template=\"You are an assistant for question-answering tasks over books. Only use the following book extracts to answer the question. If you don't know the answer, say that you don't know. \\n\\n{context}\")),\n",
       " HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question'], template='{question}'))]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BASE_QA_PROMPT.messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"You are an assistant for question-answering tasks over books. Only use the following book extracts to answer the question. If you don't know the answer, say that you don't know. \\n\\n{context}\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(BASE_QA_PROMPT.messages[0].prompt.template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.chdir('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 1 column 1 (char 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[17], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjson\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/self_scraped/factual_books_20_simple_rag_chain_search_type_similarity_k_4_results.json\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m----> 3\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/json/__init__.py:293\u001b[0m, in \u001b[0;36mload\u001b[0;34m(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload\u001b[39m(fp, \u001b[38;5;241m*\u001b[39m, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, object_hook\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, parse_float\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    275\u001b[0m         parse_int\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, parse_constant\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, object_pairs_hook\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[1;32m    276\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Deserialize ``fp`` (a ``.read()``-supporting file-like object containing\u001b[39;00m\n\u001b[1;32m    277\u001b[0m \u001b[38;5;124;03m    a JSON document) to a Python object.\u001b[39;00m\n\u001b[1;32m    278\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;124;03m    kwarg; otherwise ``JSONDecoder`` is used.\u001b[39;00m\n\u001b[1;32m    292\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 293\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobject_hook\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobject_hook\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparse_float\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparse_float\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparse_int\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparse_int\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparse_constant\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparse_constant\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobject_pairs_hook\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobject_pairs_hook\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.10/json/__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    341\u001b[0m     s \u001b[38;5;241m=\u001b[39m s\u001b[38;5;241m.\u001b[39mdecode(detect_encoding(s), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msurrogatepass\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    344\u001b[0m         parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    345\u001b[0m         parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[0;32m--> 346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_decoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    348\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONDecoder\n",
      "File \u001b[0;32m/usr/lib/python3.10/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    332\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m, s, _w\u001b[38;5;241m=\u001b[39mWHITESPACE\u001b[38;5;241m.\u001b[39mmatch):\n\u001b[1;32m    333\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;124;03m    containing a JSON document).\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \n\u001b[1;32m    336\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m     obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_w\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    338\u001b[0m     end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n\u001b[1;32m    339\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m end \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(s):\n",
      "File \u001b[0;32m/usr/lib/python3.10/json/decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    353\u001b[0m     obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscan_once(s, idx)\n\u001b[1;32m    354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 355\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpecting value\u001b[39m\u001b[38;5;124m\"\u001b[39m, s, err\u001b[38;5;241m.\u001b[39mvalue) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj, end\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
     ]
    }
   ],
   "source": [
    "import json\n",
    "with open(\"data/self_scraped/factual_books_20_simple_rag_chain_search_type_similarity_k_4_results.json\", \"r\") as f:\n",
    "    data = json.load(f)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "finalthesis-k7VKV2Vo-py3.10",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}