final_chat_bot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import openai\n",
    "import sys\n",
    "sys.path.append('../..')\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file\n",
    "\n",
    "openai.api_key  = os.environ['OPENAI_API_KEY']\n",
    "KEY  = os.environ['JINA_EMBEDDING_KEY']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip freeze > requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import PyPDFLoader\n",
    "\n",
    "# Load PDF\n",
    "loaders = [\n",
    "    # Duplicate documents on purpose - messy data\n",
    "    PyPDFLoader(\"documents/temp.pdf\"),\n",
    "    PyPDFLoader(\"documents/lm741.pdf\")\n",
    "]\n",
    "docs = []\n",
    "for loader in loaders:\n",
    "    docs.extend(loader.load())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "text_splitter = RecursiveCharacterTextSplitter(\n",
    "    chunk_size = 1500,\n",
    "    chunk_overlap = 150\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splits = text_splitter.split_documents(docs)\n",
    "len(splits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import JinaEmbeddings\n",
    "embedding = JinaEmbeddings(jina_api_key = KEY, model_name=\"jina-embeddings-v2-base-en\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import Chroma\n",
    "# persist_directory = 'docs/chroma1/'\n",
    "persist_directory = 'chroma3/'\n",
    "vectordb = Chroma.from_documents(\n",
    "    documents=splits,\n",
    "    embedding=embedding,\n",
    "    persist_directory=persist_directory\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n"
     ]
    }
   ],
   "source": [
    "print(vectordb._collection.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"is there an email i can ask for help\"\n",
    "docs = vectordb.similarity_search(question,k=3)\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectordb.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gpt-3.5-turbo\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "current_date = datetime.datetime.now().date()\n",
    "if current_date < datetime.date(2023, 9, 2):\n",
    "    llm_name = \"gpt-3.5-turbo-0301\"\n",
    "else:\n",
    "    llm_name = \"gpt-3.5-turbo\"\n",
    "print(llm_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\DELL\\Desktop\\solid-invention\\.conda\\Lib\\site-packages\\langchain_core\\_api\\deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.chat_models.openai.ChatOpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import ChatOpenAI`.\n",
      "  warn_deprecated(\n"
     ]
    }
   ],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "llm = ChatOpenAI(model_name=llm_name, temperature=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import RetrievalQA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# qa_chain = RetrievalQA.from_chain_type(\n",
    "#     llm,\n",
    "#     retriever=vectordb.as_retriever()\n",
    "# )\n",
    "# from langchain.prompts import PromptTemplate\n",
    "\n",
    "\n",
    "# question=\"how many words are there?\"\n",
    "# result = qa_chain({\"query\": question})\n",
    "# result['result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from langchain.prompts import PromptTemplate\n",
    "\n",
    "# # Build prompt\n",
    "# template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Always say \"thanks for asking!\" at the end of the answer. \n",
    "# {context}\n",
    "# Question: {question}\n",
    "# Helpful Answer:\"\"\"\n",
    "# QA_CHAIN_PROMPT = PromptTemplate.from_template(template)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Run chain\n",
    "# qa_chain = RetrievalQA.from_chain_type(\n",
    "#     llm,\n",
    "#     retriever=vectordb.as_retriever(),\n",
    "#     return_source_documents=True,\n",
    "#     chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT}\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\DELL\\Desktop\\solid-invention\\.conda\\Lib\\site-packages\\langchain_core\\_api\\deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
      "  warn_deprecated(\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Build prompt\n",
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "# template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Always say \"Fuck you !\" at the end of the answer. \n",
    "template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Must say \"Fuck You!\" at the end of the answer. \n",
    "{context}\n",
    "Question: {question}\n",
    "Helpful Answer:\"\"\"\n",
    "QA_CHAIN_PROMPT = PromptTemplate(\n",
    "    input_variables=[\"context\", \"question\"],\n",
    "    template=template,\n",
    ")\n",
    "\n",
    "# Run chain\n",
    "from langchain.chains import RetrievalQA\n",
    "\n",
    "qa_chain = RetrievalQA.from_chain_type(\n",
    "    llm,\n",
    "    retriever=vectordb.as_retriever(),\n",
    "    return_source_documents=True,\n",
    "    chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT},\n",
    ")\n",
    "\n",
    "question = \"Is probability a class topic?\"\n",
    "result = qa_chain({\"query\": question})\n",
    "result[\"result\"]\n",
    "\n",
    "\n",
    "# ### Memory\n",
    "\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
    "\n",
    "\n",
    "# ### ConversationalRetrievalChain\n",
    "\n",
    "\n",
    "from langchain.chains import ConversationalRetrievalChain\n",
    "\n",
    "retriever = vectordb.as_retriever()\n",
    "qa = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, memory=memory)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "chat_history = []\n",
    "answer = \"\"\n",
    "db_query = \"\"\n",
    "db_response = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello! How can I assist you today?\n"
     ]
    }
   ],
   "source": [
    "query =\"hi?\"\n",
    "\n",
    "result = qa({\"question\": query, \"chat_history\": chat_history})\n",
    "chat_history.extend([(query, result[\"answer\"])])\n",
    "# db_query = result[\"generated_question\"]\n",
    "# db_response = result[\"source_documents\"]\n",
    "answer = result[\"answer\"]\n",
    "print(answer)"
   ]
  }
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