{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [],

   "source": [

    "import getpass\n",

    "import os\n",

    "from dotenv import load_dotenv\n",

    "\n",

    "load_dotenv('../.env')\n",

    "openai_access_key = os.getenv('OPENAI_ACCESS_KEY')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "from typing import List, Literal, Union\n",

    "import datetime\n",

    "from typing import Literal, Optional, Tuple\n",

    "\n",

    "from langchain_core.pydantic_v1 import BaseModel, Field\n",

    "\n",

    "class Filter(BaseModel):\n",

    "    field: Literal[\"Disease\", \"Gene\", \"Protein\", \"DNAMutation\", \"ProteinMutation\", \"SNP\", \"Cell_type\", \"Drug\", \"Sign_symptom\", \"Biological_structure\", \"Date\", \n",

    "                   \"Duration\", \"Time\", \"Frequency\", \"Severity\", \"Lab_value\", \"Dosage\", \"Diagnostic_procedure\", \"Therapeutic_procedure\", \n",

    "                   \"Medication\", \"Clinical_event\", \"Outcome\", \"History\", \"Subject\", \"Family_history\", \"Detailed_description\", \"Area\"]\n",

    "    \n",

    "    comparison: Literal[\"eq\", \"lt\", \"lte\", \"gt\", \"gte\"]\n",

    "    value: Union[str] = Field(\n",

    "        ...,\n",

    "    description=\"If the field is Gene, write the official symbol from the NCBIGene knowledge bases\",\n",

    "    )\n",

    "\n",

    "\n",

    "class Search(BaseModel):\n",

    "    \"\"\"Search over a database of tutorial videos about a software library.\"\"\"\n",

    "\n",

    "    content_search: str = Field(\n",

    "        ...,\n",

    "        description=\"Similarity search query applied to video transcripts.\",\n",

    "    )\n",

    "    title_search: str = Field(\n",

    "        ...,\n",

    "        description=(\n",

    "            \"Alternate version of the content search query to apply to titles. \"\n",

    "            \"Should be succinct and only include key words that could be in a clinical trial text\"\n",

    "            \"title.\"\n",

    "        ),\n",

    "    )\n",

    "    filters: List[Filter] = Field(\n",

    "        default_factory=list,\n",

    "        description=\"Filters over specific fields. Final condition is a logical conjunction of all filters.\",\n",

    "    )\n",

    "\n",

    "    def pretty_print(self) -> None:\n",

    "        for field in self.__fields__:\n",

    "            if getattr(self, field) is not None and getattr(self, field) != getattr(\n",

    "                self.__fields__[field], \"default\", None\n",

    "            ):\n",

    "                print(f\"{field}: {getattr(self, field)}\")"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "from langchain_core.prompts import ChatPromptTemplate\n",

    "from langchain_openai import ChatOpenAI\n",

    "\n",

    "system = \"\"\"You are an expert at converting user questions into database queries. \\\n",

    "You have access to a database of tutorial videos about a software library for building LLM-powered applications. \\\n",

    "Given a question, return a database query optimized to retrieve the most relevant results.\n",

    "\n",

    "If there are acronyms or words you are not familiar with, do not try to rephrase them.\"\"\"\n",

    "prompt = ChatPromptTemplate.from_messages(\n",

    "    [\n",

    "        (\"system\", system),\n",

    "        (\"human\", \"{question}\"),\n",

    "    ]\n",

    ")\n",

    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",

    "structured_llm = llm.with_structured_output(TutorialSearch)\n",

    "query_analyzer = prompt | structured_llm"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "query_analyzer.invoke({\"question\": \"\"}).pretty_print()"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": []

  }

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