notebooks/langchain-react-astro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Astro Query  ReACT Agent\n",
    "\n",
    "#### Using Langchain Agent Library\n",
    "\n",
    "![Astro langchain](../images/astro_langchain.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: langchain-openai in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (0.1.1)\n",
      "Requirement already satisfied: langchain in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (0.1.13)\n",
      "Requirement already satisfied: pyvo in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (1.5.1)\n",
      "Requirement already satisfied: astroquery in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (0.4.7)\n",
      "Requirement already satisfied: astropy in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (6.0.0)\n",
      "Requirement already satisfied: Pillow in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (10.2.0)\n",
      "Requirement already satisfied: langchain-core<0.2.0,>=0.1.33 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain-openai) (0.1.33)\n",
      "Requirement already satisfied: openai<2.0.0,>=1.10.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain-openai) (1.14.2)\n",
      "Requirement already satisfied: tiktoken<1,>=0.5.2 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain-openai) (0.6.0)\n",
      "Requirement already satisfied: PyYAML>=5.3 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (6.0.1)\n",
      "Requirement already satisfied: SQLAlchemy<3,>=1.4 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (2.0.28)\n",
      "Requirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (3.9.3)\n",
      "Requirement already satisfied: dataclasses-json<0.7,>=0.5.7 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (0.6.4)\n",
      "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (1.33)\n",
      "Requirement already satisfied: langchain-community<0.1,>=0.0.29 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (0.0.29)\n",
      "Requirement already satisfied: langchain-text-splitters<0.1,>=0.0.1 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (0.0.1)\n",
      "Requirement already satisfied: langsmith<0.2.0,>=0.1.17 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (0.1.23)\n",
      "Requirement already satisfied: numpy<2,>=1 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (1.26.4)\n",
      "Requirement already satisfied: pydantic<3,>=1 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (2.6.4)\n",
      "Requirement already satisfied: requests<3,>=2 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (2.31.0)\n",
      "Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain) (8.2.3)\n",
      "Requirement already satisfied: beautifulsoup4>=4.8 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from astroquery) (4.12.3)\n",
      "Requirement already satisfied: html5lib>=0.999 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from astroquery) (1.1)\n",
      "Requirement already satisfied: keyring>=15.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from astroquery) (24.3.1)\n",
      "Requirement already satisfied: pyerfa>=2.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from astropy) (2.0.1.1)\n",
      "Requirement already satisfied: astropy-iers-data>=0.2023.10.30.0.29.53 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from astropy) (0.2024.3.18.0.29.47)\n",
      "Requirement already satisfied: packaging>=19.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from astropy) (23.2)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.3.1)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (23.2.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.1)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (6.0.5)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.9.4)\n",
      "Requirement already satisfied: soupsieve>1.2 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from beautifulsoup4>=4.8->astroquery) (2.5)\n",
      "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from dataclasses-json<0.7,>=0.5.7->langchain) (3.21.1)\n",
      "Requirement already satisfied: typing-inspect<1,>=0.4.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from dataclasses-json<0.7,>=0.5.7->langchain) (0.9.0)\n",
      "Requirement already satisfied: six>=1.9 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from html5lib>=0.999->astroquery) (1.16.0)\n",
      "Requirement already satisfied: webencodings in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from html5lib>=0.999->astroquery) (0.5.1)\n",
      "Requirement already satisfied: jsonpointer>=1.9 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from jsonpatch<2.0,>=1.33->langchain) (2.4)\n",
      "Requirement already satisfied: jaraco.classes in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from keyring>=15.0->astroquery) (3.3.1)\n",
      "Requirement already satisfied: anyio<5,>=3 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langchain-core<0.2.0,>=0.1.33->langchain-openai) (4.3.0)\n",
      "Requirement already satisfied: orjson<4.0.0,>=3.9.14 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from langsmith<0.2.0,>=0.1.17->langchain) (3.9.15)\n",
      "Requirement already satisfied: distro<2,>=1.7.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (1.9.0)\n",
      "Requirement already satisfied: httpx<1,>=0.23.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (0.27.0)\n",
      "Requirement already satisfied: sniffio in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (1.3.1)\n",
      "Requirement already satisfied: tqdm>4 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (4.66.2)\n",
      "Requirement already satisfied: typing-extensions<5,>=4.7 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (4.10.0)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from pydantic<3,>=1->langchain) (0.6.0)\n",
      "Requirement already satisfied: pydantic-core==2.16.3 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from pydantic<3,>=1->langchain) (2.16.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from requests<3,>=2->langchain) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from requests<3,>=2->langchain) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from requests<3,>=2->langchain) (2.2.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from requests<3,>=2->langchain) (2024.2.2)\n",
      "Requirement already satisfied: regex>=2022.1.18 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from tiktoken<1,>=0.5.2->langchain-openai) (2023.12.25)\n",
      "Requirement already satisfied: httpcore==1.* in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from httpx<1,>=0.23.0->openai<2.0.0,>=1.10.0->langchain-openai) (1.0.4)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai<2.0.0,>=1.10.0->langchain-openai) (0.14.0)\n",
      "Requirement already satisfied: mypy-extensions>=0.3.0 in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain) (1.0.0)\n",
      "Requirement already satisfied: more-itertools in /Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages (from jaraco.classes->keyring>=15.0->astroquery) (10.2.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install langchain-openai langchain pyvo astroquery astropy Pillow pandasai python-dotenv pydantic==1.10.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import dotenv\n",
    "dotenv.load_dotenv()\n",
    "\n",
    "import tiktoken\n",
    "\n",
    "from astropy import coordinates as coords, units as u\n",
    "from astroquery.simbad import Simbad\n",
    "from astroquery.skyview import SkyView\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astroquery.ipac.nexsci.nasa_exoplanet_archive import NasaExoplanetArchive\n",
    "from astroquery import nasa_ads as na\n",
    "na.ADS.TOKEN = os.getenv(\"ADS_API_KEY\")\n",
    "\n",
    "from langchain.agents import Tool\n",
    "from langchain import LLMChain, PromptTemplate\n",
    "from typing import List, Union\n",
    "from langchain.agents import (\n",
    "    AgentExecutor,\n",
    "    AgentOutputParser,\n",
    "    LLMSingleActionAgent,\n",
    ")\n",
    "from langchain.chat_models import AzureChatOpenAI, ChatOpenAI\n",
    "from langchain.prompts import BaseChatPromptTemplate\n",
    "from langchain.schema import AgentAction, AgentFinish, HumanMessage\n",
    "\n",
    "\n",
    "import pprint\n",
    "pprint = pprint.PrettyPrinter(indent=4, width=120, depth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ReACT Agent\n",
    "\n",
    "The ReACT agent model refers to a framework that integrates the reasoning capabilities of large language models (LLMs) with the ability to take actionable steps, creating a more sophisticated system that can understand and process information, evaluate situations, take appropriate actions, communicate responses, and track ongoing situations.\n",
    "\n",
    "The main components of the ReACT agent are:\n",
    "- Chain of Thought - ReACT Prompt\n",
    "- Tools for LLMs to use - ReACT Actions\n",
    "- Helper functions to control and route the Agent's actions - ReACT Controllers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prompts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#react template\n",
    "astro_template = \"\"\"\n",
    "Decode the following Astronomy query using given tools as best you can.\n",
    "The query will involve a search for astronomical objects in a specific region of the sky, drawing the region, or finding data about a specific astronomical object using bibcode.\n",
    "You have access to the following tools:\n",
    "\n",
    "{tools}\n",
    "\n",
    "Use the following format:\n",
    "\n",
    "Query: the input question you must answer\n",
    "Thought: you should always think about what to do. If the Query is not a valid astromonical data query, Provide Final Answer asking the user to provide the correct query. Any other question to the user will need to be given as Final Answer.\n",
    "Action: the action to take, should be one of [{tool_names}].\n",
    "Action Input: the input to the action\n",
    "Observation: the result of the action\n",
    "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
    "Final Answer: I now have finished \n",
    "\n",
    "Begin Loop:\n",
    "\n",
    "Query: {input}\n",
    "{agent_scratchpad}\"\"\"\n",
    "\n",
    "\n",
    "datetime_template = \"\"\"\n",
    "            convert time string: {datetime} into start and end datetime formatted as: 'temporal[]=yyyy-MM-ddTHH:mm:ssZ,yyyy-MM-ddTHH:mm:ssZ'\n",
    "            \"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tools\n",
    "\n",
    "External Tools and servides used:\n",
    "\n",
    "- Astroquery helper\n",
    "- simbad\n",
    "- SkyView API for image retrieval\n",
    "- ADS for documentation of astronomical objects\n",
    "- NASA Exoplanet Archive for exoplanet related questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_skycoords_from_text(text: str) -> str:\n",
    "    \"\"\"Converts a string representing a celestial object to its coordinates.\n",
    "\n",
    "    Args:\n",
    "        text (str): The string to convert to coordinates.\n",
    "\n",
    "    Returns:\n",
    "        str: The coordinates of the celestial object.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        coord = SkyCoord.from_name(text)\n",
    "        return coord.to_string('hmsdms')\n",
    "    except Exception as e:\n",
    "        return f\"Invalid input:, {e}\"\n",
    "\n",
    "def query_simbad_object(coord: str, radius: str=2*u.arcminute) -> str:\n",
    "    \"\"\"Query the SIMBAD database for an astronomical object.\n",
    "\n",
    "    Args:\n",
    "        RA (str): The right ascension of the object.\n",
    "        DEC (str): The declination of the object.\n",
    "        radius (str): The radius of the search area. default is 2*u.arcminute.\n",
    "        object_type (str): The type of object to search for.\n",
    "\n",
    "    Returns:\n",
    "        str: The result of the query.\n",
    "    \"\"\"\n",
    "    simbad = Simbad()\n",
    "    RA, DEC = coord.split(' ')\n",
    "    try:\n",
    "        result = simbad.query_region(\n",
    "            coords.SkyCoord(RA, DEC, frame='icrs'),\n",
    "            radius=radius,\n",
    "        )\n",
    "        # print the first 10 results\n",
    "        result.pprint(max_width=100)\n",
    "        return result\n",
    "    except Exception as e:\n",
    "        return f\"Invalid input:, {e}\"\n",
    "\n",
    "def draw_image_from_coords(coord: str, radius: str=2*u.arcminute) -> str:\n",
    "    \"\"\"Draw an image of the sky around a given set of coordinates.\n",
    "\n",
    "    Args:\n",
    "        RA (str): The right ascension of the object.\n",
    "        DEC (str): The declination of the object.\n",
    "        radius (str): The radius of the search area. default is 2*u.arcminute.\n",
    "\n",
    "    Returns:\n",
    "        str: The path to the image file.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        ra, dec = coord.split(' ')\n",
    "        position = SkyCoord(ra, dec, frame='icrs', unit=(u.hourangle, u.deg))\n",
    "\n",
    "        # Fetch the image\n",
    "        images = SkyView.get_images(position=position, survey='DSS', radius=0.1 * u.deg)\n",
    "\n",
    "        image_data = images[0][0].data\n",
    "\n",
    "        plt.figure(figsize=(8, 8))\n",
    "        plt.imshow(image_data, cmap='gray', origin='lower')\n",
    "        plt.xlabel('RA')\n",
    "        plt.ylabel('DEC')\n",
    "        plt.title('Image')\n",
    "        plt.colorbar(label='Flux')\n",
    "        plt.show()\n",
    "\n",
    "        return \"Image Plotted\"\n",
    "    except Exception as e:\n",
    "        return f\"Invalid input:, {e}\"\n",
    "\n",
    "def search_docs_with_bibcode(bibcode: str) -> str:\n",
    "    \"\"\"Search for documents with a given bibcode.\n",
    "\n",
    "    Args:\n",
    "        bibcode (str): The bibcode to search for.\n",
    "\n",
    "    Returns:\n",
    "        str: The result of the search.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        results = na.ADS.query_simple(bibcode)\n",
    "        \n",
    "        return results[-1]\n",
    "    except Exception as e:\n",
    "        return f\"Invalid input:, {e}\"\n",
    "\n",
    "def get_bibcode_from_coords(coord: str, radius: str=2*u.arcminute) -> str:\n",
    "    \"\"\"Get the bibcode for a given set of coordinates.\n",
    "\n",
    "    Args:\n",
    "        RA (str): The right ascension of the object.\n",
    "        DEC (str): The declination of the object.\n",
    "        radius (str): The radius of the search area. default is 2*u.arcminute.\n",
    "\n",
    "    Returns:\n",
    "        str: The bibcode of the object.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        ra, dec = coord.split(' ')\n",
    "        position = SkyCoord(ra, dec, frame='icrs', unit=(u.hourangle, u.deg))\n",
    "\n",
    "        result = Simbad.query_region(position, radius=radius)\n",
    "        bibcode = result['COO_BIBCODE'][0]\n",
    "        return bibcode\n",
    "    \n",
    "    except Exception as e:\n",
    "        return f\"Invalid input:, {e}\"\n",
    "\n",
    "def query_nasa_exoplanet_archive_db(name: str) -> str:\n",
    "    \"\"\"Query the NASA Exoplanet Archive database for an exoplanet.\n",
    "\n",
    "    Args:\n",
    "        name (str): The name of the exoplanet.\n",
    "\n",
    "    Returns:\n",
    "        str: The result of the query.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        result = NasaExoplanetArchive.query_object(name)\n",
    "        return result\n",
    "    except Exception as e:\n",
    "        return f\"Invalid input:, {e}\"\n",
    "\n",
    "def num_tokens_from_string(string: str, model_name: str) -> int:\n",
    "    \"\"\"Returns the number of tokens in a text string.\"\"\"\n",
    "    encoding = tiktoken.encoding_for_model(model_name)\n",
    "    num_tokens = len(encoding.encode(string))\n",
    "    return num_tokens\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## pandas AI functions\n",
    "import pandas as pd\n",
    "from pandasai.connectors import PandasConnector\n",
    "from pandasai import SmartDataframe\n",
    "from pandasai.llm import BambooLLM\n",
    "\n",
    "def save_nasa_exoplanet_archive_data_to_file(planet_name: str) -> str:\n",
    "    \n",
    "    result = NasaExoplanetArchive.query_object(planet_name)\n",
    "    \n",
    "    # os.environ[\"PANDASAI_API_KEY\"] = \"\"\n",
    "\n",
    "    df = result.to_pandas()\n",
    "    # filter columns based on these values\n",
    "    column_filters = [\"pl_name\",\n",
    "    \"pl_letter\",\n",
    "    \"hostname\",\n",
    "    \"hd_name\",\n",
    "    \"hip_name\",\n",
    "    \"tic_id\",\n",
    "    \"gaia_id\",\n",
    "    \"pl_refname\",\n",
    "    \"sy_refname\",\n",
    "    \"disc_pubdate\",\n",
    "    \"disc_year\",\n",
    "    \"discoverymethod\",\n",
    "    \"disc_locale\",\n",
    "    \"disc_facility\",\n",
    "    \"disc_instrument\",\n",
    "    \"disc_telescope\"]\n",
    "\n",
    "    df = df[column_filters]\n",
    "    df.to_csv(\"temp.csv\")\n",
    "\n",
    "    return \"Data saved to temp.csv\"\n",
    "    \n",
    "def read_exoplanet_data_from_file_and_query(query: str) -> str:\n",
    "    \n",
    "    df = pd.read_csv(\"temp.csv\")\n",
    "    field_descriptions = { \n",
    "        \"pl_name\": \"The name of the planet\",\n",
    "        \"pl_letter\": \"The letter associated with the planet\",\n",
    "        \"hostname\": \"The name of the host star\",\n",
    "        \"hd_name\": \"The HD name of the host star\",\n",
    "        \"hip_name\": \"The HIP name of the host star\",\n",
    "        \"tic_id\": \"The TIC ID of the host star\",\n",
    "        \"gaia_id\": \"The Gaia ID of the host star\",\n",
    "        \"pl_refname\": \"The reference name and document of the planet\",\n",
    "        \"sy_refname\": \"The reference name and document of the system\",\n",
    "        \"disc_pubdate\": \"The publication date of the discovery\",\n",
    "        \"disc_year\": \"The year of the discovery\",\n",
    "        \"discoverymethod\": \"The method of discovery\",\n",
    "        \"disc_locale\": \"The location of the discovery\",\n",
    "        \"disc_facility\": \"The facility of the discovery\",\n",
    "        \"disc_instrument\": \"The instrument of the discovery\",\n",
    "        \"disc_telescope\": \"The telescope of the discovery\",\n",
    "    }\n",
    "    connector = PandasConnector({\"original_df\": df}, field_descriptions=field_descriptions)\n",
    "    llm = BambooLLM()\n",
    "    return SmartDataframe(connector, config={\"llm\": llm}).chat(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Langchain\n",
    "\n",
    "Langchain is a library that provides a set of tools to interact with language models, such as GPT-3, and to build agents that can understand and process information, evaluate situations, take appropriate actions, communicate responses, and track ongoing situations. Below is a simple example of how to use the library to implement a ReACT agent.\n",
    "\n",
    "Components used:\n",
    "CustomPromptTemplate - ReACT Prompt class\n",
    "CustomOutputParser - ReACT Output controller class for routing LLM actions\n",
    "Agent - ReACT Agent class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.chat_models.azure_openai.AzureChatOpenAI` 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 AzureChatOpenAI`.\n",
      "  warn_deprecated(\n",
      "/Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain.agents.agent.LLMSingleActionAgent` was deprecated in langchain 0.1.0 and will be removed in 0.2.0. Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. instead.\n",
      "  warn_deprecated(\n"
     ]
    }
   ],
   "source": [
    "class CustomPromptTemplate(BaseChatPromptTemplate):\n",
    "    \"\"\"\n",
    "    This is a custom prompt template that uses the `cmr_template` from `prompts.py`\n",
    "    \"\"\"\n",
    "\n",
    "    template: str\n",
    "    tools: List[Tool]\n",
    "\n",
    "    def format_messages(self, **kwargs) -> str:\n",
    "        # Get the intermediate steps (AgentAction, Observation tuples)\n",
    "        # Format them in a particular way\n",
    "        intermediate_steps = kwargs.pop(\"intermediate_steps\")\n",
    "        thoughts = \"\"\n",
    "        for action, observation in intermediate_steps:\n",
    "            thoughts += action.log\n",
    "            thoughts += f\"\\nObservation: {observation}\\nThought: \"\n",
    "        kwargs[\"agent_scratchpad\"] = thoughts\n",
    "        # Create a tools variable from the list of tools provided\n",
    "        kwargs[\"tools\"] = \"\\n\".join(\n",
    "            [f\"{tool.name}: {tool.description}\" for tool in self.tools]\n",
    "        )\n",
    "        # Create a list of tool names for the tools provided\n",
    "        kwargs[\"tool_names\"] = \", \".join([tool.name for tool in self.tools])\n",
    "        formatted = self.template.format(**kwargs)\n",
    "        return [HumanMessage(content=formatted)]\n",
    "\n",
    "\n",
    "class CustomOutputParser(AgentOutputParser):\n",
    "    \"\"\"\n",
    "    This is a custom output parser that parses the output of the LLM agent\n",
    "    \"\"\"\n",
    "\n",
    "    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:\n",
    "        # Check if agent should finish\n",
    "        if \"Final Answer:\" in llm_output:\n",
    "            return AgentFinish(\n",
    "                return_values={\"output\": llm_output.split(\"Final Answer:\")[-1].strip()},\n",
    "                log=llm_output,\n",
    "            )\n",
    "        \n",
    "        regex = r\"Action\\s*\\d*\\s*:(.*?)\\nAction\\s*\\d*\\s*Input\\s*\\d*\\s*:[\\s]*(.*)\"\n",
    "        match = re.search(regex, llm_output, re.DOTALL)\n",
    "        if not match:\n",
    "            raise ValueError(f\"Could not parse LLM output: `{llm_output}`\")\n",
    "        action = match.group(1).strip()\n",
    "        action_input = match.group(2)\n",
    "        # Return the action and action input\n",
    "        return AgentAction(\n",
    "            tool=action, tool_input=action_input.strip(\" \").strip('\"'), log=llm_output\n",
    "        )\n",
    "\n",
    "\n",
    "class AstroQueryAgent:\n",
    "    \"\"\"\n",
    "    This is a custom agent that uses the `CustomPromptTemplate` and `CustomOutputParser`\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.create_tools()\n",
    "        self.tool_names = [tool.name for tool in self.tools]\n",
    "        self.prompt = CustomPromptTemplate(\n",
    "            template=astro_template,\n",
    "            tools=self.tools,\n",
    "            # This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically\n",
    "            # This includes the `intermediate_steps` variable because that is needed\n",
    "            input_variables=[\"input\", \"intermediate_steps\"],\n",
    "        )\n",
    "        self.output_parser = CustomOutputParser()\n",
    "        self.llm_chain = LLMChain(\n",
    "            # llm=ChatOpenAI(model_name=\"gpt-4-1106-preview\", temperature=0),\n",
    "            llm=AzureChatOpenAI(model_name=\"gpt-4-1106-preview\", temperature=0),\n",
    "            prompt=self.prompt,\n",
    "        )\n",
    "        self.create_agent()\n",
    "\n",
    "    def create_tools(self):\n",
    "        \"\"\"create tools for the agent\"\"\"\n",
    "        self.tools = [\n",
    "            Tool(\n",
    "                name=\"query_simbad_object\",\n",
    "                description=\"Get simbad object from RA and DEC\",\n",
    "                func=query_simbad_object,\n",
    "            ),\n",
    "            Tool(\n",
    "                name=\"get_skycoords_from_text\",\n",
    "                description=\"Get sky coordinates from text of a celestial object\",\n",
    "                func=get_skycoords_from_text,\n",
    "            ),\n",
    "            Tool(\n",
    "                name=\"draw_image_from_coords\",\n",
    "                description=\"Draw an image of the sky around a given set of coordinates\",\n",
    "                func=draw_image_from_coords,\n",
    "            ),\n",
    "            Tool(\n",
    "                name=\"search_docs_with_bibcode\",\n",
    "                description=\"Search for documents with a given bibcode\",\n",
    "                func=search_docs_with_bibcode,\n",
    "            ),\n",
    "            Tool(\n",
    "                name=\"get_bibcode_from_coords\",\n",
    "                description=\"Get the bibcode for a given set of coordinates\",\n",
    "                func=get_bibcode_from_coords,\n",
    "            ),\n",
    "            Tool(\n",
    "                name=\"save_nasa_exoplanet_archive_data_to_file\",\n",
    "                description=\"search for and save exoplanet data to file. Always use this tool for exoplanet data queries. Always follow up with read_exoplanet_data_from_file_and_query\",\n",
    "                func=save_nasa_exoplanet_archive_data_to_file,\n",
    "            ),            \n",
    "            Tool(\n",
    "                name=\"read_exoplanet_data_from_file_and_query\",\n",
    "                description=\"Read exoplanet data from file and query.\",\n",
    "                func=read_exoplanet_data_from_file_and_query,\n",
    "            ),\n",
    "        ]\n",
    "\n",
    "    def create_agent(self):\n",
    "        self.agent = LLMSingleActionAgent(\n",
    "            llm_chain=self.llm_chain,\n",
    "            output_parser=self.output_parser,\n",
    "            stop=[\"\\nObservation:\"],\n",
    "            allowed_tools=self.tool_names,\n",
    "        )\n",
    "        self.agent_executor = AgentExecutor.from_agent_and_tools(\n",
    "            agent=self.agent, tools=self.tools, verbose=True\n",
    "        )\n",
    "\n",
    "    def run_query(self, input_text: str):\n",
    "        return self.agent_executor.run(input_text)\n",
    "\n",
    "query_agent = AstroQueryAgent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mramasub/work/workshop-usecases-llm/.venv/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `run` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
      "  warn_deprecated(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: The query is asking for specific data about an exoplanet, K2-18 b. To find this information, I should use the tools provided to search for and save exoplanet data to a file, and then query that file for the discovery method of K2-18 b.\n",
      "\n",
      "Action: save_nasa_exoplanet_archive_data_to_file\n",
      "Action Input: K2-18 b\u001b[0m\n",
      "\n",
      "Observation:\u001b[38;5;200m\u001b[1;3mData saved to temp.csv\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mNow that the data is saved, I need to read the file and query it for the discovery method of K2-18 b.\n",
      "\n",
      "Action: read_exoplanet_data_from_file_and_query\n",
      "Action Input: Discovery method of K2-18 b\u001b[0m\n",
      "\n",
      "Observation:\u001b[36;1m\u001b[1;3mThe discovery method of K2-18 b is: Transit\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mFinal Answer: The discovery method of K2-18 b is Transit.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "The discovery method of K2-18 b is Transit.\n"
     ]
    }
   ],
   "source": [
    "query= \"what is the discovery method of K2 18 b?\"\n",
    "print(\n",
    "    query_agent.run_query(query)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: To provide documentation for K2-18, I need to find documents related to it. The best way to do this is by using its bibcode, which is a unique identifier for literature in astronomy and astrophysics.\n",
      "\n",
      "Action: get_skycoords_from_text\n",
      "Action Input: K2-18\u001b[0m\n",
      "\n",
      "Observation:\u001b[33;1m\u001b[1;3m11h30m14.517744s +07d35m18.255336s\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mNow that I have the sky coordinates for K2-18, the next step is to get the bibcode associated with these coordinates to find the relevant documentation.\n",
      "\n",
      "Action: get_bibcode_from_coords\n",
      "Action Input: 11h30m14.517744s +07d35m18.255336s\u001b[0m\n",
      "\n",
      "Observation:\u001b[33;1m\u001b[1;3m2020yCat.1350....0G\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mWith the bibcode obtained, I can now search for documents related to K2-18 using this identifier.\n",
      "\n",
      "Action: search_docs_with_bibcode\n",
      "Action Input: 2020yCat.1350....0G\u001b[0m\n",
      "\n",
      "Observation:\u001b[36;1m\u001b[1;3m      bibcode                                    title                                     author       aff            pub             volume  pubdate    page                  citation                  citation_count                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 abstract                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          doi             eid \n",
      "------------------- ---------------------------------------------------------------- ------------------ --- -------------------------- ------ ---------- ----- ------------------------------------------ -------------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------ -----\n",
      "2020yCat.1350....0G VizieR Online Data Catalog: Gaia EDR3 (Gaia Collaboration, 2020) Gaia Collaboration   - VizieR Online Data Catalog   1350 2020-11-00 I/350 2020AcA....70..265L .. 2024arXiv240312524H            168 Contents of Gaia EDR3: <P />The five-parameter astrometric solution - positions on the sky (alpha,delta), parallaxes, and proper motions - for around 1.5 billion (1.5x10<SUP>9</SUP>) sources, with a limiting magnitude of about G~=21 and a bright limit of about G~=3. The astrometric solution will be accompanied with some new quality indicators, like RUWE, and source image descriptors. <P />In addition, two-parameters solutions - positions on the sky (alpha,delta) - for around 300 million additional sources. <P />G magnitudes for around 1.8 billion sources. <P />GBP and GRP magnitudes for around 1.5 billion sources. <P />Please be aware that the photometric system for the G, GBP, and GRP bands in Gaia EDR3 is different from the photometric system as used in Gaia DR2 and Gaia DR1. <P />Full passband definitions for G, GBP, and GRP. More information will become available here. <P />About 1.5 million celestial reference frame (Gaia-CRF) sources. <P />Cross-matches between Gaia EDR3 sources on the one hand and Hipparcos-2, Tycho-2 + TDSC merged, 2MASS PSC (merged with 2MASX), SDSS DR13, Pan-STARRS1 DR1, SkyMapper DR1, GSC 2.3, APASS DR9, RAVE DR5, allWISE, and URAT-1 data on the other hand. <P />Additionally, a Gaia EDR3 to Gaia DR2 match will be provided. <P />Simulated data from Gaia Object Generator (GOG) and Gaia Universe Model Snapshot (GUMS) will be provided. <P />The commanded scan law covering the Gaia EDR3 data collection period will be provided. Also the major periods where data was not sent to the ground or could not be processed are identified. <P />Gaia DR3 data (both Gaia EDR3 and the full Gaia DR3) are based on data collected between 25 July 2014 (10:30 UTC) and 28 May 2017 (08:44 UTC), spanning a period of 34 months. As a comparison, Gaia DR2 was based on 22 months of data and Gaia DR1 was based on observations collected during the first 14 months of Gaia's routine operational phase. <P />The reference epoch for Gaia DR3 (both Gaia EDR3 and the full Gaia DR3) is 2016.0. Remember that the reference epoch is different for each Gaia data release (it was was J2015.5 for Gaia DR2 and J2015.0 for Gaia DR1). <P />Positions and proper motions are referred to the ICRS, to which the optical reference frame defined by Gaia EDR3 is aligned. The time coordinate for Gaia EDR3 is the barycentric coordinate time (TCB). <P />(5 data files). 10.26093/cds/vizier.1350 I/350\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThe document found with the bibcode \"2020yCat.1350....0G\" is from the VizieR Online Data Catalog and is related to the Gaia EDR3 (Gaia Early Data Release 3) by the Gaia Collaboration. This document provides comprehensive details about the contents of Gaia EDR3, including the five-parameter astrometric solution for around 1.5 billion sources, two-parameter solutions for around 300 million additional sources, G magnitudes for around 1.8 billion sources, and much more. It also mentions cross-matches with other catalogs and provides information about the data collection period, reference epoch, and the reference frame used. However, this document does not specifically mention K2-18, as it is a general catalog entry for Gaia EDR3 data. For detailed information specifically about K2-18, a more targeted search or query might be necessary, focusing on research articles or data entries that explicitly discuss K2-18.\n",
      "\n",
      "Final Answer: The document retrieved with the bibcode \"2020yCat.1350....0G\" is related to the Gaia EDR3 catalog and provides extensive information about the data collected and processed by the Gaia mission. However, it does not specifically address K2-18. For detailed information on K2-18, further targeted research would be required.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "The document retrieved with the bibcode \"2020yCat.1350....0G\" is related to the Gaia EDR3 catalog and provides extensive information about the data collected and processed by the Gaia mission. However, it does not specifically address K2-18. For detailed information on K2-18, further targeted research would be required.\n"
     ]
    }
   ],
   "source": [
    "query= \"provide documentation for K2-18\"\n",
    "print(\n",
    "    query_agent.run_query(query)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: The query is asking for an image of the Crab Nebula, which is not a galaxy but a supernova remnant. First, I need to get the sky coordinates of the Crab Nebula to draw an image of the sky around it.\n",
      "\n",
      "Action: get_skycoords_from_text\n",
      "Action Input: Crab Nebula\u001b[0m\n",
      "\n",
      "Observation:\u001b[33;1m\u001b[1;3m05h34m30.888s +22d00m52.92s\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mNow that I have the coordinates of the Crab Nebula, I can use them to draw an image of the sky around these coordinates.\n",
      "\n",
      "Action: draw_image_from_coords\n",
      "Action Input: 05h34m30.888s +22d00m52.92s\u001b[0m"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Observation:\u001b[38;5;200m\u001b[1;3mImage Plotted\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThe image of the Crab Nebula has been successfully plotted using the provided coordinates.\n",
      "\n",
      "Final Answer: I now have finished drawing the image of the Crab Nebula.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "I now have finished drawing the image of the Crab Nebula.\n"
     ]
    }
   ],
   "source": [
    "query= \"get me image of crab nebula galaxy\"\n",
    "print(\n",
    "    query_agent.run_query(query)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: The query \"What is osmosis?\" is not a valid astronomical data query. It is a question related to biology or chemistry, specifically about the movement of solvent molecules through a semipermeable membrane. Therefore, I cannot proceed with any of the given tools to answer this question.\n",
      "\n",
      "Final Answer: Please provide a correct astronomical data query.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "Please provide a correct astronomical data query.\n"
     ]
    }
   ],
   "source": [
    "query= \"What is osmosis?\"\n",
    "print(\n",
    "    query_agent.run_query(query)\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "8987afb44532b2110e1a5e1b229dd281f8440b44477d285826a54acdd52d8797"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}