LLM_tools.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Goal is to create reasonable chatbots that can choose and order the steps necessary for completing a task given a set of tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TERMINOLOGIES\n",
    "\n",
    "### Tools - Langchain interfaces, json input and corresponding functions as per their name (intuitive name and description shiuld be there)\n",
    "Eg - Wikipedia- A wrapper around wikipedia, useful for answerin general knowledge questions. Input should be a search query. {describe a input schema}\n",
    "### Tool kits - Collection of tools to be used togeather (github toolkit - fetching issues, posting comments, create a new file in repo)\n",
    "### Agents - given a task and tools, they chose how to get the work done, these are specially constructed chains (prompt template, llm, agent output parser(return agent action obj, list of agent actions, agent finish object), not responsible for executing the actions)\n",
    "### Agent Executors (Invoke the agent by user query, ouput as described above from agent,result in next action, executor checks and does as per agent action object(an obsrvation), next action variable obtained, if agent  finish return to user)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# goal is to pipe tools in a chain, create a tool from retiever, then agents and agent executors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wikipedia\n",
      "  Downloading wikipedia-1.4.0.tar.gz (27 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Requirement already satisfied: beautifulsoup4 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from wikipedia) (4.12.3)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.0.0 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from wikipedia) (2.32.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (3.4.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from requests<3.0.0,>=2.0.0->wikipedia) (2024.8.30)\n",
      "Requirement already satisfied: soupsieve>1.2 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from beautifulsoup4->wikipedia) (2.6)\n",
      "Building wheels for collected packages: wikipedia\n",
      "  Building wheel for wikipedia (setup.py): started\n",
      "  Building wheel for wikipedia (setup.py): finished with status 'done'\n",
      "  Created wheel for wikipedia: filename=wikipedia-1.4.0-py3-none-any.whl size=11705 sha256=8e82fe91ae35a99c95463b6fa0dffe8f20b7cdfdb458eeb743acbfffd9ad08f4\n",
      "  Stored in directory: c:\\users\\msi\\appdata\\local\\pip\\cache\\wheels\\5e\\b6\\c5\\93f3dec388ae76edc830cb42901bb0232504dfc0df02fc50de\n",
      "Successfully built wikipedia\n",
      "Installing collected packages: wikipedia\n",
      "Successfully installed wikipedia-1.4.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# Wikipedia tool\n",
    "%pip install wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext dotenv\n",
    "%dotenv\n",
    "import os\n",
    "cohere_api_key = os.getenv('COHERE_API_KEY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import PromptTemplate\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "from langchain_cohere import ChatCohere\n",
    "\n",
    "from langchain_community.utilities import WikipediaAPIWrapper\n",
    "from langchain_community.tools import WikipediaQueryRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\Users\\MSI\\miniconda3\\envs\\lanchain_env\\lib\\site-packages\\wikipedia\\wikipedia.py:389: GuessedAtParserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html.parser\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
      "\n",
      "The code that caused this warning is on line 389 of the file f:\\Users\\MSI\\miniconda3\\envs\\lanchain_env\\lib\\site-packages\\wikipedia\\wikipedia.py. To get rid of this warning, pass the additional argument 'features=\"html.parser\"' to the BeautifulSoup constructor.\n",
      "\n",
      "  lis = BeautifulSoup(html).find_all('li')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Page: Python (programming language)\n",
      "Summary: Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.\n",
      "Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a \"batteries included\" language due to its comprehensive standard library.\n",
      "Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.0. Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2.\n",
      "Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community.\n",
      "\n",
      "\n",
      "\n",
      "Page: Monty Python\n",
      "Summary: Monty Python (also collectively known as the Pythons) were a British comedy troupe formed in 1969 consisting of Graham Chapman, John Cleese, Terry Gilliam, Eric Idle, Terry Jones, and Michael Palin. The group came to prominence for the sketch comedy series Monty Python's Flying Circus, which aired on the BBC from 1969 to 1974. Their work then developed into a larger collection that included live shows, films, albums, books, and musicals; their influence on comedy has been compared to the Beatles' influence on music. Their sketch show has been called \"an important moment in the evolution of television comedy\".\n",
      "Monty Python's Flying Circus was loosely structured as a sketch show, but its innovative stream-of-consciousness approach and Gilliam's animation skills pushed the boundaries of what was acceptable in style and content. A self-contained comedy unit, the Pythons had creative control that allowed them to experiment with form and content, discarding rules of television comedy. They followed their television work by making  the films Monty Python and the Holy Grail (1975), Life of Brian (1979), and The Meaning of Life (1983). Their influence on British comedy has been apparent for years, while it has coloured the work of the early editions of Saturday Night Live through to absurdist trends in television comedy.\n",
      "At the 41st British Academy Film Awards in 1988, Monty Python received the BAFTA Award for Outstanding British Contribution to Cinema. In 1998, they were awarded the AFI Star Award by the American Film Institute. Holy Grail and Life of Brian are frequently ranked on lists of the greatest comedy films. A 2005 poll asked more than 300 comedians, comedy writers, producers, and directors to name the greatest comedians of all time, and half of Monty Python's members made the top 50.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "wikipedia_api = WikipediaAPIWrapper()\n",
    "# fetch articles through load() and page summaries through run()\n",
    "print(wikipedia_api.run('Python'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wikipedia_tool = WikipediaQueryRun(api_wrapper=wikipedia_api)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'query': {'description': 'query to look up on wikipedia',\n",
       "  'title': 'Query',\n",
       "  'type': 'string'}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wikipedia_tool.args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\Users\\MSI\\miniconda3\\envs\\lanchain_env\\lib\\site-packages\\wikipedia\\wikipedia.py:389: GuessedAtParserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"html.parser\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n",
      "\n",
      "The code that caused this warning is on line 389 of the file f:\\Users\\MSI\\miniconda3\\envs\\lanchain_env\\lib\\site-packages\\wikipedia\\wikipedia.py. To get rid of this warning, pass the additional argument 'features=\"html.parser\"' to the BeautifulSoup constructor.\n",
      "\n",
      "  lis = BeautifulSoup(html).find_all('li')\n"
     ]
    }
   ],
   "source": [
    "response = wikipedia_tool.invoke({'query': 'python'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Page: Python (programming language)\\nSummary: Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.\\nPython is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a \"batteries included\" language due to its comprehensive standard library.\\nGuido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.0. Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2.\\nPython consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community.\\n\\n\\n\\nPage: Monty Python\\nSummary: Monty Python (also collectively known as the Pythons) were a British comedy troupe formed in 1969 consisting of Graham Chapman, John Cleese, Terry Gilliam, Eric Idle, Terry Jones, and Michael Palin. The group came to prominence for the sketch comedy series Monty Python\\'s Flying Circus, which aired on the BBC from 1969 to 1974. Their work then developed into a larger collection that included live shows, films, albums, books, and musicals; their influence on comedy has been compared to the Beatles\\' influence on music. Their sketch show has been called \"an important moment in the evolution of television comedy\".\\nMonty Python\\'s Flying Circus was loosely structured as a sketch show, but its innovative stream-of-consciousness approach and Gilliam\\'s animation skills pushed the boundaries of what was acceptable in style and content. A self-contained comedy unit, the Pythons had creative control that allowed them to experiment with form and content, discarding rules of television comedy. They followed their television work by making  the films Monty Python and the Holy Grail (1975), Life of Brian (1979), and The Meaning of Life (1983). Their influence on British comedy has been apparent for years, while it has coloured the work of the early editions of Saturday Night Live through to absurdist trends in television comedy.\\nAt the 41st British Academy Film Awards in 1988, Monty Python received the BAFTA Award for Outstanding British Contribution to Cinema. In 1998, they were awarded the AFI Star Award by the American Film Institute. Holy Grail and Life of Brian are frequently ranked on lists of the greatest comedy films. A 2005 poll asked more than 300 comedians, comedy writers, producers, and directors to name the greatest comedians of all time, and half of Monty Python\\'s members made the top 50.\\n\\n'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pipe a tool to a simple chain\n",
    "TEMPLATE = '''\n",
    "Turn the following user input into a wikipedia search query. Don't answer the question:\n",
    "{input}\n",
    "'''\n",
    "prompt_template = PromptTemplate.from_template(template = TEMPLATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_cohere import ChatCohere\n",
    "chat = ChatCohere(cohere_api_key=cohere_api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = prompt_template | chat | StrOutputParser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python programming language creator'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({'input':'Who is the creator of the python programming language?'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = prompt_template | chat | StrOutputParser() | wikipedia_tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Page: Python (programming language)\\nSummary: Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.\\nPython is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a \"batteries included\" language due to its comprehensive standard library.\\nGuido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.0. Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2.\\nPython consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community.\\n\\n\\n\\nPage: Guido van Rossum\\nSummary: Guido van Rossum (Dutch: [ˈxidoː vɑn ˈrɔsʏm, -səm]; born 31 January 1956) is a Dutch programmer. He is the creator of the Python programming language, for which he was the \"benevolent dictator for life\" (BDFL) until he stepped down from the position on 12 July 2018. He remained a member of the Python Steering Council through 2019, and withdrew from nominations for the 2020 election.\\n\\nPage: History of Python\\nSummary: The programming language Python was conceived in the late 1980s, and its implementation was started in December 1989 by Guido van Rossum at CWI in the Netherlands as a successor to ABC capable of exception handling and interfacing with the Amoeba operating system. Van Rossum is Python\\'s principal author, and his continuing central role in deciding the direction of Python is reflected in the title given to him by the Python community, Benevolent Dictator for Life (BDFL). (However, Van Rossum stepped down as leader on July 12, 2018.). Python was named after the BBC TV show Monty Python\\'s Flying Circus.\\nPython 2.0 was released on October 16, 2000, with many major new features, including a cycle-detecting garbage collector (in addition to reference counting) for memory management and support for Unicode, along with a change to the development process itself, with a shift to a more transparent and community-backed process.\\nPython 3.0, a major, backwards-incompatible release, was released on December 3, 2008 after a long period of testing. Many of its major features have also been backported to the backwards-compatible, though now-unsupported, Python 2.6 and 2.7.'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({'input':'Who is the creator of the python programming language?'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from platform import python_version\n",
    "from langchain_core.tools import tool\n",
    "from langchain_core.tools import create_retriever_tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.vectorstores import Chroma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_cohere import CohereEmbeddings\n",
    "\n",
    "embeddings = CohereEmbeddings(\n",
    "    cohere_api_key=cohere_api_key,\n",
    "    model=\"embed-english-v3.0\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorstore = Chroma(persist_directory=\"./intro_to_ds\",\n",
    "                     embedding_function=embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = vectorstore.as_retriever(search_type='mmr', search_kwargs={'k':3,'lambda_mult':0.7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever_tool = create_retriever_tool(retriever=retriever,name='introduction_to_data_and_data_science_course_lectures',description=\"For any questions related to data and data science course, you must use this tool\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tool(name='introduction_to_data_and_data_science_course_lectures', description='For any questions related to data and data science course, you must use this tool', args_schema=<class 'langchain_core.tools.retriever.RetrieverInput'>, func=functools.partial(<function _get_relevant_documents at 0x000001A8DE0551B0>, retriever=VectorStoreRetriever(tags=['Chroma', 'CohereEmbeddings'], vectorstore=<langchain_community.vectorstores.chroma.Chroma object at 0x000001A8FA0BD420>, search_type='mmr', search_kwargs={'k': 3, 'lambda_mult': 0.7}), document_prompt=PromptTemplate(input_variables=['page_content'], input_types={}, partial_variables={}, template='{page_content}'), document_separator='\\n\\n'), coroutine=functools.partial(<function _aget_relevant_documents at 0x000001A8DE0557E0>, retriever=VectorStoreRetriever(tags=['Chroma', 'CohereEmbeddings'], vectorstore=<langchain_community.vectorstores.chroma.Chroma object at 0x000001A8FA0BD420>, search_type='mmr', search_kwargs={'k': 3, 'lambda_mult': 0.7}), document_prompt=PromptTemplate(input_variables=['page_content'], input_types={}, partial_variables={}, template='{page_content}'), document_separator='\\n\\n'))"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever_tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'query': {'description': 'query to look up in retriever',\n",
       "  'title': 'Query',\n",
       "  'type': 'string'}}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever_tool.args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Thus, we need a lot of computational power, and we can expect people to use the languages similar to those in the big data column. Apart from R, Python, and MATLAB, other, faster languages are used like Java, JavaScript, C, C++, and Scala. Cool. What we said may be wonderful, but that’s not all! By using one or more programming languages, people create application software or, as they are sometimes called, software solutions, that are adjusted for specific business needs\\n\\nWhat about big data? Apart from R and Python, people working in this area are often proficient in other languages like Java or Scala. These two have not been developed specifically for doing statistical analyses, however they turn out to be very useful when combining data from multiple sources. All right! Let’s finish off with machine learning. When it comes to machine learning, we often deal with big data\\n\\nGreat! We hope we gave you a good idea about the level of applicability of the most frequently used programming and software tools in the field of data science. Thank you for watching!'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever_tool.invoke(\"list prog languages that a data scientist should know\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "@tool\n",
    "def get_python_func() -> str:\n",
    "    '''Useful for returning the versions of python currently used'''\n",
    "    return python_version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StructuredTool(name='get_python_func', description='Useful for returning the versions of python currently used', args_schema=<class 'langchain_core.utils.pydantic.get_python_func'>, func=<function get_python_func at 0x000001A8F99AAC20>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_python_func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_python_func.args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.10.10'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_python_func.invoke({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now to create an agent we need - Prompt Template, LLM, List of tools\n",
    "# tools in hand - wiki, custom, retriever\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting langchainhub\n",
      "  Downloading langchainhub-0.1.21-py3-none-any.whl.metadata (659 bytes)\n",
      "Requirement already satisfied: packaging<25,>=23.2 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from langchainhub) (24.1)\n",
      "Requirement already satisfied: requests<3,>=2 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from langchainhub) (2.32.3)\n",
      "Requirement already satisfied: types-requests<3.0.0.0,>=2.31.0.2 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from langchainhub) (2.32.0.20241016)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from requests<3,>=2->langchainhub) (3.4.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from requests<3,>=2->langchainhub) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from requests<3,>=2->langchainhub) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in f:\\users\\msi\\miniconda3\\envs\\lanchain_env\\lib\\site-packages (from requests<3,>=2->langchainhub) (2024.8.30)\n",
      "Downloading langchainhub-0.1.21-py3-none-any.whl (5.2 kB)\n",
      "Installing collected packages: langchainhub\n",
      "Successfully installed langchainhub-0.1.21\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install langchainhub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import hub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "tools = [wikipedia_tool, retriever_tool, get_python_func]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\Users\\MSI\\miniconda3\\envs\\lanchain_env\\lib\\site-packages\\langsmith\\client.py:241: LangSmithMissingAPIKeyWarning: API key must be provided when using hosted LangSmith API\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "chat_prompt_template = hub.pull('hwchase17/openai-tools-agent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m System Message \u001b[0m================================\n",
      "\n",
      "You are a helpful assistant\n",
      "\n",
      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
      "\n",
      "\u001b[33;1m\u001b[1;3m{chat_history}\u001b[0m\n",
      "\n",
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "\u001b[33;1m\u001b[1;3m{input}\u001b[0m\n",
      "\n",
      "=============================\u001b[1m Messages Placeholder \u001b[0m=============================\n",
      "\n",
      "\u001b[33;1m\u001b[1;3m{agent_scratchpad}\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "chat_prompt_template.pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all langchain agent prompt templates available in langchain website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.agents import create_tool_calling_agent, AgentExecutor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = create_tool_calling_agent(llm=chat,tools=tools,prompt=chat_prompt_template)\n",
    "agent_executor = AgentExecutor(agent = agent, tools= tools,\n",
    "                               verbose=True,\n",
    "                               return_intermediate_steps=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `get_python_func` with `{}`\n",
      "responded: I will use the get_python_func tool to find out the version of Python the user is using.\n",
      "\n",
      "\u001b[0m\u001b[38;5;200m\u001b[1;3m3.10.10\u001b[0m\u001b[32;1m\u001b[1;3mYou are using Python version 3.10.10.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'input': 'tell me the version of python Im using',\n",
       " 'output': 'You are using Python version 3.10.10.',\n",
       " 'intermediate_steps': [(ToolAgentAction(tool='get_python_func', tool_input={}, log='\\nInvoking: `get_python_func` with `{}`\\nresponded: I will use the get_python_func tool to find out the version of Python the user is using.\\n\\n', message_log=[AIMessageChunk(content='I will use the get_python_func tool to find out the version of Python the user is using.', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '69b849db-9a39-447d-8ca2-2352b555bced', 'tool_calls': [{'id': 'bf6f1fb334454ceca999cb4e205b3756', 'function': {'name': 'get_python_func', 'arguments': '{}'}, 'type': 'function'}], 'token_count': {'input_tokens': 1045.0, 'output_tokens': 57.0}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '69b849db-9a39-447d-8ca2-2352b555bced', 'tool_calls': [{'id': 'bf6f1fb334454ceca999cb4e205b3756', 'function': {'name': 'get_python_func', 'arguments': '{}'}, 'type': 'function'}], 'token_count': {'input_tokens': 1045.0, 'output_tokens': 57.0}}, id='run-4a986dfc-9f4b-45f5-bea5-97a88ac0687b', tool_calls=[{'name': 'get_python_func', 'args': {}, 'id': 'bf6f1fb334454ceca999cb4e205b3756', 'type': 'tool_call'}], invalid_tool_calls=[{'name': 'get_python_func', 'args': 'null', 'id': '9602f564e47542adb6c6ddda152e56a6', 'error': None, 'type': 'invalid_tool_call'}], tool_call_chunks=[{'name': 'get_python_func', 'args': 'null', 'id': '9602f564e47542adb6c6ddda152e56a6', 'index': 0, 'type': 'tool_call_chunk'}, {'name': 'get_python_func', 'args': '{}', 'id': 'bf6f1fb334454ceca999cb4e205b3756', 'index': None, 'type': 'tool_call_chunk'}])], tool_call_id='bf6f1fb334454ceca999cb4e205b3756'),\n",
       "   '3.10.10')]}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_executor.invoke({'input':'tell me the version of python Im using'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'programming languages'}`\n",
      "responded: I will search for the programming languages listed in the introduction to data and data science course and also list the names of their creators. Then I will search for a hilarious topic related joke.\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mThus, we need a lot of computational power, and we can expect people to use the languages similar to those in the big data column. Apart from R, Python, and MATLAB, other, faster languages are used like Java, JavaScript, C, C++, and Scala. Cool. What we said may be wonderful, but that’s not all! By using one or more programming languages, people create application software or, as they are sometimes called, software solutions, that are adjusted for specific business needs\n",
      "\n",
      "More importantly, it will be sufficient for your need to create quick and accurate analyses. However, if your theoretical preparation is strong enough, you will find yourself restricted by software. Knowing a programming language such as R and Python, gives you the freedom to create specific, ad-hoc tools for each project you are working on\n",
      "\n",
      "Alright! So… How are the techniques used in data, business intelligence, or predictive analytics applied in real life? Certainly, with the help of computers. You can basically split the relevant tools into two categories—programming languages and software. Knowing a programming language enables you to devise programs that can execute specific operations. Moreover, you can reuse these programs whenever you need to execute the same action\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'R creator'}`\n",
      "responded: I have found the following programming languages: R, Python, MATLAB, Java, JavaScript, C, C++, and Scala. I will now search for the names of their creators.\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mAs you can see from the infographic, R, and Python are the two most popular tools across all columns. Their biggest advantage is that they can manipulate data and are integrated within multiple data and data science software platforms. They are not just suitable for mathematical and statistical computations. In other words, R, and Python are adaptable. They can solve a wide variety of business and data-related problems from beginning to the end\n",
      "\n",
      "Of course, R, and Python do have their limitations. They are not able to address problems specific to some domains. One example is ‘relational database management systems’—there, SQL is king. It was specifically created for that purpose. SQL is at its most advantageous when working with traditional, historical data. When preparing your BI analysis, for instance, you will surely employ it. Okay. When it comes to data science, mentioning MATLAB is inevitable\n",
      "\n",
      "More importantly, it will be sufficient for your need to create quick and accurate analyses. However, if your theoretical preparation is strong enough, you will find yourself restricted by software. Knowing a programming language such as R and Python, gives you the freedom to create specific, ad-hoc tools for each project you are working on\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'Python creator'}`\n",
      "responded: I have found the following programming languages: R, Python, MATLAB, Java, JavaScript, C, C++, and Scala. I will now search for the names of their creators.\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mAs you can see from the infographic, R, and Python are the two most popular tools across all columns. Their biggest advantage is that they can manipulate data and are integrated within multiple data and data science software platforms. They are not just suitable for mathematical and statistical computations. In other words, R, and Python are adaptable. They can solve a wide variety of business and data-related problems from beginning to the end\n",
      "\n",
      "More importantly, it will be sufficient for your need to create quick and accurate analyses. However, if your theoretical preparation is strong enough, you will find yourself restricted by software. Knowing a programming language such as R and Python, gives you the freedom to create specific, ad-hoc tools for each project you are working on\n",
      "\n",
      "It’s actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably, Hadoop distributes the computational tasks on multiple computers which is basically the way to handle big data nowadays. Power BI, SaS, Qlik, and especially Tableau are top-notch examples of software designed for business intelligence visualizations\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'MATLAB creator'}`\n",
      "responded: I have found the following programming languages: R, Python, MATLAB, Java, JavaScript, C, C++, and Scala. I will now search for the names of their creators.\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mIt is ideal for working with mathematical functions or matrix manipulations. That’s why it is present in all categories except for ‘big data’. While respectable, MATLAB usage is a paid service, and that’s one of the reasons why it is losing ground to open-source languages like R and Python. Either way, R, Python, and MATLAB, combined with SQL, cover most of the tools used when working with traditional data, BI, and conventional data science\n",
      "\n",
      "It’s actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably, Hadoop distributes the computational tasks on multiple computers which is basically the way to handle big data nowadays. Power BI, SaS, Qlik, and especially Tableau are top-notch examples of software designed for business intelligence visualizations\n",
      "\n",
      "In terms of predictive analytics, EViews is mostly used for working with econometric time-series models, and Stata—for academic statistical and econometric research, where techniques like regression, cluster, and factor analysis are constantly applied. As a final note, remember the following. Should you have the relevant business and theoretical knowledge, learning a software tool is relatively easy as opposed to learning a programming language\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'Java creator'}`\n",
      "responded: I have found the following programming languages: R, Python, MATLAB, Java, JavaScript, C, C++, and Scala. I will now search for the names of their creators.\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mAmong the many applications we have plotted, we can say there is an increasing amount of software designed for working with big data such as Apache Hadoop, Apache Hbase, and Mongo DB. In terms of big data, Hadoop is the name that must stick with you. Hadoop is listed as a software in the sense that it is a collection of programs, but don’t imagine it as a nice-looking application\n",
      "\n",
      "Thus, we need a lot of computational power, and we can expect people to use the languages similar to those in the big data column. Apart from R, Python, and MATLAB, other, faster languages are used like Java, JavaScript, C, C++, and Scala. Cool. What we said may be wonderful, but that’s not all! By using one or more programming languages, people create application software or, as they are sometimes called, software solutions, that are adjusted for specific business needs\n",
      "\n",
      "More importantly, it will be sufficient for your need to create quick and accurate analyses. However, if your theoretical preparation is strong enough, you will find yourself restricted by software. Knowing a programming language such as R and Python, gives you the freedom to create specific, ad-hoc tools for each project you are working on\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'JavaScript creator'}`\n",
      "responded: I have found the following programming languages: R, Python, MATLAB, Java, JavaScript, C, C++, and Scala. I will now search for the names of their creators.\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mThus, we need a lot of computational power, and we can expect people to use the languages similar to those in the big data column. Apart from R, Python, and MATLAB, other, faster languages are used like Java, JavaScript, C, C++, and Scala. Cool. What we said may be wonderful, but that’s not all! By using one or more programming languages, people create application software or, as they are sometimes called, software solutions, that are adjusted for specific business needs\n",
      "\n",
      "It’s actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably, Hadoop distributes the computational tasks on multiple computers which is basically the way to handle big data nowadays. Power BI, SaS, Qlik, and especially Tableau are top-notch examples of software designed for business intelligence visualizations\n",
      "\n",
      "In terms of predictive analytics, EViews is mostly used for working with econometric time-series models, and Stata—for academic statistical and econometric research, where techniques like regression, cluster, and factor analysis are constantly applied. As a final note, remember the following. Should you have the relevant business and theoretical knowledge, learning a software tool is relatively easy as opposed to learning a programming language\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'C creator'}`\n",
      "responded: I have found the following programming languages: R, Python, MATLAB, Java, JavaScript, C, C++, and Scala. I will now search for the names of their creators.\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mMore importantly, it will be sufficient for your need to create quick and accurate analyses. However, if your theoretical preparation is strong enough, you will find yourself restricted by software. Knowing a programming language such as R and Python, gives you the freedom to create specific, ad-hoc tools for each project you are working on\n",
      "\n",
      "As you can see from the infographic, R, and Python are the two most popular tools across all columns. Their biggest advantage is that they can manipulate data and are integrated within multiple data and data science software platforms. They are not just suitable for mathematical and statistical computations. In other words, R, and Python are adaptable. They can solve a wide variety of business and data-related problems from beginning to the end\n",
      "\n",
      "It’s actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably, Hadoop distributes the computational tasks on multiple computers which is basically the way to handle big data nowadays. Power BI, SaS, Qlik, and especially Tableau are top-notch examples of software designed for business intelligence visualizations\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'C++ creator'}`\n",
      "responded: I have found the following programming languages: R, Python, MATLAB, Java, JavaScript, C, C++, and Scala. I will now search for the names of their creators.\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mMore importantly, it will be sufficient for your need to create quick and accurate analyses. However, if your theoretical preparation is strong enough, you will find yourself restricted by software. Knowing a programming language such as R and Python, gives you the freedom to create specific, ad-hoc tools for each project you are working on\n",
      "\n",
      "Thus, we need a lot of computational power, and we can expect people to use the languages similar to those in the big data column. Apart from R, Python, and MATLAB, other, faster languages are used like Java, JavaScript, C, C++, and Scala. Cool. What we said may be wonderful, but that’s not all! By using one or more programming languages, people create application software or, as they are sometimes called, software solutions, that are adjusted for specific business needs\n",
      "\n",
      "It’s actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably, Hadoop distributes the computational tasks on multiple computers which is basically the way to handle big data nowadays. Power BI, SaS, Qlik, and especially Tableau are top-notch examples of software designed for business intelligence visualizations\u001b[0m\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'Scala creator'}`\n",
      "responded: I have found the following programming languages: R, Python, MATLAB, Java, JavaScript, C, C++, and Scala. I will now search for the names of their creators.\n",
      "\n",
      "\u001b[0m\u001b[33;1m\u001b[1;3mIt’s actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably, Hadoop distributes the computational tasks on multiple computers which is basically the way to handle big data nowadays. Power BI, SaS, Qlik, and especially Tableau are top-notch examples of software designed for business intelligence visualizations\n",
      "\n",
      "Thus, we need a lot of computational power, and we can expect people to use the languages similar to those in the big data column. Apart from R, Python, and MATLAB, other, faster languages are used like Java, JavaScript, C, C++, and Scala. Cool. What we said may be wonderful, but that’s not all! By using one or more programming languages, people create application software or, as they are sometimes called, software solutions, that are adjusted for specific business needs\n",
      "\n",
      "More importantly, it will be sufficient for your need to create quick and accurate analyses. However, if your theoretical preparation is strong enough, you will find yourself restricted by software. Knowing a programming language such as R and Python, gives you the freedom to create specific, ad-hoc tools for each project you are working on\u001b[0m\u001b[32;1m\u001b[1;3mI have found the following creators:\n",
      "- R: John Chambers\n",
      "- Python: Guido van Rossum\n",
      "- MATLAB: Cleve Moler\n",
      "- Java: James Gosling\n",
      "- JavaScript: Brendan Eich\n",
      "- C: Dennis Ritchie\n",
      "- C++: Bjarne Stroustrup\n",
      "- Scala: Martin Odersky\n",
      "\n",
      "I will now search for a hilarious topic related joke.The programming languages listed in the introduction to data and data science course are:\n",
      "- R: John Chambers\n",
      "- Python: Guido van Rossum\n",
      "- MATLAB: Cleve Moler\n",
      "- Java: James Gosling\n",
      "- JavaScript: Brendan Eich\n",
      "- C: Dennis Ritchie\n",
      "- C++: Bjarne Stroustrup\n",
      "- Scala: Martin Odersky\n",
      "\n",
      "Here's a hilarious topic related joke:\n",
      "Why did the programmer quit their job?\n",
      "Because they didn't get arrays.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "response = agent_executor.invoke({'input':'Could you list the programming languages listed in the introduction to data and data science course and also list the names of their creators.End the response with a hilarious topic related joke'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(ToolAgentAction(tool='introduction_to_data_and_data_science_course_lectures', tool_input={'query': 'programming languages'}, log=\"\\nInvoking: `introduction_to_data_and_data_science_course_lectures` with `{'query': 'programming languages'}`\\nresponded: I will search for the programming languages listed in the introduction to data and data science course and also list the names of their creators. Then I will search for a hilarious topic related joke.\\n\\n\", message_log=[AIMessageChunk(content='I will search for the programming languages listed in the introduction to data and data science course and also list the names of their creators. Then I will search for a hilarious topic related joke.', additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '70677684-8183-4199-8102-9702f2744221', 'tool_calls': [{'id': 'ed1abaeca33f4e2ca09e1f0a8a180eab', 'function': {'name': 'introduction_to_data_and_data_science_course_lectures', 'arguments': '{\"query\": \"programming languages\"}'}, 'type': 'function'}], 'token_count': {'input_tokens': 1071.0, 'output_tokens': 93.0}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': '70677684-8183-4199-8102-9702f2744221', 'tool_calls': [{'id': 'ed1abaeca33f4e2ca09e1f0a8a180eab', 'function': {'name': 'introduction_to_data_and_data_science_course_lectures', 'arguments': '{\"query\": \"programming languages\"}'}, 'type': 'function'}], 'token_count': {'input_tokens': 1071.0, 'output_tokens': 93.0}}, id='run-7936d6ec-b397-4a8c-91c3-c18ffe1c18cd', tool_calls=[{'name': 'introduction_to_data_and_data_science_course_lectures', 'args': {'query': 'programming languages'}, 'id': 'ed1abaeca33f4e2ca09e1f0a8a180eab', 'type': 'tool_call'}], invalid_tool_calls=[{'name': 'introduction_to_data_and_data_science_course_lectures', 'args': 'null\"{\\\\n    \\\\\"\"\"query\"\"\\\\\":\"\" \\\\\"\"\"programming\"\" languages\"\"\\\\\"\"\"\\\\n\"\"}\"', 'id': '0d08d4240d3b44c698054f9484f7c9a35f06436684e44993827b321a796990cb8668b93bdb7a488e9a6fc26bfd2161ecb74f62d97bd64192af42d86720f9e52527f0eb63990548919f27add576457a8643f06c69b9994fa9a0c96819d77690baa9e2e90993d24326ab191dd9eb6b702f992a340c26b14b07a0dda52644ae8e5ba6049c610da948b88a595bb0cefda1fb8185763cbd8a4b75ab9dffc3fe17e600', 'error': None, 'type': 'invalid_tool_call'}], tool_call_chunks=[{'name': 'introduction_to_data_and_data_science_course_lectures', 'args': 'null\"{\\\\n    \\\\\"\"\"query\"\"\\\\\":\"\" \\\\\"\"\"programming\"\" languages\"\"\\\\\"\"\"\\\\n\"\"}\"', 'id': '0d08d4240d3b44c698054f9484f7c9a35f06436684e44993827b321a796990cb8668b93bdb7a488e9a6fc26bfd2161ecb74f62d97bd64192af42d86720f9e52527f0eb63990548919f27add576457a8643f06c69b9994fa9a0c96819d77690baa9e2e90993d24326ab191dd9eb6b702f992a340c26b14b07a0dda52644ae8e5ba6049c610da948b88a595bb0cefda1fb8185763cbd8a4b75ab9dffc3fe17e600', 'index': 0, 'type': 'tool_call_chunk'}, {'name': 'introduction_to_data_and_data_science_course_lectures', 'args': '{\"query\": \"programming languages\"}', 'id': 'ed1abaeca33f4e2ca09e1f0a8a180eab', 'index': None, 'type': 'tool_call_chunk'}])], tool_call_id='ed1abaeca33f4e2ca09e1f0a8a180eab'),\n",
       "  'Thus, we need a lot of computational power, and we can expect people to use the languages similar to those in the big data column. Apart from R, Python, and MATLAB, other, faster languages are used like Java, JavaScript, C, C++, and Scala. Cool. What we said may be wonderful, but that’s not all! By using one or more programming languages, people create application software or, as they are sometimes called, software solutions, that are adjusted for specific business needs\\n\\nMore importantly, it will be sufficient for your need to create quick and accurate analyses. However, if your theoretical preparation is strong enough, you will find yourself restricted by software. Knowing a programming language such as R and Python, gives you the freedom to create specific, ad-hoc tools for each project you are working on\\n\\nAlright! So… How are the techniques used in data, business intelligence, or predictive analytics applied in real life? Certainly, with the help of computers. You can basically split the relevant tools into two categories—programming languages and software. Knowing a programming language enables you to devise programs that can execute specific operations. Moreover, you can reuse these programs whenever you need to execute the same action'),\n",
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       "  'It is ideal for working with mathematical functions or matrix manipulations. That’s why it is present in all categories except for ‘big data’. While respectable, MATLAB usage is a paid service, and that’s one of the reasons why it is losing ground to open-source languages like R and Python. Either way, R, Python, and MATLAB, combined with SQL, cover most of the tools used when working with traditional data, BI, and conventional data science\\n\\nIt’s actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably, Hadoop distributes the computational tasks on multiple computers which is basically the way to handle big data nowadays. Power BI, SaS, Qlik, and especially Tableau are top-notch examples of software designed for business intelligence visualizations\\n\\nIn terms of predictive analytics, EViews is mostly used for working with econometric time-series models, and Stata—for academic statistical and econometric research, where techniques like regression, cluster, and factor analysis are constantly applied. As a final note, remember the following. Should you have the relevant business and theoretical knowledge, learning a software tool is relatively easy as opposed to learning a programming language'),\n",
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       "  'Thus, we need a lot of computational power, and we can expect people to use the languages similar to those in the big data column. Apart from R, Python, and MATLAB, other, faster languages are used like Java, JavaScript, C, C++, and Scala. Cool. What we said may be wonderful, but that’s not all! By using one or more programming languages, people create application software or, as they are sometimes called, software solutions, that are adjusted for specific business needs\\n\\nIt’s actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably, Hadoop distributes the computational tasks on multiple computers which is basically the way to handle big data nowadays. Power BI, SaS, Qlik, and especially Tableau are top-notch examples of software designed for business intelligence visualizations\\n\\nIn terms of predictive analytics, EViews is mostly used for working with econometric time-series models, and Stata—for academic statistical and econometric research, where techniques like regression, cluster, and factor analysis are constantly applied. As a final note, remember the following. Should you have the relevant business and theoretical knowledge, learning a software tool is relatively easy as opposed to learning a programming language'),\n",
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'introduction_to_data_and_data_science_course_lectures', 'args': '{\"query\": \"R creator\"}', 'id': 'd71ea04342234b61b55783829d1a01fb', 'index': None, 'type': 'tool_call_chunk'}, {'name': 'introduction_to_data_and_data_science_course_lectures', 'args': '{\"query\": \"Python creator\"}', 'id': '5a35d25279894cea8c7de1e67e4f2806', 'index': None, 'type': 'tool_call_chunk'}, {'name': 'introduction_to_data_and_data_science_course_lectures', 'args': '{\"query\": \"MATLAB creator\"}', 'id': '3fa447031a9c400fb4f8e70ae35ef078', 'index': None, 'type': 'tool_call_chunk'}, {'name': 'introduction_to_data_and_data_science_course_lectures', 'args': '{\"query\": \"Java creator\"}', 'id': '43b1c3fca60e4b2d9317ae2120da3236', 'index': None, 'type': 'tool_call_chunk'}, {'name': 'introduction_to_data_and_data_science_course_lectures', 'args': '{\"query\": \"JavaScript creator\"}', 'id': 'e565a8b7cc0349ea891b18d06f310f59', 'index': None, 'type': 'tool_call_chunk'}, {'name': 'introduction_to_data_and_data_science_course_lectures', 'args': '{\"query\": \"C creator\"}', 'id': 'd0f5b4fdc157400c954f52ed48aca052', 'index': None, 'type': 'tool_call_chunk'}, {'name': 'introduction_to_data_and_data_science_course_lectures', 'args': '{\"query\": \"C++ creator\"}', 'id': '7e5b18d0b1a245d38fdb7e1c3b018f67', 'index': None, 'type': 'tool_call_chunk'}, {'name': 'introduction_to_data_and_data_science_course_lectures', 'args': '{\"query\": \"Scala creator\"}', 'id': '7afa445921e6415686122d2b69355a6c', 'index': None, 'type': 'tool_call_chunk'}])], tool_call_id='7afa445921e6415686122d2b69355a6c'),\n",
       "  'It’s actually a software framework which was designed to address the complexity of big data and its computational intensity. Most notably, Hadoop distributes the computational tasks on multiple computers which is basically the way to handle big data nowadays. Power BI, SaS, Qlik, and especially Tableau are top-notch examples of software designed for business intelligence visualizations\\n\\nThus, we need a lot of computational power, and we can expect people to use the languages similar to those in the big data column. Apart from R, Python, and MATLAB, other, faster languages are used like Java, JavaScript, C, C++, and Scala. Cool. What we said may be wonderful, but that’s not all! By using one or more programming languages, people create application software or, as they are sometimes called, software solutions, that are adjusted for specific business needs\\n\\nMore importantly, it will be sufficient for your need to create quick and accurate analyses. However, if your theoretical preparation is strong enough, you will find yourself restricted by software. Knowing a programming language such as R and Python, gives you the freedom to create specific, ad-hoc tools for each project you are working on')]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The output of agents:ToolAgentAction (each intermediate step consists of action and corresponding result)\n",
    "response['intermediate_steps']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m\n",
      "Invoking: `get_python_func` with `{}`\n",
      "responded: I will use the get_python_func tool to find the Python version.\n",
      "\n",
      "\u001b[0m\u001b[38;5;200m\u001b[1;3m3.10.10\u001b[0m\u001b[32;1m\u001b[1;3mThe Python version is 3.10.10.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "response = agent_executor.invoke({'input':'tell me the python version'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ToolAgentAction(tool='get_python_func', tool_input={}, log=\"\\nInvoking: `get_python_func` with `{}`\\nresponded: I will use the 'get_python_func' tool to find out the Python version.\\n\\n\", message_log=[AIMessage(content=\"I will use the 'get_python_func' tool to find out the Python version.\", additional_kwargs={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'd3debe1f-1188-4107-8ccd-2672904993d1', 'tool_calls': [{'id': '778a3ec52cdc47b5981307e4dae8a50b', 'function': {'name': 'get_python_func', 'arguments': '{}'}, 'type': 'function'}], 'token_count': {'input_tokens': 1042.0, 'output_tokens': 54.0}}, response_metadata={'documents': None, 'citations': None, 'search_results': None, 'search_queries': None, 'is_search_required': None, 'generation_id': 'd3debe1f-1188-4107-8ccd-2672904993d1', 'tool_calls': [{'id': '778a3ec52cdc47b5981307e4dae8a50b', 'function': {'name': 'get_python_func', 'arguments': '{}'}, 'type': 'function'}], 'token_count': {'input_tokens': 1042.0, 'output_tokens': 54.0}}, id='run-1d500fcd-47cf-48c9-8298-1ffa599cc29f-0', tool_calls=[{'name': 'get_python_func', 'args': {}, 'id': 'c2ae227a3ccd4fb89fcc2f38a6398b4e', 'type': 'tool_call'}], usage_metadata={'input_tokens': 1042, 'output_tokens': 54, 'total_tokens': 1096})], tool_call_id='c2ae227a3ccd4fb89fcc2f38a6398b4e')]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgentFinish(return_values={'output': 'The Python version is 3.10.10.'}, log='The Python version is 3.10.10.')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.invoke({'input':'tell me the python version', 'intermediate_steps':response['intermediate_steps']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agent Finish means no further steps needs to be taken"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "langchain_env",
   "language": "python",
   "name": "langchain_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}