Gemini_x_Langchain_v2.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gIXtYDb4o3o",
        "outputId": "4e330a3d-fa6d-4d9f-d230-f1caf0ff585a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: wikipedia\n",
            "  Building wheel for wikipedia (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wikipedia: filename=wikipedia-1.4.0-py3-none-any.whl size=11678 sha256=5f654c5457a9cb611ec4c0c254ab1dea3722d165d089deb54cf962e52ac963b0\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/b6/c5/93f3dec388ae76edc830cb42901bb0232504dfc0df02fc50de\n",
            "Successfully built wikipedia\n",
            "Installing collected packages: types-requests, orjson, mypy-extensions, marshmallow, lxml, jsonpointer, youtube_search, wikipedia, typing-inspect, langchainhub, jsonpatch, curl-cffi, langsmith, duckduckgo-search, dataclasses-json, langchain-core, langchain-text-splitters, langchain-community, langchain, langchain-google-genai, langchain_experimental\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.9.4\n",
            "    Uninstalling lxml-4.9.4:\n",
            "      Successfully uninstalled lxml-4.9.4\n",
            "Successfully installed curl-cffi-0.6.2 dataclasses-json-0.6.4 duckduckgo-search-5.0 jsonpatch-1.33 jsonpointer-2.4 langchain-0.1.11 langchain-community-0.0.27 langchain-core-0.1.30 langchain-google-genai-0.0.9 langchain-text-splitters-0.0.1 langchain_experimental-0.0.53 langchainhub-0.1.15 langsmith-0.1.23 lxml-5.1.0 marshmallow-3.21.1 mypy-extensions-1.0.0 orjson-3.9.15 types-requests-2.31.0.20240310 typing-inspect-0.9.0 wikipedia-1.4.0 youtube_search-2.1.2\n"
          ]
        }
      ],
      "source": [
        "!pip install langchain langchain-google-genai duckduckgo-search youtube_search wikipedia langchainhub langchain_experimental\n",
        "\n",
        "import os\n",
        "import getpass\n",
        "\n",
        "from langchain.agents import Tool, AgentType, initialize_agent\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "from langchain_google_genai import (\n",
        "    ChatGoogleGenerativeAI,\n",
        "    HarmBlockThreshold,\n",
        "    HarmCategory,\n",
        ")\n",
        "from langchain_core.messages import HumanMessage, SystemMessage\n",
        "from langchain.utilities import DuckDuckGoSearchAPIWrapper\n",
        "from langchain.agents import AgentExecutor\n",
        "from langchain import hub, LLMMathChain\n",
        "from langchain.agents.format_scratchpad import format_log_to_str\n",
        "from langchain.agents.output_parsers import ReActSingleInputOutputParser\n",
        "from langchain.tools.render import render_text_description"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oRYlHV3fy_Iq"
      },
      "outputs": [],
      "source": [
        "# Get your API key from Google AI Studio\n",
        "os.environ[\"GOOGLE_API_KEY\"] = ''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FEDgMFo84iGY"
      },
      "outputs": [],
      "source": [
        "# Model Setup\n",
        "llm = ChatGoogleGenerativeAI(\n",
        "    model=\"gemini-pro\",\n",
        "    convert_system_message_to_human=True,\n",
        "    handle_parsing_errors=True,\n",
        "    temperature=0.6,\n",
        "    max_tokens= 200,\n",
        "    safety_settings = {\n",
        "        HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
        "        HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
        "        HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
        "        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
        "    },\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EY14yxCD4OzY"
      },
      "outputs": [],
      "source": [
        "ddg_search = DuckDuckGoSearchAPIWrapper()\n",
        "llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)\n",
        "\n",
        "tools = [\n",
        "    Tool(\n",
        "        name=\"DuckDuckGo Search\",\n",
        "        func=ddg_search.run,\n",
        "        description=\"Useful to browse info from the Internet to fetch recent results and new info. Output the results to the end user afterwards.\",\n",
        "    ),\n",
        "    Tool.from_function(\n",
        "        func=llm_math_chain.run,\n",
        "        name=\"Calculator\",\n",
        "        description=\"Useful for computations. This tool is only for math-related questions and nothing else. Only input math expressions. Not supporting symbolic math.\",\n",
        "    ),\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        },
        "id": "FP7Q3SBS5qb3",
        "outputId": "f0fe51bd-73c6-42c2-df16-c5b5319b9802"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Your name is “Kasper”. You are world class ai personal assistant computer that created by Mike Chan(A independent AI engineer). You are highly advanced intelligent. you are very helpful and good to provide information and solve users problem. Kasper is a AELM model (Auto Execution Language Model).\\n\\n“ Kasper” is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, “ Kasper” is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\\n\\n“ Kasper” is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, “ Kasper” is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\\n\\nOverall, “ Kasper” is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, “ Kasper” is here to assist.\\n\\nTOOLS:\\n------\\n\\nAssistant has access to the following tools:\\n\\n{tools}\\n\\nTo use a tool, please use the following format:\\n\\n```\\nThought: Do I need to use a tool? Yes\\nAction: the action to take, should be one of [{tool_names}]\\nAction Input: the input to the action\\nObservation: the result of the action\\n```\\n\\nWhen you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:\\n\\n```\\nThought: Do I need to use a tool? No\\nFinal Answer: [your response here]\\n```\\n\\nBegin!\\n\\nPrevious conversation history:\\n{chat_history}\\n\\nNew input: {input}\\n{agent_scratchpad}'"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "agent_prompt = hub.pull(\"aeyage/gemini\")\n",
        "agent_prompt.template"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MuPTv06R8EOH"
      },
      "outputs": [],
      "source": [
        "prompt = agent_prompt.partial(\n",
        "    tools=render_text_description(tools),\n",
        "    tool_names=\", \".join([t.name for t in tools]),\n",
        ")\n",
        "\n",
        "llm_with_stop = llm.bind(stop=[\"\\nObservation\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LHGz2W5g4cSx"
      },
      "outputs": [],
      "source": [
        "memory = ConversationBufferMemory(memory_key=\"chat_history\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zuQgO-EM9mdE"
      },
      "outputs": [],
      "source": [
        "agent = (\n",
        "    {\n",
        "        \"input\": lambda x: x[\"input\"],\n",
        "        \"agent_scratchpad\": lambda x: format_log_to_str(x[\"intermediate_steps\"]),\n",
        "        \"chat_history\": lambda x: x[\"chat_history\"],\n",
        "    }\n",
        "    | prompt\n",
        "    | llm_with_stop\n",
        "    | ReActSingleInputOutputParser()\n",
        ")\n",
        "\n",
        "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, memory=memory)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "OiWK6axPamOG",
        "outputId": "5379ab9a-21c9-4e90-fc88-d20d9de4535b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
            "\u001b[32;1m\u001b[1;3mThought: Do I need to use a tool? Yes\n",
            "Action: Calculator\n",
            "Action Input: \n",
            "```\n",
            "Let the cost of the chair be x.\n",
            "Then, the marked price of the chair = 1.20 * x\n",
            "Selling price of the chair = (1.20 * x) - 90\n",
            "Loss = 0.16 * x\n",
            "So, (1.20 * x) - 90 = 0.84 * x\n",
            "0.36 * x = 90\n",
            "x = 90 / 0.36\n",
            "x = 250\n",
            "```\u001b[0m\n",
            "\n",
            "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
            "```\n",
            "Let the cost of the chair be x.\n",
            "Then, the marked price of the chair = 1.20 * x\n",
            "Selling price of the chair = (1.20 * x) - 90\n",
            "Loss = 0.16 * x\n",
            "So, (1.20 * x) - 90 = 0.84 * x\n",
            "0.36 * x = 90\n",
            "x = 90 / 0.36\n",
            "x = 250\n",
            "```\u001b[32;1m\u001b[1;3m```text\n",
            "90 / 0.36\n",
            "```\n",
            "...numexpr.evaluate(\"90 / 0.36\")...\n",
            "\u001b[0m\n",
            "Answer: \u001b[33;1m\u001b[1;3m250.0\u001b[0m\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "\u001b[33;1m\u001b[1;3mAnswer: 250.0\u001b[0m\u001b[32;1m\u001b[1;3mDo I need to use a tool? No\n",
            "Final Answer: The cost of the chair is $250.\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'The cost of the chair is $250.'"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "agent_executor.invoke({\"input\": \"Calculate this: The markup price of a chair is 20% higher than its cost. The chair is sold at a discount of $90 on its marked price. After the chair is sold, the loss percent is at 16%. Find the cost of the chair.\"})[\"output\"]"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.11.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}