Jsonformer_example.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CHETAN1KUKREJA/llm-backend-prompt-engineering/blob/main/Jsonformer_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain_core langchain_huggingface langchain_experimental"
      ],
      "metadata": {
        "id": "USzZqyH7L8F2",
        "outputId": "61b2db4b-3d5c-4c12-f200-14c9a7128897",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: python-dotenv, mypy-extensions, marshmallow, httpx-sse, typing-inspect, pydantic-settings, dataclasses-json, langchain_core, langchain_huggingface, langchain, langchain-community, langchain_experimental\n",
            "  Attempting uninstall: langchain_core\n",
            "    Found existing installation: langchain-core 0.3.21\n",
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            "    Found existing installation: langchain 0.3.9\n",
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            "Successfully installed dataclasses-json-0.6.7 httpx-sse-0.4.0 langchain-0.3.11 langchain-community-0.3.11 langchain_core-0.3.24 langchain_experimental-0.3.3 langchain_huggingface-0.1.2 marshmallow-3.23.1 mypy-extensions-1.0.0 pydantic-settings-2.6.1 python-dotenv-1.0.1 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-kogdhHO3PC",
        "outputId": "78cddb0b-0506-4303-b265-e574d6de2c4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (3.0.2)\n",
            "Downloading jsonformer-0.12.0-py3-none-any.whl (6.6 kB)\n",
            "Installing collected packages: jsonformer\n",
            "Successfully installed jsonformer-0.12.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers accelerate jsonformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9y0Jjj0sOtor",
        "outputId": "6c5ea465-384f-4aa1-8304-6fa721534f73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model and tokenizer...\n",
            "Loaded model and tokenizer\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "print(\"Loading model and tokenizer...\")\n",
        "model_name = \"databricks/dolly-v2-3b\"\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name, use_cache=True, device_map=\"auto\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True, use_cache=True)\n",
        "print(\"Loaded model and tokenizer\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "action_schemas = {\n",
        "            \"talk\": {\n",
        "                \"type\": \"object\",\n",
        "                \"properties\": {\n",
        "                    \"volume\": {\"type\": \"string\", \"enum\": [\"quiet\", \"normal\", \"loud\"]},\n",
        "                    \"content\": {\"type\": \"string\"}\n",
        "                }\n",
        "            },\n",
        "            \"take\": {\n",
        "                \"type\": \"object\",\n",
        "                \"properties\": {\n",
        "                    \"item\": {\"type\": \"string\"},\n",
        "                    \"amount\": {\"type\": \"number\"}\n",
        "                }\n",
        "            },\n",
        "            \"drop\": {\n",
        "                \"type\": \"object\",\n",
        "                \"properties\": {\n",
        "                    \"item\": {\"type\": \"string\"},\n",
        "                    \"amount\": {\"type\": \"number\"}\n",
        "                }\n",
        "            }\n",
        "            # Add more action schemas as needed\n",
        "        }"
      ],
      "metadata": {
        "id": "5JT81tRBGZLf"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "examples = {\n",
        "            \"talk\": {\n",
        "                \"query\": \"I want to ask Maria what she has to trade\",\n",
        "                \"response\": {\n",
        "                    \"action\": \"talk\",\n",
        "                    \"action_input\": {\n",
        "                        \"volume\": \"normal\",\n",
        "                        \"content\": \"Hello Maria, what do you have to trade?\"\n",
        "                    }\n",
        "                }\n",
        "            },\n",
        "            \"take\": {\n",
        "                \"query\": \"I want to take 5 apples\",\n",
        "                \"response\": {\n",
        "                    \"action\": \"take\",\n",
        "                    \"action_input\": {\n",
        "                        \"item\": \"apples\",\n",
        "                        \"amount\": 5\n",
        "                    }\n",
        "                }\n",
        "            },\n",
        "        }\n",
        ""
      ],
      "metadata": {
        "id": "2cRs_2obGDH-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt_template = \"\"\"You must respond using JSON format, with a single action and single action input. You have to read the entire human query and most of the time the output is given between '<toolcall> </toolcall>'\n",
        "          but if you dont find this you can select one action from the list that is stated in the query\n",
        "          There may be a lot of Thought process in the query you dont have to get swayed by that and focus on the action and action and action input.\n",
        "          Available actions: {actions}\n",
        "          EXAMPLES:\n",
        "          {formatted_examples}\n",
        "          BEGIN! Parse the following request into an appropriate action:\n",
        "          Human: {query}\n",
        "          Assistant:\n",
        "        \"\"\""
      ],
      "metadata": {
        "id": "OWyIzRXBGIxG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(query,available_actions=None):\n",
        "        \"\"\"Generate a prompt with examples for available actions\"\"\"\n",
        "        if available_actions is None:\n",
        "            available_actions = list(action_schemas.keys())\n",
        "\n",
        "        formatted_examples = \"\\n\".join(\n",
        "            f\"Human: {ex['query']}\\n\"\n",
        "            f\"Assistant: {ex['response']}\\n\"\n",
        "            for action in available_actions\n",
        "            if action in examples\n",
        "            for ex in [examples[action]]\n",
        "        )\n",
        "        return prompt_template.format(\n",
        "            actions=\", \".join(available_actions),\n",
        "            formatted_examples=formatted_examples,\n",
        "            query=query  # Will be filled in during parse_action\n",
        "        )"
      ],
      "metadata": {
        "id": "gEfqSd4pGKyB"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_from_LLM=\"\"\"\n",
        "## Step 0: Plan what to do for a short period\n",
        "To maximize the amount of money, the first step is to gather information about the current state of the trade centre and the forest. Since there is another agent, Maria, at the trade centre, it might be beneficial to interact with her to see if there are any trade opportunit\n",
        "ies. The plan is to:\n",
        "1. Talk to Maria to gather information about her current stock of apples and money.\n",
        "2. Ask Maria if she is willing to trade apples for money.\n",
        "3. If a trade is possible, negotiate the terms of the trade.\n",
        "4. If no trade is possible with Maria, consider going to the forest to collect apples.\n",
        "\n",
        "## Step 1: Extract a sequence of actions and parameters pairs from the plan\n",
        "### Substep 1.1.1: Output description of the action\n",
        "1. Action: talk\n",
        "2. Parameters: volume, content\n",
        "3. list:\n",
        "- Talk to Maria to gather information about her current stock of apples and money.\n",
        "- Ask Maria if she is willing to trade apples for money.\n",
        "\n",
        "### Substep 1.1.2: Verify the action and parameters\n",
        "1. The action \"talk\" appears in the plan.\n",
        "2. The sentences covering the action and parameters appear in the plan.\n",
        "3. The parameter \"content\" might need to be specified based on Maria's response, which will be answered by her.\n",
        "Considering the need to interact with Maria first, the sequence of actions starts with talking to her.\n",
        "\n",
        "## Step 2: Format the plan as function calls in JSON objects within single XML tags\n",
        "Given the plan and the actions available, the first step is to talk to Maria. The function call for this action is:\n",
        "\n",
        "<tool_call>\n",
        "{\"action\": \"talk\", \"action_input\": {\"volume\": \"normal\", \"content\": \"Hello Maria, what are you trading today?\"}}\n",
        "</tool_call>\n",
        "\n",
        "This initial interaction is aimed at gathering information and setting the stage for potential trades or other actions based on Maria's response. Further actions will depend on her answer, which could involve negotiating a trade, deciding to go to the forest, or other option\n",
        "s based on the information exchanged.\n",
        "Finished in 162.5875s\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "H3pMlPHfGMAJ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_decoder_schema(available_actions=None):\n",
        "        \"\"\"\n",
        "        Generate a decoder schema based on available actions\n",
        "\n",
        "        Args:\n",
        "            available_actions (list): List of action names that are currently available\n",
        "        \"\"\"\n",
        "        if available_actions is None:\n",
        "            available_actions = list(action_schemas.keys())\n",
        "\n",
        "        return {\n",
        "            \"type\": \"object\",\n",
        "            \"properties\": {\n",
        "                \"action\": {\n",
        "                    \"type\": \"string\",\n",
        "                    \"enum\": available_actions\n",
        "                },\n",
        "                \"action_input\": {\n",
        "                    \"type\": \"object\",\n",
        "                    \"oneOf\": [\n",
        "                        {\n",
        "                            \"if\": {\"properties\": {\"action\": {\"const\": action}}},\n",
        "                            \"then\": action_schemas[action]\n",
        "                        }\n",
        "                        for action in available_actions\n",
        "                    ]\n",
        "                }\n",
        "            }\n",
        "        }"
      ],
      "metadata": {
        "id": "Eo9n2J8FGNkK"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\"databricks/dolly-v2-3b\",use_cache=True, device_map=\"auto\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"databricks/dolly-v2-3b\", use_fast=True, use_cache=True)\n"
      ],
      "metadata": {
        "id": "4iika2aOGQWf",
        "outputId": "03e91e6d-0865-4f8a-fb58-338ec9f6c996",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415,
          "referenced_widgets": [
            "0088f72d46ad4e85badffa288e01ab6f",
            "ff00c82942a340a781e4794a11a2659f",
            "5f3f0a7a35994da99697e090a81a91bf",
            "77428da2d3f04f358fc86128da92643c",
            "eb6d70e8ffe1465b894432078dc465db",
            "c137fb216dd84ae3b419d91d21343197",
            "3e32b979df3b4df7a0695f70ef82622d",
            "cfc3b2b8e0f44e539c1a72b4a0c9f153",
            "ce2ceb36755e42559c939134d50c899d",
            "250557bbc0374d9aa00deeae8217597f",
            "6ee4859b19874431ad07ff2e178bbdb8",
            "a80e7874d1534b76928d968c1099fd38",
            "268c39cd9e41430ca19f4420bd16db68",
            "2b6baaa67fc14c8c973ad8db4eb91f91",
            "9ac8e0298c2f4e3f8930db26173cd9ca",
            "283842058a75458a80968f7ef152b53f",
            "b65ddbb2e3c2444a96b9f05cc3aaf45a",
            "37d281c240604fb9be1b37322712d7c2",
            "d6689527117d4ee0a67e88348b4a1d7f",
            "35cf07938e874397974609a192a14849",
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            "018150f2bd094ee98ce94cbec528a06b",
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            "4613999639f044a0a469165f4dfde42f",
            "bedc2a130318433f8e111b1560c7ec93",
            "11ebf141130347209edbcbbd33e88228",
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            "11ae07f042004dacaaea61766f64b35d",
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            "c5c86ba64e1e4fe097fea3c8d9434834",
            "df7f729e358a4777b24bf098fc43d3ef",
            "675988e127454243bde49d236d0d210f",
            "c229abbaa4b14bb588c8dcafae8fd994",
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            "dffd139efeb944249c76eff14b9df88e",
            "b6386caf4007496686eb1c1794c9d374"
          ]
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/819 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0088f72d46ad4e85badffa288e01ab6f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/5.68G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a80e7874d1534b76928d968c1099fd38"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/450 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d94f084f2b94eeaba0ea351a3d2938f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c79f0ac8cb645d08e06c089dd23ea21"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/228 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a1e3ffb9b43e4b55a9ea7d0de3a2e8e6"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import os\n",
        "\n",
        "import requests\n",
        "from langchain_core.tools import tool\n",
        "\n",
        "HF_TOKEN = os.environ.get(\"HUGGINGFACE_API_KEY\")\n",
        "\n",
        "\n",
        "@tool\n",
        "def ask_star_coder(query: str, temperature: float = 1.0, max_new_tokens: float = 250):\n",
        "    \"\"\"Query the BigCode StarCoder model about coding questions.\"\"\"\n",
        "    url = \"https://api-inference.huggingface.co/models/bigcode/starcoder\"\n",
        "    headers = {\n",
        "        \"Authorization\": f\"Bearer {HF_TOKEN}\",\n",
        "        \"content-type\": \"application/json\",\n",
        "    }\n",
        "    payload = {\n",
        "        \"inputs\": f\"{query}\\n\\nAnswer:\",\n",
        "        \"temperature\": temperature,\n",
        "        \"max_new_tokens\": int(max_new_tokens),\n",
        "    }\n",
        "    response = requests.post(url, headers=headers, data=json.dumps(payload))\n",
        "    response.raise_for_status()\n",
        "    return json.loads(response.content.decode(\"utf-8\"))"
      ],
      "metadata": {
        "id": "rrniu7EsLxYl"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"You must respond using JSON format, with a single action and single action input.\n",
        "You may 'ask_star_coder' for help on coding problems.\n",
        "\n",
        "{arg_schema}\n",
        "\n",
        "EXAMPLES\n",
        "----\n",
        "Human: \"So what's all this about a GIL?\"\n",
        "AI Assistant:{{\n",
        "  \"action\": \"ask_star_coder\",\n",
        "  \"action_input\": {{\"query\": \"What is a GIL?\", \"temperature\": 0.0, \"max_new_tokens\": 100}}\"\n",
        "}}\n",
        "Observation: \"The GIL is python's Global Interpreter Lock\"\n",
        "Human: \"Could you please write a calculator program in LISP?\"\n",
        "AI Assistant:{{\n",
        "  \"action\": \"ask_star_coder\",\n",
        "  \"action_input\": {{\"query\": \"Write a calculator program in LISP\", \"temperature\": 0.0, \"max_new_tokens\": 250}}\n",
        "}}\n",
        "Observation: \"(defun add (x y) (+ x y))\\n(defun sub (x y) (- x y ))\"\n",
        "Human: \"What's the difference between an SVM and an LLM?\"\n",
        "AI Assistant:{{\n",
        "  \"action\": \"ask_star_coder\",\n",
        "  \"action_input\": {{\"query\": \"What's the difference between SGD and an SVM?\", \"temperature\": 1.0, \"max_new_tokens\": 250}}\n",
        "}}\n",
        "Observation: \"SGD stands for stochastic gradient descent, while an SVM is a Support Vector Machine.\"\n",
        "\n",
        "BEGIN! Answer the Human's question as best as you are able.\n",
        "------\n",
        "Human: 'What's the difference between an iterator and an iterable?'\n",
        "AI Assistant:\"\"\".format(arg_schema=ask_star_coder.args)"
      ],
      "metadata": {
        "id": "xwDk7va9L1yq"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_huggingface import HuggingFacePipeline\n",
        "from transformers import pipeline\n",
        "\n",
        "hf_model = pipeline(\n",
        "    \"text-generation\", model=\"cerebras/Cerebras-GPT-590M\", max_new_tokens=200,device=\"cuda\"\n",
        ")\n",
        "\n",
        "original_model = HuggingFacePipeline(pipeline=hf_model)\n",
        "\n",
        "generated = original_model.predict(prompt, stop=[\"Observation:\", \"Human:\"])\n",
        "print(generated)"
      ],
      "metadata": {
        "id": "_pMY_hA2L5S2",
        "outputId": "5d5d5c0c-e30c-49c4-e24b-d07e83d836c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You must respond using JSON format, with a single action and single action input.\n",
            "You may 'ask_star_coder' for help on coding problems.\n",
            "\n",
            "{'query': {'title': 'Query', 'type': 'string'}, 'temperature': {'default': 1.0, 'title': 'Temperature', 'type': 'number'}, 'max_new_tokens': {'default': 250, 'title': 'Max New Tokens', 'type': 'number'}}\n",
            "\n",
            "EXAMPLES\n",
            "----\n",
            "Human: \"So what's all this about a GIL?\"\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"What is a GIL?\", \"temperature\": 0.0, \"max_new_tokens\": 100}\"\n",
            "}\n",
            "Observation: \"The GIL is python's Global Interpreter Lock\"\n",
            "Human: \"Could you please write a calculator program in LISP?\"\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"Write a calculator program in LISP\", \"temperature\": 0.0, \"max_new_tokens\": 250}\n",
            "}\n",
            "Observation: \"(defun add (x y) (+ x y))\n",
            "(defun sub (x y) (- x y ))\"\n",
            "Human: \"What's the difference between an SVM and an LLM?\"\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"What's the difference between SGD and an SVM?\", \"temperature\": 1.0, \"max_new_tokens\": 250}\n",
            "}\n",
            "Observation: \"SGD stands for stochastic gradient descent, while an SVM is a Support Vector Machine.\"\n",
            "\n",
            "BEGIN! Answer the Human's question as best as you are able.\n",
            "------\n",
            "Human: 'What's the difference between an iterator and an iterable?'\n",
            "AI Assistant: 'What's the difference between an iterator and an iterable?'\n",
            "Observation: 'The iterator is a function that returns a value from the iterator.'\n",
            "Human: 'What's the difference between an iterator and an iterable?'\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"What's the difference between an iterator and an iterable?\", \"temperature\": 1.0, \"max_new_tokens\": 250}\n",
            "}\n",
            "Observation: 'The iterator is a function that returns a value from the iterator.'\n",
            "Human: 'What's the difference between an iterator and an iterable?'\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"What's the difference between an iterator and an iterable?\", \"temperature\": 1.0, \"max_new_tokens\": 250\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decoder_schema = {\n",
        "    \"title\": \"Decoding Schema\",\n",
        "    \"type\": \"object\",\n",
        "    \"properties\": {\n",
        "        \"action\": {\"type\": \"string\", \"default\": ask_star_coder.name},\n",
        "        \"action_input\": {\n",
        "            \"type\": \"object\",\n",
        "            \"properties\": ask_star_coder.args,\n",
        "        },\n",
        "    },\n",
        "}"
      ],
      "metadata": {
        "id": "tkd75BCTMCn1"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_experimental.llms import JsonFormer\n",
        "\n",
        "json_former = JsonFormer(json_schema=decoder_schema, pipeline=hf_model)"
      ],
      "metadata": {
        "id": "bKn7roTlMD7O"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = json_former.predict(prompt, stop=[\"Observation:\", \"Human:\"])\n",
        "print(results)"
      ],
      "metadata": {
        "id": "MaYzg17cM_y2",
        "outputId": "32799735-6706-423f-e662-0442412b994d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You must respond using JSON format, with a single action and single action input.\n",
            "You may 'ask_star_coder' for help on coding problems.\n",
            "\n",
            "{'query': {'title': 'Query', 'type': 'string'}, 'temperature': {'default': 1.0, 'title': 'Temperature', 'type': 'number'}, 'max_new_tokens': {'default': 250, 'title': 'Max New Tokens', 'type': 'number'}}\n",
            "\n",
            "EXAMPLES\n",
            "----\n",
            "Human: \"So what's all this about a GIL?\"\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"What is a GIL?\", \"temperature\": 0.0, \"max_new_tokens\": 100}\"\n",
            "}\n",
            "Observation: \"The GIL is python's Global Interpreter Lock\"\n",
            "Human: \"Could you please write a calculator program in LISP?\"\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"Write a calculator program in LISP\", \"temperature\": 0.0, \"max_new_tokens\": 250}\n",
            "}\n",
            "Observation: \"(defun add (x y) (+ x y))\n",
            "(defun sub (x y) (- x y ))\"\n",
            "Human: \"What's the difference between an SVM and an LLM?\"\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"What's the difference between SGD and an SVM?\", \"temperature\": 1.0, \"max_new_tokens\": 250}\n",
            "}\n",
            "Observation: \"SGD stands for stochastic gradient descent, while an SVM is a Support Vector Machine.\"\n",
            "\n",
            "BEGIN! Answer the Human's question as best as you are able.\n",
            "------\n",
            "Human: 'What's the difference between an iterator and an iterable?'\n",
            "AI Assistant: 'What's the difference between an iterator and an iterable?'\n",
            "Observation: 'The iterator is a function that returns a value from the iterator.'\n",
            "Human: 'What's the difference between an iterator and an iterable?'\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"What's the difference between an iterator and an iterable?\", \"temperature\": 1.0, \"max_new_tokens\": 250}\n",
            "}\n",
            "Observation: 'The iterator is a function that returns a value from the iterator.'\n",
            "Human: 'What's the difference between an iterator and an iterable?'\n",
            "AI Assistant:{\n",
            "  \"action\": \"ask_star_coder\",\n",
            "  \"action_input\": {\"query\": \"What's the difference between an iterator and an iterable?\", \"temperature\": 1.0, \"max_new_tokens\": 250\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "id": "f_DYOWeLNGri",
        "outputId": "b8b2a89b-b2b9-4d76-91a8-581f21bd62cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: read).\n",
            "The token `balvinder` has been saved to /root/.cache/huggingface/stored_tokens\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful.\n",
            "The current active token is: `balvinder`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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"
      ],
      "metadata": {
        "id": "yWhekdLyPtAT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
        "import torch\n",
        "\n",
        "# Load model directly\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-2b-it\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"google/gemma-2b-it\")\n",
        "hf_model = pipeline(\"text-generation\", model=model, tokenizer=tokenizer,max_new_tokens=2048,device=\"cuda\")\n"
      ],
      "metadata": {
        "id": "2Z69PLwXNGiY",
        "outputId": "59098f9d-f910-4c39-885b-2901abd81a6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846,
          "referenced_widgets": [
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            "2a125d2ae59d44d0b5d320a7dfb9bb2e"
          ]
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/34.2k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d65e51963ac42a992e8071b6b3ce0d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "fe159ca3cf06459680298b6bf8769065"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "160eae94a866444f95a77c11fd0dc4da"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59f9b30f664c415f9a8990601a466a4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6a7584eb198423ca02c3448dd8bbd72"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/13.5k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e74c3ccca27d49f992100a0b28efcf65"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a959ea990764e43bd0048ca9f3a5bf4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df025307c80341c3a7344b2ee7a62a69"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model-00002-of-00002.safetensors:   0%|          | 0.00/67.1M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f52b4bd088a94052a4803c56b91ca003"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n",
            "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n",
            "`config.hidden_activation` if you want to override this behaviour.\n",
            "See https://github.com/huggingface/transformers/pull/29402 for more details.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2649eaa32b9144589f071b3ddb5222ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ebbc558ea003478d9a42c306225dccf8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_experimental.llms import JsonFormer\n",
        "\n",
        "json_former = JsonFormer(json_schema=get_decoder_schema(), pipeline=hf_model)"
      ],
      "metadata": {
        "id": "IGzZtvB3NGYx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = json_former.predict(generate_prompt(output_from_LLM), stop=[\"Observation:\", \"Human:\"])\n",
        "print(results)"
      ],
      "metadata": {
        "id": "Xq2FuFGaN7Xo",
        "outputId": "b350d8cd-07cb-4347-b4e2-28be4b93cebb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-49fb51c6d6ae>:1: LangChainDeprecationWarning: The method `BaseLLM.predict` was deprecated in langchain-core 0.1.7 and will be removed in 1.0. Use :meth:`~invoke` instead.\n",
            "  results = json_former.predict(generate_prompt(output_from_LLM), stop=[\"Observation:\", \"Human:\"])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You must respond using JSON format, with a single action and single action input. You have to read the entire human query and most of the time the output is given between '<toolcall> </toolcall>' \n",
            "          but if you dont find this you can select one action from the list that is stated in the query\n",
            "          There may be a lot of Thought process in the query you dont have to get swayed by that and focus on the action and action and action input.\n",
            "          Available actions: talk, take, drop\n",
            "          EXAMPLES:\n",
            "          Human: I want to ask Maria what she has to trade\n",
            "Assistant: {'action': 'talk', 'action_input': {'volume': 'normal', 'content': 'Hello Maria, what do you have to trade?'}}\n",
            "\n",
            "Human: I want to take 5 apples\n",
            "Assistant: {'action': 'take', 'action_input': {'item': 'apples', 'amount': 5}}\n",
            "\n",
            "          BEGIN! Parse the following request into an appropriate action:\n",
            "          Human: \n",
            "## Step 0: Plan what to do for a short period                       \n",
            "To maximize the amount of money, the first step is to gather information about the current state of the trade centre and the forest. Since there is another agent, Maria, at the trade centre, it might be beneficial to interact with her to see if there are any trade opportunit\n",
            "ies. The plan is to:                                                \n",
            "1. Talk to Maria to gather information about her current stock of apples and money.                                                      \n",
            "2. Ask Maria if she is willing to trade apples for money.           \n",
            "3. If a trade is possible, negotiate the terms of the trade.                                                                             \n",
            "4. If no trade is possible with Maria, consider going to the forest to collect apples.                                                   \n",
            "\n",
            "## Step 1: Extract a sequence of actions and parameters pairs from the plan                                                              \n",
            "### Substep 1.1.1: Output description of the action                 \n",
            "1. Action: talk                                                     \n",
            "2. Parameters: volume, content                                      \n",
            "3. list:                                                            \n",
            "- Talk to Maria to gather information about her current stock of apples and money.                                                       \n",
            "- Ask Maria if she is willing to trade apples for money.            \n",
            "\n",
            "### Substep 1.1.2: Verify the action and parameters                 \n",
            "1. The action \"talk\" appears in the plan.                           \n",
            "2. The sentences covering the action and parameters appear in the plan.                                                                  \n",
            "3. The parameter \"content\" might need to be specified based on Maria's response, which will be answered by her.                          \n",
            "Considering the need to interact with Maria first, the sequence of actions starts with talking to her.                                   \n",
            "\n",
            "## Step 2: Format the plan as function calls in JSON objects within single XML tags                                                      \n",
            "Given the plan and the actions available, the first step is to talk to Maria. The function call for this action is:                      \n",
            "\n",
            "<tool_call>                                                         \n",
            "{\"action\": \"talk\", \"action_input\": {\"volume\": \"normal\", \"content\": \"Hello Maria, what are you trading today?\"}}                               \n",
            "</tool_call>                                                        \n",
            "\n",
            "This initial interaction is aimed at gathering information and setting the stage for potential trades or other actions based on Maria's response. Further actions will depend on her answer, which could involve negotiating a trade, deciding to go to the forest, or other option\n",
            "s based on the information exchanged.                               \n",
            "Finished in 162.5875s\n",
            "\n",
            "          Assistant:\n",
            "        {'action': 'talk', 'action_input': {'volume': 'normal', 'content': 'Hello Maria, what are you trading today?'}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_jsonformer_response(response, prompt_string):\n",
        "    # Strip leading/trailing spaces from the prompt string and response\n",
        "    prompt_string = prompt_string.strip()\n",
        "    response = response.strip()\n",
        "\n",
        "    # Check if the response starts with the prompt string and remove it\n",
        "    if response.startswith(prompt_string):\n",
        "        response = response[len(prompt_string):].strip()\n",
        "\n",
        "    # Return the cleaned response\n",
        "    return response\n"
      ],
      "metadata": {
        "id": "Ks2c9VEiQ3lF"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a=clean_jsonformer_response(results,generate_prompt(output_from_LLM))"
      ],
      "metadata": {
        "id": "xDaVxx7BbgBP"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n"
      ],
      "metadata": {
        "id": "ijf6QJ61dK6K",
        "outputId": "47fa0e35-217b-413e-fc5f-7d1341e726ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'int' object has no attribute 'write'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-b2ca7eade6a0>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/lib/python3.10/json/__init__.py\u001b[0m in \u001b[0;36mdump\u001b[0;34m(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;31m# a debuggability cost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'write'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "json.dumps(a, indent=4)"
      ],
      "metadata": {
        "id": "a-wrNEEle0r4",
        "outputId": "74cafe0c-a135-4665-b94e-3e90f53a0fa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\"{\\'action\\': \\'talk\\', \\'action_input\\': {\\'volume\\': \\'normal\\', \\'content\\': \\'Hello Maria, what are you trading today?\\'}}\"'"
            ],
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import importlib\n",
        "import pkg_resources\n",
        "\n",
        "def get_library_version(lib_name):\n",
        "    try:\n",
        "        module = importlib.import_module(lib_name)\n",
        "        version = module.__version__\n",
        "        print(f\"{lib_name}=={version}\")\n",
        "    except ImportError:\n",
        "        print(f\"{lib_name} is not installed.\")\n",
        "    except AttributeError:\n",
        "        # Some modules might not have __version__, fallback to pkg_resources\n",
        "        try:\n",
        "            version = pkg_resources.get_distribution(lib_name).version\n",
        "            print(f\"{lib_name}=={version}\")\n",
        "        except pkg_resources.DistributionNotFound:\n",
        "            print(f\"{lib_name} is not installed.\")\n",
        "\n",
        "# List of libraries to check\n",
        "libraries = [\n",
        "    \"huggingface_hub\",\n",
        "    \"langchain\",\n",
        "    \"langchain_experimental\",\n",
        "    \"transformers\",\n",
        "    \"torch\"  # Include torch since it's often used with these models\n",
        "]\n",
        "\n",
        "for lib in libraries:\n",
        "    get_library_version(lib)\n"
      ],
      "metadata": {
        "id": "4eb0hvOwg6-v",
        "outputId": "fea11088-a438-4475-d75b-dc32233f8109",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-33-095191ca59d6>:2: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n",
            "  import pkg_resources\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "huggingface_hub==0.26.3\n",
            "langchain==0.3.11\n",
            "langchain_experimental==0.3.3\n",
            "transformers==4.46.3\n",
            "torch==2.5.1+cu121\n"
          ]
        }
      ]
    }
  ],
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