Generative_AI_with_LangChain_and_Hugging_Face.ipynb

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            "Requirement already satisfied: langchain-huggingface in /usr/local/lib/python3.10/dist-packages (0.1.2)\n",
            "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from langchain-huggingface) (0.26.2)\n",
            "Requirement already satisfied: langchain-core<0.4.0,>=0.3.15 in /usr/local/lib/python3.10/dist-packages (from langchain-huggingface) (0.3.15)\n",
            "Requirement already satisfied: sentence-transformers>=2.6.0 in /usr/local/lib/python3.10/dist-packages (from langchain-huggingface) (3.2.1)\n",
            "Requirement already satisfied: tokenizers>=0.19.1 in /usr/local/lib/python3.10/dist-packages (from langchain-huggingface) (0.20.3)\n",
            "Requirement already satisfied: transformers>=4.39.0 in /usr/local/lib/python3.10/dist-packages (from langchain-huggingface) (4.46.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (3.16.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (2024.10.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (4.66.6)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.23.0->langchain-huggingface) (4.12.2)\n",
            "Requirement already satisfied: jsonpatch<2.0,>=1.33 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (1.33)\n",
            "Requirement already satisfied: langsmith<0.2.0,>=0.1.125 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (0.1.142)\n",
            "Requirement already satisfied: pydantic<3.0.0,>=2.5.2 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (2.9.2)\n",
            "Requirement already satisfied: tenacity!=8.4.0,<10.0.0,>=8.1.0 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (9.0.0)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.6.0->langchain-huggingface) (2.5.0+cu121)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.6.0->langchain-huggingface) (1.5.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.6.0->langchain-huggingface) (1.13.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.6.0->langchain-huggingface) (11.0.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.39.0->langchain-huggingface) (1.26.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.39.0->langchain-huggingface) (2024.9.11)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.39.0->langchain-huggingface) (0.4.5)\n",
            "Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.10/dist-packages (from jsonpatch<2.0,>=1.33->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (3.0.0)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (0.27.2)\n",
            "Requirement already satisfied: orjson<4.0.0,>=3.9.14 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (3.10.11)\n",
            "Requirement already satisfied: requests-toolbelt<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from langsmith<0.2.0,>=0.1.125->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (1.0.0)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0.0,>=2.5.2->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0.0,>=2.5.2->langchain-core<0.4.0,>=0.3.15->langchain-huggingface) (2.23.4)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.23.0->langchain-huggingface) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.23.0->langchain-huggingface) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.23.0->langchain-huggingface) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.23.0->langchain-huggingface) (2024.8.30)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (3.1.4)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.11.0->sentence-transformers>=2.6.0->langchain-huggingface) (1.3.0)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers>=2.6.0->langchain-huggingface) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers>=2.6.0->langchain-huggingface) (3.5.0)\n",
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          ]
        }
      ],
      "source": [
        "## Libraries Required\n",
        "!pip install langchain-huggingface\n",
        "## For API Calls\n",
        "!pip install huggingface_hub\n",
        "!pip install transformers\n",
        "!pip install accelerate\n",
        "!pip install  bitsandbytes\n",
        "!pip install langchain"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_huggingface import HuggingFaceEndpoint"
      ],
      "metadata": {
        "id": "es6eIETrZViw"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from getpass import getpass\n",
        "\n",
        "HUGGINGFACEHUB_API_TOKEN = getpass()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPFIfgLnZjkt",
        "outputId": "2eeee2d8-73b0-458f-d78b-7f7fa1dde639"
      },
      "execution_count": 3,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN"
      ],
      "metadata": {
        "id": "mNYGhs2CZ6NR"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import LLMChain\n",
        "from langchain_core.prompts import PromptTemplate"
      ],
      "metadata": {
        "id": "hcRboCWkgVpU"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"Who won the FIFA World Cup in the year 1994? \"\n",
        "\n",
        "template = \"\"\"Question: {question}\n",
        "\n",
        "Answer: Let's think step by step.\"\"\"\n",
        "\n",
        "prompt = PromptTemplate.from_template(template)"
      ],
      "metadata": {
        "id": "yF9R4IMZgVlZ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "repo_id = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
        "\n",
        "llm = HuggingFaceEndpoint(\n",
        "    repo_id=repo_id,\n",
        "    max_length=128,\n",
        "    temperature=0.5,\n",
        "    huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,\n",
        ")\n",
        "llm_chain = prompt | llm\n",
        "print(llm_chain.invoke({\"question\": question}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upRV8CCOgViF",
        "outputId": "71ea31c3-4373-4922-c6de-ceb8c9f22828"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:langchain_huggingface.llms.huggingface_endpoint:WARNING! max_length is not default parameter.\n",
            "                    max_length was transferred to model_kwargs.\n",
            "                    Please make sure that max_length is what you intended.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The FIFA World Cup is an international football tournament. The year 1994 is not a difficult fact to remember, but just to be sure, let's double check. \n",
            "\n",
            "1. The FIFA World Cup has been held every four years since its inception in 1930, except for 1942 and 1946 due to World War II.\n",
            "2. Therefore, the FIFA World Cup was held in 1994 after the tournament was last held in Italy in 1990.\n",
            "\n",
            "Now, let's find out which team won the FIFA World Cup in 1994.\n",
            "\n",
            "1. The 1994 FIFA World Cup was held in the United States from June 17 to July 17, 1994.\n",
            "2. Brazil won the tournament, making it their record fifth World Cup title.\n",
            "\n",
            "So, the answer is Brazil. They won the FIFA World Cup in the year 1994.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import PromptTemplate, LLMChain\n",
        "\n",
        "question=\"Who won the Cricket World Cup in the year 2011?\"\n",
        "template = \"\"\"Question: {question}\n",
        "Answer: Let's think step by step.\"\"\"\n",
        "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12p6gftxhnXz",
        "outputId": "57664391-5675-4f30-e74c-586a0bd0bbbd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_variables=['question'] input_types={} partial_variables={} template=\"Question: {question}\\nAnswer: Let's think step by step.\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm_chain=LLMChain(llm=llm,prompt=prompt)\n",
        "print(llm_chain.invoke(question))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDzWs2xdhnOr",
        "outputId": "06a66324-156e-4bc1-ac96-8573a4aa6a72"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-70285da664b5>:1: LangChainDeprecationWarning: The class `LLMChain` was deprecated in LangChain 0.1.17 and will be removed in 1.0. Use :meth:`~RunnableSequence, e.g., `prompt | llm`` instead.\n",
            "  llm_chain=LLMChain(llm=llm,prompt=prompt)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'question': 'Who won the Cricket World Cup in the year 2011?', 'text': ' The Cricket World Cup is a global cricket tournament that takes place every four years. The tournament was held in 2011, and the winning team was India. So, the answer is India won the Cricket World Cup in the year 2011.'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_huggingface import HuggingFacePipeline\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline"
      ],
      "metadata": {
        "id": "ShC5JjnHhnLS"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model_id=\"gpt2\"\n",
        "model=AutoModelForCausalLM.from_pretrained(model_id)\n",
        "tokenizer=AutoTokenizer.from_pretrained(model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
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        },
        "id": "XYfAOqN4hnIS",
        "outputId": "68412256-1ce2-44de-b19f-c430d5346650"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "8f81335d02d34ff8badaec04bf9df154"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f79d63fb240438ba8cc561b364a2dfb"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "109ef22722964226b174e2ab9600c2ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8d0459cad9748e5859e0b423bf108ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b78697d8419464188f752f626228285"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68f345fa172d4ce8ab47e81274c8c1fb"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a219a4508df54a4fa87be356bbd8d319"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe=pipeline(\"text-generation\",model=model,tokenizer=tokenizer,max_new_tokens=100)\n",
        "hf=HuggingFacePipeline(pipeline=pipe)"
      ],
      "metadata": {
        "id": "6xMji1N4hnFg"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hf.invoke(\"What is machine learning\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "aE8-vCO1oUUG",
        "outputId": "275d2e60-1dfe-42e0-99a9-0efdd00d749d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'What is machine learning going to become?\\n\\nAt the heart of this article I want to address the key arguments made in the book from both sides of the field. One was that neural networks, in particular, are much more flexible than a regular neural network and thus will be able to handle an enormous variety of stimuli without a single malfunction. This is where machine learning comes in. It means that in order to build machines that are not only better at solving problems, but also better at predicting and adapting to our circumstances'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "## Use HuggingfacePipelines With Gpu\n",
        "gpu_llm = HuggingFacePipeline.from_model_id(\n",
        "    model_id=\"gpt2\",\n",
        "    task=\"text-generation\",\n",
        "    device=-1,  # replace with device_map=\"auto\" to use the accelerate library.\n",
        "    pipeline_kwargs={\"max_new_tokens\": 100},\n",
        ")"
      ],
      "metadata": {
        "id": "_U6XM3c0oUQg"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from langchain_core.prompts import PromptTemplate\n",
        "\n",
        "template = \"\"\"Question: {question}\n",
        "\n",
        "Answer: Let's think step by step.\"\"\"\n",
        "prompt = PromptTemplate.from_template(template)"
      ],
      "metadata": {
        "id": "ZzTC6AHmoUNY"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "chain=prompt|gpu_llm"
      ],
      "metadata": {
        "id": "rBPR1p2loUKW"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "question=\"What is artificial intelligence?\"\n",
        "chain.invoke({\"question\":question})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "id": "RdCHB60yo-63",
        "outputId": "145f26fa-b435-4fb9-bdc8-e924c110f409"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Question: What is artificial intelligence?\\n\\nAnswer: Let\\'s think step by step. Our computer systems operate on a \"memory\" that is designed to be able to read a large number of data points that the human person will be familiar with. What we see in a data center now seems a lot like a database that you get in the box. That database serves as our store of record data. Every part of this building is organized fairly like a list or a list of items that have appeared in a building.\\n\\nThe computer runs within a computer hierarchy that the real computer has'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "odjJqj2UpBF1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}