fuyu_8b_RAG_LangChain.ipynb

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          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.24.1)\n",
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            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
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            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.16.0+cu118)\n",
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            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.11.3)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (3.8.1)\n",
            "Collecting sentencepiece (from sentence-transformers)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (3.3.2)\n",
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            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth>=1.0.1->kubernetes>=28.1.0->chromadb) (0.5.0)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=2f9917c2fc378f81bea70a5853535a95f8f8971e0066360c51556c84c7d663f1\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentencepiece, sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.2 sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "!pip install accelerate bitsandbytes langchain chromadb safetensors kaleido python-multipart transformers sentence-transformers pypdf openai --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from torch import cuda\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'\n",
        "model_name = \"ybelkada/fuyu-8b-sharded\"\n",
        "device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'\n",
        "\n",
        "embed_model = HuggingFaceEmbeddings(\n",
        "    model_name=embed_model_id,\n",
        "    model_kwargs={'device': device},\n",
        "    encode_kwargs={'device': device, 'batch_size': 32}\n",
        ")"
      ],
      "metadata": {
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      },
      "execution_count": 5,
      "outputs": [
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          "metadata": {}
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        {
          "output_type": "display_data",
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    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import pipeline\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit = True,\n",
        "    bnb_4bit_use_double_quant = True,\n",
        "    bnb_4bit_quant_type = \"nf4\",\n",
        "    bnb_4bit_compute_dtype = torch.bfloat16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    load_in_4bit = True,\n",
        "    torch_dtype = torch.bfloat16,\n",
        "    quantization_config = bnb_config,\n",
        "    device_map = 'auto'\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "tokenizer.bos_token_id = 1\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    max_length = 2048,\n",
        "    temperature = 0,\n",
        "    top_p = 0.95,\n",
        "    repetition_penalty = 1.2\n",
        ")\n",
        "\n",
        "local_llm = HuggingFacePipeline(pipeline=pipe)"
      ],
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import PyPDFLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.embeddings import OpenAIEmbeddings\n",
        "from langchain.vectorstores import Chroma\n",
        "\n",
        "loader = PyPDFLoader(\"/content/cs224u-contextualreps-2023-handout.pdf\")\n",
        "docs = loader.load()\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size = 1500,\n",
        "    chunk_overlap = 150,\n",
        ")\n",
        "\n",
        "splits = text_splitter.split_documents(docs)\n",
        "\n",
        "embedding = embed_model\n",
        "\n",
        "vectordb = Chroma.from_documents(\n",
        "    documents = splits,\n",
        "    embedding = embedding,\n",
        ")"
      ],
      "metadata": {
        "id": "mkfnjuApgkgM"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "local_llm(\"What are the statical vector representations of words?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "DVp1NIF3iokM",
        "outputId": "a40dab98-eb9a-48c2-fc4f-bf9a9526e1a5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:386: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:71013 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n\\n1. Color: The representation of color is represented by shades of blue, green or yellow.\\n2. Gesture: The gesture of the characters represents their body movements and gestures. In this case, the characters are holding hands while walking together.\\n3. Space: The space between the characters and the background represents the environment in which the scene is set. In the image, there is a bench and a street nearby.\\n4. Movements: The movement of the characters and their body language represent the dynamic nature of the situation they are experiencing. The image shows two people engaged in physical activity, possibly dancing or conversing with each other.\\n5. Interaction: The interaction between the two characters can be inferred from the visual cues like touching hands and sharing the space between them. This interaction highlights the social aspect of this scene.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.prompts import PromptTemplate\n",
        "import os\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"api-key\"\n",
        "\n",
        "llm = ChatOpenAI(model_name= \"gpt-3.5-turbo-0301\", temperature= 0)\n",
        "\n",
        "template = \"\"\"Given the following context answer the question at the end. If you don't know the answer return can't answer\n",
        "{context}\n",
        "Question: {question}\n",
        "Answer:\"\"\"\n",
        "qa_chain_prompt = PromptTemplate.from_template(template)"
      ],
      "metadata": {
        "id": "N6AZiWbHkPgM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "qa_chain = RetrievalQA.from_chain_type(\n",
        "    llm,\n",
        "    retriever = vectordb.as_retriever(),\n",
        "    chain_type_kwargs = {\"prompt\": qa_chain_prompt} # You can choose whatever chaintype you want\n",
        ")"
      ],
      "metadata": {
        "id": "zeh9iEkWjbbV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What are the statical vecoter representations of words?\"\n",
        "\n",
        "result = qa_chain({\"query\": question})\n",
        "result[\"result\"]"
      ],
      "metadata": {
        "id": "ccwC4KO2jgZ0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}