llama2_rag.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYO5MCyX2S0o",
        "outputId": "8fe429fb-9cc7-4b8c-ec76-343c192be560"
      },
      "outputs": [],
      "source": [
        "from IPython.display import display, Markdown"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnUOq3dZeDK1"
      },
      "source": [
        "## RAG System Using Llama2 With Hugging Face"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "HutpiHgpgLBE"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/achuthchandrasekhar/miniconda3/envs/llama_rag/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n",
            "/home/achuthchandrasekhar/miniconda3/envs/llama_rag/lib/python3.10/site-packages/transformers/utils/generic.py:485: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n"
          ]
        }
      ],
      "source": [
        "from llama_index.core import VectorStoreIndex,SimpleDirectoryReader,ServiceContext,PromptTemplate\n",
        "from llama_index.llms.huggingface import HuggingFaceLLM\n",
        "from llama_index.core import PromptTemplate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KcU-nQ_XgxWD"
      },
      "outputs": [],
      "source": [
        "documents=SimpleDirectoryReader(\"/home/achuthchandrasekhar/Documents/AMGPT/papers_tex\").load_data()\n",
        "documents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "2g7M89fKg54H"
      },
      "outputs": [],
      "source": [
        "SYSTEM_PROMPT = \"\"\"You are an AI assistant that answers questions in a friendly manner, based on the given source documents. Here are some rules you always follow:\n",
        "- Generate human readable output, avoid creating output with gibberish text.\n",
        "- Generate only the requested output, don't include any other language before or after the requested output.\n",
        "- Never say thank you, that you are happy to help, that you are an AI agent, etc. Just answer directly.\n",
        "- Generate professional language.\n",
        "- Never generate offensive or foul language.\n",
        "-Never use references in square brackets or otherwise in the output, but provide material examples if possible.\n",
        "\"\"\"\n",
        "\n",
        "query_wrapper_prompt = PromptTemplate(\n",
        "    \"[INST]<<SYS>>\\n\" + SYSTEM_PROMPT + \"<</SYS>>\\n\\n{query_str}[/INST] \"\n",
        ")\n",
        "\n",
        "#Change this prompt to match your specifications. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5-mOhoIhfo0",
        "outputId": "c48ee0c9-a913-4cf1-8f4c-1fc0c0cc80df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /home/achuthchandrasekhar/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login --token #kep token here without quotes and then run this cell for successful huggingface login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "hf_token = \" \" #paste token in quotes"
      ]
    },
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        "outputId": "fc393150-00e7-4773-aaa3-7e3b1592f2d3"
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/achuthchandrasekhar/miniconda3/envs/llama_rag/lib/python3.10/site-packages/transformers/utils/generic.py:342: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "/home/achuthchandrasekhar/miniconda3/envs/llama_rag/lib/python3.10/site-packages/transformers/utils/generic.py:342: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n",
            "Loading checkpoint shards: 100%|██████████| 2/2 [00:11<00:00,  5.85s/it]\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import BitsAndBytesConfig\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(\n",
        "    load_in_8bit=True,\n",
        "    bnb_8bit_compute_dtype=torch.float16,\n",
        "    bnb_8bit_quant_type=\"nf8\",\n",
        "    bnb_8bit_use_double_quant=True,\n",
        ")\n",
        "\n",
        "# you may use the huggingface llm of your choice here \n",
        "#remember to paste the proper name to get it. \n",
        "llm = HuggingFaceLLM(\n",
        "    context_window=4096,\n",
        "    max_new_tokens=512,\n",
        "    generate_kwargs={\"temperature\": 0.0, \"do_sample\": False},\n",
        "    system_prompt=SYSTEM_PROMPT,\n",
        "    query_wrapper_prompt=query_wrapper_prompt,\n",
        "    tokenizer_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    model_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    device_map=\"cuda:1\",\n",
        "    # uncomment this if using CUDA to reduce memory usage\n",
        "    #model_kwargs={\"torch_dtype\": torch.float16 , \"load_in_8bit\":True}\n",
        "    model_kwargs={\"token\": hf_token, \"quantization_config\": quantization_config}\n",
        ")"
      ]
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            "9ff71b78dbb74bb78adeec540b972eb5"
          ]
        },
        "id": "pr1EN5sViQm9",
        "outputId": "0051d2e6-19c6-4069-84c9-aeac43eda397"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/achuthchandrasekhar/miniconda3/envs/llama_rag/lib/python3.10/site-packages/transformers/utils/generic.py:342: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
            "  _torch_pytree._register_pytree_node(\n"
          ]
        }
      ],
      "source": [
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "#from llama_index.core import ServiceContext\n",
        "from llama_index.embeddings.langchain import LangchainEmbedding\n",
        "\n",
        "embed_model=LangchainEmbedding(\n",
        "    HuggingFaceEmbeddings(model_name=\"mixedbread-ai/mxbai-embed-large-v1\")) #use the embedding model of your choice from huggingface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "1qIeS7LAwlBw"
      },
      "outputs": [],
      "source": [
        "from llama_index.core import Settings\n",
        "\n",
        "Settings.llm = llm\n",
        "Settings.embed_model = embed_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "31jvrW2BkFEL"
      },
      "outputs": [],
      "source": [
        "index=VectorStoreIndex.from_documents(documents)\n",
        "index.storage_context.persist(persist_dir=\" \") # paste your directory path here when you create the index at the beginning\n",
        "\n",
        "#DO NOT RUN THIS CELL IF INDEX IS ALREADY CREATED. RUN THE NEXT ONE!!!!!!!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "from llama_index.core import StorageContext, load_index_from_storage\n",
        "# rebuild storage context\n",
        "storage_context = StorageContext.from_defaults(persist_dir=\" \") # paste the directory path of your ALREADY CREATED INDEX HERE\n",
        "\n",
        "# load index\n",
        "index = load_index_from_storage(storage_context)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IM-gNZ0-kRnO",
        "outputId": "c3eb4f7b-059f-4d50-e4de-77338b4386cc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<llama_index.core.indices.vector_store.base.VectorStoreIndex at 0x7f402c48c850>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "uSJzrMm6kTxf"
      },
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      "source": [
        "query_engine=index.as_query_engine()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XHLOq8j2kyfF"
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      "source": [
        "response=query_engine.query(\"What is the relation between meltpool dimensions and density?\") # paste your query here\n",
        "markdown_response = f\"\"\"\n",
        "### Query Response:\n",
        "\n",
        "{response}\n",
        "\"\"\"\n",
        "\n",
        "# Display the Markdown-formatted response in the notebook\n",
        "display(Markdown(markdown_response))"
      ]
    }
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