RAG_Llama3_Unstructured_LangChain.ipynb

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      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SerenaHangSinclair/AlgorithmEngineeringCourseWork/blob/main/RAG_Llama3_Unstructured_LangChain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Build a RAG system with Llama 3B-Instruct for your PDFs"
      ],
      "metadata": {
        "id": "Y2m2l-vt_RSp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this quick tutorial, we'll build a simple RAG system with the latest LLM from Meta - Llama 3, specifically the `Llama-3-8B-Instruct` version that you can get on Hugging Face.\n",
        "We'll use [Unstructured API](https://unstructured.io/) for preprocessing PDF files, LangChain for RAG, FAISS for vector storage, and HuggingFace `transformers` to get the model. Let's go!"
      ],
      "metadata": {
        "id": "bKZWdc1_J5hm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install all the libraries, get your [free unstructured API key](https://unstructured.io/api-key-free), and instantiate the Unstructured client to preprocess your PDF file:"
      ],
      "metadata": {
        "id": "fKS2grloNhrM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q unstructured-client unstructured[all-docs] langchain transformers accelerate bitsandbytes sentence-transformers faiss-gpu"
      ],
      "metadata": {
        "id": "rJ9juQ-XKJKK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "os.environ[\"UNSTRUCTURED_API_KEY\"] = \"YOUR_UNSTRCTURED_API_KEY\""
      ],
      "metadata": {
        "id": "3JWGSEoZKbtN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from unstructured_client import UnstructuredClient\n",
        "\n",
        "unstructured_api_key = os.environ.get(\"UNSTRUCTURED_API_KEY\")\n",
        "\n",
        "client = UnstructuredClient(\n",
        "    api_key_auth=unstructured_api_key,\n",
        "    # if using paid API, provide your unique API URL:\n",
        "    # server_url=\"YOUR_API_URL\",\n",
        ")"
      ],
      "metadata": {
        "id": "OIG6V3swKyIZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Partition, and chunk your file so that the logical structure of the document is preserved for better RAG results."
      ],
      "metadata": {
        "id": "CwzrH-9_K6-z"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qThmnl_fJzrb"
      },
      "outputs": [],
      "source": [
        "from unstructured_client.models import shared\n",
        "from unstructured_client.models.errors import SDKError\n",
        "from unstructured.staging.base import dict_to_elements\n",
        "\n",
        "path_to_pdf=\"PATH_TO_YOUR_PDF_FILE\"\n",
        "\n",
        "with open(path_to_pdf, \"rb\") as f:\n",
        "  files=shared.Files(\n",
        "      content=f.read(),\n",
        "      file_name=path_to_pdf,\n",
        "      )\n",
        "  req = shared.PartitionParameters(\n",
        "    files=files,\n",
        "    chunking_strategy=\"by_title\",\n",
        "    max_characters=512,\n",
        "  )\n",
        "  try:\n",
        "    resp = client.general.partition(req)\n",
        "  except SDKError as e:\n",
        "    print(e)\n",
        "\n",
        "elements = dict_to_elements(resp.elements)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create LangChain documents from document chunks and their metadata, and ingest those documents into the FAISS vectorstore.\n",
        "\n",
        "Set up the retriever."
      ],
      "metadata": {
        "id": "UF1TaeByPEBR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.documents import Document\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "\n",
        "documents = []\n",
        "for element in elements:\n",
        "    metadata = element.metadata.to_dict()\n",
        "    documents.append(Document(page_content=element.text, metadata=metadata))\n",
        "\n",
        "db = FAISS.from_documents(documents, HuggingFaceEmbeddings(model_name=\"BAAI/bge-base-en-v1.5\"))\n",
        "retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 4})"
      ],
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        },
        "id": "bT1XTlQLMBX2",
        "outputId": "0fd10f26-6ae5-4183-89cc-fa53e438e073"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "282a07aee3e04d7a9902cf4621c2db7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d536f000daf4a928b9daeddf76cb375"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/94.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6766f2b04da46bdbf2df051606fce2f"
            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50550ce1b30649b9b7a382f488025af1"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
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        },
        {
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              "tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e6efa0c798d14790a6a57a566927bfb2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65383a4be13a441ba688676051fe6d29"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's finally set up llama 3 to use for text generation in the RAG system.\n",
        "\n",
        "This is a gated model, which means you first need to go to the [model's page](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), log in, review terms and conditions, and request access to it. To use the model in the notebook, you need to log in with your Hugging Face token (get it in your profile's settings)."
      ],
      "metadata": {
        "id": "gSHJYpskQKJB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub.hf_api import HfFolder\n",
        "\n",
        "HfFolder.save_token('YOUR_HF_TOKEN')"
      ],
      "metadata": {
        "id": "rmCLySVHDngw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To run this tutorial in the free Colab GPU, we'll need to quantize the model:"
      ],
      "metadata": {
        "id": "1vu81HA_b-ei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "model_name = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
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        "id": "zKG6n2JpMtu3",
        "outputId": "31da631a-0682-4302-e544-6627ff6f2d29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
          ]
        },
        {
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        {
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            ],
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set up Llama 3 and a simple RAG chain.\n",
        "Make sure to follow the prompt format for best results:\n",
        "\n",
        "```\n",
        "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
        "\n",
        "{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "{{ user_msg_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
        "\n",
        "{{ model_answer_1 }}<|eot_id|>\n",
        "```"
      ],
      "metadata": {
        "id": "aeOvh7Y2cIym"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.llms import HuggingFacePipeline\n",
        "from langchain.prompts import PromptTemplate\n",
        "from transformers import pipeline\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.runnables import RunnablePassthrough\n",
        "\n",
        "terminators = [\n",
        "    tokenizer.eos_token_id,\n",
        "    tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")\n",
        "]\n",
        "\n",
        "text_generation_pipeline = pipeline(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    task=\"text-generation\",\n",
        "    temperature=0.2,\n",
        "    do_sample=True,\n",
        "    repetition_penalty=1.1,\n",
        "    return_full_text=False,\n",
        "    max_new_tokens=200,\n",
        "    eos_token_id=terminators,\n",
        ")\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=text_generation_pipeline)\n",
        "\n",
        "prompt_template = \"\"\"\n",
        "<|start_header_id|>user<|end_header_id|>\n",
        "You are an assistant for answering questions about IPM.\n",
        "You are given the extracted parts of a long document and a question. Provide a conversational answer.\n",
        "If you don't know the answer, just say \"I do not know.\" Don't make up an answer.\n",
        "Question: {question}\n",
        "Context: {context}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
        "\"\"\"\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    input_variables=[\"context\", \"question\"],\n",
        "    template=prompt_template,\n",
        ")\n",
        "\n",
        "def format_docs(docs):\n",
        "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
        "\n",
        "rag_chain = (\n",
        "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
        "    | prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        ")"
      ],
      "metadata": {
        "id": "b-8hFNkoM0LC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tada! Your RAG is ready to use. Pass a question, the retriver will add relevant context from your document, and Llama3 will generate an answer.\n",
        "Here, my document was a chapter from a book on IPM that stands for \"Integrated Pest Management\".  "
      ],
      "metadata": {
        "id": "u18Cc6msce5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What is considered a cultural control in IPM?\"\n",
        "rag_chain.invoke(question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "i47dsLeFNVPB",
        "outputId": "ce896b69-d035-4b69-ecba-be229e68aef1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Based on the provided documents, a cultural control in IPM refers to disrupting the pest life cycle or making the environment less suited for survival. This includes practices such as rotating crops, using optimum growing conditions, and maintaining sanitation.\\n\\nFor instance, rotating crops can help break the life cycle of certain pests, while using optimum growing conditions can promote healthy plant growth and reduce the likelihood of pest infestation. Similarly, maintaining sanitation can prevent pests from finding food and shelter, thereby reducing their ability to survive and reproduce.\\n\\nThese cultural controls are often considered preventive measures, as they can help prevent pest problems from occurring in the first place. By incorporating cultural controls into an IPM program, farmers and gardeners can reduce their reliance on chemical pesticides and create a more sustainable and environmentally friendly approach to managing pests.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    }
  ]
}