RAG_car_handling_Llama.ipynb

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        "<a href=\"https://colab.research.google.com/github/sajabdoli/car_manual/blob/main/RAG_car_handling_Llama.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      },
      "source": [
        "Test the simple RAG model with Llama"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence_transformers\n",
        "!pip install faiss-gpu\n",
        "!pip install -U bitsandbytes"
      ],
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        "id": "CAwgz6LO0waE"
      },
      "execution_count": null,
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      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "import transformers\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import pandas as pd\n",
        "import faiss\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import torch"
      ]
    },
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      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "100%|██████████| 2512/2512 [00:16<00:00, 148.51it/s]\n"
          ]
        }
      ],
      "source": [
        "# Read the Markdown file\n",
        "with open('/content/sample_data/cleaned_car_manual.md', 'r') as file:\n",
        "    markdown_text = file.read()\n",
        "\n",
        "# Split the text into sections\n",
        "text_sections = markdown_text.split('\\n\\n')  # This splits by double newlines\n",
        "\n",
        "# Load Sentence Transformer model\n",
        "st_model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "# Encode the text sections with a progress bar\n",
        "tqdm.pandas(desc=\"Encoding Text\")\n",
        "embeddings = [st_model.encode(text, show_progress_bar=False) for text in tqdm(text_sections)]\n",
        "\n",
        "# Convert embeddings to a 2D numpy array\n",
        "embeddings_array = np.array(embeddings)\n",
        "\n",
        "# Create a faiss index\n",
        "dimension = embeddings_array.shape[1]\n",
        "index = faiss.IndexFlatL2(dimension)  # L2 distance\n",
        "index.add(embeddings_array)"
      ]
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        "# Retrieval function\n",
        "def retrieve_docs(query, top_k=5):\n",
        "    query_embedding = st_model.encode([query])\n",
        "    query_embedding = np.array(query_embedding).astype('float32')\n",
        "\n",
        "    distances, indices = index.search(query_embedding, top_k)\n",
        "\n",
        "    retrieved_sections = [text_sections[i] for i in indices[0]]\n",
        "\n",
        "    return retrieved_sections"
      ]
    },
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n",
            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
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      "source": [
        "# Load the text generation model\n",
        "model_id = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    pad_token_id=tokenizer.eos_token_id,\n",
        "    model=model_id,\n",
        "    model_kwargs={\"torch_dtype\": torch.bfloat16, \"load_in_4bit\": True}\n",
        ")"
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        "def generate_response(query):\n",
        "    # Retrieve relevant documents and create context\n",
        "    docs = retrieve_docs(query)\n",
        "    context = \" \".join(docs)\n",
        "\n",
        "\n",
        "    # Prepare messages with context for chat template\n",
        "    messages = [\n",
        "        {\"role\": \"system\", \"content\": \"You are a helpful assitant for the user of Toyota verena\"},\n",
        "        {\"role\": \"system\", \"content\": f\"Context: {context}\"},\n",
        "        {\"role\": \"user\", \"content\": query},\n",
        "    ]\n",
        "\n",
        "    # Generate prompt\n",
        "    prompt = pipeline.tokenizer.apply_chat_template(\n",
        "        messages,\n",
        "        tokenize=False,\n",
        "        add_generation_prompt=True\n",
        "    )\n",
        "\n",
        "\n",
        "    # Ensure prompt is not None\n",
        "    if prompt is None:\n",
        "        raise ValueError(\"The prompt could not be generated. Check the chat template and input data.\")\n",
        "\n",
        "    eos_token_id = pipeline.tokenizer.eos_token_id\n",
        "\n",
        "\n",
        "    # Generate response using the pipeline, removing the 'stream' argument\n",
        "    outputs = pipeline(\n",
        "        prompt,\n",
        "        max_new_tokens=256,\n",
        "        eos_token_id=eos_token_id,\n",
        "        do_sample=True,\n",
        "        temperature=0.6,\n",
        "        top_p=0.9,\n",
        "    )\n",
        "\n",
        "    generated_text = outputs[0]['generated_text'][len(prompt):] # Assuming the output format is a list with a dictionary\n",
        "    return generated_text"
      ]
    },
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        "outputId": "aaf20aa7-f0a7-4ccb-bb3e-dcbb580cd9f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "User: Hi, Introduce yourself please.\n",
            "Assistant: Nice to meet you! I'm your helpful assistant for the Toyota Verona. I'm here to assist you with any questions, concerns, or tasks related to your Toyota Verona. Whether you're looking for information on maintenance, troubleshooting, or simply want to know more about your vehicle's features, I'm here to help. What can I assist you with today?\n"
          ]
        }
      ],
      "source": [
        "while True:\n",
        "    user_input = input(\"User: \")\n",
        "    if user_input.lower() == 'exit':\n",
        "        break\n",
        "    response = generate_response(user_input)\n",
        "    print(\"Assistant:\", response)"
      ]
    },
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