llama_rag/llama_rag_pipeline.ipynb

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  "cells": [
    {
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      "source": [
        "# 🤖 LLM Text Generation with Retrieval-Augmented Generation (RAG)\n",
        "\n",
        "Welcome to this beginner-friendly guide! 🚀 In this project, you'll learn how to:\n",
        "- Use **LLaMA**, a powerful large language model (LLM), for **text generation**.\n",
        "- Retrieve relevant text snippets using **Sentence Transformers**, a popular tool for embedding text.\n",
        "- Combine these techniques to answer questions based on context provided from retrieved snippets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run this command to install everything you need:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNCFBlv589Hg",
        "outputId": "5e9c93ff-dad7-455e-ba35-4801a01057ff"
      },
      "outputs": [],
      "source": [
        "!pip install -Uq sentence-transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 🛠️ Setup: Installing the Necessary Libraries\n",
        "\n",
        "First, we need to install some Python libraries that will help us:\n",
        "- **`transformers`**: Provides access to LLaMA and other pre-trained models for text generation. 📚\n",
        "- **`sentence-transformers`**: Helps generate embeddings, which are essential for comparing text snippets. 🧠"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "u6Y1I-Rz89Hi"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import torch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 🔍 Step 1: Model Setup and Tokenizer"
      ]
    },
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        "id": "xIkRYJ-b89Hi",
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      "outputs": [],
      "source": [
        "chechpoint = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(chechpoint)\n",
        "llama_model = AutoModelForCausalLM.from_pretrained(chechpoint, torch_dtype=torch.bfloat16)\n",
        "generator = pipeline(\"text-generation\", model=llama_model, tokenizer=tokenizer, device=\"cuda\")              # Create a simple text generation pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 📑Step 2: Text Snippet Retrieval Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "lGXAEeN689Hj"
      },
      "outputs": [],
      "source": [
        "text_snippets = [\n",
        "    \"Fiona thanked Ethan for his unwavering support and promised to cherish their friendship.\",\n",
        "    \"As they ventured deeper into the forest, they encountered a wide array of obstacles.\",\n",
        "    \"Ethan and Fiona crossed treacherous ravines using rickety bridges, relying on each other's strength.\",\n",
        "    \"Overwhelmed with joy, Fiona thanked Ethan and disappeared into the embrace of her family.\",\n",
        "    \"Ethan returned to his cottage, heart full of memories and a smile brighter than ever before.\",\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#  🔄 Step 3: Convert text snippets to embeddings for later comparison."
      ]
    },
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        "id": "vBsgP8xP89Hk",
        "outputId": "fca91b14-74c3-433c-db6c-ec0041acec5c"
      },
      "outputs": [],
      "source": [
        "model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
        "embeddings_text_snippets = model.encode(text_snippets)                      # Generate embeddings for the text snippets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 🧮 Step 4: Create a function to retrieve the closest matching snippet using cosine similarity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "iHXqhoIh89Hk"
      },
      "outputs": [],
      "source": [
        "def retrieve_snippet(query):\n",
        "    query_embedded = model.encode([query])                                              # Encode the query to obtain its embedding\n",
        "    similarities = model.similarity(embeddings_text_snippets, query_embedded)           # Calculate cosine similarities between the query embedding and the snippet embeddings\n",
        "    retrieved_texts = text_snippets[similarities.argmax().item()]                       # Retrieve the text snippet with the highest similarity\n",
        "    return retrieved_texts\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 💬 Step 5: Create a function to generate the answer based on the retrieved snippet and query.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "8JwW2Ftv89Hk"
      },
      "outputs": [],
      "source": [
        "# In this step, we utilize the retrieved context snippets to generate a relevant answer using LLaMA, exemplifying the power of RAG in enhancing the quality of responses.\n",
        "\n",
        "def ask_query(query):\n",
        "    retrieved_texts = retrieve_snippet(query)\n",
        "\n",
        "    # Prepare the messages for the text generation pipeline\n",
        "    messages = [\n",
        "        {\"role\": \"system\", \"content\": \"You are a helpful AI assistant.\"\n",
        "                \"Provide one Answer ONLY the following query based on the context provided below. \"\n",
        "                \"Do not generate or answer any other questions. \"\n",
        "                \"Do not make up or infer any information that is not directly stated in the context. \"\n",
        "                \"Provide a concise answer.\"\n",
        "                f\"{retrieved_texts}\"},\n",
        "        {\"role\": \"user\", \"content\": query}\n",
        "    ]\n",
        "\n",
        "    # Generate a response using the text generation pipeline\n",
        "    response = generator(messages, max_new_tokens=128)[-1][\"generated_text\"][-1][\"content\"]\n",
        "    print(f\"Query: \\n\\t{query}\")\n",
        "    print(f\"Context: \\n\\t{retrieved_texts}\")\n",
        "    print(f\"Answer: \\n\\t{response}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 🗣️ Step 6: Ask a Question "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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