{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ayft6ZiJaoHw",
        "outputId": "6b336e7f-75df-47eb-d97c-1e6903ea73a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: httpx in /usr/local/lib/python3.11/dist-packages (0.28.1)\n",
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            "Collecting groq\n",
            "  Downloading groq-0.28.0-py3-none-any.whl.metadata (15 kB)\n",
            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.11/dist-packages (from groq) (4.9.0)\n",
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            "Downloading groq-0.28.0-py3-none-any.whl (130 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: groq\n",
            "Successfully installed groq-0.28.0\n"
          ]
        }
      ],
      "source": [
        "# 🔧 Install required packages\n",
        "!pip install -q sentence-transformers faiss-cpu\n",
        "!pip install httpx\n",
        "!pip install groq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zNdBhhWOa7z2"
      },
      "outputs": [],
      "source": [
        "\n",
        "passages = [\n",
        "    \"Neural networks are computing systems inspired by the human brain. They consist of layers of nodes, or neurons, which can learn complex patterns.\",\n",
        "    \"Transfer learning is a technique where a model trained on one task is reused on a second related task.\",\n",
        "    \"Attention mechanisms allow models to focus on different parts of input data when making decisions, which improves performance in tasks like translation and summarization.\",\n",
        "    \"Retrieval-augmented generation combines vector search with language models to answer questions from external documents.\",\n",
        "    \"Convolutional Neural Networks are especially effective for image classification and computer vision tasks.\"\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "omy6zkSfa_q7"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "import faiss\n",
        "import numpy as np\n",
        "import json"
      ]
    },
    {
      "cell_type": "code",
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        "id": "qDLXClvSjH4v",
        "outputId": "5c1623ab-d767-42e3-c6f7-1e81dec6f423"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2182adeab9264cd7b80ec145dba995b1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b05ff9b0cc6d427ea44100f791c4f3a7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1be2cbc6cbd24f4b9a29a413c5a6c642",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/10.5k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "eb18b6f2be5c4e6ab6c5a64ceb1d081e",
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              "version_minor": 0
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            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
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        {
          "data": {
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            "text/plain": [
              "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f0f6aeb2aa05413da304f09abe635f51",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cfc5fee1f1c0409c89c41d0b6e093cda",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b2b268ad7a0e4bd98ae6fffe01c4b344",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0359956d7a3e48cd9c12c30f0c93b6c7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3cbeffc6437c4c36896e7ac82ec46779",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b24d9b0e4f8346aeadfbee5124555a0d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# load the model\n",
        "model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0lo_nQ4GjJn5"
      },
      "outputs": [],
      "source": [
        "# Generate vector embeddings\n",
        "embeddings = model.encode(passages, convert_to_numpy=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bYPkGBQqjp8U",
        "outputId": "82bc24d6-1a02-4c6a-f639-6e7472dcc70d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5, 384)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "embeddings.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OwTpTgC1jK1H"
      },
      "outputs": [],
      "source": [
        "# Build FAISS index\n",
        "index = faiss.IndexFlatL2(embeddings.shape[1])  # 384-dim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MsejYK4Gj3GM"
      },
      "outputs": [],
      "source": [
        "index.add(embeddings)\n",
        "\n",
        "# Save passage text and embeddings (we'll use them in the next step)\n",
        "passage_store = {\n",
        "    \"passages\": passages,\n",
        "    \"embedding_dim\": embeddings.shape[1]\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DEFMrRklbC0a"
      },
      "outputs": [],
      "source": [
        "# Sample query\n",
        "query = \"How do models use context when translating sentences?\"\n",
        "\n",
        "# Embed the query\n",
        "query_vec = model.encode([query])[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ZuD1nDpoXDo"
      },
      "outputs": [],
      "source": [
        "query_vec = np.array([query_vec])  # shape (1, 384)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CoB0PBR_bEbU"
      },
      "outputs": [],
      "source": [
        "# Search top-3 most similar passages\n",
        "D, I = index.search(query_vec, k=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLn-pf1Xorog",
        "outputId": "eae11ffe-ed96-4821-9f27-a28257a1bd5e"
      },
      "outputs": [],
      "source": [
        "# Show results\n",
        "print(\" Query:\", query)\n",
        "print(\"\\nTop Matches:\")\n",
        "retrieved_passages = []\n",
        "for i in I[0]:\n",
        "    retrieved_passages.append(passages[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nYwhPVLtp2X0"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from groq import Groq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "os.environ[\"GROQ_API_KEY\"] = \"API KEY\""
      ]
    },
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      "execution_count": null,
      "metadata": {
        "id": "haSl6Vdmr_Mq"
      },
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      "source": [
        "GROQ_MODEL = \"llama3-8b-8192\""
      ]
    },
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        "def init_groq(api_key: str = None):\n",
        "    api_key = api_key or os.getenv(\"GROQ_API_KEY\")\n",
        "    return Groq(api_key=api_key)\n"
      ]
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        "def groq_chat(prompt: str, model_id: str, temperature: float = 0.7, max_tokens: int = 200):\n",
        "    client = init_groq()\n",
        "    response = client.chat.completions.create(\n",
        "        model=model_id,\n",
        "        messages=[\n",
        "            {\"role\" : \"system\", \"content\" : \"You are a helpful assistant.\"},\n",
        "            {\"role\": \"user\", \"content\": prompt}\n",
        "        ],\n",
        "        temperature=temperature,\n",
        "        max_tokens=max_tokens\n",
        "    )\n",
        "    return response.choices[0].message.content\n"
      ]
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        "outputId": "76eb9655-3660-44fc-85fe-ad8cada7c981"
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          "text": [
            "According to the provided context, an attention mechanism allows models to focus on different parts of input data when making decisions, which improves performance in tasks like translation and summarization.\n"
          ]
        }
      ],
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        "query = \"What is attention mechanism?\"\n",
        "context = \"\\n\".join(retrieved_passages)\n",
        "prompt = f\"\"\"Use the following context to answer the question.\n",
        "\n",
        "Context:\n",
        "{context}\n",
        "\n",
        "Question: {query}\n",
        "Answer:\"\"\"\n",
        "\n",
        "answer = groq_chat(prompt, model_id=\"llama3-8b-8192\")\n",
        "print(answer)\n"
      ]
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