{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "fe8e99aa",
      "metadata": {
        "id": "fe8e99aa",
        "papermill": {
          "duration": 0.006834,
          "end_time": "2024-08-27T17:13:37.902668",
          "exception": false,
          "start_time": "2024-08-27T17:13:37.895834",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Setting Up"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "153bafbb",
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "execution": {
          "iopub.execute_input": "2024-08-27T17:13:37.916183Z",
          "iopub.status.busy": "2024-08-27T17:13:37.915824Z",
          "iopub.status.idle": "2024-08-27T17:15:32.677692Z",
          "shell.execute_reply": "2024-08-27T17:15:32.676415Z"
        },
        "id": "153bafbb",
        "papermill": {
          "duration": 114.771688,
          "end_time": "2024-08-27T17:15:32.680456",
          "exception": false,
          "start_time": "2024-08-27T17:13:37.908768",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "\n",
        "%pip install langchain langchain-community\n",
        "%pip install langchainhub\n",
        "%pip install langchain-chroma\n",
        "%pip install langchain-groq\n",
        "%pip install langchain-huggingface\n",
        "%pip install gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "75c11425",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-08-27T17:15:32.695149Z",
          "iopub.status.busy": "2024-08-27T17:15:32.694425Z",
          "iopub.status.idle": "2024-08-27T17:15:33.004252Z",
          "shell.execute_reply": "2024-08-27T17:15:33.003215Z"
        },
        "id": "75c11425",
        "papermill": {
          "duration": 0.319786,
          "end_time": "2024-08-27T17:15:33.006674",
          "exception": false,
          "start_time": "2024-08-27T17:15:32.686888",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "from google.colab import userdata\n",
        "\n",
        "groq_api_key = userdata.get('GROQ_API_KEY')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "wjTFort567QH",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjTFort567QH",
        "outputId": "e10d65d4-fef0-41f1-cfac-aa50ae360f3e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  /content/archive.zip\n",
            "  inflating: /content/KaggleX_Starwars/csv/battles.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/characters.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/cities.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/droids.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/events.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/films.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/music.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/organizations.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/planets.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/quotes.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/species.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/starships.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/timeline.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/vehicles.csv  \n",
            "  inflating: /content/KaggleX_Starwars/csv/weapons.csv  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/battles.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/characters.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/cities.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/droids.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/events.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/films.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/music.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/organizations.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/planets.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/quotes.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/species.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/starships.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/timeline.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/vehicles.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/parquet_files/weapons.parquet  \n",
            "  inflating: /content/KaggleX_Starwars/star_wars.db  \n",
            "  inflating: /content/KaggleX_Starwars/star_wars.duckdb  \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/archive.zip -d /content/KaggleX_Starwars"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "77ad6fbe",
      "metadata": {
        "id": "77ad6fbe",
        "papermill": {
          "duration": 0.005808,
          "end_time": "2024-08-27T17:15:33.018770",
          "exception": false,
          "start_time": "2024-08-27T17:15:33.012962",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Groq Python API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "064b20ee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-08-27T17:15:33.032341Z",
          "iopub.status.busy": "2024-08-27T17:15:33.031981Z",
          "iopub.status.idle": "2024-08-27T17:15:34.842277Z",
          "shell.execute_reply": "2024-08-27T17:15:34.841324Z"
        },
        "id": "064b20ee",
        "outputId": "75e5f245-96b4-4135-d30f-eff3d0811a27",
        "papermill": {
          "duration": 1.819728,
          "end_time": "2024-08-27T17:15:34.844440",
          "exception": false,
          "start_time": "2024-08-27T17:15:33.024712",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A data lake is a centralized repository that stores raw, unprocessed data in its native format, allowing for easy access, processing, and analysis. Here's a breakdown of how a data lake works:\n",
            "\n",
            "**Key Components:**\n",
            "\n",
            "1. **Data Ingestion**: Data is collected from various sources, such as databases, APIs, files, and IoT devices. This data is then ingested into the data lake using tools like Apache NiFi, Apache Flume, or AWS Kinesis.\n",
            "2. **Data Storage**: The ingested data is stored in a scalable and cost-effective storage system, such as Hadoop Distributed File System (HDFS), Amazon S3, or Azure Data Lake Storage (ADLS).\n",
            "3. **Data Processing**: Data is processed using various tools and frameworks, such as Apache Spark, Apache Flink, or AWS Glue, to transform, aggregate, and analyze the data.\n",
            "4. **Data Governance**: Data governance ensures that data is properly managed, secured, and compliant with regulations. This includes data quality, metadata management, and access control.\n",
            "\n",
            "**Data Lake Architecture:**\n",
            "\n",
            "1. **Raw Data**: Raw data is stored in its native format, without any processing or transformation.\n",
            "2. **Processed Data**: Processed data is stored in a structured format, such as CSV or Parquet, for easier querying and analysis.\n",
            "3. **Metadata**: Metadata is stored separately to provide context and information about the data, such as data lineage, provenance, and schema.\n",
            "\n",
            "**Benefits:**\n",
            "\n",
            "1. **Scalability**: Data lakes can handle large volumes of data and scale horizontally to meet growing demands.\n",
            "2. **Flexibility**: Data lakes can store data in various formats, making it easier to integrate with different tools and frameworks.\n",
            "3. **Cost-Effective**: Data lakes can reduce storage costs by storing data in its native format, without the need for expensive data processing and transformation.\n",
            "4. **Improved Insights**: Data lakes enable faster and more accurate insights by providing access to raw, unprocessed data.\n",
            "\n",
            "**Common Use Cases:**\n",
            "\n",
            "1. **Data Warehousing**: Data lakes can serve as a data warehouse, providing a centralized repository for data analysis and reporting.\n",
            "2. **Machine Learning**: Data lakes can provide a vast amount of data for machine learning model training and testing.\n",
            "3. **IoT Data**: Data lakes can store and process IoT data from various devices and sensors.\n",
            "4. **Data Science**: Data lakes can provide a platform for data scientists to explore and analyze large datasets.\n",
            "\n",
            "**Challenges:**\n",
            "\n",
            "1. **Data Quality**: Ensuring data quality and consistency across different sources and formats.\n",
            "2. **Data Governance**: Managing data governance, security, and compliance in a data lake environment.\n",
            "3. **Data Integration**: Integrating data from various sources and formats into a single data lake.\n",
            "4. **Scalability**: Scaling data lakes to meet growing demands and handle large volumes of data.\n",
            "\n",
            "In summary, a data lake is a centralized repository that stores raw, unprocessed data in its native format, allowing for easy access, processing, and analysis. It provides scalability, flexibility, and cost-effectiveness, making it an ideal solution for various use cases, including data warehousing, machine learning, IoT data, and data science. However, it also presents challenges related to data quality, governance, integration, and scalability.None"
          ]
        }
      ],
      "source": [
        "from groq import Groq\n",
        "\n",
        "client = Groq(\n",
        "   api_key=groq_api_key,\n",
        ")\n",
        "\n",
        "\n",
        "chat_streaming = client.chat.completions.create(\n",
        "    messages=[\n",
        "       {\"role\": \"system\", \"content\": \"You are a professional Data Engineer.\"},\n",
        "       {\"role\": \"user\", \"content\": \"Can you explain how the data lake works?\"},\n",
        "    ],\n",
        "    model=\"llama-3.1-8b-instant\",\n",
        "    temperature=0.3,\n",
        "    max_tokens=1200,\n",
        "    top_p=1,\n",
        "    stop=None,\n",
        "    stream=True,\n",
        ")\n",
        "\n",
        "for chunk in chat_streaming:\n",
        "    print(chunk.choices[0].delta.content, end=\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a01a9a83",
      "metadata": {
        "id": "a01a9a83",
        "papermill": {
          "duration": 0.006275,
          "end_time": "2024-08-27T17:15:34.857294",
          "exception": false,
          "start_time": "2024-08-27T17:15:34.851019",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Initiating LLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed01e824",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-08-27T17:15:34.871298Z",
          "iopub.status.busy": "2024-08-27T17:15:34.870981Z",
          "iopub.status.idle": "2024-08-27T17:15:35.405798Z",
          "shell.execute_reply": "2024-08-27T17:15:35.405011Z"
        },
        "id": "ed01e824",
        "papermill": {
          "duration": 0.544475,
          "end_time": "2024-08-27T17:15:35.408162",
          "exception": false,
          "start_time": "2024-08-27T17:15:34.863687",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "from langchain_groq import ChatGroq\n",
        "\n",
        "llm = ChatGroq(model=\"llama-3.1-70b-versatile\",api_key=groq_api_key)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3dcfc2a6",
      "metadata": {
        "id": "3dcfc2a6",
        "papermill": {
          "duration": 0.006231,
          "end_time": "2024-08-27T17:15:35.422152",
          "exception": false,
          "start_time": "2024-08-27T17:15:35.415921",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Initiating Embedding Model"
      ]
    },
    {
      "cell_type": "code",
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      "id": "82d2a79a",
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        "execution": {
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          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9307039729d64b92a04bfbde3742730a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "modules.json:   0%|          | 0.00/229 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37b8e8e958564f34adf7b6853cf90c57",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/171 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "76207a784e0441be89b9730da5398599",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/114k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5043ec38597463aaa23c288a20f099d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ace847f9c00342caa111b92c0753c4a9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/677 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1f4d710ce99a4f4185486f097f5b1577",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/670M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af20b0e55f6d4e3ab208e44034a6d6f5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.24k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba25883622b042e6adba6f645d64e9cf",
              "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": "fe2e19f775db4180b12d000cbac896d5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6b66ccd5875641caa84afe669dd39dd1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/695 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "be41a5e6be984afb898baf0be20a9d46",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/297 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "embed_model = HuggingFaceEmbeddings(model_name=\"mixedbread-ai/mxbai-embed-large-v1\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "18b8da03",
      "metadata": {
        "id": "18b8da03",
        "papermill": {
          "duration": 0.008031,
          "end_time": "2024-08-27T17:16:15.133915",
          "exception": false,
          "start_time": "2024-08-27T17:16:15.125884",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Loading the CSV files\n",
        "Dataset is from https://www.kaggle.com/datasets/jsphyg/star-wars"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6b78617b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-08-27T17:16:15.152347Z",
          "iopub.status.busy": "2024-08-27T17:16:15.151454Z",
          "iopub.status.idle": "2024-08-27T17:16:15.244094Z",
          "shell.execute_reply": "2024-08-27T17:16:15.243214Z"
        },
        "id": "6b78617b",
        "papermill": {
          "duration": 0.103959,
          "end_time": "2024-08-27T17:16:15.246088",
          "exception": false,
          "start_time": "2024-08-27T17:16:15.142129",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "from langchain_community.document_loaders.csv_loader import CSVLoader\n",
        "from langchain_community.document_loaders import DirectoryLoader\n",
        "loader = DirectoryLoader(\"/Data/csv\", glob=\"**/*.csv\", loader_cls=CSVLoader)\n",
        "\n",
        "data = loader.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "85efb5d6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-08-27T17:16:15.263950Z",
          "iopub.status.busy": "2024-08-27T17:16:15.263399Z",
          "iopub.status.idle": "2024-08-27T17:16:15.269653Z",
          "shell.execute_reply": "2024-08-27T17:16:15.268793Z"
        },
        "id": "85efb5d6",
        "outputId": "f5f1f446-7979-488d-ccd7-1bfe4f7466da",
        "papermill": {
          "duration": 0.017189,
          "end_time": "2024-08-27T17:16:15.271521",
          "exception": false,
          "start_time": "2024-08-27T17:16:15.254332",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "488"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ca4a46b2",
      "metadata": {
        "id": "ca4a46b2",
        "papermill": {
          "duration": 0.008104,
          "end_time": "2024-08-27T17:16:15.287969",
          "exception": false,
          "start_time": "2024-08-27T17:16:15.279865",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Setting up VectorStore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a5c4ae82",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-08-27T17:16:15.305708Z",
          "iopub.status.busy": "2024-08-27T17:16:15.305394Z",
          "iopub.status.idle": "2024-08-27T17:16:21.540111Z",
          "shell.execute_reply": "2024-08-27T17:16:21.539087Z"
        },
        "id": "a5c4ae82",
        "papermill": {
          "duration": 6.246255,
          "end_time": "2024-08-27T17:16:21.542537",
          "exception": false,
          "start_time": "2024-08-27T17:16:15.296282",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "from langchain_chroma import Chroma\n",
        "\n",
        "vectorstore = Chroma.from_documents(\n",
        "    documents=data,\n",
        "    embedding=embed_model,\n",
        "    persist_directory=\"/content/Starwars_Vectordb\",\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "39943d49",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-08-27T17:16:21.561157Z",
          "iopub.status.busy": "2024-08-27T17:16:21.560831Z",
          "iopub.status.idle": "2024-08-27T17:16:21.611129Z",
          "shell.execute_reply": "2024-08-27T17:16:21.610066Z"
        },
        "id": "39943d49",
        "outputId": "6a06ae4e-8bef-4193-c752-7f0e63e1772c",
        "papermill": {
          "duration": 0.061749,
          "end_time": "2024-08-27T17:16:21.613105",
          "exception": false,
          "start_time": "2024-08-27T17:16:21.551356",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "id: 3\n",
            "name: Battle of Endor\n",
            "location: Endor\n",
            "date: 4 ABY\n",
            "result: Rebel Victory\n",
            "participants: Rebel Alliance, Galactic Empire\n"
          ]
        }
      ],
      "source": [
        "query = \"Which battle resulted in Rebel Victory?\"\n",
        "docs = vectorstore.similarity_search(query)\n",
        "print(docs[0].page_content)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6e2a4e7",
      "metadata": {
        "id": "a6e2a4e7",
        "papermill": {
          "duration": 0.008147,
          "end_time": "2024-08-27T17:16:21.630298",
          "exception": false,
          "start_time": "2024-08-27T17:16:21.622151",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Building Components for RAG Chain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dbceaf7b",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-08-27T17:16:21.648221Z",
          "iopub.status.busy": "2024-08-27T17:16:21.647893Z",
          "iopub.status.idle": "2024-08-27T17:16:21.652012Z",
          "shell.execute_reply": "2024-08-27T17:16:21.651186Z"
        },
        "id": "dbceaf7b",
        "papermill": {
          "duration": 0.015212,
          "end_time": "2024-08-27T17:16:21.653842",
          "exception": false,
          "start_time": "2024-08-27T17:16:21.638630",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "retriever = vectorstore.as_retriever()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1d977079",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-08-27T17:16:21.674537Z",
          "iopub.status.busy": "2024-08-27T17:16:21.674215Z",
          "iopub.status.idle": "2024-08-27T17:16:21.679597Z",
          "shell.execute_reply": "2024-08-27T17:16:21.678530Z"
        },
        "id": "1d977079",
        "papermill": {
          "duration": 0.01942,
          "end_time": "2024-08-27T17:16:21.682364",
          "exception": false,
          "start_time": "2024-08-27T17:16:21.662944",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "from langchain_core.prompts import PromptTemplate\n",
        "\n",
        "template = (\"\"\"You are a Star Wars assistant for answering questions.\n",
        "    Use the provided context to answer the question.\n",
        "    If you don't know the answer, say so. Explain your answer in detail.\n",
        "    Do not discuss the context in your response; just provide the answer directly.\n",
        "\n",
        "    Context: {context}\n",
        "\n",
        "    Question: {question}\n",
        "\n",
        "    Answer:\"\"\")\n",
        "\n",
        "rag_prompt = PromptTemplate.from_template(template)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5d27898f",
      "metadata": {
        "id": "5d27898f",
        "papermill": {
          "duration": 0.008079,
          "end_time": "2024-08-27T17:16:21.698849",
          "exception": false,
          "start_time": "2024-08-27T17:16:21.690770",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Building the RAG Chain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "65df32fa",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2024-08-27T17:16:21.716708Z",
          "iopub.status.busy": "2024-08-27T17:16:21.716401Z",
          "iopub.status.idle": "2024-08-27T17:16:21.721518Z",
          "shell.execute_reply": "2024-08-27T17:16:21.720720Z"
        },
        "id": "65df32fa",
        "papermill": {
          "duration": 0.016108,
          "end_time": "2024-08-27T17:16:21.723266",
          "exception": false,
          "start_time": "2024-08-27T17:16:21.707158",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.runnables import RunnablePassthrough\n",
        "\n",
        "rag_chain = (\n",
        "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
        "    | rag_prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6107626",
      "metadata": {
        "id": "a6107626",
        "papermill": {
          "duration": 0.008304,
          "end_time": "2024-08-27T17:16:21.739830",
          "exception": false,
          "start_time": "2024-08-27T17:16:21.731526",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "## Testing the Chain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b49ab40",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 46
        },
        "execution": {
          "iopub.execute_input": "2024-08-27T17:16:21.758687Z",
          "iopub.status.busy": "2024-08-27T17:16:21.758031Z",
          "iopub.status.idle": "2024-08-27T17:16:22.138002Z",
          "shell.execute_reply": "2024-08-27T17:16:22.137077Z"
        },
        "id": "1b49ab40",
        "outputId": "a9be8591-218a-456d-fca8-ee4e37b20d4c",
        "papermill": {
          "duration": 0.391731,
          "end_time": "2024-08-27T17:16:22.140058",
          "exception": false,
          "start_time": "2024-08-27T17:16:21.748327",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "The Battle of Endor and the Battle of Yavin resulted in Rebel Victory."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import display, Markdown\n",
        "\n",
        "response = rag_chain.invoke(\"Which battle resulted in Rebel Victory?\")\n",
        "Markdown(response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f47dfe6c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2024-08-27T17:16:22.159262Z",
          "iopub.status.busy": "2024-08-27T17:16:22.158565Z",
          "iopub.status.idle": "2024-08-27T17:16:23.018815Z",
          "shell.execute_reply": "2024-08-27T17:16:23.017824Z"
        },
        "id": "f47dfe6c",
        "outputId": "96ae045c-d1d7-4d02-b8b5-7412a209b108",
        "papermill": {
          "duration": 0.872252,
          "end_time": "2024-08-27T17:16:23.021064",
          "exception": false,
          "start_time": "2024-08-27T17:16:22.148812",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Based on the provided documents, the timeline of the Star Wars cannot be determined with certainty. However, there are two relevant pieces of information:\n",
            "\n",
            "1. The Battle of Endor occurred 4 years after the Battle of Yavin (presumably the Battle of Yavin occurred at the time of the events in Episode IV: A New Hope, as Episode IV: A New Hope is the fourth installment in the original trilogy, and the title of the document with the Battle of Endor is \"id: 3\\nevent: Battle of Endor\\nyear: 4 ABY\").\n",
            "2. Based on the release dates of the films, the order of the films is as follows: Episode I: The Phantom Menace (1999), Episode II: (not provided in the documents), Episode III: (not provided in the documents), Episode IV: A New Hope (1977), Episode V: The Empire Strikes Back (1980), Episode VI: (not provided in the documents), Episode VII: (not provided in the documents), Episode VIII: (not provided in the documents), Episode IX: (not provided in the documents), Episode X: (not provided in the documents), and Episode XI: (not provided in the documents).\n",
            "\n",
            "However, it is worth noting that Episodes I-III are a prequel trilogy, which chronologically occurs before the original trilogy (Episodes IV-VI), and Episodes VII-IX are a sequel trilogy, which chronologically occurs after the original trilogy.\n",
            "\n",
            "However, since Episodes II and III are not provided in the documents, and Episodes VII-IX, and the release dates of the prequels, the timeline of the Star Wars is incomplete and cannot be accurately determined based on the provided information."
          ]
        }
      ],
      "source": [
        "query = \"What is the timeline of the Starwars?\"\n",
        "\n",
        "for chunk in rag_chain.stream(query):\n",
        "            print(chunk, end=\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QyAH56wo0F3-",
      "metadata": {
        "id": "QyAH56wo0F3-"
      },
      "source": [
        "## Gradio App"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "LIL8HO7p_Tdj",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "LIL8HO7p_Tdj",
        "outputId": "e2301fea-4183-4a40-9094-cfd4437e9a55"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://f03fd9f0388410af8e.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co./spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://f03fd9f0388410af8e.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": []
          },
          "execution_count": 48,
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        "import gradio as gr\n",
        "\n",
        "def rag_memory_stream(text):\n",
        "    partial_text = \"\"\n",
        "    for new_text in rag_chain.stream(text):\n",
        "        partial_text += new_text\n",
        "        # Yield an empty string to cleanup the message textbox and the updated conversation history\n",
        "        yield partial_text\n",
        "\n",
        "\n",
        "title = \"Real-time AI App with Groq API and LangChain\"\n",
        "demo = gr.Interface(\n",
        "    title=title,\n",
        "    fn=rag_memory_stream,\n",
        "    inputs=\"text\",\n",
        "    outputs=\"text\",\n",
        "    live=True,\n",
        "    batch=True,\n",
        "    max_batch_size=10000,\n",
        "    concurrency_limit=16\n",
        ")\n",
        "\n",
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