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null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "29f48cfa7ebe4a3fa9448830fd953bf0": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# **Branched RAG Implementation using LangGraph**" ], "metadata": { "id": "7MbZBHtoyLK_" } }, { "cell_type": "markdown", "source": [ "## **Importing required libraries**" ], "metadata": { "id": "2bU7v8cn1AYn" } }, { "cell_type": "code", "source": [ "from typing import TypedDict, List\n", "\n", "from langchain_core.documents import Document\n", "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "from langchain_huggingface import HuggingFaceEmbeddings\n", "from langchain_community.vectorstores import FAISS\n", "\n", "from langchain_google_genai import ChatGoogleGenerativeAI\n", "from langgraph.graph import START, StateGraph, END\n" ], "metadata": { "id": "TOw2bDlky2n4" }, "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "source": [ "## **Creating Dummy Data**" ], "metadata": { "id": "uTNFXSxy1Mj2" } }, { "cell_type": "code", "source": [ "documents = [\n", " Document(page_content=\"Retrieval Augmented Generation combines retrieval with LLMs.\"),\n", " Document(page_content=\"Fine-tuning adapts models using training data.\"),\n", " Document(page_content=\"RAG reduces hallucinations.\"),\n", " Document(page_content=\"Branched RAG uses multiple queries.\")\n", "]\n" ], "metadata": { "id": "VxS1AZiOy2r1" }, "execution_count": 24, "outputs": [] }, { "cell_type": "markdown", "source": [ "## **Splitting the documents**" ], "metadata": { "id": "EEmwvLei1V3a" } }, { "cell_type": "code", "source": [ "splitter = RecursiveCharacterTextSplitter(\n", " chunk_size=100,\n", " chunk_overlap=20\n", ")\n", "\n", "chunks = splitter.split_documents(documents)\n" ], "metadata": { "id": "nOmcAyeSy_j9" }, "execution_count": 25, "outputs": [] }, { "cell_type": "markdown", "source": [ "## **Creating Embeddings and storing them in FAISS vectorstore**" ], "metadata": { "id": "JxwnOQod1fHq" } }, { "cell_type": "code", "source": [ "embeddings = HuggingFaceEmbeddings(\n", " model_name=\"sentence-transformers/all-MiniLM-L6-v2\"\n", ")\n", "\n", "vectorstore = FAISS.from_documents(chunks, embeddings)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 661, "referenced_widgets": [ "cbbcc3c1ed6d4530acca62fbe01a8e8d", "224d1d067e3e4c238e37c0336df7a781", "0e862371c9414679afbb1ab077b2075f", "a4372016fc0d4a139291f46c505c40be", 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`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" ], "image/png": 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\n" }, "metadata": {}, "execution_count": 32 } ] }, { "cell_type": "markdown", "source": [ "## **Invoking the graph with a query**" ], "metadata": { "id": "6GlRdsnU2rrb" } }, { "cell_type": "code", "source": [ "result = graph.invoke({\n", " \"query\": \"Explain Branched RAG\"\n", "})\n", "\n", "print(result[\"answer\"])\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 0 }, "id": "jWQw9qUVzWuT", "outputId": "fa062ee8-d58e-41f9-fa52-7866fcca2a75" }, "execution_count": 33, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Branched RAG is a specific approach within the broader Retrieval Augmented Generation (RAG) framework.\n", "\n", "Its defining characteristic, as stated in the context, is that it **uses multiple queries**.\n", "\n", "This means that instead of a single retrieval query, the system generates and executes several distinct queries to gather information. This approach aims to:\n", "\n", "* Obtain a more comprehensive and diverse set of relevant documents or data points.\n", "* Address different facets or sub-questions of a complex user prompt.\n", "\n", "By leveraging multiple retrieval paths, Branched RAG enhances the quality and breadth of the context provided to the Large Language Model (LLM), further contributing to RAG's goal of reducing hallucinations and generating more accurate and informed responses.\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "WEHDMu_xxWod" }, "execution_count": null, "outputs": [] } ] }