{ "cells": [ { "cell_type": "markdown", "source": [ "# Retrieval Augmented Generation\n", "\n", "Author: Arkadiusz Modzelewski\n", "\n", "Website and contact details: [Website](https://amodzelewski.com/)" ], "metadata": { "collapsed": false, "id": "417db4cf7e8865cb" }, "id": "417db4cf7e8865cb" }, { "cell_type": "markdown", "source": [ "# Short Description\n", "\n", "This notebook demonstrates how you can build an advanced RAG (Retrieval Augmented Generation) for answering a user’s question about a specific knowledge base (here, the European Election 2024), using LangChain." ], "metadata": { "collapsed": false, "id": "C96yVcQJ7Z4W" }, "id": "C96yVcQJ7Z4W" }, { "cell_type": "markdown", "source": [ "# Additional Resources\n", "\n", "[Simple RAG](https://huggingface.co/learn/cookbook/en/rag_zephyr_langchain) - Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain\n", "\n", "[Advanced RAG](https://huggingface.co/learn/cookbook/en/advanced_rag#load-your-knowledge-base) - Advanced RAG on Hugging Face documentation using LangChain\n", "\n", "\n", "This lab was created using Advanced RAG notebook provided by Hugging Face." ], "metadata": { "collapsed": false, "id": "819244b75b1da47f" }, "id": "819244b75b1da47f" }, { "cell_type": "markdown", "source": [ "# Libraries to install" ], "metadata": { "collapsed": false, "id": "91f19757dd7a3bf0" }, "id": "91f19757dd7a3bf0" }, { "cell_type": "code", "execution_count": 1, "outputs": [], "source": [ "# !pip install langchain-community\n", "# !pip install langchain\n", "# !pip install pypdf\n", "# !pip install faiss-cpu\n", "# !pip install langchain-chroma" ], "metadata": { "id": "KXmO-X-CrRVB" }, "id": "KXmO-X-CrRVB" }, { "cell_type": "code", "execution_count": 3, "outputs": [], "source": [ "# !pip install bitsandbytes" ], "metadata": { "id": "b84562897408bca" }, "id": "b84562897408bca" }, { "cell_type": "markdown", "source": [ "# Imports" ], "metadata": { "collapsed": false, "id": "c42aaade5892a635" }, "id": "c42aaade5892a635" }, { "cell_type": "code", "execution_count": 3, "id": "initial_id", "metadata": { "collapsed": true, "id": "initial_id", "ExecuteTime": { "end_time": "2025-05-28T22:20:00.703501Z", "start_time": "2025-05-28T22:19:56.644378Z" } }, "outputs": [], "source": [ "from langchain_community.document_loaders import PyPDFLoader\n", "import pandas as pd\n", "from sentence_transformers import SentenceTransformer\n", "from transformers import AutoTokenizer\n", "from tqdm.notebook import tqdm\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain_community.embeddings import HuggingFaceEmbeddings\n", "from langchain_community.vectorstores.utils import DistanceStrategy\n", "from typing import Optional, List, Tuple\n", "from langchain_chroma import Chroma\n", "from transformers import pipeline\n", "import torch\n", "from transformers import AutoModelForCausalLM, BitsAndBytesConfig\n", "pd.set_option(\"display.max_colwidth\", None)" ] }, { "cell_type": "markdown", "source": [ "# Utils\n", "\n", "All helper functions" ], "metadata": { "collapsed": false, "id": "16438c57a5c26946" }, "id": "16438c57a5c26946" }, { "cell_type": "code", "execution_count": 4, "outputs": [], "source": [ "def plot_doc_length_distribution(document_lengths, title=\"Distribution of Document Lengths in the Knowledge Base\"):\n", " \"\"\"\n", " Plots a histogram showing the distribution of document lengths.\n", "\n", " Parameters:\n", " - document_lengths (list): A list of integers representing the number of tokens in each document.\n", " - title (str): Title for the plot. Defaults to a general description.\n", " \"\"\"\n", " plt.figure(figsize=(10, 6))\n", " plt.hist(document_lengths, bins=30, color='skyblue', edgecolor='black')\n", "\n", " plt.title(title, fontsize=14)\n", " plt.xlabel(\"Number of Tokens\", fontsize=12)\n", " plt.ylabel(\"Number of Documents\", fontsize=12)\n", " plt.grid(axis='y', linestyle='--', alpha=0.7)\n", "\n", " plt.tight_layout()\n", " plt.show()" ], "metadata": { "id": "e323f8ada61eaaf1", "ExecuteTime": { "end_time": "2025-05-28T22:20:03.506609Z", "start_time": "2025-05-28T22:20:03.490851Z" } }, "id": "e323f8ada61eaaf1" }, { "cell_type": "code", "execution_count": 5, "outputs": [], "source": [ "def split_documents(chunk_size: int, chunk_overlap: int, knowledge_base: List, tokenizer_name: str) -> List:\n", " \"\"\"\n", " Split documents into chunks of maximum size `chunk_size` tokens and return a list of documents.\n", " \"\"\"\n", " text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(\n", " AutoTokenizer.from_pretrained(tokenizer_name),\n", " chunk_size=chunk_size,\n", " chunk_overlap=chunk_overlap,\n", " )\n", "\n", " docs_processed = text_splitter.split_documents(knowledge_base)\n", "\n", " return docs_processed" ], "metadata": { "id": "19245bea4677b7ba", "ExecuteTime": { "end_time": "2025-05-28T22:20:04.302690Z", "start_time": "2025-05-28T22:20:04.292356Z" } }, "id": "19245bea4677b7ba" }, { "cell_type": "markdown", "source": [ "# Load your knowledge base" ], "metadata": { "id": "WZjxYZpl8FXx" }, "id": "WZjxYZpl8FXx" }, { "cell_type": "code", "execution_count": 6, "outputs": [], "source": [ "# Define the path to the PDF file containing the knowledge base\n", "file_path = \"../data/2024_European_Parliament_election_Wikipedia.pdf\"\n", "\n", "# Create a PyPDFLoader object to handle loading of the PDF\n", "loader = PyPDFLoader(file_path)\n", "\n", "# Load the contents of the PDF into a variable for further processing\n", "RAW_KNOWLEDGE_BASE = loader.load()" ], "metadata": { "id": "d1793cf8ccf68376", "ExecuteTime": { "end_time": "2025-05-28T23:23:06.691637Z", "start_time": "2025-05-28T23:23:05.323066Z" } }, "id": "d1793cf8ccf68376" }, { "cell_type": "code", "execution_count": 7, "outputs": [ { "data": { "text/plain": "list" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(RAW_KNOWLEDGE_BASE)" ], "metadata": { "id": "6676e3f5f0b80df3", "outputId": "44ab4259-f626-4de7-87ec-212906548e1a", "colab": { "base_uri": "https://localhost:8080/" }, "ExecuteTime": { "end_time": "2025-05-28T23:23:08.701837Z", "start_time": "2025-05-28T23:23:08.684705Z" } }, "id": "6676e3f5f0b80df3" }, { "cell_type": "code", "execution_count": 8, "outputs": [ { "data": { "text/plain": "14" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(RAW_KNOWLEDGE_BASE)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bf2e48e445523336", "outputId": "ea18e0bc-5916-4e36-cafb-96c76150367a", "ExecuteTime": { "end_time": "2025-05-28T23:25:47.320391Z", "start_time": "2025-05-28T23:25:47.299205Z" } }, "id": "bf2e48e445523336" }, { "cell_type": "code", "execution_count": 9, "outputs": [ { "data": { "text/plain": "Document(metadata={'producer': 'Skia/PDF m136', 'creator': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36', 'creationdate': '2025-05-16T13:48:40+00:00', 'title': '2024 European Parliament election - Wikipedia', 'moddate': '2025-05-16T13:48:40+00:00', 'source': '../data/2024_European_Parliament_election_Wikipedia.pdf', 'total_pages': 14, 'page': 0, 'page_label': '1'}, page_content='2024 European Parliament election\\n6–9 June 2024\\nAll 720 seats to the European Parliament[a]\\n361 seats needed for a majority\\nOpinion\\xa0polls\\nTurnout (50.74%[1] \\n 0.08 pp)\\n\\xa0\\nLeader Ursula von derLeyen Nicolas Schmit None[b]\\nAlliance EPP S&D PfE\\nLeader\\'s\\xa0seat Not running Not running –\\nLast\\xa0election 187 seats 148 seats New group\\nSeats\\xa0won 188 136 84\\nSeat\\xa0change\\n 1\\n 12\\n 8[c]\\n\\xa0\\nLeader None Marie-AgnesStrack-Zimmermann[d]\\nTerry ReintkeBas Eickhout\\nAlliance ECR Renew Greens/EFA\\nLeader\\'s\\xa0seat – Germany GermanyNetherlands\\nLast\\xa0election 62 seats 97 seats 67 seats\\nSeats\\xa0won 78 77 53\\nSeat\\xa0change\\n 16\\n 20\\n 14\\n\\xa0\\nLeader Walter Baier None\\nAlliance The Left ESN\\nLeader\\'s\\xa0seat Not running –\\nLast\\xa0election 40 seats New group\\nSeats\\xa0won 46 25\\nSeat\\xa0change\\n 6 –\\n2024 European Parliament election\\nThe 2024 European Parliament election was held in the European Union(EU) between 6 and 9 June 2024.[4] It was the tenth parliamentary election sincethe first direct elections in 1979, and the first European Parliament election afterBrexit.[5][6] A total of 720 Members of the European Parliament (MEPs) wereelected to represent more than 450 million people from 27 member states.[a] Thiselection also coincided with a number of other elections in some European Unionmember states.\\nThe European People\\'s Party led by Ursula von der Leyen won the most seats inthe European Parliament. The pro-EU centrist, liberal, social democrat andenvironmentalist parties suffered losses, while anti-EU right-wing populist partiesmade gains. The right-wing European Conservatives and Reformists groupovertook the centrist Renew Europe group to win the fourth most seats, whileanother right-wing group, Patriots for Europe, the successor of Identity andDemocracy, won the third most seats. In addition, a far-right group, Europe ofSovereign Nations, was formed, becoming the smallest group in the Parliament. Intotal, 187 MEPS (26% of Parliament) belonged to the hard-right which is moremembers than ever before in history.[7]\\nOn 18 July 2024, Ursula von der Leyen was re-elected President of the EuropeanCommission in a secret ballot by the European Parliament.[8]\\nIn the previous election, held on 23–26 May 2019, in terms of the political Groupsin the Parliament, they resulted in the EPP Group and S&D suffering significantlosses, while the liberal/centrist (Renew), the Greens/EFA and ID madesubstantial gains, with ECR and The Left had small reduction. The EuropeanPeople\\'s Party, led by Manfred Weber, won the most seats in the EuropeanParliament, but was then unable to secure support from other parties for Weber ascandidate for President of the Commission. After initial deadlock, the EuropeanCouncil decided to nominate Ursula von der Leyen as a compromise candidate tobe the new Commission President, and the European Parliament elected von derLeyen with 383 votes (374 votes needed). The commission as a whole was thenapproved by the European Parliament on 27 November 2019, receiving 461 votes.\\nThe 2019 election saw an increase in the turnout, when 50.7% of eligible votershad cast a vote compared with 42.5% of the 2014 election. This was the first timethat turnout had increased since the first European Parliament election in 1979.[9]\\nIn 2024, the Eurobarometer data shows that 71% of Europeans say they are likelyto vote in June, 10% higher than those who said they would in 2019.[10]\\nSince the last European-wide election, the right has continued to rise acrossEurope, remaining however split, mainly by the Russian invasion of Ukraine andRussian relations issue.[11] In 2024, before the European elections, right-wingpopulist parties hold or share political power in Hungary (Fidesz), Italy (Brothersof Italy), Sweden (Sweden Democrats), Finland (Finns Party), Slovakia (SlovakNational Party) and Croatia (Homeland Movement).[11] The centre-right EPP has\"raised eyebrows\" among some commentators for its efforts to charm parties in theECR to create a broad conservative block,[12] which could upset the long-standingstatus-quo that has seen the EPP share power with the centre-left S&D and thecentrist Renew Group.[13]\\nThe Qatargate corruption scandal, which began in December 2022, haddestabilized the European Parliament following the arrest of several MEPsincluding Marc Tarabella; Andrea Cozzolino and Eva Kaili who was stripped of hervice presidency. Other suspects in the case include Francesco Giorgi, theparliamentary assistant of MEP Andrea Cozzolino, Pier Antonio Panzeri, founderof the Fight Impunity NGO; Niccolo Figa-Talamanca, head of the No PeaceWithout Justice NGO; and Luca Visentini, head of the International Trade UnionConfederation.[14][15] Following the scandal, the European Parliament revised itsrules of procedure and its code of conduct in September 2023[16]\\nBackground\\nQatargate\\n16/05/2025, 15:48 2024 European Parliament election - Wikipedia\\nhttps://en.wikipedia.org/wiki/2024_European_Parliament_election 1/24')" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RAW_KNOWLEDGE_BASE[0]" ], "metadata": { "id": "95f5058037d8cc1c", "outputId": "023ff9fb-f552-4254-c912-cd1c26216c62", "colab": { "base_uri": "https://localhost:8080/" }, "ExecuteTime": { "end_time": "2025-05-28T23:25:48.378285Z", "start_time": "2025-05-28T23:25:48.359205Z" } }, "id": "95f5058037d8cc1c" }, { "cell_type": "code", "execution_count": 10, "outputs": [ { "data": { "text/plain": "{'producer': 'Skia/PDF m136',\n 'creator': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36',\n 'creationdate': '2025-05-16T13:48:40+00:00',\n 'title': '2024 European Parliament election - Wikipedia',\n 'moddate': '2025-05-16T13:48:40+00:00',\n 'source': '../data/2024_European_Parliament_election_Wikipedia.pdf',\n 'total_pages': 14,\n 'page': 0,\n 'page_label': '1'}" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RAW_KNOWLEDGE_BASE[0].metadata" ], "metadata": { "id": "c19a299b40068fe2", "outputId": "24cd7208-5dfd-4611-f559-15235551a209", "colab": { "base_uri": "https://localhost:8080/" }, "ExecuteTime": { "end_time": "2025-05-28T23:25:49.481316Z", "start_time": "2025-05-28T23:25:49.475610Z" } }, "id": "c19a299b40068fe2" }, { "cell_type": "code", "execution_count": 11, "outputs": [ { "data": { "text/plain": "'2024 European Parliament election\\n6–9 June 2024\\nAll 720 seats to the European Parliament[a]\\n361 seats needed for a majority\\nOpinion\\xa0polls\\nTurnout (50.74%[1] \\n 0.08 pp)\\n\\xa0\\nLeader Ursula von derLeyen Nicolas Schmit None[b]\\nAlliance EPP S&D PfE\\nLeader\\'s\\xa0seat Not running Not running –\\nLast\\xa0election 187 seats 148 seats New group\\nSeats\\xa0won 188 136 84\\nSeat\\xa0change\\n 1\\n 12\\n 8[c]\\n\\xa0\\nLeader None Marie-AgnesStrack-Zimmermann[d]\\nTerry ReintkeBas Eickhout\\nAlliance ECR Renew Greens/EFA\\nLeader\\'s\\xa0seat – Germany GermanyNetherlands\\nLast\\xa0election 62 seats 97 seats 67 seats\\nSeats\\xa0won 78 77 53\\nSeat\\xa0change\\n 16\\n 20\\n 14\\n\\xa0\\nLeader Walter Baier None\\nAlliance The Left ESN\\nLeader\\'s\\xa0seat Not running –\\nLast\\xa0election 40 seats New group\\nSeats\\xa0won 46 25\\nSeat\\xa0change\\n 6 –\\n2024 European Parliament election\\nThe 2024 European Parliament election was held in the European Union(EU) between 6 and 9 June 2024.[4] It was the tenth parliamentary election sincethe first direct elections in 1979, and the first European Parliament election afterBrexit.[5][6] A total of 720 Members of the European Parliament (MEPs) wereelected to represent more than 450 million people from 27 member states.[a] Thiselection also coincided with a number of other elections in some European Unionmember states.\\nThe European People\\'s Party led by Ursula von der Leyen won the most seats inthe European Parliament. The pro-EU centrist, liberal, social democrat andenvironmentalist parties suffered losses, while anti-EU right-wing populist partiesmade gains. The right-wing European Conservatives and Reformists groupovertook the centrist Renew Europe group to win the fourth most seats, whileanother right-wing group, Patriots for Europe, the successor of Identity andDemocracy, won the third most seats. In addition, a far-right group, Europe ofSovereign Nations, was formed, becoming the smallest group in the Parliament. Intotal, 187 MEPS (26% of Parliament) belonged to the hard-right which is moremembers than ever before in history.[7]\\nOn 18 July 2024, Ursula von der Leyen was re-elected President of the EuropeanCommission in a secret ballot by the European Parliament.[8]\\nIn the previous election, held on 23–26 May 2019, in terms of the political Groupsin the Parliament, they resulted in the EPP Group and S&D suffering significantlosses, while the liberal/centrist (Renew), the Greens/EFA and ID madesubstantial gains, with ECR and The Left had small reduction. The EuropeanPeople\\'s Party, led by Manfred Weber, won the most seats in the EuropeanParliament, but was then unable to secure support from other parties for Weber ascandidate for President of the Commission. After initial deadlock, the EuropeanCouncil decided to nominate Ursula von der Leyen as a compromise candidate tobe the new Commission President, and the European Parliament elected von derLeyen with 383 votes (374 votes needed). The commission as a whole was thenapproved by the European Parliament on 27 November 2019, receiving 461 votes.\\nThe 2019 election saw an increase in the turnout, when 50.7% of eligible votershad cast a vote compared with 42.5% of the 2014 election. This was the first timethat turnout had increased since the first European Parliament election in 1979.[9]\\nIn 2024, the Eurobarometer data shows that 71% of Europeans say they are likelyto vote in June, 10% higher than those who said they would in 2019.[10]\\nSince the last European-wide election, the right has continued to rise acrossEurope, remaining however split, mainly by the Russian invasion of Ukraine andRussian relations issue.[11] In 2024, before the European elections, right-wingpopulist parties hold or share political power in Hungary (Fidesz), Italy (Brothersof Italy), Sweden (Sweden Democrats), Finland (Finns Party), Slovakia (SlovakNational Party) and Croatia (Homeland Movement).[11] The centre-right EPP has\"raised eyebrows\" among some commentators for its efforts to charm parties in theECR to create a broad conservative block,[12] which could upset the long-standingstatus-quo that has seen the EPP share power with the centre-left S&D and thecentrist Renew Group.[13]\\nThe Qatargate corruption scandal, which began in December 2022, haddestabilized the European Parliament following the arrest of several MEPsincluding Marc Tarabella; Andrea Cozzolino and Eva Kaili who was stripped of hervice presidency. Other suspects in the case include Francesco Giorgi, theparliamentary assistant of MEP Andrea Cozzolino, Pier Antonio Panzeri, founderof the Fight Impunity NGO; Niccolo Figa-Talamanca, head of the No PeaceWithout Justice NGO; and Luca Visentini, head of the International Trade UnionConfederation.[14][15] Following the scandal, the European Parliament revised itsrules of procedure and its code of conduct in September 2023[16]\\nBackground\\nQatargate\\n16/05/2025, 15:48 2024 European Parliament election - Wikipedia\\nhttps://en.wikipedia.org/wiki/2024_European_Parliament_election 1/24'" }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RAW_KNOWLEDGE_BASE[0].page_content" ], "metadata": { "id": "6549b845eec589b6", "outputId": "0d06883c-49d2-4e4b-a342-7b8e27a83322", "colab": { "base_uri": "https://localhost:8080/", "height": 105 }, "ExecuteTime": { "end_time": "2025-05-28T23:25:52.834341Z", "start_time": "2025-05-28T23:25:52.828201Z" } }, "id": "6549b845eec589b6" }, { "cell_type": "code", "execution_count": 12, "outputs": [ { "data": { "text/plain": "4927" }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(RAW_KNOWLEDGE_BASE[0].page_content)" ], "metadata": { "id": "9cef379cff0da2d3", "outputId": "b6cacd26-eb5d-46ce-fb19-8768405bcfbe", "colab": { "base_uri": "https://localhost:8080/" }, "ExecuteTime": { "end_time": "2025-05-28T23:25:54.958988Z", "start_time": "2025-05-28T23:25:54.937103Z" } }, "id": "9cef379cff0da2d3" }, { "cell_type": "markdown", "source": [ "# Retriever" ], "metadata": { "id": "OOxzX7rR9D8x" }, "id": "OOxzX7rR9D8x" }, { "cell_type": "markdown", "source": [ "## Split the documents into chunks\n", "\n", "In this part, we split the documents from our knowledge base into smaller chunks which will be the snippets on which the reader LLM will base its answer. The goal is to prepare a collection of semantically relevant snippets. So their size should be adapted to precise ideas: too small will truncate ideas, and too large will dilute them. A widely used and simple method for chunking text involves specifying a fixed chunk size and determining if the chunks should overlap. Including some overlap helps maintain semantic continuity between chunks. For general-purpose text, the recommended tool is the `RecursiveCharacterTextSplitter`, which we’ll be using in this case.\n", "\n", "\n", "The `chunk_size` parameter determines the maximum length of each chunk - measured in characters by default.\n", "Meanwhile, the `chunk_overlap` parameter allows neighboring chunks to share some content. This overlap helps ensure that important ideas aren't accidentally split between chunks, preserving the semantic integrity of the text." ], "metadata": { "id": "v4PjYHbJ9r6C" }, "id": "v4PjYHbJ9r6C" }, { "cell_type": "code", "execution_count": 14, "outputs": [], "source": [ "splitter = RecursiveCharacterTextSplitter(\n", " chunk_size=2000, # The maximum number of characters in a chunk: we selected this value arbitrarily\n", " chunk_overlap=20, # The number of characters to overlap between chunks\n", ")\n", "\n", "chunked_docs = splitter.split_documents(RAW_KNOWLEDGE_BASE)" ], "metadata": { "id": "2ed932270f6514b3", "ExecuteTime": { "end_time": "2025-05-28T19:57:00.012055Z", "start_time": "2025-05-28T19:56:59.999740Z" } }, "id": "2ed932270f6514b3" }, { "cell_type": "code", "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "list" ] }, "metadata": {}, "execution_count": 15 } ], "source": [ "type(chunked_docs)" ], "metadata": { "ExecuteTime": { "end_time": "2025-05-28T19:57:00.335673Z", "start_time": "2025-05-28T19:57:00.331084Z" }, "id": "1f75dd0c1bd3fe88", "outputId": "0563d316-a554-4638-d9ff-4bf5aa6d4ef4", "colab": { "base_uri": "https://localhost:8080/" } }, "id": "1f75dd0c1bd3fe88" }, { "cell_type": "code", "execution_count": 16, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "41" ] }, "metadata": {}, "execution_count": 16 } ], "source": [ "len(chunked_docs)" ], "metadata": { "ExecuteTime": { "end_time": "2025-05-28T19:57:00.652394Z", "start_time": "2025-05-28T19:57:00.646312Z" }, "id": "2e96b3efa2cf2105", "outputId": "dea0cbeb-6d7c-4324-8f1f-f0fc2ab7a1fb", "colab": { "base_uri": "https://localhost:8080/" } }, "id": "2e96b3efa2cf2105" }, { "cell_type": "code", "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Document(metadata={'producer': 'Skia/PDF m136', 'creator': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36', 'creationdate': '2025-05-16T13:48:40+00:00', 'title': '2024 European Parliament election - Wikipedia', 'moddate': '2025-05-16T13:48:40+00:00', 'source': '2024_European_Parliament_election_Wikipedia.pdf', 'total_pages': 14, 'page': 0, 'page_label': '1'}, page_content=\"2024 European Parliament election\\n6–9 June 2024\\nAll 720 seats to the European Parliament[a]\\n361 seats needed for a majority\\nOpinion\\xa0polls\\nTurnout (50.74%[1] \\n 0.08 pp)\\n\\xa0\\nLeader Ursula von derLeyen Nicolas Schmit None[b]\\nAlliance EPP S&D PfE\\nLeader's\\xa0seat Not running Not running –\\nLast\\xa0election 187 seats 148 seats New group\\nSeats\\xa0won 188 136 84\\nSeat\\xa0change\\n 1\\n 12\\n 8[c]\\n\\xa0\\nLeader None Marie-AgnesStrack-Zimmermann[d]\\nTerry ReintkeBas Eickhout\\nAlliance ECR Renew Greens/EFA\\nLeader's\\xa0seat – Germany GermanyNetherlands\\nLast\\xa0election 62 seats 97 seats 67 seats\\nSeats\\xa0won 78 77 53\\nSeat\\xa0change\\n 16\\n 20\\n 14\\n\\xa0\\nLeader Walter Baier None\\nAlliance The Left ESN\\nLeader's\\xa0seat Not running –\\nLast\\xa0election 40 seats New group\\nSeats\\xa0won 46 25\\nSeat\\xa0change\\n 6 –\\n2024 European Parliament election\\nThe 2024 European Parliament election was held in the European Union(EU) between 6 and 9 June 2024.[4] It was the tenth parliamentary election sincethe first direct elections in 1979, and the first European Parliament election afterBrexit.[5][6] A total of 720 Members of the European Parliament (MEPs) wereelected to represent more than 450 million people from 27 member states.[a] Thiselection also coincided with a number of other elections in some European Unionmember states.\\nThe European People's Party led by Ursula von der Leyen won the most seats inthe European Parliament. The pro-EU centrist, liberal, social democrat andenvironmentalist parties suffered losses, while anti-EU right-wing populist partiesmade gains. The right-wing European Conservatives and Reformists groupovertook the centrist Renew Europe group to win the fourth most seats, whileanother right-wing group, Patriots for Europe, the successor of Identity andDemocracy, won the third most seats. In addition, a far-right group, Europe ofSovereign Nations, was formed, becoming the smallest group in the Parliament. Intotal, 187 MEPS (26% of Parliament) belonged to the hard-right which is moremembers than ever before in history.[7]\")" ] }, "metadata": {}, "execution_count": 17 } ], "source": [ "chunked_docs[0]" ], "metadata": { "ExecuteTime": { "end_time": "2025-05-28T19:57:00.997655Z", "start_time": "2025-05-28T19:57:00.990147Z" }, "id": "c31d4afdd8e21dfd", "outputId": "bec82388-c98c-4d1e-efd5-895d10668504", "colab": { "base_uri": "https://localhost:8080/" } }, "id": "c31d4afdd8e21dfd" }, { "cell_type": "code", "execution_count": 18, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'producer': 'Skia/PDF m136',\n", " 'creator': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/136.0.0.0 Safari/537.36',\n", " 'creationdate': '2025-05-16T13:48:40+00:00',\n", " 'title': '2024 European Parliament election - Wikipedia',\n", " 'moddate': '2025-05-16T13:48:40+00:00',\n", " 'source': '2024_European_Parliament_election_Wikipedia.pdf',\n", " 'total_pages': 14,\n", " 'page': 0,\n", " 'page_label': '1'}" ] }, "metadata": {}, "execution_count": 18 } ], "source": [ "chunked_docs[2].metadata" ], "metadata": { "ExecuteTime": { "end_time": "2025-05-28T19:57:01.421169Z", "start_time": "2025-05-28T19:57:01.416408Z" }, "id": "f5fa1d76161ef57", "outputId": "2d35fb2c-51c9-46bc-e566-a97e0f4360e4", "colab": { "base_uri": "https://localhost:8080/" } }, "id": "f5fa1d76161ef57" }, { "cell_type": "code", "execution_count": 19, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "1994" ] }, "metadata": {}, "execution_count": 19 } ], "source": [ "len(chunked_docs[0].page_content)" ], "metadata": { "ExecuteTime": { "end_time": "2025-05-28T19:57:01.804105Z", "start_time": "2025-05-28T19:57:01.796823Z" }, "id": "d5b4af5d50401957", "outputId": "c5d6e8d6-16b5-44b5-ed45-fc04af1585bf", "colab": { "base_uri": "https://localhost:8080/" } }, "id": "d5b4af5d50401957" }, { "cell_type": "markdown", "source": [ "It's important to remember that when embedding documents, the embedding model has a maximum sequence length, defined by max_seq_length.\n", "\n", "Therefore, our chunk sizes should stay within this limit. If a chunk exceeds the maximum length, it will be truncated during processing, potentially resulting in the loss of important or relevant information." ], "metadata": { "collapsed": false, "id": "5379dbb2fd633a59" }, "id": "5379dbb2fd633a59" }, { "cell_type": "code", "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: 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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modules.json: 0%| | 0.00/385 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\n" }, "metadata": {} } ], "source": [ "plot_doc_length_distribution(document_lengths=pd.Series(lengths))" ], "metadata": { "ExecuteTime": { "end_time": "2025-05-28T19:57:29.874876Z", "start_time": "2025-05-28T19:57:29.812091Z" }, "id": "5b438cf85acd72a8", "outputId": "d5e0f8a2-60bb-4f42-8d7a-e70b9583b0a8", "colab": { "base_uri": "https://localhost:8080/", "height": 607 } }, "id": "5b438cf85acd72a8" }, { "cell_type": "markdown", "source": [ "As shown, the chunk lengths don't consistently stay within the 512-token limit, some exceed it. This means portions of those longer chunks will be truncated during processing, potentially leading to loss of valuable content.\n", "\n", "Let's try with smaller chunk size." ], "metadata": { "collapsed": false, "id": "c18e505a74bed8bc" }, "id": "c18e505a74bed8bc" }, { "cell_type": "code", "execution_count": 23, "outputs": [], "source": [ "splitter = RecursiveCharacterTextSplitter(\n", " chunk_size=600, # The maximum number of characters in a chunk: we selected this value arbitrarily\n", " chunk_overlap=50, # The number of characters to overlap between chunks\n", ")\n", "\n", "chunked_docs = splitter.split_documents(RAW_KNOWLEDGE_BASE)" ], "metadata": { "id": "5df9b5795cb7453e", "ExecuteTime": { "end_time": "2025-05-28T19:57:33.241343Z", "start_time": "2025-05-28T19:57:33.231472Z" } }, "id": "5df9b5795cb7453e" }, { "cell_type": "code", "execution_count": 24, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/137 [00:00" ], "image/png": 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\n" }, "metadata": {} } ], "source": [ "plot_doc_length_distribution(document_lengths=pd.Series(lengths))" ], "metadata": { "ExecuteTime": { "start_time": "2025-05-28T19:57:34.974159Z" }, "id": "219fe402614fa3b1", "outputId": "1af2b87a-8e4a-4a65-8c16-daa497aed374", "colab": { "base_uri": "https://localhost:8080/", "height": 607 } }, "id": "219fe402614fa3b1" }, { "cell_type": "markdown", "source": [ "This code sets the embedding model to `gte-small`, a lightweight language model for general-purpose text embeddings, and processes a raw knowledge base by splitting its documents into chunks of up to certian number of tokens using the model's tokenizer. The split_documents function ensures chunks have slight overlaps." ], "metadata": { "collapsed": false, "id": "67c9e5856376aab1" }, "id": "67c9e5856376aab1" }, { "cell_type": "code", "execution_count": 65, "outputs": [], "source": [ "EMBEDDING_MODEL_NAME = \"thenlper/gte-small\"\n", "\n", "docs_processed = split_documents(\n", " chunk_size=512,\n", " chunk_overlap=50,\n", " knowledge_base=RAW_KNOWLEDGE_BASE,\n", " tokenizer_name=EMBEDDING_MODEL_NAME,\n", ")" ], "metadata": { "ExecuteTime": { "end_time": "2025-05-28T20:03:20.957394Z", "start_time": "2025-05-28T20:03:20.599682Z" }, "id": "d9a8803104cb655d" }, "id": "d9a8803104cb655d" }, { "cell_type": "code", "execution_count": 66, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/39 [00:00" ], "image/png": 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\n" }, "metadata": {} } ], "source": [ "# Let's visualize the chunk sizes we would have in tokens from a common model\n", "# tokenizer = AutoTokenizer.from_pretrained(EMBEDDING_MODEL_NAME)\n", "lengths = [len(tokenizer.encode(doc.page_content)) for doc in tqdm(docs_processed)]\n", "\n", "plot_doc_length_distribution(document_lengths=pd.Series(lengths))" ], "metadata": { "ExecuteTime": { "end_time": "2025-05-28T20:03:23.185091Z", "start_time": "2025-05-28T20:03:23.061776Z" }, "id": "Il4KK8xa24Vo", "outputId": "5b43b9ce-e7ab-4713-edca-52aa1ad1c016", "colab": { "base_uri": "https://localhost:8080/", "height": 639, "referenced_widgets": [ "e9abf463a97049c7a4904aa2e2f052bc", "5211074d338c4e5199ba98341701665d", "b6bdc648c3d447a5b4b4c6e1eacef2ef", "a7f620e7858d4dde86774e178445ce1e", "a4a2b3071f9848cbac661cea6711c16c", "728c049158374b928258a19b634e4a53", "c51521db80a94ae293521cba332ca9c2", "542c7f3126da47d3bab3d4c7e73bac46", "18e4526ceccd402e90b0ac6ddb8881c0", "59ab7653cd2e402ba5bda49256ff18ab", "22170323336c456a82f5f5c920070a19" ] } }, "id": "Il4KK8xa24Vo" }, { "cell_type": "markdown", "source": [ "This code initializes a HuggingFaceEmbeddings model using the previously defined gte-small, enabling GPU acceleration and normalization of embeddings for cosine similarity." ], "metadata": { "collapsed": false, "id": "442d2bd15969989c" }, "id": "442d2bd15969989c" }, { "cell_type": "code", "execution_count": 67, "outputs": [], "source": [ "embedding_model = HuggingFaceEmbeddings(\n", " model_name=EMBEDDING_MODEL_NAME,\n", " multi_process=True,\n", " model_kwargs={\"device\": \"cuda\"},\n", " encode_kwargs={\"normalize_embeddings\": True}, # Set `True` for cosine similarity\n", ")" ], "metadata": { "id": "f05a09837bdafb42", "ExecuteTime": { "end_time": "2025-05-28T20:04:11.347349Z", "start_time": "2025-05-28T20:04:11.338711Z" } }, "id": "f05a09837bdafb42" }, { "cell_type": "markdown", "source": [ "Now we are creating a Chroma vector store (vector_store) from the preprocessed documents, storing the embeddings in a persistent directory (db1) and configuring it to use cosine similarity for nearest neighbor search." ], "metadata": { "collapsed": false, "id": "e7f6446d052d9d2b" }, "id": "e7f6446d052d9d2b" }, { "cell_type": "code", "execution_count": 68, "outputs": [], "source": [ "vector_store = Chroma.from_documents(\n", " docs_processed,\n", " embedding_model,\n", " persist_directory=\"db5\",\n", " collection_metadata={\"hnsw:space\": \"cosine\"}\n", ")" ], "metadata": { "id": "c0340887ca644c4" }, "id": "c0340887ca644c4" }, { "cell_type": "markdown", "source": [ "This code performs a semantic search in the Chroma vector store by first embedding the query **\"What are results of European Election in 2024?\"** using the `gte-small` embedding model. It then retrieves the top 4 most similar document chunks (`k=4`) based on cosine similarity." ], "metadata": { "collapsed": false, "id": "920dc937f91ad899" }, "id": "920dc937f91ad899" }, { "cell_type": "code", "execution_count": 69, "outputs": [], "source": [ "results = vector_store.similarity_search_by_vector(\n", " embedding=embedding_model.embed_query(\"What are results of European Election in 2024?\"), k=4\n", ")" ], "metadata": { "ExecuteTime": { "end_time": "2025-05-16T14:34:51.470313Z", "start_time": "2025-05-16T14:34:42.006271Z" }, "id": "7702093525cec395" }, "id": "7702093525cec395" }, { "cell_type": "code", "source": [ "len(results)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "z5eGO2trxkh4", "outputId": "444f7e1e-2d56-4bd5-f603-dfe4f0c1eb29" }, "id": "z5eGO2trxkh4", "execution_count": 70, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "4" ] }, "metadata": {}, "execution_count": 70 } ] }, { "cell_type": "markdown", "source": [ "This code loads the **Phi-3 Mini** language model (`microsoft/Phi-3-mini-4k-instruct`) with 4-bit quantization using `BitsAndBytesConfig` to reduce memory usage while maintaining performance. It sets up the model for efficient inference (e.g., using `bfloat16` for computation) and loads the corresponding tokenizer for text processing.\n", "\n", "This code sets up a configuration for loading a machine learning model, specifically a variant of Microsoft's **Phi-3** model, using **4-bit quantization** to reduce memory usage and increase inference speed. Here's what each part does:\n", "\n", "---\n", "\n", "\n", "`BitsAndBytesConfig`\n", "\n", "This sets up **quantization** settings from the `BitsAndBytes` library, often used with Hugging Face Transformers to save memory.\n", "\n", "\n", "Explanation:\n", "\n", "* `load_in_4bit=True`: Loads the model weights in 4-bit precision (saves memory).\n", "* `bnb_4bit_use_double_quant=True`: Applies double quantization for improved accuracy.\n", "* `bnb_4bit_quant_type=\"nf4\"`: Uses \"Normal Float 4\" quantization, designed to retain performance while using fewer bits.\n", "* `bnb_4bit_compute_dtype=torch.bfloat16`: Uses the `bfloat16` format during computation, offering a good tradeoff between speed and precision on modern GPUs (like Ampere or later).\n", "\n", "---\n", "\n", "### Summary\n", "\n", "This code prepares a **memory-efficient, fast-loading configuration** for using the `Phi-3-mini-4k-instruct` language model. It’s useful when deploying models on limited hardware while trying to maintain reasonable inference accuracy." ], "metadata": { "collapsed": false, "id": "baf45e75c53786dd" }, "id": "baf45e75c53786dd" }, { "cell_type": "code", "source": [ "READER_MODEL_NAME = \"microsoft/Phi-3-mini-4k-instruct\"\n", "\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_use_double_quant=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=torch.bfloat16,\n", ")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(READER_MODEL_NAME, quantization_config=bnb_config)\n", "tokenizer = AutoTokenizer.from_pretrained(READER_MODEL_NAME)" ], "metadata": { "id": "OoA41TvYy52y", "outputId": "bd139173-ba84-4506-bb7b-aa70138d7f98", "colab": { "base_uri": "https://localhost:8080/", "height": 401, "referenced_widgets": [ "e6cdea1217f84f44b56ef84e7fac8023", "dd961ffe865248c0a228c04d5b7c1b2f", "5d51aff699bd4770ba4f073975986d9e", "ac5565b4388d426682878181f1b04fbc", "d21a040b0a994a2684950e14d408ce48", "6f3111773a6c45809c655124ff2ebc26", "e7aab4bf43eb4e9eaf25868485771fa9", "6d3294f64590433480272e3e45e3a03d", "ccd507d245ad4921948e86f428751bb7", "a97acdcf3b004869a34ffd9ae83e2c95", "71e449b46a5b44abbd7eaeef4bb44a85", "5bbf4628ec734a7299c6adbf7dd251bb", "9616a6b394cd43088a55c53deefa99e2", "7c5802e5a7a4467b988112c0435ae7cd", "617870f500054a0d85e8066172635db9", "21573e4305de422bb7add150ce8bd4b3", "d254fb5f5a53445a92857b1bec2a02c6", "e063546331cb478abe648668b84cc0b1", "f091c500b3994c7dbf50780bcd953b4b", "904776bd38324b228d52c8f45479c40c", "1d493239a5f34bd68f699f4c441e7707", "64c3dce4a5f744a99484c79d5f628b43", "3da37e8ffb4c4f34b00cbde671eeb3b3", "c1f152cd091a49f4963fc12111a20aea", "7d4aec9d2f2549239e0bab60645bf194", "f0aa1c952585491b90a0ac8c7f425473", "fb182efa1d3b405e809a30fef57d6322", "60926779104140c8b2d2acfa6b5bdb89", "e51d6b9f3e3d4b1d86ecc440cff84b3f", "f97c1f0232774867906c16c5b675f6f0", "d883eb92a53a4e39a3e6146e0b9a006b", 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"035a7789c189449c939040ab6aa09a8b", "005f41b3e6bf42f794e870231a7b795b", "115720a2e5e64dd2b5fd175505733987", "cb682046ba544f82985dc33c9c851a00", "1ff951b6e0bb44a595ac92e120710126", "69b596160fdc4bc98c4fc91ad3100e39", "965a42d567024710b0e01f90449263d8", "8ad744a383ef4eb49e341c4783309a4b", "6855baa3e0a642999a2dc62124bc2244", "5466d4e1755f402aa883a72f627b120e", "bd355ce751a744ab8f2915f3cc6c06f1", "e91d80d86efe4bceab4d76e5b9725a87", "55ab6ddb8727406bbf5e1d11a0173a58", "52e7204bc1a943c09d94ad743bdf7571", "5894c0ad356e4e9eab480823a749f985", "ef18b73513a84b0ca974301f184dff90", "539cf8218d324316b1417f6849b1de7c", "1f5041558a6945fcb47aa52b30100db1", "a96552dfcd9b4c908b7777381afb6467", "c7dc96b8110848928b45c7dfaa2e7a15", "35ae1c8f8d9f4cb58882ddf48f1327bd", "d4148ec825e14c4da323e9e3a5016e56", "6a0b4a92bc8e41cb94313861bd13ba6f", "eadeff5f87314252ae43d6baf4ba48fc", "c0e0bd8858524dfbb52856963f3a004a", "87216f537f794f1ebbb173679ada88bb", "6ca1d7d7b1f44e2595b62899ab320adf", "5af8fd48c1a8488b993b38b07a9d9d26", "1d5fdafd7cb640c6b630e39be6dae93b", "1d7cd20d4c1d41228c0113195c7cb7ab", "7df9b9fba86d447392b758c9c5d18ba4", "8e622f6025f841229181e08beed5b3f7", "b136053d8e304f9ba1b71db1241623f3", "ea97e2d10f464e45a961a04729b87317", "25b42b7eae054ef0a4707161e04af8d6", "8f916b4192f64739ba0f0c98f0560e95", "817ef62d4b934c96b74a72f6eff84ef5", "29a11dd759fe4f2fbf5b01fd3af64699", "c6daaa3b86904eda83b688d08b056db6", "38696fbdcfca49efba557d9d691970f2", "c3a68e04e61d4fd3918b3ba2101b7eaf", "fcd44542450a402fb5fd728cefe8be77", "2383a43f4fef44bc9016056a6a76886c", "e4d060634b9e4cc6ae711bbf89d23113", "142e3e7a9f2e41cb9ae861a4de85b56c", "2b1ea09203924a728578a33eedcdad29", "5ca3c5377c0c4ec0a6a2b969ac781a99" ] } }, "id": "OoA41TvYy52y", "execution_count": 35, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/967 [00:00\n", "Using the information contained in the context, give a comprehensive answer to the question. Respond only to the question asked, response should be concise and relevant to the question. If the answer cannot be deduced from the context, do not give an answer.<|end|>\n", "<|user|>\n", "Context: {context}\n", " ---\n", " Now here is the question you need to answer.\n", " Question: {question}<|end|>\n", "<|assistant|>\n" ] } ], "source": [ "RAG_PROMPT_TEMPLATE = tokenizer.apply_chat_template(\n", " prompt_in_chat_format,\n", " tokenize=False, # Return a string, not token IDs\n", " add_generation_prompt=True # Ensures model knows where to start generating\n", ")\n", "\n", "print(RAG_PROMPT_TEMPLATE)" ], "metadata": { "id": "b75345cde19b2b54", "outputId": "48df00e9-5183-4853-9b4f-948c56d45674", "colab": { "base_uri": "https://localhost:8080/" } }, "id": "b75345cde19b2b54" }, { "cell_type": "markdown", "source": [ "This code extracts the text content (`page_content`) from each document in the `results` list, then builds a single `context` string by concatenating these texts. This formatted context can then be used as input for a language model to provide answers based on the retrieved documents." ], "metadata": { "collapsed": false, "id": "8192c18183c7b6d6" }, "id": "8192c18183c7b6d6" }, { "cell_type": "code", "source": [ "retrieved_docs_text = [doc.page_content for doc in results] # We only need the text of the documents\n", "context = \"\\nExtracted documents:\\n\"\n", "context += \"\".join([f\"Document {str(i)}:::\\n\" + doc for i, doc in enumerate(retrieved_docs_text)])" ], "metadata": { "id": "iiNvjdPw1lXh" }, "id": "iiNvjdPw1lXh", "execution_count": 72, "outputs": [] }, { "cell_type": "code", "source": [ "len(context)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2E8Jx2VC3aUa", "outputId": "bf5f6bd3-d0ec-4b09-9f11-1ef67c047fc5" }, "id": "2E8Jx2VC3aUa", "execution_count": 73, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "5380" ] }, "metadata": {}, "execution_count": 73 } ] }, { "cell_type": "markdown", "source": [ "This line fills in the placeholders in the `prompt_in_chat_format` by injecting the actual question **\"What are results of European Election in 2024?\"** and the previously built `context` string containing the retrieved documents. The result is a complete prompt ready to be fed into the language model for answering based on the given context." ], "metadata": { "collapsed": false, "id": "e4c5bfd2562ef2c1" }, "id": "e4c5bfd2562ef2c1" }, { "cell_type": "code", "source": [ "final_prompt = RAG_PROMPT_TEMPLATE.format(question=\"What are results of European Election in 2024?\", context=context)" ], "metadata": { "id": "ftk0YJdk1laG" }, "id": "ftk0YJdk1laG", "execution_count": 74, "outputs": [] }, { "cell_type": "markdown", "source": [ "This code sends the fully formatted prompt (`final_prompt`) to the language model pipeline `READER_LLM` to generate a concise answer based on the provided context and question. It then extracts the generated text from the model’s output and prints the final answer." ], "metadata": { "collapsed": false, "id": "a8ad9c011cfa6885" }, "id": "a8ad9c011cfa6885" }, { "cell_type": "code", "source": [ "# Redact an answer\n", "answer = READER_LLM(final_prompt)[0][\"generated_text\"]" ], "metadata": { "id": "Ph7-sjxn1ldB" }, "id": "Ph7-sjxn1ldB", "execution_count": 75, "outputs": [] }, { "cell_type": "code", "source": [ "answer" ], "metadata": { "id": "LlIUomd7nqI2", "outputId": "487f20db-3a31-4553-c840-e02382f5cdba", "colab": { "base_uri": "https://localhost:8080/", "height": 105 } }, "id": "LlIUomd7nqI2", "execution_count": 76, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "' Results of the 2024 European Parliament election show that there were significant changes among political parties across various regions within the EU. Here’s what happened according to different sources as per your query:\\n\\n**European Parliment Document Analysis:**\\n\\nIn terms of overall seat distribution post-election, we have these numbers based on document analysis:\\n\\n- **EPP Group & Renew Europe combined** gained additional members bringing their count up to 188 out of 720 available seats. They had previously secured 187 seats prior to this particular election cycle.\\n- **Socialists & Democrats (S&D), Green/Refoundation Europapartei (GRECE)/Renew Europe alliance** maintained its stronghold but increased slightly to hold onto 136 seats compared to previous counts like 136 or even higher depending upon past cycles.\\n- **Identity and Democracy (IDEN)** saw growth due to recentralization efforts gaining them another set of votes leading to a new high of 84 seats. Their predecessor faction might have varied over years.\\n- Conversely, both **Progressive Alliance of Socialists and Democrats (S&D)** and **Group of the Progressive Alliance of Socialists and Democrats (S&D) + Federation of Liberal Democrats (EFDD)** experienced slight reductions in membership despite being prominent forces earlier. Prior figures ranged significantly, indicating fluctuations typical in such large scale events.\\n- On the opposite spectrum, **National Rally (formerly National Front)** lost ground reducing their presence considerably when juxtaposed against former tallies showing stronger standings.\\n- Meanwhile, smaller entities like **Europe Écologie Les Verts (EELV)** managed modest increases maintaining relevance amongst environmental advocates.\\n\\nAs indicated through polls conducted pre-election day, several trends emerged regarding party support levels:\\n\\n- There seems to be growing discontentment towards traditional center-left and centre-right blocs evidenced by reduced percentages relative to historical norms.\\n- Right-leaning Eurosceptic movements appeared poised to continue expanding influence contrary to moderate conservative counterparts who faced challenges retaining dominance.\\n- Environmental concerns remained'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 76 } ] }, { "cell_type": "markdown", "source": [ "Let's try to run same question, but we will change this time `chunk_size` and `chunk_overlap`" ], "metadata": { "id": "PLHWor204Ryi" }, "id": "PLHWor204Ryi" }, { "cell_type": "code", "source": [ "docs_processed = split_documents(\n", " chunk_size=200,\n", " chunk_overlap=40,\n", " knowledge_base=RAW_KNOWLEDGE_BASE,\n", " tokenizer_name=EMBEDDING_MODEL_NAME,\n", ")\n", "\n", "vector_store = Chroma.from_documents(\n", " docs_processed,\n", " embedding_model,\n", " persist_directory=\"db9\",\n", " collection_metadata={\"hnsw:space\": \"cosine\"}\n", ")\n", "\n", "results = vector_store.similarity_search_by_vector(\n", " embedding=embedding_model.embed_query(\"What are results of European Election in 2024?\"), k=4\n", ")\n", "\n", "retrieved_docs_text = [doc.page_content for doc in results] # We only need the text of the documents\n", "context = \"\\nExtracted documents:\\n\"\n", "context += \"\".join([f\"Document {str(i)}:::\\n\" + doc for i, doc in enumerate(retrieved_docs_text)])\n", "\n", "final_prompt = RAG_PROMPT_TEMPLATE.format(question=\"What are results of European Election in 2024?\", context=context)\n", "\n", "# Redact an answer\n", "answer = READER_LLM(final_prompt)[0][\"generated_text\"]" ], "metadata": { "id": "HJIKZj2Z2Ge-" }, "id": "HJIKZj2Z2Ge-", "execution_count": 77, "outputs": [] }, { "cell_type": "code", "source": [ "answer" ], "metadata": { "id": "MtOPLe0H3pDj", "outputId": "8c23e1e0-102a-4a9d-e9c4-0ecf93c0874c", "colab": { "base_uri": "https://localhost:8080/", "height": 105 } }, "id": "MtOPLe0H3pDj", "execution_count": 78, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\" In the 2024 European Parliament election, there were seven political groups that won varying numbers of seats across different EU Member States. Here are their respective seat wins according to Document 1:\\n\\nEPP Group led by Nicolas Schmit gained 188 out of 720 available seats (+1).\\nS&D Group headed by Terence Reintke secured 136 seats (+1), while its predecessor had 148 before this year’s election (-13).\\nECR Group under Bas Eickhout obtained 84 seats (+8); previously they didn't exist as a separate entity but combined forces with EFDD which now holds no additional seats compared to last term due to changes within it.\\nERGA composed of Green & Yellow Alliance members managed to win 77 seats; however, prior years saw them having significantly fewer votes leading up until recent shifts towards greater support among certain demographics or regions over others—this shift isn't quantified precisely yet without further analysis into individual country performances beyond aggregate totals provided thus far per source material given above alongwith any potential coalition agreements post-vote count finalization process following standard procedures observed historically when dealing such large scale electoral events like these occurrences every five years whereupon allotted percentages may fluctuate slightly based upon localized dynamics specific enough detail remains unavailable unless one delves deeper into granular breakdowns typically found through dedicated research platforms specializing specifically tracking realtime updates regarding each nation state involved directly correlating party performance against constituency level voting patterns etcetera if interested readers wish pursue said endeavor themselves accordingly please refer back again shortly once complete set becomes publicly accessible via official channels designated responsible entities tasked sole0rship maintaining transparency whilst ensuring accuracy remain paramount objectives upheld consistently hencefordatesince beginning establishment date established initially designed purposefully created originally intended primarily aimed toward facilitating citizens ability freely exercise right choose representative best suited representing interests personal preferences desires ultimately empowerment entire populace collective society whole civilian body overall community inclusively encompasses everyone regardless status economic social background ethnicity national origin religion gender age sex marital situation health condition disability mental illness physical impairment sexual orientation religious belief system educational attainment professional experience expertise skillset qualifications certification credentials\"" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 78 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "jXn6TZGf2NPL" }, "id": "jXn6TZGf2NPL", "execution_count": null, "outputs": [] } ], "metadata": { "kernelspec": { "name": "python3", "language": "python", "display_name": "Python 3 (ipykernel)" }, 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