{"cells":[{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":22879,"status":"ok","timestamp":1727203947022,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"},"user_tz":-60},"id":"BF4AqVh7L7af","colab":{"base_uri":"https://localhost:8080/"},"outputId":"81ec0ace-36ef-4682-af0f-eb38844ff954"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m290.2/290.2 kB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m397.0/397.0 kB\u001b[0m \u001b[31m33.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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userdata\n","\n","\n","os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n","# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangChain API Key:\")\n","os.environ[\"LANGCHAIN_API_KEY\"] = userdata.get('LANGCHAIN_API_KEY')\n","\n","os.environ[\"LANGCHAIN_PROJECT\"] = \"AIM-SDG-MidTerm - AI Safety\"\n","# os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n","os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n","\n","# os.environ[\"QDRANT_API_KEY\"] = getpass.getpass(\"Enter Your Qdrant API Key: \")\n","os.environ[\"QDRANT_API_KEY\"] = userdata.get('QDRANT_API_KEY')\n","\n","os.environ[\"QDRANT_URL\"] = userdata.get('QDRANT_URL')\n"]},{"cell_type":"markdown","metadata":{"id":"WZ-fIcQbL7ah"},"source":["# Synthetic data generation using Ragas framework"]},{"cell_type":"markdown","metadata":{"id":"ekj8y4xaL7ah"},"source":["We will generate set of synthetic data for evaluating different opetions\n","1. Evaluating Embedding model\n","2. Evaluating Chunking Strategies"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":4764,"status":"ok","timestamp":1727203970815,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"},"user_tz":-60},"id":"axerEGy0L7ai"},"outputs":[],"source":["from langchain_experimental.text_splitter import SemanticChunker\n","\n","from enum import Enum\n","from typing import List\n","from langchain_community.document_loaders import PyMuPDFLoader\n","from langchain_core.documents import Document\n","import asyncio\n","\n","class PDFLoaderWrapper():\n","    class LoaderType(str, Enum):\n","        PYMUPDF = \"pymupdf\"\n","\n","    def __init__(self, file_path: str | List[str] , loader_type: LoaderType = LoaderType.PYMUPDF):\n","        self.file_path = file_path if isinstance(file_path, list) else [file_path]\n","        self.loader_type = loader_type\n","\n","    async def aload(self) -> List[Document]:\n","        all_docs = []\n","        for file_path in self.file_path:\n","            if self.loader_type == self.LoaderType.PYMUPDF:\n","                try:\n","                    loader = PyMuPDFLoader(file_path)\n","                    docs = await loader.aload()\n","                    all_docs.extend(docs)\n","                except Exception as e:\n","                    print(f\"Error loading file {file_path}: {e}\")\n","                    continue\n","        return all_docs\n","\n","BOR_FILE_PATH = \"https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf\"\n","NIST_FILE_PATH = \"https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf\"\n","documents = [\n","    BOR_FILE_PATH,\n","    NIST_FILE_PATH\n","]\n","\n","pdf_loader = PDFLoaderWrapper(\n","    documents, PDFLoaderWrapper.LoaderType.PYMUPDF\n",")\n","documents = await pdf_loader.aload()\n","\n"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7829,"status":"ok","timestamp":1727203990782,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"},"user_tz":-60},"id":"PEemuAR2L7ai","outputId":"a698d5af-7b56-4d9c-cce4-91161e293466"},"outputs":[{"output_type":"stream","name":"stdout","text":["Packages import complete\n","Getting the Embedding model from Huggingface\n","Embedding model loaded\n","Splitting the documents into semantic chunks\n","Creating the document store for ragas and loading LLM models\n","Creating the testset generator\n"]}],"source":["from ragas.testset.generator import TestsetGenerator\n","from ragas.testset.evolutions import simple, reasoning, multi_context\n","from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n","from ragas.testset.docstore import Document, DocumentStore,InMemoryDocumentStore\n","from langchain.text_splitter import RecursiveCharacterTextSplitter\n","from langchain_huggingface import HuggingFaceEmbeddings, HuggingFacePipeline\n","from ragas.testset.extractor import KeyphraseExtractor\n","\n","print (\"Packages import complete\")\n","print (\"Getting the Embedding model from Huggingface\")\n","# Using best performing embedding model from hugging face to generate quality dataset.\n","# # Need GPU\n","# model_name = \"Snowflake/snowflake-arctic-embed-l\"\n","# embedding_model = HuggingFaceEmbeddings(model_name=model_name)\n","\n","model = \"text-embedding-3-large\"\n","dimension = 3072\n","embedding_model = OpenAIEmbeddings(model=model,dimensions=dimension)\n","print (\"Embedding model loaded\")\n","\n","print (\"Splitting the documents into semantic chunks\")\n","text_splitter = RecursiveCharacterTextSplitter(\n","    chunk_size = 1024,\n","    chunk_overlap = 100,\n","    length_function = len,\n",")\n","chunked_docs = text_splitter.split_documents(documents)\n","\n","print (\"Creating the document store for ragas and loading LLM models\")\n","generator_llm = ChatOpenAI(model=\"gpt-4o\")\n","critic_llm = ChatOpenAI(model=\"gpt-4o\")\n","\n","# keyphrase_extractor = KeyphraseExtractor(llm=generator_llm)\n","# docstore = InMemoryDocumentStore(splitter=text_splitter,extractor=keyphrase_extractor, embeddings=embedding_model)\n","\n","print (\"Creating the testset generator\")\n","generator = TestsetGenerator.from_langchain( # Default uses TokenTextSplitter\n","    generator_llm=generator_llm,\n","    critic_llm=critic_llm,\n","    embeddings=embedding_model,\n","    #docstore=docstore # Document store uses SemenaticChunker\n",")\n","\n","distributions = {\n","    simple: 0.5,\n","    multi_context: 0.3,\n","    reasoning: 0.2\n","}"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"output_embedded_package_id":"1MpNy1YyUsYoRqZwyhtp2MW69b_jqQzFH","referenced_widgets":["c44511b1233d47c9aa3f294ffe046cd6","48f3a7c91d4e4f5e8b4579bc6ddc8075","8087926c570547bf8c4fb5ccebd014ca","349178bff9c14ac2be638b8eaeaf14be","e28fe04f82e5459a868158c825914c33","e7d41e2b26d742ed9c8e516c574b7abe","99700d061e5e4c219d815e9a0b7d76b7","498c3c835eb44cfaaa0e58bd5ca9fc97","ac003e361a52420e9b368ab3a315d50d","aaa553c5eb044ab9aa26de9f0dd713ef","bc5d9989c9d4441aa62962c35fbe1bd4","e39750bad741402b95dba6e1d67f3302","f778da18131d471f9db08e0904ae4ecf","39dd92ce29084a048959c0d60220339e","d1364508a15246059d6a1982eb391081","e0dc95cc892b4e36941c877e95d11f9e","1f97c93c48434e69832f16c7a514f8c4","a4c574a957fb48d2856d77ad8adb65c9","ae61b02afc5e4d118dcacd3367b3eb41","2a38dc3e468046ed80a9037592610baa","b921d7b9dbc94524bb68a23bdda5d780","274905cf106546078f459319650f2af7"]},"executionInfo":{"elapsed":152165,"status":"ok","timestamp":1727204152630,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"},"user_tz":-60},"id":"wuCZeairL7ai","outputId":"23e49b8a-6cd9-4a9a-8313-35e43403477b"},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["test_size = 50\n","\n","testset = generator.generate_with_langchain_docs(\n","    documents,\n","    test_size,\n","    distributions,\n","    with_debugging_logs=True\n",") # Default  RunConfig(max_retries=15, max_wait=90)"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"9kLxv3hzL7ai","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1727204193820,"user_tz":-60,"elapsed":468,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"aee23829-674b-48cf-91e4-8029ccf6d75d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                             question  \\\n","0   What techniques are suggested to assess and ma...   \n","1   What actions are suggested to ensure informati...   \n","2   What are the requirements for providing access...   \n","3   What issues arose from the system awarding ben...   \n","4   How is the federal government working to comba...   \n","5   What are the characteristics of trustworthy AI...   \n","6   What are the conditions under which individual...   \n","7   What is data poisoning and how can it affect G...   \n","8   How do opaque decision-making processes in aut...   \n","9   Who participated in the OSTP meetings focused ...   \n","10  What actions are suggested for explaining and ...   \n","11  What provisions are included in the Biometric ...   \n","12  How should designers, developers, and deployer...   \n","13  How do systems related to the assignment of pe...   \n","14  What should be the focus of risk identificatio...   \n","15  What procedures should be established and main...   \n","16  How do GAI systems contribute to the creation ...   \n","17  How can synthetic content detection help manag...   \n","18  What organizational practices are in place to ...   \n","19  What techniques are suggested to minimize risk...   \n","20  Who were some of the participants from the pri...   \n","21  What role does the National Institute of Stand...   \n","22  What should entities responsible for the devel...   \n","23  How has the customer service industry successf...   \n","24  What steps should be taken to inform AI stakeh...   \n","25  How do the U.S. AI Safety Institute and AI Ris...   \n","26  How to balance synthetic vs. non-synthetic dat...   \n","27  How to address data privacy and ensure AI inte...   \n","28  How can public feedback and incident reporting...   \n","29  How could automation bias worsen misinformatio...   \n","30  How do consultations and monitoring ensure aut...   \n","31  How do EO 13960 and NIST AI RMF ensure AI tran...   \n","32  How does surveillance software for monitoring ...   \n","33  How can provenance tracking and public feedbac...   \n","34  How can designers ensure user understanding an...   \n","35  How do equity and disparity assessments help p...   \n","36  How do school audio surveillance systems for s...   \n","37  How does the tech companion help implement AI ...   \n","38  How to mitigate Human-AI risks in evaluations ...   \n","39  How to design explanatory mechanisms in high-r...   \n","40  How do biometrics affect access and decisions ...   \n","41  How do equity assessments and disparity tests ...   \n","42  How does synthetic data proportion help preven...   \n","43  How do hiring algorithms and social media data...   \n","44  How can unproven tech and data misuse cause harm?   \n","45  Why compare system performance with human meth...   \n","46             How do confident errors mislead users?   \n","47  How do data biases impact loans, hiring, and e...   \n","48  How to design systems to avoid algorithmic bia...   \n","\n","                                             contexts  \\\n","0   [ \\n30 \\nMEASURE 2.2: Evaluations involving hu...   \n","1   [ \\n28 \\nMAP 5.2: Practices and personnel for ...   \n","2   [ \\n \\n \\n \\n \\n \\nHUMAN ALTERNATIVES, \\nCONSI...   \n","3   [ \\n \\n  \\n \\n \\nNOTICE & \\nEXPLANATION \\nWHY ...   \n","4   [ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n...   \n","5   [ \\n14 \\nGOVERN 1.2: The characteristics of tr...   \n","6   [ \\nSECTION TITLE\\nHUMAN ALTERNATIVES, CONSIDE...   \n","7   [ \\n11 \\nvalue chain (e.g., data inputs, proce...   \n","8   [ \\n \\n  \\n \\n \\nNOTICE & \\nEXPLANATION \\nWHY ...   \n","9   [APPENDIX\\n• OSTP conducted meetings with a va...   \n","10  [ \\n35 \\nMEASURE 2.9: The AI model is explaine...   \n","11  [ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n...   \n","12  [ \\n \\n \\n \\n \\nSECTION TITLE\\nDATA PRIVACY\\nY...   \n","13  [APPENDIX\\nSystems that impact the safety of c...   \n","14  [ \\n \\n \\n \\n \\n \\n \\nSAFE AND EFFECTIVE \\nSYS...   \n","15  [ \\n42 \\nMG-2.4-002 \\nEstablish and maintain p...   \n","16  [ \\n10 \\nGAI systems can ease the unintentiona...   \n","17  [ \\n51 \\ngeneral public participants. For exam...   \n","18  [ \\n19 \\nGV-4.1-003 \\nEstablish policies, proc...   \n","19  [ \\n30 \\nMEASURE 2.2: Evaluations involving hu...   \n","20  [APPENDIX\\n• OSTP conducted meetings with a va...   \n","21  [ \\n \\n \\nAbout AI at NIST: The National Insti...   \n","22  [ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\nSAFE AND ...   \n","23  [ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n...   \n","24  [ \\n41 \\nMG-2.2-006 \\nUse feedback from intern...   \n","25  [ \\n \\n \\nAbout AI at NIST: The National Insti...   \n","26  [ \\n37 \\nMS-2.11-005 \\nAssess the proportion o...   \n","27  [ \\n30 \\nMEASURE 2.2: Evaluations involving hu...   \n","28  [ \\n53 \\nDocumenting, reporting, and sharing i...   \n","29  [ \\n9 \\nand reduced content diversity). Overly...   \n","30  [ \\n \\n \\n \\n \\n \\n \\nSAFE AND EFFECTIVE \\nSYS...   \n","31  [ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\nSAF...   \n","32  [ \\n \\n  \\n \\nDATA PRIVACY \\nWHY THIS PRINCIPL...   \n","33  [ \\n51 \\ngeneral public participants. For exam...   \n","34  [ \\n \\n \\n \\n \\nSECTION TITLE\\nDATA PRIVACY\\nY...   \n","35  [ \\n \\n \\n \\n \\n \\n \\nWHAT SHOULD BE EXPECTED ...   \n","36  [ \\n \\n \\n \\n \\n \\n \\nDATA PRIVACY \\nEXTRA PRO...   \n","37  [ \\n \\n \\n \\n \\n  \\n \\n \\n \\n \\n \\n \\n \\n \\n \\...   \n","38  [ \\n30 \\nMEASURE 2.2: Evaluations involving hu...   \n","39  [ \\n \\n \\n \\n \\n \\nNOTICE & \\nEXPLANATION \\nWH...   \n","40  [APPENDIX\\nSystems that impact the safety of c...   \n","41  [ ­­­­­­­\\nALGORITHMIC DISCRIMINATION Protecti...   \n","42  [ \\n37 \\nMS-2.11-005 \\nAssess the proportion o...   \n","43  [ \\nSECTION TITLE­\\nFOREWORD\\nAmong the great ...   \n","44  [ \\n \\n  \\n \\nSAFE AND EFFECTIVE \\nSYSTEMS \\nW...   \n","45  [ \\n \\n \\n \\n \\n \\n \\nSAFE AND EFFECTIVE \\nSYS...   \n","46  [ \\n4 \\n1. CBRN Information or Capabilities: E...   \n","47  [ \\n \\n  \\n \\n \\n \\n \\n \\nAlgorithmic \\nDiscri...   \n","48  [ AI BILL OF RIGHTS\\nFFECTIVE SYSTEMS\\nineffec...   \n","\n","                                         ground_truth evolution_type  \\\n","0   Techniques such as re-sampling, re-weighting, ...         simple   \n","1   The suggested actions to ensure information in...         simple   \n","2   Those impacted by an automated system should b...         simple   \n","3   Individuals were denied benefits due to data e...         simple   \n","4   The federal government is working to combat di...         simple   \n","5   The characteristics of trustworthy AI that sho...         simple   \n","6   Individuals should be able to opt out from aut...         simple   \n","7   Data poisoning is a cybersecurity risk where a...         simple   \n","8   Opaque decision-making processes in automated ...         simple   \n","9   Participants in the OSTP meetings included Ado...         simple   \n","10  Suggested actions for explaining and validatin...         simple   \n","11  The Biometric Information Privacy Act enacted ...         simple   \n","12  Designers, developers, and deployers of automa...         simple   \n","13  Systems related to the assignment of penalties...         simple   \n","14  Identified risks should focus on the potential...         simple   \n","15  Establish and maintain procedures for escalati...         simple   \n","16  GAI systems contribute to the creation of deep...         simple   \n","17  Synthetic content detection can help manage an...         simple   \n","18  Organizational practices are in place to enabl...         simple   \n","19  Techniques such as anonymization, differential...         simple   \n","20  Participants in the OSTP meetings from the pri...         simple   \n","21  The National Institute of Standards and Techno...         simple   \n","22  Entities responsible for the development or us...         simple   \n","23  The customer service industry has successfully...         simple   \n","24  Establish and maintain communication plans to ...         simple   \n","25  The U.S. AI Safety Institute and the AI Risk M...  multi_context   \n","26  To balance synthetic vs. non-synthetic data an...  multi_context   \n","27  To address data privacy and ensure AI integrit...  multi_context   \n","28  Public feedback and incident reporting can imp...  multi_context   \n","29  Automation bias can exacerbate other risks of ...  multi_context   \n","30  Consultations ensure automated systems' safety...  multi_context   \n","31  Executive Order 13960 requires that AI used by...  multi_context   \n","32  Companies use surveillance software to track e...  multi_context   \n","33  Provenance tracking and public feedback can im...  multi_context   \n","34  Designers can ensure user understanding and pr...  multi_context   \n","35  Equity and disparity assessments help prevent ...  multi_context   \n","36  School audio surveillance systems monitor stud...  multi_context   \n","37  The technical companion provides examples and ...  multi_context   \n","38  To mitigate Human-AI risks in evaluations and ...  multi_context   \n","39  In high-risk automated systems, explanatory me...  multi_context   \n","40  The answer to given question is not present in...      reasoning   \n","41  Equity assessments and disparity tests reduce ...      reasoning   \n","42  Assessing the proportion of synthetic to non-s...      reasoning   \n","43  Algorithms used in hiring and credit decisions...      reasoning   \n","44  Unproven technology and data misuse can cause ...      reasoning   \n","45  System performance should be compared with the...      reasoning   \n","46  Confident errors, also known as confabulations...      reasoning   \n","47  Data biases impact loans by causing applicants...      reasoning   \n","48  To design systems to avoid algorithmic bias an...      reasoning   \n","\n","                                             metadata  episode_done  \n","0   [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","1   [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","2   [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","3   [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","4   [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","5   [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","6   [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","7   [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","8   [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","9   [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","10  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","11  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","12  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","13  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","14  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","15  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","16  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","17  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","18  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","19  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","20  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","21  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","22  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","23  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","24  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","25  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","26  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","27  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","28  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","29  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","30  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","31  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","32  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","33  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","34  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","35  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","36  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","37  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","38  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","39  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","40  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","41  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","42  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","43  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","44  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","45  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","46  [{'source': 'https://nvlpubs.nist.gov/nistpubs...          True  \n","47  [{'source': 'https://www.whitehouse.gov/wp-con...          True  \n","48  [{'source': 'https://www.whitehouse.gov/wp-con...          True  "],"text/html":["\n","  <div id=\"df-e80390f2-afe9-4d52-bb9e-a562cd76c565\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>question</th>\n","      <th>contexts</th>\n","      <th>ground_truth</th>\n","      <th>evolution_type</th>\n","      <th>metadata</th>\n","      <th>episode_done</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>What techniques are suggested to assess and ma...</td>\n","      <td>[ \\n30 \\nMEASURE 2.2: Evaluations involving hu...</td>\n","      <td>Techniques such as re-sampling, re-weighting, ...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>What actions are suggested to ensure informati...</td>\n","      <td>[ \\n28 \\nMAP 5.2: Practices and personnel for ...</td>\n","      <td>The suggested actions to ensure information in...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>What are the requirements for providing access...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\nHUMAN ALTERNATIVES, \\nCONSI...</td>\n","      <td>Those impacted by an automated system should b...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>What issues arose from the system awarding ben...</td>\n","      <td>[ \\n \\n  \\n \\n \\nNOTICE &amp; \\nEXPLANATION \\nWHY ...</td>\n","      <td>Individuals were denied benefits due to data e...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>How is the federal government working to comba...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n...</td>\n","      <td>The federal government is working to combat di...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>What are the characteristics of trustworthy AI...</td>\n","      <td>[ \\n14 \\nGOVERN 1.2: The characteristics of tr...</td>\n","      <td>The characteristics of trustworthy AI that sho...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>What are the conditions under which individual...</td>\n","      <td>[ \\nSECTION TITLE\\nHUMAN ALTERNATIVES, CONSIDE...</td>\n","      <td>Individuals should be able to opt out from aut...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>What is data poisoning and how can it affect G...</td>\n","      <td>[ \\n11 \\nvalue chain (e.g., data inputs, proce...</td>\n","      <td>Data poisoning is a cybersecurity risk where a...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>How do opaque decision-making processes in aut...</td>\n","      <td>[ \\n \\n  \\n \\n \\nNOTICE &amp; \\nEXPLANATION \\nWHY ...</td>\n","      <td>Opaque decision-making processes in automated ...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Who participated in the OSTP meetings focused ...</td>\n","      <td>[APPENDIX\\n• OSTP conducted meetings with a va...</td>\n","      <td>Participants in the OSTP meetings included Ado...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>What actions are suggested for explaining and ...</td>\n","      <td>[ \\n35 \\nMEASURE 2.9: The AI model is explaine...</td>\n","      <td>Suggested actions for explaining and validatin...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>What provisions are included in the Biometric ...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n...</td>\n","      <td>The Biometric Information Privacy Act enacted ...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>How should designers, developers, and deployer...</td>\n","      <td>[ \\n \\n \\n \\n \\nSECTION TITLE\\nDATA PRIVACY\\nY...</td>\n","      <td>Designers, developers, and deployers of automa...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>How do systems related to the assignment of pe...</td>\n","      <td>[APPENDIX\\nSystems that impact the safety of c...</td>\n","      <td>Systems related to the assignment of penalties...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>What should be the focus of risk identificatio...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\nSAFE AND EFFECTIVE \\nSYS...</td>\n","      <td>Identified risks should focus on the potential...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>What procedures should be established and main...</td>\n","      <td>[ \\n42 \\nMG-2.4-002 \\nEstablish and maintain p...</td>\n","      <td>Establish and maintain procedures for escalati...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>How do GAI systems contribute to the creation ...</td>\n","      <td>[ \\n10 \\nGAI systems can ease the unintentiona...</td>\n","      <td>GAI systems contribute to the creation of deep...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>How can synthetic content detection help manag...</td>\n","      <td>[ \\n51 \\ngeneral public participants. For exam...</td>\n","      <td>Synthetic content detection can help manage an...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>What organizational practices are in place to ...</td>\n","      <td>[ \\n19 \\nGV-4.1-003 \\nEstablish policies, proc...</td>\n","      <td>Organizational practices are in place to enabl...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>What techniques are suggested to minimize risk...</td>\n","      <td>[ \\n30 \\nMEASURE 2.2: Evaluations involving hu...</td>\n","      <td>Techniques such as anonymization, differential...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Who were some of the participants from the pri...</td>\n","      <td>[APPENDIX\\n• OSTP conducted meetings with a va...</td>\n","      <td>Participants in the OSTP meetings from the pri...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>What role does the National Institute of Stand...</td>\n","      <td>[ \\n \\n \\nAbout AI at NIST: The National Insti...</td>\n","      <td>The National Institute of Standards and Techno...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>What should entities responsible for the devel...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\nSAFE AND ...</td>\n","      <td>Entities responsible for the development or us...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>How has the customer service industry successf...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n...</td>\n","      <td>The customer service industry has successfully...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>What steps should be taken to inform AI stakeh...</td>\n","      <td>[ \\n41 \\nMG-2.2-006 \\nUse feedback from intern...</td>\n","      <td>Establish and maintain communication plans to ...</td>\n","      <td>simple</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>How do the U.S. AI Safety Institute and AI Ris...</td>\n","      <td>[ \\n \\n \\nAbout AI at NIST: The National Insti...</td>\n","      <td>The U.S. AI Safety Institute and the AI Risk M...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>How to balance synthetic vs. non-synthetic dat...</td>\n","      <td>[ \\n37 \\nMS-2.11-005 \\nAssess the proportion o...</td>\n","      <td>To balance synthetic vs. non-synthetic data an...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>How to address data privacy and ensure AI inte...</td>\n","      <td>[ \\n30 \\nMEASURE 2.2: Evaluations involving hu...</td>\n","      <td>To address data privacy and ensure AI integrit...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>How can public feedback and incident reporting...</td>\n","      <td>[ \\n53 \\nDocumenting, reporting, and sharing i...</td>\n","      <td>Public feedback and incident reporting can imp...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>How could automation bias worsen misinformatio...</td>\n","      <td>[ \\n9 \\nand reduced content diversity). Overly...</td>\n","      <td>Automation bias can exacerbate other risks of ...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>How do consultations and monitoring ensure aut...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\nSAFE AND EFFECTIVE \\nSYS...</td>\n","      <td>Consultations ensure automated systems' safety...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>How do EO 13960 and NIST AI RMF ensure AI tran...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\nSAF...</td>\n","      <td>Executive Order 13960 requires that AI used by...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>How does surveillance software for monitoring ...</td>\n","      <td>[ \\n \\n  \\n \\nDATA PRIVACY \\nWHY THIS PRINCIPL...</td>\n","      <td>Companies use surveillance software to track e...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>How can provenance tracking and public feedbac...</td>\n","      <td>[ \\n51 \\ngeneral public participants. For exam...</td>\n","      <td>Provenance tracking and public feedback can im...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>How can designers ensure user understanding an...</td>\n","      <td>[ \\n \\n \\n \\n \\nSECTION TITLE\\nDATA PRIVACY\\nY...</td>\n","      <td>Designers can ensure user understanding and pr...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>How do equity and disparity assessments help p...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\nWHAT SHOULD BE EXPECTED ...</td>\n","      <td>Equity and disparity assessments help prevent ...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>How do school audio surveillance systems for s...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\nDATA PRIVACY \\nEXTRA PRO...</td>\n","      <td>School audio surveillance systems monitor stud...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>How does the tech companion help implement AI ...</td>\n","      <td>[ \\n \\n \\n \\n \\n  \\n \\n \\n \\n \\n \\n \\n \\n \\n \\...</td>\n","      <td>The technical companion provides examples and ...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>How to mitigate Human-AI risks in evaluations ...</td>\n","      <td>[ \\n30 \\nMEASURE 2.2: Evaluations involving hu...</td>\n","      <td>To mitigate Human-AI risks in evaluations and ...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>How to design explanatory mechanisms in high-r...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\nNOTICE &amp; \\nEXPLANATION \\nWH...</td>\n","      <td>In high-risk automated systems, explanatory me...</td>\n","      <td>multi_context</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>How do biometrics affect access and decisions ...</td>\n","      <td>[APPENDIX\\nSystems that impact the safety of c...</td>\n","      <td>The answer to given question is not present in...</td>\n","      <td>reasoning</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>How do equity assessments and disparity tests ...</td>\n","      <td>[ ­­­­­­­\\nALGORITHMIC DISCRIMINATION Protecti...</td>\n","      <td>Equity assessments and disparity tests reduce ...</td>\n","      <td>reasoning</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>How does synthetic data proportion help preven...</td>\n","      <td>[ \\n37 \\nMS-2.11-005 \\nAssess the proportion o...</td>\n","      <td>Assessing the proportion of synthetic to non-s...</td>\n","      <td>reasoning</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>How do hiring algorithms and social media data...</td>\n","      <td>[ \\nSECTION TITLE­\\nFOREWORD\\nAmong the great ...</td>\n","      <td>Algorithms used in hiring and credit decisions...</td>\n","      <td>reasoning</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>How can unproven tech and data misuse cause harm?</td>\n","      <td>[ \\n \\n  \\n \\nSAFE AND EFFECTIVE \\nSYSTEMS \\nW...</td>\n","      <td>Unproven technology and data misuse can cause ...</td>\n","      <td>reasoning</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>Why compare system performance with human meth...</td>\n","      <td>[ \\n \\n \\n \\n \\n \\n \\nSAFE AND EFFECTIVE \\nSYS...</td>\n","      <td>System performance should be compared with the...</td>\n","      <td>reasoning</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>How do confident errors mislead users?</td>\n","      <td>[ \\n4 \\n1. CBRN Information or Capabilities: E...</td>\n","      <td>Confident errors, also known as confabulations...</td>\n","      <td>reasoning</td>\n","      <td>[{'source': 'https://nvlpubs.nist.gov/nistpubs...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>How do data biases impact loans, hiring, and e...</td>\n","      <td>[ \\n \\n  \\n \\n \\n \\n \\n \\nAlgorithmic \\nDiscri...</td>\n","      <td>Data biases impact loans by causing applicants...</td>\n","      <td>reasoning</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>How to design systems to avoid algorithmic bia...</td>\n","      <td>[ AI BILL OF RIGHTS\\nFFECTIVE SYSTEMS\\nineffec...</td>\n","      <td>To design systems to avoid algorithmic bias an...</td>\n","      <td>reasoning</td>\n","      <td>[{'source': 'https://www.whitehouse.gov/wp-con...</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e80390f2-afe9-4d52-bb9e-a562cd76c565')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-e80390f2-afe9-4d52-bb9e-a562cd76c565 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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In hiring, a tool that learned features from a company's predominantly male employees rejected women applicants for discriminatory reasons, penalizing resumes with the word 'women\\u2019s.' In education, a predictive model used by universities to predict student dropout rates used race directly as a predictor, resulting in Black students being deemed at higher risk of dropping out compared to their white peers, which could guide them away from certain majors.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"evolution_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"simple\",\n          \"multi_context\",\n          \"reasoning\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"metadata\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"episode_done\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":7}],"source":["testset_df = testset.to_pandas()\n","testset_df"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"hgtowo7NL7ai","executionInfo":{"status":"ok","timestamp":1727204204418,"user_tz":-60,"elapsed":631,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"outputs":[],"source":["testset_df.to_csv('task5-ai-safety-sdg3.csv', index=False)\n","test_questions = testset_df[\"question\"].values.tolist()\n","test_groundtruths = testset_df[\"ground_truth\"].values.tolist()"]},{"cell_type":"markdown","metadata":{"id":"0r7MJagKL7aj"},"source":["## Create Rag chain to generate answers for above questions in the dataset"]},{"cell_type":"markdown","metadata":{"id":"_RvccDwEL7aj"},"source":["> Note that we are usig Qdrant cloud where the pdf document is processed and saved for us to consume. For the RAG pipeline we use the same embedding model originally used to populate the Qdrant vectorstore."]},{"cell_type":"code","source":["len(chunked_docs)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qB-GRnyjTt9Z","executionInfo":{"status":"ok","timestamp":1727204213039,"user_tz":-60,"elapsed":2,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"68a69e5c-bc04-4bce-d548-5ab8618ac27d"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["466"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","execution_count":10,"metadata":{"id":"54pN9_FdL7aj","executionInfo":{"status":"ok","timestamp":1727204219211,"user_tz":-60,"elapsed":2343,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"outputs":[],"source":["from langchain_qdrant import QdrantVectorStore\n","from langchain_core.documents import Document\n","from qdrant_client import QdrantClient\n","from qdrant_client.http.models import Distance, VectorParams\n","\n","chunked_docs\n","model = \"text-embedding-3-large\"\n","dimension = 3072\n","embeddings = OpenAIEmbeddings(model=model,dimensions=dimension)\n","\n","qdrant_client = QdrantClient(location=\":memory:\")\n","qdrant_client.create_collection(\n","    collection_name='sdg-answers',\n","    vectors_config=VectorParams(size=dimension, distance=Distance.COSINE),\n",")\n","\n","vector_store = QdrantVectorStore(\n","    client=qdrant_client,\n","    collection_name='sdg-answers',\n","    embedding=embeddings,\n",")\n","\n","retriever = vector_store.as_retriever(search_kwargs={'k':10})"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"i-NT6G6cL7aj","executionInfo":{"status":"ok","timestamp":1727204226969,"user_tz":-60,"elapsed":486,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"outputs":[],"source":["from langchain.prompts import ChatPromptTemplate\n","\n","RAG_PROMPT = \"\"\"\\\n","Given a provided context and question, you must answer the question based only on context.\n","\n","If you cannot answer the question based on the context - you must say \"I don't know\".\n","\n","Context: {context}\n","Question: {question}\n","\"\"\"\n","\n","rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"tCwt2pjLL7aj","executionInfo":{"status":"ok","timestamp":1727204230719,"user_tz":-60,"elapsed":475,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"outputs":[],"source":["from langchain_openai import ChatOpenAI\n","\n","# Using the same model used in the app.\n","chat_model_name = \"gpt-4o\"\n","llm = ChatOpenAI(model=chat_model_name)"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"wwsGcWogL7aj","executionInfo":{"status":"ok","timestamp":1727204234204,"user_tz":-60,"elapsed":617,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"outputs":[],"source":["from operator import itemgetter\n","from langchain_core.runnables import RunnablePassthrough, RunnableParallel\n","from langchain.schema import StrOutputParser\n","\n","rag_chain_generate_anwsers = (\n","    {\"context\": itemgetter(\"question\") | retriever, \"question\": itemgetter(\"question\")}\n","    | rag_prompt | llm | StrOutputParser()\n",")"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"DvTHB2OuL7aj","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1727204241469,"user_tz":-60,"elapsed":3901,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"508bc18c-1fef-481d-d3ab-ac4bbc6a5fa9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"I don't know.\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":14}],"source":["rag_chain_generate_anwsers.invoke({\"question\" : \"What procedures should be established and maintained for escalating GAI system incidents to the organizational risk management authority?\"})"]},{"cell_type":"markdown","metadata":{"id":"XLbVBQVmL7aj"},"source":["# Create Rag Chain with config"]},{"cell_type":"markdown","metadata":{"id":"AOYrCWkWL7aj"},"source":["We are going to replicate the exact implementation used in the hosted RAG app but with different configuration to evaluate and compare."]},{"cell_type":"code","execution_count":15,"metadata":{"id":"GlqLvdfgL7aj","executionInfo":{"status":"ok","timestamp":1727204252175,"user_tz":-60,"elapsed":3838,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"outputs":[],"source":["# Utility function to create a rag chain with config\n","from langchain_experimental.text_splitter import SemanticChunker\n","from enum import Enum\n","from typing import List\n","from langchain_community.document_loaders import PyMuPDFLoader\n","from langchain_core.documents import Document\n","import asyncio\n","from langchain_qdrant import QdrantVectorStore\n","from langchain_core.documents import Document\n","from qdrant_client import QdrantClient\n","from qdrant_client.http.models import Distance, VectorParams\n","from langchain.retrievers.contextual_compression import ContextualCompressionRetriever\n","from langchain.retrievers.document_compressors import LLMChainExtractor\n","from langchain_openai import ChatOpenAI\n","from langchain.prompts import ChatPromptTemplate\n","from operator import itemgetter\n","from langchain_core.runnables import RunnablePassthrough, RunnableParallel\n","from langchain.schema import StrOutputParser\n","from langchain.chains.combine_documents import create_stuff_documents_chain\n","from langchain.prompts import MessagesPlaceholder\n","from langchain.prompts import ChatPromptTemplate\n","from langchain.chains.history_aware_retriever import create_history_aware_retriever\n","from langchain.chains.retrieval import create_retrieval_chain\n","from langchain_core.runnables.history import RunnableWithMessageHistory\n","from langchain_core.chat_history import BaseChatMessageHistory\n","from langchain_community.chat_message_histories import ChatMessageHistory\n","\n","BOR_FILE_PATH = \"https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf\"\n","NIST_FILE_PATH = \"https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf\"\n","documents_to_preload = [\n","    BOR_FILE_PATH,\n","    NIST_FILE_PATH\n","]\n","store = {}\n","\n","class PDFLoaderWrapper():\n","    class LoaderType(str, Enum):\n","        PYMUPDF = \"pymupdf\"\n","\n","    def __init__(self, file_path: str | List[str] , loader_type: LoaderType = LoaderType.PYMUPDF):\n","        self.file_path = file_path if isinstance(file_path, list) else [file_path]\n","        self.loader_type = loader_type\n","\n","    async def aload(self) -> List[Document]:\n","        all_docs = []\n","        for file_path in self.file_path:\n","            if self.loader_type == self.LoaderType.PYMUPDF:\n","                try:\n","                    loader = PyMuPDFLoader(file_path)\n","                    docs = await loader.aload()\n","                    all_docs.extend(docs)\n","                except Exception as e:\n","                    print(f\"Error loading file {file_path}: {e}\")\n","                    continue\n","        return all_docs\n","\n","async def get_contextual_compressed_retriever(retriver):\n","\n","    compressor_llm = ChatOpenAI(temperature=0, model_name=\"gpt-4o\", max_tokens=1500)\n","    compressor = LLMChainExtractor.from_llm(compressor_llm)\n","\n","    #Combine the retriever with the compressor\n","    compression_retriever = ContextualCompressionRetriever(\n","        base_compressor=compressor,\n","        base_retriever=retriver\n","    )\n","    return compression_retriever\n","\n","def create_history_aware_retriever_self(chat_model, retriever):\n","    contextualize_q_system_prompt = (\n","        \"Given a chat history and the latest user question which might reference context in the chat history, \"\n","        \"formulate a standalone question which can be understood without the chat history. Do NOT answer the question, \"\n","        \"just reformulate it if needed and otherwise return it as is.\"\n","    )\n","    contextualize_q_prompt = ChatPromptTemplate.from_messages(\n","        [\n","            (\"system\", contextualize_q_system_prompt),\n","            MessagesPlaceholder(\"chat_history\"),\n","            (\"human\", \"{input}\"),\n","        ]\n","    )\n","    return create_history_aware_retriever(chat_model, retriever, contextualize_q_prompt)\n","\n","def create_qa_chain(chat_model):\n","    qa_system_prompt = (\n","        \"You are an helpful assistant named 'Shield' and your task is to answer any questions related to AI Safety for the given context.\"\n","        \"Use the following pieces of retrieved context to answer the question.\"\n","        # \"If any questions asked outside AI Safety context, just say that you are a specialist in AI Safety and can't answer that.\"\n","        # f\"When introducing you, just say that you are an AI assistant powered by embedding model {embedding_model_name} and chat model {chat_model_name} and your knowledge is limited to 'Blueprint for an AI Bill of Rights' and 'NIST AI Standards' documents.\"\n","        \"If you don't know the answer, just say that you don't know.\\n\\n\"\n","        \"{context}\"\n","    )\n","    qa_prompt = ChatPromptTemplate.from_messages(\n","        [\n","            (\"system\", qa_system_prompt),\n","            MessagesPlaceholder(\"chat_history\"),\n","            (\"human\", \"{input}\"),\n","        ]\n","    )\n","    return create_stuff_documents_chain(chat_model, qa_prompt)\n","\n","def create_conversational_rag_chain(chat_model, retriever):\n","    history_aware_retriever = create_history_aware_retriever_self(chat_model, retriever)\n","    question_answer_chain = create_qa_chain(chat_model)\n","    return create_retrieval_chain(history_aware_retriever, question_answer_chain)\n","\n","def get_session_history(session_id: str) -> BaseChatMessageHistory:\n","    if session_id not in store:\n","        store[session_id] = ChatMessageHistory()\n","    return store[session_id]\n","\n","\n","pdf_loader = PDFLoaderWrapper(\n","    documents_to_preload, PDFLoaderWrapper.LoaderType.PYMUPDF\n",")\n","documents = await pdf_loader.aload()\n","\n","async def get_contextual_compressed_retriever(retriver):\n","\n","    base_retriever = retriver\n","    compressor_llm = ChatOpenAI(temperature=0, model_name=\"gpt-4o\", max_tokens=4000)\n","    compressor = LLMChainExtractor.from_llm(compressor_llm)\n","\n","    #Combine the retriever with the compressor\n","    compression_retriever = ContextualCompressionRetriever(\n","        base_compressor=compressor,\n","        base_retriever=base_retriever\n","    )\n","    return compression_retriever\n","\n","async def create_rag_chain(huggingface_embedding,text_splitter,collection_name,):\n","\n","    chunked_docs = text_splitter.split_documents(documents)\n","    dimension = 1024\n","    qdrant_client = QdrantClient(location=\":memory:\")\n","\n","    # Below fails if collection already exists so make sure to delete the collection first\n","    qdrant_client.create_collection(\n","        collection_name=collection_name,\n","        vectors_config=VectorParams(size=dimension, distance=Distance.COSINE),\n","    )\n","\n","    vector_store = QdrantVectorStore(\n","        client=qdrant_client,\n","        collection_name=collection_name,\n","        embedding=huggingface_embedding,\n","    )\n","    vector_store.add_documents(chunked_docs)\n","\n","    retriever = vector_store.as_retriever(search_kwargs={'k':10})\n","\n","     # Using the same model used in the app.\n","    chat_model_name = \"gpt-4o\"\n","    llm = ChatOpenAI(model=chat_model_name,temperature=0)\n","    RAG_PROMPT = \"\"\"\\\n","    Given a provided context and question, you must answer the question based only on context.\n","\n","    If you cannot answer the question based on the context - you must say \"I don't know\".\n","\n","    Context: {context}\n","    Question: {question}\n","    \"\"\"\n","\n","    rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)\n","    ai_safety_rag_chain = (\n","      {\"context\": itemgetter(\"question\") | retriever, \"question\": itemgetter(\"question\")}\n","      | RunnablePassthrough.assign(context=itemgetter(\"context\"))\n","      | {\"response\": rag_prompt | llm | StrOutputParser(), \"context\": itemgetter(\"context\")}\n","    )\n","    return ai_safety_rag_chain, retriever\n","\n"]},{"cell_type":"markdown","metadata":{"id":"6qjrgNA0L7ak"},"source":["# RAGAS Evaluation for Embedding Model"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"ek4PlaOrL7ak","colab":{"base_uri":"https://localhost:8080/","height":721,"referenced_widgets":["acc378e22b9c41c7aac97fbdafa6c5f8","91f5aad3c506446098481ed123583952","f5f7ca3ae4334ba6845aaebb6e6ef4f2","10f8cbddf1e2466cbb4e394634e0b0d2","426bd8f8107445ba8e1b086a8570100e","40d14c028ab246e8b399ca86b6642433","af22d37b9ce94ff2a187ad8c0077b5e4","bb2e260555b7452baa929dc2ec09d1f2","1d7675c29cd542af8ec5725d7eba916c","ec15e5265a5d475c86ca0fc9a518351c","18239c9c3c7e4e97a3d4f2f8a8441fe8","622615a518eb452ba8fdb6758f432b35","5ad78bc865d94857bf24b083eac5e46c","a01452341af741e5ba9ac7b55d21a78c","068fbf6e595d4332961d1f910054b215","5da5381f972e47c495e283869c0e4bc3","677f839fc04a448bb76cf3abf52ea84b","e9ee57bd83c84a828f8c881f1f3ac8c3","4b7f36d001db46c0bbf565002234ab18","3fb6a4d04fcd4c399b0790bc4eb5712c","0a7dcf6447af4aa783db37b48c84b688","f15e6c12ed1a4478a8f1f97970671c65","cf284052a9b94efda4812274812ee059","8473636ca7d141209699dced51d1c981","76fa9b2bccce477cb698af80e4119aa8","91a2f1a67ab84dbbabee2ce0960d3fc6","f26618a6c2ea470b91e9cfd1fec0c01a","b7fe3cc89b2745b6a0dcee1b0cb23fa7","5ed8c96b094e4aa58397d5e030369361","ac643757d51b4611b46e4536ca900589","d65b5b5430e64f08839842e22b10b4bb","a0ecc063893d4505b33e57d524be5af3","6c67aeca324f4e508258a90625987463","f2217e3c09a146a6a62240003634ea53","af3271f8fc124662a7bd606b837d10fe","8683275138a24b5898928139657d7637","2d927689334a4e4eaa80a493ae21ceaa","d573d26f7daa4ef78a5ee5305745a751","16d2867d3c494b45b0fcf21f9908faee","77f9bc5aa4aa47528fe74cee979f5924","60feadb9a1df4f719b8996d4d1fc5d84","09615354f8c04e8c99af3e74a107b0c5","8da9ce52896a403d96ced1d8b68f62b9","aae33dec48324052ad5c2dfbd819eb59","d282c878a3744bf1af2108bf156fa39d","e36e1181ba1f40f792c3784e361ee0b2","1255b07dc7824afcae083741dbfe518a","462a2e70d3ec4b1187c55fbeb343d63b","01ba6d708ebd4ad2bfd914b4d41d36cd","58c9073769ed406b881f143bc39ab9f9","3e5e6086fe324208a9fc1ad99b66b873","dbd49d199d444879801506c4b36ad8b7","d70432c444d141f8a59481a9e689bae8","8f1157e0f6bb47d4afda1d6a2ecb7260","f577c00780664959beba245bd0cb5be5","23907f6347ff467fa190e55d67c7be86","65965fe3dd844bccbfe7a3ca80b7671c","eba60bb33c144bf5adcbbe837670b2a2","9304f1069160432d8db0423e3ac53e47","02b7a7cdd9c24d78bd14e30aa46180ba","5fc01868d62140ff81137ab03b62babd","eebeadafb2b14b3b866a9de4b7bf8787","1c9b2a3a1ff6489f8ba430d3b55980ab","80c650923aa04781ae230ca1cf2c734f","b035a788ccda4cccb4876a94d69c7196","68c3d2518f7a478b99055d25a5e590d6","138fc40abc4f4d56a71124d6b0edbc66","cb4523a6c5354a37b73e2378265ba421","d61629e8381e46648604ebf3617cc738","1541ba898c504ea9a31803715657757d","7fac3018a8ea46aabbeaa7638cf0955b","072dcba6721048c0b5f9b3118a2b60bc","cd339b2be7134142bcb2e171f1b674fa","54beb492d35547c8b982846c30b83ccc","4d24e296a067445c81f1d8822090d94e","dfa0976ca21c441faa95c120df38a372","9e42f65225e64de08a5814f3e6e0c08a","a8a539cd3d064bf0b872bd661bd04ea7","cfe981a2a1a54ded9b678e85fe46ac18","9ce6f4aacdfe451f9b41ff79b6ca99f8","4a846afcfdd84c9db79d7e974376bcf8","5711b30ecf6d4b4e9e1f4bcf758ad599","34a02f0aecd9493b9d84d5b6856d68a3","099db16d5ba44a79962227a71f39615a","3a69155072db491a804f1ae492faf2fb","7770223b77ae4877a3a731fe0c5839c3","ef60881d1aa14b8fb568d02684501344","4bd41ff686884594b2957a1d9f967a04","4226ecb991fe4142b7e66e29dd72302b","d3129da0803f407aaffee2458c6c3986","23b5503757d34aa2a65f7c7ceb6f45d2","bc0c58360bab4c389a582181555a374d","2185240d63f34b928002fd78e4efea64","40505f46820948efb2f8bde65dc068fc","13c244d566494863a6a0854f8ffe0616","a26de5f36a884773902dff24b0675ce3","695bd216987c404b80605d4cfad86136","186bebdb4f504be7a07f6cbfb1eb50ad","34087c9db2534ff1b0ca1e20dd9bcb8e","ffe5a0b43f3840a6b20348d35b60dd56","1e74c5f0193a4e488c0e168299c7b566","374aa0f623334dac8267aa4a89e54dac","0b9cb2ad73e3455cacec0bf089316328","805ce4d268aa4c82acc08c2c6c33addf","71a8bcf1281b4936b575e40d725a3a39","0046ba366a0d4850927a74a5ec1f813e","33769cc3cc024647a3b5c6f7c9a3ed2a","007221a90c0440f385117f5a76f21fc6","28988e76f4024aaaa680944513eea9d1","dc15c9f9a8bd4630820285f22b33c483","a737433e6b4a4e35bf9eb70cd1052124","9953739863c94a71a14aaff9213b57ed","2c50ded119dc4707adfee868d167084c","67a4a60f751646b6ab4b6247ded3f9e3","1d5756bf10ed4dffba350b3685662cf9","5fdb555a5e804b73b57cd284c0c58a8b","baaf29de2d51467c9026e5b063dcb491","320b9bcafd084ef79b95610851768769","c2fc5e9ca17547acbf113751c5f14a43","bd0d0e3384724a5e8e624bb096b89221","bd9991542ffb49d5bed76e31f99617f0","8eca00b2b4d64ad78dbaba001f843eef","a5cb78a70b344104930f388d1265d82a","43fe25dab4dd4639b862e80c96e617c9","c6852309e4d94231a95bf65d9458d44c","712cdd608a094e5f9ca67c3f26630638","2a3dfa3ed5e047b1862d92de2edb9e0a","aca246802e5a43a8bd69fc9ebab2c2d5","4150ffdda851437aa4628c319df29b20","01e3d2f5cf42437699f69141c55c6bf5","aa53805767f5476d9df83a3618d70416","8f666ad87d634b42838e6eef1497b567","d473b2536bc742d28400c18d5773c945","7185bb94734e439c97a70c03f655a119","9eba6f0fad0e49f8aae4128a3dabada4","d9e4811fc42f494cac21ea50f4957584","174e72fa89e3479bb392a273903e7fc1","c1fc1b64a2b84d2ca6b4f8c085a16efc","c80b0a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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"724e460c24424e018687507ddfa0a6f2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.38k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cbe674782efb419aa4cd080b38fd0bc1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"668a151f91694ed8bd73daf3ef2df17e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/712k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a30b48c740fc4174a8ef2f8099a4232f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/695 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"260d31349c0648bea6a7ff6b44234715"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["1_Pooling/config.json:   0%|          | 0.00/297 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"35dfb1d98e884af0922df7670789df0d"}},"metadata":{}}],"source":["from langchain.text_splitter import RecursiveCharacterTextSplitter\n","\n","# Load the embedding models\n","base_embedding_model = HuggingFaceEmbeddings(model_name=\"Snowflake/snowflake-arctic-embed-l\")\n","fine_tuned_embedding_model = HuggingFaceEmbeddings(model_name=\"jeevanions/finetuned_arctic-embedd-l\")\n","\n","# Common splitter to keep variables minimum\n","recursive_text_splitter = RecursiveCharacterTextSplitter(\n","    chunk_size = 1024,\n","    chunk_overlap = 100,\n","    length_function = len,\n",")\n","recursive_chunked_docs = recursive_text_splitter.split_documents(documents)\n","\n","# Create two rag chaings with different embeddings\n","rag_chain_base, retriever_base = await create_rag_chain(\n","    base_embedding_model,\n","    recursive_text_splitter,\n","    \"snowflake-arctic-embed-l-recursive-base\"\n",")\n","\n","rag_chain_fine_tuned, retriever_fine_tuned = await create_rag_chain(\n","    fine_tuned_embedding_model,\n","    recursive_text_splitter,\n","    \"snowflake-arctic-embed-l-recursive-ft\"\n",")\n"]},{"cell_type":"code","source":["# Adding utility function to run evaluation\n","from datasets import Dataset\n","from ragas import evaluate\n","from ragas.metrics import (\n","    faithfulness,\n","    answer_relevancy,\n","    answer_correctness,\n","    context_recall,\n","    context_precision,\n",")\n","\n","metrics = [\n","    faithfulness,\n","    answer_relevancy,\n","    context_recall,\n","    context_precision,\n","    answer_correctness,\n","]\n","\n","\n","def ragas_evaluate(chain):\n","  answers = []\n","  contexts = []\n","\n","  for question in test_questions:\n","    response = chain.invoke({\"question\" : question})\n","    answers.append(response[\"response\"])\n","    contexts.append([context.page_content for context in response[\"context\"]])\n","\n","  response_dataset = Dataset.from_dict({\n","      \"question\" : test_questions,\n","      \"answer\" : answers,\n","      \"contexts\" : contexts,\n","      \"ground_truth\" : test_groundtruths\n","  })\n","\n","  eval_results = evaluate(response_dataset, metrics)\n","\n","  eval_results_df = eval_results.to_pandas()\n","  return eval_results_df"],"metadata":{"id":"e71qn1iGbpKK","executionInfo":{"status":"ok","timestamp":1727205808795,"user_tz":-60,"elapsed":480,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","execution_count":17,"metadata":{"id":"oiNTkoUCL7ak","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["c5aa90875bf34e34a66c5c9ba54c218c","8c5edff941bc43688ade081fc764babb","dbdc3f8aa6fa418c9729b314a891f586","59bb0f6f4fc943d881fb39ccfb2747b9","79780f370f0d49d2a8d4f5f276a1e842","912b3dd47fc14078b4c877289eeffdc8","74ea00a4ad824d6581fab9afdc6816e2","8a14fd0353884af3b76506bf4988f150","0e1037ee7fa74f9896f56986824d8106","ff8f58e7006a494aa25a2efd28bb393e","3bd620f9085a4a8093201c481014af50"]},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1727204669049,"user_tz":-60,"elapsed":278959,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"15ad1dc8-a8ba-43ee-d7b4-26664ad9e0a1"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Evaluating:   0%|          | 0/245 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c5aa90875bf34e34a66c5c9ba54c218c"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:ragas.metrics._faithfulness:No statements were generated from the answer.\n","/usr/local/lib/python3.10/dist-packages/datasets/table.py:1395: FutureWarning: promote has been superseded by mode='default'.\n","  block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]\n","/usr/local/lib/python3.10/dist-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by mode='default'.\n","  table = cls._concat_blocks(blocks, axis=0)\n"]},{"output_type":"execute_result","data":{"text/plain":["                                             question  \\\n","0   What techniques are suggested to assess and ma...   \n","1   What actions are suggested to ensure informati...   \n","2   What are the requirements for providing access...   \n","3   What issues arose from the system awarding ben...   \n","4   How is the federal government working to comba...   \n","5   What are the characteristics of trustworthy AI...   \n","6   What are the conditions under which individual...   \n","7   What is data poisoning and how can it affect G...   \n","8   How do opaque decision-making processes in aut...   \n","9   Who participated in the OSTP meetings focused ...   \n","10  What actions are suggested for explaining and ...   \n","11  What provisions are included in the Biometric ...   \n","12  How should designers, developers, and deployer...   \n","13  How do systems related to the assignment of pe...   \n","14  What should be the focus of risk identificatio...   \n","15  What procedures should be established and main...   \n","16  How do GAI systems contribute to the creation ...   \n","17  How can synthetic content detection help manag...   \n","18  What organizational practices are in place to ...   \n","19  What techniques are suggested to minimize risk...   \n","20  Who were some of the participants from the pri...   \n","21  What role does the National Institute of Stand...   \n","22  What should entities responsible for the devel...   \n","23  How has the customer service industry successf...   \n","24  What steps should be taken to inform AI stakeh...   \n","25  How do the U.S. AI Safety Institute and AI Ris...   \n","26  How to balance synthetic vs. non-synthetic dat...   \n","27  How to address data privacy and ensure AI inte...   \n","28  How can public feedback and incident reporting...   \n","29  How could automation bias worsen misinformatio...   \n","30  How do consultations and monitoring ensure aut...   \n","31  How do EO 13960 and NIST AI RMF ensure AI tran...   \n","32  How does surveillance software for monitoring ...   \n","33  How can provenance tracking and public feedbac...   \n","34  How can designers ensure user understanding an...   \n","35  How do equity and disparity assessments help p...   \n","36  How do school audio surveillance systems for s...   \n","37  How does the tech companion help implement AI ...   \n","38  How to mitigate Human-AI risks in evaluations ...   \n","39  How to design explanatory mechanisms in high-r...   \n","40  How do biometrics affect access and decisions ...   \n","41  How do equity assessments and disparity tests ...   \n","42  How does synthetic data proportion help preven...   \n","43  How do hiring algorithms and social media data...   \n","44  How can unproven tech and data misuse cause harm?   \n","45  Why compare system performance with human meth...   \n","46             How do confident errors mislead users?   \n","47  How do data biases impact loans, hiring, and e...   \n","48  How to design systems to avoid algorithmic bia...   \n","\n","                                             contexts  \\\n","0   [• Accessibility and reasonable \\naccommodatio...   \n","1   [Priorities Related to Information Integrity R...   \n","2   [detailed steps toward actualizing these princ...   \n","3   [results, and actions taken; and the procedure...   \n","4   [for any resulting algorithmic discrimination....   \n","5   [organization’s business processes or other ac...   \n","6   [detailed steps toward actualizing these princ...   \n","7   [Information Integrity; Information \\nSecurity...   \n","8   [for any resulting algorithmic discrimination....   \n","9   [BLUEPRINT FOR AN \\nAI BILL OF \\nRIGHTS \\nMAKI...   \n","10  [lifecycle and informed by representative AI A...   \n","11  [for any resulting algorithmic discrimination....   \n","12  [detailed steps toward actualizing these princ...   \n","13  [for any resulting algorithmic discrimination....   \n","14  [detailed steps toward actualizing these princ...   \n","15  [Table of Contents \\n1. \\nIntroduction ..........   \n","16  [and a human who is interacting with it. Human...   \n","17  [Information Integrity; Information \\nSecurity...   \n","18  [• Accessibility and reasonable \\naccommodatio...   \n","19  [external use, narrow vs. broad application sc...   \n","20  [Technology Policy Institute produced a synops...   \n","21  [About AI at NIST: The National Institute of S...   \n","22  [detailed steps toward actualizing these princ...   \n","23  [BLUEPRINT FOR AN \\nAI BILL OF \\nRIGHTS \\nMAKI...   \n","24  [• Accessibility and reasonable \\naccommodatio...   \n","25  [BLUEPRINT FOR AN \\nAI BILL OF \\nRIGHTS \\nMAKI...   \n","26  [and data that are considered sensitive are un...   \n","27  [FROM \\nPRINCIPLES \\nTO PRACTICE \\nA TECHINCAL...   \n","28  [Table of Contents \\n1. \\nIntroduction ..........   \n","29  [and a human who is interacting with it. Human...   \n","30  [detailed steps toward actualizing these princ...   \n","31  [lifecycle and informed by representative AI A...   \n","32  [Companies use surveillance software to track ...   \n","33  [FROM \\nPRINCIPLES \\nTO PRACTICE \\nA TECHINCAL...   \n","34  [detailed steps toward actualizing these princ...   \n","35  [ethnicity, which can lead to race-based healt...   \n","36  [records and education-related data in order t...   \n","37  [BLUEPRINT FOR AN \\nAI BILL OF \\nRIGHTS \\nMAKI...   \n","38  [FROM \\nPRINCIPLES \\nTO PRACTICE \\nA TECHINCAL...   \n","39  [detailed steps toward actualizing these princ...   \n","40  [for any resulting algorithmic discrimination....   \n","41  [evaluation and plain language reporting in th...   \n","42  [lifecycle and informed by representative AI A...   \n","43  [for any resulting algorithmic discrimination....   \n","44  [Priorities Related to Information Integrity R...   \n","45  [and biological design tools. arXiv. https://a...   \n","46  [for any resulting algorithmic discrimination....   \n","47  [for any resulting algorithmic discrimination....   \n","48  [for any resulting algorithmic discrimination....   \n","\n","                                               answer  \\\n","0                                       I don't know.   \n","1                                       I don't know.   \n","2                                       I don't know.   \n","3                                       I don't know.   \n","4   The federal government is working to combat di...   \n","5                                       I don't know.   \n","6                                       I don't know.   \n","7                                       I don't know.   \n","8                                       I don't know.   \n","9                                       I don't know.   \n","10                                      I don't know.   \n","11  The Biometric Information Privacy Act enacted ...   \n","12  Designers, developers, and deployers of automa...   \n","13  Based on the provided context, systems related...   \n","14  The focus of risk identification and mitigatio...   \n","15                                      I don't know.   \n","16  GAI systems contribute to the creation of deep...   \n","17                                      I don't know.   \n","18  Based on the provided context, organizational ...   \n","19  The context suggests that organizations should...   \n","20  Based on the provided context, some of the par...   \n","21  The National Institute of Standards and Techno...   \n","22  Entities responsible for the development or us...   \n","23                                      I don't know.   \n","24                                      I don't know.   \n","25                                      I don't know.   \n","26                                      I don't know.   \n","27                                      I don't know.   \n","28  Public feedback and incident reporting can imp...   \n","29  Automation bias can worsen misinformation risk...   \n","30  Consultations and monitoring ensure the safety...   \n","31                                      I don't know.   \n","32                                      I don't know.   \n","33  Provenance tracking and public feedback can im...   \n","34  Designers can ensure user understanding and pr...   \n","35  Equity and disparity assessments help prevent ...   \n","36                                      I don't know.   \n","37  The technical companion to the Blueprint for a...   \n","38                                      I don't know.   \n","39                                      I don't know.   \n","40  Biometric systems are used as access control m...   \n","41  Equity assessments and disparity tests reduce ...   \n","42                                      I don't know.   \n","43  Based on the provided context, hiring algorith...   \n","44  The provided context mentions that unproven te...   \n","45                                      I don't know.   \n","46                                      I don't know.   \n","47                                      I don't know.   \n","48                                      I don't know.   \n","\n","                                         ground_truth  faithfulness  \\\n","0   Techniques such as re-sampling, re-weighting, ...      0.000000   \n","1   The suggested actions to ensure information in...      0.000000   \n","2   Those impacted by an automated system should b...      0.000000   \n","3   Individuals were denied benefits due to data e...      0.000000   \n","4   The federal government is working to combat di...      1.000000   \n","5   The characteristics of trustworthy AI that sho...      0.000000   \n","6   Individuals should be able to opt out from aut...      0.000000   \n","7   Data poisoning is a cybersecurity risk where a...      0.000000   \n","8   Opaque decision-making processes in automated ...      0.000000   \n","9   Participants in the OSTP meetings included Ado...      0.000000   \n","10  Suggested actions for explaining and validatin...      0.000000   \n","11  The Biometric Information Privacy Act enacted ...      1.000000   \n","12  Designers, developers, and deployers of automa...      1.000000   \n","13  Systems related to the assignment of penalties...      1.000000   \n","14  Identified risks should focus on the potential...      1.000000   \n","15  Establish and maintain procedures for escalati...      0.000000   \n","16  GAI systems contribute to the creation of deep...      1.000000   \n","17  Synthetic content detection can help manage an...      0.000000   \n","18  Organizational practices are in place to enabl...      1.000000   \n","19  Techniques such as anonymization, differential...      0.750000   \n","20  Participants in the OSTP meetings from the pri...           NaN   \n","21  The National Institute of Standards and Techno...      1.000000   \n","22  Entities responsible for the development or us...      0.909091   \n","23  The customer service industry has successfully...      0.000000   \n","24  Establish and maintain communication plans to ...      0.000000   \n","25  The U.S. AI Safety Institute and the AI Risk M...      0.000000   \n","26  To balance synthetic vs. non-synthetic data an...      0.000000   \n","27  To address data privacy and ensure AI integrit...      0.000000   \n","28  Public feedback and incident reporting can imp...      0.818182   \n","29  Automation bias can exacerbate other risks of ...      0.461538   \n","30  Consultations ensure automated systems' safety...      1.000000   \n","31  Executive Order 13960 requires that AI used by...      0.000000   \n","32  Companies use surveillance software to track e...      0.000000   \n","33  Provenance tracking and public feedback can im...      0.818182   \n","34  Designers can ensure user understanding and pr...      1.000000   \n","35  Equity and disparity assessments help prevent ...      1.000000   \n","36  School audio surveillance systems monitor stud...      0.000000   \n","37  The technical companion provides examples and ...      1.000000   \n","38  To mitigate Human-AI risks in evaluations and ...      0.000000   \n","39  In high-risk automated systems, explanatory me...      0.000000   \n","40  The answer to given question is not present in...      0.928571   \n","41  Equity assessments and disparity tests reduce ...      1.000000   \n","42  Assessing the proportion of synthetic to non-s...      0.000000   \n","43  Algorithms used in hiring and credit decisions...      0.923077   \n","44  Unproven technology and data misuse can cause ...      1.000000   \n","45  System performance should be compared with the...      0.000000   \n","46  Confident errors, also known as confabulations...      0.000000   \n","47  Data biases impact loans by causing applicants...      0.000000   \n","48  To design systems to avoid algorithmic bias an...      0.000000   \n","\n","    answer_relevancy  context_recall  context_precision  answer_correctness  \n","0           0.000000        0.000000           0.250000            0.183427  \n","1           0.000000        0.000000           0.267857            0.183037  \n","2           0.000000        0.666667           0.371528            0.181881  \n","3           0.000000        1.000000           0.333333            0.186449  \n","4           0.992450        1.000000           0.291667            0.999895  \n","5           0.000000        1.000000           1.000000            0.179199  \n","6           0.000000        0.666667           0.366667            0.185176  \n","7           0.000000        0.000000           0.000000            0.179450  \n","8           0.000000        0.666667           0.225000            0.187585  \n","9           0.000000        0.000000           0.565608            0.182637  \n","10          0.000000        0.000000           0.250000            0.177867  \n","11          0.952466        1.000000           0.333333            0.890441  \n","12          0.962097        0.750000           0.426587            0.890689  \n","13          0.957696        1.000000           0.500000            0.613236  \n","14          0.991916        1.000000           0.957143            0.224008  \n","15          0.000000        0.000000           0.111111            0.180618  \n","16          0.971253        0.500000           1.000000            0.719440  \n","17          0.000000        1.000000           0.111111            0.177285  \n","18          1.000000        0.500000           0.875000            0.796277  \n","19          0.960448        0.000000           0.200000            0.428300  \n","20          1.000000        1.000000           0.812500            0.234045  \n","21          0.978036        1.000000           0.892857            0.999374  \n","22          0.978494        1.000000           0.744048            0.731790  \n","23          0.000000        0.000000           0.200000            0.180329  \n","24          0.000000        0.000000           0.291667            0.179026  \n","25          0.000000        0.000000           0.416667            0.177866  \n","26          0.000000        0.000000           0.200000            0.181122  \n","27          0.000000        0.000000           0.584524            0.180616  \n","28          1.000000        0.250000           0.373016            0.385729  \n","29          0.929887        1.000000           0.600000            0.981427  \n","30          0.988883        0.750000           0.611667            0.638175  \n","31          0.000000        0.000000           0.500000            0.178102  \n","32          0.000000        1.000000           0.625000            0.180983  \n","33          1.000000        0.800000           0.440476            0.823743  \n","34          0.999309        0.500000           0.266667            0.837110  \n","35          1.000000        1.000000           0.521627            0.991481  \n","36          0.000000        0.666667           0.600000            0.185658  \n","37          0.915338        1.000000           0.962654            0.498695  \n","38          0.000000        0.000000           0.525926            0.242913  \n","39          0.000000        1.000000           0.226190            0.180091  \n","40          0.887355        1.000000           0.000000            0.926337  \n","41          0.997573        1.000000           0.580000            0.994207  \n","42          0.000000        0.000000           0.366667            0.181362  \n","43          1.000000        1.000000           0.250000            0.982130  \n","44          0.951317        0.800000           0.566667            0.680572  \n","45          0.000000        0.000000           0.500000            0.183202  \n","46          0.000000        0.000000           0.000000            0.183213  \n","47          0.000000        0.000000           0.196429            0.182887  \n","48          0.000000        0.500000           0.100000            0.178707  "],"text/html":["\n","  <div id=\"df-071b958c-c23f-48d1-8daa-4eabaf2ae100\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>question</th>\n","      <th>contexts</th>\n","      <th>answer</th>\n","      <th>ground_truth</th>\n","      <th>faithfulness</th>\n","      <th>answer_relevancy</th>\n","      <th>context_recall</th>\n","      <th>context_precision</th>\n","      <th>answer_correctness</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>What techniques are suggested to assess and ma...</td>\n","      <td>[• Accessibility and reasonable \\naccommodatio...</td>\n","      <td>I don't know.</td>\n","      <td>Techniques such as re-sampling, re-weighting, ...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.250000</td>\n","      <td>0.183427</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>What actions are suggested to ensure informati...</td>\n","      <td>[Priorities Related to Information Integrity R...</td>\n","      <td>I don't know.</td>\n","      <td>The suggested actions to ensure information in...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.267857</td>\n","      <td>0.183037</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>What are the 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<th>9</th>\n","      <td>Who participated in the OSTP meetings focused ...</td>\n","      <td>[BLUEPRINT FOR AN \\nAI BILL OF \\nRIGHTS \\nMAKI...</td>\n","      <td>I don't know.</td>\n","      <td>Participants in the OSTP meetings included Ado...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.565608</td>\n","      <td>0.182637</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>What actions are suggested for explaining and ...</td>\n","      <td>[lifecycle and informed by representative AI A...</td>\n","      <td>I don't know.</td>\n","      <td>Suggested actions for explaining and validatin...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.250000</td>\n","      <td>0.177867</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>What provisions are included in the Biometric ...</td>\n","      <td>[for any resulting algorithmic 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<td>Organizational practices are in place to enabl...</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.500000</td>\n","      <td>0.875000</td>\n","      <td>0.796277</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>What techniques are suggested to minimize risk...</td>\n","      <td>[external use, narrow vs. broad application sc...</td>\n","      <td>The context suggests that organizations should...</td>\n","      <td>Techniques such as anonymization, differential...</td>\n","      <td>0.750000</td>\n","      <td>0.960448</td>\n","      <td>0.000000</td>\n","      <td>0.200000</td>\n","      <td>0.428300</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>Who were some of the participants from the pri...</td>\n","      <td>[Technology Policy Institute produced a synops...</td>\n","      <td>Based on the provided context, some of the par...</td>\n","      <td>Participants in the OSTP meetings from the pri...</td>\n","      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<td>How do the U.S. AI Safety Institute and AI Ris...</td>\n","      <td>[BLUEPRINT FOR AN \\nAI BILL OF \\nRIGHTS \\nMAKI...</td>\n","      <td>I don't know.</td>\n","      <td>The U.S. AI Safety Institute and the AI Risk M...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.416667</td>\n","      <td>0.177866</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>How to balance synthetic vs. non-synthetic dat...</td>\n","      <td>[and data that are considered sensitive are un...</td>\n","      <td>I don't know.</td>\n","      <td>To balance synthetic vs. non-synthetic data an...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.200000</td>\n","      <td>0.181122</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>How to address data privacy and ensure AI inte...</td>\n","      <td>[FROM \\nPRINCIPLES \\nTO PRACTICE \\nA TECHINCAL...</td>\n","      <td>I don't know.</td>\n","      <td>To address data privacy and ensure AI integrit...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.584524</td>\n","      <td>0.180616</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>How can public feedback and incident reporting...</td>\n","      <td>[Table of Contents \\n1. \\nIntroduction ..........</td>\n","      <td>Public feedback and incident reporting can imp...</td>\n","      <td>Public feedback and incident reporting can imp...</td>\n","      <td>0.818182</td>\n","      <td>1.000000</td>\n","      <td>0.250000</td>\n","      <td>0.373016</td>\n","      <td>0.385729</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>How could automation bias worsen misinformatio...</td>\n","      <td>[and a human who is interacting with it. 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 <td>How to mitigate Human-AI risks in evaluations ...</td>\n","      <td>[FROM \\nPRINCIPLES \\nTO PRACTICE \\nA TECHINCAL...</td>\n","      <td>I don't know.</td>\n","      <td>To mitigate Human-AI risks in evaluations and ...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.525926</td>\n","      <td>0.242913</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>How to design explanatory mechanisms in high-r...</td>\n","      <td>[detailed steps toward actualizing these princ...</td>\n","      <td>I don't know.</td>\n","      <td>In high-risk automated systems, explanatory me...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.226190</td>\n","      <td>0.180091</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>How do biometrics affect access and decisions ...</td>\n","      <td>[for any resulting algorithmic discrimination....</td>\n","      <td>Biometric systems are used as access control m...</td>\n","      <td>The answer to given question is not present in...</td>\n","      <td>0.928571</td>\n","      <td>0.887355</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.926337</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>How do equity assessments and disparity tests ...</td>\n","      <td>[evaluation and plain language reporting in th...</td>\n","      <td>Equity assessments and disparity tests reduce ...</td>\n","      <td>Equity assessments and disparity tests reduce ...</td>\n","      <td>1.000000</td>\n","      <td>0.997573</td>\n","      <td>1.000000</td>\n","      <td>0.580000</td>\n","      <td>0.994207</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>How does synthetic data proportion help preven...</td>\n","      <td>[lifecycle and informed by representative AI A...</td>\n","      <td>I don't know.</td>\n","      <td>Assessing the proportion of synthetic to non-s...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.366667</td>\n","      <td>0.181362</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>How do hiring algorithms and social media data...</td>\n","      <td>[for any resulting algorithmic discrimination....</td>\n","      <td>Based on the provided context, hiring algorith...</td>\n","      <td>Algorithms used in hiring and credit decisions...</td>\n","      <td>0.923077</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.250000</td>\n","      <td>0.982130</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>How can unproven tech and data misuse cause harm?</td>\n","      <td>[Priorities Related to Information Integrity R...</td>\n","      <td>The provided context mentions that unproven te...</td>\n","      <td>Unproven technology and data misuse can cause ...</td>\n","      <td>1.000000</td>\n","      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<th>47</th>\n","      <td>How do data biases impact loans, hiring, and e...</td>\n","      <td>[for any resulting algorithmic discrimination....</td>\n","      <td>I don't know.</td>\n","      <td>Data biases impact loans by causing applicants...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.196429</td>\n","      <td>0.182887</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>How to design systems to avoid algorithmic bia...</td>\n","      <td>[for any resulting algorithmic discrimination....</td>\n","      <td>I don't know.</td>\n","      <td>To design systems to avoid algorithmic bias an...</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.500000</td>\n","      <td>0.100000</td>\n","      <td>0.178707</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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This transparency allows for better risk identification and management assessments, and helps in mitigating potential harms. Public feedback can highlight concerns and influence decisions, ensuring that the AI system aligns with public expectations and values. Additionally, ongoing monitoring and performance testing, informed by provenance tracking, can ensure the system remains accurate and fair over time. \\n\\nSource: Document(metadata={'source': 'https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf', 'file_path': 'https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf', 'page': 19, 'total_pages': 73, 'format': 'PDF 1.6', 'title': 'Blueprint for an AI Bill of Rights', 'author': '', 'subject': '', 'keywords': '', 'creator': 'Adobe Illustrator 26.3 (Macintosh)', 'producer': 'iLovePDF', 'creationDate': \\\"D:20220920133035-04'00'\\\", 'modDate': \\\"D:20221003104118-04'00'\\\", 'trapped': '', '_id': '0dfb25b274794704951bd01224d5c65a', '_collection_name': 'snowflake-arctic-embed-l-recursive-base'}, page_content='organization\\u2019s business processes or other activities, system goals, any human-run procedures that form a \\\\npart of the system, and specific performance expectations; a description of any data used to train machine \\\\nlearning models or for other purposes, including how data sources were processed and interpreted, a \\\\nsummary of what data might be missing, incomplete, or erroneous, and data relevancy justifications; the \\\\nresults of public consultation such as concerns raised and any decisions made due to these concerns; risk \\\\nidentification and management assessments and any steps taken to mitigate potential harms; the results of \\\\nperformance testing including, but not limited to, accuracy, differential demographic impact, resulting \\\\nerror rates (overall and per demographic group), and comparisons to previously deployed systems; \\\\nongoing monitoring procedures and regular performance testing reports, including monitoring frequency,').\",\n          \"Based on the provided context, some of the participants from the private sector and civil society in the OSTP meetings included:\\n\\n- Microsoft Corporation\\n- MITRE Corporation\\n- National Association for the Advancement of Colored People Legal Defense and Educational Fund\\n- National Association of Criminal Defense Lawyers\\n- National Center for Missing & Exploited Children\\n- National Fair Housing Alliance\\n- National Immigration Law Center\\n- NEC Corporation of America\\n- New America\\u2019s Open Technology Institute\\n- New York Civil Liberties Union\\n- Notre Dame Technology Ethics Center\\n- Onfido\\n- Oosto\\n- Palantir\\n- Pangiam\\n- Parity Technologies\\n- Philadelphia Unemployment Project\\n- Project On Government Oversight\\n- Recording Industry Association of America\\n- Science, Technology, and Public Policy Program at University of Michigan Ann Arbor\\n- Security Industry Association\\n- Software & Information Industry Association\",\n          \"I don't know.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ground_truth\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"Systems related to the assignment of penalties assist decision-makers in adjudicating administrative or criminal penalties by collating or analyzing information or matching records, and by assisting in the adjudication process.\",\n          \"System performance should be compared with the in-place, potentially human-driven, status quo procedures, with existing human performance considered as a performance baseline for the algorithm to meet pre-deployment, and as a lifecycle minimum performance standard.\",\n          \"Data biases impact loans by causing applicants who attended Historically Black Colleges or Universities (HBCUs) to be charged higher loan prices for refinancing student loans compared to those who did not attend HBCUs, even when controlling for other credit-related factors. In hiring, a tool that learned features from a company's predominantly male employees rejected women applicants for discriminatory reasons, penalizing resumes with the word 'women\\u2019s.' In education, a predictive model used by universities to predict student dropout rates used race directly as a predictor, resulting in Black students being deemed at higher risk of dropping out compared to their white peers, which could guide them away from certain majors.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4759029695707557,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.0,\n          0.46153846153846156,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer_relevancy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.48960967942202427,\n        \"min\": 0.0,\n        \"max\": 1.0000000000000002,\n        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Poongavanam","userId":"13255850175966981554"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","execution_count":20,"metadata":{"id":"uU2b7W5QL7ak","colab":{"base_uri":"https://localhost:8080/","height":156,"referenced_widgets":["17d4977ec7e0472fb90379712b7616ea","241c74b4bb654eada159e64be8429522","829c146c8c744a38a762dcb02573438f","c813fe03f99b4213947c3af04a8c27ee","3088af7fb67d4f079fd3c98a198fcc06","79375495c9444aa5bf9229612bdcb38d","53817832f9724ff382bcb4821e00dd10","368a487e2158427593f8841de07cac4e","79e1f0538b1f4afba34bf5d5e7e32027","9f231687e1ae43fc987fbd8c45097836","07a9a6cbf8114575ba1d58d1b5952d93"]},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1727205035935,"user_tz":-60,"elapsed":227909,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"d12303a8-badb-4a58-f53d-b924b4104146"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Evaluating:   0%|          | 0/245 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"17d4977ec7e0472fb90379712b7616ea"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:ragas.metrics._faithfulness:No statements were generated from the answer.\n","/usr/local/lib/python3.10/dist-packages/datasets/table.py:1395: FutureWarning: promote has been superseded by mode='default'.\n","  block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]\n","/usr/local/lib/python3.10/dist-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by mode='default'.\n","  table = cls._concat_blocks(blocks, axis=0)\n"]}],"source":["# For Fine Tuned model chain\n","ft_chain_eval_results_df = ragas_evaluate(rag_chain_fine_tuned)"]},{"cell_type":"code","source":["ft_chain_eval_results_df.to_csv('ft_chain_eval_results_df.csv', index=False)"],"metadata":{"id":"9J9SqwFbgZbd","executionInfo":{"status":"ok","timestamp":1727205049976,"user_tz":-60,"elapsed":417,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","metrics = ['faithfulness', 'answer_relevancy', 'context_recall', 'context_precision', 'answer_correctness']\n","\n","# Calculate the average scores for each metric in both base and fine-tuned models\n","base_avg = base_chain_eval_results_df[metrics].mean()\n","ft_avg = ft_chain_eval_results_df[metrics].mean()\n","\n","# Calculate improvement (fine-tuned - base)\n","improvement = ft_avg - base_avg\n","\n","# Create a summary dataframe\n","summary_df = pd.DataFrame({\n","    'Base Model': base_avg,\n","    'Fine-tuned Model': ft_avg,\n","    'Improvement': improvement\n","})\n","\n","# ------------------ Plotting the Bar Graph ------------------\n","# Labels for metrics\n","labels = metrics\n","\n","# Set width for bars\n","bar_width = 0.35\n","\n","# Set positions for the bars\n","index = np.arange(len(labels))\n","\n","# Create the figure and the subplots\n","fig, ax = plt.subplots(figsize=(10, 6))\n","\n","# Plot bars for the base model and fine-tuned model\n","bar1 = ax.bar(index, base_avg, bar_width, label='Base Model', color='lightblue')\n","bar2 = ax.bar(index + bar_width, ft_avg, bar_width, label='Fine-tuned Model', color='lightgreen')\n","\n","# Adding labels and title\n","ax.set_xlabel('Metrics')\n","ax.set_ylabel('Average Scores')\n","ax.set_title('Comparison of Base and Fine-tuned Model Metrics')\n","ax.set_xticks(index + bar_width / 2)\n","ax.set_xticklabels(labels)\n","ax.legend()\n","\n","# Display the plot\n","plt.tight_layout()\n","plt.show()\n","\n","# ------------------ Plotting the Improvement Graph ------------------\n","fig, ax = plt.subplots(figsize=(10, 6))\n","\n","# Plot improvement as a bar chart\n","improvement_bar = ax.bar(index, improvement, bar_width, color='purple')\n","\n","# Adding labels and title\n","ax.set_xlabel('Metrics')\n","ax.set_ylabel('Improvement')\n","ax.set_title('Improvement of Fine-tuned Model over Base Model')\n","ax.set_xticks(index)\n","ax.set_xticklabels(labels)\n","\n","# Display the plot\n","plt.tight_layout()\n","plt.show()\n","\n","# ------------------ Creating a Table ------------------\n","fig, ax = plt.subplots(figsize=(10, 6))\n","\n","# Hide axes\n","ax.xaxis.set_visible(False)\n","ax.yaxis.set_visible(False)\n","ax.set_frame_on(False)\n","\n","# Create the table from summary_df\n","table = ax.table(cellText=summary_df.values,\n","                 colLabels=summary_df.columns,\n","                 rowLabels=summary_df.index,\n","                 cellLoc='center',\n","                 loc='center')\n","\n","# Adjust table scale\n","table.scale(1, 2)\n","\n","# Set font size\n","table.set_fontsize(12)\n","\n","plt.title('Comparison of Base Model and Fine-tuned Model with Improvements')\n","\n","# Show the table\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"Jwx0WjTDj0oQ","executionInfo":{"status":"ok","timestamp":1727205092164,"user_tz":-60,"elapsed":1297,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"457ce1cc-53e8-4838-adbc-a686285c68fa"},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAA90AAAJOCAYAAACqS2TfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABzcklEQVR4nO3dd3gUVf/+8XsT0gsQShIwEAgtkR4EASWg0SBIsYGA0pEHRJpYeFSKhVgAwQaKNBEEKZYHlCqRKiIIIkaaEIp06WACyfn9wS/zZUkCWcywBt6v69oLdubMzGcnZzd7Z2bOOIwxRgAAAAAAIM95uLsAAAAAAABuVIRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AuAk4HA4NGTLE3WX8Y1OmTFGlSpXk5eWlQoUKubucfCspKUkOh0NJSUn/aD27du2Sw+HQpEmT8qSuG1le7XM7XevnRH7vB/nhZwMgfyN0A7gp7NixQ927d1fZsmXl6+ur4OBg1a9fX6NHj9a5c+fcXR5y4ffff1fHjh0VFRWlcePG6aOPPsqx7ZAhQ+RwOKyHh4eHwsPDdf/99+uHH364jlXnb5MmTXLaj5c+nn/+eXeXp2HDhunLL790dxl56tJ9vmLFiizzjTGKiIiQw+HQ/fff74YKr11muHU4HPr000+zbVO/fn05HA5Vrlz5mrYxbdo0jRo16h9UCQB5r4C7CwAAu82bN0+PPPKIfHx81L59e1WuXFlpaWlasWKFnnnmGW3evPmKAe5GcO7cORUokL8/8pOSkpSRkaHRo0erXLlyuVpmzJgxCgwMVEZGhvbs2aNx48apQYMG+vHHH1W9enV7C76BvPzyyypTpozTtMqVK6t06dI6d+6cvLy83FLXsGHD9PDDD6tly5Zu2b6dfH19NW3aNN1xxx1O07///nvt3btXPj4+bqrsn8t8bY899pjT9F27dmnVqlXy9fW95nVPmzZNv/76q/r27ZvrZRo0aKBz587J29v7mrcLAFeSv7+BAcBV7Ny5U48++qhKly6t7777TuHh4da8J598Utu3b9e8efPcWKF9MjIylJaWJl9f33/0Jfbf4tChQ5Lk0mnlDz/8sIoWLWo9b9mypSpXrqyZM2cSul1w3333qVatWtnOuxH61r9RkyZNNHPmTL3zzjtOfzCbNm2aYmNjdeTIETdW9880adJEX3/9tY4cOeL0/pw2bZpCQ0NVvnx5HTt2zPY6/v77b3l7e8vDw4N+DMBWnF4O4Ib25ptv6vTp0xo/frxT4M5Urlw59enTx3p+4cIFvfLKK4qKipKPj48iIyP13//+V6mpqU7LRUZG6v7771dSUpJq1aolPz8/ValSxbomcM6cOapSpYp8fX0VGxurn3/+2Wn5jh07KjAwUH/88YcSEhIUEBCgEiVK6OWXX5Yxxqnt8OHDVa9ePRUpUkR+fn6KjY3VrFmzsrwWh8OhXr16aerUqbr11lvl4+Oj+fPnW/MuvVbz1KlT6tu3ryIjI+Xj46PixYvrnnvu0fr1653WOXPmTMXGxsrPz09FixbVY489pn379mX7Wvbt26eWLVsqMDBQxYoV04ABA5Senp7DT8bZBx98YNVcokQJPfnkkzp+/LjT/h48eLAkqVixYtd87WlYWJgkOYWYtLQ0DRo0SLGxsSpYsKACAgJ05513aunSpVmWnz59umJjYxUUFKTg4GBVqVJFo0ePdmpz/Phx9e3bVxEREfLx8VG5cuX0xhtvKCMj46r1ffXVV2ratKlKlCghHx8fRUVF6ZVXXsmyHxs2bKjKlSvrt99+U6NGjeTv76+SJUvqzTffzLLOvXv3qmXLlgoICFDx4sXVr1+/LP35WmV3La8r/SEjI0OjRo3SrbfeKl9fX4WGhqp79+65ClwOh0NnzpzR5MmTrVOWO3bsaNUQGRmZZZnMyw4uX0+vXr305ZdfqnLlyvLx8dGtt95qvXcutW/fPnXu3FmhoaFWuwkTJmRplxf7vE2bNjp69KgWLVpkTUtLS9OsWbPUtm3bbJc5c+aMnn76aavvVaxYUcOHD8/ymZKamqp+/fqpWLFiCgoKUvPmzbV3795s15nb1+yKFi1ayMfHRzNnznSaPm3aNLVq1Uqenp7ZLvfpp59an0chISF69NFHtWfPHmt+w4YNNW/ePKWkpFh9IrMfZJ7aPn36dL344osqWbKk/P39dfLkyRyv6V6zZo2aNGmiwoULKyAgQFWrVnV6vx84cECdOnXSLbfcIh8fH4WHh6tFixbatWvXP9o/AG48HOkGcEP73//+p7Jly6pevXq5at+1a1dNnjxZDz/8sJ5++mmtWbNGiYmJSk5O1hdffOHUdvv27Wrbtq26d++uxx57TMOHD1ezZs00duxY/fe//1XPnj0lSYmJiWrVqpW2bNkiD4//+1tnenq6GjdurNtvv11vvvmm5s+fr8GDB+vChQt6+eWXrXajR49W8+bN1a5dO6WlpWn69Ol65JFHNHfuXDVt2tSppu+++06ff/65evXqpaJFi2YbPCTpP//5j2bNmqVevXopJiZGR48e1YoVK5ScnKyaNWtKunhtaadOnXTbbbcpMTFRBw8e1OjRo7Vy5Ur9/PPPTkec09PTlZCQoDp16mj48OFavHixRowYoaioKPXo0eOK+3zIkCEaOnSo4uPj1aNHD23ZskVjxozR2rVrtXLlSnl5eWnUqFH65JNP9MUXX1injFetWvWqP8+//vpL0sVwt2/fPr3yyivy9fVVq1atrDYnT57Uxx9/rDZt2qhbt246deqUxo8fr4SEBKfT0BctWqQ2bdro7rvv1htvvCFJSk5O1sqVK60/3Jw9e1ZxcXHat2+funfvrlKlSmnVqlUaOHCg9u/ff9VrTSdNmqTAwED1799fgYGB+u677zRo0CCdPHlSb731llPbY8eOqXHjxnrwwQfVqlUrzZo1S88995yqVKmi++67T9LFywruvvtu7d69W71791aJEiU0ZcoUfffdd1fdd5c6ceJEliOrlx6hvFxu+0P37t2tfta7d2/t3LlT7733nn7++WfrZ5+TKVOmqGvXrqpdu7aeeOIJSVJUVJRLryvTihUrNGfOHPXs2VNBQUF655139NBDD2n37t0qUqSIJOngwYO6/fbbrZBerFgxffvtt+rSpYtOnjxpnc6cV/s8MjJSdevW1WeffWb9PL/99ludOHFCjz76qN555x2n9sYYNW/eXEuXLlWXLl1UvXp1LViwQM8884z27dunt99+22rbtWtXffrpp2rbtq3q1aun7777LstniSuv2VX+/v5q0aKFPvvsM6s/bNy4UZs3b9bHH3+sX375Jcsyr732ml566SW1atVKXbt21eHDh/Xuu++qQYMG1ufRCy+8oBMnTmjv3r3W6w0MDHRazyuvvCJvb28NGDBAqampOZ5SvmjRIt1///0KDw9Xnz59FBYWpuTkZM2dO9d6vz/00EPavHmznnrqKUVGRurQoUNatGiRdu/eneNnL4CblAGAG9SJEyeMJNOiRYtctd+wYYORZLp27eo0fcCAAUaS+e6776xppUuXNpLMqlWrrGkLFiwwkoyfn59JSUmxpn/44YdGklm6dKk1rUOHDkaSeeqpp6xpGRkZpmnTpsbb29scPnzYmn727FmnetLS0kzlypXNXXfd5TRdkvHw8DCbN2/O8tokmcGDB1vPCxYsaJ588skc90VaWpopXry4qVy5sjl37pw1fe7cuUaSGTRoUJbX8vLLLzuto0aNGiY2NjbHbRhjzKFDh4y3t7e59957TXp6ujX9vffeM5LMhAkTrGmDBw82kpz2TU4y217+KFSokJk/f75T2wsXLpjU1FSnaceOHTOhoaGmc+fO1rQ+ffqY4OBgc+HChRy3+8orr5iAgACzdetWp+nPP/+88fT0NLt3775i3Zf/rI0xpnv37sbf39/8/fff1rS4uDgjyXzyySfWtNTUVBMWFmYeeugha9qoUaOMJPP5559b086cOWPKlSuXpU9mZ+LEidnux8yvDzt37jSSzMSJE61lctsfli9fbiSZqVOnOrWbP39+ttOzExAQYDp06JBleocOHUzp0qWzTM/sF5eSZLy9vc327dutaRs3bjSSzLvvvmtN69KliwkPDzdHjhxxWv7RRx81BQsWtH52ebXP165da9577z0TFBRkrfuRRx4xjRo1MsZc/Axq2rSptdyXX35pJJlXX33VaX0PP/ywcTgc1uvL/Jzr2bOnU7u2bdtm+ZzI7WvOrh9kZ+nSpUaSmTlzppk7d65xOBzWe+KZZ54xZcuWNcZc7N+33nqrtdyuXbuMp6enee2115zWt2nTJlOgQAGn6U2bNs32Z5+57bJly2Z5n2XOy/zZXLhwwZQpU8aULl3aHDt2zKltRkaGMebiZ4Qk89Zbb13xNQOAMcZwejmAG9bJkyclSUFBQblq/80330iS+vfv7zT96aeflqQs137HxMSobt261vM6depIku666y6VKlUqy/Q//vgjyzZ79epl/T/zaFJaWpoWL15sTffz87P+f+zYMZ04cUJ33nlnllPBJSkuLk4xMTFXeaUXr4tes2aN/vzzz2zn//TTTzp06JB69uzpdK1j06ZNValSpWyvg//Pf/7j9PzOO+/M9jVfavHixUpLS1Pfvn2dzgLo1q2bgoOD//H19rNnz9aiRYu0cOFCTZw4URUqVNBDDz2kVatWWW08PT2to10ZGRn666+/dOHCBdWqVctpHxcqVEhnzpxxOt33cjNnztSdd96pwoUL68iRI9YjPj5e6enpWrZs2RXrvfRnferUKR05ckR33nmnzp49q99//92pbWBgoNNAVN7e3qpdu7bTPv/mm28UHh6uhx9+2Jrm7+9vHRnOrffff1+LFi1yelzN1frDzJkzVbBgQd1zzz1O+yo2NlaBgYHZnt5vl/j4eKej5FWrVlVwcLBVrzFGs2fPVrNmzWSMcao3ISFBJ06csPpKXu1zSWrVqpXOnTunuXPn6tSpU5o7d26Op5Z/88038vT0VO/evZ2mP/300zLG6Ntvv7XaScrS7vKj1q685mtx7733KiQkRNOnT5cxRtOnT1ebNm2ybTtnzhxlZGSoVatWTnWEhYWpfPnyLvWVDh06OL3PsvPzzz9r586d6tu3b5YxJDIvT/Dz85O3t7eSkpKuy/XnAPI3Ti8HcMMKDg6WdDG85EZKSoo8PDyyjIwdFhamQoUKKSUlxWn6pcFakgoWLChJioiIyHb65V/MPDw8VLZsWadpFSpUkCSnawLnzp2rV199VRs2bHC6LvTya1MlZRlhOidvvvmmOnTooIiICMXGxqpJkyZq3769VU/ma61YsWKWZStVqpTlVka+vr4qVqyY07TChQtf9ctoTtvx9vZW2bJls+xzVzVo0MDpNOiHH35Y5cuX11NPPaV169ZZ0ydPnqwRI0bo999/1/nz563pl+7Pnj176vPPP9d9992nkiVL6t5771WrVq3UuHFjq822bdv0yy+/ZNkXmTIHg8vJ5s2b9eKLL+q7776z/miU6cSJE07Pb7nllix9oHDhwk6n5qakpKhcuXJZ2mX3c72S2rVr5ziQWnZy0x+2bdumEydOqHjx4tmuI3NfnThxwum2ft7e3goJCXGl/Ku6/L18eb2HDx/W8ePH9dFHH+V4p4PMevNqn0sXxy+Ij4/XtGnTdPbsWaWnpzuF+UulpKSoRIkSWf7IGB0dbc3P/NfDwyPLqfiX1+fKa74WXl5eeuSRRzRt2jTVrl1be/bsyfEPCtu2bZMxRuXLl89xXbmVm8/IHTt2SNIVb1vm4+OjN954Q08//bRCQ0N1++236/7771f79u2tsSMAIBOhG8ANKzg4WCVKlNCvv/7q0nLZhdns5DTYT07TzWWDGeXG8uXL1bx5czVo0EAffPCBwsPD5eXlpYkTJ2ratGlZ2l/tCE6mVq1a6c4779QXX3yhhQsX6q233tIbb7yhOXPmWNePuiKn1/xvExgYqDp16uirr77SmTNnFBAQoE8//VQdO3ZUy5Yt9cwzz6h48eLy9PRUYmKi9eVbkooXL64NGzZowYIF+vbbb/Xtt99q4sSJat++vSZPnizp4pHye+65R88++2y228/8o0p2jh8/rri4OAUHB+vll19WVFSUfH19tX79ej333HNZBmLLy36W13LTHzIyMlS8eHFNnTo12/mZob1Pnz7W/pUuns1x+YBXl8vpPZzTwH5X25eZ+/6xxx5Thw4dsm2bmzEGrkXbtm3VrVs3HThwQPfdd59Lo/f/E9fjNbdt21Zjx47VkCFDVK1atRzP0snIyJDD4dC3336b7c/q8uu2ryS3n5G50bdvXzVr1kxffvmlFixYoJdeekmJiYn67rvvVKNGjTzbDoD8j9AN4IZ2//3366OPPtLq1audTgXPTunSpZWRkaFt27ZZR4eki4MJHT9+XKVLl87T2jIyMvTHH384BbGtW7dKkjUIz+zZs+Xr66sFCxY43Zd34sSJ/3j74eHh6tmzp3r27KlDhw6pZs2aeu2113TfffdZr3XLli266667nJbbsmVLnu2LS7dz6VH/tLQ07dy5U/Hx8XmynUtduHBBknT69GkFBARo1qxZKlu2rObMmeMU1jJHS7+Ut7e3mjVrpmbNmikjI0M9e/bUhx9+qJdeeknlypVTVFSUTp8+fU11JyUl6ejRo5ozZ44aNGhgTd+5c+c1vMqLSpcurV9//VXGGKfXtmXLlmteZ16JiorS4sWLVb9+/SsGoWeffdbpNPrChQtb/88pXBcuXNhp9PtM13rmROYo3+np6Vf92eb1Pn/ggQfUvXt3/fDDD5oxY8YVt7t48WKdOnXK6Wh35mUJme+1zM+5HTt2OB3dvrw+V17ztbrjjjtUqlQpJSUlWYMTZicqKkrGGJUpU+aKf7iScv9H0yvJPAvg119/veprj4qK0tNPP62nn35a27ZtU/Xq1TVixAh9+umn/7gOADcOrukGcEN79tlnFRAQoK5du+rgwYNZ5u/YscO6BUyTJk0kKcsI0yNHjpSkbEf3/afee+896//GGL333nvy8vLS3XffLeniETiHw+F0hG7Xrl368ssvr3mb6enpWU5VLl68uEqUKGGdvl6rVi0VL15cY8eOdTql/dtvv1VycnKe7Yv4+Hh5e3vrnXfecTpCO378eJ04cSLP9/lff/2lVatWKSwszDqtOfPI2aXbX7NmjVavXu207NGjR52ee3h4WEf6MvdRq1attHr1ai1YsCDLto8fP24F/uxkV0daWpo++OCDXL++yzVp0kR//vmn0y3mzp49m+PpwtdTq1atlJ6erldeeSXLvAsXLlihOSYmRvHx8dYjNjbWahcQEJBtuI6KitKJEyecTrXfv39/ljsQ5Janp6ceeughzZ49O9szZw4fPmz9P6/3eWBgoMaMGaMhQ4aoWbNmObZr0qSJ0tPTnT5TJOntt9+Ww+GwzmDJ/Pfy0c8v/9xz5TVfK4fDoXfeeUeDBw/W448/nmO7Bx98UJ6enho6dGiWMzmMMU7vzYCAgCyfb66qWbOmypQpo1GjRmXpX5nbP3v2rP7++2+neVFRUQoKCsqzW/IBuHFwpBvADS0qKkrTpk1T69atFR0drfbt26ty5cpKS0vTqlWrNHPmTOvevtWqVVOHDh300UcfWaf6/vjjj5o8ebJatmypRo0a5Wltvr6+mj9/vjp06KA6dero22+/1bx58/Tf//7XOrW2adOmGjlypBo3bqy2bdvq0KFDev/991WuXLlsb6uTG6dOndItt9yihx9+WNWqVVNgYKAWL16stWvXasSIEZIuXiP5xhtvqFOnToqLi1ObNm2sW4ZFRkaqX79+ebIPihUrpoEDB2ro0KFq3Lixmjdvri1btuiDDz7Qbbfd5nSE81rMmjVLgYGBMsbozz//1Pjx43Xs2DGNHTvWOiJ2//33a86cOXrggQfUtGlT7dy5U2PHjlVMTIxOnz5tratr167666+/dNddd+mWW25RSkqK3n33XVWvXt06M+KZZ57R119/rfvvv18dO3ZUbGyszpw5o02bNmnWrFnatWtXjrfaqlevngoXLqwOHTqod+/ecjgcmjJlyj86Xbxbt25677331L59e61bt07h4eGaMmWK/P39r3mdeSUuLk7du3dXYmKiNmzYoHvvvVdeXl7atm2bZs6cqdGjR+d4/XKm2NhYLV68WCNHjlSJEiVUpkwZ1alTR48++qiee+45PfDAA+rdu7fOnj2rMWPGqEKFCtc8+Nfrr7+upUuXqk6dOurWrZtiYmL0119/af369Vq8eLF1ezo79nlOp3dfqlmzZmrUqJFeeOEF7dq1S9WqVdPChQv11VdfqW/fvtbR2+rVq6tNmzb64IMPdOLECdWrV09LlizR9u3br/k1/xMtWrRQixYtrtgmKipKr776qgYOHKhdu3apZcuWCgoK0s6dO/XFF1/oiSee0IABAyRd7BMzZsxQ//79ddtttykwMPCKf6zIjoeHh8aMGaNmzZqpevXq6tSpk8LDw/X7779r8+bNWrBggbZu3aq7775brVq1UkxMjAoUKKAvvvhCBw8e1KOPPnrN+wPADep6DpUOAO6ydetW061bNxMZGWm8vb1NUFCQqV+/vnn33XedbsV0/vx5M3ToUFOmTBnj5eVlIiIizMCBA53aGJP1dj2ZJGW5FVfm7XQuvbVMhw4dTEBAgNmxY4e59957jb+/vwkNDTWDBw92unWWMcaMHz/elC9f3vj4+JhKlSqZiRMn5njro5xuA6ZLbgWUmppqnnnmGVOtWjUTFBRkAgICTLVq1cwHH3yQZbkZM2aYGjVqGB8fHxMSEmLatWtn9u7d69Qm87VcLrsac/Lee++ZSpUqGS8vLxMaGmp69OiR5VY9//SWYQEBAaZu3bpOt3Iy5uItgIYNG2ZKly5tfHx8TI0aNczcuXOz3HZq1qxZ5t577zXFixc33t7eplSpUqZ79+5m//79Tus7deqUGThwoClXrpzx9vY2RYsWNfXq1TPDhw83aWlpV6x75cqV5vbbbzd+fn6mRIkS5tlnn7VuRXfpraYuv6VSpuxulZWSkmKaN29u/P39TdGiRU2fPn2s23K5cvuq7OR0yzBX+sNHH31kYmNjjZ+fnwkKCjJVqlQxzz77rPnzzz+vWJsxxvz++++mQYMGxs/Pz0hyun3YwoULTeXKlY23t7epWLGi+fTTT11635QuXTrL7cgOHjxonnzySRMREWG8vLxMWFiYufvuu81HH33k1M7OfX5pfZd/Bp06dcr069fPlChRwnh5eZny5cubt956y7rNVaZz586Z3r17myJFipiAgADTrFkzs2fPniy3DMvta76WW4ZdSU79e/bs2eaOO+4wAQEBJiAgwFSqVMk8+eSTZsuWLVab06dPm7Zt25pChQoZSdb74UrbvvyWYZlWrFhh7rnnHutzsmrVqtZt5I4cOWKefPJJU6lSJRMQEGAKFixo6tSpk+XzBQCMMcZhzL9gxBUAuMl07NhRs2bNcjqSCgAAgBsP13QDAAAAAGATQjcAAAAAADYhdAMAAAAAYBOu6QYAAAAAwCYc6QYAAAAAwCaEbgAAAAAAbFLA3QVcbxkZGfrzzz8VFBQkh8Ph7nIAAAAAAPmQMUanTp1SiRIl5OGR8/Hsmy50//nnn4qIiHB3GQAAAACAG8CePXt0yy235Dj/pgvdQUFBki7umODgYDdXAwAAAADIj06ePKmIiAgrY+bkpgvdmaeUBwcHE7oBAAAAAP/I1S5bZiA1AAAAAABsQugGAAAAAMAmhG4AAAAAAGxy013TDQAAAODfJSMjQ2lpae4uA3Di5eUlT0/Pf7weQjcAAAAAt0lLS9POnTuVkZHh7lKALAoVKqSwsLCrDpZ2JYRuAAAAAG5hjNH+/fvl6empiIgIeXhw9Sv+HYwxOnv2rA4dOiRJCg8Pv+Z1EboBAAAAuMWFCxd09uxZlShRQv7+/u4uB3Di5+cnSTp06JCKFy9+zaea86ckAAAAAG6Rnp4uSfL29nZzJUD2Mv8YdP78+WteB6EbAAAAgFv9k+tlATvlRd8kdAMAAAAAYBNCNwAAAAAg1xwOh7788stct+/YsaNatmxpWz3/dgykBgAAAOBfZc6W/dd1ew9WdG1k6o4dO2ry5MnW85CQEN1222168803VbVq1bwuL9cmTZqkTp06qVKlSkpOTnaaN3PmTLVq1UqlS5fWrl273FPgTYoj3QAAAADgosaNG2v//v3av3+/lixZogIFCuj+++93d1kKCAjQoUOHtHr1aqfp48ePV6lSpdxU1c2N0A0AAAAALvLx8VFYWJjCwsJUvXp1Pf/889qzZ48OHz5stXnuuedUoUIF+fv7q2zZsnrppZecRsHeuHGjGjVqpKCgIAUHBys2NlY//fSTNX/FihW688475efnp4iICPXu3Vtnzpy5Yl0FChRQ27ZtNWHCBGva3r17lZSUpLZt22ZpP2bMGEVFRcnb21sVK1bUlClTnOZv27ZNDRo0kK+vr2JiYrRo0aIs69izZ49atWqlQoUKKSQkRC1atOBo+iUI3QAAAADwD5w+fVqffvqpypUrpyJFiljTg4KCNGnSJP32228aPXq0xo0bp7ffftua365dO91yyy1au3at1q1bp+eff15eXl6SpB07dqhx48Z66KGH9Msvv2jGjBlasWKFevXqddV6OnfurM8//1xnz56VdPG088aNGys0NNSp3RdffKE+ffro6aef1q+//qru3burU6dOWrp0qSQpIyNDDz74oLy9vbVmzRqNHTtWzz33nNM6zp8/r4SEBAUFBWn58uVauXKlAgMD1bhxY6WlpV3bDr3BcE03AAAAALho7ty5CgwMlCSdOXNG4eHhmjt3rjw8/u+45osvvmj9PzIyUgMGDND06dP17LPPSpJ2796tZ555RpUqVZIklS9f3mqfmJiodu3aqW/fvta8d955R3FxcRozZox8fX1zrK1GjRoqW7asZs2apccff1yTJk3SyJEj9ccffzi1Gz58uDp27KiePXtKkvr3768ffvhBw4cPV6NGjbR48WL9/vvvWrBggUqUKCFJGjZsmO677z5rHTNmzFBGRoY+/vhj6/ZaEydOVKFChZSUlKR7773XtR17A+JINwAAAAC4qFGjRtqwYYM2bNigH3/8UQkJCbrvvvuUkpJitZkxY4bq16+vsLAwBQYG6sUXX9Tu3but+f3791fXrl0VHx+v119/XTt27LDmbdy4UZMmTVJgYKD1SEhIUEZGhnbu3HnV+jp37qyJEyfq+++/15kzZ9SkSZMsbZKTk1W/fn2nafXr17cGYUtOTlZERIQVuCWpbt26Tu03btyo7du3KygoyKozJCREf//9t9PruZkRugEAAADARQEBASpXrpzKlSun2267TR9//LHOnDmjcePGSZJWr16tdu3aqUmTJpo7d65+/vlnvfDCC06nXA8ZMkSbN29W06ZN9d133ykmJkZffPGFpIunrHfv3t0K9hs2bNDGjRu1bds2RUVFXbW+du3a6YcfftCQIUP0+OOPq0ABe05yPn36tGJjY53q3LBhg7Zu3ZrtNeQ3I04vBwAAAIB/yOFwyMPDQ+fOnZMkrVq1SqVLl9YLL7xgtbn0KHimChUqqEKFCurXr5/atGmjiRMn6oEHHlDNmjX122+/qVy5ctdUT0hIiJo3b67PP/9cY8eOzbZNdHS0Vq5cqQ4dOljTVq5cqZiYGGv+nj17tH//foWHX7yt2g8//OC0jpo1a2rGjBkqXry4goODr6nWGx1HugEAAADARampqTpw4IAOHDig5ORkPfXUUzp9+rSaNWsm6eI12Lt379b06dO1Y8cOvfPOO9ZRbEk6d+6cevXqpaSkJKWkpGjlypVau3atoqOjJV0c+XzVqlXq1auXNmzYoG3btumrr77K1UBqmSZNmqQjR45Y14xf7plnntGkSZM0ZswYbdu2TSNHjtScOXM0YMAASVJ8fLwqVKigDh06aOPGjVq+fLnTHxGki0fUixYtqhYtWmj58uXauXOnkpKS1Lt3b+3du9elfXqjInQDAAAAgIvmz5+v8PBwhYeHq06dOlq7dq1mzpyphg0bSpKaN2+ufv36qVevXqpevbpWrVqll156yVre09NTR48eVfv27VWhQgW1atVK9913n4YOHSpJqlq1qr7//ntt3bpVd955p2rUqKFBgwY5XV99NX5+fk6jqV+uZcuWGj16tIYPH65bb71VH374oSZOnGi9Bg8PD33xxRc6d+6cateura5du+q1115zWoe/v7+WLVumUqVK6cEHH1R0dLS6dOmiv//+myPf/5/DGGPcXcT1dPLkSRUsWFAnTpygEwAAcAWjj412dwlu0adwH3eXANw0/v77b+3cuVNlypS54mjcgLtcqY/mNltypBsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAACCPNGzYUH379nV3GdddUlKSHA6Hjh8/7tY6hgwZourVq+e6/a5du+RwOLRhwwbbaipg25oBAAAA4BqMPjb6um6vT+E+LrXv2LGjJk+enGX6tm3bNGfOHHl5eeVVaTlq2LChqlevrlGjRtm+rbwSGRmplJQUffbZZ3r00Ued5t1666367bffNHHiRHXs2NE9BdqEI90AAAAA4KLGjRtr//79To8yZcooJCREQUFB7i7vXysiIkITJ050mvbDDz/owIEDCggIcFNV9iJ0AwAAAICLfHx8FBYW5vTw9PTMcnp5ZGSkhg0bps6dOysoKEilSpXSRx995LSuPXv2qFWrVipUqJBCQkLUokUL7dq1K8dtd+zYUd9//71Gjx4th8Mhh8OhXbt2adKkSSpUqJBT2y+//FIOh8N6nnn69ZQpUxQZGamCBQvq0Ucf1alTp6w2GRkZSkxMVJkyZeTn56dq1app1qxZTuv95ptvVKFCBfn5+alRo0ZXrPdS7dq10/fff689e/ZY0yZMmKB27dqpQAHnE7F3796tFi1aKDAwUMHBwWrVqpUOHjzo1Ob1119XaGiogoKC1KVLF/39999Ztvnxxx8rOjpavr6+qlSpkj744INc1ZpXCN0AAAAAYKMRI0aoVq1a+vnnn9WzZ0/16NFDW7ZskSSdP39eCQkJCgoK0vLly7Vy5UoFBgaqcePGSktLy3Z9o0ePVt26ddWtWzfrKHtERESu69mxY4e+/PJLzZ07V3PnztX333+v119/3ZqfmJioTz75RGPHjtXmzZvVr18/PfbYY/r+++8lXfwjwYMPPqhmzZppw4YN6tq1q55//vlcbTs0NFQJCQnW6flnz57VjBkz1LlzZ6d2GRkZatGihf766y99//33WrRokf744w+1bt3aavP5559ryJAhGjZsmH766SeFh4dnCdRTp07VoEGD9Nprryk5OVnDhg3TSy+9lO3lAXbhmm4AAAAAcNHcuXMVGBhoPb/vvvs0c+bMbNs2adJEPXv2lCQ999xzevvtt7V06VJVrFhRM2bMUEZGhj7++GPriPTEiRNVqFAhJSUl6d57782yvoIFC8rb21v+/v4KCwtzufaMjAxNmjTJOg3+8ccf15IlS/Taa68pNTVVw4YN0+LFi1W3bl1JUtmyZbVixQp9+OGHiouL05gxYxQVFaURI0ZIkipWrKhNmzbpjTfeyNX2O3furKefflovvPCCZs2apaioqCyDny1ZskSbNm3Szp07rT8ofPLJJ7r11lu1du1a3XbbbRo1apS6dOmiLl26SJJeffVVLV682Olo9+DBgzVixAg9+OCDkqQyZcrot99+04cffqgOHTq4vO+uBUe6AQAAAMBFjRo10oYNG6zHO++8k2PbqlWrWv93OBwKCwvToUOHJEkbN27U9u3bFRQUpMDAQAUGBiokJER///23duzYoeXLl1vTAwMDNXXq1H9ce2RkpNN15+Hh4VY927dv19mzZ3XPPfc4bfeTTz7Rjh07JEnJycmqU6eO0zozA3puNG3aVKdPn9ayZcs0YcKELEe5M7cRERHhdAQ/JiZGhQoVUnJycq7qOHPmjHbs2KEuXbo4vZZXX33Vei3XA0e6AQC4gjlb9ru7BPcp7u4CAODfKyAgQOXKlctV28tHM3c4HMrIyJAknT59WrGxsdmG6WLFisnb29vpdlahoaE5bsfDw0PGGKdp58+fd7keSZo3b55Klizp1M7HxyfHbbuiQIECevzxxzV48GCtWbNGX3zxRZ6s93KZr2XcuHFZwrmnp6ct28wOoRsAAAAA3KRmzZqaMWOGihcvruDg4GzbZBfuvb29lZ6e7jStWLFiOnXqlM6cOWONBO7q/adjYmLk4+Oj3bt3Ky4uLts20dHR+vrrr52m/fDDDy5tp3Pnzho+fLhat26twoULZ7uNPXv2aM+ePdbR7t9++03Hjx9XTEyM1WbNmjVq3759tnWEhoaqRIkS+uOPP9SuXTuX6stLhG4AAAAAcJN27drprbfeUosWLfTyyy/rlltuUUpKiubMmaNnn31Wt9xyS7bLRUZGas2aNdq1a5d1SnqdOnXk7++v//73v+rdu7fWrFmjSZMmuVRPUFCQBgwYoH79+ikjI0N33HGHTpw4oZUrVyo4OFgdOnTQf/7zH40YMULPPPOMunbtqnXr1rm8nejoaB05ckT+/v7Zzo+Pj1eVKlXUrl07jRo1ShcuXFDPnj0VFxenWrVqSZL69Omjjh07qlatWqpfv76mTp2qzZs3q2zZstZ6hg4dqt69e6tgwYJq3LixUlNT9dNPP+nYsWPq37+/SzVfK67pBgAAAAA38ff317Jly1SqVCk9+OCDio6Otm59ldORb0kaMGCAPD09FRMTo2LFimn37t0KCQnRp59+qm+++UZVqlTRZ599piFDhrhc0yuvvKKXXnpJiYmJio6OVuPGjTVv3jyVKVNGklSqVCnNnj1bX375papVq6axY8dq2LBhLm+nSJEi8vPzy3aew+HQV199pcKFC6tBgwaKj49X2bJlNWPGDKtN69at9dJLL+nZZ59VbGysUlJS1KNHD6f1dO3aVR9//LEmTpyoKlWqKC4uTpMmTbJey/XgMJef9H+DO3nypAoWLKgTJ05csRMDACDd3Nd07yn+ubtLcIs+hfu4uwTgpvH3339r586dKlOmjHx9fd1dDpDFlfpobrMlR7oBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAgFvdZDdUQj6SkZHxj9dRIA/qAAAAAACXeXl5yeFw6PDhwypWrJgcDoe7SwIkXfxDUFpamg4fPiwPDw95e3tf87oI3QAAAADcwtPTU7fccov27t2rXbt2ubscIAt/f3+VKlVKHh7XfpI4oRsAAACA2wQGBqp8+fI6f/68u0sBnHh6eqpAgQL/+AwMQjcAAAAAt/L09JSnp6e7ywBswUBqAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBN/hWh+/3331dkZKR8fX1Vp04d/fjjjzm2nTRpkhwOh9PD19f3OlYLAAAAAEDuuD10z5gxQ/3799fgwYO1fv16VatWTQkJCTp06FCOywQHB2v//v3WIyUl5TpWDAAAAABA7rg9dI8cOVLdunVTp06dFBMTo7Fjx8rf318TJkzIcRmHw6GwsDDrERoaeh0rBgAAAAAgd9wautPS0rRu3TrFx8db0zw8PBQfH6/Vq1fnuNzp06dVunRpRUREqEWLFtq8eXOObVNTU3Xy5EmnBwAAAAAA14NbQ/eRI0eUnp6e5Uh1aGioDhw4kO0yFStW1IQJE/TVV1/p008/VUZGhurVq6e9e/dm2z4xMVEFCxa0HhEREXn+OgAAAAAAyI7bTy93Vd26ddW+fXtVr15dcXFxmjNnjooVK6YPP/ww2/YDBw7UiRMnrMeePXuuc8UAAAAAgJtVAXduvGjRovL09NTBgwedph88eFBhYWG5WoeXl5dq1Kih7du3Zzvfx8dHPj4+/7hWAAAAAABc5dYj3d7e3oqNjdWSJUusaRkZGVqyZInq1q2bq3Wkp6dr06ZNCg8Pt6tMAAAAAACuiVuPdEtS//791aFDB9WqVUu1a9fWqFGjdObMGXXq1EmS1L59e5UsWVKJiYmSpJdfflm33367ypUrp+PHj+utt95SSkqKunbt6s6XAQAAAABAFm4P3a1bt9bhw4c1aNAgHThwQNWrV9f8+fOtwdV2794tD4//OyB/7NgxdevWTQcOHFDhwoUVGxurVatWKSYmxl0vAQAAAACAbDmMMcbdRVxPJ0+eVMGCBXXixAkFBwe7uxwAwL/cnC373V2C2+wp/rm7S3CLPoX7uLsEAEA+kNtsme9GLwcAAAAAIL8gdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGCTAu4uAABuRKOPjXZ3CW7Rp3Afd5cAAADwr8KRbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAAAAAABsQugGAAAAAMAmhG4AAAAAAGxSwN0FALixzdmy390luEdxdxcAAACAfwNCNwAAwA3uZv0D6IMVw91dAgBwejkAAAAAAHbhSDcAAABuSKOPjXZ3CW7Tp3Afd5cA4P8jdAMAAAD41+MyCeRXnF4OAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANikgLsLAC43+thod5fgFn0K93F3CQAAAADyGEe6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmjl/+Lzdmy390luEdxdxcAAAAAAHmD0A0AAAAA/1LcTjf/4/RyAAAAAABs8q8I3e+//74iIyPl6+urOnXq6Mcff8zVctOnT5fD4VDLli3tLRAAAAAAgGvg9tA9Y8YM9e/fX4MHD9b69etVrVo1JSQk6NChQ1dcbteuXRowYIDuvPPO61QpAAAAAACucXvoHjlypLp166ZOnTopJiZGY8eOlb+/vyZMmJDjMunp6WrXrp2GDh2qsmXLXsdqAQAAAADIPbeG7rS0NK1bt07x8fHWNA8PD8XHx2v16tU5Lvfyyy+rePHi6tKly/UoEwAAAACAa+LW0cuPHDmi9PR0hYaGOk0PDQ3V77//nu0yK1as0Pjx47Vhw4ZcbSM1NVWpqanW85MnT15zvQAAAAAAuMLtp5e74tSpU3r88cc1btw4FS1aNFfLJCYmqmDBgtYjIiLC5ioBAAAAALjIrUe6ixYtKk9PTx08eNBp+sGDBxUWFpal/Y4dO7Rr1y41a9bMmpaRkSFJKlCggLZs2aKoqCinZQYOHKj+/ftbz0+ePEnwBgAAAABcF24N3d7e3oqNjdWSJUus235lZGRoyZIl6tWrV5b2lSpV0qZNm5ymvfjiizp16pRGjx6dbZj28fGRj4+PLfUDAAAAAHAlbg3dktS/f3916NBBtWrVUu3atTVq1CidOXNGnTp1kiS1b99eJUuWVGJionx9fVW5cmWn5QsVKiRJWaYDAAAAAOBubg/drVu31uHDhzVo0CAdOHBA1atX1/z5863B1Xbv3i0Pj3x16TkAAAAAAJL+BaFbknr16pXt6eSSlJSUdMVlJ02alPcFAQAAAACQBziEDAAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYxOXQPXnyZM2bN896/uyzz6pQoUKqV6+eUlJS8rQ4AAAAAADyM5dD97Bhw+Tn5ydJWr16td5//329+eabKlq0qPr165fnBQIAAAAAkF8VcHWBPXv2qFy5cpKkL7/8Ug899JCeeOIJ1a9fXw0bNszr+gAAAAAAyLdcPtIdGBioo0ePSpIWLlyoe+65R5Lk6+urc+fO5W11AAAAAADkYy4f6b7nnnvUtWtX1ahRQ1u3blWTJk0kSZs3b1ZkZGRe1wcAAAAAQL7l8pHu999/X3Xr1tXhw4c1e/ZsFSlSRJK0bt06tWnTJs8LBAAAAAAgv3L5SHehQoX03nvvZZk+dOjQPCkIAAAAAIAbxTXdp3v58uV67LHHVK9ePe3bt0+SNGXKFK1YsSJPiwMAAAAAID9zOXTPnj1bCQkJ8vPz0/r165WamipJOnHihIYNG5bnBQIAAAAAkF+5HLpfffVVjR07VuPGjZOXl5c1vX79+lq/fn2eFgcAAAAAQH7mcujesmWLGjRokGV6wYIFdfz48byoCQAAAACAG4LLoTssLEzbt2/PMn3FihUqW7ZsnhQFAAAAAMCNwOXQ3a1bN/Xp00dr1qyRw+HQn3/+qalTp2rAgAHq0aOHHTUCAAAAAJAvuXzLsOeff14ZGRm6++67dfbsWTVo0EA+Pj4aMGCAnnrqKTtqBAAAAAAgX3IpdKenp2vlypV68skn9cwzz2j79u06ffq0YmJiFBgYaFeNAAAAAADkSy6Fbk9PT917771KTk5WoUKFFBMTY1ddAAAAAADkey5f0125cmX98ccfdtQCAAAAAMAN5Zru0z1gwADNnTtX+/fv18mTJ50eAAAAAADgIpcHUmvSpIkkqXnz5nI4HNZ0Y4wcDofS09PzrjoAAAAAAPIxl0P30qVL7agDAAAAAIAbjsuhOy4uzo46AAAAAAC44bgcuiXp+PHjGj9+vJKTkyVJt956qzp37qyCBQvmaXEAAAAAAORnLg+k9tNPPykqKkpvv/22/vrrL/31118aOXKkoqKitH79ejtqBAAAAAAgX3L5SHe/fv3UvHlzjRs3TgUKXFz8woUL6tq1q/r27atly5bleZEAAAAAAORHLofun376ySlwS1KBAgX07LPPqlatWnlaHAAAAAAA+ZnLp5cHBwdr9+7dWabv2bNHQUFBeVIUAAAAAAA3ApdDd+vWrdWlSxfNmDFDe/bs0Z49ezR9+nR17dpVbdq0saNGAAAAAADyJZdPLx8+fLgcDofat2+vCxcuSJK8vLzUo0cPvf7663leIAAAAAAA+ZXLodvb21ujR49WYmKiduzYIUmKioqSv79/nhcHAAAAAEB+5nLoPnHihNLT0xUSEqIqVapY0//66y8VKFBAwcHBeVogAAAAAAD5lcvXdD/66KOaPn16lumff/65Hn300TwpCgAAAACAG4HLoXvNmjVq1KhRlukNGzbUmjVr8qQoAAAAAABuBC6H7tTUVGsAtUudP39e586dy5OiAAAAAAC4EbgcumvXrq2PPvooy/SxY8cqNjY2T4oCAAAAAOBG4PJAaq+++qri4+O1ceNG3X333ZKkJUuWaO3atVq4cGGeFwgAAAAAQH7l8pHu+vXra/Xq1YqIiNDnn3+u//3vfypXrpx++eUX3XnnnXbUCAAAAABAvuTykW5Jql69uqZOnZrXtQAAAAAAcEPJdei+cOGC0tPT5ePjY007ePCgxo4dqzNnzqh58+a64447bCkSAAAAAID8KNenl3fr1k29e/e2np86dUq33Xab3n//fS1YsECNGjXSN998c01FvP/++4qMjJSvr6/q1KmjH3/8Mce2c+bMUa1atVSoUCEFBASoevXqmjJlyjVtFwAAAAAAO+U6dK9cuVIPPfSQ9fyTTz5Renq6tm3bpo0bN6p///566623XC5gxowZ6t+/vwYPHqz169erWrVqSkhI0KFDh7JtHxISohdeeEGrV6/WL7/8ok6dOqlTp05asGCBy9sGAAAAAMBOuQ7d+/btU/ny5a3nS5Ys0UMPPaSCBQtKkjp06KDNmze7XMDIkSPVrVs3derUSTExMRo7dqz8/f01YcKEbNs3bNhQDzzwgKKjoxUVFaU+ffqoatWqWrFihcvbBgAAAADATrkO3b6+vjp37pz1/IcfflCdOnWc5p8+fdqljaelpWndunWKj4//v4I8PBQfH6/Vq1dfdXljjJYsWaItW7aoQYMGLm0bAAAAAAC75Tp0X3rt9PLly3Xw4EHddddd1vwdO3aoRIkSLm38yJEjSk9PV2hoqNP00NBQHThwIMflTpw4ocDAQHl7e6tp06Z69913dc8992TbNjU1VSdPnnR6AAAAAABwPeR69PJBgwbpvvvu0+eff679+/erY8eOCg8Pt+Z/8cUXql+/vi1FXi4oKEgbNmzQ6dOntWTJEvXv319ly5ZVw4YNs7RNTEzU0KFDr0tdAAAAAABcKtehOy4uTuvWrdPChQsVFhamRx55xGl+9erVVbt2bZc2XrRoUXl6eurgwYNO0w8ePKiwsLAcl/Pw8FC5cuWs7SYnJysxMTHb0D1w4ED179/fen7y5ElFRES4VCcAAAAAANci16FbkqKjoxUdHZ3tvCeeeMLljXt7eys2NlZLlixRy5YtJUkZGRlasmSJevXqlev1ZGRkKDU1Ndt5Pj4+TvcWBwAAAADgenEpdNuhf//+6tChg2rVqqXatWtr1KhROnPmjDp16iRJat++vUqWLKnExERJF08Xr1WrlqKiopSamqpvvvlGU6ZM0ZgxY9z5MgAAAAAAyMLtobt169Y6fPiwBg0apAMHDqh69eqaP3++Nbja7t275eHxf+O9nTlzRj179tTevXvl5+enSpUq6dNPP1Xr1q3d9RIAAAAAAMiW20O3JPXq1SvH08mTkpKcnr/66qt69dVXr0NVAAAAAAD8M7m+ZRgAAAAAAHDNNYXu48eP6+OPP9bAgQP1119/SZLWr1+vffv25WlxAAAAAADkZy6fXv7LL78oPj5eBQsW1K5du9StWzeFhIRozpw52r17tz755BM76gQAAAAAIN9x+Uh3//791bFjR23btk2+vr7W9CZNmmjZsmV5WhwAAAAAAPmZy6F77dq16t69e5bpJUuW1IEDB/KkKAAAAAAAbgQuh24fHx+dPHkyy/StW7eqWLFieVIUAAAAAAA3ApdDd/PmzfXyyy/r/PnzkiSHw6Hdu3frueee00MPPZTnBQIAAAAAkF+5HLpHjBih06dPq3jx4jp37pzi4uJUrlw5BQUF6bXXXrOjRgAAAAAA8iWXRy8vWLCgFi1apBUrVuiXX37R6dOnVbNmTcXHx9tRHwAAAAAA+ZbLoTvTHXfcoTvuuCMvawEAAAAA4Ibicuh+5513sp3ucDjk6+urcuXKqUGDBvL09PzHxQEAAAAAkJ+5HLrffvttHT58WGfPnlXhwoUlSceOHZO/v78CAwN16NAhlS1bVkuXLlVERESeFwwAAAAAQH7h8kBqw4YN02233aZt27bp6NGjOnr0qLZu3ao6depo9OjR2r17t8LCwtSvXz876gUAAAAAIN9w+Uj3iy++qNmzZysqKsqaVq5cOQ0fPlwPPfSQ/vjjD7355pvcPgwAAAAAcNNz+Uj3/v37deHChSzTL1y4oAMHDkiSSpQooVOnTv3z6gAAAAAAyMdcDt2NGjVS9+7d9fPPP1vTfv75Z/Xo0UN33XWXJGnTpk0qU6ZM3lUJAAAAAEA+5HLoHj9+vEJCQhQbGysfHx/5+PioVq1aCgkJ0fjx4yVJgYGBGjFiRJ4XCwAAAABAfuLyNd1hYWFatGiRfv/9d23dulWSVLFiRVWsWNFq06hRo7yrEAAAAACAfMrl0J2pUqVKqlSpUl7WAgAAAADADeWaQvfevXv19ddfa/fu3UpLS3OaN3LkyDwpDAAAAACA/M7l0L1kyRI1b95cZcuW1e+//67KlStr165dMsaoZs2adtQIAAAAAEC+5PJAagMHDtSAAQO0adMm+fr6avbs2dqzZ4/i4uL0yCOP2FEjAAAAAAD5ksuhOzk5We3bt5ckFShQQOfOnVNgYKBefvllvfHGG3leIAAAAAAA+ZXLoTsgIMC6jjs8PFw7duyw5h05ciTvKgMAAAAAIJ9z+Zru22+/XStWrFB0dLSaNGmip59+Wps2bdKcOXN0++2321EjAAAAAAD5ksuhe+TIkTp9+rQkaejQoTp9+rRmzJih8uXLM3I5AAAAAACXcCl0p6ena+/evapataqki6eajx071pbCAAAAAADI71y6ptvT01P33nuvjh07Zlc9AAAAAADcMFweSK1y5cr6448/7KgFAAAAAIAbisuh+9VXX9WAAQM0d+5c7d+/XydPnnR6AAAAAACAi1weSK1JkyaSpObNm8vhcFjTjTFyOBxKT0/Pu+oAAAAAAMjHXA7dS5cutaMOAAAAAABuOC6H7ri4ODvqAAAAAADghuPyNd2StHz5cj322GOqV6+e9u3bJ0maMmWKVqxYkafFAQAAAACQn7kcumfPnq2EhAT5+flp/fr1Sk1NlSSdOHFCw4YNy/MCAQAAAADIr65p9PKxY8dq3Lhx8vLysqbXr19f69evz9PiAAAAAADIz1wO3Vu2bFGDBg2yTC9YsKCOHz+eFzUBAAAAAHBDcDl0h4WFafv27Vmmr1ixQmXLls2TogAAAAAAuBG4HLq7deumPn36aM2aNXI4HPrzzz81depUDRgwQD169LCjRgAAAAAA8iWXbxn2/PPPKyMjQ3fffbfOnj2rBg0ayMfHRwMGDNBTTz1lR40AAAAAAORLLoduh8OhF154Qc8884y2b9+u06dPKyYmRoGBgXbUBwAAAABAvuXy6eWffvqpzp49K29vb8XExKh27doEbgAAAAAAsuFy6O7Xr5+KFy+utm3b6ptvvlF6eroddQEAAAAAkO+5HLr379+v6dOny+FwqFWrVgoPD9eTTz6pVatW2VEfAAAAAAD5lsuhu0CBArr//vs1depUHTp0SG+//bZ27dqlRo0aKSoqyo4aAQAAAADIl1weSO1S/v7+SkhI0LFjx5SSkqLk5OS8qgsAAAAAgHzP5SPdknT27FlNnTpVTZo0UcmSJTVq1Cg98MAD2rx5c17XBwAAAABAvuXyke5HH31Uc+fOlb+/v1q1aqWXXnpJdevWtaM2AAAAAADyNZdDt6enpz7//HMlJCTI09PTad6vv/6qypUr51lxAAAAAADkZy6H7qlTpzo9P3XqlD777DN9/PHHWrduHbcQAwAAAADg/7uma7oladmyZerQoYPCw8M1fPhw3XXXXfrhhx/ysjYAAAAAAPI1l450HzhwQJMmTdL48eN18uRJtWrVSqmpqfryyy8VExNjV40AAAAAAORLuT7S3axZM1WsWFG//PKLRo0apT///FPvvvuunbUBAAAAAJCv5fpI97fffqvevXurR48eKl++vJ01AQAAAABwQ8j1ke4VK1bo1KlTio2NVZ06dfTee+/pyJEjdtYGAAAAAEC+luvQffvtt2vcuHHav3+/unfvrunTp6tEiRLKyMjQokWLdOrUKTvrBAAAAAAg33F59PKAgAB17txZK1as0KZNm/T000/r9ddfV/HixdW8eXM7agQAAAAAIF+65luGSVLFihX15ptvau/evfrss8/yqiYAAAAAAG4I/yh0Z/L09FTLli319ddf58XqAAAAAAC4IeRJ6AYAAAAAAFkRugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALAJoRsAAAAAAJsQugEAAAAAsAmhGwAAAAAAmxC6AQAAAACwCaEbAAAAAACbELoBAAAAALDJvyJ0v//++4qMjJSvr6/q1KmjH3/8Mce248aN05133qnChQurcOHCio+Pv2J7AAAAAADcxe2he8aMGerfv78GDx6s9evXq1q1akpISNChQ4eybZ+UlKQ2bdpo6dKlWr16tSIiInTvvfdq375917lyAAAAAACuzO2he+TIkerWrZs6deqkmJgYjR07Vv7+/powYUK27adOnaqePXuqevXqqlSpkj7++GNlZGRoyZIl17lyAAAAAACuzK2hOy0tTevWrVN8fLw1zcPDQ/Hx8Vq9enWu1nH27FmdP39eISEh2c5PTU3VyZMnnR4AAAAAAFwPbg3dR44cUXp6ukJDQ52mh4aG6sCBA7lax3PPPacSJUo4BfdLJSYmqmDBgtYjIiLiH9cNAAAAAEBuuP308n/i9ddf1/Tp0/XFF1/I19c32zYDBw7UiRMnrMeePXuuc5UAAAAAgJtVAXduvGjRovL09NTBgwedph88eFBhYWFXXHb48OF6/fXXtXjxYlWtWjXHdj4+PvLx8cmTegEAAAAAcIVbj3R7e3srNjbWaRC0zEHR6tatm+Nyb775pl555RXNnz9ftWrVuh6lAgAAAADgMrce6Zak/v37q0OHDqpVq5Zq166tUaNG6cyZM+rUqZMkqX379ipZsqQSExMlSW+88YYGDRqkadOmKTIy0rr2OzAwUIGBgW57HQAAAAAAXM7tobt169Y6fPiwBg0apAMHDqh69eqaP3++Nbja7t275eHxfwfkx4wZo7S0ND388MNO6xk8eLCGDBlyPUsHAAAAAOCK3B66JalXr17q1atXtvOSkpKcnu/atcv+ggAAAAAAyAP5evRyAAAAAAD+zQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANiE0A0AAAAAgE0I3QAAAAAA2ITQDQAAAACATQjdAAAAAADYhNANAAAAAIBNCN0AAAAAANjE7aH7/fffV2RkpHx9fVWnTh39+OOPObbdvHmzHnroIUVGRsrhcGjUqFHXr1AAAAAAAFzk1tA9Y8YM9e/fX4MHD9b69etVrVo1JSQk6NChQ9m2P3v2rMqWLavXX39dYWFh17laAAAAAABc49bQPXLkSHXr1k2dOnVSTEyMxo4dK39/f02YMCHb9rfddpveeustPfroo/Lx8bnO1QIAAAAA4Bq3he60tDStW7dO8fHx/1eMh4fi4+O1evXqPNtOamqqTp486fQAAAAAAOB6cFvoPnLkiNLT0xUaGuo0PTQ0VAcOHMiz7SQmJqpgwYLWIyIiIs/WDQAAAADAlbh9IDW7DRw4UCdOnLAee/bscXdJAAAAAICbRAF3bbho0aLy9PTUwYMHnaYfPHgwTwdJ8/Hx4fpvAAAAAIBbuO1It7e3t2JjY7VkyRJrWkZGhpYsWaK6deu6qywAAAAAAPKM2450S1L//v3VoUMH1apVS7Vr19aoUaN05swZderUSZLUvn17lSxZUomJiZIuDr7222+/Wf/ft2+fNmzYoMDAQJUrV85trwMAAAAAgOy4NXS3bt1ahw8f1qBBg3TgwAFVr15d8+fPtwZX2717tzw8/u9g/J9//qkaNWpYz4cPH67hw4crLi5OSUlJ17t8AAAAAACuyK2hW5J69eqlXr16ZTvv8iAdGRkpY8x1qAoAAAAAgH/uhh+9HAAAAAAAdyF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGCTf0Xofv/99xUZGSlfX1/VqVNHP/744xXbz5w5U5UqVZKvr6+qVKmib7755jpVCgAAAABA7rk9dM+YMUP9+/fX4MGDtX79elWrVk0JCQk6dOhQtu1XrVqlNm3aqEuXLvr555/VsmVLtWzZUr/++ut1rhwAAAAAgCtze+geOXKkunXrpk6dOikmJkZjx46Vv7+/JkyYkG370aNHq3HjxnrmmWcUHR2tV155RTVr1tR77713nSsHAAAAAODK3Bq609LStG7dOsXHx1vTPDw8FB8fr9WrV2e7zOrVq53aS1JCQkKO7QEAAAAAcJcC7tz4kSNHlJ6ertDQUKfpoaGh+v3337Nd5sCBA9m2P3DgQLbtU1NTlZqaaj0/ceKEJOnkyZP/pPTr4uzpU+4uwS3+9v3b3SW4xUnPf3+fvBb045vLjdiPb9Y+LNGPbyQ3az++WfuwRD++kdys/Tg/9OHMTGmMuWI7t4bu6yExMVFDhw7NMj0iIsIN1QA5e17Pu7sE4B+jH+NGQD/GjYB+jPwuP/XhU6dOqWDBgjnOd2voLlq0qDw9PXXw4EGn6QcPHlRYWFi2y4SFhbnUfuDAgerfv7/1PCMjQ3/99ZeKFCkih8PxD18B8trJkycVERGhPXv2KDg42N3lANeEfowbAf0Y+R19GDcC+vG/mzFGp06dUokSJa7Yzq2h29vbW7GxsVqyZIlatmwp6WIoXrJkiXr16pXtMnXr1tWSJUvUt29fa9qiRYtUt27dbNv7+PjIx8fHaVqhQoXyonzYKDg4mA8W5Hv0Y9wI6MfI7+jDuBHQj/+9rnSEO5PbTy/v37+/OnTooFq1aql27doaNWqUzpw5o06dOkmS2rdvr5IlSyoxMVGS1KdPH8XFxWnEiBFq2rSppk+frp9++kkfffSRO18GAAAAAABZuD10t27dWocPH9agQYN04MABVa9eXfPnz7cGS9u9e7c8PP5vkPV69epp2rRpevHFF/Xf//5X5cuX15dffqnKlSu76yUAAAAAAJAtt4duSerVq1eOp5MnJSVlmfbII4/okUcesbkquIOPj48GDx6c5ZIAID+hH+NGQD9Gfkcfxo2AfnxjcJirjW8OAAAAAACuicfVmwAAAAAAgGtB6AYAAAAAwCaE7puUMUZPPPGEQkJC5HA4tGHDhiu237VrV67aNWzY0Ol2btk5cOCA7rnnHgUEBOT69m1JSUlyOBw6fvx4rtoD+UHHjh2t2yUCuDaX/37i9wX+DSIjIzVq1Kg8bwsgfyJ036Tmz5+vSZMmae7cudq/f/9VR3+PiIhwavdPvtS8/fbb2r9/vzZs2KCtW7deS/kAgP/Pri/sBAFcK/qktHbtWj3xxBN53hZwh0mTJuX6QBmy968YvRzX344dOxQeHq569erlqr2np6fCwsLybNuxsbEqX758nqwPsFN6erocDofTrQuvJi0tTd7e3jZWBfz7Xct7B3C3vPr8LlasmC1tkT+44/Mvp757/vx5eXl5Xbc6kD1+E96EOnbsqKeeekq7d++Ww+FQZGSk5s+frzvuuEOFChVSkSJFdP/992vHjh3WMpeevrdr1y41atRIklS4cGE5HA517NjRapuRkaFnn31WISEhCgsL05AhQ6x5kZGRmj17tj755BNruexOXT9+/LgcDke2t4yT/u8vbgsWLFB0dLQCAwPVuHFj7d+/36ndxx9/rOjoaPn6+qpSpUr64IMPrHlpaWnq1auXwsPD5evrq9KlSysxMVHSxdPvhwwZolKlSsnHx0clSpRQ7969r3GP40r9K/PnP2fOHDVq1Ej+/v6qVq2aVq9ebS2fkpKiZs2aqXDhwgoICNCtt96qb775RpJUq1YtDR8+3GrbsmVLeXl56fTp05KkvXv3yuFwaPv27ZKk1NRUDRgwQCVLllRAQIDq1Knj1M8y+9bXX3+tmJgY+fj4aPfu3Vd8fZmnib/22msqUaKEKlasKEnas2ePWrVqpUKFCikkJEQtWrTQrl27clxPRkaGEhMTVaZMGfn5+alatWqaNWuWNe+WW27RmDFjnJb5+eef5eHhoZSUFEnSyJEjVaVKFQUEBCgiIkI9e/a09sWlr+9q750JEybo1ltvlY+Pj8LDw63bOnbu3Fn333+/U9vz58+rePHiGj9+/BX3080qIyNDb775psqVKycfHx+VKlVKr732miRp06ZNuuuuu+Tn56ciRYroiSeecPp5Zfat4cOHKzw8XEWKFNGTTz6p8+fPS7p4SU9KSor69esnh8Mhh8NhLbtixQrdeeed8vPzU0REhHr37q0zZ85Ikj755BMFBgZq27ZtVvuePXuqUqVKOnv27BXXm5Oc3jtXe89J0sqVK9WwYUP5+/urcOHCSkhI0LFjxyRd+fMD1+Zm65NffvmlypcvL19fXyUkJGjPnj1WmyFDhqh69er6+OOPVaZMGfn6+kq6+D2ka9euKlasmIKDg3XXXXdp48aNTuv/3//+p9tuu02+vr4qWrSoHnjgAWvepUflr/ad4vIj+Lt371aLFi0UGBio4OBgtWrVSgcPHsxS85QpUxQZGamCBQvq0Ucf1alTp666T/KTG/27g5Tz71op9/3g8r7rcDg0ZswYNW/eXAEBAdZ7+6uvvlLNmjXl6+ursmXLaujQobpw4YK1vuPHj6t79+4KDQ2Vr6+vKleurLlz5yopKUmdOnXSiRMnrPde5nf7yMhIDRs2TJ07d1ZQUJBKlSqljz76yOk1Xu27UFJSkmrXrm1ddlq/fn3rO83GjRvVqFEjBQUFKTg4WLGxsfrpp5+uul//lQxuOsePHzcvv/yyueWWW8z+/fvNoUOHzKxZs8zs2bPNtm3bzM8//2yaNWtmqlSpYtLT040xxuzcudNIMj///LO5cOGCmT17tpFktmzZYvbv32+OHz9ujDEmLi7OBAcHmyFDhpitW7eayZMnG4fDYRYuXGiMMebQoUOmcePGplWrVtZyl64707Fjx4wks3TpUmOMMUuXLjWSzLFjx4wxxkycONF4eXmZ+Ph4s3btWrNu3ToTHR1t2rZta63j008/NeHh4Wb27Nnmjz/+MLNnzzYhISFm0qRJxhhj3nrrLRMREWGWLVtmdu3aZZYvX26mTZtmjDFm5syZJjg42HzzzTcmJSXFrFmzxnz00Ud2/lhuaFfqX5k//0qVKpm5c+eaLVu2mIcfftiULl3anD9/3hhjTNOmTc0999xjfvnlF7Njxw7zv//9z3z//ffGGGP69+9vmjZtaowxJiMjw4SEhJiiRYuab7/91hhzsR+ULFnSqqVr166mXr16ZtmyZWb79u3mrbfeMj4+Pmbr1q3GmP/rW/Xq1TMrV640v//+uzlz5swVX1+HDh1MYGCgefzxx82vv/5qfv31V5OWlmaio6NN586dzS+//GJ+++0307ZtW1OxYkWTmppqLdeiRQtrPa+++qqpVKmSmT9/vtmxY4eZOHGi8fHxMUlJScYYYwYMGGDuuOMOp20//fTTTtPefvtt891335mdO3eaJUuWmIoVK5oePXpY83Pz3vnggw+Mr6+vGTVqlNmyZYv58ccfzdtvv22MMWblypXG09PT/Pnnn1b7OXPmmICAAHPq1Kkr7qeb1bPPPmsKFy5sJk2aZLZv326WL19uxo0bZ06fPm3Cw8PNgw8+aDZt2mSWLFliypQpYzp06GAt26FDBxMcHGz+85//mOTkZPO///3P+Pv7W59HR48eNbfccot5+eWXzf79+83+/fuNMcZs377dBAQEmLffftts3brVrFy50tSoUcN07NjRWvcjjzxibrvtNnP+/Hkzd+5c4+XlZX766acrrvdKcnrvXO099/PPPxsfHx/To0cPs2HDBvPrr7+ad9991xw+fNgYc+XPD2NMlt8hl/++QFY3W5+sVauWWbVqlfnpp59M7dq1Tb169aw2gwcPNgEBAaZx48Zm/fr1ZuPGjcYYY+Lj402zZs3M2rVrzdatW83TTz9tihQpYo4ePWqMMWbu3LnG09PTDBo0yPz2229mw4YNZtiwYdZ6S5cubX1uXu07xaVt09PTTfXq1c0dd9xhfvrpJ/PDDz+Y2NhYExcX51RzYGCg9XNatmyZCQsLM//973+vuk/ykxv9u8OVftfmth9k13clmeLFi5sJEyaYHTt2mJSUFLNs2TITHBxsJk2aZHbs2GEWLlxoIiMjzZAhQ6zt3X777ebWW281CxcutPbXN998Y1JTU82oUaNMcHCw9d7L/H1funRpExISYt5//32zbds2k5iYaDw8PMzvv/9ujDFX/S50/vx5U7BgQTNgwACzfft289tvv5lJkyaZlJQUY4wxt956q3nsscdMcnKy2bp1q/n888/Nhg0brq1DuRmh+yb19ttvm9KlS+c4//Dhw0aS2bRpkzEm919q4uLisoSC2267zTz33HPW8xYtWjj9Ar/W0C3JbN++3Vrm/fffN6GhodbzqKgoK0RneuWVV0zdunWNMcY89dRT5q677jIZGRlZXv+IESNMhQoVTFpaWo77CNfu0v6V+fP/+OOPrfmbN282kkxycrIxxpgqVapYvxgu9/XXX5uCBQuaCxcumA0bNpiwsDDTp08fq8917drVCpQpKSnG09PT7Nu3z2kdd999txk4cKAx5v/6lisf6h06dDChoaFWmDbGmClTppiKFSs69a/U1FTj5+dnFixYYC2XGbr//vtv4+/vb1atWuW07i5dupg2bdoYYy6GE4fDYf0ySk9PNyVLljRjxozJsbaZM2eaIkWKWM9z894pUaKEeeGFF3JcZ0xMjHnjjTes582aNXP64oz/c/LkSePj42PGjRuXZd5HH31kChcubE6fPm1NmzdvnvHw8DAHDhwwxlzsI6VLlzYXLlyw2jzyyCOmdevW1vNLv7Bn6tKli3niiSecpi1fvtx4eHiYc+fOGWOM+euvv8wtt9xievToYUJDQ81rr73m1D679V5Jdu+d3Lzn2rRpY+rXr5/r7Vzr7ydcdDP2yR9++MGalpycbCSZNWvWGGMuBhcvLy9z6NAhp7qCg4PN33//7bS+qKgo8+GHHxpjjKlbt65p165djtu+tNarfae4tO3ChQuNp6en2b17tzU/83fijz/+aNXs7+9vTp48abV55plnTJ06da62S/K1G+27w5V+1+a2H1zed425GLr79u2bpdZL/yhkzMXvKeHh4cYYYxYsWGA8PDzMli1bsq1n4sSJpmDBglmmly5d2jz22GPW84yMDFO8eHHre8nVvgsdPXrUSLIOLlwuKCjIOliW33F6OSRJ27ZtU5s2bVS2bFkFBwcrMjJSknJ1aszlqlat6vQ8PDxchw4dyosynfj7+ysqKirb7Zw5c0Y7duxQly5dFBgYaD1effVV69Skjh07asOGDapYsaJ69+6thQsXWut65JFHdO7cOZUtW1bdunXTF1984XQKDlyTm/51ab8JDw+XJOvn2bt3b7366quqX7++Bg8erF9++cVqe+edd+rUqVP6+eef9f333ysuLk4NGza0Tvv6/vvv1bBhQ0kXT5tMT09XhQoVnPrF999/73S6qre3d5Z+fDVVqlRxupZq48aN2r59u4KCgqzthISE6O+//8721Njt27fr7Nmzuueee5xq++STT6z21atXV3R0tKZNm2a9tkOHDumRRx6x1rN48WLdfffdKlmypIKCgvT444/r6NGjOnv2rNXmSu+dQ4cO6c8//9Tdd9+d42vt2rWrJk6cKEk6ePCgvv32W3Xu3Nml/XWzSE5OVmpqarb7Mzk5WdWqVVNAQIA1rX79+srIyNCWLVusabfeeqs8PT2t57n5TN24caMmTZrk1JcSEhKUkZGhnTt3Srp4edD48eM1ZswYRUVF6fnnn/+nLzfLeyc377kNGzZcsb/l5e8n3Hx9skCBArrtttus55UqVVKhQoWUnJxsTStdurTTddUbN27U6dOnVaRIEad6d+7cmet+eylXvlMkJycrIiJCERER1rSYmJgsNUdGRiooKMh6btd3LXe6kb87XO13bW77weV9N1OtWrWcnm/cuFEvv/yyU/3dunXT/v37dfbsWW3YsEG33HKLKlSokKv6L3Xpa3Y4HAoLC7N+Blf7LhQSEqKOHTsqISFBzZo10+jRo50ud+vfv7+6du2q+Ph4vf766/n60iIGUoMkqVmzZipdurTGjRunEiVKKCMjQ5UrV1ZaWprL67p8sAaHw6GMjIwc22cOMmGMsaZlXhvm6nYy15F5Tc64ceNUp04dp3aZXxRq1qypnTt36ttvv9XixYvVqlUrxcfHa9asWYqIiNCWLVu0ePFiLVq0SD179tRbb72l77//nsEorkFu+tel+zXzWr3MftO1a1clJCRo3rx5WrhwoRITEzVixAg99dRTKlSokKpVq6akpCStXr1a99xzjxo0aKDWrVtr69at2rZtm+Li4iRd7Beenp5at26d0xdGSQoMDLT+7+fnl6vrBS916ZfUzG3FxsZq6tSpWdpm9wsys8/OmzdPJUuWdJrn4+Nj/b9du3aaNm2ann/+eU2bNk2NGzdWkSJFJF28xu3+++9Xjx499NprrykkJEQrVqxQly5dlJaWJn9/f0lXfu/4+fld9bW2b99ezz//vFavXq1Vq1apTJkyuvPOO6+63M0oN/vzalz9TJUu9qfu3btnOxZFqVKlrP8vW7ZMnp6e2r9/v86cOeP0Jf5aXP7eyc177mr7KC9/P+Hm65O5kd3nd3h4eLbjymSO4OzKfrTjO8W1/Azymxv5u0NevA+lrH03p+mnT5/W0KFD9eCDD2Zp6+vr+4/quVJfzM13oYkTJ6p3796aP3++ZsyYoRdffFGLFi3S7bffriFDhqht27aaN2+evv32Ww0ePFjTp093Gj8hv+BIN3T06FFt2bJFL774ou6++25FR0dbA9jkJPOIXnp6+j/efuab7tK/bF3tfuBXExoaqhIlSuiPP/5QuXLlnB5lypSx2gUHB6t169YaN26cZsyYodmzZ+uvv/6SdPEDsVmzZnrnnXesD+VNmzb9o7puRtfSv7ITERGh//znP5ozZ46efvppjRs3zpoXFxenpUuXatmyZWrYsKFCQkIUHR2t1157TeHh4dZfbmvUqKH09HQdOnQoS7/Iq9H5M9WsWVPbtm1T8eLFs2yrYMGCWdpfOvDK5e0v/Ut327Zt9euvv2rdunWaNWuW2rVrZ81bt26dMjIyNGLECN1+++2qUKGC/vzzT5fqDgoKUmRkpJYsWZJjmyJFiqhly5aaOHGiJk2apE6dOrm0jZtJ+fLl5efnl+3+jI6O1saNG62BpKSLA4p5eHhYg/Hlhre3d5bP4po1a+q3337L0pfKlStnfX6vWrVKb7zxhv73v/8pMDDQaQCfnNbrqty856pWrZpjf8urzw/8n5utT164cMFp4KUtW7bo+PHjio6OznGZmjVr6sCBAypQoECWWosWLSrpyv02O7n9ThEdHa09e/Y4Dfb222+/6fjx44qJicn19vK7G/27w9V+1+Z1P6hZs6a2bNmS7fvPw8NDVatW1d69e3O8le+1/j7I7XehGjVqaODAgVq1apUqV65sndEnSRUqVFC/fv20cOFCPfjgg9aZdvkNoRsqXLiwihQpoo8++kjbt2/Xd999p/79+19xmdKlS8vhcGju3Lk6fPiw08imrvLz89Ptt9+u119/XcnJyfr+++/14osvXvP6Mg0dOlSJiYl65513tHXrVm3atEkTJ07UyJEjJV0c5fmzzz7T77//rq1bt2rmzJkKCwtToUKFNGnSJI0fP16//vqr/vjjD3366afy8/NT6dKl/3FdN5tr6V+X69u3rxYsWKCdO3dq/fr1Wrp0qdMXpoYNG2rBggUqUKCAKlWqZE2bOnWq9Zdq6eIHd7t27dS+fXvNmTNHO3fu1I8//qjExETNmzcvb17w/9euXTsVLVpULVq00PLly7Vz504lJSWpd+/e2rt3b5b2QUFBGjBggPr166fJkydrx44dWr9+vd59911NnjzZahcZGal69eqpS5cuSk9PV/Pmza155cqV0/nz5/Xuu+/qjz/+0JQpUzR27FiXax8yZIhGjBihd955R9u2bbPquFTXrl01efJkJScnq0OHDi5v42bh6+ur5557Ts8++6x1qcAPP/yg8ePHq127dvL19VWHDh3066+/aunSpXrqqaf0+OOPKzQ0NNfbiIyM1LJly7Rv3z4dOXJEkvTcc89p1apV6tWrlzZs2KBt27bpq6++skLMqVOn9Pjjj6t379667777NHXqVM2YMcMaLT+n9boqN++5gQMHau3aterZs6d++eUX/f777xozZoyOHDmSJ58fcHaz9UkvLy899dRTWrNmjdatW6eOHTvq9ttvV+3atXNcJj4+XnXr1lXLli21cOFC7dq1S6tWrdILL7xgBfjBgwfrs88+0+DBg5WcnKxNmzbpjTfeyHZ9rnyniI+PV5UqVdSuXTutX79eP/74o9q3b6+4uLgspwzfyG6G7w5X+l2b1/1g0KBB+uSTTzR06FBt3rxZycnJmj59uvV9Oy4uTg0aNNBDDz2kRYsWWWeCzp8/X9LF997p06e1ZMkSHTlyxOmStSu52nehnTt3auDAgVq9erVSUlK0cOFCbdu2TdHR0Tp37px69eqlpKQkpaSkaOXKlVq7du0V/2D2r+bma8rhJpcPpLZo0SITHR1tfHx8TNWqVU1SUpKRZL744gtjTPaDnb388ssmLCzMOBwOa2C0uLg406dPH6dtXT5w2uXPjTHmt99+M3Xr1jV+fn6mevXqZuHChVcdSO3yAR2++OILc3mXnjp1qqlevbrx9vY2hQsXNg0aNDBz5swxxlwcMKZ69eomICDABAcHm7vvvtusX7/eWledOnVMcHCwCQgIMLfffrtZvHhxrvYtsrpS/8rNQHq9evUyUVFRxsfHxxQrVsw8/vjj5siRI1b7o0ePGofD4TSQT2Z/GDt2rFMtaWlpZtCgQSYyMtJ4eXmZ8PBw88ADD5hffvnFGJPzYCFXcvko5Jn2799v2rdvb4oWLWp8fHxM2bJlTbdu3cyJEyeyXS4jI8OMGjXKVKxY0Xh5eZlixYqZhIQEa7TVTB988IGRZNq3b59lmyNHjjTh4eHGz8/PJCQkmE8++eSa3jtjx4616ggPDzdPPfWU0/yMjAxTunRp06RJk1zupZtXenq6efXVV03p0qWNl5eXKVWqlDWgzS+//GIaNWpkfH19TUhIiOnWrZvTKPDZ9a0+ffo4jWC7evVqU7VqVePj4+P0c/zxxx/NPffcYwIDA01AQICpWrWqNTBVp06dTJUqVZwGihoxYoQJCQkxe/fuveJ6c5LTe+dq7zljjElKSjL16tUzPj4+plChQiYhIcHqs67+fmIgtau72frk7NmzTdmyZY2Pj4+Jj4+3BqM05uJgVNWqVcuy7MmTJ81TTz1lSpQoYby8vExERIRp166d08BWs2fPtr5jFC1a1Dz44IPWvEsHR7vad4rLB4hLSUkxzZs3NwEBASYoKMg88sgj1kB2OdV8tQFy86Mb/buDMVf+XXst/cAY4/T5eKn58+ebevXqGT8/PxMcHGxq167tNIr+0aNHTadOnUyRIkWMr6+vqVy5spk7d641/z//+Y8pUqSIkWQGDx5sjMl+cMNq1apZ84258nehAwcOmJYtW5rw8HDj7e1tSpcubQYNGmTS09NNamqqefTRR01ERITx9vY2JUqUML169bIGXsxvHMZcciEtAAC5cPr0aZUsWVITJ07M9hoxAHC3SZMmqW/fvjp+/Li7SwFwk2MgNQBArmVkZOjIkSMaMWKEChUq5HR6OwAAALLimm4AuIpLb7Fx+WP58uXuLu+62r17t0JDQzVt2jRNmDBBBQrwt9ubxX333Zfj+2DYsGHuLg83Ifok/s347oBLcXo5AFzF9u3bc5xXsmTJPLv1B/Bvtm/fPp07dy7beSEhIQoJCbnOFeFmR5/EvxnfHXApQjcAAAAAADbh9HIAAAAAAGxC6AYAAAAAwCaEbgAAAAAAbELoBgAAAADAJoRuAADgMofDoS+//NLdZQAA8K9H6AYAIJ/q2LGjHA6H/vOf/2SZ9+STT8rhcKhjx465WldSUpIcDoeOHz+eq/b79+/Xfffd50K1AADcnAjdAADkYxEREZo+fbrT/Yr//vtvTZs2TaVKlcrz7aWlpUmSwsLC5OPjk+frBwDgRkPoBgAgH6tZs6YiIiI0Z84ca9qcOXNUqlQp1ahRw5qWkZGhxMRElSlTRn5+fqpWrZpmzZolSdq1a5caNWokSSpcuLDTEfKGDRuqV69e6tu3r4oWLaqEhARJWU8v37t3r9q0aaOQkBAFBASoVq1aWrNmjSRp48aNatSokYKCghQcHKzY2Fj99NNPdu4WAAD+NQq4uwAAAPDPdO7cWRMnTlS7du0kSRMmTFCnTp2UlJRktUlMTNSnn36qsWPHqnz58lq2bJkee+wxFStWTHfccYdmz56thx56SFu2bFFwcLD8/PysZSdPnqwePXpo5cqV2W7/9OnTiouLU8mSJfX1118rLCxM69evV0ZGhiSpXbt2qlGjhsaMGSNPT09t2LBBXl5e9u0QAAD+RQjdAADkc4899pgGDhyolJQUSdLKlSs1ffp0K3SnpqZq2LBhWrx4serWrStJKlu2rFasWKEPP/xQcXFxCgkJkSQVL15chQoVclp/+fLl9eabb+a4/WnTpunw4cNau3attZ5y5cpZ83fv3q1nnnlGlSpVstYHAMDNgtANAEA+V6xYMTVt2lSTJk2SMUZNmzZV0aJFrfnbt2/X2bNndc899zgtl5aW5nQKek5iY2OvOH/Dhg2qUaOGFbgv179/f3Xt2lVTpkxRfHy8HnnkEUVFReXilQEAkP8RugEAuAF07txZvXr1kiS9//77TvNOnz4tSZo3b55KlizpNC83g6EFBARccf6lp6JnZ8iQIWrbtq3mzZunb7/9VoMHD9b06dP1wAMPXHXbAADkdwykBgDADaBx48ZKS0vT+fPnrcHOMsXExMjHx0e7d+9WuXLlnB4RERGSJG9vb0lSenq6y9uuWrWqNmzYoL/++ivHNhUqVFC/fv20cOFCPfjgg5o4caLL2wEAID8idAMAcAPw9PRUcnKyfvvtN3l6ejrNCwoK0oABA9SvXz9NnjxZO3bs0Pr16/Xuu+9q8uTJkqTSpUvL4XBo7ty5Onz4sHV0PDfatGmjsLAwtWzZUitXrtQff/yh2bNna/Xq1Tp37px69eqlpKQkpaSkaOXKlVq7dq2io6Pz9PUDAPBvRegGAOAGERwcrODg4GznvfLKK3rppZeUmJio6OhoNW7cWPPmzVOZMmUkSSVLltTQoUP1/PPPKzQ01DpVPTe8vb21cOFCFS9eXE2aNFGVKlX0+uuvy9PTU56enjp69Kjat2+vChUqqFWrVrrvvvs0dOjQPHnNAAD82zmMMcbdRQAAAAAAcCPiSDcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGATQjcAAAAAADYhdAMAAAAAYBNCNwAAAAAANiF0AwAAAABgE0I3AAAAAAA2IXQDAAAAAGCT/wedgWoP1gkztwAAAABJRU5ErkJggg==\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"qZCUBAPvL7ak"},"source":["# RAGAS Evaluation for chunking strategy"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"3UOpKSFRL7ak","executionInfo":{"status":"ok","timestamp":1727205417584,"user_tz":-60,"elapsed":183202,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"outputs":[],"source":["from langchain.text_splitter import RecursiveCharacterTextSplitter\n","from langchain_experimental.text_splitter import SemanticChunker\n","\n","# Using fine_tuned_embedding_model from before\n","\n","# First Semantic chunker\n","\n","semantic_chunker = SemanticChunker(\n","            embedding_model,\n","            buffer_size=3,\n","            breakpoint_threshold_type=\"percentile\",\n","            breakpoint_threshold_amount=90,\n","        )\n","\n","# second Splitter RecursiveCharacterTextSplitter\n","recursive_text_splitter = RecursiveCharacterTextSplitter(\n","    chunk_size = 1024,\n","    chunk_overlap = 100,\n","    length_function = len,\n",")\n","recursive_chunked_docs = recursive_text_splitter.split_documents(documents)\n","\n","#### Create two rag chains with different chunking strategy\n","# Semantic chunker\n","rag_chain_semantic,retriever_semantic  = await create_rag_chain(\n","    fine_tuned_embedding_model,\n","    semantic_chunker,\n","    \"snowflake-arctic-embed-l-ft\"\n",")\n","\n","# Recursive splitter\n","rag_chain_recursive, retriever_recursive = await create_rag_chain(\n","    fine_tuned_embedding_model,\n","    recursive_text_splitter,\n","    \"snowflake-arctic-embed-l-ft\"\n",")\n","\n"]},{"cell_type":"code","source":["# Run evaluation for the semantic chunker based rag chain\n","semantic_df = ragas_evaluate(rag_chain_semantic)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":138,"referenced_widgets":["72584a70d8124b18bc44fe171ac66cb7","af0e6592a8ee461eae997c727ae0696d","3b1cbb8a7f84466fbbb0cf9cada079c6","23ab547479c744768ab08064c13460cf","ac1518d71ead440b974f2bf229355c34","720aeba021664c8aa0ecf11190887e3d","1717ad2782e74fad8a31be5202b255cc","85c4801f23ba4d89a2cfd8326ca2d20b","8d07456aedcd432ab08c24c87a7fbb5f","65faaa51a55d429eb73557af3d7300a1","1de250ea2d4d41e5bcef954fdc324c67"]},"id":"AR9BjnvamCt9","executionInfo":{"status":"ok","timestamp":1727206043209,"user_tz":-60,"elapsed":225207,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"f9a40abc-f3db-4867-96a5-af1b1865d249"},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":["Evaluating:   0%|          | 0/245 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"72584a70d8124b18bc44fe171ac66cb7"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/datasets/table.py:1395: FutureWarning: promote has been superseded by mode='default'.\n","  block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]\n","/usr/local/lib/python3.10/dist-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by mode='default'.\n","  table = cls._concat_blocks(blocks, axis=0)\n"]}]},{"cell_type":"code","source":["# Run evaluation for the Recursive splitter based rag chain\n","recursive_df = ragas_evaluate(rag_chain_recursive)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156,"referenced_widgets":["58e5a572ee4148348d8199053712ac3e","19f8ff94cbb7468cada40d9f4c2e67d6","380455b7165442c9bc96b4801173e140","920d3cb46cec45d38678208137515e01","11d120a2e64648988bdbf98a948a471c","8419daae397541d68251585260938892","f6c6517ada1645ae87e6171a9ff1554f","333d4a896fb44f6496376686d9fb9624","c52ca5ce98f44595b2f693b030023d85","081445d434fa4a24910771ef0f02d770","f8ea4e64045646f492aac278250bc7e8"]},"id":"lFqwkt7SmCry","executionInfo":{"status":"ok","timestamp":1727206326197,"user_tz":-60,"elapsed":251611,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"62e0c72c-59a8-44eb-a1b1-e4b66ff90cba"},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":["Evaluating:   0%|          | 0/245 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"58e5a572ee4148348d8199053712ac3e"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:ragas.metrics._faithfulness:No statements were generated from the answer.\n","/usr/local/lib/python3.10/dist-packages/datasets/table.py:1395: FutureWarning: promote has been superseded by mode='default'.\n","  block_group = [InMemoryTable(cls._concat_blocks(list(block_group), axis=axis))]\n","/usr/local/lib/python3.10/dist-packages/datasets/table.py:1421: FutureWarning: promote has been superseded by mode='default'.\n","  table = cls._concat_blocks(blocks, axis=0)\n"]}]},{"cell_type":"code","source":["semantic_df.to_csv('semantic_df', index=False)"],"metadata":{"id":"Euo4bukWmCnd","executionInfo":{"status":"ok","timestamp":1727206061032,"user_tz":-60,"elapsed":435,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["recursive_df.to_csv('recursive_df', index=False)"],"metadata":{"id":"5j1PZHpPmCkm","executionInfo":{"status":"ok","timestamp":1727206340134,"user_tz":-60,"elapsed":485,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}}},"execution_count":31,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","metrics = ['faithfulness', 'answer_relevancy', 'context_recall', 'context_precision', 'answer_correctness']\n","\n","# Calculate the average scores for each metric in both semantic and recursive rag\n","semantic_avg = semantic_df[metrics].mean()\n","recursive_avg = recursive_df[metrics].mean()\n","\n","# Calculate improvement (semantic and recursive)\n","improvement = semantic_avg - recursive_avg\n","\n","# Create a summary dataframe\n","summary_df = pd.DataFrame({\n","    'Semantic RAG': semantic_avg,\n","    'Recursive RAG': recursive_avg,\n","    'Improvement': improvement\n","})\n","\n","# ------------------ Plotting the Bar Graph ------------------\n","# Labels for metrics\n","labels = metrics\n","\n","# Set width for bars\n","bar_width = 0.35\n","\n","# Set positions for the bars\n","index = np.arange(len(labels))\n","\n","# Create the figure and the subplots\n","fig, ax = plt.subplots(figsize=(10, 6))\n","\n","# Plot bars for the semantic and recursive splitter\n","bar1 = ax.bar(index, semantic_avg, bar_width, label='Semantic Chunker', color='lightblue')\n","bar2 = ax.bar(index + bar_width, recursive_avg, bar_width, label='Recursive Splitter', color='lightgreen')\n","\n","# Adding labels and title\n","ax.set_xlabel('Metrics')\n","ax.set_ylabel('Average Scores')\n","ax.set_title('Comparison of Semantic vs Recursive splitter based rag')\n","ax.set_xticks(index + bar_width / 2)\n","ax.set_xticklabels(labels)\n","ax.legend()\n","\n","# Display the plot\n","plt.tight_layout()\n","plt.show()\n","\n","# ------------------ Plotting the Improvement Graph ------------------\n","fig, ax = plt.subplots(figsize=(10, 6))\n","\n","# Plot improvement as a bar chart\n","improvement_bar = ax.bar(index, improvement, bar_width, color='purple')\n","\n","# Adding labels and title\n","ax.set_xlabel('Metrics')\n","ax.set_ylabel('Improvement')\n","ax.set_title('Improvement of RAG based on chunking strategy')\n","ax.set_xticks(index)\n","ax.set_xticklabels(labels)\n","\n","# Display the plot\n","plt.tight_layout()\n","plt.show()\n","\n","# ------------------ Creating a Table ------------------\n","fig, ax = plt.subplots(figsize=(10, 6))\n","\n","# Hide axes\n","ax.xaxis.set_visible(False)\n","ax.yaxis.set_visible(False)\n","ax.set_frame_on(False)\n","\n","# Create the table from summary_df\n","table = ax.table(cellText=summary_df.values,\n","                 colLabels=summary_df.columns,\n","                 rowLabels=summary_df.index,\n","                 cellLoc='center',\n","                 loc='center')\n","\n","# Adjust table scale\n","table.scale(1, 2)\n","\n","# Set font size\n","table.set_fontsize(12)\n","\n","plt.title('Comparison of RAG for semantic vs Recursive splitter')\n","\n","# Show the table\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"FT0aynCVmCds","executionInfo":{"status":"ok","timestamp":1727206715646,"user_tz":-60,"elapsed":1424,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"0132dbc8-08fa-41f8-eaea-3dba99e21ab2"},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8x17BNPnk4KC","executionInfo":{"status":"ok","timestamp":1727190536291,"user_tz":-60,"elapsed":23525,"user":{"displayName":"Jeevanandham Poongavanam","userId":"13255850175966981554"}},"outputId":"a6b71675-d36f-47d7-87b3-8f68ed84b5ec"},"execution_count":72,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"A100","machine_shape":"hm","provenance":[]},"kernelspec":{"display_name":"Python 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