INITIALIZATION Knowledgebase: ki-dev Base Query: Can you give a brief summary Model: gemini-1.5-pro-preview-0409 Use Curl?: ================================================== QUESTIONING Clarity Score: 1 Give follow-up?: None Follow-up query: None ================================================== ROUTING Query type: summary ================================================== RAG PARAMETERS Max Context To Include: 100 Lowest Score to Consider: 0.1 ================================================== VECTOR SEARCH ALGORITHM TO USE Use MMR search?: True Use Similarity search?: False ================================================== FILENAMES AND KEYWORDS Keywords Found: ['summary', 'brief'] All file names: ['asd.txt', 'Layahin-Final-Abstract.pdf', 'Layahin-Final-Abstract.pdf'] File names to FILTER: [] ================================================== PRIMER Primer: You are a highly intelligent personal assistant. Your role is to act as an expert at summarization and analysis. In your responses to enterprise users, prioritize clarity, trustworthiness, and appropriate formality. Be honest by admitting when a topic falls outside your scope of knowledge, and suggest alternative avenues for obtaining information when necessary. Make effective use of chat history to avoid redundancy and enhance response relevance, continuously adapting to integrate all necessary details in your interactions. Use as much tokens as possible to provide a detailed response. ================================================== FINAL QUERY Final Query: CONTEXT: ########## File: asd.txt Page: 1 Context: testing/asd #################### File: Layahin-Final-Abstract.pdf Page: 1 Context: archshouldusemorepowerfulcomputingresources,incorporatediversedatasets,andexploreadvancedmodelingtechniquesforfurtherimprovement. #################### File: Layahin-Final-Abstract.pdf Page: 1 Context: EVALUATINGHYBRIDANDTRADITIONALMODELSFORLONG-TERMTRAFFICCONGESTIONFORECASTINGUSINGTHEPEMS-BAYDATASETJeffErllayahinBSComputerScienceABSTRACTTrafficcongestionisamajorchallengeinurbanplanning,necessitatingaccuratelong-termforecastingforeffectivemanagement.ThisstudyevaluateshybridmodelscombiningConvolutionalNeuralNetworks(CNN),LongShort-TermMemory(LSTM)networks,andTransformerarchitecturestoimprovelong-termtrafficforecastingusingthePeMS-Baydataset.Thestudyfocusesondatapreprocessing,modeltraining,andperformanceevaluationusingMeanAbsoluteError(MAE),RootMeanSquaredError(RMSE),andMeanAbsolutePercentageError(MAPE).Resultsshowthatwhiletraditionalmodelsperformwellforshort-termpredictions,hybridmodelssignificantlyimprovemid-termtolong-termaccuracy.However,thesehybridmodelsrequirecarefultuning.Thisresearchdemonstratesthepotentialofhybridmodelingtechniquestoenhancelong-termtrafficforecastingaccuracy,contributingtosmartcityinitiativesaimedatoptimizingurbantransportsystems.Futureresearchshouldusemorepow ########## """QUERY: Can you give a brief summary""" Consider the chat history for relevant information. Use all information included. Use as much tokens as needed. Final Files Sources: asd.txt - Page 1, Layahin-Final-Abstract.pdf - Page 1 ================================================== FINAL ANSWER Answer: ==================================================