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Chatbot to Query Enterprise Documents using Large Language Model (LLM) | by\nMilan’s Outlook – https://www.milanoutlook.com/ | Techno Leeway |\nMedium\\n===============\\n\n\\n\\nOpen in app^[1]\\n\\nSign\nup\\n\\nSign in^[2]\\n\\n^[3]\\n\\nWrite^[4]\\n\\n^[5]\\n\\nSign\nup\\n\\nSign in^[6]\\n\\n

\\n\\nMember-only\nstory\\n\\nChatbot to Query Enterprise Documents using Large Language Model\n(LLM)\\n======================================================================\\n\\n

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^[8]\\n\\nMilan’s Outlook – https://www.milanoutlook.com/^[9]\\n\\n·Follow^[10]\\n\\nPublished\nin\\n\\nTechno Leeway^[11]\\n\\n·\\n\\n4\nmin read\\n\\n·\\n\\nOct 23,\n2023\\n\\n^[12]\\n\\n12\\n\\n^[13]\\n\\nListen^[14]\\n\\nShare\\n\\nThe\nattached image shows how to build a large language model (LLM)-based chatbot to\nquery an enterprise knowledge\nbase.\\n\\n

\\n\\nEnterprise\nDocument Repository\\n==============================\\n\\nAn enterprise document\nrepository is a system for storing, organizing, and managing electronic\ndocuments. It is typically used by large organizations to store a wide variety\nof documents, such as contracts, financial records, customer data, and product\nspecifications. Your internal knowledge base documents in a format suitable for\nquerying.\\n\\nExample: Microsoft SharePoint, Google Workspace, Box, Dropbox\nBusiness, IBM FileNet, DocuWare\\n\\nEmbeddings via Embedding\nModel\\n==============================\\n\\nEmbeddings are a key component of\nlarge language models (LLMs). They are used to represent words and phrases in a\nway that computers can process — as vectors, or N-dimensional arrays, of\nnumbers. These numerical vectors capture the meaning of words by representing\ndifferent characteristics of a word with numbers in an array.\\n\\nIn this step,\nEmbed the text chunks using an embedding model. This transforms each chunk of\ntext into a numerical representation that captures its essence.\\n\\nCreate an\naccount to read the full\nstory.\\n\\n\\n———————————————\\n\\nThe author\nmade this story available to Medium members only. \\nIf you’re new to Medium,\ncreate a new account to read this story on\nus.\\n\\nContinue in app^[15]\\n\\nOr,\ncontinue in mobile\nweb\\n\\nSign up with Google^[16]\\n\\nSign up with Facebook^[17]\\n\\nSign\nup with email\\n\\nAlready have an account?\nSign in^[18]\\n\\n^[19]\\n\\n12\\n\\n^[20]\\n\\n12\\n\\n^[21]\\n\\n

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^[23]\\n\\nFollow\\n\\nPublished in Techno Leeway ————————–^[24]\\n\\n4 Followers^[25]\\n\\n·Last published Dec 9, 2023^[26]\\n\\nThis\npublication is for AI, Cloud, ML Enthusiasts. We’re here to provide you with the\nlatest news, insights, and trends in the world of technology, from cutting-edge\nresearch to real-world applications. Whether you’re a seasoned AI, ML or Cloud\nexpert or just starting\nout.\\n\\nFollow\\n\\n

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^[28]\\n\\nFollow\\n\\nWritten by Milan’s Outlook – https://www.milanoutlook.com/ ———————————————————-^[29]\\n\\n98 Followers^[30]\\n\\n·14 Following^[31]\\n\\nMilan\nDhore,Growth-Driven Enterprise Strategist & Transformation Leader | Pioneering\nLeader in Cloud, Generative AI, ML,and Emerging Technologies\\n\\nFollow\\n\\nNo\nresponses\nyet\\n—————-\\n\\n^[32]\\n\\nWhat are your thoughts?^[33]\\n\\nCancel\\n\\nRespond\\n\\nRespond\\n\\nAlso\npublish to my profile\\n\\nMore from Milan’s Outlook -\nhttps://www.milanoutlook.com/ and Techno\nLeeway\\n—————————————————————————\\n\\n

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^[34]\\n\\nMilan’s Outlook – https://www.milanoutlook.com/^[35]\\n\\nGenerative AI Design Patterns using LLM. —————————————- ### The Art of Design Patterns^[36]\\n\\nApr\n22,\n2024\\n\\n34 1^[37]\\n\\n^[38]\\n\\n

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^[39]\\n\\nIn\\n\\nTechno Leeway^[40]\\n\\nby\\n\\nMilan’s Outlook – https://www.milanoutlook.com/^[41]\\n\\nBenefits of Onion Architecture in Domain-Driven Design —————————————————— ### In the ever-evolving world of software development, the need for robust, maintainable, and scalable software systems has never been…^[42]\\n\\nNov\n20,\n2023\\n\\n^[43]\\n\\n^[44]\\n\\n

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^[45]\\n\\nIn\\n\\nTechno Leeway^[46]\\n\\nby\\n\\nMilan’s Outlook – https://www.milanoutlook.com/^[47]\\n\\nIs Google’s Gemini AI new ChatGPT in Generative AI? ————————————————— ### Hold on tight, because we’re about to jump into the thrilling world of artificial intelligence! On December 6, 2023, Google unveiled its…^[48]\\n\\nDec\n9,\n2023\\n\\n^[49]\\n\\n^[50]\\n\\n

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^[51]\\n\\nMilan’s Outlook – https://www.milanoutlook.com/^[52]\\n\\nHow do I Design Enterprise Strategy and Architecture: ADS to TSA —————————————————————- ### Through hundreds of architecture design sessions, I have learned their importance in crafting winning solutions. This article walks you…^[53]\\n\\nJun\n2,\n2024\\n\\n25^[54]\\n\\n^[55]\\n\\nSee all from Milan’s Outlook – https://www.milanoutlook.com/^[56]\\n\\nSee all from Techno Leeway^[57]\\n\\nRecommended\nfrom\nMedium\\n———————–\\n\\n

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^[58]\\n\\nIn\\n\\nAI Advances^[59]\\n\\nby\\n\\nKaitai Dong^[60]\\n\\nRailway Ontology Inspired Fine-Tuning and Evaluation of LLMs Using MLflow for More Effective… ——————————————————————————————— ### Effectively tailor LLMs with domain-specific knowledge and ontology to better identify defective rail components and recommend consistent…^[61]\\n\\n5d\nago\\n\\n340 4^[62]\\n\\n^[63]\\n\\n

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^[64]\\n\\nIn\\n\\nITNEXT^[65]\\n\\nby\\n\\nNima Mahmoudi^[66]\\n\\nBuilding an LLM Router using OpenAI Embeddings ———————————————- ### With the trend of using increasingly more specialized prompts and LLMs for different applications, organizations need to be able to route…^[67]\\n\\n4d\nago\\n\\n92^[68]\\n\\n^[69]\\n\\nLists\\n—–\\n\\n

Natural Language Processing ————————— 1882 stories·1518 saves^[70]\\n\\n

Predictive Modeling w/ Python —————————– 20 stories·1751 saves^[71]\\n\\n

The New Chatbots: ChatGPT, Bard, and Beyond ——————————————- 12 stories·534 saves^[72]\\n\\n

Practical Guides to Machine Learning ———————————— 10 stories·2126 saves^[73]\\n\\n

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^[74]\\n\\nChris Yan^[75]\\n\\nBuilding a Chatbot for Microsoft Teams Using OpenAI API and Microsoft Teams Conversation Bot ——————————————————————————————– ### The integration of conversational AI with collaboration platforms has seen a massive surge in recent years. Microsoft Teams, a widely…^[76]\\n\\nOct\n8,\n2024\\n\\n^[77]\\n\\n^[78]\\n\\n

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^[79]\\n\\nShiva Lagisetty^[80]\\n\\nIs RAG relevant in 2025 ? ————————- ### Retrieval-Augmented Generation (RAG) was introduced in 2020 with the publication of the seminal paper “Retrieval-Augmented Generation for…^[81]\\n\\nDec\n22,\n2024\\n\\n1^[82]\\n\\n^[83]\\n\\n

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^[84]\\n\\nIn\\n\\nAI Advances^[85]\\n\\nby\\n\\nDebmalya Biswas^[86]\\n\\nAI Agents Marketplace & Discovery for Multi-agent Systems ——————————————————— ### Why LLMs as a run-time execution engine for Agentic AI systems do not Scale?^[87]\\n\\nDec\n26,\n2024\\n\\n668 10^[88]\\n\\n^[89]\\n\\n

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^[90]\\n\\nIn\\n\\nTowards Data Science^[91]\\n\\nby\\n\\nThuwarakesh Murallie^[92]\\n\\nHow to Build Helpful RAGs with Query Routing. ——————————————— ### An LLM can handle general routing. Semantic search can handle private data better. Which one should you pick?^[93]\\n\\nAug\n16,\n2024\\n\\n530 2^[94]\\n\\n^[95]\\n\\nSee more recommendations^[96]\\n\\nHelp^[97]\\n\\nStatus^[98]\\n\\nAbout^[99]\\n\\nCareers^[100]\\n\\nPress^[101]\\n\\nBlog^[102]\\n\\nPrivacy^[103]\\n\\nTerms^[104]\\n\\nText to speech^[105]\\n\\nTeams^[106]

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Generative AI Design Patterns using LLM. —————————————- ### The Art of Design Patterns: https://medium.com/@itsmybestview/generative-ai-design-patterns-using-llm-01eca97d0a1c?source=post_page—author_recirc–b0bde0a7e901—-0———————1519a614_ef45_45f0_ad0b_6c7aa12394fd——-

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AI Agents Marketplace & Discovery for Multi-agent Systems ——————————————————— ### Why LLMs as a run-time execution engine for Agentic AI systems do not Scale?: https://medium.com/ai-advances/ai-agents-marketplace-discovery-for-multi-agent-systems-27a31b6b1ca6?source=post_page—read_next_recirc–b0bde0a7e901—-2———————7117cc7e_79b2_4d04_97fb_481a54bde152——-

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How to Build Helpful RAGs with Query Routing. ——————————————— ### An LLM can handle general routing. Semantic search can handle private data better. Which one should you pick?: https://medium.com/towards-data-science/rags-with-query-routing-5552e4e41c54?source=post_page—read_next_recirc–b0bde0a7e901—-3———————7117cc7e_79b2_4d04_97fb_481a54bde152——-

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