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* added the first set of docs for our technical docs * more docuemtnation changes * added support for prompt processing and updated life of a request * updated docs to including getting help sections and updated life of a request * committing local changes for getting started guide, sample applications, and full reference spec for prompt-config * updated configuration reference, added sample app skeleton, updated favico * fixed the configuration refernce file, and made minor changes to the intent detection. commit v1 for now --------- Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-261.local> Co-authored-by: Adil Hafeez <adil@katanemo.com>
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Retrieval-Augmented Generation (RAG)
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====================================
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The following section describes how Arch can help you build faster, more smarter Retrieval-Augmented Generation (RAG) applications.
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Intent Markers (Multi-Turn Chat)
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----------------------------------
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Developers struggle to handle follow-up questions, or clarifying questions from users in their AI applications. Specifically, when
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users ask for modifications or additions to previous responses, their AI applications often generates entirely new responses instead
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of adjusting the previous ones. Developers are facing challenges in maintaining context across interactions, despite using tools like
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ConversationBufferMemory and chat_history from Langchain.
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There are several documented cases of this issue, `here <https://www.reddit.com/r/ChatGPTPromptGenius/comments/17dzmpy/how_to_use_rag_with_conversation_history_for/?>`_,
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`and here <https://www.reddit.com/r/LocalLLaMA/comments/18mqwg6/best_practice_for_rag_with_followup_chat/>`_ and `again here <https://www.reddit.com/r/LangChain/comments/1bajhg8/chat_with_rag_further_questions/>`_.
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Arch helps developer with intent detection tracking. Arch uses its lightweight NLI and embedding-based intent detection models to know
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if the user's last prompt represents a new intent or not. This way developers can easily build an intent tracker and only use a subset of prompts
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to process from the history to improve the retrieval and speed of their applications.
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.. literalinclude:: /_include/intent_detection.py
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:language: python
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:linenos:
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:lines: 77-
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:emphasize-lines: 15-22
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:caption: :download:`intent-detection-python-example.py </_include/intent_detection.py>`
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