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52 lines
2.6 KiB
ReStructuredText
52 lines
2.6 KiB
ReStructuredText
.. _arch_rag_guide:
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RAG Apps
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========
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The following section describes how Arch can help you build faster, smarter and more accurate
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Retrieval-Augmented Generation (RAG) applications, including fast and accurate RAG in multi-turn
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converational scenarios.
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What is Retrieval-Augmented Generation (RAG)?
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---------------------------------------------
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RAG applications combine retrieval-based methods with generative AI models to provide more accurate,
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contextually relevant, and reliable outputs. These applications leverage external data sources to augment
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the capabilities of Large Language Models (LLMs), enabling them to retrieve and integrate specific information
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rather than relying solely on the LLM's internal knowledge.
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Parameter Extraction for RAG
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----------------------------
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To build RAG (Retrieval Augmented Generation) applications, you can configure prompt targets with parameters,
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enabling Arch to retrieve critical information in a structured way for processing. This approach improves the
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retrieval quality and speed of your application. By extracting parameters from the conversation, you can pull
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the appropriate chunks from a vector database or SQL-like data store to enhance accuracy. With Arch, you can
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streamline data retrieval and processing to build more efficient and precise RAG applications.
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Step 1: Define Prompt Targets
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. literalinclude:: includes/rag/prompt_targets.yaml
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:language: yaml
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:caption: Prompt Targets
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:linenos:
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Step 2: Process Request Parameters in Flask
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Once the prompt targets are configured as above, handling those parameters is
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.. literalinclude:: includes/rag/parameter_handling.py
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:language: python
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:caption: Parameter handling with Flask
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:linenos:
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Multi-Turn RAG (Follow-up Questions)
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-------------------------------------
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Developers often `struggle <https://www.reddit.com/r/LocalLLaMA/comments/18mqwg6/best_practice_for_rag_with_followup_chat/>`_ to efficiently handle
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``follow-up`` or ``clarification`` questions. Specifically, when users ask for changes or additions to previous responses, it requires developers to
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re-write prompts using LLMs with precise prompt engineering techniques. This process is slow, manual, error prone and adds signifcant latency to the
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user experience. Arch
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Arch is highly capable of accurately detecting and processing prompts in a multi-turn scenarios so that you can buil fast and accurate RAG apps in
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minutes. For additional details on how to build multi-turn RAG applications please refer to our :ref:`multi-turn <arch_multi_turn_guide>` docs.
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