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14.3-32 32-32H416c17.7 0 32 14.3 32 32s-14.3 32-32 32H32c-17.7 0-32-14.3-32-32zM448 416c0 17.7-14.3 32-32 32H32c-17.7 0-32-14.3-32-32s14.3-32 32-32H416c17.7 0 32 14.3 32 32z"></path></svg></button><div class="ml-4 flex text-sm leading-6 whitespace-nowrap min-w-0 space-x-3"><div class="flex items-center space-x-3"><span>Guides</span><svg width="3" height="24" viewBox="0 -9 3 24" class="h-5 rotate-0 overflow-visible fill-gray-400"><path d="M0 0L3 3L0 6" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round"></path></svg></div><div class="font-semibold text-gray-900 truncate dark:text-gray-200">Working with Tables and CSV</div></div></div></div></div></div></div><div class="max-w-8xl px-4 mx-auto lg:px-8 min-h-screen"><div class="z-20 hidden lg:block fixed bottom-0 right-auto w-[18rem] top-[4rem]" id="sidebar"><div class="absolute inset-0 z-10 stable-scrollbar-gutter overflow-auto pr-8 pb-10" id="sidebar-content"><div class="relative lg:text-sm lg:leading-6"><div 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dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="introduction.html"><div class="flex-1 flex items-center space-x-2.5"><div>Introduction</div></div></a></li></ul></div><div class="mt-12 lg:mt-8"><h5 class="pl-4 mb-3.5 lg:mb-2.5 font-semibold text-gray-900 dark:text-gray-200">Guides</h5><ul><li id="/creating-first-chatbot" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="creating-first-chatbot.html"><div class="flex-1 flex items-center space-x-2.5"><div>Create Your First Chatbot</div></div></a></li><li id="/lead-collection" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="lead-collection.html"><div class="flex-1 flex items-center space-x-2.5"><div>Lead Collection</div></div></a></li><li id="/human-support-escalation" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="human-support-escalation.html"><div class="flex-1 flex items-center space-x-2.5"><div>Human Support Escalation</div></div></a></li><li id="/inbox-notifications" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="inbox-notifications.html"><div class="flex-1 flex items-center space-x-2.5"><div>Inbox Notifications</div></div></a></li><li id="/conversation-labeling" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="conversation-labeling.html"><div class="flex-1 flex items-center space-x-2.5"><div>Conversation Labeling</div></div></a></li><li id="/multi-agents-chatbot" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="multi-agents-chatbot.html"><div class="flex-1 flex items-center space-x-2.5"><div>Multi-Agents Chatbot</div></div></a></li><li id="/fine-tuning-agent-intents" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="fine-tuning-agent-intents.html"><div class="flex-1 flex items-center space-x-2.5"><div>Fine Tuning Agent Intents</div></div></a></li><li id="/supervisor-overrides" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="supervisor-overrides.html"><div class="flex-1 flex items-center space-x-2.5"><div>AI Supervisor Overrides</div></div></a></li><li id="/byok-pricing-guide" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="byok-pricing-guide.html"><div class="flex-1 flex items-center space-x-2.5"><div>Bring Your Own Key (BYOK) and Pricing</div></div></a></li><li id="/working-with-tables" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl bg-primary/10 text-primary font-semibold dark:text-primary-light dark:bg-primary-light/10" style="padding-left:1rem" href="working-with-tables.html"><div class="flex-1 flex items-center space-x-2.5"><div>Working with Tables and CSV</div></div></a></li><li id="/best-practices" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="best-practices.html"><div class="flex-1 flex items-center space-x-2.5"><div>Best practices for preparing training data</div></div></a></li><li id="/help" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="help.html"><div class="flex-1 flex items-center space-x-2.5"><div>Why does my chatbot not answer correctly?</div></div></a></li></ul></div><div class="mt-12 lg:mt-8"><h5 class="pl-4 mb-3.5 lg:mb-2.5 font-semibold text-gray-900 dark:text-gray-200">Function Calling</h5><ul><li id="/rag-from-external-data-provider" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="rag-from-external-data-provider.html"><div class="flex-1 flex items-center space-x-2.5"><div>RAG from an External Data Provider</div></div></a></li></ul></div><div class="mt-12 lg:mt-8"><h5 class="pl-4 mb-3.5 lg:mb-2.5 font-semibold text-gray-900 dark:text-gray-200">Authentication Webhook</h5><ul><li id="/user-identity" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="user-identity.html"><div class="flex-1 flex items-center space-x-2.5"><div>User Identity Verification</div></div></a></li></ul></div></div></div></div></div><div class="" id="content-container"><div class="flex flex-row gap-12 box-border w-full pt-40 lg:pt-10"><div class="relative grow box-border flex-col w-full mx-auto px-1 lg:pl-[23.7rem] lg:-ml-12 xl:w-[calc(100%-28rem)]" id="content-area"><header id="header" class="relative"><div class="mt-0.5 space-y-2.5"><div class="eyebrow h-5 text-primary dark:text-primary-light text-sm font-semibold">Guides</div><div class="flex items-center"><h1 class="inline-block text-2xl sm:text-3xl font-extrabold text-gray-900 tracking-tight dark:text-gray-200">Working with Tables and CSV</h1></div></div></header><div class="flex flex-col gap-8"><div class="flex flex-col gap-6 xl:hidden [&:not(:empty)]:mt-8"></div></div><div class="relative mt-8 prose prose-gray dark:prose-invert"><p>GPT-trainer allows you to upload training data in the form of <strong>simple</strong> CSV or Excel tables.
Simple tables refer to tabulated data that do not contain merged cells, have a single primary
key column with unique elements, unique and informative column / row titles, and either
column-wise or row-column structural configurations.</p>
<p>Example column-only simple table:</p>
<div><div class="p-2 not-prose relative bg-gray-50/50 rounded-2xl overflow-hidden dark:bg-gray-800/25"><div style="background-position:10px 10px" class="absolute inset-0 bg-grid-neutral-200/20 [mask-image:linear-gradient(0deg,#fff,rgba(255,255,255,0.6))] dark:bg-grid-white/5 dark:[mask-image:linear-gradient(0deg,rgba(255,255,255,0.1),rgba(255,255,255,0.5))]"></div><div class="relative rounded-xl overflow-hidden flex justify-center"></div><div class="absolute inset-0 pointer-events-none border border-black/5 rounded-2xl dark:border-white/5"></div></div></div>
<p><em>Data across each row corresponds to a single product key in the first column</em></p>
<p>Example row / column simple table:</p>
<div><div class="p-2 not-prose relative bg-gray-50/50 rounded-2xl overflow-hidden dark:bg-gray-800/25"><div style="background-position:10px 10px" class="absolute inset-0 bg-grid-neutral-200/20 [mask-image:linear-gradient(0deg,#fff,rgba(255,255,255,0.6))] dark:bg-grid-white/5 dark:[mask-image:linear-gradient(0deg,rgba(255,255,255,0.1),rgba(255,255,255,0.5))]"></div><div class="relative rounded-xl overflow-hidden flex justify-center"></div><div class="absolute inset-0 pointer-events-none border border-black/5 rounded-2xl dark:border-white/5"></div></div></div>
<p><em>Each row-column combination points to a corresponding cell value.</em></p>
<p>While there is no maximum number of columns, there is a limit to the total number of tokens that can
be included within each row of the table. If you exceed that, the training will result in an error.
As of March 2024, the maximum number of tokens per row is ~8000 tokens (this number includes non-cell-value
JSON code used to represent table structure, so actual maximum reserved for cell-values is lower
and depends on column / row name lengths).</p>
<p>If your data matches the format above, you can proceed to upload your table onto GPT-trainer as a
training source. To do so, simply go to Sources -> Add Sources -> Tables:</p>
<div><div class="p-2 not-prose relative bg-gray-50/50 rounded-2xl overflow-hidden dark:bg-gray-800/25"><div style="background-position:10px 10px" class="absolute inset-0 bg-grid-neutral-200/20 [mask-image:linear-gradient(0deg,#fff,rgba(255,255,255,0.6))] dark:bg-grid-white/5 dark:[mask-image:linear-gradient(0deg,rgba(255,255,255,0.1),rgba(255,255,255,0.5))]"></div><div class="relative rounded-xl overflow-hidden flex justify-center"></div><div class="absolute inset-0 pointer-events-none border border-black/5 rounded-2xl dark:border-white/5"></div></div></div>
<p>For row-column tables, after you upload it, select it and go to the three-dot-menu on the rightmost
edge and click “Edit Table Data”:</p>
<div><div class="p-2 not-prose relative bg-gray-50/50 rounded-2xl overflow-hidden dark:bg-gray-800/25"><div style="background-position:10px 10px" class="absolute inset-0 bg-grid-neutral-200/20 [mask-image:linear-gradient(0deg,#fff,rgba(255,255,255,0.6))] dark:bg-grid-white/5 dark:[mask-image:linear-gradient(0deg,rgba(255,255,255,0.1),rgba(255,255,255,0.5))]"></div><div class="relative rounded-xl overflow-hidden flex justify-center"></div><div class="absolute inset-0 pointer-events-none border border-black/5 rounded-2xl dark:border-white/5"></div></div></div>
<p>Then, in the table edit dialog, select “Row-Column Header” and click “Save Changes”. This ensures that
your data is pre-processed correctly for LLM understanding.</p>
<div><div class="p-2 not-prose relative bg-gray-50/50 rounded-2xl overflow-hidden dark:bg-gray-800/25"><div style="background-position:10px 10px" class="absolute inset-0 bg-grid-neutral-200/20 [mask-image:linear-gradient(0deg,#fff,rgba(255,255,255,0.6))] dark:bg-grid-white/5 dark:[mask-image:linear-gradient(0deg,rgba(255,255,255,0.1),rgba(255,255,255,0.5))]"></div><div class="relative rounded-xl overflow-hidden flex justify-center"></div><div class="absolute inset-0 pointer-events-none border border-black/5 rounded-2xl dark:border-white/5"></div></div></div>
<p>You can then set the tables as referenced data when assigning knowledge base to your Agents.</p>
<p>Please note that based on our experience, GPT-4 series significantly outperform GPT-3.5 series in
retrieval accuracy and consistency when working with tables.</p>
<div><div class="p-2 not-prose relative bg-gray-50/50 rounded-2xl overflow-hidden dark:bg-gray-800/25"><div style="background-position:10px 10px" class="absolute inset-0 bg-grid-neutral-200/20 [mask-image:linear-gradient(0deg,#fff,rgba(255,255,255,0.6))] dark:bg-grid-white/5 dark:[mask-image:linear-gradient(0deg,rgba(255,255,255,0.1),rgba(255,255,255,0.5))]"></div><div class="relative rounded-xl overflow-hidden flex justify-center"></div><div class="absolute inset-0 pointer-events-none border border-black/5 rounded-2xl dark:border-white/5"></div></div></div>
<p><em>Example LLM retrieval using the sample tables above.</em></p>
<h2 class="flex whitespace-pre-wrap group" id="my-agent-or-chatbot-doesnt-seem-to-understand-or-interpret-my-table-correctly"><div class="absolute"><a href="#my-agent-or-chatbot-doesnt-seem-to-understand-or-interpret-my-table-correctly" class="-ml-10 flex items-center opacity-0 border-0 group-hover:opacity-100" aria-label="Navigate to header"><div class="w-6 h-6 text-gray-400 rounded-md flex items-center justify-center zinc-box bg-white ring-1 ring-gray-400/30 dark:ring-gray-700/25 hover:ring-gray-400/60 dark:hover:ring-white/20"><svg xmlns="http://www.w3.org/2000/svg" fill="gray" height="12px" viewBox="0 0 576 512"><path d="M0 256C0 167.6 71.6 96 160 96h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C98.1 144 48 194.1 48 256s50.1 112 112 112h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C71.6 416 0 344.4 0 256zm576 0c0 88.4-71.6 160-160 160H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c61.9 0 112-50.1 112-112s-50.1-112-112-112H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c88.4 0 160 71.6 160 160zM184 232H392c13.3 0 24 10.7 24 24s-10.7 24-24 24H184c-13.3 0-24-10.7-24-24s10.7-24 24-24z"></path></svg></div></a></div><span class="cursor-pointer">My Agent or chatbot doesn’t seem to understand or interpret my table correctly!</span></h2>
<p>Large language models (LLMs) like GPT-4 are extremely adept at working with unstructured text data.
With a multimodal training method, LLMs can even interpret visual or image data and associate it with natural
language (like GPT-Vision). However, tabulated data is very different. There is no universal rule set or
standard language patterns when it comes to representing structured information. Given the probabilistic nature
of LLMs, they are not innately proficient at handling this type of data directly.</p>
<p>An article from <a href="https://www.microsoft.com/en-us/research/blog/improving-llm-understanding-of-structured-data-and-exploring-advanced-prompting-methods/" target="_blank" rel="noreferrer">Microsoft Research</a>
recently evaluated GPT-4’s performance when processing structured data. Within it, model performance against
numerous programmatic representations of structured data are discussed.</p>
<div><div class="p-2 not-prose relative bg-gray-50/50 rounded-2xl overflow-hidden dark:bg-gray-800/25"><div style="background-position:10px 10px" class="absolute inset-0 bg-grid-neutral-200/20 [mask-image:linear-gradient(0deg,#fff,rgba(255,255,255,0.6))] dark:bg-grid-white/5 dark:[mask-image:linear-gradient(0deg,rgba(255,255,255,0.1),rgba(255,255,255,0.5))]"></div><div class="relative rounded-xl overflow-hidden flex justify-center"></div><div class="absolute inset-0 pointer-events-none border border-black/5 rounded-2xl dark:border-white/5"></div></div></div>
<p><em>Comparing GPT-3.5 vs. GPT-4 performance across a variety of table operations and structured data representations.</em>
<em>Source: <a href="https://www.microsoft.com/en-us/research/blog/improving-llm-understanding-of-structured-data-and-exploring-advanced-prompting-methods/" target="_blank" rel="noreferrer">https://www.microsoft.com/en-us/research/blog/improving-llm-understanding-of-structured-data-and-exploring-advanced-prompting-methods/</a></em></p>
<p>GPT-trainer currently uses a version of JSON to handle tables. While not perfect, it does support a limited range
of use cases when bot makers want to train chatbots with structured data.</p>
<p>We understand that many use cases involve much larger and more complex datasets that may even be updated in real-time.
The static table feature within GPT-trainer is not optimized for them, and we instead recommend that you look into
function-calling.</p>
<p>The most robust and “legitimate” way to do retrieval augmented generation (RAG) on structured data is via function calling,
where you design and host custom functions with <strong>templated</strong> SQL queries to retrieve specific data snippets and feed it to
your GPT-trainer chatbot as additional RAG context on-demand. However, this requires a bit of programming and server hosting
setup on your end. We are working on some concrete examples of this setup, so we appreciate your patience as we improve our
documentation.</p></div><div class="leading-6 mt-14"><div class="mb-12 px-0.5 flex items-center text-sm font-semibold text-gray-700 dark:text-gray-200"><a class="flex items-center space-x-3 group" href="byok-pricing-guide.html"><svg viewBox="0 0 3 6" class="h-1.5 stroke-gray-400 overflow-visible group-hover:stroke-gray-600 dark:group-hover:stroke-gray-300"><path d="M3 0L0 3L3 6" fill="none" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="group-hover:text-gray-900 dark:group-hover:text-white">Bring Your Own Key (BYOK) and Pricing</span></a><a class="flex items-center ml-auto space-x-3 group" href="best-practices.html"><span class="group-hover:text-gray-900 dark:group-hover:text-white">Best practices for preparing training data</span><svg viewBox="0 0 3 6" class="rotate-180 h-1.5 stroke-gray-400 overflow-visible group-hover:stroke-gray-600 dark:group-hover:stroke-gray-300"><path d="M3 0L0 3L3 6" fill="none" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg></a></div><footer id="footer" class="flex gap-12 justify-between pt-10 border-t border-gray-100 sm:flex dark:border-gray-800/50 pb-28"><div class="flex items-center justify-between"><div class="sm:flex"><a href="https://mintlify.com/preview-request?utm_campaign=poweredBy&utm_medium=docs&utm_source=guide.gpt-trainer.com" target="_blank" rel="noreferrer" class="text-sm text-gray-500 dark:text-gray-400 hover:text-gray-700 dark:hover:text-gray-300">Powered by Mintlify</a></div></div></footer></div></div><div class="z-10 hidden xl:flex pl-10 box-border w-[19rem]" id="table-of-contents"><div id="table-of-contents-content" class="fixed text-gray-600 text-sm leading-6 w-[16.5rem] overflow-y-auto space-y-2 h-[calc(100%-7rem)]"><div class="text-gray-700 dark:text-gray-300 font-medium flex items-center space-x-2"><svg width="16" height="16" viewBox="0 0 16 16" fill="none" stroke="currentColor" stroke-width="2" xmlns="http://www.w3.org/2000/svg" class="h-3 w-3"><path d="M2.44434 12.6665H13.5554" stroke-linecap="round" stroke-linejoin="round"></path><path d="M2.44434 3.3335H13.5554" stroke-linecap="round" stroke-linejoin="round"></path><path d="M2.44434 8H7.33323" stroke-linecap="round" stroke-linejoin="round"></path></svg><span>On this page</span></div><ul><li><a href="#my-agent-or-chatbot-doesnt-seem-to-understand-or-interpret-my-table-correctly" class="py-1 block hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300">My Agent or chatbot doesn’t seem to understand or interpret my table correctly!</a></li></ul></div></div></div></div></div></div></main></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"mdxSource":{"compiledSource":"\"use strict\";\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n const _components = {\n a: \"a\",\n em: \"em\",\n p: \"p\",\n strong: \"strong\",\n ..._provideComponents(),\n ...props.components\n }, {Frame, Heading, ZoomImage} = _components;\n if (!Frame) _missingMdxReference(\"Frame\", true);\n if (!Heading) _missingMdxReference(\"Heading\", true);\n if (!ZoomImage) _missingMdxReference(\"ZoomImage\", true);\n return _jsxs(_Fragment, {\n children: [_jsxs(_components.p, {\n children: [\"GPT-trainer allows you to upload training data in the form of \", _jsx(_components.strong, {\n children: \"simple\"\n }), \" CSV or Excel tables.\\nSimple tables refer to tabulated data that do not contain merged cells, have a single primary\\nkey column with unique elements, unique and informative column / row titles, and either\\ncolumn-wise or row-column structural configurations.\"]\n }), \"\\n\", _jsx(_components.p, {\n children: \"Example column-only simple table:\"\n }), \"\\n\", _jsx(Frame, {\n children: _jsx(ZoomImage, {\n children: _jsx(\"img\", {\n src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/working-with-tables-1.png\"\n })\n })\n }), \"\\n\", _jsx(_components.p, {\n children: _jsx(_components.em, {\n children: \"Data across each row corresponds to a single product key in the first column\"\n })\n }), \"\\n\", _jsx(_components.p, {\n children: \"Example row / column simple table:\"\n }), \"\\n\", _jsx(Frame, {\n children: _jsx(ZoomImage, {\n children: _jsx(\"img\", {\n src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/working-with-tables-2.png\"\n })\n })\n }), \"\\n\", _jsx(_components.p, {\n children: _jsx(_components.em, {\n children: \"Each row-column combination points to a corresponding cell value.\"\n })\n }), \"\\n\", _jsx(_components.p, {\n children: \"While there is no maximum number of columns, there is a limit to the total number of tokens that can\\nbe included within each row of the table. 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If you exceed that, the training will result in an error.\\nAs of March 2024, the maximum number of tokens per row is ~8000 tokens (this number includes non-cell-value\\nJSON code used to represent table structure, so actual maximum reserved for cell-values is lower\\nand depends on column / row name lengths).\"\n }), \"\\n\", _jsx(_components.p, {\n children: \"If your data matches the format above, you can proceed to upload your table onto GPT-trainer as a\\ntraining source. To do so, simply go to Sources -\u003e Add Sources -\u003e Tables:\"\n }), \"\\n\", _jsx(Frame, {\n children: _jsx(ZoomImage, {\n children: _jsx(\"img\", {\n src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/working-with-tables-3.png\"\n })\n })\n }), \"\\n\", _jsx(_components.p, {\n children: \"For row-column tables, after you upload it, select it and go to the three-dot-menu on the rightmost\\nedge and click “Edit Table Data”:\"\n }), \"\\n\", _jsx(Frame, {\n children: _jsx(ZoomImage, {\n children: _jsx(\"img\", {\n src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/working-with-tables-4.png\"\n })\n })\n }), \"\\n\", _jsx(_components.p, {\n children: \"Then, in the table edit dialog, select “Row-Column Header” and click “Save Changes”. This ensures that\\nyour data is pre-processed correctly for LLM understanding.\"\n }), \"\\n\", _jsx(Frame, {\n children: _jsx(ZoomImage, {\n children: _jsx(\"img\", {\n src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/working-with-tables-5.png\"\n })\n })\n }), \"\\n\", _jsx(_components.p, {\n children: \"You can then set the tables as referenced data when assigning knowledge base to your Agents.\"\n }), \"\\n\", _jsx(_components.p, {\n children: \"Please note that based on our experience, GPT-4 series significantly outperform GPT-3.5 series in\\nretrieval accuracy and consistency when working with tables.\"\n }), \"\\n\", _jsx(Frame, {\n children: _jsx(ZoomImage, {\n children: _jsx(\"img\", {\n src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/working-with-tables-6.png\"\n })\n })\n }), \"\\n\", _jsx(_components.p, {\n children: _jsx(_components.em, {\n children: \"Example LLM retrieval using the sample tables above.\"\n })\n }), \"\\n\", _jsx(Heading, {\n level: \"2\",\n id: \"my-agent-or-chatbot-doesnt-seem-to-understand-or-interpret-my-table-correctly\",\n children: \"My Agent or chatbot doesn’t seem to understand or interpret my table correctly!\"\n }), \"\\n\", _jsx(_components.p, {\n children: \"Large language models (LLMs) like GPT-4 are extremely adept at working with unstructured text data.\\nWith a multimodal training method, LLMs can even interpret visual or image data and associate it with natural\\nlanguage (like GPT-Vision). However, tabulated data is very different. There is no universal rule set or\\nstandard language patterns when it comes to representing structured information. Given the probabilistic nature\\nof LLMs, they are not innately proficient at handling this type of data directly.\"\n }), \"\\n\", _jsxs(_components.p, {\n children: [\"An article from \", _jsx(_components.a, {\n href: \"https://www.microsoft.com/en-us/research/blog/improving-llm-understanding-of-structured-data-and-exploring-advanced-prompting-methods/\",\n children: \"Microsoft Research\"\n }), \"\\nrecently evaluated GPT-4’s performance when processing structured data. Within it, model performance against\\nnumerous programmatic representations of structured data are discussed.\"]\n }), \"\\n\", _jsx(Frame, {\n children: _jsx(ZoomImage, {\n children: _jsx(\"img\", {\n src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/working-with-tables-7.png\"\n })\n })\n }), \"\\n\", _jsxs(_components.p, {\n children: [_jsx(_components.em, {\n children: \"Comparing GPT-3.5 vs. GPT-4 performance across a variety of table operations and structured data representations.\"\n }), \"\\n\", _jsxs(_components.em, {\n children: [\"Source: \", _jsx(_components.a, {\n href: \"https://www.microsoft.com/en-us/research/blog/improving-llm-understanding-of-structured-data-and-exploring-advanced-prompting-methods/\",\n children: \"https://www.microsoft.com/en-us/research/blog/improving-llm-understanding-of-structured-data-and-exploring-advanced-prompting-methods/\"\n })]\n })]\n }), \"\\n\", _jsx(_components.p, {\n children: \"GPT-trainer currently uses a version of JSON to handle tables. While not perfect, it does support a limited range\\nof use cases when bot makers want to train chatbots with structured data.\"\n }), \"\\n\", _jsx(_components.p, {\n children: \"We understand that many use cases involve much larger and more complex datasets that may even be updated in real-time.\\nThe static table feature within GPT-trainer is not optimized for them, and we instead recommend that you look into\\nfunction-calling.\"\n }), \"\\n\", _jsxs(_components.p, {\n children: [\"The most robust and “legitimate” way to do retrieval augmented generation (RAG) on structured data is via function calling,\\nwhere you design and host custom functions with \", _jsx(_components.strong, {\n children: \"templated\"\n }), \" SQL queries to retrieve specific data snippets and feed it to\\nyour GPT-trainer chatbot as additional RAG context on-demand. However, this requires a bit of programming and server hosting\\nsetup on your end. We are working on some concrete examples of this setup, so we appreciate your patience as we improve our\\ndocumentation.\"]\n })]\n });\n}\nfunction MDXContent(props = {}) {\n const {wrapper: MDXLayout} = {\n ..._provideComponents(),\n ...props.components\n };\n return MDXLayout ? _jsx(MDXLayout, {\n ...props,\n children: _jsx(_createMdxContent, {\n ...props\n })\n }) : _createMdxContent(props);\n}\nreturn {\n default: MDXContent\n};\nfunction _missingMdxReference(id, component) {\n throw new Error(\"Expected \" + (component ? \"component\" : \"object\") + \" `\" + id + \"` to be defined: you likely forgot to import, pass, or provide it.\");\n}\n","frontmatter":{},"scope":{"mintConfig":{"$schema":"https://mintlify.com/schema.json","name":"GPT-trainer API","logo":{"light":"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/logo/light.svg","dark":"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/logo/dark.svg"},"favicon":"/logo/favicon.png","api":{"baseUrl":"https://app.gpt-trainer.com/api","auth":{"method":"bearer"}},"colors":{"primary":"#2E3F51","light":"#516F90","dark":"#0D001D","background":{"dark":"#111827"},"anchors":{"from":"#ED727B","to":"#F6B7BB"}},"topbarLinks":[{"url":"mailto:hello@gpt-trainer.com","name":"Support","_id":"676a0adaff1411a490c729a2"}],"navigation":[{"group":"Getting Started","pages":["introduction"]},{"group":"Guides","pages":["creating-first-chatbot","lead-collection","human-support-escalation","inbox-notifications","conversation-labeling","multi-agents-chatbot","fine-tuning-agent-intents","supervisor-overrides","byok-pricing-guide","working-with-tables","best-practices","help"]},{"group":"Function Calling","pages":["rag-from-external-data-provider"]},{"group":"API Usage Guides","pages":["api-reference/api-key-setup","api-reference/guide-00-chatbot-create","api-reference/guide-01-chat","api-reference/guide-02-source"]},{"group":"Authentication Webhook","pages":["user-identity"]},{"group":"Chatbots","pages":["api-reference/chatbots/properties-reference","api-reference/chatbots/create","api-reference/chatbots/update","api-reference/chatbots/fetch","api-reference/chatbots/fetch_multi","api-reference/chatbots/delete"]},{"group":"Agents","pages":["api-reference/agents/properties-reference","api-reference/agents/create","api-reference/agents/update","api-reference/agents/fetch_multi","api-reference/agents/delete"]},{"group":"Chatbot Sessions","pages":["api-reference/sessions/properties-reference","api-reference/sessions/create","api-reference/sessions/fetch","api-reference/sessions/fetch_multi","api-reference/sessions/delete","api-reference/sessions/delete_multi"]},{"group":"Session Messages","pages":["api-reference/messages/properties-reference","api-reference/messages/create","api-reference/messages/fetch_multi","api-reference/messages/delete","api-reference/messages/delete_multi"]},{"group":"Data Sources","pages":["api-reference/data-sources/properties-reference","api-reference/data-sources/create-file","api-reference/data-sources/create-qa","api-reference/data-sources/create-url","api-reference/data-sources/update","api-reference/data-sources/fetch_multi","api-reference/data-sources/retrain","api-reference/data-sources/delete","api-reference/data-sources/delete_multi"]},{"group":"Data Source Tags","pages":["api-reference/source-tags/create","api-reference/source-tags/fetch-multi","api-reference/source-tags/update","api-reference/source-tags/delete"]},{"group":"Tool Guides","pages":["tools/tools-intro"]},{"group":"Tools","pages":["tools/weekday"]},{"group":"Whitelabel Dashboard","pages":["whitelabel/whitelabel-intro","whitelabel/whitelabel-plans","whitelabel/whitelabel-users"]},{"group":"Integrations","pages":["whitelabel/whitelabel-zapier","whitelabel/whitelabel-make","whitelabel/whitelabel-meta"]}],"primaryTab":{"name":"Documentation"},"anchors":[{"name":"API References","url":"api-reference","icon":"code","_id":"676a0adaff1411a490c7299f"},{"name":"Tools","url":"tools","icon":"gear","_id":"676a0adaff1411a490c729a0"},{"name":"Whitelabel","url":"whitelabel","icon":"browser","_id":"676a0adaff1411a490c729a1"}],"repo":{"github":{"owner":"ks-collab","repo":"gpt-trainer-docs","contentDirectory":"","deployBranch":"main","isPrivate":false}}},"pageMetadata":{"title":"Working with Tables and CSV","description":null,"href":"/working-with-tables"}}}},"__N_SSG":true},"page":"/_sites/[subdomain]/[[...slug]]","query":{"subdomain":"guide.gpt-trainer.com","slug":["working-with-tables"]},"buildId":"pChs_9tFT1YAEINLWWPhQ","isFallback":false,"isExperimentalCompile":false,"gsp":true,"scriptLoader":[]}</script>
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