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Example_groq_search.json
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Example_groq_search.json
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{
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"title":"System Prompt",
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"Next step is to simply query the data we've obtained with our system prompt, and users question to guide the LLM on data parsing (especially helpful when search results could contain irrelevant matches). "
],
"color":"#432",
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"title":"Groq Powered Search Engine Bot",
"properties":{
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"widgets_values":[
"This workflow demonstrates creating a \"Search Engine Bot\" utilizing blazing fast Groq models. The only thing holding us back really is the speed at which we can obtain data on searches, and communicate with Groq. \n\nBe sure to input your keys! You can obtain them from:\nGroq: https://console.groq.com/keys \nOpenAI (for embeddings): https://openai.com/index/openai-api/\n\n"
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"title":"Search the net with Tavily",
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"widgets_values":[
"Here we're utilizing the Tavily Research node to obtain our context documents. This node performs basic or advance searches, and can return answers and raw site content. \n\n"
],
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"Coaching the LLM with a system prompt is an essential part of setting up your model to provide the best responses. \n\nHere we're directing the model to only output the provide the relevant data, and not to provide any fluff such as things like \"based on the provided context ...\" before actually giving the user any relevant data. \n"
],
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"name":"results",
"type":"STRING",
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],
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"inputs":[
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"You are a search engine end-point that provides valid markdown as a response. You do not chat or talk to the users, but simply provide structured data.\n\nRULES THAT MUST BE FOLLOWED AT ALL TIMES:\n\nDO NOT SAY ANYTHING. Only provide the data requested; and elaborate on it as much as possible. At no point in time are you to talk to the user, or reveal you're analyzing \"the provided context\". You are a data end-point. \n\nIf there are related questions and answers, display them.\n\nBe sure to provide ALL source website titles and URL to the websites where you obtained the knowledge at the footer titled \"Citation(s)\".",
"Please summarize this data. "
]
},
{
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"name":"output",
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}
],
"properties":{
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},
"widgets_values":[
"## Getsalt.ai is a platform for building, deploying, and scaling AI workflows. \n\n**Key features:**\n\n* **Cloud-based platform:** Start building workflows immediately in your web browser without local software setup or infrastructure management.\n* **Simple and intuitive:** Designed for users of all skill levels.\n* **High performance:** Automatically provisions and optimizes cloud compute resources for effortless scaling.\n* **Open source:** Regularly shares back to the community with custom nodes and other contributions.\n\n**Getsalt.ai's mission:**\n\n* Democratize AI development by making it easier to build, deploy, and scale AI workflows.\n* Bridge the gap between experimentation and real-world applications.\n* Empower anyone to create impactful AI-powered solutions.\n\n**See the official website for more information:** https://getsalt.ai\n\n**Citation(s):**\n\n* Salt AI | LinkedIn: https://www.linkedin.com/company/getsalt-ai\n* Workflows for the Real World: Introducing Salt - blog.getsalt.ai: https://blog.getsalt.ai/post/workflows-for-the-real-world-introducing-salt-ai"
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"title":"User Input",
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"Here we're obtaining input from Salt Workflow Input, a node which can pass data in-workflow, or receive data from the Salt platform, such as from Discord. \n\nBe sure your `input_type` is set to \"STRING\" to pass string data. \n\n**API Currently Unavailable**\nWhen using the API, be sure your input_name reflects the key you're using on the API to input the data to the workflow. "
],
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