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Show HN: ElectionGPT – easy-to-consume information about U.S. candidates

electiongpt.ai

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Re: Show HN: ElectionGPT – easy-to-consume information about U.S. candidates

#3

Post by brianjking »

Kennedy isn't running as a Democrat.

Secondly, without disclosing where you're obtaining the data you're generating embeddings from, this feels pretty dangerous. I hope you're using actual news sources and then providing them in the retrieval.

Re: Show HN: ElectionGPT – easy-to-consume information about U.S. candidates

#5

Post by picohen »

brianjking wrote: post #3

Kennedy isn't running as a Democrat. Secondly, without disclosing where you're obtaining the data you're generating embeddings from, this feels pretty dangerous. I hope you're using actual news sources and then providing them in the retrieval.

Thanks! Yes. We ingest from a variety of sources. You can check in the about section of the page but essentially with Neum - Disclaimer, I'm the co-founder of Neum (https://neum.ai) - we power the RAG for the chatbot.

The bulk of the data is getting refreshed by Tweets from all of the candidates. We also pull in data from public sources such as wikipedia and ballotpedia (the bot outputs the sources used) And we also pull in from transcripts of interviews the candidates have had, again, if a piece of indo was used from any of these sources, we show it to the user

Re: Show HN: ElectionGPT – easy-to-consume information about U.S. candidates

#7

Post by picohen »

spencerchubb wrote: post #4

I've been waiting for someone to make something like this! If you are the creator, could you share about your approach?

Hey! Yes! I am the creator, anything specific you wanted to know about? We published the tech-stack in this tweet https://x.com/kevin_neum/status/1712915693874958604?s=20 but essentially the way it works is: 1. Vercel and nextjs for frontend code and deployment 2. Neum to power the rag pipelines for the chatbot to query up-to-date information which we pull from a variety of sources 2.a) the text embeddings are stored in Weaviate (vector db) 3. We then create a prompt/some code with langchain to help query openai/stream the response back!
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