content-chatbot
ludwig
content-chatbot | ludwig | |
---|---|---|
5 | 3 | |
510 | 10,827 | |
- | 1.0% | |
6.9 | 9.5 | |
3 months ago | 5 days ago | |
Python | Python | |
- | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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content-chatbot
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Show HN: Document Q&A with GPT: web, .pdf, .docx, etc.
I built this repo to do this for your own website content, that should get you a good starting point:
https://github.com/mpaepper/content-chatbot
- Your website's content -> Q&A bot / chatbot
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Repo to create embeddings of your website's content for a Q&A bot / chatbot
Thanks for sharing the code. What happen when the existing content get updated and new contents created, would it need to create embeddings for all contents again? The current approach is not good as create embeddings cost money? Please see https://github.com/mpaepper/content-chatbot/blob/main/create.... Would it be possible progressively update the vector store?
Please advise. Thank you.
ludwig
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Show HN: Toolkit for LLM Fine-Tuning, Ablating and Testing
This is a great project, little bit similar to https://github.com/ludwig-ai/ludwig, but it includes testing capabilities and ablation.
questions regarding the LLM testing aspect: How extensive is the test coverage for LLM use cases, and what is the current state of this project area? Do you offer any guarantees, or is it considered an open-ended problem?
Would love to see more progress toward this area!
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Python projects with best practices on Github?
Two random examples I found from 30 seconds of googling: Here’s Netflix using it in their crisis management tool, and here’s Uber using it in their deep learning framework.
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Most Frequent 600 Coding Questions on LeetCode
They list themselves all over the internet as an "open source contributor" to Uber, which as far I can tell is based entirely on... reporting that there was an issue with a favicon. To me, it seems like they'll be cheating anybody who employs them based on this, ahem, "experience". And that feels like the tip of the iceberg.
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
nlp-recipes - Natural Language Processing Best Practices & Examples
faiss - A library for efficient similarity search and clustering of dense vectors.
data-structures-and-algorithms - Resources that I used to crack some big tech & startups interviews
simple-llm-finetuner - Simple UI for LLM Model Finetuning
aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
paper-qa - LLM Chain for answering questions from documents with citations
Robo-Semantic-Segmentation - Just a simple semantic segmentation library that I developed to speed up the image segmentation pipeline
slothbot - SlothBot | A generally useful analytical Discord bot that does support and writes SQL.
clip-as-service - 🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
ai-deadlines - :alarm_clock: AI conference deadline countdowns
Python_Storage_Tracker - Py Storage Tracker is a cross-platform command line tool using Python to track storage and other system related information.