serviette

Hello everyone! I am Jan, CTO and one of the creators of Pathway, the real-time data processing framework. I’m excited to share Pathway’s ready-to-use AI Pipelines, configurable with just YAML! These frameworks offer out-of-the-box solutions for AI search, RAG, and more—optimized for real-time indexing and in-memory processing. What makes it simple? YAML templates! The pipeline templates are fully customizable using YAMLs to fit your needs, from changing the data sources to the choice of the LLM model, all without touching Pathway’s Python code. Thanks to the Pathway data processing engine, these pipelines are optimized for real-time, updating the pipeline by ingesting the data as it arrives. Why it matters: * YAML templates: that’s it, no Python code required. * Full customization: Configure everything, from LLM models and indexes to tok

We’re excited to share our fully local RAG setup, which has received great feedback from the community. Full app: https://github.com/pathwaycom/llm-app/tree/main/examples/pip... We also feature tools and parsers for various RAG use cases, including document parsing with local vision LMs and some other cool stuff.

Sure: when a new response is produced because some source documents have changed we ask an LLM to compare the responses and tell if they are significantly different. Even a simplistic prompt, like the one used in the example would do: Are the two following responses deviating? Answer with Yes or No. First response: "{old}" Second response: "{new}" (used in https://github.com/pathwaycom/llm-app/blob/69709a2cf58cdf6ea...)

Hi I am Jan, CTO @ Pathway. A use case we have been working on with LLMs is to let people know when an answer to their query changes due to revisions of source documents. Obviously, we want to avoid periodically re-computing all queries for the LLM. Why I think it’s cool? - We don’t spin in a loop to repeat with the LLM. - Alerts are LLM-deduplicated - no spamming users with typo fixes - And the best - our framework, Pathway takes care of handling the updates, the example looks nearly like a regular, static RAG chatbot. More context + GIF of how it works for Google Drive document alerts: https://pathway.com/developers/showcases/llm-alert-pathway Happy to have your thoughts!

To quickly get to the application sources please go to: - https://github.com/pathwaycom/llm-app/blob/main/llm_app/path... for the simplest contextless app - https://github.com/pathwaycom/llm-app/blob/main/llm_app/path... for the default app that builds a reactive index of context documents - https://github.com/pathwaycom/llm-app/blob/main/llm_app/path... for the contextful app reading data from s3 - https://github.com/pathwaycom/llm-app/blob/main/llm_app/path... for the app using locally available models

Hi HN, I am Jan, CTO and co-founder of Pathway.com. We’ve built a LLM microservice that answers questions about a corpus of documents, while automatically reacting to additions of new docs. The single, self-contained service fully replaces a complex multi-system pipeline that scans in real-time for new documents, indexes them into a specialized database and queries it to generate answers. Everyone can have their own real-time vector now. Github: https://github.com/pathwaycom/llm-app Demo video: https://youtu.be/kcrJSk00duw I am eager to hear your thoughts and comments!