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olruasManul from Pathway
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Rebranding public repo (#10390)
GitOrigin-RevId: 81e91c947bd0dffcef2dfee55258aad3db51348a
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.github/ISSUE_TEMPLATE/bug_report.yml

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id: pathway_version
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attributes:
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label: Version
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description: On which version of Pathway are you working?
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description: On which version of the Pathway Live Data Framework are you working?
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validations:
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required: true
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- type: input
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id: os_version
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attributes:
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label: OS
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description: On which OS did you run Pathway?
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description: On which OS did you run the Pathway Live Data Framework?
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options:
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- Linux
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- MacOS
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- type: dropdown
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id: cpu_architecture
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attributes:
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label: On which CPU architecture did you run Pathway?
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label: On which CPU architecture did you run the Pathway Live Data Framework?
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multiple: false
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options:
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- x86-64

.github/pull_request_template.md

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### Introduction
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To contribute code to the Pathway project, start by discussing your proposed changes on Discord or by filing an issue.
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To contribute code to the Pathway Live Data Framework project, start by discussing your proposed changes on Discord or by filing an issue.
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Once approved, follow the fork + pull request model against the main branch, ensuring you've signed the contributor license agreement.
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### Context

CONTRIBUTING.md

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# Contributing to Pathway
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# Contributing to the Pathway Live Data Framework
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Welcome! This guide is intended to help developers that are new to the community
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to make contributions to the Pathway project. Please be sure to also read our [code of conduct](CODE_OF_CONDUCT.md). We work hard to make this a welcoming community for all, and we're excited to have you on board!
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to make contributions to the Pathway Live Data Framework project. Please be sure to also read our [code of conduct](CODE_OF_CONDUCT.md). We work hard to make this a welcoming community for all, and we're excited to have you on board!
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## The basics:
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## Contributing Code
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We are eager to build Pathway together with the community and are excited to have you here!
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We are eager to build the Pathway Live Data Framework together with the community and are excited to have you here!
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Important: Before you contribute any changes to our repositories (make a Pull Request) please get in touch with us and explain what you want to work on. You can do so by filing an issue, or asking us on Discord. That way you can get coding advice and speedier reviews. If you start to work on an issue before hearing back from a Pathway core team engineer there is a chance that we will not be able to accept your changes - something we all want to avoid!
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README.md

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<div align="center">
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# Pathway AI Pipelines
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# Pathway Live Data Framework AI Pipelines
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<a href="https://trendshift.io/repositories/4400" target="_blank"><img src="https://trendshift.io/api/badge/repositories/4400" alt="pathwaycom%2Fllm-app | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
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The apps can be run as **Docker containers**, and expose an **HTTP API** to connect the frontend. To allow quick testing and demos, some app templates also include an optional Streamlit UI which connects to this API.
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The apps rely on the [Pathway Live Data framework](https://github.com/pathwaycom/pathway) for data source synchronization and for serving API requests (Pathway is a standalone Python library with a Rust engine built into it). They bring you a **simple and unified application logic** for back-end, embedding, retrieval, LLM tech stack. There is no need to integrate and maintain separate modules for your Gen AI app: ~Vector Database (e.g. Pinecone/Weaviate/Qdrant) + Cache (e.g. Redis) + API Framework (e.g. Fast API)~. Pathway's default choice of **built-in vector index** is based on the lightning-fast [usearch](https://github.com/unum-cloud/usearch) library, and **hybrid full-text indexes** make use of [Tantivy](https://github.com/quickwit-oss/tantivy) library. Everything works out of the box.
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The apps rely on the [Pathway Live Data Framework](https://github.com/pathwaycom/pathway) for data source synchronization and for serving API requests (Pathway is a standalone Python library with a Rust engine built into it). They bring you a **simple and unified application logic** for back-end, embedding, retrieval, LLM tech stack. There is no need to integrate and maintain separate modules for your Gen AI app: ~Vector Database (e.g. Pinecone/Weaviate/Qdrant) + Cache (e.g. Redis) + API Framework (e.g. Fast API)~. Pathway's default choice of **built-in vector index** is based on the lightning-fast [usearch](https://github.com/unum-cloud/usearch) library, and **hybrid full-text indexes** make use of [Tantivy](https://github.com/quickwit-oss/tantivy) library. Everything works out of the box.
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## Getting started
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templates/question_answering_rag/ui/ui.py

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PATHWAY_HOST = os.environ.get("PATHWAY_HOST", "app")
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PATHWAY_PORT = os.environ.get("PATHWAY_PORT", 8000)
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st.set_page_config(page_title="Pathway Live Data Framework RAG App", page_icon="favicon.ico")
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st.set_page_config(
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page_title="Pathway Live Data Framework RAG App", page_icon="favicon.ico"
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)
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logging.basicConfig(
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level=logging.INFO,

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