<?xml version="1.0" encoding="UTF-8"?>
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    <title>rag</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/492/feed"/>
    <updated>2026-09-17T08:09:28+00:00</updated>
    <id>https://links.biapy.com/guest/tags/492/feed</id>
            <entry>
            <id>https://links.biapy.com/links/13563</id>
            <title type="text"><![CDATA[LEANN]]></title>
            <link rel="alternate" href="https://github.com/StarTrail-org/LEANN" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13563"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The smallest vector index in the world. RAG Everything with LEANN! 

[MLsys2026]: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

LEANN is an innovative vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using 97% less storage than traditional solutions without accuracy loss.]]>
            </summary>
            <updated>2026-08-07T05:46:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/13150</id>
            <title type="text"><![CDATA[Osmantic Deployment System (ODS)]]></title>
            <link rel="alternate" href="https://github.com/Light-Heart-Labs/ODS" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/13150"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Turn your PC, Mac, or Linux box into an AI server. LLM inference, chat UI, voice, agents, workflows, RAG, and image generation. 

Related contents:

- [Dream Server - Un serveur IA complet chez vous en une commande @ Korben :fr:](https://korben.info/dream-server-ia-locale-auto-hebergee.html).]]>
            </summary>
            <updated>2026-06-29T08:53:53+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12788</id>
            <title type="text"><![CDATA[Dream Server]]></title>
            <link rel="alternate" href="https://github.com/Light-Heart-Labs/DreamServer" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12788"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Local AI anywhere, for everyone — LLM inference, chat UI, voice, agents, workflows, RAG, and image generation. No cloud, no subscriptions. 

Dream Server is the exit. A local-first AI stack — LLM inference, chat, voice, agents, workflows, RAG, image generation, and privacy tools — deployed on your hardware with a single command. No cloud required. No subscriptions required. No one watching. Cloud and hybrid API modes are optional when you choose them.]]>
            </summary>
            <updated>2026-05-18T11:57:20+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12600</id>
            <title type="text"><![CDATA[RAG-Anything]]></title>
            <link rel="alternate" href="https://github.com/HKUDS/RAG-Anything" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12600"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[All-in-One RAG Framework.

RAG-Anything eliminates the need for multiple specialized tools. It provides seamless processing and querying across all content modalities within a single integrated framework. Unlike conventional RAG approaches that struggle with non-textual elements, our all-in-one system delivers comprehensive multimodal retrieval capabilities.

Users can query documents containing interleaved text, visual diagrams, structured tables, and mathematical formulations through one cohesive interface. This consolidated approach makes RAG-Anything particularly valuable for academic research, technical documentation, financial reports, and enterprise knowledge management where rich, mixed-content documents demand a unified processing framework.]]>
            </summary>
            <updated>2026-04-22T12:48:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12379</id>
            <title type="text"><![CDATA[Langflow]]></title>
            <link rel="alternate" href="https://www.langflow.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12379"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Low-code AI builder for agentic and RAG applications.
 Langflow is a powerful tool for building and deploying AI-powered agents and workflows. 

Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Langflow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.

- [Langflow @ GitHub](https://github.com/langflow-ai/langflow).

Related contents:

- [CVE-2026-33017 Detail @ NIST](https://nvd.nist.gov/vuln/detail/CVE-2026-33017).
- [Using AI for Terraform: running locally with Langflow, OpenSearch, &amp;amp; Ollama @ Rosemary Wang&amp;#039;s dev.to](https://dev.to/joatmon08/using-ai-for-terraform-running-a-locally-with-langflow-opensearch-ollama-5co6).]]>
            </summary>
            <updated>2026-04-08T05:59:06+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12376</id>
            <title type="text"><![CDATA[Onyx AI]]></title>
            <link rel="alternate" href="https://onyx.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12376"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Source Enterprise Search &amp;amp; AI Assistant.
Give your team superpowers.

Onyx is the open-source AI chat connected to your docs, apps, and people.
 Open Source AI Platform - AI Chat with advanced features that works with every LLM.


Onyx is a feature-rich, self-hostable Chat UI that works with any LLM. It is easy to deploy and can run in a completely airgapped environment.
Onyx comes loaded with advanced features like Agents, Web Search, RAG, MCP, Deep Research, Connectors to 40+ knowledge sources, and more.

- [Onyx @ GitHub](https://github.com/onyx-dot-app/onyx)]]>
            </summary>
            <updated>2026-03-30T18:47:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12359</id>
            <title type="text"><![CDATA[Paperwise]]></title>
            <link rel="alternate" href="https://paperwise.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12359"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Documents, structured and queryable.
Structured and Queryable Documents.

Paperwise helps you OCR, extract, organize, and query documents on your own infrastructure. Run it locally or self-host it, and keep full control of your data. 

- [Paperwise @ GitHub](https://github.com/zellux/paperwise).]]>
            </summary>
            <updated>2026-03-29T15:31:44+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12241</id>
            <title type="text"><![CDATA[Project N.O.M.A.D]]></title>
            <link rel="alternate" href="https://github.com/Crosstalk-Solutions/project-nomad" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12241"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Node for Offline Media, Archives, and Data.

 Project N.O.M.A.D, is a self-contained, offline survival computer packed with critical tools, knowledge, and AI to keep you informed and empowered—anytime, anywhere.]]>
            </summary>
            <updated>2026-03-23T14:42:45+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12166</id>
            <title type="text"><![CDATA[RAGFlow]]></title>
            <link rel="alternate" href="https://ragflow.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12166"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Build a superior context layer for AI agents.

Empower your AI agents through the leading open-source RAG engine, delivering reliable context and an integrated agent platform, built for enterprise.

 RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs 

- [RAGFlow @ GitHub](https://github.com/infiniflow/ragflow).]]>
            </summary>
            <updated>2026-03-17T16:36:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12165</id>
            <title type="text"><![CDATA[Dify]]></title>
            <link rel="alternate" href="https://dify.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12165"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Leading Agentic Workflow Builder.

Dify offers everything you need — agentic workflows, RAG pipelines, integrations, and observability — all in one place, putting AI power into your hands.

Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (including Opik, Langfuse, and Arize Phoenix) and more, letting you quickly go from prototype to production. Here&amp;#039;s a list of the core features:

- [Dify @ GitHub](https://github.com/langgenius/dify).]]>
            </summary>
            <updated>2026-03-17T16:34:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12148</id>
            <title type="text"><![CDATA[OpenRAG]]></title>
            <link rel="alternate" href="https://www.openr.ag/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12148"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[OpenRAG is a comprehensive, single package Retrieval-Augmented Generation platform built on Langflow, Docling, and Opensearch. 

Users can upload, process, and query documents through a chat interface backed by large language models and semantic search capabilities. The system utilizes Langflow for document ingestion, retrieval workflows, and intelligent nudges, providing a seamless RAG experience.

- [OpenRAG @ GitHub](https://github.com/langflow-ai/openrag).

Related contents:

- [Veille \#47 — L&amp;#039;actu de la semaine @ Camille Roux :fr:](https://www.camilleroux.com/veille-47-lactu-de-la-semaine/).
- [OpenRAG - Le RAG clé en main qui vous évite 3 jours de galère @ Korben :fr:](https://korben.info/openrag-rag-cle-en-main-documents-ia.html).]]>
            </summary>
            <updated>2026-03-23T15:21:46+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/12087</id>
            <title type="text"><![CDATA[RCLI]]></title>
            <link rel="alternate" href="https://github.com/RunanywhereAI/rcli" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12087"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Talk to your Mac, query your docs, no cloud required. On-device voice AI + RAG. 

RCLI is an on-device voice AI for macOS. A complete STT + LLM + TTS pipeline running natively on Apple Silicon — 38 macOS actions via voice, local RAG over your documents, sub-200ms end-to-end latency. No cloud, no API keys.]]>
            </summary>
            <updated>2026-03-11T12:12:31+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11901</id>
            <title type="text"><![CDATA[GitNexus]]></title>
            <link rel="alternate" href="https://gitnexus.vercel.app/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11901"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Zero-Server Code Intelligence Engine - GitNexus is a client-side knowledge graph creator that runs entirely in your browser. Drop in a GitHub repo or ZIP file, and get an interactive knowledge graph wit a built in Graph RAG Agent. Perfect for code exploration 

- [GitNexus @ GitHub](https://github.com/abhigyanpatwari/GitNexus).]]>
            </summary>
            <updated>2026-02-23T12:55:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11692</id>
            <title type="text"><![CDATA[Systematically Improving RAG Applications]]></title>
            <link rel="alternate" href="https://567-labs.github.io/systematically-improving-rag/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11692"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Most RAG implementations struggle in production because teams focus on model selection and prompt engineering while overlooking the fundamentals: measurement, feedback, and systematic improvement.

This guide presents practical frameworks for building RAG systems that become more valuable over time through continuous learning and data-driven optimization.

- [Systematically Improving RAG Applications @ GitHub](https://github.com/567-labs/systematically-improving-rag/).]]>
            </summary>
            <updated>2026-02-03T13:23:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11651</id>
            <title type="text"><![CDATA[PageIndex]]></title>
            <link rel="alternate" href="https://pageindex.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11651"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Human-like Document AI

PageIndex is a vectorless, reasoning-based RAG engine that mirrors how humans read, delivering traceable, explainable, and context-aware retrieval, without vector databases or chunking.

- [PageIndex @ GitHub](https://github.com/VectifyAI/PageIndex).]]>
            </summary>
            <updated>2026-01-30T12:52:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11629</id>
            <title type="text"><![CDATA[PaddleOCR :cn:]]></title>
            <link rel="alternate" href="https://www.paddleocr.ai/latest/en/index.html" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11629"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages. 

PaddleOCR is an industry-leading, production-ready OCR and document AI engine, offering end-to-end solutions from text extraction to intelligent document understanding

- [PaddleOCR @ GitHub](https://github.com/PaddlePaddle/PaddleOCR).

Related contents:

- [Episode \#125: The state of homelab tech (2026) @ Changelog &amp;amp; Friends](https://changelog.com/friends/125).]]>
            </summary>
            <updated>2026-01-27T07:12:46+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11464</id>
            <title type="text"><![CDATA[WeKnora :cn:]]></title>
            <link rel="alternate" href="https://weknora.weixin.qq.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11464"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answers using RAG paradigm.

It adopts a modular architecture that combines multimodal preprocessing, semantic vector indexing, intelligent retrieval, and large language model inference. At its core, WeKnora follows the RAG (Retrieval-Augmented Generation) paradigm, enabling high-quality, context-aware answers by combining relevant document chunks with model reasoning.

- [WeKnora @ GitHub](https://github.com/Tencent/WeKnora).]]>
            </summary>
            <updated>2026-01-15T06:50:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11310</id>
            <title type="text"><![CDATA[HelixML]]></title>
            <link rel="alternate" href="https://helix.ml/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11310"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Agents on a Private GenAI Stack.

 ♾️ Helix is a private GenAI stack for building AI agents with declarative pipelines, knowledge (RAG), API bindings, and first-class testing. 

- [HelixML @ GitHub](https://github.com/helixml/helix).

Related contents:

- [We Mass-Deployed 15-Year-Old Screen Sharing Technology and It&amp;#039;s Actually Better @ HelixML](https://blog.helix.ml/p/we-mass-deployed-15-year-old-screen).]]>
            </summary>
            <updated>2025-12-24T12:44:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/11141</id>
            <title type="text"><![CDATA[Skald]]></title>
            <link rel="alternate" href="https://useskald.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/11141"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Production-ready RAG in your infrastructure.

Skald gives you a production-ready RAG in minutes through a plug-and-play API, and then let&amp;#039;s you configure your RAG engine exactly to your needs.

Our solid defaults will work for most use cases, but you can tune every part of your RAG to better suit your needs. That means configurable vector search params, reranking, models, query rewriting, chunking (soon), and more.

- [Skald @ GitHub](https://github.com/skaldlabs/skald).

Related contents:

- [So you wanna build a local RAG? @ yakko&amp;#039;s blog](https://blog.yakkomajuri.com/blog/local-rag).]]>
            </summary>
            <updated>2025-12-01T10:13:07+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10768</id>
            <title type="text"><![CDATA[LightRAG]]></title>
            <link rel="alternate" href="https://github.com/HKUDS/LightRAG" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10768"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Simple and Fast Retrieval-Augmented Generation.

The LightRAG Server is designed to provide Web UI and API support. The Web UI facilitates document indexing, knowledge graph exploration, and a simple RAG query interface. LightRAG Server also provide an Ollama compatible interfaces, aiming to emulate LightRAG as an Ollama chat model. This allows AI chat bot, such as Open WebUI, to access LightRAG easily.]]>
            </summary>
            <updated>2025-10-27T11:25:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10751</id>
            <title type="text"><![CDATA[Agentset]]></title>
            <link rel="alternate" href="https://agentset.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10751"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Build Frontier RAG Apps.
 The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more. 

Ground AI agents in your knowledge base, minimize hallucinations, and impress out of the box.
Agentset is the open-source platform to build, evaluate, and ship production-ready RAG and agentic applications. It provides end-to-end tooling: ingestion, vector indexing, evaluation/benchmarks, chat playground, hosting, and a clean API with first-class developer experience.

- [Agentset @ GitHub](https://github.com/agentset-ai/agentset).

Related contents:

- [Production RAG: what I learned from processing 5M+ documents @ Abdellatif Abdelfattah](https://blog.abdellatif.io/production-rag-processing-5m-documents).]]>
            </summary>
            <updated>2025-10-21T11:51:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10334</id>
            <title type="text"><![CDATA[Pinecone]]></title>
            <link rel="alternate" href="https://www.pinecone.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10334"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The vector database to build knowledgeable AI.

The vector database for machine learning applications. Build vector-based personalization, ranking, and search systems that are accurate, fast, and scalable.

Related contents:

- [Building a Hybrid Search RAG System with Pinecone and LangChain @ Arpan Roy&amp;#039;s Medium](https://medium.com/@arpanroy_43094/building-a-hybrid-search-ragsystem-with-pinecone-and-langchain-efed2cbf1f88).]]>
            </summary>
            <updated>2025-09-22T07:00:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10259</id>
            <title type="text"><![CDATA[DSPy (Declarative Self-improving Python)]]></title>
            <link rel="alternate" href="https://dspy.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10259"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The framework for programming—not prompting—language models 

DSPy is a declarative framework for building modular AI software. It allows you to iterate fast on structured code, rather than brittle strings, and offers algorithms that compile AI programs into effective prompts and weights for your language models, whether you&amp;#039;re building simple classifiers, sophisticated RAG pipelines, or Agent loops.

- [DSPy @ GitHub](https://github.com/stanfordnlp/dspy).

Related contents:

- [Building and Optimizing Multi-Agent RAG Systems with DSPy and GEPA @ Isaac Kargar&amp;#039;s Medium](https://kargarisaac.medium.com/building-and-optimizing-multi-agent-rag-systems-with-dspy-and-gepa-2b88b5838ce2).]]>
            </summary>
            <updated>2025-09-18T05:57:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10179</id>
            <title type="text"><![CDATA[LobeChat]]></title>
            <link rel="alternate" href="https://lobechat.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10179"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Your personal AI productivity tool for a smarter brain.

 🤯 Lobe Chat - an open-source, modern design AI chat framework. Supports multiple AI providers (OpenAI / Claude 4 / Gemini / DeepSeek / Ollama / Qwen), Knowledge Base (file upload / RAG ), one click install MCP Marketplace and Artifacts / Thinking. One-click FREE deployment of your private AI Agent application. 

- [LobeChat @ GitHub](https://github.com/lobehub/lobe-chat).

Related contents:

- [Episode 586: Kexec with Determination @ Linux Unplugged](https://linuxunplugged.com/586).]]>
            </summary>
            <updated>2026-04-09T06:11:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10046</id>
            <title type="text"><![CDATA[LEANN]]></title>
            <link rel="alternate" href="https://github.com/yichuan-w/LEANN" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10046"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device. 

LEANN is an innovative vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using 97% less storage than traditional solutions without accuracy loss.

Related contents:

- [LEANN - L&amp;#039;IA personnelle qui écrase 97% de ses concurrents (en taille) @ Korben :fr:](https://korben.info/leann-rag.html).]]>
            </summary>
            <updated>2025-09-08T10:28:00+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/321</id>
            <title type="text"><![CDATA[Morphik]]></title>
            <link rel="alternate" href="https://www.morphik.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/321"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Build Agents that Never Hallucinate.
Deploy the most accurate RAG in the world in two lines of code.

 The most accurate document search and store for building AI apps.

- [Morphik @ GitHub](https://github.com/morphik-org/morphik-core).

Related contents:

- [Don&amp;#039;t bother parsing: Just use images for RAG @ Morphik](https://www.morphik.ai/blog/stop-parsing-docs).]]>
            </summary>
            <updated>2026-01-13T13:25:14+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/392</id>
            <title type="text"><![CDATA[React Native ExecuTorch]]></title>
            <link rel="alternate" href="https://docs.swmansion.com/react-native-executorch/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/392"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Declarative way to run AI models in React Native on device, powered by ExecuTorch.

React Native ExecuTorch is a declarative way to run AI models in React Native on device, powered by ExecuTorch 🚀. It offers out-of-the-box support for many LLMs, computer vision models, and many many more. Feel free to check them out on our HuggingFace page.

ExecuTorch is a novel framework created by Meta that enables running AI models on devices such as mobile phones or microcontrollers.

- [React Native ExecuTorch @ GitHub](https://github.com/software-mansion/react-native-executorch).

Related contents:

- [Introducing React Native RAG: Local &amp;amp; Offline Retrieval-Augmented Generation @ Software Mansion&amp;#039;s Medium](https://blog.swmansion.com/introducing-react-native-rag-fbb62efa4991).]]>
            </summary>
            <updated>2026-01-20T15:31:46+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/676</id>
            <title type="text"><![CDATA[Memvid]]></title>
            <link rel="alternate" href="https://github.com/Olow304/memvid" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/676"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Video-Based AI Memory 🧠📹.

Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic search. No database needed. 

Memvid revolutionizes AI memory management by encoding text data into videos, enabling lightning-fast semantic search across millions of text chunks with sub-second retrieval times. Unlike traditional vector databases that consume massive amounts of RAM and storage, Memvid compresses your knowledge base into compact video files while maintaining instant access to any piece of information.]]>
            </summary>
            <updated>2025-08-28T17:50:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/697</id>
            <title type="text"><![CDATA[HelixDB]]></title>
            <link rel="alternate" href="https://www.helix-db.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/697"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Native Graph-Vector Database.

HelixDB is a powerful, open-source, graph-vector database built in Rust for intelligent data storage for RAG and AI. 

- [HelixDB @ GitHub](https://github.com/HelixDB/helix-db/).]]>
            </summary>
            <updated>2025-08-28T17:54:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/911</id>
            <title type="text"><![CDATA[Exmeralda]]></title>
            <link rel="alternate" href="https://exmeralda.chat/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/911"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Exmeralda helps you ask questions about Elixir libraries and get accurate, version-specific answers. Powered by Retrieval-Augmented Generation (RAG), it combines the latest AI with real documentation to deliver helpful, grounded responses. 

- [Exmeralda @ GitHub](https://github.com/bitcrowd/exmeralda/).]]>
            </summary>
            <updated>2025-08-28T18:30:39+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1471</id>
            <title type="text"><![CDATA[Gitingest]]></title>
            <link rel="alternate" href="https://gitingest.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1471"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Turn any Git repository into a simple text digest of its codebase.
Replace &amp;#039;hub&amp;#039; with &amp;#039;ingest&amp;#039; in any github url to get a prompt-friendly extract of a codebase.

This is useful for feeding a codebase into any LLM. 

- [Gitingest @ GitHub](https://github.com/cyclotruc/gitingest).

Related contents:

- [GitIngest - Transformez votre code en prompts pour LLM @ Korben :fr:](https://korben.info/gitingest-transformez-code-prompts-llm.html).]]>
            </summary>
            <updated>2025-08-28T20:01:18+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1779</id>
            <title type="text"><![CDATA[Haystack]]></title>
            <link rel="alternate" href="https://haystack.deepset.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1779"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Production-Ready Open Source AI Framework.

 AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it&amp;#039;s best suited for building RAG, question answering, semantic search or conversational agent chatbots. 

- [Haystack @ GitHub](https://github.com/deepset-ai/haystack).]]>
            </summary>
            <updated>2025-08-28T20:52:51+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1835</id>
            <title type="text"><![CDATA[Common Crawl]]></title>
            <link rel="alternate" href="https://commoncrawl.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1835"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open Repository of Web Crawl Data.

Common Crawl maintains a free, open repository of web crawl data that can be used by anyone.

Related contents:

- [S5E7 - Sommes-nous à l&amp;#039;aube d&amp;#039;un effondrement des IA ? @ Underscore_&amp;#039;s acast :fr:](https://shows.acast.com/micode-underscore/episodes/s5e7-sommes-nous-a-laube-dun-effondrement-des-ia).]]>
            </summary>
            <updated>2025-08-28T21:01:56+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1849</id>
            <title type="text"><![CDATA[DataBridge]]></title>
            <link rel="alternate" href="https://databridge.gitbook.io/databridge-docs" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1849"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Multi-modal modular data ingestion and retrieval.

DataBridge is an open source library for natural language search and management of multi-modal data. Get started by installing databridge now!

DataBridge is a powerful document processing and retrieval system designed for building intelligent document-based applications. It provides a robust foundation for semantic search, document processing, and AI-powered document interactions.]]>
            </summary>
            <updated>2025-08-28T21:04:04+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1880</id>
            <title type="text"><![CDATA[Repomix]]></title>
            <link rel="alternate" href="https://repomix.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1880"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Pack your codebase into AI-friendly formats.

 📦 Repomix (formerly Repopack) is a powerful tool that packs your entire repository into a single, AI-friendly file. Perfect for when you need to feed your codebase to Large Language Models (LLMs) or other AI tools like Claude, ChatGPT, and Gemini. 

- [Repomix @ GitHub](https://github.com/yamadashy/repomix).]]>
            </summary>
            <updated>2025-08-28T21:09:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1897</id>
            <title type="text"><![CDATA[yek]]></title>
            <link rel="alternate" href="https://github.com/bodo-run/yek" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1897"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A fast tool to read text-based files in a repository or directory, chunk them, and serialize them for LLM consumption.]]>
            </summary>
            <updated>2025-08-28T21:12:08+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2091</id>
            <title type="text"><![CDATA[Crawl4AI]]></title>
            <link rel="alternate" href="https://crawl4ai.com/mkdocs/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2091"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open-Source LLM-Friendly Web Crawler &amp;amp; Scraper.

Crawl4AI delivers blazing-fast, AI-ready web crawling tailored for large language models, AI agents, and data pipelines. Fully open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.

- [Crawl4AI @ GitHub](https://github.com/unclecode/crawl4ai).]]>
            </summary>
            <updated>2025-08-28T21:44:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2108</id>
            <title type="text"><![CDATA[Khoj AI]]></title>
            <link rel="alternate" href="https://khoj.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2108"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Your AI Second Brain.
Ask anything, understand documents, create new content.

Khoj is a personal AI app to extend your capabilities. It smoothly scales up from an on-device personal AI to a cloud-scale enterprise AI.

 Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free. 

- [Khoj @ GitHub](https://github.com/khoj-ai/khoj).

Related contents:

- [Khoj - Un assistant IA privé qui vous accompagne au quotidien @ Korben :fr:](https://korben.info/khoj-assistant-ia-prive-productivite.html).]]>
            </summary>
            <updated>2025-08-28T21:48:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2208</id>
            <title type="text"><![CDATA[Pathway]]></title>
            <link rel="alternate" href="https://pathway.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2208"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Power Your AI with Live Data.

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. 

- [Pathway @ GitHub](https://github.com/pathwaycom/pathway).]]>
            </summary>
            <updated>2025-09-10T11:29:30+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2220</id>
            <title type="text"><![CDATA[MarkItDown]]></title>
            <link rel="alternate" href="https://github.com/microsoft/markitdown" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2220"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Python tool for converting files and office documents to Markdown.
MarkItDown is a utility for converting various files to Markdown (e.g., for indexing, text analysis, etc).

- [MarkItDown-MCP @ GitHub](https://github.com/microsoft/markitdown/tree/main/packages/markitdown-mcp).

Related contents:

- [MarkItDown - Convertissez tous vos documents en Markdown très facilement @ Korben :fr:](https://korben.info/markitdown-convertisseur-fichiers-markdown.html).
- [Convertir un document au format Markdown avec MarkItDown : Word, PDF, PowerPoint, Excel, etc. @ IT-Connect :fr:](https://www.it-connect.fr/convertir-un-document-au-format-markdown-avec-markitdown/).]]>
            </summary>
            <updated>2026-03-06T14:52:52+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2223</id>
            <title type="text"><![CDATA[OmniParse]]></title>
            <link rel="alternate" href="https://omniparse.cognitivelab.in/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2223"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Convert Anything into Structured Actionable Data.

 Ingest, parse, and optimize any data format ➡️ from documents to multimedia ➡️ for enhanced compatibility with GenAI frameworks.

OmniParse is a platform that ingests and parses any unstructured data into structured, actionable data optimized for GenAI (LLM) applications. Whether you are working with documents, tables, images, videos, audio files, or web pages, OmniParse prepares your data to be clean, structured, and ready for AI applications such as RAG, fine-tuning, and more

-  [OmniParse @ GitHub](https://github.com/adithya-s-k/omniparse).]]>
            </summary>
            <updated>2025-08-28T22:06:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2473</id>
            <title type="text"><![CDATA[DataFuel]]></title>
            <link rel="alternate" href="https://www.datafuel.dev/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2473"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Turn websites into LLM - ready   data.

DataFuel API scrapes entire websites and knowledge bases in a single query. Get clean, markdown-structured web data instantly for your RAG systems and AI models. No complex scraping code needed.]]>
            </summary>
            <updated>2025-08-28T22:48:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2511</id>
            <title type="text"><![CDATA[🌟 Awesome LLM Apps]]></title>
            <link rel="alternate" href="https://github.com/Shubhamsaboo/awesome-llm-apps" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2511"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Collection of awesome LLM apps with RAG using OpenAI, Anthropic, Gemini and opensource models. 

A curated collection of awesome LLM apps built with RAG and AI agents. This repository features LLM apps that use models from OpenAI, Anthropic, Google, and even open-source models like LLaMA that you can run locally on your computer.]]>
            </summary>
            <updated>2025-08-28T22:54:59+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2711</id>
            <title type="text"><![CDATA[Mastra]]></title>
            <link rel="alternate" href="https://mastra.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2711"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Typescript AI framework.

Mastra is an opinionated Typescript framework that helps you build AI applications and features quickly. It gives you the set of primitives you need: workflows, agents, RAG, integrations, syncs and evals. You can run Mastra on your local machine, or deploy to a serverless cloud.

- [Mastra @ GitHub](https://github.com/mastra-ai/mastra).

Related contents:

- [\#107 - Les news web dev pour mars 2025 @ Double Slash :fr:](https://double-slash.dev/podcasts/news-mar-25/).
- [Mastra 101: Learn how to build agents @ Mastra University](https://mastra.ai/course).]]>
            </summary>
            <updated>2025-08-28T23:29:17+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2722</id>
            <title type="text"><![CDATA[TiDB]]></title>
            <link rel="alternate" href="https://tidb.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2722"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[AI Assistant. Knowledge Graph based RAG built with TiDB Serverless Vector Storage and LlamaIndex.

An open source GraphRAG (Knowledge Graph) built on top of TiDB Vector and LlamaIndex and DSPy.
pingcap/autoflow is a Graph RAG based and conversational knowledge base tool built with TiDB Serverless Vector Storage.

- [autoflow @ GitHub](https://github.com/pingcap/autoflow).]]>
            </summary>
            <updated>2025-08-28T23:30:16+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2819</id>
            <title type="text"><![CDATA[Documind]]></title>
            <link rel="alternate" href="https://www.documind.xyz/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2819"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Extract structured data from PDFs.
Stop wasting time extracting PDFs.
Transform your PDF documents into structured data with Documind. Simple, powerful and open-source.

Documind is an advanced document processing tool that leverages AI to extract structured data from PDFs. It is built to handle PDF conversions, extract relevant information, and format results as specified by customizable schemas.

- [Documind @ GitHub](https://github.com/DocumindHQ/documind/).]]>
            </summary>
            <updated>2025-08-28T23:46:25+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2820</id>
            <title type="text"><![CDATA[Fast GraphRAG]]></title>
            <link rel="alternate" href="https://github.com/circlemind-ai/fast-graphrag" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2820"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[RAG that intelligently adapts to your use case, data, and queries.

Streamlined and promptable Fast GraphRAG framework designed for interpretable, high-precision, agent-driven retrieval workflows.]]>
            </summary>
            <updated>2025-08-28T23:46:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2832</id>
            <title type="text"><![CDATA[MinerU]]></title>
            <link rel="alternate" href="https://mineru.readthedocs.io/en/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2832"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[MinerU is a tool that converts PDFs into machine-readable formats (e.g., markdown, JSON), allowing for easy extraction into any format. MinerU was born during the pre-training process of InternLM. We focus on solving symbol conversion issues in scientific literature and hope to contribute to technological development in the era of large models. Compared to well-known commercial products, MinerU is still young. If you encounter any issues or if the results are not as expected, please submit an issue on issue and attach the relevant PDF.

- [MinerU @ GitHub](https://github.com/opendatalab/MinerU).]]>
            </summary>
            <updated>2025-08-28T23:49:28+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2900</id>
            <title type="text"><![CDATA[Chonkie]]></title>
            <link rel="alternate" href="https://github.com/bhavnicksm/chonkie" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2900"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🦛 CHONK your texts with Chonkie ✨ - The no-nonsense RAG chunking library.
The no-nonsense RAG chunking library that&amp;#039;s lightweight, lightning-fast, and ready to CHONK your texts]]>
            </summary>
            <updated>2025-08-28T23:59:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2949</id>
            <title type="text"><![CDATA[Docling]]></title>
            <link rel="alternate" href="https://docling-project.github.io/docling/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2949"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Docling parses documents and exports them to the desired format with ease and speed.
🗂️ Reads popular document formats (PDF, DOCX, PPTX, Images, HTML, AsciiDoc, Markdown) and exports to Markdown and JSON.

- [Docling @ GitHub](https://github.com/docling-project/docling).

Related contents:

- [Docling - Pour convertir vos documents sans prise de tête @ Korben :fr:](https://korben.info/docling-convertisseur-documents-multi-formats.html).
- [Episode \#125: The state of homelab tech (2026) @ Changelog &amp;amp; Friends](https://changelog.com/friends/125).]]>
            </summary>
            <updated>2026-01-27T07:14:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3117</id>
            <title type="text"><![CDATA[Vanna.AI]]></title>
            <link rel="alternate" href="https://vanna.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3117"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄. 

Personalized AI SQL Agent. Let Vanna.AI write your SQL for you

The fastest way to get actionable insights from your database just by asking questions.

- [Vanna @ GitHub](https://github.com/vanna-ai/vanna).]]>
            </summary>
            <updated>2025-08-29T00:36:34+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3146</id>
            <title type="text"><![CDATA[📦 Repopack]]></title>
            <link rel="alternate" href="https://github.com/yamadashy/repopack" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3146"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[📦 Repopack is a powerful tool that packs your entire repository into a single, AI-friendly file. Perfect for when you need to feed your codebase to Large Language Models (LLMs) or other AI tools like Claude, ChatGPT, and Gemini.]]>
            </summary>
            <updated>2025-08-29T00:40:45+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3239</id>
            <title type="text"><![CDATA[OmniParse]]></title>
            <link rel="alternate" href="https://docs.cognitivelab.in/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3239"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[OmniParse is a platform that ingests/parses any unstructured data into structured, actionable data optimized for GenAI (LLM) applications. Whether working with documents, tables, images, videos, audio files, or web pages, OmniParse prepares your data to be clean, structured and ready for AI applications, such as RAG , fine-tuning and more.

- [OmniParse @ GitHub](https://github.com/adithya-s-k/omniparse).]]>
            </summary>
            <updated>2025-08-29T00:56:54+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3383</id>
            <title type="text"><![CDATA[AutoArena]]></title>
            <link rel="alternate" href="https://www.kolena.com/autoarena/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3383"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Rank LLMs, RAG systems, and prompts using automated judge evaluation.

- [AutoArena @ GitHub](https://github.com/kolenaIO/autoarena).
- [Your October Dose of Data - October 2024 @ Data Council](https://mailchi.mp/datacouncil/october-2024).]]>
            </summary>
            <updated>2025-08-29T01:21:01+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3453</id>
            <title type="text"><![CDATA[Kotaemon]]></title>
            <link rel="alternate" href="https://cinnamon.github.io/kotaemon/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3453"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An open-source RAG-based tool for chatting with your documents. 

An open-source clean &amp;amp; customizable RAG UI for chatting with your documents. Built with both end users and developers in mind.

- [Kotaemon @ GitHub](https://github.com/Cinnamon/kotaemon).]]>
            </summary>
            <updated>2025-08-29T01:33:04+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3576</id>
            <title type="text"><![CDATA[Dot]]></title>
            <link rel="alternate" href="https://dotapp.uk/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3576"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Text-To-Speech, RAG, and LLMs. All local!

Dot is a standalone, open-source application designed for seamless interaction with documents and files using local LLMs and Retrieval Augmented Generation (RAG). It is inspired by solutions like Nvidia&amp;#039;s Chat with RTX, providing a user-friendly interface for those without a programming background. Using the Phi-3 LLM by default, Dot ensures accessibility and simplicity right out of the box.

- [Dot – L’app IA locale pour interagir avec vos documents (RAG) @ Korben :fr:](https://korben.info/dot-app-ia-locale-interagir-documents.html).]]>
            </summary>
            <updated>2025-08-29T01:53:15+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3719</id>
            <title type="text"><![CDATA[GraphRAG]]></title>
            <link rel="alternate" href="https://microsoft.github.io/graphrag/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3719"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs.

GraphRAG is a structured, hierarchical approach to Retrieval Augmented Generation (RAG), as opposed to naive semantic-search approaches using plain text snippets. The GraphRAG process involves extracting a knowledge graph out of raw text, building a community hierarchy, generating summaries for these communities, and then leveraging these structures when perform RAG-based tasks.

- [GraphRAG @ GitHub](https://github.com/microsoft/graphrag).
- [GraphRAG: Microsoft’s Open-Source Solution for Enhanced Document Understanding @ Towards AI&amp;#039;s Medium](https://pub.towardsai.net/graphrag-microsofts-open-source-solution-for-enhanced-document-understanding-b42d05f5fec5).]]>
            </summary>
            <updated>2025-08-29T02:17:27+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3773</id>
            <title type="text"><![CDATA[InstructLab]]></title>
            <link rel="alternate" href="https://instructlab.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3773"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A new community-based approach to build truly open-source LLMs.

InstructLab Command-Line Interface. Use this to chat with a model and execute the InstructLab workflow to train a model using custom taxonomy data. 

- [InstructLab 🐶 (ilab) @ GitHub](https://github.com/instructlab/instructlab).
- [Spécial été 2024 : retour sur la conférence WeAreDevs et les tendances Tech @ AXOPEN YouTube :fr:](https://www.youtube.com/watch?v=daVHfuM1ios).
- [InstructLab: Advancing generative AI through open source @ Red Hat Developer](https://developers.redhat.com/articles/2024/05/07/instructlab-open-source-generative-ai).]]>
            </summary>
            <updated>2025-08-29T02:25:33+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/3841</id>
            <title type="text"><![CDATA[Verba]]></title>
            <link rel="alternate" href="https://github.com/weaviate/Verba" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/3841"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Retrieval Augmented Generation (RAG) chatbot powered by Weaviate.

Welcome to Verba: The Golden RAGtriever, an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. In just a few easy steps, explore your datasets and extract insights with ease, either locally with HuggingFace and Ollama or through LLM providers such as OpenAI, Cohere, and Google.]]>
            </summary>
            <updated>2025-08-29T02:37:38+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/4029</id>
            <title type="text"><![CDATA[PostgresML]]></title>
            <link rel="alternate" href="https://postgresml.org/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/4029"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Infra for RAG apps that work in prod.
You know Postgres. Now you know machine learning.

Index, filter &amp;amp; rank vectors. Create embeddings. Generate real-time, fact-based outputs.

Korvus is a search SDK that unifies the entire RAG pipeline in a single database query. Built on top of Postgres with bindings for Python, JavaScript and Rust, Korvus delivers high-performance, customizable search capabilities with minimal infrastructure concerns.

- [Korvus @ GitHub](https://github.com/postgresml/korvus).]]>
            </summary>
            <updated>2025-08-29T03:07:58+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5387</id>
            <title type="text"><![CDATA[Milvus]]></title>
            <link rel="alternate" href="https://milvus.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5387"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.

- [Milvus @ GitHub](https://github.com/milvus-io/milvus).

Related contents:

- [RAG Against The Machine @ Quoi de neuf les devs ? :fr:](https://happytodev.substack.com/p/quoi-de-neuf-les-devs-153-veille?open=false#%C2%A7rag-against-the-machine).]]>
            </summary>
            <updated>2025-10-29T13:14:35+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5399</id>
            <title type="text"><![CDATA[Trafilatura]]></title>
            <link rel="alternate" href="https://trafilatura.readthedocs.io/en/latest/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5399"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A Python package &amp;amp; command-line tool to gather text on the Web.

Trafilatura is a Python package and command-line tool designed to gather text on the Web. It includes discovery, extraction and text processing components. Its main applications are web crawling, downloads, scraping, and extraction of main texts, metadata and comments. It aims at staying handy and modular: no database is required, the output can be converted to various commonly used formats.

- [Trafilatura @ GitHub](https://github.com/adbar/trafilatura).

Related contents:

- [Alimenter les RAG/LLM avec Trafilatura @ DevSecOps :fr:](https://blog.stephane-robert.info/docs/developper/programmation/python/trafilatura/).]]>
            </summary>
            <updated>2025-08-29T06:56:50+00:00</updated>
        </entry>
    </feed>
