Dive into the world of multi-agent orchestration with LlamaIndex's AgentWorkflow! Learn how it simplifies complex scenarios through an easy-to-understand example. Check out my latest article!
#DataScience #AI #MachineLearning #DeepLearning #LlamaIndex
https://www.dataleadsfuture.com/diving-into-llamaindex-agentworkflow-a-nearly-perfect-multi-agent-orchestration-solution/

Ich will mal ein paar #GenAI #RAG Tests machen und habe überlegt, mit welchem mir bekannten Korpus ich das machen könnte. Nachdem ab Dienstag erstmal dystopische Zeiten bevorstehen, habe ich mich für "Jonas, Der letzte Detektiv" entschieden (https://de.wikipedia.org/wiki/Der_letzte_Detektiv) entschieden, das habe ich in meiner Jugend immer Donnerstag Abend in Bayern 2 gehört

Prozess: Download > Transkript mit #Whisper -> Vorbereitung mit #Llamaindex

GraphRAGでポケモン図鑑の構築に挑戦【LlamaIndex × Neptune 】
https://qiita.com/TKG_KM/items/42c31a852470ae4d28db?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

Docling: AI-powered document processing!
PDFs & DOCXs in your AI workflow? Docling makes it easy! Converts to markdown & JSON for RAG and more. Blazing fast!

https://youtu.be/zSCxbqgqeJ8?si=mede5eJL_iGRFwAJ

Reverse engineering LlamaIndex has been a fascinating dive into understanding how its Workflow processes are structured to handle dynamic data retrieval and integration seamlessly. At its core, a LlamaIndex Workflow orchestrates the interaction between indexes, queries, and retrieval logic, ensuring efficient and context-aware results. By analyzing its modular design, I found that each task—whether building an index or querying it—is highly decoupled, enabling scalability and customization. The workflow’s use of adaptive heuristics and stateful operations allows it to fine-tune results in real-time while handling diverse data sources. This design not only ensures flexibility but also showcases how workflows in LlamaIndex intelligently manage complexity in knowledge retrieval tasks. Understanding these processes provides valuable insights for building robust, modular AI systems. #LlamaIndex #ReverseEngineering #AIWorkflows #KnowledgeRetrieval

Virtuoso Open Source (VOS) 7.2.14 is out!

Our high-performance, multi-model #VirtuosoRDBMS supports:

• Relational tables & graphs
• #SQL, #SPARQL & #GraphQL
• Seamless AI integration w/ #LangChain & #LlamaIndex via #HTTP, #ODBC, or #JDBC

Read on for links etc..

Comprehensive Guide to Building with #Groq API

Key Components:
• Complete examples for building chatbots, RAG systems & #SQL applications using #LangChain, #LlamaIndex & #DuckDB
• Integration tutorials with popular tools like #Streamlit, #Portkey, #JigsawStack & #E2B
• Ready-to-use #Replit examples for quick experimentation with different #LLM implementations
• Step-by-step guides for setting up #CodeGPT in VSCode with #Groq

Featured Implementations:
• Text-to-SQL applications with JSON mode & function calling
• Presidential speeches RAG with #Pinecone
• Stock market analysis using #Llama3 function calling
• Newsletter summarizer using #Composio
• #CrewAI machine learning assistant

Perfect for developers looking to leverage Groq's lightning-fast inference speeds in production applications.

Full documentation & examples available at: https://github.com/groq/groq-api-cookbook

Learn how to build AI RAG apps with the ObjectBox LlamaIndex Integration using Google's Gemini in this hands-on tutorial from Shubham Panchal

https://shubham0204.github.io/blogpost/programming/objectbox-llamaindex

#LlamaCloud è un ambiente cloud che permette di rendere usabile #LlamaIndex attraverso un'interfaccia web. In poche parole permette di creare sistemi #RAG in modo più semplice. Attualmente è in versione alpha, ma lascia intuire applicazioni potenti.
L'immagine è un esempio di una pipeline RAG multimodale basata su dati complessi non strutturati, che consente di analizzare, indicizzare, interrogare elementi visivi in slide deck di marketing, contratti legali, report finanziari, ecc..

#LlamaParse è un nuovo progetto in grado di elaborare informazioni sui documenti, comprendendo anche elementi visuali e tabelle.
Supporta documenti in qualunque formato: PDF, docx, pptx, xlrx, HTML.
Il sistema si integra direttamente con #LlamaIndex.
Lo schema custom ha ancora qualche problema, ma è una versione beta che promette molto bene.

Il progetto: https://github.com/run-llama/llama_parse

#Opik è un nuovo sistema open source per il tracciamento e la valutazione delle applicazioni basate sui #LLM.
Permette di tenere traccia di tutte le interazioni, e si interfaccia nativamente con #OpenAI, #LangChain, #LlamaIndex, #Ollama, e molti altri sistemi.
Consente di individuare facilmente se il modello introduce informazioni fuori contesto, e di confrontare versioni diverse dell'applicazione, fino alla produzione.
Il progetto: https://github.com/comet-ml/opik

LlamaIndexとNeo4jでGraphRAGを作る（勝手にPANKRATION 2024延長線）
https://qiita.com/moritalous/items/d76bb199b865b746309b?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

ragapp/ragapp: The easiest way to use Agentic #RAG in any enterprise

https://github.com/ragapp/ragapp?tab=readme-ov-file

As simple to configure as #OpenAI custom sGPTs, but deployable in your own cloud infrastructure using Docker. Built using #LlamaIndex.

 #Llamaindex mostra le soluzioni per creare un sistema #RAG potenziato da knowledge graph.
È possibile realizzarlo in diversi modi, ad esempio con la ricerca vettoriale + il confronto sul grafo, oppure con un sistema text-to-cypher.
L'uso della ricerca vettoriale guidata dal grafo permette soluzioni più affidabili e sicure.

Just completed the short course on Building Agentic RAG with Llamaindex from http://deeplearning.ai! Ready to leverage these new skills for upcoming projects. #DeepLearningAI #AI #llamaindex #RAG

https://learn.deeplearning.ai/accomplishments/9d38b964-c618-43ee-9699-03f8f9f87d2e?usp=sharing

[ A story from Plaban Nayak on Medium ]
Read this story from Plaban Nayak on Medium: https://medium.com/the-ai-forum/multi-document-agentic-rag-using-llama-index-and-mistral-b334fa45d3ee #RAG #Agent #LlamaIndex #Mistral

i'm using llama.cpp and llamaindex to run a query engine-chatbot. The text it needs to query over is very short. Each generation takes about 2 minutes. running a 7b parameter gguf file locally. Is this normal? #LlamaIndex #chatbots #help

[ cookbook/llamaindex_agentic_rag.ipynb at main · mistralai/cookbook · GitHub ]
https://github.com/mistralai/cookbook/blob/main/llamaindex_agentic_rag.ipynb #MistralAI #LlamaIndex

Writing #frontend code using a component framework feels a little like writing a fanfic. Actually, I get the same feeling when writing #LLM pipelines using #LlamaIndex. This is someone else's fantasy world that I'm playing in. I have to enjoy reading their docs. I have to feel inspired.

I'll be honest, I really like LlamaIndex's team, and I like some of their docs, but it's not exactly the nightstand reading that the #Railsguides are. They are juicy and fun!

This post inspired by #BulmaCSS.

If you use LlamaIndex in your generative AI applications, there are some breaking changes in version 0.10 (as I discovered this weekend)

Project blog post: https://blog.llamaindex.ai/llamaindex-v0-10-838e735948f8
Jerry Liu video https://youtu.be/swJsw9Jvgoc?si=qKXkGRdqt-dIi2o7