# llamaindex LlamaIndex is an open-source framework and data framework for building LLM-powered agents and applications over private data, with tools for ingestion, indexing, retrieval, and workflows. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How LlamaIndex uses Temporal to scale reliable document orchestration DevFeed: [How LlamaIndex uses Temporal to scale reliable document orchestration]() Original publisher: [Read original article]() Author: Adrian Lyjak Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [llamaindex](), [Orchestration](), [RabbitMQ](), [Concurrency]() Tags: [api](), [concurrency](), [llamaindex](), [orchestration](), [rabbitmq](), [scale](), [temporal](), [temporal-voices]() ### AI overview This article explains how LlamaIndex moved document-processing workloads from RabbitMQ to Temporal. It describes the complexity of processing documents page by page and reports that Temporal helped the team improve durability and concurrency while processing tens of millions of pages per day for the Batch API. ### Source excerpt Learn how LlamaIndex moved from RabbitMQ to Temporal to make LlamaParse more durable, improve concurrency, and process tens of millions of pages per day. ## The Real Python Podcast - Episode #306: Programmatically Developing LLM Prompts With DSPy DevFeed: [The Real Python Podcast - Episode #306: Programmatically Developing LLM Prompts With DSPy]() Original publisher: [Read original article]() Author: Real Python Published: 2026-08-07T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [DSPy](), [Large Language Model](), [Prompt Engineering](), [Retrieval Augmented Generation (RAG)](), [Python](), [llamaindex]() Tags: [dspy](), [llamaindex](), [llm](), [podcast](), [programming](), [prompt-engineering](), [python](), [rag]() ### AI overview This podcast episode explains how DSPy programmatically develops and optimizes prompts for LLM applications. It covers declarative signatures, prompt compilation and tuning, and the limitations of manual prompt engineering. The episode also highlights a course on using LlamaIndex for RAG in Python. ### Source excerpt How can you move from manually writing prompts for an LLM application toward defining them programmatically? This week on the show, Brett Kennedy returns to discuss his new book "Building LLM Applications with DSPy." ## DigitalOcean Gradient™ AI Platform Now Integrates with LlamaIndex DevFeed: [DigitalOcean Gradient™ AI Platform Now Integrates with LlamaIndex]() Original publisher: [Read original article]() Author: Narasimha Badrinath Published: 2026-02-18T20:23:52Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [llamaindex](), [Retrieval Augmented Generation (RAG)](), [AI Platform](), [Large Language Model](), [Embeddings]() Tags: [ai-platform](), [async](), [digitalocean](), [llamaindex](), [product-updates](), [pypi](), [rag](), [streaming]() ### AI overview DigitalOcean Gradient AI Platform now natively integrates with LlamaIndex through two PyPI packages. The integration connects Gradient Knowledge Bases and hosted LLMs to LlamaIndex workflows, supporting hybrid search, metadata filtering, streaming responses, and asynchronous operations. ### Source excerpt We're excited to announce that DigitalOcean Gradient™ AI Platform now integrates natively with LlamaIndex - one of the most popular frameworks for building RAG applications. This means you can now connect your Gradient AI Platform Knowledge Base and LLMs directly to LlamaIndex workflows, using the abstractions you already know. No additional infrastructure. No complex setup. Just install two packages and start building. Why This Matters If you've built RAG applications before, you know the drill: provision a vector database, set up an embedding pipeline, manage credentials across services, and stitch everything together. It's a lot of overhead before you write a single line of application logic. With these new integrations, we've done the heavy lifting. Your Knowledge Base handles document ingestion, chunking, and embeddings. The LlamaIndex retriever connects directly to it. Add our LLM integration, and you have a complete RAG pipeline running on managed DigitalOcean infrastructure. What's New Two packages are now available on PyPI: llama-index-retrievers-digitalocean-gradientai Connect to your Knowledge Base as a LlamaIndex retriever. Supports hybrid search (keyword + semantic), metadata filtering, and async operations. llama-index-llms-digitalocean-gradientai Use Gradient AI Platform-hosted LLMs in your LlamaIndex workflows. Supports streaming responses and async for high-throughput applications. Both packages work with LlamaIndex query engines, chat engines, callbacks, and the broader ecosystem. Get Started in Minutes Install the packages: pip install llama-index-retrievers-digitalocean-gradientai llama-index-llms-digitalocean-gradientai From there, configure your Gradient AI Platform credentials and drop the retriever and LLM into your existing LlamaIndex code. Check out our documentation for a complete walkthrough and code examples. What You Can Build These integrations open up a range of possibilities: Support assistants grounded in your product documentation ## Building Data-Aware AI Applications with Heroku AI and LlamaIndex DevFeed: [Building Data-Aware AI Applications with Heroku AI and LlamaIndex]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2025-08-19T15:00:00Z Content type: tutorial Language: en Sources: [Heroku]() Topics: [Heroku](), [Retrieval Augmented Generation (RAG)](), [llamaindex](), [AI Development](), [Large Language Model](), [Model Context Protocol (MCP)]() Tags: [ai](), [building](), [data](), [engineering](), [heroku-ai](), [how-to](), [llamaindex](), [llm-applications](), [managed-inference-and-agents](), [product-features](), [rag]() ### AI overview This tutorial explains how to build retrieval-augmented generation applications that use private data with Heroku AI and LlamaIndex. It describes LlamaIndex's role in orchestrating data ingestion, indexing, retrieval, and LLM context, along with Heroku AI capabilities including managed inference, embeddings, vector search through pgvector, and connections via Model Context Protocol and AppLink. ### Source excerpt Building AI applications that can interact with private data is a common goal for many organizations. The challenge often lies in connecting large language models (LLMs) with proprietary datasets. A combination of Heroku Managed Inference and Agents and LlamaIndex provides an elegant stack for this purpose. This post explores how to use these tools to [...] The post Building Data-Aware AI Applications with Heroku AI and LlamaIndex appeared first on Heroku. ## Building RagRabbit, An Open Source RAG Search with Postgres as the Vector Store DevFeed: [Building RagRabbit, An Open Source RAG Search with Postgres as the Vector Store]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-03-17T17:40:19Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Retrieval Augmented Generation (RAG)](), [Databases](), [Web Scraping](), [llamaindex](), [Model Context Protocol (MCP)](), [Appwrite](), [Claude](), [Vercel](), [Serverless]() Tags: [ai](), [building](), [case-studies](), [claude](), [cursor](), [databases](), [embedding](), [llamaindex](), [mcp](), [openai](), [rag](), [search](), [serverless](), [source](), [vercel]() ### AI overview This article introduces RagRabbit, an open-source toolkit for building retrieval-augmented generation workflows with Postgres and pgVector. It crawls websites, converts pages to Markdown, creates LLM-friendly text files, stores embeddings in Postgres, and provides AI question answering through OpenAI or Claude. It also offers an MCP server for supplying relevant document chunks to Cursor and Claude Desktop, with deployment on Vercel and Neon. ### Source excerpt "When I started RagRabbit, I did testing on vector databases, but I didn't see a real advantage. Postgres with Pgvector covers everything I need, and it's very performant for the number of rows I handle" (Marco D'Alia, Software Architect behind RagRabbit) While experimenting with... ## Visual Document Retrieval Goes Multilingual DevFeed: [Visual Document Retrieval Goes Multilingual]() Original publisher: [Read original article]() Author: Marco Cimolai; Logan Markewich Published: 2025-01-10T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [datasets](), [Embeddings](), [hugging face](), [llamaindex](), [multimodal](), [Open Source](), [Inference](), [data](), [Query (disambiguation)]() Tags: [artificial-intelligence](), [benchmarks](), [cv](), [data](), [datasets](), [embeddings](), [hugging-face](), [inference](), [integrations](), [llamaindex](), [multimodal](), [nlp](), [open-source](), [open-source-collab](), [retrieval](), [synthetic](), [vector](), [vlms]() ### AI overview The article introduces a multilingual embedding model for visual document retrieval and its English-only counterpart. The models encode document page screenshots into dense single-vector representations, enabling visual search across languages without OCR or document-chunking pipelines. The article also presents a 500,000-sample open-source multilingual synthetic dataset, reports faster inference and lower VRAM usage, and describes cross-lingual retrieval and Matryoshka Representation Learning capabilities. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.