CI/CD preprocessing pipelines in LLM applications
Open-source framework for building and running AI agents, connects to tools and APIs, no invite code needed
Build Real-Time RAG Pipelines with CocoIndex, Amazon S3, and AWS SQS
Serving LLM for 100K Parallel Queries
A Developer's Guide to Hosting 3B LLM for Millions of Queries in Parallel Whether you are building agents, RAG bots, or LLM apps, the core of your product is usually an LLM accessed via API. Providers like Together.ai optimize these models for efficient and scalable use, rather than hosting them in full precision.
Pipeline of Agents Pattern: Building Maintainable AI Workflows with LangGraph
PipesHub is the workplace AI platform for enterprises to improve how businesses operate and help employees and AI agents work more efficiently. In most companies, important work data is spread across multiple apps like Google Workspace, Microsoft 365, Slack, Jira, Confluence, and more. PipesHub AI helps you quickly find the right information using natural language search—just like Google. It can answer questions, provide insights, and more. The platform not only delivers the most relevant results but also shows where the information came from, with proper citations, using Knowledge Graphs and Page Ranking. Beyond search, our platform allows enterprises to create custom apps and AI agents using a No-Code interface.
In this tutorial, we build a fully local, API-free agentic storytelling system using Griptape and a lightweight Hugging Face model. We walk through creating an agent with tool-use abilities, generating a fictional world, designing characters, and orchestrating a multi-stage workflow that produces a coherent short story.
How to Build a Production-Grade RAG Pipeline
In this article, I will demonstrate how to build and deploy a production-grade RAG pipeline that answers questions about U.S. federal copyright laws.