ragbits and Building-Natural-Language-and-LLM-Pipelines
These are complementary tools: ragbits provides reusable building blocks and abstractions for RAG/agentic systems, while the Packt book offers practical implementation patterns and recipes demonstrating how to construct such systems using specific frameworks (Haystack, RAGAS, LangGraph).
About ragbits
deepsense-ai/ragbits
Building blocks for rapid development of GenAI applications
This project offers robust building blocks for quickly creating Generative AI applications. It allows you to feed various document types, like PDFs and spreadsheets, into an AI system to get accurate, context-aware answers. It's designed for AI developers and engineers looking to build scalable and reliable AI assistants, chatbots, or intelligent search tools.
About Building-Natural-Language-and-LLM-Pipelines
PacktPublishing/Building-Natural-Language-and-LLM-Pipelines
Building RAG and Agentic Applications with Haystack 2.0, RAGAS and LangGraph 1.0 published by Packt
This repository provides code examples for building reliable AI applications that use large language models (LLMs). It guides you through creating robust systems that can retrieve specific information from your own documents and automate complex tasks using AI agents. This is ideal for AI/ML engineers and data scientists looking to implement advanced natural language processing solutions.
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