Image created with OpenAI GPT-Image-1. Image prompt: Cheesy late-night infomercial freeze-frame—file cabinet pop-out labelled “RAG RETRIEVE-AND-GO™”; steel blue gels, VHS hum, high-resolution
🚀 Building RAG with native Qdrant nodes in @n8n_io Fully automated RAG pipeline that can be deployed, iterated, and extended entirely from within the n8n canvas. Chunk documents with @ChonkieAI, embed with @JinaAI_, generate vector payloads via @FastAPI, and return responses https://x.com/qdrant_engine/status/1935928598524797236
We are going to officially kill the “RAG is dead” bullshit once and for all thanks to @bclavie https://x.com/HamelHusain/status/1935851069915242913
5/ @superagent_ai allows any developer to add powerful AI assistants to their applications. These assistants use large language models (LLM), retrieval augmented generation (RAG), and generative AI to help users. https://x.com/AtomSilverman/status/1932988822322868326
📄🔍 GraphRAG Contract Analysis A powerful solution combining GraphRAG and LangGraph agents to analyze legal contracts using Neo4j knowledge graphs, featuring benchmarks across multiple LLMs. Check out the full implementation guide ➡️ https://x.com/LangChainAI/status/1934294834086387829
2/ @llama_index provides a data framework for LLM applications, facilitating efficient data indexing and retrieval. It is the simplest framework for connecting custom data sources to large language models. https://x.com/AtomSilverman/status/1932988797115117740
