Retrieval-unterstützte Generierung (RAG) Jobs

The expected deliverables include a fully functional web platform and mobile application with course management, payments (one-time and subscription), certificates, coupons, reviews, AI-powered course search (RAG system), and role-based dashboards. The developer must provide complete source code, deployment (staging and production), database setup, Stripe integration, AI embedding pipeline, documentation, and secure, production-ready architecture. The estimated timeline is 10–12 weeks, covering both web platform and mobile app development.

RAG must be developed as an independent regulatory validation engine running after FINAL MERGE, using a closed-domain approach that operates only on uploaded official documents without external web search. It should run after final_merged_text is completed and Vision results are appended, connected from n8n only via a Side-Car API call. RAG must be deployed as a separate Docker container with a vector database in channel-specific namespaces already made in current workflow Input data should include final_merged_text and Vision tags, and RAG must not influence generation logic, only validate final outputs. The output must be a structured JSON validation report containing legal references, not just OK/NG. Because this is a closed-document RAG structure, it provides high accuracy and relativ...

RAG Engine Construction & Data Training Integration ​We have an existing n8n-based AI video automation system. The task is to develop the features listed below and ensure seamless integration with the current system. UI designs provided. ​Difficulty: Low / Estimated Time: 4–5 hours ​Scope: Review the existing design of the Google-based RAG engine for large document training. Modify and connect data pipelines to ensure seamless integration with downstream n8n workflows and UI/UX connections. ​[Mandatory Deliverable]: A Google Sheets-based manual including step-by-step screenshots, prompts, and configuration values (Video + Text). [Mandatory] tell me your portfolio related to this task. and Tell me price and timeline.

I’m building a retrieval-augmented generation (RAG) pipeline and need a specialist to stand up the vector database layer for my large-language-model workflow. All content going into the store will be purely textual—think markdown files, knowledge-base articles, and long-form documents—so the schema, chunking strategy, and embedding approach should be optimised for fast, accurate text search. Here’s what I’d like from you: • Recommend and deploy a production-ready vector database (Pinecone, Weaviate, Chroma, Milvus or a comparable option). • Design a text-specific embedding and metadata schema, including parameters such as chunk size, overlap, and namespace strategy. • Build ingestion scripts that batch-process my existing documents, generate em...

1. CONTEXTO Y DESAFÍO REAL Proyecto del sector de la trefilería y el galvanizado con más de 40 líneas de producción activas. El desafío no es la falta de información, sino que el conocimiento crítico es volátil: reside en la experiencia de supervisores y operarios veteranos y se transmite de forma verbal. Cuando surge una solución técnica en planta, esta no se documenta y se pierde para el siguiente turno. Buscamos desarrollar un ecosistema de IA que no solo responda preguntas, sino que capture, valide, estructure y democratice el conocimiento técnico que surge en el día a día, creando una infraestructura de inteligencia industrial sostenible a largo plazo. 2. LA SOLUCIÓN: "THE ...