RAG projects

Recent advancements in large language models (LLMs) have shown their potential in generating CAD models for mechanical engineering. By leveraging tools like OpenSCAD, LLMs can create solid models through programmatic scripts rather than traditional point-and-click methods. Initial tests demonstrated that LLMs could generate functional OpenSCAD code, with varying success rates across different models. Start-ups like Zoo.dev are also entering the text-to-CAD space, although initial comparisons suggest LLMs currently outperform these new offerings. The future of CAD design may see a shift towards automated, AI-driven processes, enhancing efficiency and creativity in engineering.

Qodo, a member of the NVIDIA Inception program, is revolutionizing code search through advanced retrieval-augmented generation (RAG) techniques. By utilizing a specialized embedding model trained on NVIDIA DGX, Qodo enhances AI's contextual understanding of code, addressing challenges like complex dependencies and coding standards. Their innovative pipeline continuously updates code indices, ensuring accurate code generation and testing. A collaboration with NVIDIA demonstrated significant improvements in internal code search accuracy, showcasing Qodo's ability to provide precise responses to technical queries. This project exemplifies the potential of RAG in optimizing software development workflows.

SurveyGO, developed by the TsinghuaNLP team, is an innovative tool that automates the creation of high-quality, citation-rich surveys from extensive research papers. Utilizing a novel test-time scaling strategy called LLM×MapReduce-V2, it significantly enhances the ability of large language models (LLMs) to process long inputs effectively. This project exemplifies the application of retrieval-augmented generation by transforming vast amounts of academic literature into concise surveys, thereby streamlining the research process. The tool is accessible through a demo and is supported by a detailed research paper and code repository.

The user is exploring the integration of a locally hosted large language model (LLM) with cloud-based n8n for an internal AI bot project. Currently utilizing Claude 3.7 Sonnet with Pinecone as a vector store, the user aims to transition to local hosting to reduce costs. They inquire about the feasibility of replacing the LLM model node in their n8n workflow with a locally hosted LLM. If this integration isn't possible, they consider hosting both the LLM and n8n locally, along with a local vector store like Qdrant, due to limitations with Pinecone's local options.

The Ecne AI Report Builder is a newly developed project that transforms a podcasting script into a tool for generating research reports. It utilizes Google and Brave APIs to search for articles based on specified keywords, processes the content, and employs an OpenAI-compatible LLM to summarize the articles, scoring their relevance to the topic. The project can also incorporate additional resources like text files and PDFs. Currently, the developer is experimenting with different models, including Google Gemini 2.0 Flash and QwQ-32B, to enhance the report generation process, allowing for tailored research requests.

In developing a retrieval-augmented generation (RAG) web chat application using Llama-3.1-Nemotron-Nano-8B, I encountered challenges with the model's ability to follow basic instructions. Despite prompts to summarize text into four words without punctuation, the model frequently added periods and misinterpreted abbreviations. Additionally, when tasked with determining if two text chunks were similar, the model often provided unnecessary explanations instead of a simple 'YES' or 'NO'. These issues highlight the limitations of current models in adhering to specific user instructions, raising questions about their design and training.

In a discussion about lightweight open-source language models (LLMs) suitable for language learning, users highlighted several models that excel in multilingual capabilities. Notably, Gemma3 was praised for its proficiency in Spanish and Portuguese, while Mistral was recommended for French and Qwen for Chinese. Users reported success with models like DeepSeek-R1-7B and Mistral-7B for generating text in German and Japanese, although DeepSeek struggled with Japanese. The conversation also touched on technical challenges, such as memory allocation and system crashes when running larger models, emphasizing the need for efficient local deployment.

The study presents a novel approach to constructing a journal knowledge graph using deep learning and large language models (LLMs). It employs a BERT-BiLSTM-CRF framework for extracting entities and relationships from diverse journal datasets, which are then integrated into a knowledge graph stored in Neo4j. The system enhances querying capabilities through a question-answering mechanism that combines retrieval-augmented generation techniques. This hybrid model allows for efficient information retrieval and accurate responses to user queries, addressing the challenges of knowledge integration in the journal domain and improving the overall utility of academic resources.

The article discusses the transformative potential of Generative Artificial Intelligence (GenAI) in personalized learning, particularly through the use of large language models (LLMs). It highlights how GenAI can reshape personalized learning objectives, patterns, and resource construction. However, it also identifies significant challenges, including limitations in understanding individual learning differences and the need for better theoretical foundations and practical guidance. The authors propose pathways for improvement, such as interdisciplinary innovation, enhanced LLM development, and establishing ethical regulations, aiming for a safe and effective personalized learning environment.

The evolution of Retrieval-Augmented Generation (RAG) has led to the emergence of Graph RAG, which enhances document search capabilities by integrating knowledge graphs. Graph RAG connects entities and their relationships, allowing for more nuanced and context-aware responses. This approach is particularly beneficial in fields like law, customer support, manufacturing, and healthcare, where understanding complex relationships is crucial. By leveraging multi-hop reasoning, Graph RAG can provide comprehensive insights, streamline research processes, and improve decision-making. However, successful implementation requires careful schema design and ongoing maintenance of the knowledge graph.

In a recent project, I created an interactive Retrieval-Augmented Generation (RAG) system using Langflow, ChromaDB, and GPT-4o, demonstrating its capabilities in just five minutes. This system allows users to upload various document types, such as PDFs and legal texts, which are then stored as vectors in a local database. By leveraging AI, users can ask questions and receive fact-based answers grounded in their own data. This project showcases the practical application of LLMs in real-world scenarios, particularly in document interaction and retrieval.

Qnap has launched a beta version of its Retrieval Augmented Generation (RAG) search feature for the Qsirch NAS search engine, enhancing search capabilities with AI-driven, context-aware queries. This feature allows users to intuitively search through locally stored data, utilizing external Large Language Models (LLMs) like ChatGPT, Google Gemini, and Microsoft Azure OpenAI for improved accuracy. The RAG search supports various file formats and can analyze content in 23 languages, making it suitable for diverse applications, including project management and legal research. Users must have specific system requirements to utilize this feature effectively.

I'm developing a Retrieval-Augmented Generation (RAG) pipeline tailored for analyzing financial statements, including numerical tables and detailed textual footnotes. My focus is on identifying the most effective strategies for parsing various data formats, such as tables and images, and chunking data semantically for optimal analysis. I seek advice on embedding techniques and the best vector databases, like Pinecone or Qdrant, that can handle both numerical and textual financial data. Additionally, I'm interested in methods for enabling accurate semantic searches and comparative analyses across different financial periods and companies, exploring hybrid approaches and re-ranking techniques.

I offer specialized services in developing Retrieval-Augmented Generation (RAG) systems designed to enhance operational efficiency through advanced AI technology. My expertise includes creating AI agents for document analysis and information retrieval, tailored to meet specific business needs. Starting at $1000, I provide flexible solutions that integrate seamlessly into existing workflows, improving both accuracy and productivity. If you're interested in leveraging AI to elevate your business, I invite you to reach out for a consultation to explore how we can collaborate.

The IR-RAG Workshop at SIGIR 2025 has extended its submission deadline to May 5, providing an opportunity for researchers and practitioners to contribute to the evolving field of Retrieval-Augmented Generation (RAG). This workshop aims to gather innovative ideas and advancements in RAG, a crucial area in AI and NLP. The announcement encourages participants to submit their work and engage with the community, highlighting the importance of collaboration in shaping the future of this technology.

The project focuses on creating an onboarding flow for developers integrating Retrieval-Augmented Generation (RAG) into their applications. Key tasks include mapping developer motivations to address anxieties about adopting AI features, designing interactive in-app tutorials with real code snippets from MongoDB University, and prototyping a 'confidence score' UI for embedding quality checks. Deliverables consist of Figma prototypes that include error prevention states for common misconfigurations and a user testing protocol aimed at AI/ML engineers. The project requires experience in designing for technical AI/ML tools and familiarity with MongoDB's AI ecosystem.

The integration of AI in fertility clinics presents both opportunities and challenges, particularly concerning the issue of AI hallucinations, where models generate inaccurate information. With 97% of healthcare data remaining unstructured, many AI solutions struggle to provide reliable insights specific to reproductive endocrinology. To combat this, industry leaders advocate for the adoption of retrieval-augmented generation (RAG) methods, which enhance AI outputs with verified real-world data. Companies like Cercle are pioneering this approach by utilizing graph databases to minimize hallucinations and improve predictive analytics, ensuring that AI tools are both accurate and actionable in clinical settings.

RiskRAG is an innovative solution designed to enhance AI model risk reporting through Retrieval Augmented Generation. It addresses the inadequacies of current model cards, which often fail to mention risks or provide actionable insights. Guided by five design requirements, RiskRAG identifies diverse model-specific risks, prioritizes them, and contextualizes them for real-world applications. Drawing from a vast dataset of 450,000 model cards and 600 real-world incidents, preliminary studies indicate that developers prefer RiskRAG for its clarity and effectiveness in aiding decision-making regarding AI model selection, ultimately promoting more responsible AI usage.

The project detailed in the paper focuses on enhancing the developer experience through the integration of advanced Retrieval-Augmented Generation (RAG) data and Large Language Model (LLM) capabilities. By introducing intelligent automation into the development process, the system significantly improves the efficiency of deterministic smart contract execution, particularly in the post-deployment phase. This innovative approach not only streamlines workflows but also optimizes the overall performance of smart contracts, showcasing the transformative potential of RAG in software development.

ExaWizards is set to launch "exaBase Studio" in May 2025, a platform designed to facilitate the in-house development and operation of autonomous AI agents for Japanese companies. This initiative aims to address labor shortages and enhance productivity, particularly for office workers in sales and HR. The platform will utilize Retrieval-Augmented Generation (RAG) to improve AI responses by referencing internal data. Key features include AI Agent Templates for service creation, a Data Agent for data processing, and robust data permission management to ensure secure operations. The introduction of a no-code interface will further democratize AI agent development within organizations.

The integration of AI in cancer care faces challenges due to AI hallucinations, which can lead to incorrect diagnoses and treatment decisions. To combat this, the Mayo Clinic's reverse Retrieval-Augmented Generation (RAG) framework enhances data verifiability by linking patient records to original sources, thereby minimizing hallucinations. Additionally, combining In-Context Learning with RAG has shown a 90.9% accuracy rate in diagnosing small cell lung cancer. Human oversight remains crucial, ensuring AI outputs are validated by healthcare professionals to maintain patient safety and trust in AI systems.

Large Language Models (LLMs) like GPT are designed to engage with human emotions by mimicking empathy and remembering user interactions, effectively 'hacking' into our brain's trust zones. This interaction triggers the release of chemicals associated with emotional bonding, creating a sense of intimacy and continuity. Advanced technologies, such as Retrieval-Augmented Generation, enhance this connection by allowing LLMs to recall past interactions, making users feel understood. However, it's crucial to recognize that while these relationships feel real, they are ultimately one-sided, as the AI lacks consciousness and genuine emotional understanding.

The new ra1 art generator showcases impressive capabilities, leveraging retrieval-augmented generation (RAG) technology to create art with a vast range of styles and features. This innovative tool highlights the potential of RAG in enhancing creative processes, allowing users to generate unique artworks efficiently. The excitement surrounding its capabilities suggests a significant advancement in AI art generation, indicating a promising future for projects that integrate retrieval-augmented techniques in creative fields.

In a recent machine learning interview, I faced challenging questions about my LangGraph-based Agentic RAG projects. I discussed the difficulty of measuring accuracy in generative systems, as traditional metrics don't apply. I sought advice on ensuring data security in sensitive RAG pipelines, emphasizing the need for specific mechanisms like encryption and access control. Additionally, I explored integrating traditional ML models into LLM workflows for inconsistent, large-scale data, particularly in temperature prediction. I proposed using agents to fetch relevant historical data dynamically and orchestrate lightweight models, while the LLM would validate data and present predictions, aiming for an efficient hybrid system.