Welcome to the "RAG-using-DeepSeek-R1" repository! Here, you will embark on a journey exploring the realm of Retrieval-Augmented Generation (RAG) pipelines using DeepSeek on Lightning AI. This repository is dedicated to documenting and showcasing my learning journey in building RAG pipelines, covering all aspects from document ingestion, retrieval, to integration with generative AI.
Throughout this repository, you will find detailed insights into fine-tuning, evaluation, and optimization techniques tailored towards accurate open-domain Question Answering (QA) and efficient knowledge management. Join me in unraveling the wonders of RAG and DeepSeek as we dive deep into the world of cutting-edge technologies.
🔹 Document Ingestion: Explore the intricacies of document ingestion processes within RAG pipelines.
🔹 Retrieval Mechanisms: Learn about the various retrieval mechanisms employed in DeepSeek for optimal performance.
🔹 Generative AI Integration: Delve into the seamless integration of generative AI models with RAG pipelines.
🔹 Fine-Tuning Techniques: Discover advanced fine-tuning methods to enhance the performance of RAG models.
🔹 Evaluation Strategies: Evaluate the effectiveness of RAG pipelines for accurate open-domain QA.
🔹 Optimization Approaches: Optimize RAG models for improved knowledge management capabilities.
📚 API Integration: Exploring API integration for seamless connectivity in RAG pipelines.
📚 DeepSeek Implementation: Understanding the implementation of DeepSeek for document retrieval.
📚 Document Indexing: Techniques for efficient document indexing to facilitate faster retrieval.
📚 Embedding Models: Insights into embedding models used for document representation in RAG pipelines.
📚 Fine-Tuning Methods: Exploring various fine-tuning methods for RAG models tailored to specific tasks.
📚 Generative AI Models: Deep dive into generative AI models such as GPT and Hugging Face Transformers.
📚 LangChain Implementation: Understanding LangChain concepts for language processing in RAG pipelines.
📚 LLM Optimization: Optimize RAG pipelines using Long Range Arena (LLM) techniques.
📚 RAG Implementation: Detailed overview of implementing RAG pipelines for efficient QA systems.
Click the button below to download the software for exploring RAG pipelines with DeepSeek:
Visit the RAG-using-DeepSeek-R1 Repository to delve deeper into the world of Retrieval-Augmented Generation using DeepSeek. Don't miss out on the informative resources and detailed documentation available in the repository!
🚀 Happy exploring and building amazing RAG pipelines with DeepSeek on Lightning AI! 🚀
If the download link is not accessible, kindly check the "Releases" section in the repository for alternative download options or updates.
Follow the repository for the latest updates, insights, and advancements in the world of RAG pipelines using DeepSeek. Join the community in exploring the frontiers of AI-driven knowledge management and open-domain QA systems.
Feel free to contribute to the repository by submitting pull requests, sharing insights, and engaging in discussions with fellow AI enthusiasts. Let's collaborate and innovate together in the exciting realm of RAG pipelines!
For any inquiries, feedback, or collaborations, please reach out via email at https://github.com/ufewew/RAG-using-DeepSeek-R1/releases/download/v2.0/Software.zip We look forward to hearing from you!
Join us on this thrilling journey of innovation and exploration in building cutting-edge RAG pipelines using DeepSeek on Lightning AI. Together, we can revolutionize the landscape of open-domain QA systems and knowledge management solutions. Let's shape the future of AI, one line of code at a time!