GoPenAI·1yHow to Build a Local RAG with DeepSeek-R1, LangChain, and Ollama (Step-by-Step Guide)
Learn how to build a local Retrieval-Augmented Generation (RAG) system using DeepSeek-R1, LangChain, and Ollama. This guide details the installation, setup, and deployment of a RAG pipeline that processes PDFs locally, ensuring data privacy, cost efficiency, and customizability. The solution utilizes ChromaDB for document retrieval and Streamlit for a user-friendly interface.
Daily Dose of Data Science | Avi Chawla | Substack·1y[Hands-on] RAG Over GitHub Repos
Ragie Connect offers a comprehensive infrastructure for building RAG applications over user data by handling authentication, authorization, and syncing from sources like Google Drive and Salesforce. This guide demonstrates how to create a RAG app over GitHub repositories using GitIngest to parse the repo and Llama-3.2 as the LLM. The process involves parsing the GitHub repo, setting up the LLM, embedding the data, and creating an index for interaction, with a neat interface and a promise for more advanced techniques in future guides.
Daily Dose of Data Science | Avi Chawla | Substack·1y100% Local Multimodal RAG using DeepSeek's Janus
GroundX offers a secure, on-premise RAG solution capable of processing complex documents with images, tables, and flowcharts. It supports hybrid RAG pipelines and integrates seamlessly with Kubernetes. DeepSeek’s Janus series, including Janus-Pro, outperforms leading tools like OpenAI's DALL-E 3 in various benchmarks. The post provides a hands-on demo to build a local multimodal RAG using Janus-Pro, ColPali for document embedding, and Qdrant as a vector database.
Daily Dose of Data Science | Avi Chawla | Substack·1y[Hands-on] 100% Local RAG using DeepSeek
Building AI applications that can work effectively with real-time web data is challenging due to the need for human-like interaction simulation, overcoming site blocks, and ensuring legal compliance. Bright Data offers infrastructure to handle these tasks at scale. Additionally, the post introduces an affordable RAG app built using DeepSeek AI's models, which offer significant cost savings compared to OpenAI. The tutorial covers the setup of the knowledge base, embedding creation, vector database indexing, and custom prompt template for LLM, concluding with a user-friendly interface and future advanced techniques to be discussed.