ALT Building a Local RAG Application with DeepSeek, Ollama, Qdrant, Docling, Huggingface, Chainlit and LangChain Fully local Retrieval-Augmented Generation (RAG) application using the DeepSeek model via Ollama. The architecture involves utilising Docling to extract information from PDFs, segmenting the document into smaller chunks, and then embedding these segments using HuggingFace embeddings. The segments are stored in a local Qdrant Vectorstore using Docker. When a query is made, embeddings are generated and queried against the knowledge base to retrieve relevant information. We leverage LangChain for orchestration and interface with a UI bot using Chainlit, implementing a step-by-step approach that highlights the capabilities of DeepSeek in handling and reasoning over the retrieved data. The video also discusses prerequisites like installing Docker and setting up the environment, while walking through the code implementation and testing the application with sample queries.