The latest articles on DEV Community by Srinu Tallavalasa (@srinu_tallavalasa_b83c6d9). https://dev.to/srinu_tallavalasa_b83c6d9 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4188704%2F33b91c0d-91ac-4fda-aef9-9410a2d9c777.png https://dev.to/srinu_tallavalasa_b83c6d9 en Srinu Tallavalasa Fri, 16 Oct 2026 11:11:52 +0000 https://dev.to/srinu_tallavalasa_b83c6d9/title-converting-recallos-into-a-terminal-based-local-ai-document-assistant-1gno https://dev.to/srinu_tallavalasa_b83c6d9/title-converting-recallos-into-a-terminal-based-local-ai-document-assistant-1gno <ol> <li>RecallOS is a Python-based AI document memory and search assistant built using Streamlit, Ollama, and ChromaDB.</li> <li>I want to convert it into a terminal-only application that runs directly inside VS Code without opening a browser.</li> <li>The application should support PDF, DOCX, TXT, and Markdown documents.</li> <li>It should extract text from documents and generate embeddings using Sentence Transformers.</li> <li>ChromaDB should store document embeddings and support semantic search.</li> <li>Ollama with Gemma 3 (4B) should generate answers locally using Retrieval-Augmented Generation (RAG).</li> <li>The application should display relevant source documents and passages alongside answers.</li> <li>It should preserve local chat history and allow users to search previous conversations.</li> <li>I want to reuse my existing <code>main.py</code>, <code>model.py</code>, and <code>tools.py</code> functionality wherever possible.</li> <li>Please suggest a reliable architecture and provide step-by-step implementation guidance with complete code while preserving privacy and existing features.</li> </ol> llm python rag