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A fully local retrieval-augmented generation system that supports building private knowledge bases from documents, YouTube videos, and web content. It provides query services via REST API, CLI, and browser dashboard with no cloud dependency.
Section 01
AtlasOrc is a fully local retrieval-augmented generation system that supports building private knowledge bases from documents, YouTube videos, and web content. It provides query services via REST API, CLI, and browser dashboard with no cloud dependency. Its core goal is to meet users' needs for data privacy and local deployment—all data processing, vector storage, and model inference are completed locally.
Section 02
With the popularization of AI applications today, data privacy and local deployment have become core user demands. AtlasOrc takes "local-first" as its core concept, distinguishing itself from cloud API-dependent solutions. All operations are performed on the user's local machine, ensuring sensitive information never leaves the local network—suitable for privacy-sensitive scenarios and offline environments.
Section 03
AtlasOrc adopts a layered architecture: The embedding model layer uses nomic-embed-text (run via Ollama); the large language model layer defaults to qwen3:8b (deployed via Ollama); the vector storage layer uses ChromaDB; the API service layer is based on FastAPI; the user interface layer is a single-file HTML dashboard. All components are loosely coupled and support custom extensions.
Section 04
The system supports content ingestion from multiple sources: Document processing (automatic extraction and chunking for PDF, Word, etc.), YouTube video transcription (captions are included in the knowledge base), and web content extraction (filtering irrelevant elements to capture main content)—helping users integrate various types of materials.
Section 05
Built-in file monitoring module processes new files in real time; provides status query and logging functions; optional extensions include Cloudflare Tunnel for remote access and n8n workflow integration to expand automation scenarios.
Section 06
Deployment steps: Install Ollama and pull models, install Python dependencies and configure API keys, create directories; Usage: Open the single-file dashboard in a browser, enter the key, then you can perform content ingestion and intelligent queries.
Section 07
Suitable for scenarios like personal knowledge management, team document retrieval, offline technical queries, sensitive data Q&A, etc.; Its open-source nature supports deep customization, representing the future direction of AI tools combining "intelligent experience + data control".
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