Corpus

Corpus takes PDFs and web pages, answers questions about them in plain language, and links each answer back to the passage it came from. Documents group into workspaces and document sets, so a research pile stays separated by project instead of merging into one searchable heap.

The backend is FastAPI with PostgreSQL as the primary database, Elasticsearch for full-text search, RabbitMQ as the message queue, Temporal for workflow orchestration, Redis for caching and S3 for document storage. The frontend is React with Vite. A Docker Compose file starts the whole stack, including a LocalStack S3 and the Temporal web interface.

The product sits on a handful of moving parts.

• Citations: every answer carries links back into the source text.
• Cross-correlation: the same question can be compared across several documents at once.
• Workflows: Temporal runs automated workflows whose output can be reviewed and exported.
• Model choice: OpenAI, Anthropic, Google and xAI models are supported for answering.
• Embeddings: OpenAI and Voyage AI both work for the vector side.

Running everything in containers needs only Docker and Docker Compose, while running the services directly needs Python 3.11 or newer with Poetry and Alembic, plus Node.js 20 for the frontend. Video walkthroughs in the repository cover getting started, cross-correlation and workflow creation, and the license is AGPL-3.0.