Docling

Component Category

Document AI inference

Component Description

Docling converts uploaded documents into structured content for retrieval and document-intelligence workflows. BullSequana AI deploys Docling through the BSQAI API chart and uses Redis-backed RQ workers so conversion state is shared across backend replicas.

Why It Is Used

In BullSequana AI, Docling provides one conversion path for text, layout, tables, OCR, and other document structure needed by downstream embedding, retrieval, and extraction workflows.

Learn More

• Docling documentation
• docling-project/docling on GitHub

Developer Guidance

Applications use the BSQAI API upload flow at /v1/files/upload or /v1/files/upload-stream instead of calling Docling directly. The backend applies validation, bounded polling, Temporal heartbeats, batching, storage, and tenant-scoped authorization around conversion.

GPU acceleration

Docling runs in CPU mode by default. An environment override selects the CUDA image and adds NVIDIA GPU resources. See Enable GPU for AI components.

Interacts With

• BSQAI API, for document ingestion, extraction, and status operations.
• Temporal, for durable document-processing workflows and heartbeats.
• Redis, for shared conversion job state and worker queues.
• Milvus, for embedded document chunks used in retrieval.
• Rook-Ceph or external S3-compatible storage, for source documents and artifacts.