# Docling (/docs/ai/components/docling)



## Component Category [#component-category]

Document AI inference

## Component Description [#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 [#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 [#learn-more]

* [Docling documentation](https://docling-project.github.io/docling/)
* [docling-project/docling on GitHub](https://github.com/docling-project/docling)

## Developer Guidance [#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 [#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](/docs/deployment/playbooks/enable-gpu).

## Interacts With [#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.