Importer Quick Start

Install the Importer and turn your first document into a knowledge graph stored in ArangoDB

Prerequisites

An Arango Contextual Data Platform deployment.
A project in your target database. The project name prefixes the collection names, so it must follow ArangoDB naming rules. See Projects.
LLM and embedding API access (OpenAI-compatible).
A valid JWT (Authorization: Bearer ...).
A document to import (.txt, .md, .pdf, or a supported Office file such as .docx). See Format support.

Import your first document

You reach the Importer through the data platform API gateway on port 8529. Every call includes an Authorization: Bearer <token> header.

Install the Importer service

Install and start the service through the Arango Control Plane. Configure your LLM and embedding providers at install time.

POST https://<EXTERNAL_ENDPOINT>:8529/_platform/acp/v1/graphragimporter

Note the serviceIdPostfix from the response - you need it for the following calls.

Submit an import

Submit a single file (use /import-multiple for a batch). Keep the default rag_mode: "full_graphrag" to build a complete knowledge graph.

POST https://<EXTERNAL_ENDPOINT>:8529/graphrag/importer/:serviceIdPostfix/v1/import

Monitor progress

For single-file imports, watch the platform service status. For multi-file imports, poll the job:

GET https://<EXTERNAL_ENDPOINT>:8529/graphrag/importer/:serviceIdPostfix/v1/jobs/:job_id

Verify the result

Open your ArangoDB database and confirm the knowledge-graph collections (prefixed with your project name) were created and populated. See Verify and explore.

You now have a knowledge graph in ArangoDB. Query it with AutoRAG or with AQL directly.

Next steps

LLM Configuration: Configure chat and embedding providers.
Import Files: Single-file and multi-file workflows.
Architecture: Collections, vector indexes, and the async-job lifecycle.