Importer Service
The Importer service transforms your text documents into a knowledge graph stored in ArangoDB, ready for semantic search and AutoRAG-driven Q&A
What is the Importer?
The Importer service turns documents into a knowledge graph stored in your ArangoDB database. It chunks text, calls configured chat and embedding models, extracts entities and communities (in full GraphRAG mode), writes the graph data, and creates vector indexes where embeddings exist.
The resulting knowledge graph is the data layer your applications query with AutoRAG or with AQL directly.
When to use it
The Importer fits three usage patterns:
| Pattern | How you use it |
|---|---|
| Web interface | The fastest path. Configure, run, and inspect imports through AutoGraph Studio without writing code. |
| Direct API | Call the Importer over HTTP API when you want full control - custom partitions, custom prompts, batch automation, or integration into an existing pipeline. |
| Driven by AutoGraph | For large or heterogeneous corpora, AutoGraph discovers domains, assigns a RAG strategy per domain, and orchestrates Importer workers automatically. You don’t call the Importer directly in this mode. See AutoGraph Integration. |
RAG modes
The Importer supports two operational modes that determine how documents are processed and what knowledge-graph elements are created:
- Full GraphRAG (
rag_mode: "full_graphrag", default): Extracts entities, relationships, and community structures to build a complete knowledge graph. Best for queries that require understanding relationships between concepts. - Vector RAG (
rag_mode: "vector_rag"): Faster processing using only document chunks and embeddings. Best for straightforward semantic search use cases that don’t need the full graph structure.
See Architecture for the collections each mode populates.
Next steps
- Setup: Prerequisites, installation, and your first import call.
- Architecture: Knowledge-graph collections, vector indexes, and the async-job lifecycle.
- LLM Configuration: Configure your chat and embedding providers (OpenAI-compatible APIs or Triton Inference Server).
- Import Files: Single-file and multi-file import workflows with examples.
- Incremental Updates: Rebuild the community layer of a partition without importing the documents again, and how documents are removed and replaced in the knowledge graph.
- Verify and explore: Check that your import succeeded and inspect the resulting collections.
- Semantic Units (optional): Process images and multimedia content.
- AutoGraph Integration (optional): How the Importer is driven by AutoGraph for multi-partition builds.
- Reference: HTTP endpoints, full parameter reference, and error handling.
API reference
For the full machine-readable API, see the GraphRAG Importer API Reference .