FengZhenfei / carrel

# Carrel
English | 中文
**Ontology-Augmented Generation for AI agents.**
Carrel is a self-hosted knowledge base for AI agents. It automates document
parsing, indexing, and retrieval, with optional knowledge graphs. Agents use
its API to retrieve source passages, images, and related facts, then generate
their own answers.
Use it to give an agent access to a collection of product documents, research,
contracts, meeting notes, or other files that change over time. A web console
manages knowledge bases, models, and processing tasks. A bundled
search skill connects agents such as Claude
Code and Codex to the retrieval service.
Features · Quick start · Agent integration · Documentation
## Features
- **Document parsing:** PDF, Word, PowerPoint, spreadsheets, images, HTML,
 Markdown, and source code, with processing suited to each format.
- **Automatic updates:** scan for file changes, update indexes, and maintain
 enabled knowledge graphs through scheduled tasks.
- **Hybrid retrieval:** combine vector, keyword, graph, and visual search,
 with automatic knowledge-base selection and optional reranking.
- **Knowledge graphs:** extract entity types, relationships, and facts from
 your documents; retain sources, time information, and conflict indicators.
- **Agent access:** search through an API, fetch surrounding text or original
 images, and follow graph relationships when more evidence is needed.
- **Web console:** configure processing, inspect chunks and graphs, track
 tasks, and manage services. Available in English and Chinese.
## How it works
 [code] 
Carrel uses MinerU and native parsers for ingestion, Qdrant for vectors,
OpenSearch for keywords, and Neo4j for graph queries. SQLite tracks files and
tasks. Model services can run locally or through compatible hosted endpoints.
See Architecture for the pipeline and retrieval design.
## Requirements
- Python 3.12+ and Docker 25+ with Compose v2.
- A text embedding endpoint; a vision-language endpoint for image descriptions;
 and a chat model for knowledge graph construction, if enabled.
- An NVIDIA GPU and NVIDIA Container Toolkit to run the optional local model
 stack. CPU parsing is also supported. Resource needs depend on the models
 and collection size.
| Platform | Deployment |
|---|---|
| Linux with NVIDIA GPU | GPU parsing and optional local model services; systemd scheduling |
| Linux without GPU | CPU parsing; connect to model services on another host or a provider |
| macOS with Docker Desktop | CPU containers and native Python services; arrange scheduling separately |
| Windows | Use WSL2; native Windows deployment is not supported |
Example setup describes the author's installation
on an NVIDIA DGX Spark and some lighter alternatives.
## Quick start
### 1. Deploy the services
 [code] 
The script detects the host, starts the infrastructure, installs the Python
app, and creates configuration files. On Linux with user systemd available,
it also enables services and scheduled tasks. Other hosts rec