FastGPT

FastGPT is an open-source knowledge-base and AI-workflow platform. You can register OpenAI-compatible models, so FastGPT can work with the ScitiX Model Inference API for chat/workflow apps and dataset (RAG) indexing. Deploy FastGPT with Docker Compose by following the official guide, then log in with the initial root account. Supported chat models include glm-5.2 and DeepSeek-V4-Flash. The embedding model in this guide is Qwen/Qwen3-Embedding-8B. See the model catalog.

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Configure

Create an API Key on the API Keys page, then open Account → Model Providers.

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1. Configure a chat model

Add or edit a model. In Model parameter editing, set: Add glm-5.2 as an LLM model. The Custom url is the full endpoint. Chat uses https://api.scitix.ai/model-api/v1/chat/completions, and embeddings use .../v1/embeddings. When a per-model Custom url is set, FastGPT calls it verbatim and does not append a path.

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2. Configure an embedding model

If you use datasets, add Qwen/Qwen3-Embedding-8B as an Embedding model. Its Custom url ends with /v1/embeddings.

• Model type matters. Add glm-5.2 as an LLM and Qwen/Qwen3-Embedding-8B as an embedding/index model. Datasets need an embedding model.
• Model IDs must match ScitiX exactly, including the Qwen/ prefix on the embedding model.
• Choose a chat model with native tool calling if you use workflow tool nodes or agents.

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Verify

Create a simple app in Studio, select glm-5.2, and send a message in Chat Preview. A normal reply confirms that the connection works.