FastGPT
FastGPT is a low-code AI application development platform for large language models, enabling users to build intelligent question-answering, conversational, and automation applications through visual workflow orchestration. Its core features include knowledge base management with Retrieval-Augmented Generation (RAG), multi-model integration, and node-based drag-and-drop process orchestration. It is designed for enterprises and developers to rapidly build customized AI application scenarios.
Core Facts
Timeline (last 90 days)
FastGPT uses MinIO to store the original documents uploaded to the knowledge base, the parsed chunk data, and files such as vector indexes.
Once an embedding model is chosen in FastGPT, changing it later will invalidate existing vectors and require re-indexing, so it is recommended to decide on a long-term usable embedding model provider early in deployment.
On the test machine, FastGPT's memory usage at idle is about 2.1 GB, a VPS with 2GB RAM would struggle even to start, and a machine with at least 4GB of memory is recommended.
FastGPT's memory usage mainly comes from the Node.js backend service, PostgreSQL and its vector extension pgvector, MinIO, Redis, and optional auxiliary services such as OneAPI, with total baseline memory overhead at idle of around 2 GB.
When FastGPT's knowledge base document count exceeds tens of thousands or concurrent users exceed a few dozen, it is recommended to use 8GB of memory or a separate database instance directly, otherwise it easily triggers the OOM Killer and causes containers to be forcibly terminated by the system.
In 2025 MinIO officially adjusted its image distribution strategy, removing the community edition image from Docker Hub and instead publishing it through its own site and some cloud vendors' image repositories, causing default Compose files that depend on minio/minio to report a pull access denied error when running docker compose up.
A feasible solution to the MinIO image pull failure is to switch to the same version of MinIO in FastGPT's official image source: registry.cn-hangzhou.aliyuncs.com/fastgpt/minio.
FastGPT supports batch import of question-answer pairs via CSV files, requiring a header format of three columns q,a,indexes; a missing indexes column will cause the system to reject the import and report a format error.
FastGPT's knowledge base retrieval relies on vector embeddings; configuring only a chat model (such as DeepSeek) cannot create a knowledge base and will prompt System not embedding model, so an embedding model must also be configured.
FastGPT's knowledge base retrieval process is divided into two steps: first, an embedding model chunks the uploaded documents and converts each chunk into a vector stored in a vector database; when a user asks a question, the same embedding model converts the question into a vector, and by computing cosine similarity or inner product the most relevant document chunks are found, which are finally handed to the chat model to generate an answer.
4 more timeline events
All Facts (20)
Open WebUI supports multiple backends including Ollama and the OpenAI API.
80%VerifiedCoze is an agent building platform launched by ByteDance, while Dify and FastGPT are open-source low-code agent solutions.
65%Unverified与Coze类似的AI应用开发平台还有Dify、FastGPT、Langflow等
90%Unverified53AI Hub支持与Coze、Dify、FastGPT、RAGFlow等主流AI开发平台无缝集成
90%UnverifiedFastGPT以TypeScript全栈开发，侧重于快速搭建基于知识库的对话机器人
85%Unverified在RAG框架市场中，LangChain和LlamaIndex作为编排层框架，Dify、FastGPT、RAGFlow是面向业务用户的完整平台产品
85%UnverifiedLow-code agent platforms are unable to meet requirements when cross-microservice distributed transactions, user authentication, fine-grained concurrency control, or integration with infrastructure such as Nacos, Redis, and MQ are needed.
60%UnverifiedFastGPT uses MinIO to store the original documents uploaded to the knowledge base, the parsed chunk data, and files such as vector indexes.
50%UnverifiedOnce an embedding model is chosen in FastGPT, changing it later will invalidate existing vectors and require re-indexing, so it is recommended to decide on a long-term usable embedding model provider early in deployment.
50%UnverifiedOn the test machine, FastGPT's memory usage at idle is about 2.1 GB, a VPS with 2GB RAM would struggle even to start, and a machine with at least 4GB of memory is recommended.
50%UnverifiedFastGPT's memory usage mainly comes from the Node.js backend service, PostgreSQL and its vector extension pgvector, MinIO, Redis, and optional auxiliary services such as OneAPI, with total baseline memory overhead at idle of around 2 GB.
50%UnverifiedWhen FastGPT's knowledge base document count exceeds tens of thousands or concurrent users exceed a few dozen, it is recommended to use 8GB of memory or a separate database instance directly, otherwise it easily triggers the OOM Killer and causes containers to be forcibly terminated by the system.
50%UnverifiedA feasible solution to the MinIO image pull failure is to switch to the same version of MinIO in FastGPT's official image source: registry.cn-hangzhou.aliyuncs.com/fastgpt/minio.
50%UnverifiedFastGPT supports batch import of question-answer pairs via CSV files, requiring a header format of three columns q,a,indexes; a missing indexes column will cause the system to reject the import and report a format error.
50%UnverifiedFastGPT's knowledge base retrieval relies on vector embeddings; configuring only a chat model (such as DeepSeek) cannot create a knowledge base and will prompt System not embedding model, so an embedding model must also be configured.
50%UnverifiedFastGPT's knowledge base retrieval process is divided into two steps: first, an embedding model chunks the uploaded documents and converts each chunk into a vector stored in a vector database; when a user asks a question, the same embedding model converts the question into a vector, and by computing cosine similarity or inner product the most relevant document chunks are found, which are finally handed to the chat model to generate an answer.
50%UnverifiedOnce the thinking framework for breaking down requirements is mastered, even if the platform changes from Coze to Dify, FastGPT, or other agent frameworks, the core process design capability remains applicable.
50%UnverifiedLow-code platforms such as Coze, Dify, and FastGPT provide drag-and-drop development with configurable nodes, mountable knowledge bases, and support for the MCP protocol.
50%UnverifiedPlatforms similar to Dify include LangChain, Flowise, FastGPT, and others.
50%UnverifiedIn 2025 MinIO officially adjusted its image distribution strategy, removing the community edition image from Docker Hub and instead publishing it through its own site and some cloud vendors' image repositories, causing default Compose files that depend on minio/minio to report a pull access denied error when running docker compose up.
50%