In today's digital era, enterprises' demand for intelligent services is growing day by day, and application scenarios such as AI customer service and merchant knowledge Q&A are becoming more and more common. Many friends have reported to me that they need to build a similar question and answer system within the company, hoping to find open source solutions and understand how to integrate them into existing code. In this video, I will lead you to use the open source framework FastGPT to implement an enterprise internal knowledge question and answer system. In fact, the process is not complicated, let’s take a look.
FastGPT is a knowledge base question and answer system based on the LLM large language model and has many practical functions. It not only provides out-of-the-box data processing and model calling functions, but also performs workflow orchestration through process visualization to realize complex question and answer scenarios. From the architecture diagram, the left side is responsible for core data and vector data storage. The model gateway is built through an API in the middle. The lower part can connect various large models, and even locally deployed open source large models can be used. The selection is very rich.
Due to the high demand for GPU resources for large model operations, if a pure CPU is used, the question and answer response may take more than a minute. Therefore, this time I chose a GPU-first server and used the GPUEZ intelligent computing cloud platform. On this platform, we rent an instance with 48G of video memory on demand. Since this is a demo project, I chose to pay as you go, rent it for an hour, and specify the base image. For scientific research and exploration related to machine learning and artificial intelligence, the platform is very convenient to use. After the instance is running, both SSH connection and Jupiter Lab mode are supported. After entering, select terminal, similar to SSH login console. You can enter LINUX commands and get feedback.
In order to install FastGPT, we use the docker compose method, which is suitable for LINUX, Mac OSX and Windows systems. The operation is simple and can be called "brainless installation". The only configuration required is model access to a single API platform. Taking the access to a large domestic public welfare model as an example, it is not difficult to follow the documents provided by FastGPT.
The GPUEZ intelligent computing cloud platform used this time has an excellent experience. It supports pay-as-you-go, flexible and convenient; large single-card video memory such as 32G and 48G is rare on other platforms; it provides a pre-installed environment, and self-made images can be saved and reused; it has a rich set of machine learning and model training data. It is of great help to scientific research and exploration. The platform cooperates with teachers and students from many universities across the country and researchers from scientific research institutions, so it is safe, stable and guaranteed. Register now to get a 5 yuan trial reward and a 20% consumption discount. The link has been placed in the comments section. Friends who need computing power rental may wish to give it a try.
Through the above steps, we learned to use FastGPT to build an internal knowledge base question and answer system for the enterprise. The whole process is simple and easy to understand. I hope everyone can try to build their own enterprise-level AI question and answer system. If you have any questions or ideas, please leave a message in the comment area to share. See you in the next video!
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