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BratBot is an open-source AI chatbot built with FastAPI and Ollama. It allows users to control the style of AI responses via an adjustable 'attitude level' parameter—ranging from polite and professional to playful and sarcastic—providing an interesting example of personalized interaction for LLM application development.
Section 01
BratBot is an open-source AI chatbot API built with FastAPI and Ollama. Its core feature is the ability to control response styles via an 'attitude level' parameter (ranging from polite and professional to playful and sarcastic), offering an example of personalized interaction for LLM application development.
Section 02
In terms of background: traditional AI assistants are mostly designed with a neutral and polite image, but in some scenarios, users need more interesting and human-like conversation experiences. BratBot originated from the pursuit of personalized AI interactions. Technically, it uses FastAPI (high-performance asynchronous API service), Ollama (local LLM inference engine supporting GPU acceleration), and Python (concise and efficient, easy to extend and maintain). It supports local deployment and can achieve low-latency real-time conversations.
Section 03
BratBot's core feature is the 'attitude level' system. Users can specify the 'sassiness' degree of responses via API parameters: low levels are polite and professional (suitable for formal business scenarios), medium levels have playful humor (suitable for casual social scenarios), and high levels have sarcastic and tsundere styles (suitable for entertainment and creative scenarios). This design dynamically adjusts AI personality through prompt engineering and system prompts.
Section 04
BratBot applies to multiple scenarios: 1. Entertainment and creative writing (generating dialogue content in specific styles); 2. Education and training (simulating objects with different attitudes to help practice communication skills); 3. Product prototype development (quickly verifying personalized AI concepts); 4. Developer tools and experiments (learning how to integrate FastAPI with Ollama).
Section 05
Technical highlights include: priority on local inference (protecting data privacy, no need for cloud APIs), GPU acceleration support (close to commercial API response speed), modular design (easy to extend new attitude styles or LLM backends), and concise API design (callable by specifying attitude parameters, lowering the threshold for use).
Section 06
Deployment steps: 1. Environment preparation (install Python 3.8+ and Ollama); 2. Model download (obtain models like Llama2, Mistral via Ollama); 3. Dependency installation (install FastAPI and other dependencies via pip); 4. Service startup (run the FastAPI application); 5. API call (send HTTP requests with specified attitude level parameters). The project documentation provides detailed configuration instructions and sample code.
Section 07
BratBot touches on important topics of personalization and user experience in AI application development, providing feasible technical references for AI personalization. In the future, it can expand dimensions such as emotional states and language styles, helping to build truly 'understanding' AI assistants. It is one of the early explorers of the personalized AI vision.
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