#Knowledge Base

Explore FastGPT, an AI-driven knowledge base and Q&A system utilizing large language models for efficient data handling and model use. It features visual workflow tools, supporting complex question-answering and various document formats. Incorporating OpenAPI, voice input/output, and a friendly interface, FastGPT provides versatile interaction and effective knowledge management.

The solution combines Feishu-OpenAI's functionalities with comprehensive knowledge management, including features like CSV data import, vector creation, and database administration. Utilizing Embeddings and Qdrant, it ensures efficient context retrieval, reduces token costs, and minimizes redundant queries. Learn about seamless deployment and effective utilization of this advanced toolset.

MaxKB is an open-source Q&A system using large language models and RAG to optimize enterprise knowledge, customer service, and educational interactions. The system allows for effortless document integration and intelligent responses with reduced errors. It is compatible with various local and global models and includes a robust workflow engine for complex AI tasks, ensuring seamless system integration without coding.

QAnything provides a local knowledge base Q&A system supporting multiple file formats for secure, offline applications. This system handles formats like PDFs and Word documents, offering reliable responses. Version 2.0 improves usability and efficiency, merging previous versions into a Docker-based setup. Compatible with various platforms and file types, it supports cross-language interaction in a CPU environment. Key features include high parsing success, two-stage vector sorting, easy installation, improved PDF parsing, configurable APIs, and support for retrieval-only or file-free Q&A modes.

The project utilizes a Chinese medical dataset from the QiZhen Medical Knowledge Base to fine-tune language models such as Chinese-LLaMA-Plus-7B, CaMA-13B, and ChatGLM-6B. Focusing initially on drug-related queries, these models significantly improve performance in Chinese medical scenarios. Future enhancements will address disease, surgery, and diagnostics inquiries. QiZhenGPT provides accurate medical insights via structured datasets from real doctor-patient interactions. MedCopilot, leveraging QiZhenGPT, merges the QiZhen Medical Knowledge Base and clinical data to bolster healthcare management and decision-making, reflecting significant innovation in the medical field.

Tianji is an open-source project providing tools for understanding human etiquette and interaction through prompt applications, knowledge databases, and customizable agents. Regular updates enhance its capability to support applications in areas like Etiquette and Hospitality. Explore models such as Wines Etiquette and Wish-sending integrated with frameworks like LangChain and LlamaIndex, and access comprehensive datasets on Hugging Face. Collaborate with the community on GitHub and WeChat for insights into AI-driven social practices.

STaRK provides a comprehensive benchmark for assessing the retrieval effectiveness of large language models on knowledge bases. It includes practical applications in areas like product search, academic inquiry, and biomedicine, offering realistic query challenges to spur advancements in retrieval technology. With easy installation via pip, resources on Hugging Face, and a dedicated leaderboard, STaRK supports researchers in refining context-specific retrieval strategies.