The open source platform for AI-native application development.
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Updated
Dec 2, 2024 - Python
The open source platform for AI-native application development.
Advanced Game Hacking Library for C, Modern C++, Rust and Python (Windows/Linux/FreeBSD) (Process/Memory Hacking) (Hooking/Detouring) (Cross Platform) (x86/x64) (DLL/SO Injection) (Internal/External) (Assembler/Disassembler)
🔧 Repair JSON!Solution for JSON Anomalies from LLMs.
Higher performance OpenAI LLM service than vLLM serve: A pure C++ high-performance OpenAI LLM service implemented with GPRS+TensorRT-LLM+Tokenizers.cpp, supporting chat and function call, AI agents, distributed multi-GPU inference, multimodal capabilities, and a Gradio chat interface.
The Rysana AI TS library.
Java AI(智能体) 全场景应用开发框架(LLM,Function Call,RAG,Embedding,Reranking,Flow,MCP Server,Mcp Client,Mcp Proxy)。同时兼容 java8 ~ java24。也可嵌入到 SpringBoot2、jFinal、Vert.x 等框架中使用。
Started out as Dynamic Function Calling for OAI. Upon reviewing a research paper released (LATM) This is/has become a implementation of such system using: OpenAI and Autogen
A sample app to demonstrate Function calling using the latest format in Chat Completions API and also in Assistants API.
A simple example that demonstrates how to use the function call feature of the OpenAI API
Claudetools is a Python library that enables function calling with the Claude 3 family of language models from Anthropic.
Find function-level association impacts of code changes
akshare-gpt 是一个开源工具,旨在将 Akshare 集成到 GPT 的工具中,实现自然语言问答。
Your AI Interface in Command Line
This is a sample React project that generates a grocery list of ingredients based on a menu or a list of dishes. It is powered by the OpenAI Chat Completion API and built using Next.js 13.
A large model communication platform based on Baidu ERNIE and STreamlit
✨ 基于代码生成和函数调用(function call)的大语言模型(LLM)智能体 ✨ 通过自然语言提问,使用大语言模型智能解析数据库结构,对数据进行智能多表结构化查询和统计计算,根据查询结果智能绘制多种图表。 支持自定义函数(function call)和Agent调用,多智能体协同。 基于代码生成的思维链(COT)。 实现智能体对用户的反问,解决用户提问模糊、不完整的情况。
Enable any LLM to call tool in gpt format. Development based on Qwen2. If it cannot adapt to your LLM, your Pull Request is wanted.
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