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OpenAI 接口管理 & 分发系统，支持 Azure、Anthropic Claude、Google PaLM 2 & Gemini、智谱 ChatGLM、百度文心一言、讯飞星火认知、阿里通义千问、360 智脑以及腾讯混元，可用于二次分发管理 key，仅单可执行文件，已打包好 Docker 镜像，一键部署，开箱即用. OpenAI key management & redistributi…
A Strict JSON Framework for LLM Outputs
调用大模型已经是如今做 ai 项目习以为常的工作的，但是大模型的输出很多时候是不可控的，我们又需要使用大模型去做各种下游任务，实现可控可解析的输出。我们探索了一种和 python 开发可以紧密合作的开发方法。
整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。
A distributed, fast open-source graph database featuring horizontal scalability and high availability
A lightweight framework for building LLM-based agents
🔍 An LLM-based Multi-agent Framework of Web Search Engine (like Perplexity.ai Pro and SearchGPT)
Enforce the output format (JSON Schema, Regex etc) of a language model
GraphRAG using Local LLMs - Features robust API and multiple apps for Indexing/Prompt Tuning/Query/Chat/Visualizing/Etc. This is meant to be the ultimate GraphRAG/KG local LLM app.
Local models support for Microsoft's graphrag using ollama (llama3, mistral, gemma2 phi3)- LLM & Embedding extraction
The platform for building AI from enterprise data
Search for words, documents, images, videos, news, maps and text translation using the DuckDuckGo.com search engine. Downloading files and images to a local hard drive.
Proof of concept app using LangChain and LLMs to retrieve information from graphs, built with the IMDB dataset
Neo4j graph construction from unstructured data using LLMs
《开源大模型食用指南》基于Linux环境快速部署开源大模型，更适合中国宝宝的部署教程
the resources about the application based on LLM with RAG pattern
pycorrector is a toolkit for text error correction. 文本纠错，实现了Kenlm，T5，MacBERT，ChatGLM3，LLaMA等模型应用在纠错场景，开箱即用。
A collection of papers and resources about knowledge graph enhanced large language models (KGLLM)
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Updated Jun 21, 2023
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
Official Implementation of ICLR 2024 paper: "Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning"
LangChain结合了大型语言模型、知识库和计算逻辑，可以用于快速开发强大的AI应用。这个仓库包含了我对LangChain的学习和实践经验，包括教程和代码案例。让我们一起探索LangChain的可能性，共同推动人工智能领域的进步！
Integrating ONgDB database into langchain ecosystem
Explore Langchain agents with LLMs and neo4j data generation and viz