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Langchain-Chatchat（原Langchain-ChatGLM）基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
🏆 实时 零代码、全功能、强安全 ORM 库 🚀 后端接口和文档零代码，前端(客户端) 定制返回 JSON 的数据和结构 🏆 Real-Time coding-free, powerful and secure ORM 🚀 providing APIs and Docs without coding by Backend, and the returned JSON of API can be customized by Frontend(Client) users
Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
Open Source Deep Research Alternative to Reason and Search on Private Data. Written in Python.
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.
Visualize hnsw, faiss and other anns index
腾讯 APIJSON 各种语言、各种框架 的 使用示例项目、上手文档、测试数据 SQL 文件 等。Demo projects with document and SQL files for Tencent APIJSON with different programming languages and different frameworks.
Radient turns many data types (not just text) into vectors for similarity search, RAG, regression analysis, and more.
Code from tutorials presented on the "Code AI with Rok" YouTube channel
Akcio is a demonstration project for Retrieval Augmented Generation (RAG). It leverages the power of LLM to generate responses and uses vector databases to fetch relevant documents to enhance the quality and relevance of the output.
A web UI Project In order to learn the large language model. This project includes features such as chat, quantization, fine-tuning, prompt engineering templates, and multimodality.
Comprehensive Vector Data Tooling. The universal interface for all vector database, datasets and RAG platforms. Easily export, import, backup, re-embed (using any model) or access your vector data from any vector databases or repository.
Open Source, Cloud Native, RESTful Search Engine Powered by Neural Networks
A Web Crawler based on LLMs implemented with Ray and Huggingface. The embeddings are saved into a vector database for fast clustering and retrieval. Use it for your RAG.