Hysen Labsquivr
GitHub describes it as Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.. The repository metadata lists Python as its primary language. The metadata lists the NOASSERTION license. This article stays within the project description and details documented in the GitHub repository README.
QuivrHQ/quivr: Quivr - Your Second Brain, Empowered by Generative AI
GitHub describes it as Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.. The repository metadata lists Python as its primary language. The metadata lists the NOASSERTION license. This article stays within the project description and details documented in the GitHub repository README.
Repository scope
GitHub describes it as Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.. The repository metadata lists Python as its primary language. The metadata lists the NOASSERTION license. The README describes the project this way: Quivr, helps you build your second brain, utilizes the power of GenerativeAI to be your personal assistant !
Key Features 🎯
The README section "Key Features 🎯" states: - Opiniated RAG : We created a RAG that is opinionated, fast and efficient so you can focus on your product - LLMs : Quivr works with any LLM, you can use it with OpenAI, Anthropic, Mistral, Gemma, etc. - Any File : Quivr works with any file, you can use it with PDF, TXT, Markdown, etc and even add your own parsers. - Customize your RAG : Quivr allows you to customize your RAG, add internet search, add tools, etc. - Integrations with Megaparse : Quivr works with Megaparse, so you can ingest your files with Megaparse and use the RAG with Quivr.
Key Features 🎯
The README section "Key Features 🎯" states: We take care of the RAG so you can focus on your product. Simply install quivr-core and add it to your project. You can now ingest your files and ask questions.
Workflows
The README section "Workflows" states: Quivr supports APIs from Anthropic, OpenAI, and Mistral. It also supports local models using Ollama.
Editorial conclusion
The repository README is the source for this review. It does not replace a local installation or an independent test.
Community notes