Quivr
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Quivr is an open-source, self-hostable Retrieval Augmented Generation (RAG) platform designed to function as a user's 'second brain.' It enables users to upload and ingest diverse data types—from text documents and web pages to audio and video files—into a personal knowledge base. Users can then interact with this knowledge base through natural language queries, leveraging large language models (LLMs) to retrieve contextually relevant information and generate informed responses. It stands out by offering complete data ownership and flexibility through its open-source nature, empowering users to control their data and customize their AI assistant.
Why was this tool discontinued?
Automatically marked inactive after 7 consecutive failed health checks (last error: SSL error)
What It Does
Quivr ingests various data formats, including PDFs, Notion pages, YouTube videos, and audio recordings, converting them into embeddings stored in a vector database. When a user poses a question, it retrieves the most relevant information from this database and feeds it to an LLM to generate a coherent and contextually accurate answer. This process augments the LLM's knowledge with personal data, preventing hallucinations and ensuring tailored responses based on the user's specific information.
Pricing
Pricing Plans
Ideal for small teams and initial AI support adoption.
- 500 monthly queries
- 1 agent
- Basic analytics
Best for growing businesses needing expanded AI support.
- 5000 monthly queries
- 3 agents
- Advanced analytics
- Integrations
Tailored for large enterprises requiring comprehensive, scalable AI support.
- Unlimited queries
- Unlimited agents
- Dedicated support
- Custom integrations
Key Features
Quivr offers multi-modal data ingestion, allowing users to upload a wide array of file types to build a comprehensive knowledge base from diverse sources. Its core RAG architecture ensures accurate, context-aware responses by integrating personal data with LLMs, significantly reducing factual errors. The platform is open-source, providing complete control over data and customization options, and includes features like streaming answers for real-time interaction, support for various LLM providers, and an API for seamless integration into other applications.
Target Audience
Quivr primarily targets developers, researchers, and knowledge workers who need a powerful, private, and customizable personal knowledge management system. It's also ideal for individuals or teams looking to build AI-powered applications that leverage their proprietary data without relying on third-party hosted solutions. Those prioritizing data ownership, open-source flexibility, and the ability to self-host their AI infrastructure will find it particularly valuable.
Value Proposition
Improves customer satisfaction and operational efficiency by automating support, leading to faster resolutions, reduced costs, and a more responsive and consistent customer experience.
Use Cases
Automating FAQ responses, providing instant solutions to common issues, triaging complex queries, offering 24/7 customer assistance, and reducing human agent workload.
Frequently Asked Questions
Quivr is a paid tool. Available plans include: Starter, Pro, Business.
Quivr ingests various data formats, including PDFs, Notion pages, YouTube videos, and audio recordings, converting them into embeddings stored in a vector database. When a user poses a question, it retrieves the most relevant information from this database and feeds it to an LLM to generate a coherent and contextually accurate answer. This process augments the LLM's knowledge with personal data, preventing hallucinations and ensuring tailored responses based on the user's specific information.
Quivr is best suited for Quivr primarily targets developers, researchers, and knowledge workers who need a powerful, private, and customizable personal knowledge management system. It's also ideal for individuals or teams looking to build AI-powered applications that leverage their proprietary data without relying on third-party hosted solutions. Those prioritizing data ownership, open-source flexibility, and the ability to self-host their AI infrastructure will find it particularly valuable..
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