Mnemo FAQs

Question 1

What does 'plugin-first' mean for Mnemo users?

Accepted Answer

The plugin-first architecture allows extensive customization and flexibility. Users can easily integrate diverse data sources through extensible source adapters (e.g., Git, folder, web) and process various content types using content extractors (e.g., plain text, Office documents, PDFs, HTML).

Question 2

Can AI agents directly interact with Mnemo for retrieval?

Accepted Answer

Yes, Mnemo includes an integrated MCP (Meta-Content Protocol) server. This enables seamless querying and interaction by AI agents such as Claude Desktop, Cursor, and Continue.dev, allowing them to leverage Mnemo's robust retrieval capabilities directly.

Question 3

How does Mnemo achieve 'freshness-aware hybrid search'?

Accepted Answer

Mnemo combines the power of vector search (using Qdrant) with lexical search (using Meilisearch). This hybrid approach, coupled with its event bus and change tracking, ensures that retrieval results are not only relevant but also reflect the most up-to-date information from your sources.

Question 4

What is Mnemo and how does it enhance AI stacks?

Accepted Answer

Mnemo is a self-hosted, headless retrieval plane designed for AI stacks. It provides freshness-aware hybrid search capabilities and a plugin-first architecture, significantly enhancing the accuracy and relevance of your Retrieval Augmented Generation (RAG) infrastructure.

Question 5

What are the deployment options for Mnemo?

Accepted Answer

Mnemo is designed for self-hosted infrastructure and can be easily deployed using Docker Compose. This makes it ideal for setups ranging from development on laptops and homelabs to single-host production environments. Kubernetes manifests are also provided as a reference for advanced users.

Mnemo

Mnemo

主要功能

使用案例

主要功能

使用案例