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This page defines the core concepts you’ll use across Moss.

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Index

Structure that powers fast, local search. You add documents, we build an efficient index for sub-10ms queries.

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Document schema

  • id (string), text (string), metadata (optional string map)
  • Upserts replace matching ids; keep ids stable for updates

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Embeddings

Semantic vector representations of text. Can be generated locally or via a remote model, then stored alongside your index.

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Chunking

  • Aim for ~200–500 tokens per chunk; overlap 10–20%
  • Smaller chunks improve recall; overlap preserves context continuity

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Retrieval

How results are fetched. Options include vector similarity, keyword, and hybrid methods with scoring and reranking.

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Retrieval knobs

  • top_k: number of results to return
  • alpha: blend semantic (1.0) vs keyword (0.0); defaults semantic-heavy
  • Filters: constrain by metadata (e.g., category/lang)
  • Rerank: reorder top-k for precision

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Storage & Sync

Persist indexes locally (desktop/mobile). Optionally sync segments to the cloud for backup and sharing.

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Models

  • moss-minilm (default): fast, lightweight for edge/offline
  • moss-mediumlm: higher accuracy with reasonable performance

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Authentication

Used for optional cloud features like syncing or hosted embedding models. Local mode requires no network access.

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Lifecycle

  • Create index → upsert docs → load → query → delete when done
  • Supports multiple indexes per project

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Performance expectations

  • Sub-10ms local queries (hardware-dependent)
  • Sync is optional; compute stays on-device