Models

Memvid uses local models for semantic search (embeddings), reranking, visual search (CLIP), Logic‑Mesh entity extraction (NER), and local enrichment workflows. Models are cached under MEMVID_MODELS_DIR (default ~/.memvid/models). Some models are installed explicitly via memvid models install, while embedding/reranker models are auto-downloaded on first use (unless offline).

Quick Start

# See what's available / installed (no downloads)
memvid models list

# Install optional CLIP + NER models
memvid models install --clip mobileclip-s2
memvid models install --ner distilbert-ner

# Install optional local LLM for enrichment (GGUF)
memvid models install phi-3.5-mini

Model Types

Type	How it’s fetched	Used by
Embedding	Auto-download on first use	`put --embedding`, `find --mode sem/auto`, `ask --mode sem/hybrid`
Reranker	Auto-download on first use	Hybrid `ask`/`find` (disable with `--no-rerank`)
CLIP	`memvid models install --clip …`	`put --clip`, `find --mode clip`
NER	`memvid models install --ner …`	`put --logic-mesh`, `follow …`
LLM (Enrich)	`memvid models install …`	`enrich`, `put --contextual --contextual-model local`
Whisper	Auto-download on first use	`put --transcribe`

Embedding Models (Text Vectors)

Select the default embedding model with the global -m/--embedding-model flag. It can appear before or after the subcommand:

memvid put knowledge.mv2 --input docs/ --embedding -m bge-small
memvid -m openai-small put knowledge.mv2 --input docs/ --embedding

Common choices

Model	Dimensions	Notes
`bge-small`	384	Default local model (fastembed)
`bge-base`	768	Higher quality local model
`nomic`	768	High accuracy local model
`gte-large`	1024	Best local semantic depth
`openai-small`	1536	`OPENAI_API_KEY` required
`openai-large`	3072	`OPENAI_API_KEY` required
`openai`	3072	Alias for `openai-large`
`openai-ada`	1536	Legacy OpenAI model

External embedding APIs

export OPENAI_API_KEY=sk-...
memvid put knowledge.mv2 --input docs/ --embedding -m openai-small

ask/find auto-detect the correct embedding runtime from the .mv2 when vectors are present. Use --query-embedding-model (or global -m) only when you need an explicit override.

Reranking (Hybrid Precision)

memvid ask may use a cross-encoder reranker (auto-downloaded on first use). Disable it in gated/offline environments:

memvid ask knowledge.mv2 --question "…" --no-rerank

CLIP Models (Visual Search)

Install a CLIP model:

memvid models install --clip mobileclip-s2
memvid models install --clip mobileclip-s2-fp16
memvid models install --clip siglip-base

Use it during ingestion and search:

memvid put photos.mv2 --input ./images --clip
memvid find photos.mv2 --query "sunset over ocean" --mode clip

NER Model (Logic‑Mesh)

Install NER:

memvid models install --ner distilbert-ner

Enable Logic‑Mesh during ingestion:

memvid put graph.mv2 --input docs/ --logic-mesh
memvid follow graph.mv2 traverse --start "Microsoft" --hops 2

Enrichment LLM Models

These are local GGUF models used by enrichment workflows (not by memvid ask):

memvid models install phi-3.5-mini
memvid models install phi-3.5-mini-q8

For memvid ask, choose a synthesis model with --use-model (e.g. --use-model openai, --use-model gemini-2.0-flash, or --use-model "ollama:qwen2.5:1.5b"). See Local Models with Ollama.

List / Verify / Remove

# Filter model list
memvid models list --model-type embedding
memvid models list --model-type clip --json

# Verify installed enrichment LLM models (phi-3.5-*)
memvid models verify
memvid models verify phi-3.5-mini

# Remove an enrichment LLM model
memvid models remove phi-3.5-mini --yes

Offline Mode

Set MEMVID_OFFLINE=1 to prevent downloads. In offline mode:

memvid models install … fails (it can’t download).
Embedding/reranker auto-download is blocked; run a semantic command once while online to populate caches.

Environment Variables

Variable	Purpose
`MEMVID_MODELS_DIR`	Model cache directory (default `~/.memvid/models`)
`MEMVID_OFFLINE`	Skip downloads/network (`1` to enable)
`MEMVID_CLIP_MODEL`	Default local CLIP model (e.g. `mobileclip-s2`)
`OPENAI_API_KEY`	OpenAI embeddings / CLIP / LLM providers
`GEMINI_API_KEY`	Gemini providers
`ANTHROPIC_API_KEY`	Claude providers
`COHERE_API_KEY`	Cohere embeddings
`VOYAGE_API_KEY`	Voyage embeddings

Get Started

Comparisons

Install

Hosting

Architecture

Search & Retrieval

Enrichment

Media Processing

Embeddings

Security & Limits

Performance

CLI

Python SDK

Node.js SDK

Examples & Packages

Testing

Help

Quick Start

Model Types

Embedding Models (Text Vectors)

Common choices

External embedding APIs

Reranking (Hybrid Precision)

CLIP Models (Visual Search)

NER Model (Logic‑Mesh)

Enrichment LLM Models

List / Verify / Remove

Offline Mode

Environment Variables

​Quick Start

​Model Types

​Embedding Models (Text Vectors)

​Common choices

​External embedding APIs

​Reranking (Hybrid Precision)

​CLIP Models (Visual Search)

​NER Model (Logic‑Mesh)

​Enrichment LLM Models

​List / Verify / Remove

​Offline Mode

​Environment Variables

Quick Start

Model Types

Embedding Models (Text Vectors)

Common choices

External embedding APIs

Reranking (Hybrid Precision)

CLIP Models (Visual Search)

NER Model (Logic‑Mesh)

Enrichment LLM Models

List / Verify / Remove

Offline Mode

Environment Variables