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FAQ

1. My building time seems long

You can speed up the process by using a lightweight embedding model. Add this to your arguments:

--embedding-model sentence-transformers/all-MiniLM-L6-v2

Model sizes: all-MiniLM-L6-v2 (30M parameters), facebook/contriever (~100M parameters), Qwen3-0.6B (600M parameters)

2. When should I use prompt templates?

Use prompt templates ONLY with task-specific embedding models like Google's EmbeddingGemma. These models are specially trained to use different prompts for documents vs queries.

DO NOT use with regular models like nomic-embed-text, text-embedding-3-small, or bge-base-en-v1.5 - adding prompts to these models will corrupt the embeddings.

Example usage with EmbeddingGemma:

# Build with document prompt
leann build my-docs --embedding-prompt-template "title: none | text: "

# Search with query prompt
leann search my-docs --query "your question" --embedding-prompt-template "task: search result | query: "

See the Configuration Guide: Task-Specific Prompt Templates for detailed usage.

3. Why is LM Studio loading multiple copies of my model?

This was fixed in recent versions. LEANN now properly unloads models after querying metadata, respecting your LM Studio JIT auto-evict settings.

If you still see duplicates:

  • Update to the latest LEANN version
  • Restart LM Studio to clear loaded models
  • Check that you have JIT auto-evict enabled in LM Studio settings

How it works now:

  1. LEANN loads model temporarily to get context length
  2. Immediately unloads after query
  3. LM Studio JIT loads model on-demand for actual embeddings
  4. Auto-evicts per your settings

4. Do I need Node.js and @lmstudio/sdk?

No, it's completely optional. LEANN works perfectly fine without them using a built-in token limit registry.

Benefits if you install it:

  • Automatic context length detection for LM Studio models
  • No manual registry maintenance
  • Always gets accurate token limits from the model itself

To install (optional):

npm install -g @lmstudio/sdk

See Configuration Guide: LM Studio Auto-Detection for details.

5. leann build fails with Security Violation [pathsec.open]: refusing multiply-linked file (Linux)

uv installs packages by hardlinking files from its cache into the environment by default. Recent nltk releases ship a hardened file loader (nltk/pathsec.py) that refuses to open multiply-linked files (st_nlink > 1, CWE-59 guard) - and llama-index, which LEANN uses for document parsing, loads its bundled nltk stopwords through exactly that path. The result on an uv-installed LEANN:

PermissionError: Security Violation [pathsec.open]: refusing multiply-linked file
'.../site-packages/llama_index/core/_static/nltk_cache/corpora/stopwords/english' (st_nlink=2);
a hardlink can point at an outside-root inode (CWE-59)

Fix: reinstall with copy mode so no hardlinks are created:

UV_LINK_MODE=copy uv pip install --reinstall leann
# or, for the global CLI/MCP install:
UV_LINK_MODE=copy uv tool install --reinstall leann-core --with leann

--reinstall matters: without it uv leaves the already-hardlinked packages in place (satisfied requirements are skipped, and uv tool install refuses to overwrite an existing tool), so the error persists. pip installs are unaffected (pip copies by default).