62 lines
2.5 KiB
Markdown
62 lines
2.5 KiB
Markdown
---
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title: "LM Studio — Embeddings Endpoint"
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aliases: [lmstudio-embeddings, lm-studio-embeddings, local-embeddings]
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tags: [lmstudio, embeddings, openai-compat, local-llm, vectors]
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sources: [raw/Embeddings.md]
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created: 2026-04-30
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updated: 2026-04-30
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---
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# LM Studio — Embeddings Endpoint
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LM Studio exposes an OpenAI-compatible `/v1/embeddings` endpoint for generating dense vector representations of text. Drop-in compatible with the `openai` Python SDK.
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## Endpoint
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- **Method:** `POST /v1/embeddings`
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- **Base URL:** `http://localhost:1234/v1`
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- **API key:** any non-empty string (e.g. `"lm-studio"`)
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- **Spec:** mirrors [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings)
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## Python Example
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:1234/v1", api_key="lm-studio")
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def get_embedding(text, model="model-identifier"):
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text = text.replace("\n", " ")
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return client.embeddings.create(input=[text], model=model).data[0].embedding
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print(get_embedding("Once upon a time, there was a cat."))
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```
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- Replace `"model-identifier"` with the name of an embedding model loaded in LM Studio
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- Newlines are stripped before embedding — standard preprocessing step
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- Returns a flat Python list of floats (`data[0].embedding`)
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## Usage Notes
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- Must have an embedding model loaded in LM Studio (not a chat model)
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- Common embedding models: `nomic-embed-text`, `text-embedding-3-small` clones, `all-MiniLM-L6-v2`
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- The `model` param must match the identifier shown in LM Studio's loaded models list
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- Batch inputs: pass multiple strings in the `input` list for efficiency
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## Key Takeaways
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- LM Studio's `/v1/embeddings` is a drop-in OpenAI replacement — zero code changes beyond `base_url` and `api_key`
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- Use any non-empty string as the API key; auth is not enforced locally
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- Strip newlines before embedding for cleaner vectors
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- Return value is `response.data[0].embedding` — a list of floats
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- Pair with a vector store (FAISS, Chroma, pgvector) to build a fully local RAG pipeline
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## Related Articles
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- [[wiki/claude-code/lmstudio-anthropic-compat|LM Studio — Anthropic Compat]] — redirect Claude Code / Anthropic SDK to local LM Studio
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- [[wiki/claude-code/lmstudio-chat-completions|LM Studio — Chat Completions]] — `/v1/chat/completions` with full param list
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- [[wiki/architecture/rag-pattern|RAG Pattern]] — retrieval-augmented generation using embeddings
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## Sources
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- `raw/Embeddings.md` — clipped from https://lmstudio.ai/docs/developer/openai-compat/embeddings
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