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@@ -143,7 +143,7 @@ The defaults are reasonable for getting started. When you outgrow them, there ar
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- **Embedding engine.** The default (SentenceTransformers `all-MiniLM-L6-v2`) runs locally on CPU and consumes roughly 500 MB of RAM per worker. For any multi-user deployment, point at an external embeddings API (OpenAI, or Ollama with `nomic-embed-text`) via `RAG_EMBEDDING_ENGINE`.
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- **Content extraction engine.** The default uses `pypdf`, which leaks memory during heavy ingestion. For anything beyond casual use, switch to **Tika** or **Docling** via `CONTENT_EXTRACTION_ENGINE`.
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- **Vector database.** The default ChromaDB (local SQLite-backed) does not tolerate multi-worker deployments. At scale, switch to **PGVector**. It is the only vector database officially supported and maintained by the Open WebUI team. Milvus, Qdrant, and MariaDB Vector are also available but are community-maintained: they may break on upgrades and fixes depend on community contributions. See the [env-configuration reference](/reference/env-configuration#vector_db) for setup and the community disclaimers on each provider.
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- **Vector database.** The default ChromaDB (local SQLite-backed) does not tolerate multi-worker deployments. At scale, switch to **PGVector**. It is the only vector database officially supported and maintained by the Open WebUI team. Milvus, Qdrant, and MariaDB Vector are also available as non-core integrations: they may break on upgrades, and fixes come from outside the core Open WebUI maintenance path. See the [env-configuration reference](/reference/env-configuration#vector_db) for setup and the integration disclaimers on each provider.
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:::note When to worry about this
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None of these matter for "a single user with a handful of PDFs." All of them start mattering the moment you have 100 documents or 10 concurrent users.
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