> ## Documentation Index > Fetch the complete documentation index at: https://docs.pipeshub.com/llms.txt > Use this file to discover all available pages before exploring further. # Default (System Provided) > PipesHub's built-in BAAI/bge-large-en-v1.5 embedding model — no configuration required # Default Embedding Model BAAI/bge-large-en-v1.5 Model Selection *The system-provided BAAI/bge-large-en-v1.5 embedding model is available in PipesHub with no setup required* PipesHub ships with **BAAI/bge-large-en-v1.5** as its built-in embedding model. It is available immediately — no API key, no external service, no configuration needed. If you do not need a custom embedding provider, this model is ready to use out of the box. ## No Configuration Required This is the only embedding provider that requires no credentials. Simply click **Configure** on the Default provider card and click **Add Model** to activate it as your embedding model. > No API key or external service is needed. The model runs locally inside PipesHub. ## About BAAI/bge-large-en-v1.5 BAAI/bge-large-en-v1.5 is developed by the Beijing Academy of Artificial Intelligence (BAAI). It is one of the highest-performing models on the Massive Text Embedding Benchmark (MTEB) and is optimised for semantic search and document retrieval in English. **Key characteristics:** * 1024-dimensional embeddings * Optimised for semantic search and document retrieval * Runs entirely inside your PipesHub instance — data never leaves your infrastructure * No usage costs or rate limits ## Configuration Steps 1. Click **Configure** on the Default (System Provided) provider card 2. Optionally set a **Model Friendly Name** for this configuration 3. Click **Add Model** to activate the model > This provider has no required fields. It works immediately after clicking Add Model. ## Usage Considerations * No external API calls are made — all embedding happens locally * No API key or billing setup required * Processing speed depends on your PipesHub server's available CPU/memory * Best suited for English-language content ## When to use an external provider instead Consider connecting an external embedding provider if you need: * Multilingual embedding support * Higher-dimensional vectors for specialised retrieval tasks * A specific model your organisation has standardised on For additional support, refer to the [PipesHub documentation](/ai-models/overview) or contact PipesHub support.