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no-static-default-onboarding-behavior

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Mendon Kissling 2025-09-26 13:35:42 -04:00
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docs/docs/core-components/knowledge.mdx
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@ -134,15 +134,14 @@ A new filter is created with default settings that match everything.
## OpenRAG default configuration ## OpenRAG default configuration
OpenRAG creates a specialized OpenSearch index called `documents` with the values defined at `src/config/settings.py`. OpenRAG automatically detects and configures the correct vector dimensions for embedding models, ensuring optimal search performance and compatibility.
* **Vector Dimensions**: 1536-dimensional embeddings using OpenAI's `text-embedding-3-small` model.
* **KNN Vector Type**: Uses `knn_vector` field type with `disk_ann` method and `jvector` engine.
* **Distance Metric**: L2 (Euclidean) distance for vector similarity.
* **Performance Optimization**: Configured with `ef_construction: 100` and `m: 16` parameters.
OpenRAG supports hybrid search, which combines semantic and keyword search.
The complete list of supported models is available at [/src/services/models_service.py](https://github.com/langflow-ai/openrag/blob/main/src/services/models_service.py).
You can use custom embedding models by specifying them in your configuration.
If you use an unknown embedding model, OpenRAG will automatically fall back to `1536` dimensions and log a warning. The system will continue to work, but search quality may be affected if the actual model dimensions differ from `1536`.
The default embedding dimension is `1536` and the default model is `text-embedding-3-small`.
For models with known vector dimensions, see [/src/config/settings.py](https://github.com/langflow-ai/openrag/blob/main/src/config/settings.py).