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lukekim merged 1 commit into from
Jul 29, 2026
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docs(embeddings): drop ONNX from the embedding formats (vNext) #2008

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2 changes: 1 addition & 1 deletion website/docs/components/embeddings/huggingface/deployment.md
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Expand Up @@ -61,7 +61,7 @@ When `pooling` is unset, the loader defaults to `mean` and logs a warning. Set t

Local file mode (HF-downloaded) requires:

- Model weights (accepted formats: `.onnx`, `.gguf`, `.ggml`, `.safetensors`, `pytorch_model.bin`).
- Model weights (accepted formats: `.gguf`, `.ggml`, `.safetensors`, `pytorch_model.bin`).
- `config.json`
- `tokenizer.json`

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20 changes: 10 additions & 10 deletions website/docs/components/embeddings/index.md
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Expand Up @@ -19,16 +19,16 @@ Embeddings enable vector-based and similarity search, such as document retrieval

Spice supports a variety of embedding model sources and formats:

| Name | Description | Status | ML Format(s) | LLM Format(s)\* |
| -------------------------- | --------------------------------------- | ----------------- | ------------ | ------------------------------- |
| [`file`][file] | Local filesystem | Release Candidate | ONNX | GGUF, GGML, SafeTensor |
| [`huggingface`][hf] | Models hosted on HuggingFace | Release Candidate | ONNX | GGUF, GGML, SafeTensor |
| [`openai`][openai] | OpenAI (or compatible) LLM endpoint | Release Candidate | - | OpenAI-compatible HTTP endpoint |
| [`azure`][azure] | Azure OpenAI | Alpha | - | OpenAI-compatible HTTP endpoint |
| [`google`][google] | Google AI embedding models | Alpha | - | OpenAI-compatible HTTP endpoint |
| [`databricks`][databricks] | Models deployed to Databricks Mosaic AI | Alpha | - | OpenAI-compatible HTTP endpoint |
| [`bedrock`][bedrock] | Models deployed on Amazon Bedrock | Alpha | - | OpenAI-compatible HTTP endpoint |
| [`model2vec`][model2vec] | Model2Vec static word embeddings | Alpha | - | Model2Vec format |
| Name | Description | Status | Format(s) |
| -------------------------- | --------------------------------------- | ----------------- | ------------------------------- |
| [`file`][file] | Local filesystem | Release Candidate | GGUF, GGML, SafeTensor |
| [`huggingface`][hf] | Models hosted on HuggingFace | Release Candidate | GGUF, GGML, SafeTensor |
| [`openai`][openai] | OpenAI (or compatible) LLM endpoint | Release Candidate | OpenAI-compatible HTTP endpoint |
| [`azure`][azure] | Azure OpenAI | Alpha | OpenAI-compatible HTTP endpoint |
| [`google`][google] | Google AI embedding models | Alpha | OpenAI-compatible HTTP endpoint |
| [`databricks`][databricks] | Models deployed to Databricks Mosaic AI | Alpha | OpenAI-compatible HTTP endpoint |
| [`bedrock`][bedrock] | Models deployed on Amazon Bedrock | Alpha | OpenAI-compatible HTTP endpoint |
| [`model2vec`][model2vec] | Model2Vec static word embeddings | Alpha | Model2Vec format |

[file]: embeddings/local
[hf]: embeddings/huggingface
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2 changes: 1 addition & 1 deletion website/docs/components/embeddings/local/deployment.md
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Expand Up @@ -55,7 +55,7 @@ When `pooling` is unset, the loader defaults to `mean` and logs a warning. Set t

Local embedding requires all of the following in the model directory:

- Model weights (accepted formats: `.onnx`, `.gguf`, `.ggml`, `.safetensors`, `pytorch_model.bin`).
- Model weights (accepted formats: `.gguf`, `.ggml`, `.safetensors`, `pytorch_model.bin`).
- `config.json`
- `tokenizer.json`

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2 changes: 1 addition & 1 deletion website/docs/features/data-ingestion/index.md
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Expand Up @@ -151,7 +151,7 @@ datasets:

Spice.ai OSS includes built-in data ingestion support for collecting the latest data from edge nodes for use in subsequent queries. This feature eliminates the need for additional ETL pipelines and improves the speed of the feedback loop.

For example, consider CPU usage anomaly detection. When CPU metrics are sent to the Spice OpenTelemetry endpoint, the loaded machine learning model can use the most recent observations for inferencing and provide recommendations to the edge node. This process occurs quickly on the edge itself, within milliseconds, and without generating network traffic.
For example, consider CPU usage anomaly detection. When CPU metrics are sent to the Spice OpenTelemetry endpoint, they are immediately queryable alongside accelerated data, so an application can compare the most recent observations against historical baselines and act on the edge node. This process occurs quickly on the edge itself, within milliseconds, and without generating network traffic.

Additionally, Spice will periodically replicate the data to the data connector for further use.

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