From 8b6340f12b913832bf8d4f2f9c2fd22d286275f1 Mon Sep 17 00:00:00 2001 From: claudespice Date: Wed, 29 Jul 2026 15:35:25 -0700 Subject: [PATCH] docs(embeddings): drop ONNX from the embedding formats (vNext) ONNX inference was removed in vNext (spiceai#11684) and no ONNX runtime remains in the build: Cargo.lock on trunk has zero ort/tract entries, and the only text-embeddings backend resolved is text-embeddings-backend-candle. Dropped the now-empty "ML Format(s)" column from the embedding-provider table, removed .onnx from the accepted weight formats in both local embedding deployment guides, and reframed the data-ingestion edge example away from ONNX inference. --- .../embeddings/huggingface/deployment.md | 2 +- website/docs/components/embeddings/index.md | 20 +++++++++---------- .../components/embeddings/local/deployment.md | 2 +- website/docs/features/data-ingestion/index.md | 2 +- 4 files changed, 13 insertions(+), 13 deletions(-) diff --git a/website/docs/components/embeddings/huggingface/deployment.md b/website/docs/components/embeddings/huggingface/deployment.md index aeb2a89b7..b800ca31b 100644 --- a/website/docs/components/embeddings/huggingface/deployment.md +++ b/website/docs/components/embeddings/huggingface/deployment.md @@ -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` diff --git a/website/docs/components/embeddings/index.md b/website/docs/components/embeddings/index.md index 2aa7df62e..6bef99bfd 100644 --- a/website/docs/components/embeddings/index.md +++ b/website/docs/components/embeddings/index.md @@ -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 diff --git a/website/docs/components/embeddings/local/deployment.md b/website/docs/components/embeddings/local/deployment.md index f060cbc94..f69a7cdc4 100644 --- a/website/docs/components/embeddings/local/deployment.md +++ b/website/docs/components/embeddings/local/deployment.md @@ -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` diff --git a/website/docs/features/data-ingestion/index.md b/website/docs/features/data-ingestion/index.md index 4b00bb795..51c5d85f7 100644 --- a/website/docs/features/data-ingestion/index.md +++ b/website/docs/features/data-ingestion/index.md @@ -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.