OpenPipe

# OpenPipe ## Docs - [Delete Dataset](https://docs.openpipe.ai/api-reference/delete-dataset.md): Delete a dataset. - [Delete Model](https://docs.openpipe.ai/api-reference/delete-model.md): Delete an existing model. - [Get Model](https://docs.openpipe.ai/api-reference/get-getModel.md): Get a model by ID. - [List Datasets](https://docs.openpipe.ai/api-reference/get-listDatasets.md): List datasets for a project. - [List Models](https://docs.openpipe.ai/api-reference/get-listModels.md): List all models for a project. - [Chat Completions](https://docs.openpipe.ai/api-reference/post-chatcompletions.md): OpenAI-compatible route for generating inference and optionally logging the request. - [Create Dataset](https://docs.openpipe.ai/api-reference/post-createDataset.md): Create a new dataset. - [Add Entries to Dataset](https://docs.openpipe.ai/api-reference/post-createDatasetEntries.md): Add new dataset entries. - [Create Model](https://docs.openpipe.ai/api-reference/post-createModel.md): Train a new model. - [Judge Criteria](https://docs.openpipe.ai/api-reference/post-criteriajudge.md): Get a judgement of a completion against the specified criterion - [Report](https://docs.openpipe.ai/api-reference/post-report.md): Record request logs from OpenAI models - [Report Anthropic](https://docs.openpipe.ai/api-reference/post-report-anthropic.md): Record request logs from Anthropic models - [Update Metadata](https://docs.openpipe.ai/api-reference/post-updatemetadata.md): Update tags metadata for logged calls matching the provided filters. - [Base Models](https://docs.openpipe.ai/base-models.md): Train and compare across a range of the most powerful base models. - [Caching](https://docs.openpipe.ai/features/caching.md): Improve performance and reduce costs by caching previously generated responses. - [Anthropic Proxy](https://docs.openpipe.ai/features/chat-completions/anthropic.md) - [Proxying to External Models](https://docs.openpipe.ai/features/chat-completions/external-models.md) - [Gemini Proxy](https://docs.openpipe.ai/features/chat-completions/gemini.md) - [Chat Completions](https://docs.openpipe.ai/features/chat-completions/overview.md) - [Criterion Alignment Sets](https://docs.openpipe.ai/features/criteria/alignment-set.md): Use alignment sets to test and improve your criteria. - [API Endpoints](https://docs.openpipe.ai/features/criteria/api.md): Use the Criteria API for runtime evaluation and offline testing. - [Criteria](https://docs.openpipe.ai/features/criteria/overview.md): Align LLM judgements with human ratings to evaluate and improve your models. - [Criteria Quick Start](https://docs.openpipe.ai/features/criteria/quick-start.md): Create and align your first criterion. - [Exporting Data](https://docs.openpipe.ai/features/datasets/exporting-data.md): Export your past requests as a JSONL file in their raw form. - [Importing Request Logs](https://docs.openpipe.ai/features/datasets/importing-logs.md): Search and filter your past LLM requests to inspect your responses and build a training dataset. - [Datasets](https://docs.openpipe.ai/features/datasets/overview.md): Collect, evaluate, and refine your training data. - [Datasets Quick Start](https://docs.openpipe.ai/features/datasets/quick-start.md): Create your first dataset and import training data. - [Relabeling Data](https://docs.openpipe.ai/features/datasets/relabeling-data.md): Use powerful models to generate new outputs for your data before training. - [Uploading Data](https://docs.openpipe.ai/features/datasets/uploading-data.md): Upload external data to kickstart your fine-tuning process. Use the OpenAI chat fine-tuning format. - [Deployment Types](https://docs.openpipe.ai/features/deployments.md): Learn about serverless, hourly, and dedicated deployments. - [Direct Preference Optimization (DPO)](https://docs.openpipe.ai/features/dpo/overview.md) - [DPO Quick Start](https://docs.openpipe.ai/features/dpo/quick-start.md): Train your first DPO fine-tuned model with OpenPipe. - [Co […gekürzt]