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docs/explorer/api/annotations.md

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## Annotation Format
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You can add annotations to traces at upload time. For this, both during [file upload](./Uploading_Traces/file_uploads.md) and via the [Push API](./Uploading_Traces/push_api.md), you can include an `annotations` field in the trace data. This field should be an array of objects, each representing an annotation. Each annotation object should have the following fields:
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You can add annotations to traces at upload time. For this, both during [file upload](../uploading-traces/file-uploads) and via the [Push API](../uploading-traces/push-api), you can include an `annotations` field in the trace data. This field should be an array of objects, each representing an annotation. Each annotation object should have the following fields:
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##### `content` <span class='type'>string</span> <span class='required'/>
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docs/explorer/api/uploading-traces/file-uploads.md

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#### Raw Event Lists
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Raw event lists are `jsonl` files where each line is a JSON array of events. Each event is a dictionary with at least a `role` and `content` field according to the trace format described [in this chapter](../2_traces.md).
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Raw event lists are `jsonl` files where each line is a JSON array of events. Each event is a dictionary with at least a `role` and `content` field according to the trace format described [in this chapter](../../trace-format).
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```json
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[{ "role": "user", "content": "Hello, world!" }, { "role": "assistant", "content": "Hi!" }]
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#### Annotated Event Lists
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Annotated event lists support the same format as raw event lists, but also include [annotations](../3_annotations.md).
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Annotated event lists support the same format as raw event lists, but also include [annotations](../../annotations).
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For this, each line is a JSON object with the fields `messages`, `annotations` (optional) and `metadata` (optional, for trace-level metadata).
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docs/explorer/api/uploading-traces/push-api.md

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This represents the traces in a dataset. Each `List[Dict]` is a single trace within the dataset. Each `dict` is a single message within a trace - these can represet a user prompt, a tool call, a tool output, etc.
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Must be in the [required trace format](../2_traces.md). Must not be empty.
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Must be in the [required trace format](../../trace-format). Must not be empty.
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##### `annotations` <span class='type'>Optional[List[List[AnnotationCreate]]</span> <span class='optional'/>
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Each metadata dictionary can have arbitrary keys and values for storing additional information about the trace.
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See [File Uploads](../Uploading_Traces/file_uploads.md) for more information on metadata.
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See [File Uploads](../file-uploads) for more information on metadata.
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### `AnnotationCreate`
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The `AnnotationCreate` class holds the data for an annotation to be created.
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See [Annotations](../3_annotations.md) for more information on annotations.
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See [Annotations](../../annotations) for more information on annotations.
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##### `content` <span class='type'>str</span> <span class='required'/>
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docs/explorer/benchmarks.md

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### Step 3: Associate Your Agent Dataset with a Benchmark and Score
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[Instructions on updating the datasets's metadata](./Explorer_API/Dataset_Metadata/update_dataset_metadata_api.md) to include the `benchmark`, `name` and `accuracy` fields, that will associate your agent dataset with a specific benchmark and score.
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[Instructions on updating the datasets's metadata](./api/dataset-metadata/update.md) to include the `benchmark`, `name` and `accuracy` fields, that will associate your agent dataset with a specific benchmark and score.
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For instance, to associate your `gpt-4o` agent dataset with the `webarena` benchmark and an accuracy score of `0.5`, you can update the dataset's metadata as follows:
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docs/explorer/index.md

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## 3. Install the Invariant SDK
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Next, install the Invariant SDK in your Python environment, by running the following command. See [Installation](Explorer_API/installation.md) for alternative methods using different package managers.
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Next, install the Invariant SDK in your Python environment, by running the following command. See [Installation](api/sdk-installation) for alternative methods using different package managers.
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```bash
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pip install invariant-sdk
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This will upload your traces to the Invariant Explorer under a new dataset named `my-first-dataset`. If the corresponding dataset already exists, the traces will be appended to it.
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To learn more about the expected trace format see the chapter on the [trace format requirements](Explorer_API/2_traces.md).
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To learn more about the expected trace format see the chapter on the [trace format requirements](api/trace-format.md).
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## Work With Your Traces in Explorer
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docs/explorer/self-hosted.md

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## Using the Self-Hosted Explorer
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The self-hosted version of Explorer is configured to run on `http://localhost`. You can access it in your browser at this address. The local instance provides the same API as the managed cloud instance, so you can use the [Invariant SDK](./Explorer_API/1_client_setup.md) to connect to it.
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The self-hosted version of Explorer is configured to run on `http://localhost`. You can access it in your browser at this address. The local instance provides the same API as the managed cloud instance, so you can use the [Invariant SDK](./api/client-setup) to connect to it.
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### Storage
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The self-hosted version of Explorer will create a `data/` directory in the current working directory to store traces and other data.
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### Usage and Access
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You can access the self-hosted version of Explorer at `http://localhost`. To use it with the [Invariant SDK](./Explorer_API/1_client_setup.md) you can set the `INVARIANT_API_ENDPOINT` environment variable to `http://localhost/`. For security reasons, you'll still need to create and provide an API key to access the self-hosted version of Explorer.
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You can access the self-hosted version of Explorer at `http://localhost`. To use it with the [Invariant SDK](./api/client-setup) you can set the `INVARIANT_API_ENDPOINT` environment variable to `http://localhost/`. For security reasons, you'll still need to create and provide an API key to access the self-hosted version of Explorer.
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### Updates
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`invariant explorer` will automatically check for updates and pull the latest stable version of the Explorer Docker images. If you want to try the latest development version, you can use the `--version=main` flag. Note however, that database migrations may be required when switching between versions, which may not always be backwards compatible.

docs/testing/index.md

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# },
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# ]
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```
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The test result provides information about which assertion failed but also [localizes the assertion failure precisely](./Writing_Tests/tests.md) in the provided list of agent messages.
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The test result provides information about which assertion failed but also [localizes the assertion failure precisely](writing/tests) in the provided list of agent messages.
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**Visual Test Viewer (Explorer):**
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As an alternative to the command line, you can also [visualize test results](./Running_Tests/Visual_Debugger.md) on the [Invariant Explorer](https://explorer.invariantlabs.ai/):
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As an alternative to the command line, you can also [visualize test results](running/visual-debugging) on the [Invariant Explorer](https://explorer.invariantlabs.ai/):
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## Features
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* Comprehensive [`Trace` API](Writing_Tests/1_Traces.ipynb) for easily navigating and checking agent traces.
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* [Assertions library](Writing_Tests/2_Assertions.md) to check agent behavior, including fuzzy checkers such as _Levenshtein distance_, _semantic similarity_ and _LLM-as-a-judge_ pipelines.
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* Full [`pytest` compatibility](Running_Tests/PyTest_Compatibility.md) for easy integration with existing test and CI/CD pipelines.
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* Parameterized tests for [testing multiple scenarios](Writing_Tests/parameterized-tests) with a single test function.
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* [Visual test viewer](Writing_Tests/4_Visual_Test_Viewer.md) for exploring large traces and debugging test failures.
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* Comprehensive [`Trace` API](writing/traces) for easily navigating and checking agent traces.
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* [Assertions library](writing/matchers) to check agent behavior, including fuzzy checkers such as _Levenshtein distance_, _semantic similarity_ and _LLM-as-a-judge_ pipelines.
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* Full [`pytest` compatibility](running/pytest-compatibility) for easy integration with existing test and CI/CD pipelines.
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* Parameterized tests for [testing multiple scenarios](writing/parameterized-tests) with a single test function.
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* [Visual test viewer](running/visual-debugging) for exploring large traces and debugging test failures.
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## Next Steps
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docs/testing/running/pytest-compatibility.md

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Flag to indicate whether to [push data to Explorer](./Visual_Debugger.md). If set to true,
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Flag to indicate whether to [push data to Explorer](./visual-debugging.md). If set to true,
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the `INVARIANT_API_KEY` environment variable must be set. Visit the [Explorer Documentation](https://explorer.invariantlabs.ai/docs/explorer) to learn how to obtain your own API key.
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### Example Commands

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