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# Argilla | ||
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![Argilla - Open-source data platform for LLMs](https://argilla.io/og.png) | ||
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>[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs. | ||
> Using Argilla, everyone can build robust language models through faster data curation | ||
> using both human and machine feedback. We provide support for each step in the MLOps cycle, | ||
> from data labelling to model monitoring. | ||
>[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs. | ||
> Using `Argilla`, everyone can build robust language models through faster data curation | ||
> using both human and machine feedback. `Argilla` provides support for each step in the MLOps cycle, | ||
> from data labeling to model monitoring. | ||
## Installation and Setup | ||
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First, you'll need to install the `argilla` Python package as follows: | ||
Get your [API key](https://platform.openai.com/account/api-keys). | ||
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Install the Python package: | ||
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```bash | ||
pip install argilla --upgrade | ||
pip install argilla | ||
``` | ||
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If you already have an Argilla Server running, then you're good to go; but if | ||
you don't, follow the next steps to install it. | ||
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If you don't you can refer to [Argilla - 🚀 Quickstart](https://docs.argilla.io/en/latest/getting_started/quickstart.html#Running-Argilla-Quickstart) to deploy Argilla either on HuggingFace Spaces, locally, or on a server. | ||
## Callbacks | ||
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## Tracking | ||
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See a [usage example of `ArgillaCallbackHandler`](/docs/integrations/callbacks/argilla). | ||
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```python | ||
from langchain.callbacks import ArgillaCallbackHandler | ||
``` | ||
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See an [example](/docs/integrations/callbacks/argilla). |
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# Confident AI | ||
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![Confident - Unit Testing for LLMs](https://github.com/confident-ai/deepeval) | ||
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>[DeepEval](https://confident-ai.com) package for unit testing LLMs. | ||
> Using Confident, everyone can build robust language models through faster iterations | ||
> using both unit testing and integration testing. We provide support for each step in the iteration | ||
>[Confident AI](https://confident-ai.com) is a creator of the `DeepEval`. | ||
> | ||
>[DeepEval](https://github.com/confident-ai/deepeval) is a package for unit testing LLMs. | ||
> Using `DeepEval`, everyone can build robust language models through faster iterations | ||
> using both unit testing and integration testing. `DeepEval provides support for each step in the iteration | ||
> from synthetic data creation to testing. | ||
## Installation and Setup | ||
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First, you'll need to install the `DeepEval` Python package as follows: | ||
You need to get the [DeepEval API credentials](https://app.confident-ai.com). | ||
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You need to install the `DeepEval` Python package: | ||
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```bash | ||
pip install deepeval | ||
``` | ||
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Afterwards, you can get started in as little as a few lines of code. | ||
## Callbacks | ||
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See an [example](/docs/integrations/callbacks/confident). | ||
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```python | ||
from langchain.callbacks import DeepEvalCallback | ||
from langchain.callbacks.confident_callback import DeepEvalCallbackHandler | ||
``` |
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# Fiddler | ||
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>[Fiddler](https://www.fiddler.ai/) provides a unified platform to monitor, explain, analyze, | ||
> and improve ML deployments at an enterprise scale. | ||
## Installation and Setup | ||
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Set up your model [with Fiddler](https://demo.fiddler.ai): | ||
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* The URL you're using to connect to Fiddler | ||
* Your organization ID | ||
* Your authorization token | ||
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Install the Python package: | ||
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```bash | ||
pip install fiddler-client | ||
``` | ||
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## Callbacks | ||
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```python | ||
from langchain_community.callbacks.fiddler_callback import FiddlerCallbackHandler | ||
``` | ||
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See an [example](/docs/integrations/callbacks/fiddler). |