mirror of https://github.com/hwchase17/langchain
docs: `providers` update (#18527)
Added missed pages. Added links and descriptions. Foratted to the consistent form.pull/18548/head^2
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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.
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> Using Argilla, everyone can build robust language models through faster data curation
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> using both human and machine feedback. We provide support for each step in the MLOps cycle,
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> from data labelling to model monitoring.
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>[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs.
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> Using `Argilla`, everyone can build robust language models through faster data curation
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> using both human and machine feedback. `Argilla` provides support for each step in the MLOps cycle,
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> from data labeling to model monitoring.
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## Installation and Setup
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First, you'll need to install the `argilla` Python package as follows:
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Get your [API key](https://platform.openai.com/account/api-keys).
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Install the Python package:
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```bash
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pip install argilla --upgrade
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pip install argilla
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```
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If you already have an Argilla Server running, then you're good to go; but if
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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.
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## 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
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from langchain.callbacks import ArgillaCallbackHandler
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```
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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.
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> Using Confident, everyone can build robust language models through faster iterations
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> using both unit testing and integration testing. We provide support for each step in the iteration
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>[Confident AI](https://confident-ai.com) is a creator of the `DeepEval`.
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>
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>[DeepEval](https://github.com/confident-ai/deepeval) is a package for unit testing LLMs.
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> Using `DeepEval`, everyone can build robust language models through faster iterations
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> using both unit testing and integration testing. `DeepEval provides support for each step in the iteration
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> from synthetic data creation to testing.
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## Installation and Setup
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First, you'll need to install the `DeepEval` Python package as follows:
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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
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pip install deepeval
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```
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Afterwards, you can get started in as little as a few lines of code.
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## Callbacks
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See an [example](/docs/integrations/callbacks/confident).
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```python
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from langchain.callbacks import DeepEvalCallback
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from langchain.callbacks.confident_callback import DeepEvalCallbackHandler
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```
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@ -0,0 +1,27 @@
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# Fiddler
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>[Fiddler](https://www.fiddler.ai/) provides a unified platform to monitor, explain, analyze,
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> and improve ML deployments at an enterprise scale.
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## 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
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* Your organization ID
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* Your authorization token
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Install the Python package:
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```bash
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pip install fiddler-client
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```
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## Callbacks
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```python
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from langchain_community.callbacks.fiddler_callback import FiddlerCallbackHandler
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```
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See an [example](/docs/integrations/callbacks/fiddler).
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