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
parent
7d6de96186
commit
114d64d4a7
@ -1,29 +1,25 @@
|
|||||||
# Argilla
|
# Argilla
|
||||||
|
|
||||||
![Argilla - Open-source data platform for LLMs](https://argilla.io/og.png)
|
>[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
|
||||||
>[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs.
|
> using both human and machine feedback. `Argilla` provides support for each step in the MLOps cycle,
|
||||||
> Using Argilla, everyone can build robust language models through faster data curation
|
> from data labeling to model monitoring.
|
||||||
> using both human and machine feedback. We provide support for each step in the MLOps cycle,
|
|
||||||
> from data labelling to model monitoring.
|
|
||||||
|
|
||||||
## Installation and Setup
|
## Installation and Setup
|
||||||
|
|
||||||
First, you'll need to install the `argilla` Python package as follows:
|
Get your [API key](https://platform.openai.com/account/api-keys).
|
||||||
|
|
||||||
|
Install the Python package:
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
pip install argilla --upgrade
|
pip install argilla
|
||||||
```
|
```
|
||||||
|
|
||||||
If you already have an Argilla Server running, then you're good to go; but if
|
## Callbacks
|
||||||
you don't, follow the next steps to install it.
|
|
||||||
|
|
||||||
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.
|
|
||||||
|
|
||||||
## Tracking
|
|
||||||
|
|
||||||
See a [usage example of `ArgillaCallbackHandler`](/docs/integrations/callbacks/argilla).
|
|
||||||
|
|
||||||
```python
|
```python
|
||||||
from langchain.callbacks import ArgillaCallbackHandler
|
from langchain.callbacks import ArgillaCallbackHandler
|
||||||
```
|
```
|
||||||
|
|
||||||
|
See an [example](/docs/integrations/callbacks/argilla).
|
||||||
|
@ -1,22 +1,26 @@
|
|||||||
# Confident AI
|
# Confident AI
|
||||||
|
|
||||||
![Confident - Unit Testing for LLMs](https://github.com/confident-ai/deepeval)
|
>[Confident AI](https://confident-ai.com) is a creator of the `DeepEval`.
|
||||||
|
>
|
||||||
>[DeepEval](https://confident-ai.com) package for unit testing LLMs.
|
>[DeepEval](https://github.com/confident-ai/deepeval) is a package for unit testing LLMs.
|
||||||
> Using Confident, everyone can build robust language models through faster iterations
|
> Using `DeepEval`, 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
|
> using both unit testing and integration testing. `DeepEval provides support for each step in the iteration
|
||||||
> from synthetic data creation to testing.
|
> from synthetic data creation to testing.
|
||||||
|
|
||||||
## Installation and Setup
|
## Installation and Setup
|
||||||
|
|
||||||
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).
|
||||||
|
|
||||||
|
You need to install the `DeepEval` Python package:
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
pip install deepeval
|
pip install deepeval
|
||||||
```
|
```
|
||||||
|
|
||||||
Afterwards, you can get started in as little as a few lines of code.
|
## Callbacks
|
||||||
|
|
||||||
|
See an [example](/docs/integrations/callbacks/confident).
|
||||||
|
|
||||||
```python
|
```python
|
||||||
from langchain.callbacks import DeepEvalCallback
|
from langchain.callbacks.confident_callback import DeepEvalCallbackHandler
|
||||||
```
|
```
|
||||||
|
@ -0,0 +1,27 @@
|
|||||||
|
# Fiddler
|
||||||
|
|
||||||
|
>[Fiddler](https://www.fiddler.ai/) provides a unified platform to monitor, explain, analyze,
|
||||||
|
> and improve ML deployments at an enterprise scale.
|
||||||
|
|
||||||
|
## Installation and Setup
|
||||||
|
|
||||||
|
Set up your model [with Fiddler](https://demo.fiddler.ai):
|
||||||
|
|
||||||
|
* The URL you're using to connect to Fiddler
|
||||||
|
* Your organization ID
|
||||||
|
* Your authorization token
|
||||||
|
|
||||||
|
Install the Python package:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
pip install fiddler-client
|
||||||
|
```
|
||||||
|
|
||||||
|
## Callbacks
|
||||||
|
|
||||||
|
|
||||||
|
```python
|
||||||
|
from langchain_community.callbacks.fiddler_callback import FiddlerCallbackHandler
|
||||||
|
```
|
||||||
|
|
||||||
|
See an [example](/docs/integrations/callbacks/fiddler).
|
Loading…
Reference in New Issue