docs: providers update (#18527)

Added missed pages. Added links and descriptions. Foratted to the
consistent form.

docs/docs/integrations/callbacks/fiddler.ipynb

@@ -5,9 +5,9 @@
    "id": "0cebf93b",
    "metadata": {},
    "source": [
-    "## Fiddler Langchain integration Quick Start Guide\n",
+    "# Fiddler\n",
     "\n",
-    "Fiddler is the pioneer in enterprise Generative and Predictive system ops, offering a unified platform that enables Data Science, MLOps, Risk, Compliance, Analytics, and other LOB teams to monitor, explain, analyze, and improve ML deployments at enterprise scale. "
+    ">[Fiddler](https://www.fiddler.ai/) is the pioneer in enterprise Generative and Predictive system ops, offering a unified platform that enables Data Science, MLOps, Risk, Compliance, Analytics, and other LOB teams to monitor, explain, analyze, and improve ML deployments at enterprise scale. "
    ]
   },
   {
@@ -25,7 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# langchain langchain-community langchain-openai fiddler-client"
+    "#!pip install langchain langchain-community langchain-openai fiddler-client"
    ]
   },
   {
@@ -207,7 +207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/providers/argilla.mdx

@@ -1,29 +1,25 @@
 # 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
 
-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
-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
 from langchain.callbacks import ArgillaCallbackHandler
 ```
+
+See an [example](/docs/integrations/callbacks/argilla).

docs/docs/integrations/providers/comet_tracking.ipynb

@@ -4,7 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Comet"
+    "# Comet\n",
+    "\n",
+    ">[Comet](https://www.comet.com/) machine learning platform integrates with your existing infrastructure\n",
+    ">and tools so you can manage, visualize, and optimize models—from training runs to production monitoring"
    ]
   },
   {
@@ -318,6 +321,26 @@
     "print(synopsis_chain.apply(test_prompts, callbacks=callbacks))\n",
     "comet_callback.flush_tracker(synopsis_chain, finish=True)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Callback Tracer\n",
+    "\n",
+    "There is another integration with Comet:\n",
+    "\n",
+    "See an [example](/docs/integrations/callbacks/comet_tracing).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.tracers.comet import CometTracer"
+   ]
   }
  ],
  "metadata": {
@@ -336,9 +359,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/providers/confident.mdx

@@ -1,22 +1,26 @@
 # 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.
 
 ## 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
 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
-from langchain.callbacks import DeepEvalCallback
+from langchain.callbacks.confident_callback import DeepEvalCallbackHandler
 ```

docs/docs/integrations/providers/fiddler.md

@@ -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).