Repository: run-llama/llama_index
Pull request: Argilla Callback Handler README updated
# Description

Closes #13244, updates the README for the new version of the Argilla Callback Handler.

Fixes # (issue)

## New Package?

Did I fill in the `tool.llamahub` section in the `pyproject.toml` and provide a detailed README.md for my new integration or package?

- [X] Yes
- [ ] No

## Version Bump?

Did I bump the version in the `pyproject.toml` file of the package I am updating? (Except for the `llama-index-core` package)

- [ ] Yes
- [X] No

## Type of Change

Please delete options that are not relevant.

- [ ] Bug fix (non-breaking change which fixes an issue)
- [X] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
- [ ] This change requires a documentation update

## How Has This Been Tested?

Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration

- [ ] Added new unit/integration tests
- [ ] Added new notebook (that tests end-to-end)
- [X] I stared at the code and made sure it makes sense

## Suggested Checklist:

- [x] I have performed a self-review of my own code
- [ ] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [ ] I have added Google Colab support for the newly added notebooks.
- [ ] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] I ran `make format; make lint` to appease the lint gods


Problem statement:
[Feature Request]: Argilla Callback Handler updated, new README
### Feature Description

Hi! Ignacio from Argilla here. We just released version 1.0.0 of the Argilla LlamaIndex Callback Handler, and we wanted to update a bit the README that we had empty on your side. Now, the callback works fine after the refactor on your side :)

### Reason

_No response_

### Value of Feature

_No response_

Hints from issue discussion:

Commit message: readme updayed

Patch to review:
diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-argilla/README.md b/llama-index-integrations/callbacks/llama-index-callbacks-argilla/README.md
index 5574bbe2b1..cc96324c48 100644
--- a/llama-index-integrations/callbacks/llama-index-callbacks-argilla/README.md
+++ b/llama-index-integrations/callbacks/llama-index-callbacks-argilla/README.md
@@ -1 +1,82 @@
-# LlamaIndex Callbacks Integration: Argilla
+<div align="center">
+  <h1>✨🦙 Argilla's LlamaIndex Integration</h1>
+  <p><em> Argilla integration into the LlamaIndex workflow</em></p>
+</div>
+
+> [!TIP]
+> To discuss, get support, or give feedback [join Argilla's Slack Community](https://join.slack.com/t/rubrixworkspace/shared_invite/zt-whigkyjn-a3IUJLD7gDbTZ0rKlvcJ5g) and you will be able to engage with our amazing community and also with the core developers of `argilla` and `distilabel`.
+
+
+This integration allows the user to include the feedback loop that Argilla offers into the LlamaIndex ecosystem. It's based on a callback handler to be run within the LlamaIndex workflow. 
+
+Don't hesitate to check out both [LlamaIndex](https://github.com/run-llama/llama_index) and [Argilla](https://github.com/argilla-io/argilla)
+
+## Getting Started
+
+You first need to install argilla and argilla-llama-index as follows:
+
+```bash
+pip install argilla-llama-index
+```
+
+You will need to an Argilla Server running to monitor the LLM. You can either install the server locally or have it on HuggingFace Spaces. For a complete guide on how to install and initialize the server, you can refer to the [Quickstart Guide](https://docs.argilla.io/en/latest/getting_started/quickstart_installation.html).
+
+## Usage
+
+It requires just a simple step to log your data into Argilla within your LlamaIndex workflow. We just need to call the handler before starting production with your LLM.
+
+We will use GPT3.5 from OpenAI as our LLM. For this, you will need a valid API key from OpenAI. You can have more info and get one via [this link](https://openai.com/blog/openai-api).
+
+After you get your API key, the easiest way to import it is through an environment variable, or via *getpass()*.
+
+```python
+import os
+from getpass import getpass
+
+openai_api_key = os.getenv("OPENAI_API_KEY", None) or getpass("Enter OpenAI API key:")
+```
+
+Let's now write all the necessary imports
+
+```python
+from llama_index.core import VectorStoreIndex, ServiceContext, SimpleDirectoryReader, set_global_handler
+from llama_index.llms.openai import OpenAI
+```
+
+What we need to do is to set Argilla as the global handler as below. Within the handler, we need to provide the dataset name that we will use. If the dataset does not exist, it will be created with the given name. You can also set the API KEY, API URL, and the Workspace name. You can learn more about the variables that controls Argilla initialization [here](https://docs.argilla.io/en/latest/getting_started/installation/configurations/workspace_management.html)
+
+> [!TIP]
+> Remember that the default Argilla workspace name is `admin`. If you want to use a custom Workspace, you'll need to create it and grant access to the desired users. The link above also explains how to do that.
+
+
+```python
+set_global_handler("argilla", dataset_name="query_model")
+```
+
+Let's now create the llm instance, using GPT-3.5 from OpenAI.
+
+```python
+llm = OpenAI(model="gpt-3.5-turbo", temperature=0.8, openai_api_key=openai_api_key)
+```
+
+With the code snippet below, you can create a basic workflow with LlamaIndex. You will also need a txt file as the data source within a folder named "data". For a sample data file and more info regarding the use of Llama Index, you can refer to the [Llama Index documentation](https://docs.llamaindex.ai/en/stable/getting_started/starter_example.html).
+
+```python
+service_context = ServiceContext.from_defaults(llm=llm)
+docs = SimpleDirectoryReader("data").load_data()
+index = VectorStoreIndex.from_documents(docs, service_context=service_context)
+query_engine = index.as_query_engine()
+```
+
+Now, let's run the `query_engine` to have a response from the model.
+
+```python
+response = query_engine.query("What did the author do growing up?")
+response
+```
+
+```bash
+The author worked on two main things outside of school before college: writing and programming. They wrote short stories and tried writing programs on an IBM 1401. They later got a microcomputer, built it themselves, and started programming on it.
+```
+
+The prompt given and the response obtained will be logged in to Argilla server.


Code review comments:
diff_hunk: @@ -1 +1,82 @@
-# LlamaIndex Callbacks Integration: Argilla
+<div align="center">
+  <h1>✨🦙 Argilla's LlamaIndex Integration</h1>
+  <p><em> Argilla integration into the LlamaIndex workflow</em></p>
+</div>
+
+> [!TIP]
+> To discuss, get support, or give feedback [join Argilla's Slack Community](https://join.slack.com/t/rubrixworkspace/shared_invite/zt-whigkyjn-a3IUJLD7gDbTZ0rKlvcJ5g) and you will be able to engage with our amazing community and also with the core developers of `argilla` and `distilabel`.
+
+
+This integration allows the user to include the feedback loop that Argilla offers into the LlamaIndex ecosystem. It's based on a callback handler to be run within the LlamaIndex workflow. 
+
+Don't hesitate to check out both [LlamaIndex](https://github.com/run-llama/llama_index) and [Argilla](https://github.com/argilla-io/argilla)
+
+## Getting Started
+
+You first need to install argilla and argilla-llama-index as follows:
+
+```bash
+pip install argilla-llama-index
path: llama-index-integrations/callbacks/llama-index-callbacks-argilla/README.md
text: @user1:
don't we also need to install `lama-index-callbacks-argilla` ?

@user1:
I updated the deps and version, and also changed this line