Merge pull request #25 from argilla-io/feat/update-argilla-2.0

[FEAT] Update argilla 2.0

.github/workflows/integration-tests.yml

@@ -9,54 +9,78 @@ jobs:
   integration_tests:
     name: Running integration tests, which require an Argilla instance running
     runs-on: ubuntu-latest
-    services:
-      argilla:
-        image: argilla/argilla-quickstart:latest
-        ports:
-          - 6900:6900
+
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+
     defaults:
       run:
         shell: bash -l {0}
+
     steps:
-      - name: 🪑 Wait for argilla-quickstart
-        run: |
-          while ! curl -XGET http://localhost:6900/api/_status; do sleep 30; done
       - name: 🛎 Checkout Code 
         uses: actions/checkout@v3
+
       - name: 🐍 Setup Conda Env 
         uses: conda-incubator/setup-miniconda@v2
         with:
           miniforge-variant: Mambaforge
           miniforge-version: latest
           use-mamba: true
           activate-environment: argilla
+
       - name: 🐍 Get date for Conda cache
         id: get-date
         run: echo "::set-output name=today::$(/bin/date -u '+%Y%m%d')"
         shell: bash
+
       - name: 🐍 Cache Conda env
         uses: actions/cache@v3
         id: cache
         with:
           path: ${{ env.CONDA }}/envs
           key: conda-${{ runner.os }}--${{ runner.arch }}--${{ steps.get-date.outputs.today }}-${{ hashFiles('environment_dev.yml') }}-${{ env.CACHE_NUMBER }}
+
       - name: 👜 Cache pip 
         uses: actions/cache@v3
         with:
           path: ~/.cache/pip
           key: ${{ runner.os }}-pip-${{ env.CACHE_NUMBER }}-${{ hashFiles('pyproject.toml') }}
+
       - name: 🛜 Netsat
         run: |
           apt update && apt install sudo
           sudo apt install net-tools
           netstat -lt
-      - name: 🗃️ Install pytest
+
+      - name: 📦 Download Docker Compose file
         run: |
-          pip install pytest
-          pip install -e .
-      - name: 📈 Run end2end examples 
+          curl https://raw.githubusercontent.com/argilla-io/argilla/main/examples/deployments/docker/docker-compose.yaml -o docker-compose.yaml
+        working-directory: .
+
+      - name: 📦 Set up Docker Compose
+        run: |
+          docker compose -f docker-compose.yaml up -d
+        working-directory: .
+
+      - name: 🪑 Wait for services to be ready
+        run: |
+          while ! curl -XGET http://localhost:6900/api/_status; do sleep 30; done
+
+      - name: 🗃️ Install dependencies
+        run: |
+          pip install -e ".[dev,tests]"
+
+      - name: 📈 Run tests
         env:
           ARGILLA_ENABLE_TELEMETRY: 0
         run: |
           pytest tests
 
+      - name: 📦 Tear down Docker Compose
+        if: always()
+        run: |
+          docker compose -f docker-compose.yaml down
+        working-directory: .

.gitignore

@@ -129,6 +129,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+.python-version
 
 # Spyder project settings
 .spyderproject

README.md

@@ -4,81 +4,74 @@
 </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`.
+> To discuss, get support, or give feedback [join Discord](http://hf.co/join/discord) in #argilla-distilabel-general and #argilla-distilabel-help. You will be able to engage with our amazing community and 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. 
+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:
+You first need to install 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).
+If you already have deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following [this guide](https://docs.argilla.io/latest/getting_started/quickstart/).
 
-## Usage
+## Basic 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.
+To easily log your data into Argilla within your LlamaIndex workflow, you only need a simple step. Just call the Argilla global handler for Llama Index 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).
+- `dataset_name`: The name of the dataset. If the dataset does not exist, it will be created with the specified name. Otherwise, it will be updated.
+- `api_url`: The URL to connect to the Argilla instance.
+- `api_key`: The API key to authenticate with the Argilla instance.
+- `number_of_retrievals`: The number of retrieved documents to be logged. Defaults to 0.
+- `workspace_name`: The name of the workspace to log the data. By default, the first available workspace.
 
-After you get your API key, the easiest way to import it is through an environment variable, or via *getpass()*.
+> For more information about the credentials, check the documentation for [users](https://docs.argilla.io/latest/how_to_guides/user/) and [workspaces](https://docs.argilla.io/latest/how_to_guides/workspace/).
 
 ```python
-import os
-from getpass import getpass
-
-openai_api_key = os.getenv("OPENAI_API_KEY", None) or getpass("Enter OpenAI API key:")
+from llama_index.core import set_global_handler
+
+set_global_handler(
+    "argilla",
+    dataset_name="query_model",
+    api_url="http://localhost:6900",
+    api_key="argilla.apikey",
+    number_of_retrievals=2,
+)
 ```
 
-Let's now write all the necessary imports
+Let's log some data into Argilla. With the code below, you can create a basic LlamaIndex workflow. We will use GPT3.5 from OpenAI as our LLM ([OpenAI API key](https://openai.com/blog/openai-api)). Moreover, we will use an example `.txt` file obtained from the [Llama Index documentation](https://docs.llamaindex.ai/en/stable/getting_started/starter_example.html).
 
-```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")
-```
+import os 
 
-Let's now create the llm instance, using GPT-3.5 from OpenAI.
+from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
+from llama_index.llms.openai import OpenAI
 
-```python
-llm = OpenAI(model="gpt-3.5-turbo", temperature=0.8, openai_api_key=openai_api_key)
-```
+# LLM settings
+Settings.llm = OpenAI(
+  model="gpt-3.5-turbo", temperature=0.8, openai_api_key=os.getenv("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).
+# Load the data and create the index
+documents = SimpleDirectoryReader("data").load_data()
+index = VectorStoreIndex.from_documents(documents)
 
-```python
-service_context = ServiceContext.from_defaults(llm=llm)
-docs = SimpleDirectoryReader("data").load_data()
-index = VectorStoreIndex.from_documents(docs, service_context=service_context)
+# Create the query engine
 query_engine = index.as_query_engine()
 ```
 
-Now, let's run the `query_engine` to have a response from the model.
+Now, let's run the `query_engine` to have a response from the model. The generated response will be logged into Argilla.
 
 ```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. You can check the data on Argilla's UI:
-
-![Argilla Dataset](docs/assets/argilla-ui-dataset.png)
+![Argilla UI](/docs/assets/UI-screenshot.png)