Merge pull request #3 from allegro/open-source-documentation

README.md updated and examples/introduction.ipynb added
allegro · Feb 19, 2024 · 056082a · 056082a
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diff --git a/README.md b/README.md
@@ -3,30 +3,65 @@
 ___
 ## About
 
-`llm-wrapper` is a versatile and powerful library designed to streamline the process of querying large language models, offering a user-friendly experience.
-The` llm-wrapper` package is designed to simplify interactions with the underlying models by providing the following features:
+llm-wrapper is a versatile and powerful library designed to streamline the process of querying Large Language Models
+(LLMs) 🤖💬
 
-* **Simple and User-Friendly Interface**: The module offers an intuitive and easy-to-use interface, making it straightforward to work with the model.
+Developed by the Allegro engineers, llm-wrapper is based on popular libraries like transformers, pydantic, and langchain. It takes care 
+of the boring boiler-plate code you write around your LLM applications, quickly enabling you to prototype ideas, and eventually helping you to scale up 
+for production use-cases!
 
-* **Asynchronous Querying**: Requests to the model are processed asynchronously by default, ensuring efficient and non-blocking interactions.
+Among the llm-wrapper most notable features, you will find:
 
-* **Automatic Retrying Mechanism**: The module includes an automatic retrying mechanism, which helps handle transient errors and ensures that queries to the model are robust.
+* **😊 Simple and User-Friendly Interface**: The module offers an intuitive and easy-to-use interface, making it straightforward to work with the model.
 
-* **Error Handling and Management**: Errors that may occur during interactions with the model are handled and managed gracefully, providing informative error messages and potential recovery options.
+* **🔀 Asynchronous Querying**: Requests to the model are processed asynchronously by default, ensuring efficient and non-blocking interactions.
 
-* **Output Parsing**: The module simplifies the process of defining the model's output format as well as parsing and working with it, allowing you to easily extract the information you need.
+* **🔄 Automatic Retrying Mechanism** : The module includes an automatic retrying mechanism, which helps handle transient errors and ensures that queries to the model are robust.
+
+* **🛠️ Error Handling and Management**: Errors that may occur during interactions with the model are handled and managed gracefully, providing informative error messages and potential recovery options.
+
+* **⚙️ Output Parsing**: The module simplifies the process of defining the model's output format as well as parsing and working with it, allowing you to easily extract the information you need.
 
 ___
 
 ## Documentation
 
-Are you interested in using `llm-wrapper` in your project? Consult the [Official Documentation](# TODO: open-source)!
+Full documentation available at **[llm-wrapper.allegro.tech](https://llm-wrapper.allegro.tech/)**
+
+Get familiar with llm-wrapper 🚀: [introductory jupyter notebook](https://github.com/allegro/llm-wrapper/blob/main/examples/introduction.ipynb)
 
 ___
 
+## Quickstart
+
+Install the package via pip:
+
+```
+pip install llm-wrapper
+```
+
+Configure endpoint credentials and start querying the model!
+
+```python
+from llm_wrapper.models import AzureOpenAIModel
+from llm_wrapper.domain.configuration import AzureOpenAIConfiguration
+
+configuration = AzureOpenAIConfiguration(
+    api_key="<OPENAI_API_KEY>",
+    base_url="<OPENAI_API_BASE>",
+    api_version="<OPENAI_API_VERSION>",
+    deployment="<OPENAI_API_DEPLOYMENT_NAME>",
+    model_name="<OPENAI_API_MODEL_NAME>"
+)
+
+gpt_model = AzureOpenAIModel(config=configuration)
+gpt_response = gpt_model.generate("Plan me a 3-day holiday trip to Italy")
+```
+___
+
 ## Local Development
 
-### Installation
+### Installation from the source
 
 We assume that you have python `3.10.*` installed on your machine. 
 You can set it up using [pyenv](https://github.com/pyenv/pyenv#installationbrew) 
@@ -59,6 +94,13 @@ In order to execute tests, run:
 make tests
 ```
 
+### Updating the documentation
+
+Run `mkdocs serve` to serve a local instance of the documentation.
+
+Modify the content of `docs` directory to update the documentation. The updated content will be deployed
+via the github action `.github/workflows/docs.yml`
+
 ### Make a new release
 
 When a new version of `llm-wrapper` is ready to be released, do the following operations:

diff --git a/docs/tutorial/index.md b/docs/tutorial/index.md
diff --git a/docs/tutorial/batch_query.md → docs/usage/batch_query.md b/docs/tutorial/batch_query.md → docs/usage/batch_query.md
diff --git a/docs/tutorial/deploy_llama2_on_azure.md → docs/usage/deploy_llama2_on_azure.md b/docs/tutorial/deploy_llama2_on_azure.md → docs/usage/deploy_llama2_on_azure.md
diff --git a/docs/tutorial/error_handling.md → docs/usage/error_handling.md b/docs/tutorial/error_handling.md → docs/usage/error_handling.md
diff --git a/docs/tutorial/forcing_response_format.md → docs/usage/forcing_response_format.md b/docs/tutorial/forcing_response_format.md → docs/usage/forcing_response_format.md
diff --git a/docs/tutorial/single_query.md → docs/usage/single_query.md b/docs/tutorial/single_query.md → docs/usage/single_query.md