Update "Quick Start" page (#212)

* Update deepset description in the footer

* Add runnable code snippet to the quick-start page

* Update code explanations

content/overview/quick-start.md

@@ -9,53 +9,101 @@ toc: true
 aliases: [get-started]
 ---
 
-## The Haystack Source Code
+<!-- ## Haystack Source Code
 
-Haystack is an open source framework that helps developers build NLP empowered search systems.
-On our Github, you can find the source code for Haystack.
-This is also the main channel for raising issues and contributing to the project.
+Haystack is an open source Python framework that helps developers build LLM empowered custom application.
 
-{{< button url="https://github.com/deepset-ai/haystack" text="View Source Code" color="green">}}
+You can find the source code for Haystack on GitHub. This is also the main channel for raising issues, asking questions and contributing to the project.
 
-## Quick Installation
+{{< button url="https://github.com/deepset-ai/haystack" text="View Source Code" color="green">}} -->
 
-{{< tabs totalTabs="2">}}
-{{< tab tabName="Basic Install"  >}}
+## Installation
 
-The most straightforward way to install the latest release of Haystack is through pip.
+These are the instructions for installing Haystack. The most straightforward way to install the latest release of Haystack is through [pip](https://github.com/pypa/pip).
 
-This command will install everything needed for Pipelines that use an Elasticsearch Document Store. See <a href="https://docs.haystack.deepset.ai/docs/installation">Installation</a> for more details.
+{{< tabs totalTabs="3">}}
+
+{{< tab tabName="Minimal"  >}}
+
+This command installs everything needed for basic Pipelines that use an InMemoryDocumentStore and external LLM provider (e.g. OpenAI).
 
 ```python
 pip install farm-haystack
 ```
 
 {{< /tab >}}
 
-{{< tab tabName="Full Install" >}}
+{{< tab tabName="Basic"  >}}
+
+This command installs everything you need for basic Pipelines that use an InMemoryDocumentStore, as well as all necessary dependencies for model inference on local machine, including torch.
 
-If you plan to be using more advanced features like Milvus, FAISS, Weaviate, OCR or Ray, you will need to install a full version of Haystack.
+```python
+pip install farm-haystack[inference]
+```
 
-The following command will install the latest version on the main branch.
+{{< /tab >}}
+
+{{< tab tabName="Full" >}}
+
+This command installs further dependencies for more advanced features, like certain DocumentStores, FileConverters, OCR, or Ray.
 
 ```python
-git clone https://github.com/deepset-ai/haystack.git
-cd haystack
-pip install -e .[all]
+pip install --upgrade pip
+pip install 'farm-haystack[all]' ## or 'all-gpu' for the GPU-enabled dependencies
 ```
 
 {{< /tab >}}
 {{< /tabs >}}
 
-For a more comprehensive guide to installation, see our documentation.
+For a more comprehensive installation guide, inlcuding methods for various operating systems, refer to our documentation.
+
+{{< button url="https://docs.haystack.deepset.ai/docs/installation" text="Docs: Installation" color="green">}}
+
+## Build Your First RAG Pipeline
+
+Haystack is built around the concept of pipelines. A pipeline is a powerful structure made up of components that can be used to perform a task.
+For example, you can connect together a Retriever and a PromptNode to build a Generative Question Answering pipeline.
 
-{{< button url="https://docs.haystack.deepset.ai/docs/installation" text="Installation" color="green">}}
+Try out how Haystack answers questions about Game of Thrones using **Retrieval Augmented Generation (RAG)** approach 👇
 
-## Build Your First Pipeline
+Install Haystack in the minimal form:
+```bash
+pip install farm-haystack
+```
+Ask a question on your data after indexing your data to the DocumentStore and building a RAG pipeline: 
+```python
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.utils import build_pipeline, add_example_data, print_answers
+
+# We are model agnostic :) Here, you can choose from: "anthropic", "cohere", "huggingface", and "openai".
+provider = "openai"
+API_KEY = "sk-..." # ADD YOUR KEY HERE
+
+# We support many different databases. Here we load a simple and lightweight in-memory database.
+document_store = InMemoryDocumentStore(use_bm25=True)
+
+# Download and add Game of Thrones TXT articles to Haystack DocumentStore.
+# You can also provide a folder with your local documents.
+add_example_data(document_store, "data/GoT_getting_started")
 
-Haystack is built around the concept of Pipelines. A Pipeline is a sequence of connected components that can be used to perform a task.
-For example, you can chain together a Reader and a Retriever to build a Question Answering Pipeline.
+# Build a pipeline with a Retriever to get relevant documents to the query and a PromptNode interacting with LLMs using a custom prompt.
+pipeline = build_pipeline(provider, API_KEY, document_store)
+
+# Ask a question on the data you just added.
+result = pipeline.run(query="Who is the father of Arya Stark?")
+
+# For details, like which documents were used to generate the answer, look into the <result> object
+print_answers(result, details="medium")
+```
+The output of the pipeline will look like this, referencing the documents used to generate the answer:
+
+```text
+'Query: Who is the father of Arya Stark?'
+'Answers:'
+[{'answer': 'The father of Arya Stark is Lord Eddard Stark of '
+                'Winterfell. [Document 1, Document 4, Document 5]'}]
+```
 
-For a hands-on guide to building your first Pipeline, see our tutorials
+For a hands-on guide to build your first RAG Pipeline, see our tutorial.
 
-{{< button url="/tutorials/" text="Tutorials" color="green">}}
+{{< button url="https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode" text="Tutorial: Creating a RAG Pipeline" color="green">}}

themes/haystack/layouts/partials/footer.html

@@ -166,8 +166,7 @@ <h3>Sign up for community updates</h3>
 
         {{/* Tagline */}}
         <p class="footer-tagline">
-          Building a semantic layer for the modern tech stack — driven by the
-          latest NLP and open source.
+          Building products, technology and solutions for LLM-enabled applications.
         </p>
 
         {{/* Social icons */}}