Add Azure integration page (#186)

integrations/azure.md

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+---
+layout: integration
+name: Azure
+description: Use OpenAI models deployed through Azure services with Haystack
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: https://www.linkedin.com/company/deepset-ai
+pypi: https://pypi.org/project/haystack-ai/
+repo: https://github.com/deepset-ai/haystack
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack/issues
+logo: /logos/azure.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Haystack 2.0](#haystack-20)
+  - [Installation](#installation)
+  - [Usage](#usage)
+    - [Embedding Models](#embedding-models)
+    - [Generative Models (LLMs)](#generative-models-llms)
+
+## Overview
+
+[Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) provides REST API access to OpenAI's powerful language models including the GPT-4, GPT-4 Turbo with Vision, GPT-3.5-Turbo, and Embeddings model series. To get access to Azure OpenAI endpoints, visit [Azure OpenAI Service REST API reference](https://learn.microsoft.com/en-us/azure/ai-services/openai/reference).
+
+## Installation
+
+Install Haystack 2.0:
+
+```bash
+pip install haystack-ai
+```
+
+## Usage
+
+To work with Azure components, you will need an Azure OpenAI API key, an [Azure Active Directory Token](https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id) as well as an Azure OpenAI Endpoint.
+
+### Components
+
+- [AzureOpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/azureopenaigenerator)
+- [AzureOpenAIChatGenerator](https://docs.haystack.deepset.ai/v2.0/docs/azureopenaichatgenerator)
+- [AzureOpenAITextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/azureopenaitextembedder)
+- [AzureOpenAIDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/azureopenaidocumentembedder)
+
+All components use `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_AD_TOKEN` environment variables by default. Otherwise, you can pass `api_key` and `azure_ad_token` at initialization using `Secret` class. Read more about [Secret Handling](https://docs.haystack.deepset.ai/v2.0/docs/secret-management#structured-secret-handling).
+
+### Embedding Models
+
+You can leverage embedding models from Azure OpenAI through two components: [AzureOpenAITextEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/azureopenaitextembedder) and [AzureOpenAIDocumentEmbedder](https://docs.haystack.deepset.ai/v2.0/docs/azureopenaidocumentembedder).
+
+To create semantic embeddings for documents, use `AzureOpenAIDocumentEmbedder` in your indexing pipeline. For generating embeddings for queries, use `AzureOpenAITextEmbedder`. Once you've selected the suitable component for your specific use case, initialize the component with required parameters.
+
+Below is the example indexing pipeline with `InMemoryDocumentStore`, `AzureOpenAIDocumentEmbedder` and  `DocumentWriter`:
+
+```python
+from haystack import Document, Pipeline
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.embedders import AzureOpenAITextEmbedder, AzureOpenAIDocumentEmbedder
+from haystack.components.writers import DocumentWriter
+
+os.environ["AZURE_OPENAI_API_KEY"] = "Your Azure OpenAI API key"
+os.environ["AZURE_OPENAI_AD_TOKEN"] = "Your Azure Active Directory Token"
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+documents = [Document(content="My name is Wolfgang and I live in Berlin"),
+             Document(content="I saw a black horse running"),
+             Document(content="Germany has many big cities")]
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_component("embedder", AzureOpenAIDocumentEmbedder(azure_endpoint="https://example-resource.azure.openai.com/", azure_deployment="text-embedding-ada-002"))
+indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
+indexing_pipeline.connect("embedder", "writer")
+
+indexing_pipeline.run({"embedder": {"documents": documents}})
+```
+
+### Generative Models (LLMs)
+
+You can leverage Azure OpenAI models through two components: [AzureOpenAIGenerator](https://docs.haystack.deepset.ai/v2.0/docs/azureopenaigenerator) and [AzureOpenAIChatGenerator](https://docs.haystack.deepset.ai/v2.0/docs/azureopenaichatgenerator).
+
+To use OpenAI models deployed through Azure services for text generation, initialize a `AzureOpenAIGenerator` with `azure_deployment` and `azure_endpoint`. You can then use the `AzureOpenAIGenerator` instance in a pipeline after the `PromptBuilder`.  
+
+Below is the example of generative questions answering pipeline using RAG with `PromptBuilder` and  `AzureOpenAIGenerator`:
+
+```python
+from haystack import Pipeline
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack.components.generators import AzureOpenAIGenerator
+
+os.environ["AZURE_OPENAI_API_KEY"] = "Your Azure OpenAI API key"
+os.environ["AZURE_OPENAI_AD_TOKEN"] = "Your Azure Active Directory Token"
+
+template = """
+Given the following information, answer the question.
+
+Context: 
+{% for document in documents %}
+    {{ document.content }}
+{% endfor %}
+
+Question: What's the official language of {{ country }}?
+"""
+pipe = Pipeline()
+
+pipe.add_component("retriever", InMemoryBM25Retriever(document_store=document_store))
+pipe.add_component("prompt_builder", PromptBuilder(template=template))
+pipe.add_component("llm", AzureOpenAIGenerator(azure_endpoint="https://example-resource.azure.openai.com/", azure_deployment="gpt-35-turbo"))
+pipe.connect("retriever", "prompt_builder.documents")
+pipe.connect("prompt_builder", "llm")
+
+pipe.run({
+    "prompt_builder": {
+        "country": "France"
+    }
+})   
+
+
+