Update Anthropic integration tile (#211)

* Update Anthropic integration tile

* add docs links

integrations/anthropic.md

@@ -8,28 +8,131 @@ authors:
         github: deepset-ai
         twitter: deepset_ai
         linkedin: deepset-ai
-pypi: https://pypi.org/project/farm-haystack
-repo: https://github.com/deepset-ai/haystack
+pypi: https://pypi.org/project/anthropic-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/anthropic
 type: Model Provider
-report_issue: https://github.com/deepset-ai/haystack/issues
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
 logo: /logos/anthropic.png
+version: Haystack 2.0
+toc: true
 ---
 
-You can use [Anhtropic Claude](https://docs.anthropic.com/claude/reference/getting-started-with-the-api) in your Haystack pipelines with the [PromptNode](https://docs.haystack.deepset.ai/v1.25/docs/prompt_node#using-anthropic-generative-models), which can also be used with and [Agent](https://docs.haystack.deepset.ai/v1.25/docs/agent).
+### **Table of Contents**
 
-## Installation
+- [Haystack 2.0](#haystack-20)
+  - [Installation](#installation)
+  - [Usage](#usage)
+- [Haystack 1.x](#haystack-1x)
+  - [Installation (1.x)](#installation-1x)
+  - [Usage (1.x)](#usage-1x)
+
+## Overview
+
+This integration supports Anthropic Claude models provided through Anthropic’s own inferencing infrastructure. For a full list of available models, check out [the Anthropic Claude documentation](https://www.anthropic.com/claude).
+
+## Haystack 2.0
+
+You can use Anthropic models with [`AnthropicGenerator`](https://docs.haystack.deepset.ai/docs/anthropicgenerator) and [`AnthropicChatGenerator`](https://docs.haystack.deepset.ai/docs/anthropicchatgenerator).
+
+Currently, available models are:
+
+- `claude-2.1`
+- `claude-3-haiku-20240307`
+- `claude-3-sonnet-20240229` (default)
+- `claude-3-opus-20240229`
+
+### Installation
+
+```bash
+pip install anthropic-haystack
+```
+
+### Usage
+
+Based on your use case, you can choose between [`AnthropicGenerator`](https://docs.haystack.deepset.ai/docs/anthropicgenerator) or [`AnthropicChatGenerator`](https://docs.haystack.deepset.ai/docs/anthropicchatgenerator) to work with Anthropic models. To learn more about the difference, visit the [Generators vs Chat Generators](https://docs.haystack.deepset.ai/docs/generators-vs-chat-generators) guide.  
+Before using, make sure to set the `ANTHROPIC_API_KEY` environment variable.
+
+#### Using `AnthropicChatGenerator`
+
+Below is an example RAG Pipeline where we answer a predefined question using the contents from the below mentioned URL pointing to Anthropic prompt engineering guide. We fetch the contents of the URL and generate an answer with the `AnthropicChatGenerator`.
+
+```python
+from haystack import Pipeline
+from haystack.components.builders import DynamicChatPromptBuilder
+from haystack.components.converters import HTMLToDocument
+from haystack.components.fetchers import LinkContentFetcher
+from haystack.components.generators.utils import print_streaming_chunk
+from haystack.dataclasses import ChatMessage
+from haystack.utils import Secret
+
+from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator
+
+messages = [
+    ChatMessage.from_system("You are a prompt expert who answers questions based on the given documents."),
+    ChatMessage.from_user("Here are the documents: {{documents}} \\n Answer: {{query}}"),
+]
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component("fetcher", LinkContentFetcher())
+rag_pipeline.add_component("converter", HTMLToDocument())
+rag_pipeline.add_component("prompt_builder", DynamicChatPromptBuilder(runtime_variables=["documents"]))
+rag_pipeline.add_component(
+    "llm",
+    AnthropicChatGenerator(
+        api_key=Secret.from_env_var("ANTHROPIC_API_KEY"),
+        model="claude-3-sonnet-20240229",
+        streaming_callback=print_streaming_chunk,
+    ),
+)
+
+
+rag_pipeline.connect("fetcher", "converter")
+rag_pipeline.connect("converter", "prompt_builder")
+rag_pipeline.connect("prompt_builder", "llm")
+
+question = "What are the best practices in prompt engineering?"
+rag_pipeline.run(
+    data={
+        "fetcher": {"urls": ["https://docs.anthropic.com/claude/docs/prompt-engineering"]},
+        "prompt_builder": {"template_variables": {"query": question}, "prompt_source": messages},
+    }
+)
+```
+
+#### Using `AnthropicGenerator`
+
+Below is an example of using `AnthropicGenerator`:
+
+```python
+from haystack_integrations.components.generators.anthropic import AnthropicGenerator
+
+client = AnthropicGenerator(model="claude-2.1")
+response = client.run("What's Natural Language Processing? Be brief.")
+print(response)
+
+>>{'replies': ['Natural language processing (NLP) is a branch of artificial intelligence focused on enabling
+>>computers to understand, interpret, and manipulate human language. The goal of NLP is to read, decipher,
+>> understand, and make sense of the human languages in a manner that is valuable.'], 'meta': {'model':
+>> 'claude-2.1', 'index': 0, 'finish_reason': 'end_turn', 'usage': {'input_tokens': 18, 'output_tokens': 58}}}
+```
+
+## Haystack 1.x
+
+You can use [Anhtropic Claude](https://docs.anthropic.com/claude/reference/getting-started-with-the-api) in your Haystack 1.x pipelines with the [PromptNode](https://docs.haystack.deepset.ai/v1.25/docs/prompt_node#using-anthropic-generative-models), which can also be used with and [Agent](https://docs.haystack.deepset.ai/v1.25/docs/agent).
+
+### Installation (1.x)
 
 ```bash
 pip install farm-haystack[inference]
 ```
 
-## Usage
+### Usage (1.x)
 
 You can use Anthropic models in various ways:
 
-### Using Claude with PromptNode
+#### Using Claude with PromptNode
 
-To use Claude for prompting and generating answers, initialize a `PromptNode` with the model name, your Anthrpic API key and a prompt template. You can then use this `PromptNode` in a question answering pipeline to generate answers based on the given context.  
+To use Claude for prompting and generating answers, initialize a `PromptNode` with the model name, your Anthropic API key and a prompt template. You can then use this `PromptNode` in a question answering pipeline to generate answers based on the given context.  
 
 Below is the example of a `PromptNode` that uses a custom `PromptTemplate`
 
@@ -63,4 +166,4 @@ from haystack.nodes import PromptNode
 
 prompt_node = PromptNode(model_name_or_path="YOUR_ANTHROPIC_API_KEY", api_key=anthropic_key, stop_words=["Observation:"])
 agent = Agent(prompt_node=prompt_node)
-```
+```