[Bedrock] Added Amazon Bedrock examples (#635)

* add ChatGenerator example

* add Generator, Embedders example

* move system prompt from inference params to messages

integrations/amazon_bedrock/examples/chatgenerator_example.py

@@ -0,0 +1,36 @@
+# To run this example, you will need to
+# 1) set `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` and `AWS_DEFAULT_REGION` environment variables
+# 2) enable access to the selected model in Amazon Bedrock
+# Note: if you change the model, update the model-specific inference parameters.
+
+
+from haystack.dataclasses import ChatMessage
+
+from haystack_integrations.components.generators.amazon_bedrock import AmazonBedrockChatGenerator
+
+generator = AmazonBedrockChatGenerator(
+    model="anthropic.claude-3-haiku-20240307-v1:0",
+    # model-specific inference parameters
+    generation_kwargs={
+        "max_tokens": 500,
+        "temperature": 0.0,
+    },
+)
+
+system_prompt = """
+You are a helpful assistant that helps users learn more about AWS services.
+Your audience is engineers with a decent technical background.
+Be very concise and specific in your answers, keeping them short.
+You may use technical terms, jargon, and abbreviations that are common among practitioners.
+"""
+
+# Even though Anthropic Claud models support only messages with `user` and `assistant` roles,
+# internal handling converts message with `system` role into `system` inference parameter for Claude
+# which allows for more portablability of code across generators
+messages = [
+    ChatMessage.from_system(system_prompt),
+    ChatMessage.from_user("Which service should I use to train custom Machine Learning models?"),
+]
+
+results = generator.run(messages)
+results["replies"]

integrations/amazon_bedrock/examples/embedders_generator_with_rag_example.py

@@ -0,0 +1,74 @@
+# To run this example, you will need to
+# 1) set `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` and `AWS_DEFAULT_REGION` environment variables
+# 2) enable access to the selected model in Amazon Bedrock
+# Note: if you change the model, update the model-specific inference parameters.
+
+from haystack import Document, Pipeline
+from haystack.components.builders import PromptBuilder
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+
+from haystack_integrations.components.embedders.amazon_bedrock import (
+    AmazonBedrockDocumentEmbedder,
+    AmazonBedrockTextEmbedder,
+)
+from haystack_integrations.components.generators.amazon_bedrock import AmazonBedrockGenerator
+
+generator_model_name = "amazon.titan-text-lite-v1"
+embedder_model_name = "amazon.titan-embed-text-v1"
+
+prompt_template = """
+Context:
+{% for document in documents %}
+    {{ document.content }}
+{% endfor %}
+
+Given the context above, answer the question.
+Write a full detailed answer.
+Provide explanation of why the answer is relevant to the question.
+If you cannot answer the question, output "I do not know".
+
+Question: {{ question }}?
+"""
+
+docs = [
+    Document(content="User ABC is using Amazon SageMaker to train ML models."),
+    Document(content="User XYZ is using Amazon EC2 instances to train ML models."),
+]
+
+
+doc_embedder = AmazonBedrockDocumentEmbedder(model=embedder_model_name)
+docs_with_embeddings = doc_embedder.run(docs)["documents"]
+
+doc_store = InMemoryDocumentStore()
+doc_store.write_documents(docs_with_embeddings)
+
+
+pipe = Pipeline()
+pipe.add_component("text_embedder", AmazonBedrockTextEmbedder(embedder_model_name))
+pipe.add_component("retriever", InMemoryEmbeddingRetriever(doc_store, top_k=1))
+pipe.add_component("prompt_builder", PromptBuilder(prompt_template))
+pipe.add_component(
+    "generator",
+    AmazonBedrockGenerator(
+        model=generator_model_name,
+        # model-specific inference parameters
+        generation_kwargs={
+            "maxTokenCount": 1024,
+            "temperature": 0.0,
+        },
+    ),
+)
+pipe.connect("text_embedder", "retriever")
+pipe.connect("retriever", "prompt_builder")
+pipe.connect("prompt_builder", "generator")
+
+
+question = "Which user is using IaaS services for Machine Learning?"
+results = pipe.run(
+    {
+        "text_embedder": {"text": question},
+        "prompt_builder": {"question": question},
+    }
+)
+results["generator"]["replies"]