Replaces occurences of dynamic prompt builders (#250)

Co-authored-by: Amna Mubashar <amna.mubashar@Amnas-MBP.fritz.box>

Author: Amnah199

integrations/anthropic.md

@@ -58,7 +58,7 @@ Below is an example RAG Pipeline where we answer a predefined question using the
 
 ```python
 from haystack import Pipeline
-from haystack.components.builders import DynamicChatPromptBuilder
+from haystack.components.builders import ChatPromptBuilder
 from haystack.components.converters import HTMLToDocument
 from haystack.components.fetchers import LinkContentFetcher
 from haystack.components.generators.utils import print_streaming_chunk
@@ -75,7 +75,7 @@ messages = [
 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("prompt_builder", ChatPromptBuilder())
 rag_pipeline.add_component(
     "llm",
     AnthropicChatGenerator(
@@ -88,13 +88,13 @@ rag_pipeline.add_component(
 
 rag_pipeline.connect("fetcher", "converter")
 rag_pipeline.connect("converter", "prompt_builder")
-rag_pipeline.connect("prompt_builder", "llm")
+rag_pipeline.connect("prompt_builder.prompt", "llm.messages")
 
 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},
+        "prompt_builder": {"template_variables": {"query": question}, "template": messages},
     }
 )
 ```

integrations/cohere.md

@@ -117,13 +117,13 @@ Similar to the above example, you can also use [`CohereChatGenerator`](https://d
 
 ```python
 from haystack import Pipeline
-from haystack.components.builders import DynamicChatPromptBuilder
+from haystack.components.builders import ChatPromptBuilder
 from haystack.dataclasses import ChatMessage
 from haystack_integrations.components.generators.cohere.chat import CohereChatGenerator
 
 
 pipe = Pipeline()
-pipe.add_component("prompt_builder", DynamicChatPromptBuilder())
+pipe.add_component("prompt_builder", ChatPromptBuilder())
 pipe.add_component("llm", CohereChatGenerator())
 pipe.connect("prompt_builder", "llm")
 

integrations/context-ai.md

@@ -59,7 +59,7 @@ import uuid
 import os
 
 from haystack.components.generators.chat import OpenAIChatGenerator
-from haystack.components.builders import DynamicChatPromptBuilder
+from haystack.components.builders import ChatPromptBuilder
 from haystack import Pipeline
 from haystack.dataclasses import ChatMessage
 
@@ -70,7 +70,7 @@ model = "gpt-3.5-turbo"
 os.environ["GETCONTEXT_TOKEN"] = "GETCONTEXT_TOKEN"
 os.environ["OPENAI_API_KEY"] = "OPENAI_API_KEY"
 
-prompt_builder = DynamicChatPromptBuilder()
+prompt_builder = ChatPromptBuilder()
 llm = OpenAIChatGenerator(model=model)
 prompt_analytics = ContextAIAnalytics()
 assistant_analytics = ContextAIAnalytics()

integrations/langfuse.md

@@ -132,14 +132,14 @@ Once you've run these code samples, you can also [use the Langfuse dashboard to
 
 ```python
 from haystack import Pipeline
-from haystack.components.builders import DynamicChatPromptBuilder
+from haystack.components.builders import ChatPromptBuilder
 from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.dataclasses import ChatMessage
 from haystack_integrations.components.connectors.langfuse import LangfuseConnector
 
 pipe = Pipeline()
 pipe.add_component("tracer", LangfuseConnector("Chat example"))
-pipe.add_component("prompt_builder", DynamicChatPromptBuilder())
+pipe.add_component("prompt_builder", ChatPromptBuilder())
 pipe.add_component("llm", OpenAIChatGenerator(model="gpt-3.5-turbo"))
 
 pipe.connect("prompt_builder.prompt", "llm.messages")
@@ -149,7 +149,7 @@ messages = [
 ]
 
 response = pipe.run(
-    data={"prompt_builder": {"template_variables": {"location": "Berlin"}, "prompt_source": messages}}
+    data={"prompt_builder": {"template_variables": {"location": "Berlin"}, "template": messages}}
 )
 print(response["llm"]["replies"][0])
 print(response["tracer"]["trace_url"])

integrations/mistral.md

@@ -125,6 +125,7 @@ from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack_integrations.components.embedders.mistral.document_embedder import MistralDocumentEmbedder
 from haystack_integrations.components.embedders.mistral.text_embedder import MistralTextEmbedder
 from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.components.builders import ChatPromptBuilder
 
 os.environ["MISTRAL_API_KEY"] = "YOUR_MISTRAL_API_KEY"
 
@@ -140,7 +141,7 @@ document_store.write_documents(documents)
 
 text_embedder = MistralTextEmbedder()
 retriever = InMemoryEmbeddingRetriever(document_store=document_store)
-prompt_builder = DynamicChatPromptBuilder(runtime_variables=["documents"])
+prompt_builder = ChatPromptBuilder()
 llm = MistralChatGenerator(streaming_callback=print_streaming_chunk)
 
 messages = [ChatMessage.from_user("Here are some the documents: {{documents}} \\n Answer: {{query}}")]
@@ -161,7 +162,7 @@ question = "Who lives in Berlin?"
 result = rag_pipeline.run(
     {
         "text_embedder": {"text": question},
-        "prompt_builder": {"template_variables": {"query": question}, "prompt_source": messages},
+        "prompt_builder": {"template_variables": {"query": question}, "template": messages},
         "llm": {"generation_kwargs": {"max_tokens": 165}},
     }
 )