Remove explicit mention of Haystack "2.x" in tutorials (#384)

julian-risch · web-flow · commit d0e6e93d5400 · 2025-03-11T11:01:30.000+01:00
* remove 2.x note at the top

* remove more 2.0 and 2.x mentions

* remove 1.x tutorials from readme overview page

* remove outdated multiplexer tutorial from overview in readme
diff --git a/README.md b/README.md
diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb
@@ -12,8 +12,7 @@
     "- **Time to complete**: 10 minutes\n",
     "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder), [`OpenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n",
     "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys).\n",
-    "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIChatGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+    "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIChatGenerator to build a generative question-answering pipeline with retrieval-augmentation."
    ]
   },
   {
@@ -24,7 +23,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) for creating a template prompt, and [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator) for generating responses.\n",
+    "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) for creating a template prompt, and [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator) for generating responses.\n",
     "\n",
     "For this tutorial, you'll use the Uncyclopedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"
    ]
diff --git a/tutorials/28_Structured_Output_With_Loop.ipynb b/tutorials/28_Structured_Output_With_Loop.ipynb
@@ -14,10 +14,8 @@
     "- **Components Used**: `PromptBuilder`, `OpenAIChatGenerator`, `OutputValidator` (Custom component)\n",
     "- **Goal**: After completing this tutorial, you will have built a system that extracts unstructured data, puts it in a JSON schema, and automatically corrects errors in the JSON output from a large language model (LLM) to make sure it follows the specified structure.\n",
     "\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
-    "\n",
     "## Overview\n",
-    "This tutorial demonstrates how to use Haystack 2.0's advanced [looping pipelines](https://docs.haystack.deepset.ai/docs/pipelines#loops) with LLMs for more dynamic and flexible data processing. You'll learn how to extract structured data from unstructured data using an LLM, and to validate the generated output against a predefined schema.\n",
+    "This tutorial demonstrates how to use Haystack's advanced [looping pipelines](https://docs.haystack.deepset.ai/docs/pipelines#loops) with LLMs for more dynamic and flexible data processing. You'll learn how to extract structured data from unstructured data using an LLM, and to validate the generated output against a predefined schema.\n",
     "\n",
     "This tutorial uses `gpt-4o-mini` to change unstructured passages into JSON outputs that follow the [Pydantic](https://github.com/pydantic/pydantic) schema. It uses a custom OutputValidator component to validate the JSON and loop back to make corrections, if necessary."
    ]
diff --git a/tutorials/29_Serializing_Pipelines.ipynb b/tutorials/29_Serializing_Pipelines.ipynb
@@ -12,9 +12,7 @@
     "- **Time to complete**: 10 minutes\n",
     "- **Components Used**: [`HuggingFaceLocalChatGenerator`](https://docs.haystack.deepset.ai/docs/huggingfacelocalchatgenerator), [`ChatPromptBuilder`](https://docs.haystack.deepset.ai/docs/chatpromptbuilder)\n",
     "- **Prerequisites**: None\n",
-    "- **Goal**: After completing this tutorial, you'll understand how to serialize and deserialize between YAML and Python code.\n",
-    "\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+    "- **Goal**: After completing this tutorial, you'll understand how to serialize and deserialize between YAML and Python code."
    ]
   },
   {
diff --git a/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb b/tutorials/30_File_Type_Preprocessing_Index_Pipeline.ipynb
@@ -12,8 +12,6 @@
     "- **Time to complete**: 15 minutes\n",
     "- **Goal**: After completing this tutorial, you'll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the `FileTypeRouter`.\n",
     "\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
-    "\n",
     "> 💡 (Optional): After creating the indexing pipeline in this tutorial, there is an optional section that shows you how to create a RAG pipeline on top of the document store you just created. You must have a [Hugging Face API Key](https://huggingface.co/settings/tokens) for this section\n",
     "\n",
     "## Components Used\n",
diff --git a/tutorials/31_Metadata_Filtering.ipynb b/tutorials/31_Metadata_Filtering.ipynb
@@ -12,9 +12,7 @@
     "- **Time to complete**: 5 minutes\n",
     "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever)\n",
     "- **Prerequisites**: None\n",
-    "- **Goal**: Filter documents in a document store based on given metadata\n",
-    "\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+    "- **Goal**: Filter documents in a document store based on given metadata"
    ]
   },
   {
diff --git a/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb b/tutorials/32_Classifying_Documents_and_Queries_by_Language.ipynb
@@ -12,9 +12,7 @@
     "- **Time to complete**: 15 minutes\n",
     "- **Components Used**: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`DocumentLanguageClassifier`](https://docs.haystack.deepset.ai/docs/documentlanguageclassifier), [`MetadataRouter`](https://docs.haystack.deepset.ai/docs/metadatarouter), [`DocumentWriter`](https://docs.haystack.deepset.ai/docs/documentwriter), [`TextLanguageRouter`](https://docs.haystack.deepset.ai/docs/textlanguagerouter), [`DocumentJoiner`](https://docs.haystack.deepset.ai/docs/documentjoiner), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever), [`ChatPromptBuilder`](https://docs.haystack.deepset.ai/docs/chatpromptbuilder), [`OpenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n",
     "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline to classify documents based on the (human) language they were written in.\n",
-    "- Optionally, at the end you'll also incorporate language clasification and query routing into a RAG pipeline, so you can query documents based on the language a question was written in.\n",
-    "\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro).\n"
+    "- Optionally, at the end you'll also incorporate language clasification and query routing into a RAG pipeline, so you can query documents based on the language a question was written in."
    ]
   },
   {
@@ -679,7 +677,7 @@
     "If you've been following along, now you know how to incorporate language detection into query and indexing Haystack piplines. Go forth and build the international application of your dreams. 🗺️\n",
     "\n",
     "\n",
-    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+    "If you liked this tutorial, there's more to learn about Haystack:\n",
     "- [Serializing Haystack Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
     "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
     "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -12,9 +12,7 @@
     "- **Time to complete**: 15 minutes\n",
     "- **Components Used**: [`DocumentSplitter`](https://docs.haystack.deepset.ai/docs/documentsplitter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`DocumentJoiner`](https://docs.haystack.deepset.ai/docs/documentjoiner), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), and [`TransformersSimilarityRanker`](https://docs.haystack.deepset.ai/docs/transformerssimilarityranker)\n",
     "- **Prerequisites**: None\n",
-    "- **Goal**: After completing this tutorial, you will have learned about creating a hybrid retrieval and when it's useful.\n",
-    "\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+    "- **Goal**: After completing this tutorial, you will have learned about creating a hybrid retrieval and when it's useful."
    ]
   },
   {
diff --git a/tutorials/34_Extractive_QA_Pipeline.ipynb b/tutorials/34_Extractive_QA_Pipeline.ipynb
@@ -11,10 +11,7 @@
     "- **Level**: Beginner\n",
     "- **Time to complete**: 15 minutes\n",
     "- **Components Used**: [`ExtractiveReader`](https://docs.haystack.deepset.ai/docs/extractivereader), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [`DocumentWriter`](https://docs.haystack.deepset.ai/docs/documentwriter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder)\n",
-    "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is.\n",
-    "\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro).\n",
-    "\n"
+    "- **Goal**: After completing this tutorial, you'll have learned how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is."
    ]
   },
   {
@@ -108,7 +105,7 @@
     "\n",
     "The data has already been cleaned and preprocessed, so turning it into Haystack `Documents` is fairly straightfoward.\n",
     "\n",
-    "Using an `InMemoryDocumentStore` here keeps things simple. However, this general approach would work with [any document store that Haystack 2.0 supports](https://docs.haystack.deepset.ai/docs/document-store).\n",
+    "Using an `InMemoryDocumentStore` here keeps things simple. However, this general approach would work with [any document store that Haystack supports](https://docs.haystack.deepset.ai/docs/document-store).\n",
     "\n",
     "The `SentenceTransformersDocumentEmbedder` transforms each `Document` into a vector. Here we've used [`sentence-transformers/multi-qa-mpnet-base-dot-v1`](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1). You can substitute any embedding model you like, as long as you use the same one in your extractive pipeline.\n",
     "\n",
@@ -656,10 +653,10 @@
    "source": [
     "## Wrapping it up\n",
     "\n",
-    "If you've been following along, now you know how to build an extractive question answering pipeline with Haystack 2.0. 🎉 Thanks for reading!\n",
+    "If you've been following along, now you know how to build an extractive question answering pipeline with Haystack. 🎉 Thanks for reading!\n",
     "\n",
     "\n",
-    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+    "If you liked this tutorial, there's more to learn about Haystack:\n",
     "- [Classifying Documents & Queries by Language](https://haystack.deepset.ai/tutorials/32_classifying_documents_and_queries_by_language)\n",
     "-  [Generating Structured Output with Loop-Based Auto-Correction](https://haystack.deepset.ai/tutorials/28_structured_output_with_loop)\n",
     "- [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline)\n",
diff --git a/tutorials/35_Evaluating_RAG_Pipelines.ipynb b/tutorials/35_Evaluating_RAG_Pipelines.ipynb
@@ -12,9 +12,7 @@
     "- **Time to complete**: 15 minutes\n",
     "- **Components Used**: `InMemoryDocumentStore`, `InMemoryEmbeddingRetriever`, `ChatPromptBuilder`, `OpenAIChatGenerator`, `DocumentMRREvaluator`, `FaithfulnessEvaluator`, `SASEvaluator`\n",
     "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-4o-mini model by OpenAI: https://platform.openai.com/api-keys\n",
-    "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines both with model-based, and statistical metrics available in the Haystack evaluation offering. You'll also see which other evaluation frameworks are integrated with Haystack.\n",
-    "\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro)."
+    "- **Goal**: After completing this tutorial, you'll have learned how to evaluate your RAG pipelines both with model-based, and statistical metrics available in the Haystack evaluation offering. You'll also see which other evaluation frameworks are integrated with Haystack."
    ]
   },
   {
diff --git a/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb b/tutorials/36_Building_Fallbacks_with_Conditional_Routing.ipynb
@@ -12,9 +12,7 @@
     "- **Time to complete**: 10 minutes\n",
     "- **Components Used**: [`ConditionalRouter`](https://docs.haystack.deepset.ai/docs/conditionalrouter), [`SerperDevWebSearch`](https://docs.haystack.deepset.ai/docs/serperdevwebsearch), [`ChatPromptBuilder`](https://docs.haystack.deepset.ai/docs/chatpromptbuilder), [`OpenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n",
     "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and a [Serper API Key](https://serper.dev/api-key) for this tutorial\n",
-    "- **Goal**: After completing this tutorial, you'll have learned how to create a pipeline with conditional routing that can fallback to websearch if the answer is not present in your dataset.\n",
-    "\n",
-    "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro).\n"
+    "- **Goal**: After completing this tutorial, you'll have learned how to create a pipeline with conditional routing that can fallback to websearch if the answer is not present in your dataset."
    ]
   },
   {
@@ -540,9 +538,9 @@
    "source": [
     "## What's next\n",
     "\n",
-    "🎉 Congratulations! You've built a pipeline with conditional routing! You can now customize the condition for your specific use case and create a custom Haystack 2.0 pipeline to meet your needs.\n",
+    "🎉 Congratulations! You've built a pipeline with conditional routing! You can now customize the condition for your specific use case and create a custom Haystack pipeline to meet your needs.\n",
     "\n",
-    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
+    "If you liked this tutorial, there's more to learn about Haystack:\n",
     "- [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline)\n",
     "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
     "\n",
diff --git a/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb b/tutorials/37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb
diff --git a/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb b/tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb

Original file line number	Diff line number	Diff line change
`@@ -12,8 +12,7 @@`
`12`	`12`	`"- Time to complete: 10 minutes\n",`
`13`	`13`	"- Components Used: [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`SentenceTransformersTextEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [`PromptBuilder`](https://docs.haystack.deepset.ai/docs/promptbuilder), [`OpenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n",
`14`	`14`	`"- Prerequisites: You must have an [OpenAI API Key](https://platform.openai.com/api-keys).\n",`
`15`		`- "- Goal: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIChatGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n",`
`16`		- "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro)."
	`15`	`+ "- Goal: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and OpenAIChatGenerator to build a generative question-answering pipeline with retrieval-augmentation."`
`17`	`16`	`]`
`18`	`17`	`},`
`19`	`18`	`{`
`@@ -24,7 +23,7 @@`
`24`	`23`	`"source": [`
`25`	`24`	`"## Overview\n",`
`26`	`25`	`"\n",`
`27`		- "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) for creating a template prompt, and [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator) for generating responses.\n",
	`26`	+ "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack. The process involves four main components: [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder) for creating an embedding for the user query, [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever) for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/docs/promptbuilder) for creating a template prompt, and [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator) for generating responses.\n",
`28`	`27`	`"\n",`
`29`	`28`	`"For this tutorial, you'll use the Uncyclopedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n"`
`30`	`29`	`]`
Original file line number	Diff line number	Diff line change
`@@ -12,9 +12,7 @@`
`12`	`12`	`"- Time to complete: 10 minutes\n",`
`13`	`13`	"- Components Used: [`HuggingFaceLocalChatGenerator`](https://docs.haystack.deepset.ai/docs/huggingfacelocalchatgenerator), [`ChatPromptBuilder`](https://docs.haystack.deepset.ai/docs/chatpromptbuilder)\n",
`14`	`14`	`"- Prerequisites: None\n",`
`15`		`- "- Goal: After completing this tutorial, you'll understand how to serialize and deserialize between YAML and Python code.\n",`
`16`		`- "\n",`
`17`		- "> This tutorial uses the latest version of Haystack 2.x (`haystack-ai`). For more information on Haystack 2.0, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro)."
	`15`	`+ "- Goal: After completing this tutorial, you'll understand how to serialize and deserialize between YAML and Python code."`
`18`	`16`	`]`
`19`	`17`	`},`
`20`	`18`	`{`