index.toml

[config]
layout = "tutorial"
toc = true
colab = "https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/"

[[tutorial]]
title = "Creating Your First QA Pipeline with Retrieval-Augmentation"
description = "Build your first generative QA pipeline with OpenAI GPT models"
level = "beginner"
weight = 5
notebook = "27_First_RAG_Pipeline.ipynb"
aliases = []
completion_time = "10 min"
created_at = 2023-12-05
dependencies = ["datasets>=2.6.1", "sentence-transformers-haystack", "mistral-haystack", "transformers-haystack"]
featured = true

[[tutorial]]
title = "Generating Structured Output with OpenAI"
description = "Learn how to generate structured output with OpenAI models using Pydantic models or JSON schema"
level = "beginner"
weight = 71
notebook = "28_Structured_Output_With_OpenAI.ipynb"
aliases = ["28_Structured_Output_With_Loop"]
completion_time = "10 min"
created_at = 2023-11-30
dependencies = []

[[tutorial]]
title = "Serializing LLM Pipelines"
description = "Learn how to serialize and deserialize your pipelines between YAML and Python"
level = "beginner"
weight = 9
notebook = "29_Serializing_Pipelines.ipynb"
aliases = []
completion_time = "10 min"
created_at = 2024-01-29
dependencies = ["transformers-haystack"]

[[tutorial]]
title = "Preprocessing Different File Types"
description = "Learn how to build an indexing pipeline that will preprocess files based on their file type"
level = "beginner"
weight = 7
notebook = "30_File_Type_Preprocessing_Index_Pipeline.ipynb"
aliases = []
completion_time = "15 min"
created_at = 2024-01-30
dependencies = [
    "sentence-transformers-haystack",
    "huggingface-api-haystack",
    "markdown-it-py",
    "mdit_plain",
    "pypdf",
    "gdown"
]

[[tutorial]]
title = "Filtering Documents with Metadata"
description = "Learn how to filter down to specific documents at retrieval time using metadata"
level = "beginner"
weight = 6
notebook = "31_Metadata_Filtering.ipynb"
aliases = []
completion_time = "5 min"
created_at = 2024-01-30
dependencies = []

[[tutorial]]
title = "Classifying Documents & Queries by Language"
description = "Learn how to classify documents and route queries by language, for both indexing and RAG pipelines"
level = "intermediate"
weight = 75
notebook = "32_Classifying_Documents_and_Queries_by_Language.ipynb"
aliases = []
completion_time = "15 min"
created_at = 2024-02-06
dependencies = ["langdetect-haystack"]

[[tutorial]]
title = "Creating a Hybrid Retrieval Pipeline"
description = "Learn how to combine keyword-based retrieval and dense retrieval to enhance retrieval"
level = "intermediate"
weight = 56
notebook = "33_Hybrid_Retrieval.ipynb"
aliases = []
completion_time = "15 min"
created_at = 2024-02-13
dependencies = ["datasets>=2.6.1", "sentence-transformers-haystack"]
needs_gpu = true

[[tutorial]]
title = "Build an Extractive QA Pipeline"
description = "Learn how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is."
level = "beginner"
weight = 15
notebook = "34_Extractive_QA_Pipeline.ipynb"
aliases = []
completion_time = "10 min"
created_at = 2024-02-09
dependencies = ["sentence-transformers-haystack", "datasets", "transformers-haystack"]

[[tutorial]]
title = "Evaluating RAG Pipelines"
description = "Learn how to evaluate your RAG pipelines using statistical and model-based evaluation metrics"
level = "intermediate"
weight = 77
notebook = "35_Evaluating_RAG_Pipelines.ipynb"
aliases = ["35_model_based_evaluation_of_rag_pipelines"]
completion_time = "15 min"
created_at = 2024-02-12
dependencies = ["datasets>=2.6.1", "sentence-transformers-haystack"]

[[tutorial]]
title = "Building an Agentic RAG with Fallback to Websearch"
description = "Learn how to direct the query to a web-based RAG route when necessary"
level = "intermediate"
weight = 81
notebook = "36_Building_Fallbacks_with_Conditional_Routing.ipynb"
aliases = []
completion_time = "10 min"
created_at = 2024-02-16
dependencies = ["serperdev-haystack"]
featured = true

[[tutorial]]
title = "Embedding Metadata for Improved Retrieval"
description = "Learn how to embed metadata while indexing, to improve the quality of retrieval results"
level = "beginner"
weight = 8
notebook = "39_Embedding_Metadata_for_Improved_Retrieval.ipynb"
aliases = []
completion_time = "10 min"
created_at = 2024-02-20
dependencies = ["wikipedia", "sentence-transformers-haystack"]

[[tutorial]]
title = "Building a Chat Agent with Function Calling"
description = "Learn how to build chat applications that have agent-like behavior with OpenAI function calling"
level = "advanced"
weight = 100
notebook = "40_Building_Chat_Application_with_Function_Calling.ipynb"
aliases = []
completion_time = "20 min"
created_at = 2024-03-05
dependencies = ["sentence-transformers-haystack", "gradio", "pytz"]

[[tutorial]]
title = "Query Classification with TransformersTextRouter and TransformersZeroShotTextRouter"
description = "Learn how to route user questions and other text inputs with classification models"
level = "intermediate"
weight = 105
notebook = "41_Query_Classification_with_TransformersTextRouter_and_TransformersZeroShotTextRouter.ipynb"
aliases = []
completion_time = "25 min"
created_at = 2024-10-15
dependencies = ["sentence-transformers-haystack", "gradio", "datasets", "transformers-haystack"]

[[tutorial]]
title = "Retrieving a Context Window Around a Sentence"
description = "Learn how to use the SentenceWindowRetriever to retrieve a context window"
level = "beginner"
weight = 105
notebook = "42_Sentence_Window_Retriever.ipynb"
aliases = []
completion_time = "10 min"
created_at = 2024-10-16
# https://github.com/nltk/nltk/issues/3730
dependencies = ["nltk!=3.10.1"]

[[tutorial]]
title = "Evaluation"
description = "A guided walkthrough to learn everything about evaluation"
weight = 110
notebook = "guide_evaluation.ipynb"
aliases = []
guide = true
colab = false
download = false
created_at = 2024-07-17

[[tutorial]]
title = "Build a Tool-Calling Agent"
description = "Learn how to create an Agent that can use a web search tool to answer questions"
level = "beginner"
weight = 7
notebook = "43_Building_a_Tool_Calling_Agent.ipynb"
aliases = []
completion_time = "10 min"
created_at = 2025-04-03
dependencies = ["docstring-parser", "trafilatura", "serperdev-haystack"]
featured = true

[[tutorial]]
title = "Creating Custom SuperComponents"
description = "Learn how to use the @super_component decorator to create custom SuperComponents with input and output mappings"
level = "intermediate"
weight = 8
notebook = "44_Creating_Custom_SuperComponents.ipynb"
aliases = []
completion_time = "20 min"
created_at = 2025-04-22
dependencies = ["sentence-transformers-haystack", "datasets"]

[[tutorial]]
title = "Creating a Multi-Agent System with Haystack"
description = "Use agents specialized in specific tasks to build more complex, modular agent workflows"
level = "advanced"
weight = 11
notebook = "45_Creating_a_Multi_Agent_System.ipynb"
aliases = []
completion_time = "20 min"
created_at = 2025-06-02
dependencies = ["duckduckgo-api-haystack", "docstring-parser"]
featured = true

[[tutorial]]
title = "Creating Vision+Text RAG Pipelines"
description = "Build a multimodal RAG pipeline that can answer questions grounded in both images and text."
level = "advanced"
weight = 11
notebook = "46_Multimodal_RAG.ipynb"
aliases = []
completion_time = "20 min"
created_at = 2025-08-04
dependencies = ["pillow", "pypdf", "pypdfium2", "sentence-transformers-haystack"]
featured = true

[[tutorial]]
title = "Human-in-the-Loop with Haystack Agents"
description = "Learn how to use confirmation strategies to get user input before tool execution for safer, more controllable AI systems."
level = "advanced"
weight = 10
notebook = "47_Human_in_the_Loop_Agent.ipynb"
aliases = []
completion_time = "20 min"
created_at = 2025-10-30
dependencies = ["haystack-ai>=3.0.0", "rich"]
featured = true

[[tutorial]]
title = "Conversational RAG Agent using InMemoryChatMessageStore"
description = "Learn how to use conversational history for RAG to enable multi-turn conversations grounded in documents"
level = "advanced"
weight = 9
notebook = "48_Conversational_RAG.ipynb"
aliases = []
completion_time = "20 min"
created_at = 2025-12-30
dependencies = ["haystack-experimental==0.19.0.post1", "datasets"]
featured = true

[[tutorial]]
title = "Compress the KV Cache with TurboQuant and Haystack"
description = "Use TurboQuant KV cache compression to run large LLMs on consumer GPUs with significant memory reduction"
level = "advanced"
weight = 12
notebook = "49_TurboQuant_Quantization_with_HuggingFace.ipynb"
aliases = []
completion_time = "20 min"
created_at = 2026-03-30
dependencies = ["haystack-ai", "turboquant-vllm", "transformers-haystack"]
featured = false
python_version = "3.12"

[[tutorial]]
title = "Using Pre-Built Agents from Agent Pack"
description = "Run and customize the ready-made Advanced RAG and Deep Research agents from the experimental Agent Pack"
level = "advanced"
weight = 13
notebook = "50_Using_Pre_Built_Agents_from_Agent_Pack.ipynb"
aliases = []
completion_time = "25 min"
created_at = 2026-07-20
dependencies = ["haystack-ai>=3.0", "agent-pack-haystack", "arrow", "tavily-haystack", "trafilatura", "pypdf"]
featured = false