haystack-tutorials
/
index.toml
413 строк · 12.2 Кб
1[config]
2layout = "tutorial"
3toc = true
4colab = "https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/"
5
6[[tutorial]]
7title = "Build Your First Question Answering System"
8description = "Get Started by creating a Retriever Reader pipeline."
9level = "beginner"
10weight = 10
11notebook = "01_Basic_QA_Pipeline.ipynb"
12aliases = ["first-qa-system", "without-elasticsearch", "03_basic_qa_pipeline_without_elasticsearch"]
13completion_time = "15 min"
14created_at = 2023-01-11
15
16[[tutorial]]
17title = "Fine-Tuning a Model on Your Own Data"
18description = "Improve the performance of your Reader by performing fine-tuning."
19level = "intermediate"
20weight = 50
21notebook = "02_Finetune_a_model_on_your_data.ipynb"
22aliases = ["fine-tuning-a-model"]
23created_at = 2021-08-12
24completion_time = "15 min"
25needs_gpu = true
26
27[[tutorial]]
28title = "Build a Scalable Question Answering System"
29description = "Create a scalable Retriever Reader pipeline that uses an ElasticsearchDocumentStore."
30level = "beginner"
31weight = 15
32notebook = "03_Scalable_QA_System.ipynb"
33aliases = []
34completion_time = "20 min"
35created_at = 2023-01-11
36
37[[tutorial]]
38title = "Utilizing Existing FAQs for Question Answering"
39description = "Create a smarter way to answer new questions using your existing FAQ documents."
40level = "beginner"
41weight = 20
42notebook = "04_FAQ_style_QA.ipynb"
43aliases = ["existing-faqs"]
44created_at = 2021-08-12
45
46[[tutorial]]
47title = "Evaluation of a QA System"
48description = "Learn how to evaluate the performance of individual nodes as well as entire pipelines."
49level = "advanced"
50weight = 105
51notebook = "05_Evaluation.ipynb"
52aliases = ["evaluation"]
53created_at = 2021-08-12
54
55[[tutorial]]
56title = "Better Retrieval with Embedding Retrieval"
57description = "Use Transformer based dense Retrievers to improve your system’s performance."
58level = "intermediate"
59weight = 55
60notebook = "06_Better_Retrieval_via_Embedding_Retrieval.ipynb"
61aliases = ["embedding-retrieval"]
62created_at = 2022-03-08
63
64[[tutorial]]
65title = "Generative QA with RAGenerator"
66description = "Try out a generative model in place of the extractive Reader."
67level = "intermediate"
68weight = 60
69notebook = "07_RAG_Generator.ipynb"
70aliases = []
71created_at = 2021-08-12
72haystack_version = "1.17.2"
73hidden = true
74sitemap_exclude = true
75
76[[tutorial]]
77title = "Preprocessing Your Documents"
78description = "Start converting, cleaning, and splitting Documents using Haystack’s preprocessing capabilities."
79level = "beginner"
80weight = 25
81notebook = "08_Preprocessing.ipynb"
82aliases = ["preprocessing"]
83created_at = 2021-08-12
84
85[[tutorial]]
86title = "Training Your Own Dense Passage Retrieval Model"
87description = "Learn about training a Dense Passage Retrieval model and the data needed to do so."
88level = "advanced"
89weight = 110
90notebook = "09_DPR_training.ipynb"
91aliases = ["train-dpr"]
92created_at = 2021-08-12
93needs_gpu = true
94
95[[tutorial]]
96title = "Question Answering on a Knowledge Graph"
97description = "Experiment with a question answering system that draws upon knowledge graph.h"
98level = "advanced"
99weight = 120
100notebook = "10_Knowledge_Graph.ipynb"
101aliases = ["knowledge-graph"]
102created_at = 2021-08-12
103haystack_version = "1.16.1"
104hidden = true
105sitemap_exclude = true
106
107[[tutorial]]
108title = "How to Use Pipelines"
109description = "Learn about the many ways which you can route queries through the nodes in a pipeline."
110level = "beginner"
111weight = 40
112notebook = "11_Pipelines.ipynb"
113aliases = ["pipelines"]
114created_at = 2021-08-12
115
116[[tutorial]]
117title = "Generative QA with Seq2SeqGenerator"
118description = "Try out a generative model in place of the extractive Reader."
119level = "intermediate"
120weight = 70
121notebook = "12_LFQA.ipynb"
122aliases = ["lfqa"]
123created_at = 2021-08-12
124haystack_version = "1.17.2"
125hidden = true
126sitemap_exclude = true
127
128[[tutorial]]
129title = "Question Generation"
130description = "Generate a set of questions that can be answered by a given Document."
131level = "intermediate"
132weight = 75
133notebook = "13_Question_generation.ipynb"
134aliases = ["question-generation"]
135created_at = 2021-08-12
136needs_gpu = true
137
138[[tutorial]]
139title = "Query Classifier"
140description = "Classify incoming queries so that they can be routed to the nodes that are best at handling them."
141level = "intermediate"
142weight = 80
143notebook = "14_Query_Classifier.ipynb"
144aliases = ["query-classifier"]
145created_at = 2021-08-12
146
147[[tutorial]]
148title = "Open-Domain QA on Tables"
149description = "Perform question answering on tabular data."
150level = "advanced"
151weight = 130
152notebook = "15_TableQA.ipynb"
153aliases = ["table-qa"]
154created_at = 2021-08-12
155
156[[tutorial]]
157title = "Document Classification at Index Time"
158description = "Generate and attach classification labels to your Documents when indexing."
159level = "intermediate"
160weight = 85
161notebook = "16_Document_Classifier_at_Index_Time.ipynb"
162aliases = ["doc-class-index"]
163created_at = 2021-08-12
164
165[[tutorial]]
166title = "Make Your QA Pipelines Talk!"
167description = "Convert text Answers into speech."
168level = "intermediate"
169weight = 90
170notebook = "17_Audio.ipynb"
171aliases = ["audio"]
172created_at = 2022-06-07
173
174[[tutorial]]
175title = "Generative Pseudo Labeling for Domain Adaptation"
176description = "Use a Retriever and a query generator to perform unsupervised domain adaptation."
177level = "advanced"
178weight = 140
179notebook = "18_GPL.ipynb"
180aliases = ["gpl"]
181created_at = 2022-06-07
182needs_gpu = true
183
184[[tutorial]]
185title = "Text-To-Image Search Pipeline with Multimodal Retriever"
186description = "Use a MultiModalRetriever to build a cross-modal search pipeline."
187level = "intermediate"
188weight = 95
189notebook = "19_Text_to_Image_search_pipeline_with_MultiModal_Retriever.ipynb"
190aliases = ["multimodal"]
191completion_time = "20 min"
192created_at = 2022-07-11
193
194[[tutorial]]
195title = "Using Haystack with REST API"
196description = "Create a production-ready pipeline and interact with Haystack REST API."
197level = "advanced"
198weight = 115
199notebook = "20_Using_Haystack_with_REST_API.ipynb"
200aliases = ["using-haystack-with-rest-api"]
201colab = false
202completion_time = "30 min"
203created_at = 2023-01-11
204
205[[tutorial]]
206title = "Customizing PromptNode for NLP Tasks"
207description = "Use PromptNode and PromptTemplate for your custom NLP tasks"
208level = "intermediate"
209weight = 57
210notebook = "21_Customizing_PromptNode.ipynb"
211aliases = ["customizing-promptnode"]
212completion_time = "20 min"
213created_at = 2023-02-16
214
215[[tutorial]]
216title = "Answering Multihop Questions with Agents"
217description = "Use Agent to answer multihop questions with extractive models"
218level = "intermediate"
219weight = 63
220notebook = "23_Answering_Multihop_Questions_with_Agents.ipynb"
221aliases = ["multihop-qa-with-agents"]
222completion_time = "10 min"
223created_at = 2023-03-27
224
225[[tutorial]]
226title = "Creating a Generative QA Pipeline with Retrieval-Augmentation"
227description = "Use a large language model in your search system through PromptNode"
228level = "intermediate"
229weight = 61
230notebook = "22_Pipeline_with_PromptNode.ipynb"
231aliases = ["pipeline-with-promptnode", "retrieval-augmented-generation"]
232completion_time = "15 min"
233created_at = 2023-03-13
234featured = true
235
236[[tutorial]]
237title = "Building a Conversational Chat App"
238description = "Use ConversationalAgent to build a human-like chat application"
239level = "intermediate"
240weight = 64
241notebook = "24_Building_Chat_App.ipynb"
242aliases = ["building-chat-app"]
243completion_time = "10 min"
244created_at = 2023-05-30
245
246[[tutorial]]
247title = "Customizing Agent to Chat with Your Documents"
248description = "Advanced Customizations for Agents with Memory"
249level = "advanced"
250weight = 117
251notebook = "25_Customizing_Agent.ipynb"
252aliases = ["customizing-agent"]
253completion_time = "15 min"
254created_at = 2023-07-19
255featured = true
256
257[[tutorial]]
258title = "Creating a Hybrid Retrieval Pipeline"
259description = "Learn how to combine Retrievers to enhance retrieval"
260level = "intermediate"
261weight = 63
262notebook = "26_Hybrid_Retrieval.ipynb"
263aliases = ["hybrid-retrieval"]
264completion_time = "15 min"
265created_at = 2023-10-10
266featured = true
267
268[[tutorial]]
269title = "Creating Your First QA Pipeline with Retrieval-Augmentation"
270description = "Build your first generative QA pipeline with OpenAI GPT models"
271level = "beginner"
272weight = 5
273notebook = "27_First_RAG_Pipeline.ipynb"
274aliases = []
275completion_time = "10 min"
276created_at = 2023-12-05
277haystack_2 = true
278featured = true
279
280[[tutorial]]
281title = "Generating Structured Output with Loop-Based Auto-Correction"
282description = "Learn how to extract structured data using an LLM, and to validate the generated output against a predefined schema."
283level = "intermediate"
284weight = 71
285notebook = "28_Structured_Output_With_Loop.ipynb"
286aliases = []
287completion_time = "15 min"
288created_at = 2023-11-30
289haystack_2 = true
290featured = true
291
292[[tutorial]]
293title = "Serializing LLM Pipelines"
294description = "Learn how to serialize and deserialize your pipelines between YAML and Python"
295level = "beginner"
296weight = 9
297notebook = "29_Serializing_Pipelines.ipynb"
298aliases = []
299completion_time = "10 min"
300created_at = 2024-01-29
301haystack_2 = true
302
303[[tutorial]]
304title = "Preprocessing Different File Types"
305description = "Learn how to build an indexing pipeline that will preprocess files based on their file type"
306level = "beginner"
307weight = 7
308notebook = "30_File_Type_Preprocessing_Index_Pipeline.ipynb"
309aliases = []
310completion_time = "15 min"
311created_at = 2024-01-30
312haystack_2 = true
313
314[[tutorial]]
315title = "Filtering Documents with Metadata"
316description = "Learn how to filter down to specific documents at retrieval time using metadata"
317level = "beginner"
318weight = 6
319notebook = "31_Metadata_Filtering.ipynb"
320aliases = []
321completion_time = "5 min"
322created_at = 2024-01-30
323haystack_2 = true
324
325[[tutorial]]
326title = "Classifying Documents & Queries by Language"
327description = "Learn how to classify documents and route queries by language, for both indexing and RAG pipelines"
328level = "intermediate"
329weight = 75
330notebook = "32_Classifying_Documents_and_Queries_by_Language.ipynb"
331aliases = []
332completion_time = "15 min"
333created_at = 2024-02-06
334haystack_2 = true
335
336[[tutorial]]
337title = "Creating a Hybrid Retrieval Pipeline"
338description = "Learn how to combine keyword-based retrieval and dense retrieval to enhance retrieval"
339level = "intermediate"
340weight = 56
341notebook = "33_Hybrid_Retrieval.ipynb"
342aliases = []
343completion_time = "15 min"
344created_at = 2024-02-13
345haystack_2 = true
346
347[[tutorial]]
348title = "Build an Extractive QA Pipeline"
349description = "Learn how to build a Haystack pipeline that uses an extractive model to display where the answer to your query is."
350level = "beginner"
351weight = 15
352notebook = "34_Extractive_QA_Pipeline.ipynb"
353aliases = []
354completion_time = "10 min"
355created_at = 2024-02-09
356haystack_2 = true
357
358[[tutorial]]
359title = "Model-Based Evaluation of RAG Pipelines"
360description = "Learn how to evaluate your RAG pipelines using some of the model-based evaluation frameworkes integerated into Haystack"
361level = "intermediate"
362weight = 77
363notebook = "35_Model_Based_Evaluation_of_RAG_Pipelines.ipynb"
364aliases = []
365completion_time = "15 min"
366created_at = 2024-02-12
367haystack_2 = true
368
369[[tutorial]]
370title = "Building Fallbacks to Websearch with Conditional Routing"
371description = "Learn how to direct the query to a web-based RAG route when necessary"
372level = "intermediate"
373weight = 81
374notebook = "36_Building_Fallbacks_with_Conditional_Routing.ipynb"
375aliases = []
376completion_time = "10 min"
377created_at = 2024-02-16
378haystack_2 = true
379featured = true
380
381[[tutorial]]
382title = "Simplifying Pipeline Inputs with Multiplexer"
383description = "Learn how to declutter the inputs of complex pipelines"
384level = "intermediate"
385weight = 84
386notebook = "37_Simplifying_Pipeline_Inputs_with_Multiplexer.ipynb"
387aliases = []
388completion_time = "10 min"
389created_at = 2024-02-19
390haystack_2 = true
391
392[[tutorial]]
393title = "Embedding Metadata for Improved Retrieval"
394description = "Learn how to embed metadata while indexing, to improve the quality of retrieval results"
395level = "beginner"
396weight = 8
397notebook = "39_Embedding_Metadata_for_Improved_Retrieval.ipynb"
398aliases = []
399completion_time = "10 min"
400created_at = 2024-02-20
401haystack_2 = true
402
403[[tutorial]]
404title = "Building a Chat Application with Function Calling"
405description = "Learn how to build chat applications that have agent-like behavior with OpenAI function calling"
406level = "advanced"
407weight = 100
408notebook = "40_Building_Chat_Application_with_Function_Calling.ipynb"
409aliases = []
410completion_time = "20 min"
411created_at = 2024-03-05
412haystack_2 = true
413featured = true
414