[{"data":1,"prerenderedAt":5343},["ShallowReactive",2],{"\u002Fdevelopers\u002Ftemplates\u002Fyaml-snippets\u002Ffull-pipelines-examples-app":3,"navigation-developers":1820,"\u002Fdevelopers\u002Ftemplates\u002Fyaml-snippets\u002Ffull-pipelines-examples":3773,"surr-\u002Fdevelopers\u002Ftemplates\u002Fyaml-snippets\u002Ffull-pipelines-examples":5340},{"id":4,"title":5,"author":6,"body":7,"description":1811,"extension":1812,"meta":1813,"navigation":165,"path":1815,"seo":1816,"sitemap":1817,"stem":1818,"__hash__":1819},"content\u002F2.developers\u002F7.templates\u002F39.yaml-snippets\u002F30.full-pipelines-examples.md","Full Templates Examples",null,{"type":8,"value":9,"toc":1809},"minimark",[10,15,25,37,1805],[11,12,14],"h1",{"id":13},"full-yaml-templates-examples","Full YAML Templates Examples",[16,17,18,19,24],"p",{},"This page gathers entire YAML configuration files for Pathway Live Data Framework RAG templates.\nFor each template, you can find an example for the different data sources: file system, SharePoint, Google Drive, and S3.\nYou can find more example on how to configure the data sources on the ",[20,21,23],"a",{"href":22},"\u002Fdevelopers\u002Ftemplates\u002Fyaml-snippets\u002Fdata-sources-examples","dedicated page",".",[16,26,27,28,32,33,24],{},"For further configurations, you can learn ",[20,29,31],{"href":30},"\u002Fdevelopers\u002Ftemplates\u002Fconfigure-yaml","how to configure YAML templates"," and see ",[20,34,36],{"href":35},"\u002Fdevelopers\u002Ftemplates\u002Fyaml-snippets\u002Frag-configuration-examples","our different YAML examples for each component",[38,39,40,49,54],"openable-list",{},[41,42,44],"template",{"v-slot:title":43},"",[45,46,48],"h2",{"id":47},"adaptive-rag","Adaptive RAG",[41,50,51],{"v-slot:description":43},[16,52,53],{},"Configuration of Pathway Live Data Framework Adaptive RAG pipeline.",[41,55,56,69,72],{"v-slot:content":43},[16,57,58,59,62,63,24],{},"The Pathway Live Data Framework provides an advanced RAG technique called ",[20,60,48],{"href":61},"\u002Fdevelopers\u002Ftemplates\u002Frag\u002Fadaptive-rag"," that lowers the costs of the queries.\nYou can find the template on ",[20,64,68],{"href":65,"rel":66},"https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fllm-app\u002Fblob\u002Fmain\u002Fexamples\u002Fpipelines\u002Fadaptive-rag",[67],"nofollow","GitHub",[16,70,71],{},"Here are the",[73,74,75,570,1003,1405],"code-group-tabs",{},[76,77,82],"pre",{"className":78,"code":79,"filename":80,"language":81,"meta":43,"style":43},"language-yaml shiki shiki-themes material-theme-palenight","$sources:\n  # File System connector, reading data locally.\n  - !pw.io.fs.read\n    path: data                   # Path to the data directory\n    format: binary               # Format of the data to be read\n    with_metadata: true          # Include metadata in the data\n\n# Configures the LLM model settings for generating responses.\n$llm: !pw.xpacks.llm.llms.OpenAIChat\n  model: \"gpt-4o-mini\" \n  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy\n    max_retries: 6 \n  cache_strategy: !pw.udfs.DefaultCache\n  temperature: 0\n  capacity: 8\n\n# Specifies the embedder model for converting text into embeddings.\n$embedder: !pw.xpacks.llm.embedders.OpenAIEmbedder\n  model: \"text-embedding-ada-002\"\n  cache_strategy: !pw.udfs.DefaultCache\n\n# Defines the splitter settings for dividing text into smaller chunks.\n$splitter: !pw.xpacks.llm.splitters.TokenCountSplitter\n  max_tokens: 400\n\n# Configures the parser for processing and extracting information from documents.\n$parser: !pw.xpacks.llm.parsers.DoclingParser\n  cache_strategy: !pw.udfs.DefaultCache\n\n# Sets up the retriever factory for indexing and retrieving documents.\n$retriever_factory: !pw.stdlib.indexing.BruteForceKnnFactory\n  reserved_space: 1000\n  embedder: $embedder\n  metric: !pw.stdlib.indexing.BruteForceKnnMetricKind.COS\n\n# Manages the storage and retrieval of documents for the RAG template.\n$document_store: !pw.xpacks.llm.document_store.DocumentStore\n  docs: $sources\n  parser: $parser\n  splitter: $splitter\n  retriever_factory: $retriever_factory\n\n# Configures the question-answering component using the RAG approach.\nquestion_answerer: !pw.xpacks.llm.question_answering.AdaptiveRAGQuestionAnswerer\n  llm: $llm\n  indexer: $document_store\n  n_starting_documents: 2\n  factor: 2\n  max_iterations: 4\n","File System","yaml",[83,84,85,98,105,115,131,145,160,167,173,184,205,216,230,241,252,263,268,274,285,300,309,314,320,331,342,347,353,364,373,378,384,395,406,417,428,433,439,450,461,472,483,494,499,505,516,527,538,549,559],"code",{"__ignoreMap":43},[86,87,90,94],"span",{"class":88,"line":89},"line",1,[86,91,93],{"class":92},"s-wAU","$sources",[86,95,97],{"class":96},"sAklC",":\n",[86,99,101],{"class":88,"line":100},2,[86,102,104],{"class":103},"saEQR","  # File System connector, reading data locally.\n",[86,106,108,111],{"class":88,"line":107},3,[86,109,110],{"class":96},"  -",[86,112,114],{"class":113},"sJ14y"," !pw.io.fs.read\n",[86,116,118,121,124,128],{"class":88,"line":117},4,[86,119,120],{"class":92},"    path",[86,122,123],{"class":96},":",[86,125,127],{"class":126},"sfyAc"," data",[86,129,130],{"class":103},"                   # Path to the data directory\n",[86,132,134,137,139,142],{"class":88,"line":133},5,[86,135,136],{"class":92},"    format",[86,138,123],{"class":96},[86,140,141],{"class":126}," binary",[86,143,144],{"class":103},"               # Format of the data to be read\n",[86,146,148,151,153,157],{"class":88,"line":147},6,[86,149,150],{"class":92},"    with_metadata",[86,152,123],{"class":96},[86,154,156],{"class":155},"sbqyR"," true",[86,158,159],{"class":103},"          # Include metadata in the data\n",[86,161,163],{"class":88,"line":162},7,[86,164,166],{"emptyLinePlaceholder":165},true,"\n",[86,168,170],{"class":88,"line":169},8,[86,171,172],{"class":103},"# Configures the LLM model settings for generating responses.\n",[86,174,176,179,181],{"class":88,"line":175},9,[86,177,178],{"class":92},"$llm",[86,180,123],{"class":96},[86,182,183],{"class":113}," !pw.xpacks.llm.llms.OpenAIChat\n",[86,185,187,190,192,195,198,201],{"class":88,"line":186},10,[86,188,189],{"class":92},"  model",[86,191,123],{"class":96},[86,193,194],{"class":96}," \"",[86,196,197],{"class":126},"gpt-4o-mini",[86,199,200],{"class":96},"\"",[86,202,204],{"class":203},"s0W1g"," \n",[86,206,208,211,213],{"class":88,"line":207},11,[86,209,210],{"class":92},"  retry_strategy",[86,212,123],{"class":96},[86,214,215],{"class":113}," !pw.udfs.ExponentialBackoffRetryStrategy\n",[86,217,219,222,224,228],{"class":88,"line":218},12,[86,220,221],{"class":92},"    max_retries",[86,223,123],{"class":96},[86,225,227],{"class":226},"sx098"," 6",[86,229,204],{"class":203},[86,231,233,236,238],{"class":88,"line":232},13,[86,234,235],{"class":92},"  cache_strategy",[86,237,123],{"class":96},[86,239,240],{"class":113}," !pw.udfs.DefaultCache\n",[86,242,244,247,249],{"class":88,"line":243},14,[86,245,246],{"class":92},"  temperature",[86,248,123],{"class":96},[86,250,251],{"class":226}," 0\n",[86,253,255,258,260],{"class":88,"line":254},15,[86,256,257],{"class":92},"  capacity",[86,259,123],{"class":96},[86,261,262],{"class":226}," 8\n",[86,264,266],{"class":88,"line":265},16,[86,267,166],{"emptyLinePlaceholder":165},[86,269,271],{"class":88,"line":270},17,[86,272,273],{"class":103},"# Specifies the embedder model for converting text into embeddings.\n",[86,275,277,280,282],{"class":88,"line":276},18,[86,278,279],{"class":92},"$embedder",[86,281,123],{"class":96},[86,283,284],{"class":113}," !pw.xpacks.llm.embedders.OpenAIEmbedder\n",[86,286,288,290,292,294,297],{"class":88,"line":287},19,[86,289,189],{"class":92},[86,291,123],{"class":96},[86,293,194],{"class":96},[86,295,296],{"class":126},"text-embedding-ada-002",[86,298,299],{"class":96},"\"\n",[86,301,303,305,307],{"class":88,"line":302},20,[86,304,235],{"class":92},[86,306,123],{"class":96},[86,308,240],{"class":113},[86,310,312],{"class":88,"line":311},21,[86,313,166],{"emptyLinePlaceholder":165},[86,315,317],{"class":88,"line":316},22,[86,318,319],{"class":103},"# Defines the splitter settings for dividing text into smaller chunks.\n",[86,321,323,326,328],{"class":88,"line":322},23,[86,324,325],{"class":92},"$splitter",[86,327,123],{"class":96},[86,329,330],{"class":113}," !pw.xpacks.llm.splitters.TokenCountSplitter\n",[86,332,334,337,339],{"class":88,"line":333},24,[86,335,336],{"class":92},"  max_tokens",[86,338,123],{"class":96},[86,340,341],{"class":226}," 400\n",[86,343,345],{"class":88,"line":344},25,[86,346,166],{"emptyLinePlaceholder":165},[86,348,350],{"class":88,"line":349},26,[86,351,352],{"class":103},"# Configures the parser for processing and extracting information from documents.\n",[86,354,356,359,361],{"class":88,"line":355},27,[86,357,358],{"class":92},"$parser",[86,360,123],{"class":96},[86,362,363],{"class":113}," !pw.xpacks.llm.parsers.DoclingParser\n",[86,365,367,369,371],{"class":88,"line":366},28,[86,368,235],{"class":92},[86,370,123],{"class":96},[86,372,240],{"class":113},[86,374,376],{"class":88,"line":375},29,[86,377,166],{"emptyLinePlaceholder":165},[86,379,381],{"class":88,"line":380},30,[86,382,383],{"class":103},"# Sets up the retriever factory for indexing and retrieving documents.\n",[86,385,387,390,392],{"class":88,"line":386},31,[86,388,389],{"class":92},"$retriever_factory",[86,391,123],{"class":96},[86,393,394],{"class":113}," !pw.stdlib.indexing.BruteForceKnnFactory\n",[86,396,398,401,403],{"class":88,"line":397},32,[86,399,400],{"class":92},"  reserved_space",[86,402,123],{"class":96},[86,404,405],{"class":226}," 1000\n",[86,407,409,412,414],{"class":88,"line":408},33,[86,410,411],{"class":92},"  embedder",[86,413,123],{"class":96},[86,415,416],{"class":126}," $embedder\n",[86,418,420,423,425],{"class":88,"line":419},34,[86,421,422],{"class":92},"  metric",[86,424,123],{"class":96},[86,426,427],{"class":113}," !pw.stdlib.indexing.BruteForceKnnMetricKind.COS\n",[86,429,431],{"class":88,"line":430},35,[86,432,166],{"emptyLinePlaceholder":165},[86,434,436],{"class":88,"line":435},36,[86,437,438],{"class":103},"# Manages the storage and retrieval of documents for the RAG template.\n",[86,440,442,445,447],{"class":88,"line":441},37,[86,443,444],{"class":92},"$document_store",[86,446,123],{"class":96},[86,448,449],{"class":113}," !pw.xpacks.llm.document_store.DocumentStore\n",[86,451,453,456,458],{"class":88,"line":452},38,[86,454,455],{"class":92},"  docs",[86,457,123],{"class":96},[86,459,460],{"class":126}," $sources\n",[86,462,464,467,469],{"class":88,"line":463},39,[86,465,466],{"class":92},"  parser",[86,468,123],{"class":96},[86,470,471],{"class":126}," $parser\n",[86,473,475,478,480],{"class":88,"line":474},40,[86,476,477],{"class":92},"  splitter",[86,479,123],{"class":96},[86,481,482],{"class":126}," $splitter\n",[86,484,486,489,491],{"class":88,"line":485},41,[86,487,488],{"class":92},"  retriever_factory",[86,490,123],{"class":96},[86,492,493],{"class":126}," $retriever_factory\n",[86,495,497],{"class":88,"line":496},42,[86,498,166],{"emptyLinePlaceholder":165},[86,500,502],{"class":88,"line":501},43,[86,503,504],{"class":103},"# Configures the question-answering component using the RAG approach.\n",[86,506,508,511,513],{"class":88,"line":507},44,[86,509,510],{"class":92},"question_answerer",[86,512,123],{"class":96},[86,514,515],{"class":113}," !pw.xpacks.llm.question_answering.AdaptiveRAGQuestionAnswerer\n",[86,517,519,522,524],{"class":88,"line":518},45,[86,520,521],{"class":92},"  llm",[86,523,123],{"class":96},[86,525,526],{"class":126}," $llm\n",[86,528,530,533,535],{"class":88,"line":529},46,[86,531,532],{"class":92},"  indexer",[86,534,123],{"class":96},[86,536,537],{"class":126}," $document_store\n",[86,539,541,544,546],{"class":88,"line":540},47,[86,542,543],{"class":92},"  n_starting_documents",[86,545,123],{"class":96},[86,547,548],{"class":226}," 2\n",[86,550,552,555,557],{"class":88,"line":551},48,[86,553,554],{"class":92},"  factor",[86,556,123],{"class":96},[86,558,548],{"class":226},[86,560,562,565,567],{"class":88,"line":561},49,[86,563,564],{"class":92},"  max_iterations",[86,566,123],{"class":96},[86,568,569],{"class":226}," 4\n",[76,571,574],{"className":78,"code":572,"filename":573,"language":81,"meta":43,"style":43},"$sources:\n  # Connect to your SharePoint data.\n  - !pw.xpacks.connectors.sharepoint.read \n    url: $SHAREPOINT_URL       # URL of the SharePoint site\n    tenant: $SHAREPOINT_TENANT # Tenant ID for SharePoint\n    client_id: $SHAREPOINT_CLIENT_ID # Client ID for authentication\n    cert_path: sharepointcert.pem # Path to the certificate file\n    thumbprint: $SHAREPOINT_THUMBPRINT # Thumbprint of the certificate\n    root_path: $SHAREPOINT_ROOT # Root path in SharePoint\n    with_metadata: true        # Include metadata in the data\n    refresh_interval: 30       # Interval to refresh data (in seconds)\n\n# Configures the LLM model settings for generating responses.\n$llm: !pw.xpacks.llm.llms.OpenAIChat\n  model: \"gpt-4o-mini\" \n  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy\n    max_retries: 6 \n  cache_strategy: !pw.udfs.DefaultCache\n  temperature: 0\n  capacity: 8\n\n# Specifies the embedder model for converting text into embeddings.\n$embedder: !pw.xpacks.llm.embedders.OpenAIEmbedder\n  model: \"text-embedding-ada-002\"\n  cache_strategy: !pw.udfs.DefaultCache\n\n# Defines the splitter settings for dividing text into smaller chunks.\n$splitter: !pw.xpacks.llm.splitters.TokenCountSplitter\n  max_tokens: 400\n\n# Configures the parser for processing and extracting information from documents.\n$parser: !pw.xpacks.llm.parsers.DoclingParser\n  cache_strategy: !pw.udfs.DefaultCache\n\n# Sets up the retriever factory for indexing and retrieving documents.\n$retriever_factory: !pw.stdlib.indexing.BruteForceKnnFactory\n  reserved_space: 1000\n  embedder: $embedder\n  metric: !pw.stdlib.indexing.BruteForceKnnMetricKind.COS\n\n# Manages the storage and retrieval of documents for the RAG template.\n$document_store: !pw.xpacks.llm.document_store.DocumentStore\n  docs: $sources\n  parser: $parser\n  splitter: $splitter\n  retriever_factory: $retriever_factory\n\n# Configures the question-answering component using the RAG approach.\nquestion_answerer: !pw.xpacks.llm.question_answering.AdaptiveRAGQuestionAnswerer\n  llm: $llm\n  indexer: $document_store\n  n_starting_documents: 2\n  factor: 2\n  max_iterations: 4\n","SharePoint",[83,575,576,582,587,596,609,622,635,648,661,674,685,698,702,706,714,728,736,746,754,762,770,774,778,786,798,806,810,814,822,830,834,838,846,854,858,862,870,878,886,894,898,902,910,918,926,934,942,946,950,958,967,976,985,994],{"__ignoreMap":43},[86,577,578,580],{"class":88,"line":89},[86,579,93],{"class":92},[86,581,97],{"class":96},[86,583,584],{"class":88,"line":100},[86,585,586],{"class":103},"  # Connect to your SharePoint data.\n",[86,588,589,591,594],{"class":88,"line":107},[86,590,110],{"class":96},[86,592,593],{"class":113}," !pw.xpacks.connectors.sharepoint.read",[86,595,204],{"class":203},[86,597,598,601,603,606],{"class":88,"line":117},[86,599,600],{"class":92},"    url",[86,602,123],{"class":96},[86,604,605],{"class":126}," $SHAREPOINT_URL",[86,607,608],{"class":103},"       # URL of the SharePoint site\n",[86,610,611,614,616,619],{"class":88,"line":133},[86,612,613],{"class":92},"    tenant",[86,615,123],{"class":96},[86,617,618],{"class":126}," $SHAREPOINT_TENANT",[86,620,621],{"class":103}," # Tenant ID for SharePoint\n",[86,623,624,627,629,632],{"class":88,"line":147},[86,625,626],{"class":92},"    client_id",[86,628,123],{"class":96},[86,630,631],{"class":126}," $SHAREPOINT_CLIENT_ID",[86,633,634],{"class":103}," # Client ID for authentication\n",[86,636,637,640,642,645],{"class":88,"line":162},[86,638,639],{"class":92},"    cert_path",[86,641,123],{"class":96},[86,643,644],{"class":126}," sharepointcert.pem",[86,646,647],{"class":103}," # Path to the certificate file\n",[86,649,650,653,655,658],{"class":88,"line":169},[86,651,652],{"class":92},"    thumbprint",[86,654,123],{"class":96},[86,656,657],{"class":126}," $SHAREPOINT_THUMBPRINT",[86,659,660],{"class":103}," # Thumbprint of the certificate\n",[86,662,663,666,668,671],{"class":88,"line":175},[86,664,665],{"class":92},"    root_path",[86,667,123],{"class":96},[86,669,670],{"class":126}," $SHAREPOINT_ROOT",[86,672,673],{"class":103}," # Root path in SharePoint\n",[86,675,676,678,680,682],{"class":88,"line":186},[86,677,150],{"class":92},[86,679,123],{"class":96},[86,681,156],{"class":155},[86,683,684],{"class":103},"        # Include metadata in the data\n",[86,686,687,690,692,695],{"class":88,"line":207},[86,688,689],{"class":92},"    refresh_interval",[86,691,123],{"class":96},[86,693,694],{"class":226}," 30",[86,696,697],{"class":103},"       # Interval to refresh data (in 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 # Connect to your data in Google Drive\n  - !pw.io.gdrive.read\n    object_id: $DRIVE_ID\n    service_user_credentials_file: gdrive_indexer.json\n    file_name_pattern:\n      - \"*.pdf\"\n      - \"*.pptx\"\n    object_size_limit: null\n    with_metadata: true\n    refresh_interval: 30\n\n# Configures the LLM model settings for generating responses.\n$llm: !pw.xpacks.llm.llms.OpenAIChat\n  model: \"gpt-4o-mini\" \n  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy\n    max_retries: 6 \n  cache_strategy: !pw.udfs.DefaultCache\n  temperature: 0\n  capacity: 8\n\n# Specifies the embedder model for converting text into embeddings.\n$embedder: !pw.xpacks.llm.embedders.OpenAIEmbedder\n  model: \"text-embedding-ada-002\"\n  cache_strategy: !pw.udfs.DefaultCache\n\n# Defines the splitter settings for dividing text into smaller chunks.\n$splitter: !pw.xpacks.llm.splitters.TokenCountSplitter\n  max_tokens: 400\n\n# Configures the parser for processing and extracting information from documents.\n$parser: !pw.xpacks.llm.parsers.DoclingParser\n  cache_strategy: !pw.udfs.DefaultCache\n\n# Sets up the retriever factory for indexing and retrieving documents.\n$retriever_factory: !pw.stdlib.indexing.BruteForceKnnFactory\n  reserved_space: 1000\n  embedder: $embedder\n  metric: !pw.stdlib.indexing.BruteForceKnnMetricKind.COS\n\n# Manages the storage and retrieval of documents for the RAG template.\n$document_store: !pw.xpacks.llm.document_store.DocumentStore\n  docs: $sources\n  parser: $parser\n  splitter: $splitter\n  retriever_factory: $retriever_factory\n\n# Configures the question-answering component using the RAG approach.\nquestion_answerer: !pw.xpacks.llm.question_answering.AdaptiveRAGQuestionAnswerer\n  llm: $llm\n  indexer: $document_store\n  n_starting_documents: 2\n  factor: 2\n  max_iterations: 4\n","Google 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gdrive_indexer.json\n",[86,1048,1049,1052],{"class":88,"line":147},[86,1050,1051],{"class":92},"    file_name_pattern",[86,1053,97],{"class":96},[86,1055,1056,1059,1061,1064],{"class":88,"line":162},[86,1057,1058],{"class":96},"      -",[86,1060,194],{"class":96},[86,1062,1063],{"class":126},"*.pdf",[86,1065,299],{"class":96},[86,1067,1068,1070,1072,1075],{"class":88,"line":169},[86,1069,1058],{"class":96},[86,1071,194],{"class":96},[86,1073,1074],{"class":126},"*.pptx",[86,1076,299],{"class":96},[86,1078,1079,1082,1084],{"class":88,"line":175},[86,1080,1081],{"class":92},"    object_size_limit",[86,1083,123],{"class":96},[86,1085,1086],{"class":96}," null\n",[86,1088,1089,1091,1093],{"class":88,"line":186},[86,1090,150],{"class":92},[86,1092,123],{"class":96},[86,1094,1095],{"class":155}," true\n",[86,1097,1098,1100,1102],{"class":88,"line":207},[86,1099,689],{"class":92},[86,1101,123],{"class":96},[86,1103,1104],{"class":226}," 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,{"class":96},[86,1404,569],{"class":226},[76,1406,1409],{"className":78,"code":1407,"filename":1408,"language":81,"meta":43,"style":43},"$sources:\n  # Connect to your data in S3\n  - !pw.io.s3.read\n    path: $path\n    format: \"binary\"\n    aws_s3_setting: !pw.io.s3.AwsS3Settings\n      bucket_name: $bucket\n      region: \"eu-west-3\"\n      access_key: $s3_access_key\n      secret_access_key: $s3_secret_access_key\n\n# Configures the LLM model settings for generating responses.\n$llm: !pw.xpacks.llm.llms.OpenAIChat\n  model: \"gpt-4o-mini\" \n  retry_strategy: !pw.udfs.ExponentialBackoffRetryStrategy\n    max_retries: 6 \n  cache_strategy: !pw.udfs.DefaultCache\n  temperature: 0\n  capacity: 8\n\n# Specifies the embedder model for converting text into embeddings.\n$embedder: !pw.xpacks.llm.embedders.OpenAIEmbedder\n  model: \"text-embedding-ada-002\"\n  cache_strategy: !pw.udfs.DefaultCache\n\n# Defines the splitter settings for dividing text into smaller chunks.\n$splitter: !pw.xpacks.llm.splitters.TokenCountSplitter\n  max_tokens: 400\n\n# Configures the parser for processing and extracting information from 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