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Create question answer generation system consisting of QuestionGenerator and rci reader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "rci_reader"}]}]}
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Generate Haystack search summarization using TransformersSummarizer, in memory document store and elasticsearch filter only retriever
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{"version": "1.8.0", "components": [{"name": "in_memory_document_store", "type": "InMemoryDocumentStore", "params": {"index": "document", "label_index": "label", "embedding_dim": 768, "return_embedding": false, "similarity": "dot_product", "progress_bar": true, "duplicate_documents": "overwrite", "use_gpu": true, "scoring_batch_size": 500000}}, {"name": "elasticsearch_filter_only_retriever", "type": "ElasticsearchFilterOnlyRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_filter_only_retriever"}, {"inputs": ["elasticsearch_filter_only_retriever"], "name": "transformers_summarizer"}]}]}
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Generate question generation pipeline
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Build Haystack ExtractiveQAPipeline consisting of FAISSDocumentStore, BM25Retriever and transformers reader
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "transformers_reader"}]}]}
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Make Haystack question answer generation pipeline using QuestionGenerator and TransformersReader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "transformers_reader"}]}]}
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Create Haystack faq pipeline consisting of open distro elasticsearch document store and elasticsearch retriever
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "elasticsearch_retriever", "type": "ElasticsearchRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_retriever"}, {"inputs": ["elasticsearch_retriever"]}]}]}
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Create Haystack GenerativeQAPipeline consisting of tfidf retriever, in memory document store and ra generator
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{"version": "1.8.0", "components": [{"name": "in_memory_document_store", "type": "InMemoryDocumentStore", "params": {"index": "document", "label_index": "label", "embedding_dim": 768, "return_embedding": false, "similarity": "dot_product", "progress_bar": true, "duplicate_documents": "overwrite", "use_gpu": true, "scoring_batch_size": 500000}}, {"name": "tfidf_retriever", "type": "TfidfRetriever", "params": {"top_k": 10}}, {"name": "ra_generator", "type": "RAGenerator", "params": {"model_name_or_path": "facebook/rag-token-nq", "generator_type": "token", "top_k": 2, "max_length": 200, "min_length": 2, "num_beams": 2, "embed_title": true, "use_gpu": true, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "tfidf_retriever"}, {"inputs": ["tfidf_retriever"], "name": "ra_generator"}]}]}
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Build Haystack generative pipeline using seq2 seq generator, faiss document store and BM25Retriever
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "seq2_seq_generator", "type": "Seq2SeqGenerator", "params": {"top_k": 1, "max_length": 200, "min_length": 2, "num_beams": 8, "use_gpu": true, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "seq2_seq_generator"}]}]}
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Make SearchSummarizationPipeline with TransformersSummarizer, OpenDistroElasticsearchDocumentStore and bm25 retriever
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "transformers_summarizer"}]}]}
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Make Haystack ExtractiveQAPipeline consisting of ElasticsearchFilterOnlyRetriever, FAISSDocumentStore and farm reader
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "elasticsearch_filter_only_retriever", "type": "ElasticsearchFilterOnlyRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}, {"name": "farm_reader", "type": "FARMReader", "params": {"context_window_size": 150, "batch_size": 50, "use_gpu": true, "no_ans_boost": 0.0, "return_no_answer": false, "top_k": 10, "top_k_per_candidate": 3, "top_k_per_sample": 1, "num_processes": 0, "max_seq_len": 256, "doc_stride": 128, "progress_bar": true, "duplicate_filtering": 0, "use_confidence_scores": true, "confidence_threshold": 0, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_filter_only_retriever"}, {"inputs": ["elasticsearch_filter_only_retriever"], "name": "farm_reader"}]}]}
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Generate Haystack question answer generation system with QuestionGenerator and rci reader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "rci_reader"}]}]}
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Make document search system with dense passage retriever and DeepsetCloudDocumentStore
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{"version": "1.8.0", "components": [{"name": "deepset_cloud_document_store", "type": "DeepsetCloudDocumentStore", "params": {"workspace": "default", "duplicate_documents": "overwrite", "similarity": "dot_product", "return_embedding": false, "label_index": "default", "embedding_dim": 768}}, {"name": "dense_passage_retriever", "type": "DensePassageRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_title": true, "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "dense_passage_retriever"}, {"inputs": ["dense_passage_retriever"]}]}]}
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Generate Haystack generative qa pipeline using open distro elasticsearch document store, EmbeddingRetriever and seq2 seq generator
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "seq2_seq_generator", "type": "Seq2SeqGenerator", "params": {"top_k": 1, "max_length": 200, "min_length": 2, "num_beams": 8, "use_gpu": true, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"], "name": "seq2_seq_generator"}]}]}
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Build search pipeline using FAISSDocumentStore and elasticsearch retriever
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "elasticsearch_retriever", "type": "ElasticsearchRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_retriever"}, {"inputs": ["elasticsearch_retriever"]}]}]}
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Generate Haystack ExtractiveQAPipeline consisting of multihop embedding retriever, faiss document store and transformers reader
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "multihop_embedding_retriever", "type": "MultihopEmbeddingRetriever", "params": {"num_iterations": 2, "use_gpu": true, "batch_size": 32, "max_seq_len": 512, "model_format": "farm", "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "multihop_embedding_retriever"}, {"inputs": ["multihop_embedding_retriever"], "name": "transformers_reader"}]}]}
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Build extractive qa using TableReader, OpenDistroElasticsearchDocumentStore and bm25 retriever
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "table_reader", "type": "TableReader", "params": {"model_name_or_path": "google/tapas-base-finetuned-wtq", "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answer": false, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "table_reader"}]}]}
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Build Haystack generative qa system using Seq2SeqGenerator, sql document store and TableTextRetriever
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{"version": "1.8.0", "components": [{"name": "sql_document_store", "type": "SQLDocumentStore", "params": {"url": "sqlite://", "index": "document", "label_index": "label", "duplicate_documents": "overwrite", "check_same_thread": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "seq2_seq_generator", "type": "Seq2SeqGenerator", "params": {"top_k": 1, "max_length": 200, "min_length": 2, "num_beams": 8, "use_gpu": true, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "seq2_seq_generator"}]}]}
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Generate generative qa system consisting of TfidfRetriever, DeepsetCloudDocumentStore and OpenAIAnswerGenerator
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{"version": "1.8.0", "components": [{"name": "deepset_cloud_document_store", "type": "DeepsetCloudDocumentStore", "params": {"workspace": "default", "duplicate_documents": "overwrite", "similarity": "dot_product", "return_embedding": false, "label_index": "default", "embedding_dim": 768}}, {"name": "tfidf_retriever", "type": "TfidfRetriever", "params": {"top_k": 10}}, {"name": "open_ai_answer_generator", "type": "OpenAIAnswerGenerator", "params": {"model": "text-curie-001", "max_tokens": 7, "top_k": 5, "temperature": 0, "presence_penalty": -2.0, "frequency_penalty": -2.0, "progress_bar": true}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "tfidf_retriever"}, {"inputs": ["tfidf_retriever"], "name": "open_ai_answer_generator"}]}]}
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Build generative qa pipeline with OpenDistroElasticsearchDocumentStore, EmbeddingRetriever and Seq2SeqGenerator
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "seq2_seq_generator", "type": "Seq2SeqGenerator", "params": {"top_k": 1, "max_length": 200, "min_length": 2, "num_beams": 8, "use_gpu": true, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"], "name": "seq2_seq_generator"}]}]}
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Generate question generation system
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Create ExtractiveQAPipeline consisting of bm25 retriever, TableReader and elasticsearch document store
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{"version": "1.8.0", "components": [{"name": "elasticsearch_document_store", "type": "ElasticsearchDocumentStore", "params": {"port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "scheme": "http", "verify_certs": true, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "dot_product", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "table_reader", "type": "TableReader", "params": {"model_name_or_path": "google/tapas-base-finetuned-wtq", "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answer": false, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "table_reader"}]}]}
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Create question generation pipeline
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Make Haystack QuestionGenerationPipeline
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Make SearchSummarizationPipeline using embedding retriever, TransformersSummarizer and sql document store
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{"version": "1.8.0", "components": [{"name": "sql_document_store", "type": "SQLDocumentStore", "params": {"url": "sqlite://", "index": "document", "label_index": "label", "duplicate_documents": "overwrite", "check_same_thread": false}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"], "name": "transformers_summarizer"}]}]}
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Make faq system with ElasticsearchRetriever and DeepsetCloudDocumentStore
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{"version": "1.8.0", "components": [{"name": "deepset_cloud_document_store", "type": "DeepsetCloudDocumentStore", "params": {"workspace": "default", "duplicate_documents": "overwrite", "similarity": "dot_product", "return_embedding": false, "label_index": "default", "embedding_dim": 768}}, {"name": "elasticsearch_retriever", "type": "ElasticsearchRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_retriever"}, {"inputs": ["elasticsearch_retriever"]}]}]}
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Create search pipeline with in memory document store and ElasticsearchRetriever
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{"version": "1.8.0", "components": [{"name": "in_memory_document_store", "type": "InMemoryDocumentStore", "params": {"index": "document", "label_index": "label", "embedding_dim": 768, "return_embedding": false, "similarity": "dot_product", "progress_bar": true, "duplicate_documents": "overwrite", "use_gpu": true, "scoring_batch_size": 500000}}, {"name": "elasticsearch_retriever", "type": "ElasticsearchRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_retriever"}, {"inputs": ["elasticsearch_retriever"]}]}]}
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Make qa system consisting of TableReader, weaviate document store and TableTextRetriever
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{"version": "1.8.0", "components": [{"name": "weaviate_document_store", "type": "WeaviateDocumentStore", "params": {"port": 0, "timeout_config": [5, 15], "index": "Document", "embedding_dim": 768, "content_field": "content", "name_field": "name", "similarity": "cosine", "index_type": "hnsw", "return_embedding": false, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "table_reader", "type": "TableReader", "params": {"model_name_or_path": "google/tapas-base-finetuned-wtq", "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answer": false, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "table_reader"}]}]}
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Generate Haystack document search system consisting of weaviate document store and embedding retriever
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{"version": "1.8.0", "components": [{"name": "weaviate_document_store", "type": "WeaviateDocumentStore", "params": {"port": 0, "timeout_config": [5, 15], "index": "Document", "embedding_dim": 768, "content_field": "content", "name_field": "name", "similarity": "cosine", "index_type": "hnsw", "return_embedding": false, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"]}]}]}
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Build Haystack faq pipeline using OpenDistroElasticsearchDocumentStore and embedding retriever
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"]}]}]}
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Create SearchSummarizationPipeline with PineconeDocumentStore, transformers summarizer and TableTextRetriever
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{"version": "1.8.0", "components": [{"name": "pinecone_document_store", "type": "PineconeDocumentStore", "params": {"environment": "us-west1-gcp", "embedding_dim": 768, "return_embedding": false, "index": "document", "similarity": "cosine", "replicas": 1, "shards": 1, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false, "validate_index_sync": true}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "transformers_summarizer"}]}]}
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Generate Haystack QuestionAnswerGenerationPipeline with RCIReader and question generator
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "rci_reader"}]}]}
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Make Haystack faq system consisting of tfidf retriever and elasticsearch document store
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{"version": "1.8.0", "components": [{"name": "elasticsearch_document_store", "type": "ElasticsearchDocumentStore", "params": {"port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "scheme": "http", "verify_certs": true, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "dot_product", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "tfidf_retriever", "type": "TfidfRetriever", "params": {"top_k": 10}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "tfidf_retriever"}, {"inputs": ["tfidf_retriever"]}]}]}
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Make question generation pipeline
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Generate Haystack GenerativeQAPipeline consisting of OpenAIAnswerGenerator, OpenDistroElasticsearchDocumentStore and TableTextRetriever
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "open_ai_answer_generator", "type": "OpenAIAnswerGenerator", "params": {"model": "text-curie-001", "max_tokens": 7, "top_k": 5, "temperature": 0, "presence_penalty": -2.0, "frequency_penalty": -2.0, "progress_bar": true}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "open_ai_answer_generator"}]}]}
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Create Haystack generative qa system consisting of BM25Retriever, DeepsetCloudDocumentStore and RAGenerator
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{"version": "1.8.0", "components": [{"name": "deepset_cloud_document_store", "type": "DeepsetCloudDocumentStore", "params": {"workspace": "default", "duplicate_documents": "overwrite", "similarity": "dot_product", "return_embedding": false, "label_index": "default", "embedding_dim": 768}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "ra_generator", "type": "RAGenerator", "params": {"model_name_or_path": "facebook/rag-token-nq", "generator_type": "token", "top_k": 2, "max_length": 200, "min_length": 2, "num_beams": 2, "embed_title": true, "use_gpu": true, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "ra_generator"}]}]}
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Generate Haystack QuestionAnswerGenerationPipeline consisting of table reader and QuestionGenerator
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "table_reader", "type": "TableReader", "params": {"model_name_or_path": "google/tapas-base-finetuned-wtq", "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answer": false, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "table_reader"}]}]}
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Create ExtractiveQAPipeline consisting of bm25 retriever, FAISSDocumentStore and TransformersReader
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "transformers_reader"}]}]}
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Create Haystack FAQPipeline consisting of FilterRetriever and in memory document store
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{"version": "1.8.0", "components": [{"name": "in_memory_document_store", "type": "InMemoryDocumentStore", "params": {"index": "document", "label_index": "label", "embedding_dim": 768, "return_embedding": false, "similarity": "dot_product", "progress_bar": true, "duplicate_documents": "overwrite", "use_gpu": true, "scoring_batch_size": 500000}}, {"name": "filter_retriever", "type": "FilterRetriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "filter_retriever"}, {"inputs": ["filter_retriever"]}]}]}
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Make Haystack qa pipeline with BM25Retriever, FAISSDocumentStore and TransformersReader
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "transformers_reader"}]}]}
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Build document search system consisting of elasticsearch retriever and pinecone document store
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{"version": "1.8.0", "components": [{"name": "pinecone_document_store", "type": "PineconeDocumentStore", "params": {"environment": "us-west1-gcp", "embedding_dim": 768, "return_embedding": false, "index": "document", "similarity": "cosine", "replicas": 1, "shards": 1, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false, "validate_index_sync": true}}, {"name": "elasticsearch_retriever", "type": "ElasticsearchRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_retriever"}, {"inputs": ["elasticsearch_retriever"]}]}]}
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Make faq search pipeline consisting of in memory document store and filter retriever
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{"version": "1.8.0", "components": [{"name": "in_memory_document_store", "type": "InMemoryDocumentStore", "params": {"index": "document", "label_index": "label", "embedding_dim": 768, "return_embedding": false, "similarity": "dot_product", "progress_bar": true, "duplicate_documents": "overwrite", "use_gpu": true, "scoring_batch_size": 500000}}, {"name": "filter_retriever", "type": "FilterRetriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "filter_retriever"}, {"inputs": ["filter_retriever"]}]}]}
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Generate Haystack question generation system
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Make Haystack document search pipeline consisting of tfidf retriever and open search document store
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{"version": "1.8.0", "components": [{"name": "open_search_document_store", "type": "OpenSearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "dot_product", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false, "knn_engine": "nmslib"}}, {"name": "tfidf_retriever", "type": "TfidfRetriever", "params": {"top_k": 10}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "tfidf_retriever"}, {"inputs": ["tfidf_retriever"]}]}]}
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Build document search system with DeepsetCloudDocumentStore and EmbeddingRetriever
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{"version": "1.8.0", "components": [{"name": "deepset_cloud_document_store", "type": "DeepsetCloudDocumentStore", "params": {"workspace": "default", "duplicate_documents": "overwrite", "similarity": "dot_product", "return_embedding": false, "label_index": "default", "embedding_dim": 768}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"]}]}]}
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Make Haystack question answer generation pipeline using QuestionGenerator and TransformersReader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "transformers_reader"}]}]}
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Generate QuestionGenerationPipeline
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Make Haystack QuestionAnswerGenerationPipeline with RCIReader and QuestionGenerator
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "rci_reader"}]}]}
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Make Haystack document search system consisting of faiss document store and tfidf retriever
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "tfidf_retriever", "type": "TfidfRetriever", "params": {"top_k": 10}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "tfidf_retriever"}, {"inputs": ["tfidf_retriever"]}]}]}
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Make Haystack SearchSummarizationPipeline using transformers summarizer, table text retriever and elasticsearch document store
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{"version": "1.8.0", "components": [{"name": "elasticsearch_document_store", "type": "ElasticsearchDocumentStore", "params": {"port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "scheme": "http", "verify_certs": true, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "dot_product", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "transformers_summarizer"}]}]}
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Build FAQPipeline using pinecone document store and FilterRetriever
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{"version": "1.8.0", "components": [{"name": "pinecone_document_store", "type": "PineconeDocumentStore", "params": {"environment": "us-west1-gcp", "embedding_dim": 768, "return_embedding": false, "index": "document", "similarity": "cosine", "replicas": 1, "shards": 1, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false, "validate_index_sync": true}}, {"name": "filter_retriever", "type": "FilterRetriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "filter_retriever"}, {"inputs": ["filter_retriever"]}]}]}
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Create FAQPipeline with tfidf retriever and InMemoryDocumentStore
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{"version": "1.8.0", "components": [{"name": "in_memory_document_store", "type": "InMemoryDocumentStore", "params": {"index": "document", "label_index": "label", "embedding_dim": 768, "return_embedding": false, "similarity": "dot_product", "progress_bar": true, "duplicate_documents": "overwrite", "use_gpu": true, "scoring_batch_size": 500000}}, {"name": "tfidf_retriever", "type": "TfidfRetriever", "params": {"top_k": 10}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "tfidf_retriever"}, {"inputs": ["tfidf_retriever"]}]}]}
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Create Haystack FAQPipeline with OpenDistroElasticsearchDocumentStore and EmbeddingRetriever
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"]}]}]}
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Make Haystack search summarization pipeline consisting of EmbeddingRetriever, SQLDocumentStore and TransformersSummarizer
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{"version": "1.8.0", "components": [{"name": "sql_document_store", "type": "SQLDocumentStore", "params": {"url": "sqlite://", "index": "document", "label_index": "label", "duplicate_documents": "overwrite", "check_same_thread": false}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"], "name": "transformers_summarizer"}]}]}
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Build question answer generation pipeline with QuestionGenerator and RCIReader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "rci_reader"}]}]}
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Generate question generation pipeline
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Generate faq pipeline consisting of filter retriever and InMemoryDocumentStore
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{"version": "1.8.0", "components": [{"name": "in_memory_document_store", "type": "InMemoryDocumentStore", "params": {"index": "document", "label_index": "label", "embedding_dim": 768, "return_embedding": false, "similarity": "dot_product", "progress_bar": true, "duplicate_documents": "overwrite", "use_gpu": true, "scoring_batch_size": 500000}}, {"name": "filter_retriever", "type": "FilterRetriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "filter_retriever"}, {"inputs": ["filter_retriever"]}]}]}
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Create faq search pipeline using MultihopEmbeddingRetriever and weaviate document store
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{"version": "1.8.0", "components": [{"name": "weaviate_document_store", "type": "WeaviateDocumentStore", "params": {"port": 0, "timeout_config": [5, 15], "index": "Document", "embedding_dim": 768, "content_field": "content", "name_field": "name", "similarity": "cosine", "index_type": "hnsw", "return_embedding": false, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false}}, {"name": "multihop_embedding_retriever", "type": "MultihopEmbeddingRetriever", "params": {"num_iterations": 2, "use_gpu": true, "batch_size": 32, "max_seq_len": 512, "model_format": "farm", "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "multihop_embedding_retriever"}, {"inputs": ["multihop_embedding_retriever"]}]}]}
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Generate Haystack generative qa system using table text retriever, PineconeDocumentStore and open ai answer generator
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{"version": "1.8.0", "components": [{"name": "pinecone_document_store", "type": "PineconeDocumentStore", "params": {"environment": "us-west1-gcp", "embedding_dim": 768, "return_embedding": false, "index": "document", "similarity": "cosine", "replicas": 1, "shards": 1, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false, "validate_index_sync": true}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "open_ai_answer_generator", "type": "OpenAIAnswerGenerator", "params": {"model": "text-curie-001", "max_tokens": 7, "top_k": 5, "temperature": 0, "presence_penalty": -2.0, "frequency_penalty": -2.0, "progress_bar": true}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "open_ai_answer_generator"}]}]}
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Generate generative qa system using Seq2SeqGenerator, open distro elasticsearch document store and ElasticsearchRetriever
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "elasticsearch_retriever", "type": "ElasticsearchRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}, {"name": "seq2_seq_generator", "type": "Seq2SeqGenerator", "params": {"top_k": 1, "max_length": 200, "min_length": 2, "num_beams": 8, "use_gpu": true, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_retriever"}, {"inputs": ["elasticsearch_retriever"], "name": "seq2_seq_generator"}]}]}
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Build QuestionGenerationPipeline
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Make question answer generation pipeline using QuestionGenerator and RCIReader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "rci_reader"}]}]}
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Build QuestionAnswerGenerationPipeline using question generator and transformers reader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "transformers_reader"}]}]}
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Generate Haystack search summarization pipeline consisting of table text retriever, TransformersSummarizer and PineconeDocumentStore
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{"version": "1.8.0", "components": [{"name": "pinecone_document_store", "type": "PineconeDocumentStore", "params": {"environment": "us-west1-gcp", "embedding_dim": 768, "return_embedding": false, "index": "document", "similarity": "cosine", "replicas": 1, "shards": 1, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false, "validate_index_sync": true}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "transformers_summarizer"}]}]}
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Create Haystack faq search pipeline using filter retriever and InMemoryDocumentStore
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{"version": "1.8.0", "components": [{"name": "in_memory_document_store", "type": "InMemoryDocumentStore", "params": {"index": "document", "label_index": "label", "embedding_dim": 768, "return_embedding": false, "similarity": "dot_product", "progress_bar": true, "duplicate_documents": "overwrite", "use_gpu": true, "scoring_batch_size": 500000}}, {"name": "filter_retriever", "type": "FilterRetriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "filter_retriever"}, {"inputs": ["filter_retriever"]}]}]}
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Make Haystack qa pipeline with sql document store, transformers reader and MultihopEmbeddingRetriever
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{"version": "1.8.0", "components": [{"name": "sql_document_store", "type": "SQLDocumentStore", "params": {"url": "sqlite://", "index": "document", "label_index": "label", "duplicate_documents": "overwrite", "check_same_thread": false}}, {"name": "multihop_embedding_retriever", "type": "MultihopEmbeddingRetriever", "params": {"num_iterations": 2, "use_gpu": true, "batch_size": 32, "max_seq_len": 512, "model_format": "farm", "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "multihop_embedding_retriever"}, {"inputs": ["multihop_embedding_retriever"], "name": "transformers_reader"}]}]}
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Build extractive qa system using FARMReader, faiss document store and multihop embedding retriever
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "multihop_embedding_retriever", "type": "MultihopEmbeddingRetriever", "params": {"num_iterations": 2, "use_gpu": true, "batch_size": 32, "max_seq_len": 512, "model_format": "farm", "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "farm_reader", "type": "FARMReader", "params": {"context_window_size": 150, "batch_size": 50, "use_gpu": true, "no_ans_boost": 0.0, "return_no_answer": false, "top_k": 10, "top_k_per_candidate": 3, "top_k_per_sample": 1, "num_processes": 0, "max_seq_len": 256, "doc_stride": 128, "progress_bar": true, "duplicate_filtering": 0, "use_confidence_scores": true, "confidence_threshold": 0, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "multihop_embedding_retriever"}, {"inputs": ["multihop_embedding_retriever"], "name": "farm_reader"}]}]}
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Create generative qa pipeline with FilterRetriever, Seq2SeqGenerator and PineconeDocumentStore
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{"version": "1.8.0", "components": [{"name": "pinecone_document_store", "type": "PineconeDocumentStore", "params": {"environment": "us-west1-gcp", "embedding_dim": 768, "return_embedding": false, "index": "document", "similarity": "cosine", "replicas": 1, "shards": 1, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false, "validate_index_sync": true}}, {"name": "filter_retriever", "type": "FilterRetriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "seq2_seq_generator", "type": "Seq2SeqGenerator", "params": {"top_k": 1, "max_length": 200, "min_length": 2, "num_beams": 8, "use_gpu": true, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "filter_retriever"}, {"inputs": ["filter_retriever"], "name": "seq2_seq_generator"}]}]}
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Build generative qa system using open ai answer generator, pinecone document store and TableTextRetriever
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{"version": "1.8.0", "components": [{"name": "pinecone_document_store", "type": "PineconeDocumentStore", "params": {"environment": "us-west1-gcp", "embedding_dim": 768, "return_embedding": false, "index": "document", "similarity": "cosine", "replicas": 1, "shards": 1, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false, "validate_index_sync": true}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "open_ai_answer_generator", "type": "OpenAIAnswerGenerator", "params": {"model": "text-curie-001", "max_tokens": 7, "top_k": 5, "temperature": 0, "presence_penalty": -2.0, "frequency_penalty": -2.0, "progress_bar": true}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "open_ai_answer_generator"}]}]}
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Create Haystack faq system with TfidfRetriever and ElasticsearchDocumentStore
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{"version": "1.8.0", "components": [{"name": "elasticsearch_document_store", "type": "ElasticsearchDocumentStore", "params": {"port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "scheme": "http", "verify_certs": true, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "dot_product", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "tfidf_retriever", "type": "TfidfRetriever", "params": {"top_k": 10}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "tfidf_retriever"}, {"inputs": ["tfidf_retriever"]}]}]}
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Create Haystack faq pipeline consisting of faiss document store and DensePassageRetriever
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "dense_passage_retriever", "type": "DensePassageRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_title": true, "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "dense_passage_retriever"}, {"inputs": ["dense_passage_retriever"]}]}]}
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Build qa pipeline using weaviate document store, table reader and TableTextRetriever
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{"version": "1.8.0", "components": [{"name": "weaviate_document_store", "type": "WeaviateDocumentStore", "params": {"port": 0, "timeout_config": [5, 15], "index": "Document", "embedding_dim": 768, "content_field": "content", "name_field": "name", "similarity": "cosine", "index_type": "hnsw", "return_embedding": false, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "table_reader", "type": "TableReader", "params": {"model_name_or_path": "google/tapas-base-finetuned-wtq", "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answer": false, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "table_reader"}]}]}
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Generate question answer generation pipeline with TransformersReader and QuestionGenerator
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "transformers_reader"}]}]}
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Create QuestionAnswerGenerationPipeline using question generator and FARMReader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "farm_reader", "type": "FARMReader", "params": {"context_window_size": 150, "batch_size": 50, "use_gpu": true, "no_ans_boost": 0.0, "return_no_answer": false, "top_k": 10, "top_k_per_candidate": 3, "top_k_per_sample": 1, "num_processes": 0, "max_seq_len": 256, "doc_stride": 128, "progress_bar": true, "duplicate_filtering": 0, "use_confidence_scores": true, "confidence_threshold": 0, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "farm_reader"}]}]}
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Make Haystack DocumentSearchPipeline using FAISSDocumentStore and elasticsearch retriever
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "elasticsearch_retriever", "type": "ElasticsearchRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_retriever"}, {"inputs": ["elasticsearch_retriever"]}]}]}
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Build Haystack QuestionGenerationPipeline
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Make Haystack faq search pipeline with DeepsetCloudDocumentStore and ElasticsearchRetriever
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{"version": "1.8.0", "components": [{"name": "deepset_cloud_document_store", "type": "DeepsetCloudDocumentStore", "params": {"workspace": "default", "duplicate_documents": "overwrite", "similarity": "dot_product", "return_embedding": false, "label_index": "default", "embedding_dim": 768}}, {"name": "elasticsearch_retriever", "type": "ElasticsearchRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_retriever"}, {"inputs": ["elasticsearch_retriever"]}]}]}
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Make Haystack FAQPipeline with bm25 retriever and OpenSearchDocumentStore
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{"version": "1.8.0", "components": [{"name": "open_search_document_store", "type": "OpenSearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "dot_product", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false, "knn_engine": "nmslib"}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"]}]}]}
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Build Haystack search pipeline consisting of DeepsetCloudDocumentStore and EmbeddingRetriever
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{"version": "1.8.0", "components": [{"name": "deepset_cloud_document_store", "type": "DeepsetCloudDocumentStore", "params": {"workspace": "default", "duplicate_documents": "overwrite", "similarity": "dot_product", "return_embedding": false, "label_index": "default", "embedding_dim": 768}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"]}]}]}
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Build question answer generation system with TransformersReader and question generator
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "transformers_reader", "type": "TransformersReader", "params": {"model_name_or_path": "distilbert-base-uncased-distilled-squad", "context_window_size": 70, "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answers": false, "max_seq_len": 256, "doc_stride": 128, "batch_size": 16, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "transformers_reader"}]}]}
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Make Haystack GenerativeQAPipeline using weaviate document store, embedding retriever and open ai answer generator
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{"version": "1.8.0", "components": [{"name": "weaviate_document_store", "type": "WeaviateDocumentStore", "params": {"port": 0, "timeout_config": [5, 15], "index": "Document", "embedding_dim": 768, "content_field": "content", "name_field": "name", "similarity": "cosine", "index_type": "hnsw", "return_embedding": false, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "open_ai_answer_generator", "type": "OpenAIAnswerGenerator", "params": {"model": "text-curie-001", "max_tokens": 7, "top_k": 5, "temperature": 0, "presence_penalty": -2.0, "frequency_penalty": -2.0, "progress_bar": true}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"], "name": "open_ai_answer_generator"}]}]}
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Generate Haystack extractive qa consisting of pinecone document store, rci reader and BM25Retriever
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{"version": "1.8.0", "components": [{"name": "pinecone_document_store", "type": "PineconeDocumentStore", "params": {"environment": "us-west1-gcp", "embedding_dim": 768, "return_embedding": false, "index": "document", "similarity": "cosine", "replicas": 1, "shards": 1, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false, "metadata_config": {}, "validate_index_sync": true}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "rci_reader"}]}]}
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Create search summarization using pinecone document store, tfidf retriever and TransformersSummarizer
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{"version": "1.8.0", "components": [{"name": "pinecone_document_store", "type": "PineconeDocumentStore", "params": {"environment": "us-west1-gcp", "embedding_dim": 768, "return_embedding": false, "index": "document", "similarity": "cosine", "replicas": 1, "shards": 1, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false, "validate_index_sync": true}}, {"name": "tfidf_retriever", "type": "TfidfRetriever", "params": {"top_k": 10}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "tfidf_retriever"}, {"inputs": ["tfidf_retriever"], "name": "transformers_summarizer"}]}]}
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Generate Haystack document search system using OpenDistroElasticsearchDocumentStore and ElasticsearchFilterOnlyRetriever
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "elasticsearch_filter_only_retriever", "type": "ElasticsearchFilterOnlyRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "elasticsearch_filter_only_retriever"}, {"inputs": ["elasticsearch_filter_only_retriever"]}]}]}
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Create question answer generation system using table reader and question generator
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "table_reader", "type": "TableReader", "params": {"model_name_or_path": "google/tapas-base-finetuned-wtq", "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answer": false, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "table_reader"}]}]}
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Create Haystack faq system consisting of TableTextRetriever and OpenDistroElasticsearchDocumentStore
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{"version": "1.8.0", "components": [{"name": "open_distro_elasticsearch_document_store", "type": "OpenDistroElasticsearchDocumentStore", "params": {"scheme": "https", "username": "admin", "password": "admin", "port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "verify_certs": false, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "cosine", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"]}]}]}
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Build Haystack qa pipeline consisting of multihop embedding retriever, FARMReader and faiss document store
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{"version": "1.8.0", "components": [{"name": "faiss_document_store", "type": "FAISSDocumentStore", "params": {"sql_url": "sqlite:///faiss_document_store.db", "vector_dim": 0, "embedding_dim": 768, "faiss_index_factory_str": "Flat", "return_embedding": false, "index": "document", "similarity": "dot_product", "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "n_links": 64, "ef_search": 20, "ef_construction": 80, "validate_index_sync": true}}, {"name": "multihop_embedding_retriever", "type": "MultihopEmbeddingRetriever", "params": {"num_iterations": 2, "use_gpu": true, "batch_size": 32, "max_seq_len": 512, "model_format": "farm", "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "farm_reader", "type": "FARMReader", "params": {"context_window_size": 150, "batch_size": 50, "use_gpu": true, "no_ans_boost": 0.0, "return_no_answer": false, "top_k": 10, "top_k_per_candidate": 3, "top_k_per_sample": 1, "num_processes": 0, "max_seq_len": 256, "doc_stride": 128, "progress_bar": true, "duplicate_filtering": 0, "use_confidence_scores": true, "confidence_threshold": 0, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "multihop_embedding_retriever"}, {"inputs": ["multihop_embedding_retriever"], "name": "farm_reader"}]}]}
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Create search summarization system using ElasticsearchDocumentStore, transformers summarizer and TableTextRetriever
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{"version": "1.8.0", "components": [{"name": "elasticsearch_document_store", "type": "ElasticsearchDocumentStore", "params": {"port": 0, "index": "document", "label_index": "label", "content_field": "content", "name_field": "name", "embedding_field": "embedding", "embedding_dim": 768, "analyzer": "standard", "scheme": "http", "verify_certs": true, "recreate_index": false, "create_index": true, "refresh_type": "wait_for", "similarity": "dot_product", "timeout": 30, "return_embedding": false, "duplicate_documents": "overwrite", "index_type": "flat", "scroll": "1d", "skip_missing_embeddings": true, "synonym_type": "synonym", "use_system_proxy": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "transformers_summarizer"}]}]}
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Generate generative qa using RAGenerator, weaviate document store and table text retriever
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{"version": "1.8.0", "components": [{"name": "weaviate_document_store", "type": "WeaviateDocumentStore", "params": {"port": 0, "timeout_config": [5, 15], "index": "Document", "embedding_dim": 768, "content_field": "content", "name_field": "name", "similarity": "cosine", "index_type": "hnsw", "return_embedding": false, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "ra_generator", "type": "RAGenerator", "params": {"model_name_or_path": "facebook/rag-token-nq", "generator_type": "token", "top_k": 2, "max_length": 200, "min_length": 2, "num_beams": 2, "embed_title": true, "use_gpu": true, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_generator_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "ra_generator"}]}]}
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Generate Haystack qa system using weaviate document store, MultihopEmbeddingRetriever and RCIReader
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{"version": "1.8.0", "components": [{"name": "weaviate_document_store", "type": "WeaviateDocumentStore", "params": {"port": 0, "timeout_config": [5, 15], "index": "Document", "embedding_dim": 768, "content_field": "content", "name_field": "name", "similarity": "cosine", "index_type": "hnsw", "return_embedding": false, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false}}, {"name": "multihop_embedding_retriever", "type": "MultihopEmbeddingRetriever", "params": {"num_iterations": 2, "use_gpu": true, "batch_size": 32, "max_seq_len": 512, "model_format": "farm", "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "multihop_embedding_retriever"}, {"inputs": ["multihop_embedding_retriever"], "name": "rci_reader"}]}]}
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Build question generation system
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Make extractive qa consisting of FARMReader, weaviate document store and BM25Retriever
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{"version": "1.8.0", "components": [{"name": "weaviate_document_store", "type": "WeaviateDocumentStore", "params": {"port": 0, "timeout_config": [5, 15], "index": "Document", "embedding_dim": 768, "content_field": "content", "name_field": "name", "similarity": "cosine", "index_type": "hnsw", "return_embedding": false, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false}}, {"name": "bm25_retriever", "type": "BM25Retriever", "params": {"top_k": 10, "all_terms_must_match": false, "scale_score": true}}, {"name": "farm_reader", "type": "FARMReader", "params": {"context_window_size": 150, "batch_size": 50, "use_gpu": true, "no_ans_boost": 0.0, "return_no_answer": false, "top_k": 10, "top_k_per_candidate": 3, "top_k_per_sample": 1, "num_processes": 0, "max_seq_len": 256, "doc_stride": 128, "progress_bar": true, "duplicate_filtering": 0, "use_confidence_scores": true, "confidence_threshold": 0, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"], "name": "farm_reader"}]}]}
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Create qa system consisting of table reader, TableTextRetriever and sql document store
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{"version": "1.8.0", "components": [{"name": "sql_document_store", "type": "SQLDocumentStore", "params": {"url": "sqlite://", "index": "document", "label_index": "label", "duplicate_documents": "overwrite", "check_same_thread": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}, {"name": "table_reader", "type": "TableReader", "params": {"model_name_or_path": "google/tapas-base-finetuned-wtq", "use_gpu": true, "top_k": 10, "top_k_per_candidate": 3, "return_no_answer": false, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"], "name": "table_reader"}]}]}
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Build Haystack question generation system
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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Build Haystack faq search pipeline using WeaviateDocumentStore and TableTextRetriever
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{"version": "1.8.0", "components": [{"name": "weaviate_document_store", "type": "WeaviateDocumentStore", "params": {"port": 0, "timeout_config": [5, 15], "index": "Document", "embedding_dim": 768, "content_field": "content", "name_field": "name", "similarity": "cosine", "index_type": "hnsw", "return_embedding": false, "embedding_field": "embedding", "progress_bar": true, "duplicate_documents": "overwrite", "recreate_index": false}}, {"name": "table_text_retriever", "type": "TableTextRetriever", "params": {"max_seq_len_query": 64, "max_seq_len_passage": 256, "max_seq_len_table": 256, "top_k": 10, "use_gpu": true, "batch_size": 16, "embed_meta_fields": ["name", "section_title", "caption"], "use_fast_tokenizers": true, "similarity_function": "dot_product", "global_loss_buffer_size": 150000, "progress_bar": true, "use_auth_token": false, "scale_score": true, "use_fast": true}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "table_text_retriever"}, {"inputs": ["table_text_retriever"]}]}]}
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Generate Haystack search summarization system with transformers summarizer, SQLDocumentStore and EmbeddingRetriever
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{"version": "1.8.0", "components": [{"name": "sql_document_store", "type": "SQLDocumentStore", "params": {"url": "sqlite://", "index": "document", "label_index": "label", "duplicate_documents": "overwrite", "check_same_thread": false}}, {"name": "embedding_retriever", "type": "EmbeddingRetriever", "params": {"use_gpu": true, "batch_size": 32, "max_seq_len": 512, "pooling_strategy": "reduce_mean", "emb_extraction_layer": -1, "top_k": 10, "progress_bar": true, "use_auth_token": false, "scale_score": true}}, {"name": "transformers_summarizer", "type": "TransformersSummarizer", "params": {"model_name_or_path": "google/pegasus-xsum", "max_length": 200, "min_length": 5, "use_gpu": true, "clean_up_tokenization_spaces": true, "separator_for_single_summary": " ", "generate_single_summary": false, "batch_size": 16, "progress_bar": true, "use_auth_token": false}}], "pipelines": [{"name": "my_query_pipeline", "nodes": [{"inputs": ["Query"], "name": "embedding_retriever"}, {"inputs": ["embedding_retriever"], "name": "transformers_summarizer"}]}]}
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Make Haystack question answer generation system with FARMReader and question generator
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "farm_reader", "type": "FARMReader", "params": {"context_window_size": 150, "batch_size": 50, "use_gpu": true, "no_ans_boost": 0.0, "return_no_answer": false, "top_k": 10, "top_k_per_candidate": 3, "top_k_per_sample": 1, "num_processes": 0, "max_seq_len": 256, "doc_stride": 128, "progress_bar": true, "duplicate_filtering": 0, "use_confidence_scores": true, "confidence_threshold": 0, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "farm_reader"}]}]}
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Generate Haystack question answer generation system consisting of rci reader and question generator
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "rci_reader"}]}]}
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Make Haystack question answer generation pipeline with question generator and rci reader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "rci_reader"}]}]}
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Generate question answer generation system consisting of QuestionGenerator and RCIReader
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{"version": "1.8.0", "components": [{"name": "question_generator", "type": "QuestionGenerator", "params": {"sep_token": "<sep>", "batch_size": 16, "progress_bar": true, "use_auth_token": false}}, {"name": "rci_reader", "type": "RCIReader", "params": {"row_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-row", "column_model_name_or_path": "michaelrglass/albert-base-rci-wikisql-col", "use_gpu": true, "top_k": 10, "max_seq_len": 256, "use_auth_token": false}}], "pipelines": [{"name": "my_question_answering_pipeline", "nodes": [{"inputs": ["Query"], "name": "question_generator"}, {"inputs": ["question_generator"], "name": "rci_reader"}]}]}
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Build question generation pipeline
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{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}
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