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haystack-pipelines

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Build faq search pipeline using DensePassageRetriever and FAISSDocumentStore

{"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"]}]}]}

Generate Haystack SearchSummarizationPipeline consisting of BM25Retriever, faiss document store and transformers summarizer

{"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_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"}]}]}

Build Haystack faq system using EmbeddingRetriever and open distro elasticsearch document store

{"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"]}]}]}

Make Haystack extractive qa system with FAISSDocumentStore, multihop embedding retriever and TransformersReader

{"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"}]}]}

Generate Haystack SearchSummarizationPipeline using embedding retriever, sql document store and TransformersSummarizer

{"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"}]}]}

Build Haystack question generation pipeline

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Create faq system consisting of tfidf retriever and ElasticsearchDocumentStore

{"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"]}]}]}

Make generative qa system consisting of pinecone document store, tfidf retriever and OpenAIAnswerGenerator

{"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": "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"}]}]}

Generate question generation system

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Make Haystack qa pipeline using FARMReader, FAISSDocumentStore and ElasticsearchFilterOnlyRetriever

{"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"}]}]}

Create qa system with multihop embedding retriever, sql document store and TransformersReader

{"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"}]}]}

Generate Haystack QuestionGenerationPipeline

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Build Haystack question answer generation system with QuestionGenerator and FARMReader

{"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"}]}]}

Create search summarization with TableTextRetriever, TransformersSummarizer and pinecone document store

{"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"}]}]}

Build QuestionAnswerGenerationPipeline using QuestionGenerator and transformers reader

{"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"}]}]}

Make QuestionGenerationPipeline

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Make question answer generation system using table reader and QuestionGenerator

{"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"}]}]}

Create Haystack question answer generation system consisting of transformers reader and QuestionGenerator

{"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"}]}]}

Make SearchSummarizationPipeline consisting of faiss document store, BM25Retriever and TransformersSummarizer

{"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_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"}]}]}

Create generative qa pipeline consisting of OpenAIAnswerGenerator, weaviate document store and DensePassageRetriever

{"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": "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}}, {"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": "dense_passage_retriever"}, {"inputs": ["dense_passage_retriever"], "name": "open_ai_answer_generator"}]}]}

Generate Haystack faq pipeline with multihop embedding retriever and WeaviateDocumentStore

{"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"]}]}]}

Create search summarization pipeline with InMemoryDocumentStore, FilterRetriever and TransformersSummarizer

{"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}}, {"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": "filter_retriever"}, {"inputs": ["filter_retriever"], "name": "transformers_summarizer"}]}]}

Build Haystack faq pipeline using TableTextRetriever and open search document store

{"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": "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"]}]}]}

Make extractive qa using bm25 retriever, weaviate document store and FARMReader

{"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"}]}]}

Make Haystack generative qa system with filter retriever, open search document store and ra generator

{"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": "filter_retriever", "type": "FilterRetriever", "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": "filter_retriever"}, {"inputs": ["filter_retriever"], "name": "ra_generator"}]}]}

Make Haystack question generation system

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Make Haystack faq system using open distro elasticsearch document store and BM25Retriever

{"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}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"]}]}]}

Build Haystack extractive qa consisting of in memory document store, elasticsearch filter only retriever and TableReader

{"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": "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": "elasticsearch_filter_only_retriever"}, {"inputs": ["elasticsearch_filter_only_retriever"], "name": "table_reader"}]}]}

Make SearchSummarizationPipeline using embedding retriever, sql document store and TransformersSummarizer

{"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"}]}]}

Create Haystack faq pipeline consisting of elasticsearch retriever and DeepsetCloudDocumentStore

{"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"]}]}]}

Build qa system using TableReader, TableTextRetriever and SQLDocumentStore

{"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"}]}]}

Make Haystack search summarization pipeline consisting of dense passage retriever, weaviate document store and TransformersSummarizer

{"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": "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}}, {"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": "dense_passage_retriever"}, {"inputs": ["dense_passage_retriever"], "name": "transformers_summarizer"}]}]}

Make question generation pipeline

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Create question generation system

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Build Haystack search summarization consisting of OpenSearchDocumentStore, transformers summarizer and TfidfRetriever

{"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}}, {"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"}]}]}

Build document search system consisting of multihop embedding retriever and OpenSearchDocumentStore

{"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": "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"]}]}]}

Build Haystack faq pipeline with OpenDistroElasticsearchDocumentStore and BM25Retriever

{"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}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"]}]}]}

Make Haystack FAQPipeline with elasticsearch document store and TfidfRetriever

{"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"]}]}]}

Make faq search pipeline using open distro elasticsearch document store and TableTextRetriever

{"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"]}]}]}

Make generative qa system with TfidfRetriever, DeepsetCloudDocumentStore and OpenAIAnswerGenerator

{"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"}]}]}

Generate Haystack search summarization pipeline with transformers summarizer, embedding retriever and SQLDocumentStore

{"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"}]}]}

Create question answer generation system using QuestionGenerator and farm reader

{"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"}]}]}

Create Haystack QuestionAnswerGenerationPipeline with RCIReader and question generator

{"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"}]}]}

Build qa pipeline with TransformersReader, bm25 retriever and FAISSDocumentStore

{"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"}]}]}

Build qa system using elasticsearch document store, filter retriever and rci reader

{"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": "filter_retriever", "type": "FilterRetriever", "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": "filter_retriever"}, {"inputs": ["filter_retriever"], "name": "rci_reader"}]}]}

Generate Haystack search pipeline with multihop embedding retriever and open search document store

{"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": "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"]}]}]}

Make QuestionGenerationPipeline

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Generate Haystack document search system with SQLDocumentStore and dense passage retriever

{"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": "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"]}]}]}

Build Haystack question answer generation pipeline with transformers reader and question generator

{"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"}]}]}

Create generative qa system consisting of ElasticsearchFilterOnlyRetriever, WeaviateDocumentStore and ra generator

{"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": "elasticsearch_filter_only_retriever", "type": "ElasticsearchFilterOnlyRetriever", "params": {"top_k": 10, "all_terms_must_match": false}}, {"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": "elasticsearch_filter_only_retriever"}, {"inputs": ["elasticsearch_filter_only_retriever"], "name": "ra_generator"}]}]}

Generate search pipeline with open search document store and multihop embedding retriever

{"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": "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"]}]}]}

Build qa system with FilterRetriever, WeaviateDocumentStore and TableReader

{"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": "filter_retriever", "type": "FilterRetriever", "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": "filter_retriever"}, {"inputs": ["filter_retriever"], "name": "table_reader"}]}]}

Make faq pipeline with in memory document store and TfidfRetriever

{"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"]}]}]}

Make Haystack generative qa system using Seq2SeqGenerator, elasticsearch retriever and OpenDistroElasticsearchDocumentStore

{"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"}]}]}

Make SearchSummarizationPipeline using bm25 retriever, pinecone document store and TransformersSummarizer

{"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": "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"}]}]}

Build Haystack QuestionAnswerGenerationPipeline with table reader and question generator

{"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"}]}]}

Generate search summarization pipeline with ElasticsearchFilterOnlyRetriever, TransformersSummarizer and open search document store

{"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": "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"}]}]}

Generate Haystack QuestionGenerationPipeline

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Make Haystack extractive qa pipeline consisting of table reader, OpenDistroElasticsearchDocumentStore and BM25Retriever

{"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"}]}]}

Create Haystack QuestionAnswerGenerationPipeline using FARMReader and QuestionGenerator

{"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"}]}]}

Build Haystack SearchSummarizationPipeline consisting of transformers summarizer, SQLDocumentStore and embedding retriever

{"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"}]}]}

Make Haystack search summarization with transformers summarizer, OpenDistroElasticsearchDocumentStore and elasticsearch filter only retriever

{"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}}, {"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"}]}]}

Make question answer generation system consisting of table reader and question generator

{"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"}]}]}

Create search summarization consisting of filter retriever, WeaviateDocumentStore and TransformersSummarizer

{"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": "filter_retriever", "type": "FilterRetriever", "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": "filter_retriever"}, {"inputs": ["filter_retriever"], "name": "transformers_summarizer"}]}]}

Generate Haystack question generation system

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Create Haystack question answer generation system consisting of FARMReader and question generator

{"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"}]}]}

Create Haystack document search system with MultihopEmbeddingRetriever and ElasticsearchDocumentStore

{"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": "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"]}]}]}

Create SearchSummarizationPipeline using transformers summarizer, in memory document store and filter retriever

{"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}}, {"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": "filter_retriever"}, {"inputs": ["filter_retriever"], "name": "transformers_summarizer"}]}]}

Build generative pipeline consisting of open ai answer generator, TfidfRetriever and DeepsetCloudDocumentStore

{"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"}]}]}

Generate Haystack document search system using bm25 retriever and deepset cloud document store

{"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}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"]}]}]}

Make Haystack generative qa system with pinecone document store, Seq2SeqGenerator and FilterRetriever

{"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"}]}]}

Create Haystack generative qa system with pinecone document store, tfidf retriever and OpenAIAnswerGenerator

{"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": "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"}]}]}

Generate QuestionAnswerGenerationPipeline with QuestionGenerator and FARMReader

{"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"}]}]}

Build Haystack extractive qa pipeline with weaviate document store, BM25Retriever and farm reader

{"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"}]}]}

Make document search pipeline with PineconeDocumentStore and elasticsearch retriever

{"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"]}]}]}

Generate question answer generation pipeline consisting of FARMReader and question generator

{"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"}]}]}

Make Haystack SearchSummarizationPipeline with embedding retriever, SQLDocumentStore and TransformersSummarizer

{"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"}]}]}

Generate Haystack question answer generation system using question generator and FARMReader

{"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"}]}]}

Create question generation system

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Make faq search pipeline with WeaviateDocumentStore and multihop embedding retriever

{"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"]}]}]}

Build question generation system

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Make Haystack faq system with MultihopEmbeddingRetriever and weaviate document store

{"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"]}]}]}

Create faq search pipeline with EmbeddingRetriever and open distro elasticsearch document store

{"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"]}]}]}

Generate Haystack QuestionGenerationPipeline

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Generate Haystack ExtractiveQAPipeline with MultihopEmbeddingRetriever, RCIReader and weaviate document store

{"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"}]}]}

Generate Haystack faq search pipeline using pinecone document store and embedding retriever

{"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": "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"]}]}]}

Make Haystack generative qa pipeline consisting of Seq2SeqGenerator, elasticsearch retriever and OpenDistroElasticsearchDocumentStore

{"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"}]}]}

Create Haystack SearchSummarizationPipeline consisting of TableTextRetriever, WeaviateDocumentStore and transformers summarizer

{"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": "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"}]}]}

Make extractive qa system with weaviate document store, table text retriever and table reader

{"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"}]}]}

Generate Haystack qa system with InMemoryDocumentStore, FARMReader and ElasticsearchFilterOnlyRetriever

{"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": "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"}]}]}

Build Haystack faq pipeline consisting of in memory document store and TfidfRetriever

{"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"]}]}]}

Generate faq system using bm25 retriever and OpenSearchDocumentStore

{"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"]}]}]}

Create Haystack QuestionAnswerGenerationPipeline consisting of QuestionGenerator and TransformersReader

{"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"}]}]}

Make Haystack generative qa system with open ai answer generator, EmbeddingRetriever and weaviate document store

{"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"}]}]}

Build QuestionGenerationPipeline

{"version": "1.8.0", "components": [{"name": "QuestionGenerator", "params": {}, "type": "QuestionGenerator"}], "pipelines": [{"name": "query", "nodes": [{"inputs": ["Query"], "name": "QuestionGenerator"}]}]}

Generate Haystack generative qa system with elasticsearch retriever, PineconeDocumentStore and seq2 seq generator

{"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}}, {"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"}]}]}

Build Haystack search pipeline using deepset cloud document store and BM25Retriever

{"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}}], "pipelines": [{"name": "my_doc_search_pipeline", "nodes": [{"inputs": ["Query"], "name": "bm25_retriever"}, {"inputs": ["bm25_retriever"]}]}]}

Build Haystack generative qa system with OpenAIAnswerGenerator, weaviate document store and EmbeddingRetriever

{"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"}]}]}

Create Haystack document search pipeline consisting of OpenSearchDocumentStore and tfidf retriever

{"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"]}]}]}

Build Haystack question answer generation pipeline consisting of QuestionGenerator and TransformersReader

{"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"}]}]}