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v1.11.0

21 Nov 11:22
@masci masci
v1.11.0
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v1.11.0

⭐ Highlights

Expanding Haystack’s LLM support further with the new CohereEmbeddingEncoder (#3356)

Now you can easily create document and query embeddings using Cohere’s large language models: if you have a Cohere account, all you have to do is set the name of one of the supported models (small, medium, or large) and add your API key to the EmbeddingRetriever component in your pipelines (see docs).

Extracting headlines from Markdown and PDF files (#3445 #3488)

Using the MarkdownConverter or the ParsrConverter you can set the parameter extract_headlines to True to extract the headlines out of your files together with their start start position in the file and their level. Headlines are stored as a list of dictionaries in the Document's meta field "headlines" and are structured as followed:

{
    "headline": <THE HEADLINE STRING>,
    "start_idx": <IDX OF HEADLINE START IN document.content >,
    "level": <LEVEL OF THE HEADLINE>
}

Introducing the proposals design process (#3333)

We've introduced the proposal design process for substantial changes. A proposal is a single Markdown file that explains why a change is needed and how it would be implemented. You can find a detailed explanation of the process and a proposal template in the proposals directory.

⚠️ Breaking change: removing Milvus1DocumentStore

From this version onwards, Haystack no longer supports version 1 of Milvus. We still support Milvus version 2. We removed Milvus1DocumentStore and renamed Milvus2DocumentStore to MilvusDocumentStore.

What's Changed

Breaking Changes

  • bug: removed duplicated meta "name" field addition to content before embedding in update_embeddings workflow by @mayankjobanputra in #3368
  • BREAKING CHANGE: remove Milvus1DocumentStore along with support for Milvus < 2.x by @masci in #3552

Pipeline

  • fix: Fix the error of wrong page numbers when documents contain empty pages. by @brunnurs in #3330
  • bug: change type of split_by to Literal including None by @julian-risch in #3389
  • Fix: update pyworld pin by @anakin87 in #3435
  • feat: send event if number of queries exceeds threshold by @vblagoje in #3419
  • Feat: allow decreasing size of datasets loaded from BEIR by @ugm2 in #3392
  • feat: add __cointains__ to Span by @ZanSara in #3446
  • Bug: Fix prompt length computation by @Timoeller in #3448
  • Add indexing pipeline type by @vblagoje in #3461
  • fix: warning if doc store similarity function is incompatible with Sentence Transformers model by @anakin87 in #3455
  • feat: Add CohereEmbeddingEncoder to EmbeddingRetriever by @vblagoje in #3453
  • feat: Extraction of headlines in markdown files by @bogdankostic in #3445
  • bug: replace decorator with counter attribute for pipeline event by @julian-risch in #3462
  • feat: add document_store to all BaseRetriever.retrieve() and BaseRetriever.retrieve_batch() implementations by @ZanSara in #3379
  • refactor: TableReader by @sjrl in #3456
  • fix: do not reference package directory in PDFToTextOCRConverter.convert() by @ZanSara in #3478
  • feat: Create the TextIndexingPipeline by @brandenchan in #3473
  • refactor: remove YAML save/load methods for subclasses of BaseStandardPipeline by @ZanSara in #3443
  • fix: strip whitespaces safely from FARMReader's answers by @ZanSara in #3526

DocumentStores

  • Document Store test refactoring by @masci in #3449
  • fix: support long texts for labels in ElasticsearchDocumentStore by @anakin87 in #3346
  • feat: add SQLDocumentStore tests by @masci in #3517
  • refactor: Refactor Weaviate tests by @masci in #3541
  • refactor: Pinecone tests by @masci in #3555
  • fix: write metadata to SQL Document Store when duplicate_documents!="overwrite" by @anakin87 in #3548
  • fix: Elasticsearch / OpenSearch brownfield function does not incorporate meta by @tstadel in #3572
  • fix: discard metadata fields if not set in Weaviate by @masci in #3578

UI / Demo

Documentation

Other Changes

New Contributors

Full Changelog: v1.10.0...v1.11.0rc1

Contributors

masci, vblagoje, and 12 other contributors
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v1.11.0rc1

18 Nov 07:38
@bogdankostic bogdankostic
v1.11.0rc1
893d2d4
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v1.11.0rc1 Pre-release
Pre-release

⭐ Highlights

Expanding Haystack’s LLM support further with the new CohereEmbeddingEncoder (#3356)

Now you can easily create document and query embeddings using Cohere’s large language models: if you have a Cohere account, all you have to do is set the name of one of the supported models (small, medium, or large) and add your API key to the EmbeddingRetriever component in your pipelines (see docs).

Extracting headlines from Markdown and PDF files (#3445 #3488)

Using the MarkdownConverter or the ParsrConverter you can set the parameter extract_headlines to True to extract the headlines out of your files together with their start start position in the file and their level. Headlines are stored as a list of dictionaries in the Document's meta field "headlines" and are structured as followed:

{
    "headline": <THE HEADLINE STRING>,
    "start_idx": <IDX OF HEADLINE START IN document.content >,
    "level": <LEVEL OF THE HEADLINE>
}

Introducing the proposals design process (#3333)

We've introduced the proposal design process for substantial changes. A proposal is a single Markdown file that explains why a change is needed and how it would be implemented. You can find a detailed explanation of the process and a proposal template in the proposals directory.

⚠️ Breaking change: removing Milvus1DocumentStore

From this version onwards, Haystack no longer supports version 1 of Milvus. We still support Milvus version 2. We removed Milvus1DocumentStore and renamed Milvus2DocumentStore to MilvusDocumentStore.

What's Changed

Breaking Changes

  • bug: removed duplicated meta "name" field addition to content before embedding in update_embeddings workflow by @mayankjobanputra in #3368
  • BREAKING CHANGE: remove Milvus1DocumentStore along with support for Milvus < 2.x by @masci in #3552

Pipeline

  • fix: Fix the error of wrong page numbers when documents contain empty pages. by @brunnurs in #3330
  • bug: change type of split_by to Literal including None by @julian-risch in #3389
  • Fix: update pyworld pin by @anakin87 in #3435
  • feat: send event if number of queries exceeds threshold by @vblagoje in #3419
  • Feat: allow decreasing size of datasets loaded from BEIR by @ugm2 in #3392
  • feat: add __cointains__ to Span by @ZanSara in #3446
  • Bug: Fix prompt length computation by @Timoeller in #3448
  • Add indexing pipeline type by @vblagoje in #3461
  • fix: warning if doc store similarity function is incompatible with Sentence Transformers model by @anakin87 in #3455
  • feat: Add CohereEmbeddingEncoder to EmbeddingRetriever by @vblagoje in #3453
  • feat: Extraction of headlines in markdown files by @bogdankostic in #3445
  • bug: replace decorator with counter attribute for pipeline event by @julian-risch in #3462
  • feat: add document_store to all BaseRetriever.retrieve() and BaseRetriever.retrieve_batch() implementations by @ZanSara in #3379
  • refactor: TableReader by @sjrl in #3456
  • fix: do not reference package directory in PDFToTextOCRConverter.convert() by @ZanSara in #3478
  • feat: Create the TextIndexingPipeline by @brandenchan in #3473
  • refactor: remove YAML save/load methods for subclasses of BaseStandardPipeline by @ZanSara in #3443
  • fix: strip whitespaces safely from FARMReader's answers by @ZanSara in #3526

DocumentStores

  • Document Store test refactoring by @masci in #3449
  • fix: support long texts for labels in ElasticsearchDocumentStore by @anakin87 in #3346
  • feat: add SQLDocumentStore tests by @masci in #3517
  • refactor: Refactor Weaviate tests by @masci in #3541
  • refactor: Pinecone tests by @masci in #3555
  • fix: write metadata to SQL Document Store when duplicate_documents!="overwrite" by @anakin87 in #3548
  • fix: Elasticsearch / OpenSearch brownfield function does not incorporate meta by @tstadel in #3572
  • fix: discard metadata fields if not set in Weaviate by @masci in #3578

UI / Demo

Documentation

Other Changes

New Contributors

Full Changelog: v1.10.0...v1.11.0rc1

Contributors

masci, vblagoje, and 12 other contributors
Assets 2
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v1.10.0

25 Oct 13:47
@masci masci
v1.10.0
3a2714e
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v1.10.0

⭐ Highlights

Expanding Haystack's LLM support with the new OpenAIEmbeddingEncoder (#3356)

Now you can easily create document and query embeddings using large language models: if you have an OpenAI account, all you have to do is set the name of one of the supported models (ada, babbage, davinci or curie) and add your API key to the EmbeddingRetriever component in your pipelines (see docs).

Multimodal retrieval is here! (#2891)

Multimodality with Haystack just made a big leap forward with the addition of MultiModalRetriever: a Retriever that can handle different modalities for query and documents independently. Take it for a spin and experiment with new Document formats, like images. You can now use the same Retriever for text-to-image, text-to-table, and text-to-text retrieval but also image similarity, table similarity, and more! Feed your favorite multimodal model to MultiModalRetriever and see it in action.

retriever = MultiModalRetriever(
    document_store=InMemoryDocumentStore(embedding_dim=512),
    query_embedding_model = "sentence-transformers/clip-ViT-B-32",
    query_type="text",
    document_embedding_models = {"image": "sentence-transformers/clip-ViT-B-32"}
)

Multi-platform Docker images

Starting with 1.10, we're making the deepset/haystack images available for linux/amd64 and linux/arm64.

⚠️ Breaking change in embed_queries method (#3252)

We've changed the text argument in the embed_queries method for DensePassageRetriever and EmbeddingRetriever to queries.

What's Changed

Breaking Changes

Pipeline

  • fix: ONNX FARMReader model conversion is broken by @vblagoje in #3211
  • bug: JoinDocuments nodes produce incorrect results if preceded by another JoinDocuments node by @JeffRisberg in #3170
  • fix: eval() with add_isolated_node_eval=True breaks if no node supports it by @tstadel in #3347
  • feat: extract label aggregation by @tstadel in #3363
  • feat: Add OpenAIEmbeddingEncoder to EmbeddingRetriever by @vblagoje in #3356
  • fix: stable YAML schema generation by @ZanSara in #3388
  • fix: Update how schema is ordered by @sjrl in #3399
  • feat: MultiModalRetriever by @ZanSara in #2891

DocumentStores

  • feat: FAISS in OpenSearch: Support HNSW for cosine by @tstadel in #3217
  • feat: add support for Elasticsearch 7.16.2 by @masci in #3318
  • refactor: remove dead code from FAISSDocumentStore by @anakin87 in #3372
  • fix: allow same vector_id in different indexes for SQL-based Document stores by @anakin87 in #3383

UI / Demo

  • fix: demo won't start through Docker compose on Apple M1 by @masci in #3337

Documentation

Other Changes

  • refactor: make TransformersDocumentClassifier output consistent between different types of classification by @anakin87 in #3224
  • Classify pipeline's type based on its components by @vblagoje in #3132
  • docs: sync Haystack API with Readme by @brandenchan in #3223
  • fix: MostSimilarDocumentsPipeline doesn't have pipeline property by @vblagoje in #3265
  • bug: make ElasticSearchDocumentStore use batch_size in get_documents_by_id by @anakin87 in #3166
  • refactor: better tests for TransformersDocumentClassifier by @anakin87 in #3270
  • fix: AttributeError in TranslationWrapperPipeline by @nickchomey in #3290
  • refactor: remove Inferencer multiprocessing by @vblagoje in #3283
  • fix: opensearch script score with filters by @tstadel in #3321
  • feat: Adding filters param to MostSimilarDocumentsPipeline run and run_batch by @JacdDev in #3301
  • feat: add multi-platform Docker images by @masci in #3354
  • fix: Added checks for DataParallel and WrappedDataParallel by @sjrl in #3366
  • fix: QuestionGenerator generates wrong document questions for non-default num_queries_per_doc parameter by @vblagoje in #3381
  • bug: Adds better way of checking query in BaseRetriever and Pipeline.run() by @ugm2 in #3304
  • feat: Updated EntityExtractor to handle long texts and added better postprocessing by @sjrl in #3154
  • docs: Add comment about the generation of no-answer samples in FARMReader training by @brandenchan in #3404
  • feat: Speed up integration tests (nodes) by @sjrl in #3408
  • fix: Fix the error of wrong page numbers when documents contain empty pages. by @brunnurs in #3330
  • bug: change type of split_by to Literal including None by @julian-risch in #3389
  • feat: Add exponential backoff decorator; apply it to OpenAI requests by @vblagoje in #3398

New Contributors

Full Changelog: v1.9.1...v1.10.0rc1

Contributors

masci, tanertopal, and 13 other contributors
Assets 2
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v1.10.0rc1

20 Oct 15:37
@masci masci
v1.10.0rc1
0405d70
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v1.10.0rc1 Pre-release
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⭐ Highlights

Expanding Haystack's LLM support with the new OpenAIEmbeddingEncoder (#3356)

Now you can easily create document and query embeddings using large language models: if you have an OpenAI account, all you have to do is set the name of one of the supported models (ada, babbage, davinci or curie) and add your API key to the EmbeddedRetriver component in your pipelines.

Multimodal retrieval is here! (#2891)

Multimodality with Haystack just made a big leap forward with the addition of MultiModalRetriever: a Retriever that can handle different modalities for query and documents independently. Take it for a spin and experiment with new Document formats, like images. You can now use the same Retriever for text-to-image, text-to-table, and text-to-text retrieval but also image similarity, table similarity, and more! Feed your favorite multimodal model to MultiModalRetriever and see it in action.

retriever = MultiModalRetriever(
    document_store=InMemoryDocumentStore(embedding_dim=512),
    query_embedding_model = "sentence-transformers/clip-ViT-B-32",
    query_type="text",
    document_embedding_models = {"image": "sentence-transformers/clip-ViT-B-32"}
)

Multi-platform Docker images

Starting with 1.10, we're making the deepset/haystack images available for linux/amd64 and linux/arm64.

⚠️ Breaking change in embed_queries method (#3252)

We've changed the text argument in the embed_queries method for DensePassageRetriever and EmbeddingRetriever to queries.

What's Changed

Breaking Changes

Pipeline

  • fix: ONNX FARMReader model conversion is broken by @vblagoje in #3211
  • bug: JoinDocuments nodes produce incorrect results if preceded by another JoinDocuments node by @JeffRisberg in #3170
  • fix: eval() with add_isolated_node_eval=True breaks if no node supports it by @tstadel in #3347
  • feat: extract label aggregation by @tstadel in #3363
  • feat: Add OpenAIEmbeddingEncoder to EmbeddingRetriever by @vblagoje in #3356
  • fix: stable YAML schema generation by @ZanSara in #3388
  • fix: Update how schema is ordered by @sjrl in #3399
  • feat: MultiModalRetriever by @ZanSara in #2891

DocumentStores

  • feat: FAISS in OpenSearch: Support HNSW for cosine by @tstadel in #3217
  • feat: add support for Elasticsearch 7.16.2 by @masci in #3318
  • refactor: remove dead code from FAISSDocumentStore by @anakin87 in #3372
  • fix: allow same vector_id in different indexes for SQL-based Document stores by @anakin87 in #3383

UI / Demo

  • fix: demo won't start through Docker compose on Apple M1 by @masci in #3337

Documentation

Other Changes

  • refactor: make TransformersDocumentClassifier output consistent between different types of classification by @anakin87 in #3224
  • Classify pipeline's type based on its components by @vblagoje in #3132
  • docs: sync Haystack API with Readme by @brandenchan in #3223
  • fix: MostSimilarDocumentsPipeline doesn't have pipeline property by @vblagoje in #3265
  • bug: make ElasticSearchDocumentStore use batch_size in get_documents_by_id by @anakin87 in #3166
  • refactor: better tests for TransformersDocumentClassifier by @anakin87 in #3270
  • fix: AttributeError in TranslationWrapperPipeline by @nickchomey in #3290
  • refactor: remove Inferencer multiprocessing by @vblagoje in #3283
  • fix: opensearch script score with filters by @tstadel in #3321
  • feat: Adding filters param to MostSimilarDocumentsPipeline run and run_batch by @JacdDev in #3301
  • feat: add multi-platform Docker images by @masci in #3354
  • fix: Added checks for DataParallel and WrappedDataParallel by @sjrl in #3366
  • fix: QuestionGenerator generates wrong document questions for non-default num_queries_per_doc parameter by @vblagoje in #3381
  • bug: Adds better way of checking query in BaseRetriever and Pipeline.run() by @ugm2 in #3304
  • feat: Updated EntityExtractor to handle long texts and added better postprocessing by @sjrl in #3154
  • docs: Add comment about the generation of no-answer samples in FARMReader training by @brandenchan in #3404
  • feat: Speed up integration tests (nodes) by @sjrl in #3408
  • fix: Fix the error of wrong page numbers when documents contain empty pages. by @brunnurs in #3330
  • bug: change type of split_by to Literal including None by @julian-risch in #3389
  • feat: Add exponential backoff decorator; apply it to OpenAI requests by @vblagoje in #3398

New Contributors

Full Changelog: v1.9.1...v1.10.0rc1

Contributors

masci, tanertopal, and 13 other contributors
Assets 2
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v1.9.1

10 Oct 13:16
@masci masci
v1.9.1
c1e8d14
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v1.9.1

What's Changed

  • fix: Allow less restrictive values for parameters in Pipeline configurations by @bogdankostic in #3345

Full Changelog: v1.9.0...v1.9.1rc1

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bogdankostic
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v1.9.1rc1

10 Oct 12:37
@masci masci
v1.9.1rc1
256321d
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v1.9.1rc1 Pre-release
Pre-release

What's Changed

  • fix: Allow less restrictive values for parameters in Pipeline configurations by @bogdankostic in #3345

Full Changelog: v1.9.0...v1.9.1rc1

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bogdankostic
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v1.9.0

21 Sep 11:23
@masci masci
v1.9.0
ce36be8
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v1.9.0

⭐ Highlights

Haystack 1.9 comes with nice performance improvements and two important pieces of news about its ecosystem. Let's see it in more detail!

Logging speed set to ludicrous (#3212)

This feature alone makes Haystack 1.9 worth testing out, just sayin'... We switched from f-strings to the string formatting operator when composing a log message, observing an astonishing speed of up to 120% in some pipelines.

Tutorials moved out! (#3244)

They grow up so fast! Tutorials now have their own git repository, CI, and release cycle, making it easier than ever to contribute ideas, fixes, and bug reports. Have a look at the tutorials repo, Star it, and open an issue if you have an idea for a new tutorial!

Docker pull deepset/haystack (#3162)

A new Docker image is ready to be pulled shipping Haystack 1.9, providing different flavors and versions that you can specify with the proper Docker tag - have a look at the README.
On this occasion, we also revamped the build process so that it's now using bake, while the older images are deprecated (see below).

⚠️ Deprecation notice

With the release of the new Docker image deepset/haystack, the following images are now deprecated and won't be updated any more starting with Haystack 1.10:

New Documentation Site and Haystack Website Revamp:

The Haystack website is going through a make-over to become a developer portal that surrounds Haystack and NLP topics beyond pure documentation. With that, we've published our new documentation site. From now on, content surrounding pure developer documentation will live under Haystack Documentation, while the Haystack website becomes a place for the community with tutorials, learning material and soon, a place where the community can share their own content too.

What's Changed

Pipeline

  • feat: standardize devices parameter and device initialization by @vblagoje in #3062
  • fix: Reduce GPU to CPU copies at inference by @sjrl in #3127
  • test: lower low boundary for accuracy in test_calculate_context_similarity_on_non_matching_contexts by @ZanSara in #3199
  • bug: fix pdftotext installation verification by @banjocustard in #3233
  • chore: remove f-strings from logs for performance reasons by @ZanSara in #3212
  • bug: reactivate benchmarks with quick fixes by @tholor in #2766

Models

  • fix: Replace multiprocessing tokenization with batched fast tokenization by @vblagoje in #3089

DocumentStores

  • bug: OpensearchDocumentStore.custom_mapping should accept JSON strings at validation by @ZanSara in #3065
  • feat: Add warnings to PineconeDocumentStore about indexing metadata if filters return no documents by @Namoush in #3086
  • bug: validate custom_mapping as an object by @ZanSara in #3189

Tutorials

  • docs: Fix the word length splitting; should be set to 100 not 1,000 by @stevenhaley in #3133
  • chore: remove tutorials from the repo by @masci in #3244

Other Changes

  • chore: Upgrade and pin transformers to 4.21.2 by @vblagoje in #3098
  • bug: adapt UI random question for streamlit 1.12 and pin to streamlit>=1.9.0 by @anakin87 in #3121
  • build: pin pydantic to 1.9.2 by @masci in #3126
  • fix: document FARMReader.train() evaluation report log level by @brandenchan in #3129
  • feat: add a security policy for Haystack by @masci in #3130
  • refactor: update dependencies and remove pins by @danielbichuetti in #3147
  • refactor: update package strategy in rest_api by @masci in #3148
  • fix: give default index for torch.device('cuda') in initialize_device_settings by @sjrl in #3161
  • fix: add type hints to all component init constructor parameters by @vblagoje in #3152
  • fix: Add 15 min timeout for downloading cached HF models by @vblagoje in #3179
  • fix: replace torch.device("cuda") with torch.device("cuda:0") in devices initialization by @vblagoje in #3184
  • feat: add health check endpoint to rest api by @danielbichuetti in #3168
  • refactor: improve support for dataclasses by @danielbichuetti in #3142
  • feat: Updates docs and types for language param in PreProcessor by @sjrl in #3186
  • feat: Add option to use MultipleNegativesRankingLoss for EmbeddingRetriever training with sentence-transformers by @bglearning in #3164
  • refactoring: reimplement Docker strategy by @masci in #3162
  • refactor: remove pre haystack-1.0 import paths support by @ZanSara in #3204
  • feat: exponential backoff with exp decreasing batch size for opensearch and elasticsearch client by @ArzelaAscoIi in #3194
  • feat: add public layout-base extraction support on PDFToTextConverter by @danielbichuetti in #3137
  • bug: fix embedding_dim mismatch in DocumentStore by @kalki7 in #3183
  • fix: update rest_api Docker Compose yamls for recent refactoring of rest_api by @nickchomey in #3197
  • chore: fix Windows CI by @masci in #3222
  • fix: type of temperature param and adjust defaults for OpenAIAnswerGenerator by @tholor in #3073
  • fix: handle Documents containing dataframes in Multilabel constructor by @masci in #3237
  • fix: make pydoc-markdown hook correctly resolve paths relative to repo root by @masci in #3238
  • fix: proper retrieval of answers for batch eval by @vblagoje in #3245
  • chore: updating colab links in older docs versions by @TuanaCelik in #3250
  • docs: establish API docs sync between v1.9.x and Readme by @brandenchan in #3266

New Contributors

Full Changelog: v1.8.0...v1.9.0

Contributors

masci, vblagoje, and 14 other contributors
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v1.8.0

26 Aug 16:08
@julian-risch julian-risch
v1.8.0
4e518cd
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v1.8.0

⭐ Highlights

This release comes with a bunch of new features, improvements and bug fixes. Let us know how you like it on our brand new Haystack Discord server! Here are the highlights of the release:

Pipeline Evaluation in Batch Mode #2942

The evaluation of pipelines often uses large datasets and with this new feature batches of queries can be processed at the same time on a GPU. Thereby, the time needed for an evaluation run is decreased and we are working on further speed improvements. To try it out, you only need to replace the call to pipeline.eval() with pipeline.eval_batch() when you evaluate your question answering pipeline:

...
pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)
eval_result = pipeline.eval_batch(labels=eval_labels, params={"Retriever": {"top_k": 5}})

Early Stopping in Reader and Retriever Training #3071

When training a reader or retriever model, you need to specify the number of training epochs. If the model doesn't further improve after the first few epochs, the training usually still continues for the rest of the specified number of epochs. Early Stopping can now automatically monitor how much the model improves during training and stop the process when there is no significant improvement. Various metrics can be monitored, including loss, EM, f1, and top_n_accuracy for FARMReader or loss, acc, f1, and average_rank for DensePassageRetriever. For example, reader training can be stopped when loss doesn't further decrease by at least 0.001 compared to the previous epoch:

from haystack.nodes import FARMReader
from haystack.utils.early_stopping import EarlyStopping
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2-distilled")
reader.train(data_dir="data/squad20", train_filename="dev-v2.0.json", early_stopping=EarlyStopping(min_delta=0.001), use_gpu=True, n_epochs=8, save_dir="my_model")

PineconeDocumentStore Without SQL Database #2749

Thanks to @jamescalam the PineconeDocumentStore does not depend on a local SQL database anymore. So when you initialize a PineconeDocumentStore from now on, all you need to provide is a Pinecone API key:

from haystack.document_stores import PineconeDocumentStore
document_store = PineconeDocumentStore(api_key="...")
docs = [Document(content="...")]
document_store.write_documents(docs)

FAISS in OpenSearchDocumentStore: #3101 #3029

OpenSearch supports different approximate k-NN libraries for indexing and search. In Haystack's OpenSearchDocumentStore you can now set the knn_engine parameter to choose between nmslib and faiss. When loading an existing index you can also specify a knn_engine and Haystack checks if the same engine was used to create the index. If not, it falls back to slow exact vector calculation.

Highlighted Bug Fixes

A bug was fixed that prevented users from loading private models in some components because the authentication token wasn't passed on correctly. A second bug was fixed in the schema files affecting parameters that are of type Optional[List[]], in which case the validation failed if the parameter was explicitly set to None.

  • fix: Use use_auth_token in all cases when loading from the HF Hub by @sjrl in #3094
  • bug: handle Optional params in schema validation by @anakin87 in #2980

Other Changes

DocumentStores

  • feat: Allow exact list matching with field in Elasticsearch filtering by @masci in #2988

Documentation

Crawler

  • fix: update ChromeDriver options on restricted environments and add ChromeDriver options as function parameter by @danielbichuetti in #3043
  • fix: Crawler quits ChromeDriver on destruction by @danielbichuetti in #3070

Other Changes

  • fix(translator): write translated text to output documents, while keeping input untouched by @danielbichuetti in #3077
  • test: Use random_sample instead of ndarray for random array in OpenSearchDocumentStore test by @bogdankostic in #3083
  • feat: add progressbar to upload_files() for deepset Cloud client by @tholor in #3069
  • refactor: update package metadata by @ofek in #3079

New Contributors

❤️ Big thanks to all contributors and the whole community!

Full Changelog: v1.7.1...v1.8.0

Contributors

masci, tholor, and 9 other contributors
Assets 2
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v1.7.1

19 Aug 11:32
@julian-risch julian-risch
v1.7.1
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v1.7.1

Patch Release

Main Changes

  • feat: take the list of models to cache instead of hardcoding one by @masci in #3060

Other Changes

  • fix: pin version of pyworld to 0.2.12 by @sjrl in #3047
  • test: update filtering of Pinecone mock to imitate doc store by @jamescalam in #3020

Full Changelog: v1.7.0...v1.7.1

Contributors

masci, sjrl, and jamescalam
Assets 2
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v1.7.0

15 Aug 12:43
@tstadel tstadel
v1.7.0
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v1.7.0

⭐ Highlights

This time we have a couple of smaller yet important feature highlights: lots of them coming from you, our amazing community!
🥂 Alongside that, as we notice more frequent and great contributions from our community, we are also announcing our brand new Haystack Discord server to help us interact better with the people that make Haystack what it is! 🥳

Here's what you'll find in Haystack 1.7:

Support for OpenAI GPT-3

If you always wanted to know how OpenAI's famous GPT-3 model compares to other models, now your time has come. It's been fully integrated into Haystack, so you can use it as any other model. Just sign up to OpenAI, copy your API key from here and run the following code.To compare it to other models, check out our evaluation guide.

from haystack.nodes import OpenAIAnswerGenerator
from haystack import Document

reader = OpenAIAnswerGenerator(api_key="<your-api-token>", max_tokens=15, temperature=0.3)

docs = [Document(content="""The Big Bang Theory is an American sitcom.
                            The four main characters are all avid fans of nerd culture. 
                            Among their shared interests are science fiction, fantasy, comic books and collecting memorabilia. 
                            Star Trek in particular is frequently referenced""")]
res = reader.predict(query="Do the main characters of big bang theory like Star Trek?", documents=docs)
print(res)

#2605
#3036

Zero-Shot Query Classification

Till now, TransformersQueryClassifier was very closely built around the excellent binary query-type classifier model of hahrukhx01. Although it was already possible to use other Transformer models, the choice was restricted to the models that output binary labels. One of our amazing community contributions now lifted this restriction.
But that's not all: @anakin87 added support for zero-shot classification models as well!
So now that you're completely free to choose the classification categories you want, you can let your creativity run wild. One thing you could do is customize the behavior of your pipeline based on the semantic category of the query, like this:

from haystack.nodes import TransformersQueryClassifier

# In zero-shot-classification, you are free to choose the labels
labels = ["music", "cinema", "food"]

query_classifier = TransformersQueryClassifier(
    model_name_or_path="typeform/distilbert-base-uncased-mnli",
    use_gpu=True,
    task="zero-shot-classification",
    labels=labels,
)

queries = [
    "In which films does John Travolta appear?",  # query about cinema
    "What is the Rolling Stones first album?",  # query about music
    "Who was Sergio Leone?",  # query about cinema
]

for query in queries:
    result = query_classifier.run(query=query)
    print(f'Query "{query}" was sent to {result[1]}')

#2965

Adding Page Numbers to Document Meta

Sometimes it's not enough to find the right answer or paragraph inside a document and just print it on the screen. Context matters and thus, for search applications, it's essential to send the user exactly to the place where the information came from. For huge documents, we're just halfway there if the user clicks a result and the document opens. To get to the right position, they still need to search the document using the document viewer. To make it easier, we added the parameter add_page_number to ParsrConverter, AzureConverter and PreProcessor. If you set it to True, it adds a meta field "page" to documents containing the page number of the text snippet or a table within the original file.

from haystack.nodes import PDFToTextConverter, PreProcessor
from haystack.document_stores import InMemoryDocumentStore

converter = PDFToTextConverter()
preprocessor = PreProcessor(add_page_number=True)
document_store = InMemoryDocumentStore()

pipeline = Pipeline()
pipeline.add_node(component=converter, name="Converter", inputs=["File"])
pipeline.add_node(component=preprocessor, name="Preprocessor", inputs=["Converter"])
pipeline.add_node(component=document_store, name="DocumentStore", inputs=["PreProcessor"])

#2932

Gradient Accumulation for FARMReader

Training big Transformer models in low-resource environments is hard. Batch size plays a significant role when it comes to hyper-parameter tuning during the training process. The number of batches you can run on your machine is restricted by the amount of memory that fits into your GPUs. Gradient accumulation is a well-known technique to work around that restriction: adding up the gradients across iterations and running the backward pass only once after a certain number of iterations.
We tested it when we fine-tuned roberta-base on SQuAD, which led to nearly the same results as using a higher batch size. We also used it for training deepset/deberta-v3-large, which significantly outperformed its predecessors (see Question Answering on SQuAD).

from haystack.nodes import FARMReader

reader = FARMReader(model_name_or_path="distilbert-base-uncased-distilled-squad", use_gpu=True)
data_dir = "data/squad20"
reader.train(
    data_dir=data_dir, 
    train_filename="dev-v2.0.json", 
    use_gpu=True, n_epochs=1, 
    save_dir="my_model", 
    grad_acc_steps=8
)

#2925

Extended Ray Support

Another great contribution from our community comes from @zoltan-fedor: it's now possible to run more complex pipelines with dual-retriever setup on Ray. Also, we now support ray serve deployment arguments in Pipeline YAMLs so that you can fully control your ray deployments.

pipelines:
  - name: ray_query_pipeline
    nodes:
      - name: EmbeddingRetriever
        replicas: 2
        inputs: [ Query ]
        serve_deployment_kwargs:
          num_replicas: 2
          version: Twenty
          ray_actor_options:
            num_gpus: 0.25
            num_cpus: 0.5
          max_concurrent_queries: 17
      - name: Reader
        inputs: [ EmbeddingRetriever ]

#2981
#2918

Support for Custom Sentence Tokenizers in Preprocessor

On some specific domains (for example, legal with lots of custom abbreviations), the default sentence tokenizer can be improved by some extra training on the domain data. To support a custom model for sentence splitting, @danielbichuetti added the tokenizer_model_folder parameter to Preprocessor.

from haystack.nodes import PreProcessor

preprocessor = PreProcessor(
        split_length=10,
        split_overlap=0,
        split_by="sentence",
        split_respect_sentence_boundary=False,
        language="pt",
        tokenizer_model_folder="/home/user/custom_tokenizer_models",
    )

#2783

Making it Easier to Switch Document Stores

We had yet another amazing community contribution by @zoltan-fedor about the support for BM25 with the Weaviate document store.
Besides that we streamlined methods of BaseDocumentStore and added update_document_meta() to InMemoryDocumentStore. These are all steps to make it easier for you to run the same pipeline with different document stores (for example, for quick prototyping, use in-memory, then head to something more production-ready).
#2860
#2689

Almost 2x Performance Gain for Electra Reader Models

We did a major refactoring of our language_modeling module resolving a bug that caused Electra models to execute the forward pass twice.
#2703.

⚠️ Breaking Changes

⚠️ Breaking Changes for Contributors

Default Branch will be Renamed to main on Tuesday, 16th of August

We will rename the default branch from master to main after this release. For a nice recap about good reasons for doing this, have a look at the Software Freedom Conservancy's blog.
Whether coming from this repository or from a fork, local clones of the Haystack repository will need to be updated by running the following commands:

git branch -m master main
git fetch origin
git branch -u origin/main main
git remote set-head origin -a

Pre-Commit Hooks Instead of CI Jobs

To give you full control over your changes, we switched from CI jobs that automatically reformat files, generate schemas, and so on, to pre-commit hooks. To install them, run:

pre-commit install

For more information, check our contributing guidelines.
#2819

Other Changes

Pipelin...

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Contributors

masci, vblagoje, and 23 other contributors
Assets 2
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