v.1.23.0

⭐️ Highlights

🪨 Amazon Bedrock support for PromptNode (#6226)

Haystack now supports Amazon Bedrock models, including all existing and previously announced
models, like Llama-2-70b-chat. To use these models, simply pass the model ID in the
model_name_or_path parameter, like you do for any other model. For details, see
Amazon Bedrock Documentation.

For example, the following code loads the Llama 2 Chat 13B model:

from haystack.nodes import PromptNode

prompt_node = PromptNode(model_name_or_path="meta.llama2-13b-chat-v1")

🗺️ Support for MongoDB Atlas Document Store (#6471)

With this release, we introduce support for MongoDB Atlas as a Document Store. Try it with:

from haystack.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore

document_store = MongoDBAtlasDocumentStore(
    mongo_connection_string=f"mongodb+srv://USER:PASSWORD@HOST/?{'retryWrites': 'true', 'w': 'majority'}",
    database_name="database",
    collection_name="collection",
)
...
document_store.write_documents(...)

Note that you need MongoDB Atlas credentials to fill the connection string. You can get such credentials by registering here: https://www.mongodb.com/cloud/atlas/register

⬆️ Upgrade Notes

• Remove deprecated OpenAIAnswerGenerator, BaseGenerator, GenerativeQAPipeline, and related tests.
  GenerativeQA Pipelines should use PromptNode instead. See https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode.

🚀 New Features

• Add PptxConverter: a node to convert pptx files to Haystack Documents.

• Add split_length by token in PreProcessor.

• Support for dense embedding instructions used in retrieval models such as BGE and LLM-Embedder.

• You can use Amazon Bedrock models in Haystack.

• Add MongoDBAtlasDocumentStore, providing support for MongoDB Atlas as a document store.

⚡️ Enhancement Notes

• Change PromptModel constructor parameter invocation_layer_class to accept a str too.
  If a str is used the invocation layer class will be imported and used.
  This should ease serialisation to YAML when using invocation_layer_class with PromptModel.

• Users can now define the number of pods and pod type directly when creating a PineconeDocumentStore instance.

• Add batch_size to the init method of FAISS Document Store. This works as the default value for all methods of
  FAISS Document Store that support batch_size.

• Introduces a new timeout keyword argument in PromptNode, addressing and fixing the issue #5380 for enhanced control over individual calls to OpenAI.

• Upgrade Transformers to the latest version 4.35.2
  This version adds support for DistilWhisper, Fuyu, Kosmos-2, SeamlessM4T, Owl-v2.

• Upgraded openai-whisper to version 20231106 and simplified installation through re-introduced audio extra.
  The latest openai-whisper version unpins its tiktoken dependency, which resolved a version conflict with Haystack's dependencies.

• Make it possible to load additional fields from the SQUAD format file into the meta field of the Labels.

• Add new variable model_kwargs to the ExtractiveReader so we can pass different loading options supported by
  HuggingFace.

• Add new token limit for gpt-4-1106-preview model.

🐛 Bug Fixes

• Fix Pipeline.load_from_deepset_cloud to work with the latest version of deepset Cloud.

• When using JoinDocuments with join_mode=concatenate (default) and
  passing duplicate documents, including some with a null score, this
  node raised an exception.
  Now this case is handled correctly and the documents are joined as expected.

• Adds LostInTheMiddleRanker, DiversityRanker, and RecentnessRanker to haystack/nodes/__init__.py and thus
  ensures that they are included in JSON schema generation.

• Adds LostInTheMiddleRanker, DiversityRanker, and RecentnessRanker to haystack/nodes/__init__.py and thus
  ensures that they are included in JSON schema generation.

v2.0.0-beta.1

Introduction

We are happy to officially share Haystack 2.0-beta with you. The new version is a complete rework of the pipeline, our core concept, with production readiness, ease of use, and customizability in mind.

Haystack 2.0-Beta Documentation.
Check the available features in this Beta release (see section below).
Try out Haystack 2.0-Beta in “Advent of Code”.

What does the “Beta” mean for me?

Production readiness means also caring about stability. Therefore, we decided to release a beta version now and test it thoroughly in public over the next weeks. We will add more features and we might add breaking changes until the stable 2.0 release in late Q1 2024.

We invite you to try this beta version and give candid feedback, it will be heard and we will change Haystack accordingly. We’ve put together 10 code challenges for you in our “Advent of Haystack” to get your hands on it. We don’t recommend migrating your production pipelines yet to 2.0 beta.

We will support Haystack 1.x with updates and important features being added to the codebase even after the final 2.0.0 release, to give users time to migrate.

⭐️ What’s changed?

For a detailed overview of what’s changed in this Beta release, check out our article “Introducing Haystack 2.0 and Advent of Haystack”.

The bulk of the work in this release introduces changes to the fundamental design of:

• Haystack Pipelines
• Haystack Components

In the last few months, we've been working with our community members and partners to already start adding some integrations for Haystack 2.0. Today, along with the beta package you can also try integrations tagged with Haystack 2.0 in our Integration inventory!

🚀 Getting started

One way to get started with Haystack 2.0 Beta is to participate in the “Advent of Haystack” and give us feedback on how you got along.

To install the new package:

pip install haystack-ai

To use a simple RAG pipeline:

from haystack import Document
from haystack.document_stores import InMemoryDocumentStore
from haystack.pipeline_utils import build_rag_pipeline

API_KEY = "sk-xxx" # ADD YOUR OPENAI API KEY

# We support many different databases. Here we load a simple and lightweight in-memory document store.
document_store = InMemoryDocumentStore()

# Create some example documents and add them to the document store.
documents = [
    Document(content="My name is Jean and I live in Paris."),
    Document(content="My name is Mark and I live in Berlin."),
    Document(content="My name is Giorgio and I live in Rome."),
]
document_store.write_documents(documents)

# Let's now build a simple RAG pipeline that uses a generative model to answer questions.
rag_pipeline = build_rag_pipeline(llm_api_key=API_KEY, document_store=document_store)
answers = rag_pipeline.run(query="Who lives in Rome?")
print(answers.data)

For more details on how to get started see: https://docs.haystack.deepset.ai/v2.0/docs/get_started

🪶 List of Features

✅ Ready in this Beta release

🏗️ Under construction

Feature	Haystack 2.0-Beta
Document Stores
InMemoryDocumentStore	✅
ElasticsearchDocumentstore	✅
OpenSearchDocumentStore	✅
ChromaDocumentStore	✅
MarqoDocumentStore	✅
FAISSDocumentStore	🏗️
PineconeDocumentStore	🏗️
WeaviateDocumentStore	🏗️
MilvusDocumentStore	🏗️
QdrantDocumentStore	🏗️
PGVectorDocumentStore	🏗️
MongoDBAtlasDocumentStore	🏗️
Generators
GPTGenerator	✅
HuggingFaceLocalGenerator	✅
HuggingFaceTGIGenerator	✅
GradientGenerator	✅
Anthropic - Claude	🏗️
Cohere - generate	✅
AzureGPT	🏗️
AWS Bedrock	🏗️
AWS SageMaker	🏗️
PromptNode	🏗️
PromptBuilder	✅
AnswerBuilder	✅
Embedders
OpenAI Embedder	✅
SentenceTransformers Embedder	✅
Cohere - embed	🏗️
Gradient Embedder (external)	✅
Retrievers
InMemoryBM25Retriever	✅
InMemoryEmbeddingRetriever	✅
ElasticsearchBM25Retriever	✅
ElasticsearchEmbeddingRetriever	✅
OpensearchBM25Retriever	✅
OpensearchEmbeddingRetriever	✅
SerperDevWebSearch	✅
MultiModalRetriever	🏗️
TableTextRetriever	🏗️
DensePassageRetriever	🏗️
Rankers
TransformersSimilarityRanker	✅
CohereRanker	🏗️
DiversityRanker	🏗️
LostInTheMiddleRanker	🏗️
RecentnessRanker	🏗️
MetaFieldRanker	✅
Readers
ExtractiveReader
(successor of both FARMReader and TransformersReader)	✅
TableReader	🏗️
Data Processing
Local + Remote WhisperTranscriber	✅
UrlCacheChecker	✅
LinkContentFetcher	✅
AzureOCRDocumentConverter	✅
HTMLToDocument	✅
PyPDFToDocument	✅
TikaDocumentConverter	✅
TextFileToDocument	✅
MarkdownToDocument	✅
DocumentCleaner	✅
TextDocumentSplitter	✅
TextLanguageClassifier	✅
FileTypeRouter	✅
MetadataRouter	✅
DocumentWriter	✅
DocumentJoiner	✅
Misc
Evaluation	🏗️
Agents	🏗️
Conversational Agent	🏗️
TopPSampler	✅
TransformersSummarizer	🏗️
TransformersTranslator	🏗️

v1.22.1

Release Notes

v1.22.1

Enhancement Notes

• Add new token limit for gpt-4-1106-preview model

Bug Fixes

• When using JoinDocuments with join_mode=concatenate (default) and passing duplicate documents, including some with a null score, this node raised an exception. Now this case is handled correctly and the documents are joined as expected.

v1.22.0

Release Notes

v1.22.0

⭐️ Highlights

Some additions to Haystack 2.0 preview:

New additions include a ByteStream type for binary data abstraction and the ChatMessage data class to streamline chat LLM component integration. AzureOCRDocumentConverter, HTMLToDocument and PyPDFToDocument further expand capability in document conversion. TransformersSimilarityRanker and TopPSampler improve document ranking and query handling capabilities. HuggingFaceLocalGenerator adds to ever-growing LLM components. These significant updates, along with a host of minor fixes and refinements, mark a significant step towards the upcoming Haystack 2.0 beta release.

⬆️ Upgrade Notes

• This update enables all Pinecone index types to be used, including Starter. Previously, Pinecone Starter index type couldn't be used as document store. Due to limitations of this index type (https://docs.pinecone.io/docs/starter-environment), in current implementation fetching documents is limited to Pinecone query vector limit (10000 vectors). Accordingly, if the number of documents in the index is above this limit, some of PineconeDocumentStore functions will be limited.
• Removes the audio, ray, onnx and beir extras from the extra group all.

🚀 New Features

• Add experimental support for asynchronous Pipeline run

⚡️ Enhancement Notes

• Added support for Apple Silicon GPU acceleration through "mps pytorch", enabling better performance on Apple M1 hardware.
• Document writer returns the number of documents written.
• added support for using on_final_answer trough Agent callback_manager
• Add asyncio support to the OpenAI invocation layer.
• PromptNode can now be run asynchronously by calling the arun method.
• Add search_engine_kwargs param to WebRetriever so it can be propagated to WebSearch. This is useful, for example, to pass the engine id when using Google Custom Search.
• Upgrade Transformers to the latest version 4.34.1. This version adds support for the new Mistral, Persimmon, BROS, ViTMatte, and Nougat models.
• Make JoinDocuments return only the document with the highest score if there are duplicate documents in the list.
• Add list_of_paths argument to utils.convert_files_to_docs to allow input of list of file paths to be converted, instead of, or as well as, the current dir_path argument.
• Optimize particular methods from PineconeDocumentStore (delete_documents and _get_vector_count)
• Update the deepset Cloud SDK to the new endpoint format for new saving pipeline configs.
• Add alias names for Cohere embed models for an easier map between names

⚠️ Deprecation Notes

• Deprecate OpenAIAnswerGenerator in favor of PromptNode. OpenAIAnswerGenerator will be removed in Haystack 1.23.

🐛 Bug Fixes

• Adds LostInTheMiddleRanker, DiversityRanker, and RecentnessRanker to haystack/nodes/__init__.py and thus ensures that they are included in JSON schema generation.
• Fixed the bug that prevented the correct usage of ChatGPT invocation layer in 1.21.1. Added async support for ChatGPT invocation layer.
• Added documents_store.update_embeddings call to pipeline examples so that embeddings are calculated for newly added documents.
• Remove unsupported medium and finance-sentiment models from supported Cohere embed model list

🩵 Haystack 2.0 preview

• Add AzureOCRDocumentConverter to convert files of different types using Azure's Document Intelligence Service.
• Add ByteStream type to send binary raw data across components in a pipeline.
• Introduce ChatMessage data class to facilitate structured handling and processing of message content within LLM chat interactions.
• Adds ChatMessage templating in PromptBuilder
• Adds HTMLToDocument component to convert HTML to a Document.
• Adds SimilarityRanker, a component that ranks a list of Documents based on their similarity to the query.
• Introduce the StreamingChunk dataclass for efficiently handling chunks of data streamed from a language model, encapsulating both the content and associated metadata for systematic processing.
• Adds TopPSampler, a component selects documents based on the cumulative probability of the Document scores using top p (nucleus) sampling.
• Add dumps, dump, loads and load methods to save and load pipelines in Yaml format.
• Adopt Hugging Face token instead of the deprecated use_auth_token. Add this parameter to ExtractiveReader and SimilarityRanker to allow loading private models. Proper handling of token during serialization: if it is a string (a possible valid token) it is not serialized.
• Add mime_type field to ByteStream dataclass.
• The Document dataclass checks if id_hash_keys is None or empty in __post_init__. If so, it uses the default factory to set a default valid value.
• Rework Document.id generation, if an id is not explicitly set it's generated using all Document field' values, score is not used.
• Change Document's embedding field type from numpy.ndarray to List[float]
• Fixed a bug that caused TextDocumentSplitter and DocumentCleaner to ignore id_hash_keys and create Documents with duplicate ids if the documents differed only in their metadata.
• Fix TextDocumentSplitter failing when run with an empty list
• Better management of API key in GPT Generator. The API key is never serialized. Make the api_base_url parameter really used (previously it was ignored).
• Add a minimal version of HuggingFaceLocalGenerator, a component that can run Hugging Face models locally to generate text.
• Migrate RemoteWhisperTranscriber to OpenAI SDK.
• Add OpenAI Document Embedder. It computes embeddings of Documents using OpenAI models. The embedding of each Document is stored in the embedding field of the Document.
• Add the TextDocumentSplitter component for Haystack 2.0 that splits a Document with long text into multiple Documents with shorter texts. Thereby the texts match the maximum length that the language models in Embedders or other components can process.
• Refactor OpenAIDocumentEmbedder to enrich documents with embeddings instead of recreating them.
• Refactor SentenceTransformersDocumentEmbedder to enrich documents with embeddings instead of recreating them.
• Remove "api_key" from serialization of AzureOCRDocumentConverter and SerperDevWebSearch.
• Removed implementations of from_dict and to_dict from all components where they had the same effect as the default implementation from Canals: https://github.com/deepset-ai/canals/blob/main/canals/serialization.py#L12-L13 This refactoring does not change the behavior of the components.
• Remove array field from Document dataclass.
• Remove id_hash_keys field from Document dataclass. id_hash_keys has been also removed from Components that were using it:
  • DocumentCleaner
  • TextDocumentSplitter
  • PyPDFToDocument
  • AzureOCRDocumentConverter
  • HTMLToDocument
  • TextFileToDocument
  • TikaDocumentConverter
• Enhanced file routing capabilities with the introduction of ByteStream handling, and improved clarity by renaming the router to FileTypeRouter.
• Rename MemoryDocumentStore to InMemoryDocumentStore Rename MemoryBM25Retriever to InMemoryBM25Retriever Rename MemoryEmbeddingRetriever to InMemoryEmbeddingRetriever
• Renamed ExtractiveReader's input from document to documents to match its type List[Document].
• Rename SimilarityRanker to TransformersSimilarityRanker, as there will be more similarity rankers in the future.
• Allow specifying stopwords to stop text generation for HuggingFaceLocalGenerator.
• Add basic telemetry to Haystack 2.0 pipelines
• Added DocumentCleaner, which removes extra whitespace, empty lines, headers, etc. from Documents containing text. Useful as a preprocessing step before splitting...

v1.22.0-rc3

Release Notes

v1.22.0-rc2

Bug Fixes

• Adds LostInTheMiddleRanker, DiversityRanker, and RecentnessRanker to haystack/nodes/__init__.py and thus ensures that they are included in JSON schema generation.

v1.22.0-rc1

Upgrade Notes

• This update enables all Pinecone index types to be used, including Starter. Previously, Pinecone Starter index type couldn't be used as document store. Due to limitations of this index type (https://docs.pinecone.io/docs/starter-environment), in current implementation fetching documents is limited to Pinecone query vector limit (10000 vectors). Accordingly, if the number of documents in the index is above this limit, some of PineconeDocumentStore functions will be limited.

• Removes the audio, ray, onnx and beir extras from the extra group all.

New Features

• Add experimental support for asynchronous Pipeline run

Enhancement Notes

• Added support for Apple Silicon GPU acceleration through "mps pytorch", enabling better performance on Apple M1 hardware.

• Document writer returns the number of documents written.

• added support for using on_final_answer trough Agent callback_manager

• Add asyncio support to the OpenAI invocation layer.

• PromptNode can now be run asynchronously by calling the arun method.

• Add search_engine_kwargs param to WebRetriever so it can be propagated to WebSearch. This is useful, for example, to pass the engine id when using Google Custom Search.

• Upgrade Transformers to the latest version 4.34.1. This version adds support for the new Mistral, Persimmon, BROS, ViTMatte, and Nougat models.

• Make JoinDocuments return only the document with the highest score if there are duplicate documents in the list.

• Add list_of_paths argument to utils.convert_files_to_docs to allow input of list of file paths to be converted, instead of, or as well as, the current dir_path argument.

• Optimize particular methods from PineconeDocumentStore (delete_documents and _get_vector_count)

• Update the deepset Cloud SDK to the new endpoint format for new saving pipeline configs.

• Add alias names for Cohere embed models for an easier map between names

Deprecation Notes

• Deprecate OpenAIAnswerGenerator in favor of PromptNode. OpenAIAnswerGenerator will be removed in Haystack 1.23.

Bug Fixes

• Fixed the bug that prevented the correct usage of ChatGPT invocation layer in 1.21.1. Added async support for ChatGPT invocation layer.

• Added documents_store.update_embeddings call to pipeline examples so that embeddings are calculated for newly added documents.

• Remove unsupported medium and finance-sentiment models from supported Cohere embed model list

Haystack 2.0 preview

• Add AzureOCRDocumentConverter to convert files of different types using Azure's Document Intelligence Service.

• Add ByteStream type to send binary raw data across components in a pipeline.

• Introduce ChatMessage data class to facilitate structured handling and processing of message content within LLM chat interactions.

• Adds ChatMessage templating in PromptBuilder

• Adds HTMLToDocument component to convert HTML to a Document.

• Adds SimilarityRanker, a component that ranks a list of Documents based on their similarity to the query.

• Introduce the StreamingChunk dataclass for efficiently handling chunks of data streamed from a language model, encapsulating both the content and associated metadata for systematic processing.

• Adds TopPSampler, a component selects documents based on the cumulative probability of the Document scores using top p (nucleus) sampling.

• Add dumps, dump, loads and load methods to save and load pipelines in Yaml format.

• Adopt Hugging Face token instead of the deprecated use_auth_token. Add this parameter to ExtractiveReader and SimilarityRanker to allow loading private models. Proper handling of token during serialization: if it is a string (a possible valid token) it is not serialized.

• Add mime_type field to ByteStream dataclass.

• The Document dataclass checks if id_hash_keys is None or empty in __post_init__. If so, it uses the default factory to set a default valid value.

• Rework Document.id generation, if an id is not explicitly set it's generated using all Document field' values, score is not used.

• Change Document's embedding field type from numpy.ndarray to List[float]

• Fixed a bug that caused TextDocumentSplitter and DocumentCleaner to ignore id_hash_keys and create Documents with duplicate ids if the documents differed only in their metadata.

• Fix TextDocumentSplitter failing when run with an empty list

• Better management of API key in GPT Generator. The API key is never serialized. Make the api_base_url parameter really used (previously it was ignored).

• Add a minimal version of HuggingFaceLocalGenerator, a component that can run Hugging Face models locally to generate text.

• Migrate RemoteWhisperTranscriber to OpenAI SDK.

• Add OpenAI Document Embedder. It computes embeddings of Documents using OpenAI models. The embedding of each Document is stored in the embedding field of the Document.

• Add the TextDocumentSplitter component for Haystack 2.0 that splits a Document with long text into multiple Documents with shorter texts. Thereby the texts match the maximum length that the language models in Embedders or other components can process.

• Refactor OpenAIDocumentEmbedder to enrich documents with embeddings instead of recreating them.

• Refactor SentenceTransformersDocumentEmbedder to enrich documents with embeddings instead of recreating them.

• Remove "api_key" from serialization of AzureOCRDocumentConverter and SerperDevWebSearch.

• Removed implementations of from_dict and to_dict from all components where they had the same effect as the default implementation from Canals: https://github.com/deepset-ai/canals/blob/main/canals/serialization.py#L12-L13 This refactoring does not change the behavior of the components.

• Remove array field from Document dataclass.

• Remove id_hash_keys field from Document dataclass. id_hash_keys has been also removed from Components that were using it:
  • DocumentCleaner
  • TextDocumentSplitter
  • PyPDFToDocument
  • AzureOCRDocumentConverter
  • HTMLToDocument
  • TextFileToDocument
  • TikaDocumentConverter

• Enhanced file routing capabilities with the introduction of ByteStream handling, and improved clarity by renaming the router to FileTypeRouter.

• Rename MemoryDocumentStore to InMemoryDocumentStore Rename MemoryBM25Retriever to InMemoryBM25Retriever Rename MemoryEmbeddingRetriever to InMemoryEmbeddingRetriever

• Renamed ExtractiveReader's input from document to documents to match its type List[Document].

• Rename SimilarityRanker to TransformersSimilarityRanker, as there will be more similarity rankers in the future.

• Allow specifying stopwords to stop text generation for HuggingFaceLocalGenerator.

• Add basic telemetry to Haystack 2.0 pipelines

• Added DocumentCleaner, which removes extra whitespace, empty lines, headers, etc. from Documents containing text. Useful as a preprocessing step before sp...

v1.22.0-rc1

v1.22.0-rc1

Upgrade Notes

• This update enables all Pinecone index types to be used, including
  Starter. Previously, Pinecone Starter index type couldn't be used as
  document store. Due to limitations of this index type
  (https://docs.pinecone.io/docs/starter-environment), in current
  implementation fetching documents is limited to Pinecone query
  vector limit (10000 vectors). Accordingly, if the number of
  documents in the index is above this limit, some of
  PineconeDocumentStore functions will be limited.
• Removes the audio,
  ray,
  onnx and
  beir extras from the extra group
  all.

New Features

• Add experimental support for asynchronous
  Pipeline run

Enhancement Notes

• Added support for Apple Silicon GPU acceleration through "mps
  pytorch", enabling better performance on Apple M1 hardware.
• Document writer returns the number of documents written.
• added support for using
  on_final_answer trough
  Agent
  callback_manager
• Add asyncio support to the OpenAI invocation layer.
• PromptNode can now be run asynchronously by calling the
  arun method.
• Add search_engine_kwargs param to
  WebRetriever so it can be propagated to WebSearch. This is useful,
  for example, to pass the engine id when using Google Custom Search.
• Upgrade Transformers to the latest version 4.34.1. This version adds
  support for the new Mistral, Persimmon, BROS, ViTMatte, and Nougat
  models.
• Make JoinDocuments return only the document with the highest score
  if there are duplicate documents in the list.
• Add list_of_paths argument to
  utils.convert_files_to_docs to allow
  input of list of file paths to be converted, instead of, or as well
  as, the current dir_path argument.
• Optimize particular methods from PineconeDocumentStore
  (delete_documents and _get_vector_count)
• Update the deepset Cloud SDK to the new endpoint format for new
  saving pipeline configs.
• Add alias names for Cohere embed models for an easier map between
  names

Deprecation Notes

• Deprecate OpenAIAnswerGenerator in
  favor of PromptNode.
  OpenAIAnswerGenerator will be removed
  in Haystack 1.23.

Bug Fixes

• Fixed the bug that prevented the correct usage of ChatGPT invocation
  layer in 1.21.1. Added async support for ChatGPT invocation layer.
• Added documents_store.update_embeddings call to pipeline examples so
  that embeddings are calculated for newly added documents.
• Remove unsupported medium and
  finance-sentiment models from
  supported Cohere embed model list

Haystack 2.0 preview

• Add AzureOCRDocumentConverter to convert files of different types
  using Azure's Document Intelligence Service.
• Add ByteStream type to send binary raw data across components in a
  pipeline.
• Introduce ChatMessage data class to facilitate structured handling
  and processing of message content within LLM chat interactions.
• Adds ChatMessage templating in
  PromptBuilder
• Adds HTMLToDocument component to convert HTML to a Document.
• Adds SimilarityRanker, a component that ranks a list of Documents
  based on their similarity to the query.
• Introduce the StreamingChunk dataclass for efficiently handling
  chunks of data streamed from a language model, encapsulating both
  the content and associated metadata for systematic processing.
• Adds TopPSampler, a component selects documents based on the
  cumulative probability of the Document scores using top p (nucleus)
  sampling.
• Add dumps,
  dump,
  loads and
  load methods to save and load
  pipelines in Yaml format.
• Adopt Hugging Face token instead of
  the deprecated use_auth_token. Add
  this parameter to ExtractiveReader
  and SimilarityRanker to allow loading
  private models. Proper handling of
  token during serialization: if it is
  a string (a possible valid token) it is not serialized.
• Add mime_type field to
  ByteStream dataclass.
• The Document dataclass checks if
  id_hash_keys is None or empty in
  __post_init__. If so, it uses the default factory to set a
  default valid value.
• Rework Document.id generation, if an
  id is not explicitly set it's
  generated using all Document field'
  values, score is not used.
• Change Document's
  embedding field type from
  numpy.ndarray to
  List[float]
• Fixed a bug that caused TextDocumentSplitter and DocumentCleaner to
  ignore id_hash_keys and create Documents with duplicate ids if the
  documents differed only in their metadata.
• Fix TextDocumentSplitter failing when run with an empty list
• Better management of API key in GPT Generator. The API key is never
  serialized. Make the api_base_url
  parameter really used (previously it was ignored).
• Add a minimal version of HuggingFaceLocalGenerator, a component that
  can run Hugging Face models locally to generate text.
• Migrate RemoteWhisperTranscriber to OpenAI SDK.
• Add OpenAI Document Embedder. It computes embeddings of Documents
  using OpenAI models. The embedding of each Document is stored in the
  embedding field of the Document.
• Add the TextDocumentSplitter
  component for Haystack 2.0 that splits a Document with long text
  into multiple Documents with shorter texts. Thereby the texts match
  the maximum length that the language models in Embedders or other
  components can process.
• Refactor OpenAIDocumentEmbedder to enrich documents with embeddings
  instead of recreating them.
• Refactor SentenceTransformersDocumentEmbedder to enrich documents
  with embeddings instead of recreating them.
• Remove "api_key" from serialization of AzureOCRDocumentConverter and
  SerperDevWebSearch.
• Removed implementations of from_dict and to_dict from all components
  where they had the same effect as the default implementation from
  Canals:
  https://github.com/deepset-ai/canals/blob/main/canals/serialization.py#L12-L13
  This refactoring does not change the behavior of the components.
• Remove array field from
  Document dataclass.
• Remove id_hash_keys field from
  Document dataclass.
  id_hash_keys has been also removed
  from Components that were using it:
  • DocumentCleaner
  • TextDocumentSplitter
  • PyPDFToDocument
  • AzureOCRDocumentConverter
  • HTMLToDocument
  • TextFileToDocument
  • TikaDocumentConverter
• Enhanced file routing capabilities with the introduction of
  ByteStream handling, and improved
  clarity by renaming the router to
  FileTypeRouter.
• Rename MemoryDocumentStore to
  InMemoryDocumentStore Rename
  MemoryBM25Retriever to
  InMemoryBM25Retriever Rename
  MemoryEmbeddingRetriever to
  InMemoryEmbeddingRetriever
• Renamed ExtractiveReader's input from
  document to
  documents to match its type
  List[Document].
• Rename SimilarityRanker to
  TransformersSimilarityRanker, as
  there will be more similarity rankers in the future.
• Allow specifying stopwords to stop text generation for
  HuggingFaceLocalGenerator.
• Add basic telemetry to Haystack 2.0 pipelines
• Added DocumentCleaner, which removes extra whitespace, empty lines,
  headers, etc. from Documents containing text. Useful as a
  preprocessing step before splitting into shorter text documents.
• Add TextLanguageClassifier component so that an input string, for
  example a query, can be routed to different components based on the
  detected language.
• Upgrade canals to 0.9.0 to support variadic inputs for Joiner
  c...

v1.21.2

🐛 Bug Fixes

• Fixed the bug that prevented the correct usage of ChatGPT invocation layer in 1.21.1.
  Added async support for ChatGPT invocation layer.

v1.21.1

✨ Enhancements

• Added experimental support for asynchronous Pipeline run.
• Added asyncio support to the OpenAI invocation layer.
• PromptNode can now be run asynchronously by calling the arun method.

⏰ Deprecations

• Deprecated OpenAIAnswerGenerator in favor of PromptNode. OpenAIAnswerGenerator will be removed in Haystack v1.23.0

v1.21.0

⭐ Highlights

🚀 Support for gpt-3.5-turbo-instruct

We are happy to announce that Haystack now supports OpenAI's new gpt-3.5-turbo-instruct model! Simply provide the model name in the PromptNode to use it:

pn = PromptNode("gpt-3.5-turbo-instruct", api_key=os.environ.get("OPENAI_API_KEY"))

2️⃣ Preview Installation Extra

Excited about the upcoming Haystack 2.0? We have introduced a new installation extra called preview which you can install to try out the Haystack 2.0 preview! This extra also makes Haystack's core dependencies leaner and thus speeds up installation. If you would like to start experiencing the new Haystack 2.0 components and pipeline design right away, run:

pip install farm-haystack[preview]

⚡️ WeaviateDocumentStore Performance

We fixed a bottleneck in WeaviateDocumentStore which was slowing down the indexing. The fix led to a notable performance improvement, reducing the indexing time of one million documents by 6 times!

🐣 PineconeDocumentStore Robustness

The PineconeDocumentStore now uses metadata instead of namespaces for the distinction between documents with embeddings, documents without embeddings, and labels. This is a breaking change and it makes the PineconeDocumentStore more robust to use in Haystack pipelines. If you want to retrieve all documents with an embedding, specify the metadata instead of the namespace as follows:

from haystack.document_stores.pinecone import DOCUMENT_WITH_EMBEDDING
# docs = doc_store.get_all_documents(namespace="vectors") # old way using namespaces
docs = doc_store.get_all_documents(type_metadata=DOCUMENT_WITH_EMBEDDING)

Additionally, if you want to retrieve all documents without an embedding, specify the metadata instead of the namespace:

# docs = doc_store.get_all_documents(namespace="no-vectors") # old way using namespaces
docs = doc_store_.get_all_documents(type_metadata="no-vector")

⬆️ Upgrade Notes

• SklearnQueryClassifier is removed and users should switch to the more powerful TransformersQueryClassifier instead. #5447

• Refactor PineconeDocumentStore to use metadata instead of namespaces for the distinction between documents with embeddings, documents without embeddings, and labels.

✨ Enhancements

• ci: Fix typos discovered by codespell running in pre-commit.

• Support OpenAI's new gpt-3.5-turbo-instruct model

🐛 Bug Fixes

• Fix EntityExtractor output not JSON serializable.

• Fix model_max_length not being set in the Tokenizer in DefaultPromptHandler.

• Fixed a bottleneck in Weaviate document store which was slowing down the indexing.

• gpt-35-turbo-16k model from Azure can integrate correctly.

• Upgrades tiktoken to 0.5.1 to account for a breaking release.

👁️ Haystack 2.0 preview

• Add the AnswerBuilder component for Haystack 2.0 that creates Answer objects from the string output of Generators.

• Adds LinkContentFetcher component to Haystack 2.0. LinkContentFetcher fetches content from a given URL and
  converts it into a Document object, which can then be used within the Haystack 2.0 pipeline.

• Add MetadataRouter, a component that routes documents to different edges based on the content of their fields.

• Adds support for PDF files to the Document converter via pypdf library.

• Adds SerperDevWebSearch component to retrieve URLs from the web. See https://serper.dev/ for more information.

• Add TikaDocumentConverter component to convert files of different types to Documents.

• This adds an ExtractiveReader for v2. It should be a replacement where
  FARMReader would have been used before for inference.
  The confidence scores are calculated differently from FARMReader because
  each span is considered to be an independent binary classification task.

• Introduce GPTGenerator, a class that can generate completions using OpenAI Chat models like GPT3.5 and GPT4.

• Remove id parameter from Document constructor as it was ignored and a new one was generated anyway.
  This is a backwards incompatible change.

• Add generators module for LLM generator components.

• Adds GPT4Generator, an LLM component based on GPT35Generator.

• Add embedding_retrieval method to MemoryDocumentStore,
  which allows to retrieve the relevant Documents, given a query embedding.
  It will be called the MemoryEmbeddingRetriever.

• Rename MemoryRetriever to MemoryBM25Retriever
  Add MemoryEmbeddingRetriever, which takes as input a query embedding and
  retrieves the most relevant Documents from a MemoryDocumentStore.

• Adds proposal for an extended Document class in Haystack 2.0.

• Adds the implementation of said class.

• Add OpenAI Text Embedder.
  It is a component that uses OpenAI models to embed strings into vectors.

• Revert #5826 and optionally take the id in the Document
  class constructor.

• Create a dedicated dependency list for the preview package, farm-haystack[preview].
  Using haystack-ai is still the recommended way to test Haystack 2.0.

• Add PromptBuilder component to render prompts from template strings.

• Add prefix and suffix attributes to SentenceTransformersDocumentEmbedder.
  They can be used to add a prefix and suffix to the Document text before
  embedding it. This is necessary to take full advantage of modern embedding
  models, such as E5.

• Add support for dates in filters.

• Add UrlCacheChecker to support Web retrieval pipelines.
  Check if documents coming from a given list of URLs are already present in the store and if so, returns them.
  All URLs with no matching documents are returned on a separate connection.

v1.20.1

Changelog

• fix: temporary pin tiktoken #5774

Full Changelog: v1.20.0...v1.20.1