Release Notes

v2.0.0-beta.6

⬆️ Upgrade Notes

• Upgraded the default converter in PyPDFToDocument to insert page breaks "f" between each extracted page. This allows for downstream components and applications to better be able to keep track of the original PDF page a portion of text comes from.

• ⚠️ Breaking change: Update secret handling for components using the Secret type. The following components are affected: RemoteWhisperTranscriber, AzureOCRDocumentConverter, AzureOpenAIDocumentEmbedder, AzureOpenAITextEmbedder, HuggingFaceTEIDocumentEmbedder, HuggingFaceTEITextEmbedder, OpenAIDocumentEmbedder, SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder, AzureOpenAIGenerator, AzureOpenAIChatGenerator, HuggingFaceLocalChatGenerator, HuggingFaceTGIChatGenerator, OpenAIChatGenerator, HuggingFaceLocalGenerator, HuggingFaceTGIGenerator, OpenAIGenerator, TransformersSimilarityRanker, SearchApiWebSearch, SerperDevWebSearch

  The default init parameters for api_key, token, azure_ad_token have been adjusted to use environment variables wherever possible. The azure_ad_token_provider parameter has been removed from Azure-based components. Components based on Hugging Face are now required to either use a token or an environment variable if authentication is required - The on-disk local token file is no longer supported.

Required actions to take:
To make fixes to accommodate to this breaking change check the expected environment variable name for the api_key of the affected component you are using. Make sure to provide your API keys via this environment variable. Alternatively, if that's not an option, use the Secret.from_token function to wrap any bare/string API tokens. Mind that pipelines using token secrets cannot be serialized/deserialized.

🚀 New Features

• Expose a Secret type to provide consistent API for any component that requires secrets for authentication. Currently supports string tokens and environment variables. Token-based secrets are automatically prevented from being serialized to disk (to prevent accidental leakage of secrets).

  from haystack.utils import Secret
  
  @component
  class MyComponent:
    def __init__(self, api_key: Optional[Secret] = None, **kwargs):
      self.api_key = api_key
      self.backend = None
  
    def warm_up(self):
      # Call resolve_value to yield a single result. The semantics of the result is policy-dependent.
      # Currently, all supported policies will return a single string token.
      self.backend = SomeBackend(api_key=self.api_key.resolve_value() if self.api_key else None, ...)
  
    def to_dict(self):
      # Serialize the policy like any other (custom) data. If the policy is token-based, it will
      # raise an error.
      return default_to_dict(self, api_key=self.api_key.to_dict() if self.api_key else None, ...)
  
    @classmethod
    def from_dict(cls, data):
      # Deserialize the policy data before passing it to the generic from_dict function.
      api_key_data = data["init_parameters"]["api_key"]
      api_key = Secret.from_dict(api_key_data) if api_key_data is not None else None
      data["init_parameters"]["api_key"] = api_key
      return default_from_dict(cls, data)
  
  # No authentication.
  component = MyComponent(api_key=None)
  # Token based authentication
  component = MyComponent(api_key=Secret.from_token("sk-randomAPIkeyasdsa32ekasd32e"))
  component.to_dict() # Error! Can't serialize authentication tokens
  # Environment variable based authentication
  component = MyComponent(api_key=Secret.from_env("OPENAI_API_KEY"))
  component.to_dict() # This is fine

• Adds support for the Exact Match metric to EvaluationResult.calculate_metrics(...):

  from haystack.evaluation.metrics import Metric 
  exact_match_metric = eval_result.calculate_metrics(Metric.EM, output_key="answers")

• Adds support for the F1 metric to EvaluationResult.calculate_metrics(...):

  from haystack.evaluation.metrics import Metric 
  f1_metric = eval_result.calculate_metrics(Metric.F1, output_key="answers")

• Adds support for the Semantic Answer Similarity (SAS) metric to EvaluationResult.calculate_metrics(...):

  from haystack.evaluation.metrics import Metric 
  sas_metric = eval_result.calculate_metrics(     
      Metric.SAS, output_key="answers", model="sentence-transformers/paraphrase-multilingual-mpnet-base-v2" )

• Introducing the HuggingFaceLocalChatGenerator, a new chat-based generator designed for leveraging chat models from Hugging Face's (HF) model hub. Users can now perform inference with chat-based models in a local runtime, utilizing familiar HF generation parameters, stop words, and even employing custom chat templates for custom message formatting. This component also supports streaming responses and is optimized for compatibility with a variety of devices.

  Here is an example of how to use the HuggingFaceLocalChatGenerator:

  from haystack.components.generators.chat import HuggingFaceLocalChatGenerator
  from haystack.dataclasses import ChatMessage
  
  generator = HuggingFaceLocalChatGenerator(model="HuggingFaceH4/zephyr-7b-beta")
  generator.warm_up()
  messages = [ChatMessage.from_user("What's Natural Language Processing? Be brief.")] 
  print(generator.run(messages))

⚡️ Enhancement Notes

• Change Pipeline.add_component() to fail if the Component instance has already been added in another Pipeline.
• Use device_map when loading a TransformersSimilarityRanker and ExtractiveReader. This allows for multi-device inference and for loading quantized models (e.g. load_in_8bit=True)
• Add meta parameter to ByteStream.from_file_path() and ByteStream.from_string().
• Add query and document prefix options for the TransformerSimilarityRanker
• The default in default_streaming_callback was confusing, this function was the go-to-helper one would use to quickly print the generated tokens as they come, but it was not used by default. The function was then renamed to print_streaming_chunk.
• Speed up import of Document dataclass. Importing Document was slowed down cause we were importing the whole pandas and numpy packages. This has now been changed to import only the necessary classes and functions.
• Introduces weighted score normalization for the DocumentJoiner's reciprocal rank fusion, enhancing the relevance of document sorting by allowing customizable influence on the final scores

🐛 Bug Fixes

• Fix auto-complete never working for any Component
• Fix Haystack imports failing when using local development environment that doesn't have haystack-ai installed.
• Remove all mentions of Canals by renaming some variables. __canals_input__ and __canals_ouput__ have been renamed respectively to __haystack_input__ and __haystack_ouput__. CANALS_VARIADIC_ANNOTATION has been renamed to HAYSTACK_VARIADIC_ANNOTATION and it's value changed from __canals__variadic_t to __haystack__variadic_t. Default Pipeline debug_path has been changed from .canals_debug to .haystack_debug.