farm-haystack 1.4.0


Neural Question Answering & Semantic Search at Scale. Use modern transformer based models like BERT

Neural Question Answering & Semantic Search at Scale. Use modern transformer based models like BERT to find answers in large document collections

Stars: 4642, Watchers: 4642, Forks: 747, Open Issues: 195

The deepset-ai/haystack repo was created 2 years ago and was last updated 10 hours ago.
The project is very popular with an impressive 4642 github stars!

How to Install farm-haystack

You can install farm-haystack using pip

pip install farm-haystack

or add it to a project with poetry

poetry add farm-haystack

Package Details

Apache License 2.0
GitHub Repo


  • Scientific/Engineering/Artificial Intelligence
No  farm-haystack  pypi packages just yet.


A list of common farm-haystack errors.

Code Examples

Here are some farm-haystack code examples and snippets.

GitHub Issues

The farm-haystack package has 195 open issues on GitHub

  • Add ADR template for transparent architecture decisions
  • OpenSearchDocumentStore: Support cosine similarity on dot_product embedding fields
  • allow different filters per query in pipeline evaluation
  • Update tutorials, utilities, tests and dependencies to run on Milvus2
  • What is the basic machine requirement for deploying ?
  • Population based training for fine tuning?
  • DPR embedding is not "invalidated" after calling DocumentStore.update_document_meta
  • ElasticSearchDocumentStore create_index param: Why is this useful?
  • Unable to make connection with existing Elasticstore with data.
  • add metadata to summarizer response
  • ranker should return scores for later usage
  • Autogenerate OpenAPI specs file
  • ✨ Add JSON Schema autogeneration for Pipeline YAML files - alternative 2
  • Make version of pipeline config/YAML configurable
  • Add Haystack CLI utility

See more issues on GitHub

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