farm-haystack 1.4.0

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

Author

deepset.ai

License

Apache License 2.0

Homepage

https://github.com/deepset-ai/haystack

PyPi

https://pypi.org/project/farm-haystack/

GitHub Repo

https://github.com/deepset-ai/haystack

Classifiers

• Scientific/Engineering/Artificial Intelligence

Related Packages

Errors

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