farm-haystack 1.26.3

LLM framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data.

Stars: 17099, Watchers: 17099, Forks: 1869, Open Issues: 123

The deepset-ai/haystack repo was created 4 years ago and the last code push was Yesterday.
The project is extremely popular with a mindblowing 17099 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

None

License

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Homepage

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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 123 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