haystack
https://github.com/deepset-ai/haystack
Python
:mag: LLM orchestration 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. With advanced retrieval methods, it's best suited
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- Issues
- I am using Faiss Document store. When I call get_document_count function along with filters, it is returning zero
- File_path in meta should optionally be just the filename
- Update Document.__eq__ to intelligently compare floats
- feat: Add a Azure OCR Converter that uses the `azure-ai-documentintelligence` library
- feat: Update required_variables parameter in PromptBuilder to accept `"*"` to require all defined jinja variables
- chore: `renaming deserialize_document_store_in_init_params_inplace`
- Rename internal mentions of `from_socket` and `to_socket` to `sender_socket` and `receiver_socket`
- Update meta images for docs
- OpenAIGenerator uses chat_completions endpoint. Error with model that has no chat_template in config
- Option to enable structured outputs with OpenAI Generators
- Docs
- Python not yet supported