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		d048bb5352
		
			
		
	
	
	
	
		
			
			* init * Change question * Add TODO comment * Addressing feedback * Add local folder option. Move additional functions inside haystack.utils for easier imports * Apply Daria's review suggestions Co-authored-by: Daria Fokina <daria.fokina@deepset.ai> * Add integration test * change string formatting Co-authored-by: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com> * Add outputparser to HF * Exclude anthropic test --------- Co-authored-by: Daria Fokina <daria.fokina@deepset.ai> Co-authored-by: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
		
			
				
	
	
		
			35 lines
		
	
	
		
			1.5 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			35 lines
		
	
	
		
			1.5 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| from haystack.document_stores import InMemoryDocumentStore
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| from haystack.utils import build_pipeline, add_example_data, print_answers
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| 
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| 
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| def getting_started(provider, API_KEY):
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|     """
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|     This getting_started example shows you how to use LLMs with your data with a technique called Retrieval Augmented Generation - RAG.
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| 
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|     :param provider: We are model agnostic :) Here, you can choose from: "anthropic", "cohere", "huggingface", and "openai".
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|     :param API_KEY: The API key matching the provider.
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| 
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|     """
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| 
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|     # We support many different databases. Here we load a simple and lightweight in-memory database.
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|     document_store = InMemoryDocumentStore(use_bm25=True)
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| 
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|     # Pipelines are the main abstraction in Haystack, they connect components like LLMs and databases.
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|     pipeline = build_pipeline(provider, API_KEY, document_store)
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| 
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|     # Download and add Game of Thrones TXT articles to Haystack's database.
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|     # You can also provide a folder with your local documents.
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|     # You might need to install additional dependencies - look inside the function for more information.
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|     add_example_data(document_store, "data/GoT_getting_started")
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| 
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|     # Ask a question on the data you just added.
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|     result = pipeline.run(query="Who is the father of Arya Stark?")
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| 
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|     # For details such as which documents were used to generate the answer, look into the <result> object.
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|     print_answers(result, details="medium")
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|     return result
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| 
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| 
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| if __name__ == "__main__":
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|     getting_started(provider="openai", API_KEY="ADD KEY HERE")
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