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https://github.com/deepset-ai/haystack.git
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540d0fad97
* Introduce DiversityRanker * improve most_diverse_order speed * Compute mean for numerical stability * Add release note * Add cosine similarity * Test both dot product and cosine similarity * Add pydocs hook --------- Co-authored-by: Michel Bartels <login@michelbartels.com>
50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
import logging
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import os
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from haystack import Pipeline
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from haystack.nodes import PromptNode, PromptTemplate, TopPSampler, DocumentMerger
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from haystack.nodes.ranker.diversity import DiversityRanker
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from haystack.nodes.retriever.web import WebRetriever
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search_key = os.environ.get("SERPERDEV_API_KEY")
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if not search_key:
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raise ValueError("Please set the SERPERDEV_API_KEY environment variable")
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openai_key = os.environ.get("OPENAI_API_KEY")
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if not openai_key:
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raise ValueError("Please set the OPENAI_API_KEY environment variable")
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prompt_text = """
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Synthesize a comprehensive answer from the following most relevant paragraphs and the given question.
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Provide a clear and concise response that summarizes the key points and information presented in the paragraphs.
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Your answer should be in your own words and be no longer than 50 words.
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\n\n Paragraphs: {documents} \n\n Question: {query} \n\n Answer:
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"""
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prompt_node = PromptNode(
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"gpt-3.5-turbo", default_prompt_template=PromptTemplate(prompt_text), api_key=openai_key, max_length=256
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)
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web_retriever = WebRetriever(api_key=search_key, top_search_results=10, mode="preprocessed_documents", top_k=25)
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sampler = TopPSampler(top_p=0.95)
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ranker = DiversityRanker()
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merger = DocumentMerger(separator="\n\n")
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pipeline = Pipeline()
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pipeline.add_node(component=web_retriever, name="Retriever", inputs=["Query"])
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pipeline.add_node(component=sampler, name="Sampler", inputs=["Retriever"])
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pipeline.add_node(component=ranker, name="Ranker", inputs=["Sampler"])
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pipeline.add_node(component=merger, name="Merger", inputs=["Ranker"])
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pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Merger"])
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logger = logging.getLogger("boilerpy3")
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logger.setLevel(logging.CRITICAL)
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questions = ["What are the reasons for long-standing animosities between Russia and Poland?"]
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for q in questions:
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print(f"Question: {q}")
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response = pipeline.run(query=q)
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print(f"Answer: {response['results'][0]}")
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