wehub-resource-sync
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
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""" This example illustrates a common two-step retrieval pattern using a SLIM NER model:
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Step 1: Extract named entity information from a text. In this case, the name of a musician.
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Step 2: Use the extracted name information as the basis for a retrieval. In this case, we will use the
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extracted named entities to do a lookup in Wikipedia. """
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from llmware.agents import LLMfx
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from llmware.parsers import WikiParser
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def ner_lookup_retrieval():
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text = ("The new Miko Marks album is one of the best I have ever heard in a number of years. "
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"She is definitely an artist worth exploring further.")
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# create agent
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agent = LLMfx()
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agent.load_work(text)
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agent.load_tool("ner")
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named_entities = agent.ner()
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ner_dict= named_entities["llm_response"]
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# take named entities found and package into a lookup list
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lookup = []
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for keys, value in ner_dict.items():
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if value:
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lookup.append(value)
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for entries in lookup:
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# run a wiki topic query with each of the named entities found
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wiki_info = WikiParser().add_wiki_topic(entries, target_results=1)
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print("update: wiki_info - ", wiki_info)
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summary = wiki_info["articles"][0]["summary"]
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print("update: summary - ", summary)
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return 0
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if __name__ == "__main__":
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ner_lookup_retrieval()
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