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38 lines
1.5 KiB
Python
38 lines
1.5 KiB
Python
"""Test in memory docstore."""
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from langchain.output_parsers.regex_dict import RegexDictParser
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DEF_EXPECTED_RESULT = {"action": "Search", "action_input": "How to use this class?"}
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DEF_OUTPUT_KEY_TO_FORMAT = {"action": "Action", "action_input": "Action Input"}
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DEF_README = """We have just received a new result from the LLM, and our next step is
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to filter and read its format using regular expressions to identify specific fields,
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such as:
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- Action: Search
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- Action Input: How to use this class?
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- Additional Fields: "N/A"
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To assist us in this task, we use the regex_dict class. This class allows us to send a
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dictionary containing an output key and the expected format, which in turn enables us to
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retrieve the result of the matching formats and extract specific information from it.
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To exclude irrelevant information from our return dictionary, we can instruct the LLM to
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use a specific command that notifies us when it doesn't know the answer. We call this
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variable the "no_update_value", and for our current case, we set it to "N/A". Therefore,
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we expect the result to only contain the following fields:
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{
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{key = action, value = search}
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{key = action_input, value = "How to use this class?"}.
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}"""
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def test_regex_dict_result() -> None:
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"""Test regex dict result."""
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regex_dict_parser = RegexDictParser(
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output_key_to_format=DEF_OUTPUT_KEY_TO_FORMAT, no_update_value="N/A"
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)
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result_dict = regex_dict_parser.parse(DEF_README)
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print("parse_result:", result_dict)
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assert DEF_EXPECTED_RESULT == result_dict
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