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@@ -565,7 +565,7 @@ class EmbedChain(JSONSerializable):
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dry_run=False,
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where: Optional[Dict[str, str]] = None,
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**kwargs: Dict[str, Any],
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- ) -> str:
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+ ) -> Union[Tuple[str, List[Tuple[str, str, str]]], str]:
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"""
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Queries the vector database on the given input query.
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Gets relevant doc based on the query and then passes it to an
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@@ -590,13 +590,10 @@ class EmbedChain(JSONSerializable):
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or the dry run result
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:rtype: str, if citations is False, otherwise Tuple[str,List[Tuple[str,str,str]]]
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"""
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- if "citations" in kwargs:
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- citations = kwargs.pop("citations")
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- else:
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- citations = False
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-
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+ citations = kwargs.get("citations", False)
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+ db_kwargs = {key: value for key, value in kwargs.items() if key != "citations"}
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contexts = self._retrieve_from_database(
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- input_query=input_query, config=config, where=where, citations=citations, **kwargs
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+ input_query=input_query, config=config, where=where, citations=citations, **db_kwargs
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)
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if citations and len(contexts) > 0 and isinstance(contexts[0], tuple):
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contexts_data_for_llm_query = list(map(lambda x: x[0], contexts))
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