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Runtime error
Runtime error
Some frontend cosmetics and refactoring.
Browse files- filterminutes.py +3 -3
- public_app.py +3 -3
filterminutes.py
CHANGED
@@ -67,15 +67,15 @@ def search_with_filter(vector_store, query, filter_dict, target_k=5, init_k=100,
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if len_docs_begin >= target_k:
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log.info(f'Initial search contains {len_docs_begin} documents. Expansion not required. ')
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return context
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-
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-
for top_k_docs in np.arange(init_k,
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log.info(f'Context contains {len(context)} documents')
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log.info(f'Expanding search with k={top_k_docs}')
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context = filter_docs_by_meta(vector_store.similarity_search(query, k=int(top_k_docs)), filter_dict)
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if len(context) >= target_k:
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log.info(f'Success. Context contains {len(context)} documents matching the filtering criteria')
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return context
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-
log.info(f'Failed to reach target number of documents
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f' context contains {len(context)} documents matching the filtering criteria')
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return context
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if len_docs_begin >= target_k:
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log.info(f'Initial search contains {len_docs_begin} documents. Expansion not required. ')
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return context
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+
MAX_K = 50000 # This is more than the number of actual documents.
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+
for top_k_docs in np.arange(init_k, MAX_K, step):
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log.info(f'Context contains {len(context)} documents')
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log.info(f'Expanding search with k={top_k_docs}')
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context = filter_docs_by_meta(vector_store.similarity_search(query, k=int(top_k_docs)), filter_dict)
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if len(context) >= target_k:
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log.info(f'Success. Context contains {len(context)} documents matching the filtering criteria')
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return context
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+
log.info(f'Failed to reach target number of documents,'
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f' context contains {len(context)} documents matching the filtering criteria')
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return context
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public_app.py
CHANGED
@@ -81,7 +81,7 @@ if __name__ == '__main__':
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'cultural, financial, and political developments occurring at a given time.'
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' The model actively looks for the presence of date elements in the query '
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'and will stop the execution if cannot find them to minimize the risk of model '
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-
'hallucination. Nevertheless, the usual caveats for
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' Click the query examples below to see some possible outputs from the model.',
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article='**Disclaimer**: This app is for demonstration purposes only, and no assurance of uninterrupted'
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' functionality can be given at this time. Answers may take some'
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@@ -89,9 +89,9 @@ if __name__ == '__main__':
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'during periods of heavy usage. There is still significant work planned ahead. Please be patient :)',
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analytics_enabled=True,
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allow_flagging="manual",
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flagging_options=["error", "ambiguous", "not
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outputs=gr.Textbox(lines=1, label='Answer'),
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-
title='Search the FED minutes',
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examples=FedMinutesSearch,
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cache_examples=False
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)
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'cultural, financial, and political developments occurring at a given time.'
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' The model actively looks for the presence of date elements in the query '
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'and will stop the execution if cannot find them to minimize the risk of model '
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+
'hallucination. Nevertheless, the usual caveats for applications making use of generative AI apply.'
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' Click the query examples below to see some possible outputs from the model.',
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article='**Disclaimer**: This app is for demonstration purposes only, and no assurance of uninterrupted'
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' functionality can be given at this time. Answers may take some'
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'during periods of heavy usage. There is still significant work planned ahead. Please be patient :)',
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analytics_enabled=True,
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allow_flagging="manual",
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+
flagging_options=["error", "ambiguous", "not true"],
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outputs=gr.Textbox(lines=1, label='Answer'),
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title='Search the FED minutes archive',
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examples=FedMinutesSearch,
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cache_examples=False
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)
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