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66238004d8
For consistent, more granular verbosity controls across app Allows user to increase verbosity by passing -vvv flags passed to main.py
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
from typing import Optional
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from fastapi import FastAPI
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from search_type import asymmetric
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import argparse
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import pathlib
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import uvicorn
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app = FastAPI()
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@app.get('/search')
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def search(q: str, n: Optional[int] = 5, t: Optional[str] = 'notes'):
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if q is None or q == '':
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print(f'No query param (q) passed in API call to initiate search')
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return {}
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user_query = q
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results_count = n
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if t == 'notes':
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# query notes
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hits = asymmetric.query_notes(
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q,
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corpus_embeddings,
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entries,
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bi_encoder,
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cross_encoder,
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top_k)
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# collate and return results
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return asymmetric.collate_results(hits, entries, results_count)
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else:
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return {}
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if __name__ == '__main__':
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# Setup Argument Parser
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parser = argparse.ArgumentParser(description="Expose API for Semantic Search")
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parser.add_argument('--compressed-jsonl', '-j', type=pathlib.Path, default=pathlib.Path(".notes.jsonl.gz"), help="Compressed JSONL formatted notes file to compute embeddings from")
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parser.add_argument('--embeddings', '-e', type=pathlib.Path, default=pathlib.Path(".notes_embeddings.pt"), help="File to save/load model embeddings to/from")
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parser.add_argument('--verbose', action='count', help="Show verbose conversion logs. Default: 0")
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args = parser.parse_args()
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# Initialize Model
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bi_encoder, cross_encoder, top_k = asymmetric.initialize_model()
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# Extract Entries
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entries = asymmetric.extract_entries(args.compressed_jsonl, args.verbose)
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# Compute or Load Embeddings
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corpus_embeddings = asymmetric.compute_embeddings(entries, bi_encoder, args.embeddings, args.verbose)
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# Start Application Server
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uvicorn.run(app)
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