mirror of
https://github.com/khoj-ai/khoj.git
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76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
# Standard Packages
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import sys
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import argparse
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import pathlib
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from typing import Optional
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# External Packages
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import uvicorn
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from fastapi import FastAPI
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# Internal Packages
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from search_type import asymmetric
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from processor.org_mode.org_to_jsonl import org_to_jsonl
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from utils.helpers import is_none_or_empty
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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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user_query,
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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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@app.get('/regenerate')
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def regenerate():
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# Extract Entries, Generate Embeddings
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extracted_entries, computed_embeddings, _, _, _ = asymmetric.setup(args.input_files, args.input_filter, args.compressed_jsonl, args.embeddings, regenerate=True, verbose=args.verbose)
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# Now Update State
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# update state variables after regeneration complete
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# minimize time the application is in inconsistent, partially updated state
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global corpus_embeddings
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global entries
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entries = extracted_entries
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corpus_embeddings = computed_embeddings
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return {'status': 'ok', 'message': 'regeneration completed'}
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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('--input-files', '-i', nargs='*', help="List of org-mode files to process")
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parser.add_argument('--input-filter', type=str, default=None, help="Regex filter for org-mode files to process")
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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('--regenerate', action='store_true', default=False, help="Regenerate embeddings from org-mode files. Default: false")
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parser.add_argument('--verbose', action='count', default=0, help="Show verbose conversion logs. Default: 0")
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args = parser.parse_args()
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entries, corpus_embeddings, bi_encoder, cross_encoder, top_k = asymmetric.setup(args.input_files, args.input_filter, args.compressed_jsonl, args.embeddings, args.regenerate, args.verbose)
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# Start Application Server
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uvicorn.run(app)
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