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initial commit
Browse files- src/main.py +18 -5
src/main.py
CHANGED
@@ -9,28 +9,41 @@ from .parameters import *
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from fastapi import FastAPI, Header, HTTPException, BackgroundTasks
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from fastapi.responses import FileResponse
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app = FastAPI()
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vector_db = VectorDB(emb_model, db_location, full_actions_list_file_path, num_sub_vectors, batch_size)
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open_ai_connector = OpenAIConnector()
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@app.get("/find-action")
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async def find_action(query: str):
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#data = vector_db.get_embedding_db_as_pandas()
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#print(data)
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prefiltered_names, prefiltered_descriptions = vector_db.retrieve_prefiltered_hits(query, K)
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print(prefiltered_names)
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response = open_ai_connector.query_open_ai(query, prefiltered_names, prefiltered_descriptions)
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return {'success': True, 'query': query, 'response': response}
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from fastapi import FastAPI, Header, HTTPException, BackgroundTasks
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from fastapi.responses import FileResponse
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import logging
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import sys
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app = FastAPI()
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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logger.addHandler(logging.StreamHandler(sys.stdout))
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vector_db = VectorDB(emb_model, db_location, full_actions_list_file_path, num_sub_vectors, batch_size)
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open_ai_connector = OpenAIConnector()
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@app.get("/find-action")
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async def find_action(query: str):
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#logging.basicConfig(filename='myapp.log', level=logging.INFO)
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logger.info('Started')
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#print('start action')
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#data = vector_db.get_embedding_db_as_pandas()
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#print(data)
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prefiltered_names, prefiltered_descriptions = vector_db.retrieve_prefiltered_hits(query, K)
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logger.info('prefiltered list')
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#print(prefiltered_names)
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logger.info('start query openAI')
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response = open_ai_connector.query_open_ai(query, prefiltered_names, prefiltered_descriptions)
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logger.info(response)
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logger.info('Finished')
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return {'success': True, 'query': query, 'response': response}
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