Utils
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import openai
import logging
import sys
import time
import openai
import logging
import sys
import time
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from config import *
from config import *
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logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[
logging.FileHandler("debug.log"),
logging.StreamHandler(sys.stdout)
]
)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[
logging.FileHandler("debug.log"),
logging.StreamHandler(sys.stdout)
]
)
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def get_pinecone_id_for_file_chunk(session_id, filename, chunk_index):
return str(session_id+"-!"+filename+"-!"+str(chunk_index))
def get_pinecone_id_for_file_chunk(session_id, filename, chunk_index):
return str(session_id+"-!"+filename+"-!"+str(chunk_index))
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def get_embedding(text, engine):
return openai.Engine(id=engine).embeddings(input=[text])["data"][0]["embedding"]
def get_embedding(text, engine):
return openai.Engine(id=engine).embeddings(input=[text])["data"][0]["embedding"]
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def get_embeddings(text_array, engine):
# Parameters for exponential backoff
max_retries = 5 # Maximum number of retries
base_delay = 1 # Base delay in seconds
factor = 2 # Factor to multiply the delay by after each retry
while True:
try:
return openai.Engine(id=engine).embeddings(input=text_array)["data"]
except Exception as e:
if max_retries > 0:
logging.info(f"Request failed. Retrying in {base_delay} seconds.")
time.sleep(base_delay)
max_retries -= 1
base_delay *= factor
else:
raise e
def get_embeddings(text_array, engine):
# Parameters for exponential backoff
max_retries = 5 # Maximum number of retries
base_delay = 1 # Base delay in seconds
factor = 2 # Factor to multiply the delay by after each retry
while True:
try:
return openai.Engine(id=engine).embeddings(input=text_array)["data"]
except Exception as e:
if max_retries > 0:
logging.info(f"Request failed. Retrying in {base_delay} seconds.")
time.sleep(base_delay)
max_retries -= 1
base_delay *= factor
else:
raise e