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remove pyq goodbye model and rime synthesizer and fix environment loading

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This commit is contained in:
Ajay Raj 2023-03-28 10:20:36 -07:00
commit 1dc7bc74c3
28 changed files with 143 additions and 285 deletions
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63
vocode/streaming/utils/goodbye_model.py
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@ -1,19 +1,12 @@
import os
import asyncio
import openai
from dotenv import load_dotenv
import numpy as np
import requests
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")
from vocode import getenv
PLATFORM = "pyq" if os.getenv("USE_PYQ_EMBEDDINGS", "false") == "true" else "openai"
SIMILARITY_THRESHOLD = 0.9
SIMILARITY_THRESHOLD_PYQ = 0.7
EMBEDDING_SIZE = 1536
PYQ_EMBEDDING_SIZE = 768
GOODBYE_PHRASES = [
"bye",
"goodbye",
@ -24,7 +17,6 @@ GOODBYE_PHRASES = [
"have a good day",
"have a good night",
]
PYQ_API_URL = "https://embeddings.pyqai.com"
class GoodbyeModel:
@ -34,12 +26,10 @@ class GoodbyeModel:
os.path.dirname(__file__), "goodbye_embeddings"
),
):
openai.api_key = getenv("OPENAI_API_KEY")
self.goodbye_embeddings = self.load_or_create_embeddings(
f"{embeddings_cache_path}/goodbye_embeddings.npy"
)
self.goodbye_embeddings_pyq = self.load_or_create_embeddings(
f"{embeddings_cache_path}/goodbye_embeddings_pyq.npy"
)
def load_or_create_embeddings(self, path):
if os.path.exists(path):
@ -49,50 +39,33 @@ class GoodbyeModel:
np.save(path, embeddings)
return embeddings
def create_embeddings(self, platform=PLATFORM):
def create_embeddings(self):
print("Creating embeddings...")
size = EMBEDDING_SIZE if platform == "openai" else PYQ_EMBEDDING_SIZE
size = EMBEDDING_SIZE
embeddings = np.empty((size, len(GOODBYE_PHRASES)))
for i, goodbye_phrase in enumerate(GOODBYE_PHRASES):
embeddings[:, i] = self.create_embedding(goodbye_phrase, platform=platform)
embeddings[:, i] = self.create_embedding(goodbye_phrase)
return embeddings
async def is_goodbye(self, text: str, platform=PLATFORM) -> bool:
async def is_goodbye(self, text: str) -> bool:
if "bye" in text.lower():
return True
embedding = self.create_embedding(text.strip().lower(), platform=platform)
goodbye_embeddings = (
self.goodbye_embeddings
if platform == "openai"
else self.goodbye_embeddings_pyq
)
threshold = (
SIMILARITY_THRESHOLD if platform == "openai" else SIMILARITY_THRESHOLD_PYQ
)
similarity_results = embedding @ goodbye_embeddings
return np.max(similarity_results) > threshold
embedding = self.create_embedding(text.strip().lower())
similarity_results = embedding @ self.goodbye_embeddings
return np.max(similarity_results) > SIMILARITY_THRESHOLD
def create_embedding(self, text, platform=PLATFORM) -> np.array:
if platform == "openai":
return np.array(
openai.Embedding.create(input=text, model="text-embedding-ada-002")[
"data"
][0]["embedding"]
)
elif platform == "pyq":
return np.array(
requests.post(
PYQ_API_URL,
headers={
"Content-Type": "application/json",
"Authorization": os.getenv("PYQ_API_KEY"),
},
json={"input_sequence": [text], "account_id": "400"},
).json()["response"][0]
)
def create_embedding(self, text) -> np.array:
return np.array(
openai.Embedding.create(input=text, model="text-embedding-ada-002")["data"][
0
]["embedding"]
)
if __name__ == "__main__":
from dotenv import load_dotenv
load_dotenv()
async def main():
model = GoodbyeModel()