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from environs import Env
env = Env()
try:
env.read_env("/Users/kanasani/Documents/api_keys/.env.llm")
print("Using local .env.llm file")
except:
env.read_env()
print(".env file from repo secrets is used")
import openai
openai.api_type = env("API_TYPE")
openai.api_base = env("API_BASE")
openai.api_version = env("API_VERSION")
openai.api_key = env("AZURE_OPENAI_KEY")
def check_password():
import streamlit as st
"""Returns `True` if the user had the correct password."""
def password_entered():
"""Checks whether a password entered by the user is correct."""
if st.session_state["password"] == env("st_password"):
st.session_state["password_correct"] = True
del st.session_state["password"] # don't store password
else:
st.session_state["password_correct"] = False
if "password_correct" not in st.session_state:
# First run, show input for password.
st.text_input(
"Password", type="password", on_change=password_entered, key="password"
)
return False
elif not st.session_state["password_correct"]:
# Password not correct, show input + error.
st.text_input(
"Password", type="password", on_change=password_entered, key="password"
)
st.error("😕 Password incorrect")
return False
else:
# Password correct.
return True
def submit_prompt_to_gpt(input_list_of_prompts):
response = openai.ChatCompletion.create(
engine=env("DEPLOYMENT_NAME"),
messages=input_list_of_prompts,
temperature=1,
max_tokens=256,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
)
response_content = response["choices"][0]["message"]["content"]
return response_content
def get_hf_embeddings():
from langchain.embeddings import HuggingFaceHubEmbeddings
embeddings = HuggingFaceHubEmbeddings(
repo_id="sentence-transformers/all-mpnet-base-v2",
task="feature-extraction",
huggingfacehub_api_token=env("HUGGINGFACEHUB_API_TOKEN"),
)
return embeddings
def get_openAI_chat_model():
import openai
from langchain.chat_models.azure_openai import AzureChatOpenAI
chat_model = AzureChatOpenAI(deployment_name=env("DEPLOYMENT_NAME"),
openai_api_version=env("API_VERSION"),
openai_api_base=env("API_BASE"),
openai_api_type=env("API_TYPE"),
openai_api_key=env("AZURE_OPENAI_KEY"),
verbose=True)
return chat_model
def get_hf_model(repo_id = "google/flan-t5-xxl"):
from langchain import HuggingFaceHub
hf_llm = HuggingFaceHub(
repo_id=repo_id,
model_kwargs={"temperature": 0.1, "max_length": 1024},
huggingfacehub_api_token = env("HUGGINGFACEHUB_API_TOKEN"),
)
return hf_llm
def get_local_gpt4_model(model = "GPT4All-13B-snoozy.ggmlv3.q4_0.bin"):
from langchain.llms import GPT4All
gpt4_llm = GPT4All(model=".models/"+model,
verbose=True)
return gpt4_llm
def set_LangChain_tracking(project="Chat with your PDF"):
import os
os.environ['LANGCHAIN_PROJECT'] = project
print("LangChain tracking is set to : ", project)
def unset_LangChain_tracking():
import os
os.environ.pop('LANGCHAIN_API_KEY', None)
os.environ.pop('LANGCHAIN_TRACING_V2', None)
os.environ.pop('LANGCHAIN_ENDPOINT', None)
os.environ.pop('LANGCHAIN_PROJECT', None)
print("LangChain tracking is removed .") |