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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 .") |