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import os
import logging
import json
import openai
import streamlit as st
# Set up the page, enable logging
from dotenv import load_dotenv,find_dotenv
load_dotenv(find_dotenv(),override=True)
def load_sidebar(config_file,
index_data_file,
vector_databases=False,
embeddings=False,
rag_type=False,
index_name=False,
llm=False,
model_options=False,
secret_keys=False):
"""
Sets up the sidebar based no toggled options. Returns variables with options.
"""
sb_out={}
with open(config_file, 'r') as f:
config = json.load(f)
databases = {db['name']: db for db in config['databases']}
llms = {m['name']: m for m in config['llms']}
logging.info('Loaded: '+config_file)
with open(index_data_file, 'r') as f:
index_data = json.load(f)
logging.info('Loaded: '+index_data_file)
if vector_databases:
# Vector databases
st.sidebar.title('Vector database')
sb_out['index_type']=st.sidebar.selectbox('Index type', list(databases.keys()), index=1)
logging.info('Index type: '+sb_out['index_type'])
if embeddings:
# Embeddings
st.sidebar.title('Embeddings')
if sb_out['index_type']=='RAGatouille': # Default to selecting hugging face model for RAGatouille, otherwise select alternates
sb_out['query_model']=st.sidebar.selectbox('Hugging face rag models', databases[sb_out['index_type']]['hf_rag_models'], index=0)
else:
sb_out['query_model']=st.sidebar.selectbox('Embedding models', databases[sb_out['index_type']]['embedding_models'], index=0)
if sb_out['query_model']=='Openai':
sb_out['embedding_name']='text-embedding-ada-002'
elif sb_out['query_model']=='Voyage':
sb_out['embedding_name']='voyage-02'
logging.info('Query type: '+sb_out['query_model'])
if 'embedding_name' in locals() or 'embedding_name' in globals():
logging.info('Embedding name: '+sb_out['embedding_name'])
if rag_type:
if sb_out['index_type']!='RAGatouille': # RAGatouille doesn't have a rag_type
# RAG Type
st.sidebar.title('RAG Type')
sb_out['rag_type']=st.sidebar.selectbox('RAG type', config['rag_types'], index=0)
sb_out['smart_agent']=st.sidebar.checkbox('Smart agent?')
logging.info('RAG type: '+sb_out['rag_type'])
logging.info('Smart agent: '+str(sb_out['smart_agent']))
if index_name:
# Index Name
st.sidebar.title('Index Name')
sb_out['index_name']=index_data[sb_out['index_type']][sb_out['query_model']]
st.sidebar.markdown('Index name: '+sb_out['index_name'])
logging.info('Index name: '+sb_out['index_name'])
if llm:
# LLM
st.sidebar.title('LLM')
sb_out['llm_source']=st.sidebar.selectbox('LLM model', list(llms.keys()), index=0)
logging.info('LLM source: '+sb_out['llm_source'])
if sb_out['llm_source']=='OpenAI':
sb_out['llm_model']=st.sidebar.selectbox('OpenAI model', llms[sb_out['llm_source']]['models'], index=0)
if sb_out['llm_source']=='Hugging Face':
sb_out['llm_model']=st.sidebar.selectbox('Hugging Face model', llms[sb_out['llm_source']]['models'], index=0)
if model_options:
# Add input fields in the sidebar
st.sidebar.title('LLM Options')
temperature = st.sidebar.slider('Temperature', min_value=0.0, max_value=2.0, value=0.0, step=0.1)
output_level = st.sidebar.selectbox('Level of Output', ['Concise', 'Detailed'], index=1)
st.sidebar.title('Retrieval Options')
k = st.sidebar.number_input('Number of items per prompt', min_value=1, step=1, value=4)
if sb_out['index_type']!='RAGatouille':
search_type = st.sidebar.selectbox('Search Type', ['similarity', 'mmr'], index=0)
sb_out['model_options']={'output_level':output_level,
'k':k,
'search_type':search_type,
'temperature':temperature}
else:
sb_out['model_options']={'output_level':output_level,
'k':k,
'temperature':temperature}
logging.info('Model options: '+str(sb_out['model_options']))
if secret_keys:
# Add a section for secret keys
st.sidebar.title('Secret keys')
st.sidebar.markdown('If .env file is in directory, will use that first.')
sb_out['keys']={}
if 'llm_source' in sb_out and sb_out['llm_source'] == 'OpenAI':
sb_out['keys']['OPENAI_API_KEY'] = st.sidebar.text_input('OpenAI API Key', type='password')
elif 'query_model' in sb_out and sb_out['query_model'] == 'Openai':
sb_out['keys']['OPENAI_API_KEY'] = st.sidebar.text_input('OpenAI API Key', type='password')
if 'llm_source' in sb_out and sb_out['llm_source']=='Hugging Face':
sb_out['keys']['HUGGINGFACEHUB_API_TOKEN'] = st.sidebar.text_input('Hugging Face API Key', type='password')
if 'query_model' in sb_out and sb_out['query_model']=='Voyage':
sb_out['keys']['VOYAGE_API_KEY'] = st.sidebar.text_input('Voyage API Key', type='password')
if 'index_type' in sb_out and sb_out['index_type']=='Pinecone':
sb_out['keys']['PINECONE_API_KEY']=st.sidebar.text_input('Pinecone API Key',type='password')
return sb_out
def set_secrets(sb):
"""
Sets secrets from environment file, or from sidebar if not available.
"""
secrets={}
secrets['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')
openai.api_key = secrets['OPENAI_API_KEY']
if not secrets['OPENAI_API_KEY']:
secrets['OPENAI_API_KEY'] = sb['keys']['OPENAI_API_KEY']
os.environ['OPENAI_API_KEY'] = secrets['OPENAI_API_KEY']
openai.api_key = secrets['OPENAI_API_KEY']
secrets['VOYAGE_API_KEY'] = os.getenv('VOYAGE_API_KEY')
if not secrets['VOYAGE_API_KEY']:
secrets['VOYAGE_API_KEY'] = sb['keys']['VOYAGE_API_KEY']
os.environ['VOYAGE_API_KEY'] = secrets['VOYAGE_API_KEY']
secrets['PINECONE_API_KEY'] = os.getenv('PINECONE_API_KEY')
if not secrets['PINECONE_API_KEY']:
secrets['PINECONE_API_KEY'] = sb['keys']['PINECONE_API_KEY']
os.environ['PINECONE_API_KEY'] = secrets['PINECONE_API_KEY']
secrets['HUGGINGFACEHUB_API_TOKEN'] = os.getenv('HUGGINGFACEHUB_API_TOKEN')
if not secrets['HUGGINGFACEHUB_API_TOKEN']:
secrets['HUGGINGFACEHUB_API_TOKEN'] = sb['keys']['HUGGINGFACEHUB_API_TOKEN']
os.environ['HUGGINGFACEHUB_API_TOKEN'] = secrets['HUGGINGFACEHUB_API_TOKEN']
return secrets |