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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 |