# Standard Packages import sys import json from typing import Optional # External Packages import uvicorn from fastapi import FastAPI, Request, Body from fastapi.responses import HTMLResponse from fastapi.staticfiles import StaticFiles from fastapi.templating import Jinja2Templates from pydantic import BaseModel, validator # Internal Packages from src.search_type import asymmetric, symmetric_ledger, image_search from src.utils.helpers import get_absolute_path from src.utils.cli import cli from src.utils.config import SearchType, SearchModels, TextSearchConfig, ImageSearchConfig, SearchConfig, ProcessorConfig, ConversationProcessorConfig from src.utils.rawconfig import FullConfig from src.processor.conversation.gpt import converse, message_to_prompt # Application Global State model = SearchModels() search_config = SearchConfig() processor_config = ProcessorConfig() config = {} app = FastAPI() app.mount("/views", StaticFiles(directory="views"), name="views") templates = Jinja2Templates(directory="views/") @app.get('/ui', response_class=HTMLResponse) def ui(request: Request): return templates.TemplateResponse("config.html", context={'request': request}) @app.get('/config', response_model=FullConfig) def config(): return config @app.post('/config') async def config(updated_config: FullConfig): print(updated_config) return updated_config @app.get('/search') def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None): if q is None or q == '': print(f'No query param (q) passed in API call to initiate search') return {} user_query = q results_count = n if (t == SearchType.Notes or t == None) and model.notes_search: # query notes hits = asymmetric.query(user_query, model.notes_search) # collate and return results return asymmetric.collate_results(hits, model.notes_search.entries, results_count) if (t == SearchType.Music or t == None) and model.music_search: # query music library hits = asymmetric.query(user_query, model.music_search) # collate and return results return asymmetric.collate_results(hits, model.music_search.entries, results_count) if (t == SearchType.Ledger or t == None) and model.ledger_search: # query transactions hits = symmetric_ledger.query(user_query, model.ledger_search) # collate and return results return symmetric_ledger.collate_results(hits, model.ledger_search.entries, results_count) if (t == SearchType.Image or t == None) and model.image_search: # query transactions hits = image_search.query(user_query, results_count, model.image_search) # collate and return results return image_search.collate_results( hits, model.image_search.image_names, search_config.image.input_directory, results_count) else: return {} @app.get('/regenerate') def regenerate(t: Optional[SearchType] = None): if (t == SearchType.Notes or t == None) and search_config.notes: # Extract Entries, Generate Embeddings model.notes_search = asymmetric.setup(search_config.notes, regenerate=True) if (t == SearchType.Music or t == None) and search_config.music: # Extract Entries, Generate Song Embeddings model.music_search = asymmetric.setup(search_config.music, regenerate=True) if (t == SearchType.Ledger or t == None) and search_config.ledger: # Extract Entries, Generate Embeddings model.ledger_search = symmetric_ledger.setup(search_config.ledger, regenerate=True) if (t == SearchType.Image or t == None) and search_config.image: # Extract Images, Generate Embeddings model.image_search = image_search.setup(search_config.image, regenerate=True) return {'status': 'ok', 'message': 'regeneration completed'} @app.get('/chat') def chat(q: str): # Load Conversation History conversation_history = processor_config.conversation.conversation_history # Converse with OpenAI GPT gpt_response = converse(q, conversation_history, api_key=processor_config.conversation.openai_api_key) # Update Conversation History processor_config.conversation.conversation_history = message_to_prompt(q, conversation_history, gpt_response) return {'status': 'ok', 'response': gpt_response} def initialize_search(config, regenerate, verbose): model = SearchModels() search_config = SearchConfig() # Initialize Org Notes Search search_config.notes = TextSearchConfig.create_from_dictionary(config, ('content-type', 'org'), verbose) if search_config.notes: model.notes_search = asymmetric.setup(search_config.notes, regenerate=regenerate) # Initialize Org Music Search search_config.music = TextSearchConfig.create_from_dictionary(config, ('content-type', 'music'), verbose) if search_config.music: model.music_search = asymmetric.setup(search_config.music, regenerate=regenerate) # Initialize Ledger Search search_config.ledger = TextSearchConfig.create_from_dictionary(config, ('content-type', 'ledger'), verbose) if search_config.ledger: model.ledger_search = symmetric_ledger.setup(search_config.ledger, regenerate=regenerate) # Initialize Image Search search_config.image = ImageSearchConfig.create_from_dictionary(config, ('content-type', 'image'), verbose) if search_config.image: model.image_search = image_search.setup(search_config.image, regenerate=regenerate) return model, search_config def initialize_processor(config, verbose): processor_config = ProcessorConfig() # Initialize Conversation Processor processor_config.conversation = ConversationProcessorConfig.create_from_dictionary(config, ('processor', 'conversation'), verbose) # Load or Initialize Conversation History from Disk conversation_logfile = processor_config.conversation.conversation_logfile if processor_config.conversation.verbose: print('Saving conversation logs to disk...') if conversation_logfile.expanduser().absolute().is_file(): with open(get_absolute_path(conversation_logfile), 'r') as f: processor_config.conversation.conversation_history = json.load(f).get('chat', '') else: processor_config.conversation.conversation_history = '' return processor_config @app.on_event('shutdown') def shutdown_event(): if processor_config.conversation.verbose: print('Saving conversation logs to disk...') # Save Conversation History to Disk conversation_logfile = get_absolute_path(processor_config.conversation.conversation_logfile) with open(conversation_logfile, "w+", encoding='utf-8') as logfile: json.dump({"chat": processor_config.conversation.conversation_history}, logfile) print('Conversation logs saved to disk.') if __name__ == '__main__': # Load config from CLI args = cli(sys.argv[1:]) # Store the path to the config file. config = args.config # Initialize Search from Config model, search_config = initialize_search(args.config, args.regenerate, args.verbose) # Initialize Processor from Config processor_config = initialize_processor(args.config, args.verbose) # Start Application Server if args.socket: uvicorn.run(app, proxy_headers=True, uds=args.socket) else: uvicorn.run(app, host=args.host, port=args.port)