# Standard Packages import sys, json, yaml from typing import Optional # External Packages import uvicorn from fastapi import FastAPI, Request from fastapi.responses import HTMLResponse from fastapi.staticfiles import StaticFiles from fastapi.templating import Jinja2Templates # 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_log, message_to_prompt, understand # Application Global State model = SearchModels() search_config = SearchConfig() processor_config = ProcessorConfig() config = {} config_file = "" 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): global config config = updated_config with open(config_file, 'w') as outfile: yaml.dump(yaml.safe_load(config.json(by_alias=True)), outfile) outfile.close() return 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 chat_log = processor_config.conversation.chat_log meta_log = processor_config.conversation.meta_log # Converse with OpenAI GPT user_message_metadata = understand(q, api_key=processor_config.conversation.openai_api_key) gpt_response = converse(q, chat_log, api_key=processor_config.conversation.openai_api_key) # Update Conversation History processor_config.conversation.chat_log = message_to_prompt(q, chat_log, gpt_message=gpt_response) processor_config.conversation.meta_log= message_to_log(q, user_message_metadata, gpt_response, meta_log) return {'status': 'ok', 'response': gpt_response} def initialize_search(regenerate, verbose): model = SearchModels() search_config = SearchConfig() # Initialize Org Notes Search if config.content_type.org: search_config.notes = TextSearchConfig(config.content_type.org, verbose) model.notes_search = asymmetric.setup(search_config.notes, regenerate=regenerate) # Initialize Org Music Search if config.content_type.music: search_config.music = TextSearchConfig(config.content_type.music, verbose) model.music_search = asymmetric.setup(search_config.music, regenerate=regenerate) # Initialize Ledger Search if config.content_type.ledger: search_config.ledger = TextSearchConfig(config.content_type.org, verbose) model.ledger_search = symmetric_ledger.setup(search_config.ledger, regenerate=regenerate) # Initialize Image Search if config.content_type.image: search_config.image = ImageSearchConfig(config.content_type.image, verbose) model.image_search = image_search.setup(search_config.image, regenerate=regenerate) return model, search_config def initialize_processor(verbose): if not config.processor: return processor_config = ProcessorConfig() # Initialize Conversation Processor processor_config.conversation = ConversationProcessorConfig(config.processor.conversation, verbose) conversation_logfile = processor_config.conversation.conversation_logfile if processor_config.conversation.verbose: print('INFO:\tLoading conversation logs from disk...') if conversation_logfile.expanduser().absolute().is_file(): # Load Metadata Logs from Conversation Logfile with open(get_absolute_path(conversation_logfile), 'r') as f: processor_config.conversation.meta_log = json.load(f) # Extract Chat Logs from Metadata processor_config.conversation.chat_log = ''.join( [f'\n{item["by"]}: {item["message"]}' for item in processor_config.conversation.meta_log]) print('INFO:\tConversation logs loaded from disk.') else: # Initialize Conversation Logs processor_config.conversation.meta_log = [] processor_config.conversation.chat_log = "" return processor_config @app.on_event('shutdown') def shutdown_event(): # No need to create empty log file if not processor_config.conversation.meta_log: return elif processor_config.conversation.verbose: print('INFO:\tSaving conversation logs to disk...') # Save Conversation Metadata Logs to Disk conversation_logfile = get_absolute_path(processor_config.conversation.conversation_logfile) with open(conversation_logfile, "w+", encoding='utf-8') as logfile: json.dump(processor_config.conversation.meta_log, logfile) print('INFO:\tConversation logs saved to disk.') if __name__ == '__main__': # Load config from CLI args = cli(sys.argv[1:]) # Stores the file path to the config file. config_file = args.config_file # Store the raw config data. config = args.config # Initialize Search from Config model, search_config = initialize_search(args.regenerate, args.verbose) # Initialize Processor from Config processor_config = initialize_processor(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)