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Extract configure and routers from main.py into separate modules
- Main.py was becoming too big to manage. It had both controllers/routers and component configurations (search, processors) in it - Now that the native app GUI code is also getting added to the main path, good time to split/modularize/clean main.py - Put global state into a separate file to share across modules
This commit is contained in:
Debanjum Singh Solanky
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src/router.py
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208
src/router.py
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# Standard Packages
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import yaml
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import json
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import time
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from typing import Optional
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from functools import lru_cache
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# External Packages
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from fastapi import APIRouter
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from fastapi import Request
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from fastapi.responses import HTMLResponse, FileResponse
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from fastapi.templating import Jinja2Templates
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# Internal Packages
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from src.configure import initialize_search
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from src.search_type import image_search, text_search
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from src.processor.conversation.gpt import converse, extract_search_type, message_to_log, message_to_prompt, understand, summarize
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from src.search_filter.explicit_filter import ExplicitFilter
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from src.search_filter.date_filter import DateFilter
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from src.utils.rawconfig import FullConfig
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from src.utils.config import SearchType
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from src.utils.helpers import get_absolute_path, get_from_dict
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from src.utils import constants
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router = APIRouter()
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templates = Jinja2Templates(directory=constants.web_directory)
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@router.get("/", response_class=FileResponse)
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def index():
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return FileResponse(constants.web_directory / "index.html")
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@router.get('/config', response_class=HTMLResponse)
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def config_page(request: Request):
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return templates.TemplateResponse("config.html", context={'request': request})
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@router.get('/config/data', response_model=FullConfig)
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def config_data():
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return constants.config
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@router.post('/config/data')
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async def config_data(updated_config: FullConfig):
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constants.config = updated_config
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with open(constants.config_file, 'w') as outfile:
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yaml.dump(yaml.safe_load(constants.config.json(by_alias=True)), outfile)
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outfile.close()
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return constants.config
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@router.get('/search')
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@lru_cache(maxsize=100)
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def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Optional[bool] = False):
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if q is None or q == '':
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print(f'No query param (q) passed in API call to initiate search')
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return {}
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user_query = q
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results_count = n
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results = {}
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if (t == SearchType.Org or t == None) and constants.model.orgmode_search:
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# query org-mode notes
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query_start = time.time()
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hits, entries = text_search.query(user_query, constants.model.orgmode_search, rank_results=r, device=constants.device, filters=[DateFilter(), ExplicitFilter()], verbose=constants.verbose)
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query_end = time.time()
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# collate and return results
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collate_start = time.time()
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results = text_search.collate_results(hits, entries, results_count)
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collate_end = time.time()
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if (t == SearchType.Music or t == None) and constants.model.music_search:
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# query music library
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query_start = time.time()
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hits, entries = text_search.query(user_query, constants.model.music_search, rank_results=r, device=constants.device, filters=[DateFilter(), ExplicitFilter()], verbose=constants.verbose)
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query_end = time.time()
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# collate and return results
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collate_start = time.time()
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results = text_search.collate_results(hits, entries, results_count)
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collate_end = time.time()
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if (t == SearchType.Markdown or t == None) and constants.model.orgmode_search:
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# query markdown files
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query_start = time.time()
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hits, entries = text_search.query(user_query, constants.model.markdown_search, rank_results=r, device=constants.device, filters=[ExplicitFilter(), DateFilter()], verbose=constants.verbose)
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query_end = time.time()
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# collate and return results
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collate_start = time.time()
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results = text_search.collate_results(hits, entries, results_count)
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collate_end = time.time()
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if (t == SearchType.Ledger or t == None) and constants.model.ledger_search:
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# query transactions
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query_start = time.time()
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hits, entries = text_search.query(user_query, constants.model.ledger_search, rank_results=r, device=constants.device, filters=[ExplicitFilter(), DateFilter()], verbose=constants.verbose)
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query_end = time.time()
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# collate and return results
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collate_start = time.time()
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results = text_search.collate_results(hits, entries, results_count)
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collate_end = time.time()
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if (t == SearchType.Image or t == None) and constants.model.image_search:
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# query images
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query_start = time.time()
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hits = image_search.query(user_query, results_count, constants.model.image_search)
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output_directory = constants.web_directory / 'images'
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query_end = time.time()
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# collate and return results
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collate_start = time.time()
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results = image_search.collate_results(
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hits,
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image_names=constants.model.image_search.image_names,
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output_directory=output_directory,
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image_files_url='/static/images',
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count=results_count)
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collate_end = time.time()
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if constants.verbose > 1:
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print(f"Query took {query_end - query_start:.3f} seconds")
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print(f"Collating results took {collate_end - collate_start:.3f} seconds")
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return results
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@router.get('/reload')
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def reload(t: Optional[SearchType] = None):
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constants.model = initialize_search(constants.model, constants.config, regenerate=False, t=t, device=constants.device)
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return {'status': 'ok', 'message': 'reload completed'}
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@router.get('/regenerate')
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def regenerate(t: Optional[SearchType] = None):
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constants.model = initialize_search(constants.model, constants.config, regenerate=True, t=t, device=constants.device)
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return {'status': 'ok', 'message': 'regeneration completed'}
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@router.get('/beta/search')
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def search_beta(q: str, n: Optional[int] = 1):
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# Extract Search Type using GPT
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metadata = extract_search_type(q, api_key=constants.processor_config.conversation.openai_api_key, verbose=constants.verbose)
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search_type = get_from_dict(metadata, "search-type")
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# Search
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search_results = search(q, n=n, t=SearchType(search_type))
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# Return response
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return {'status': 'ok', 'result': search_results, 'type': search_type}
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@router.get('/chat')
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def chat(q: str):
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# Load Conversation History
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chat_session = constants.processor_config.conversation.chat_session
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meta_log = constants.processor_config.conversation.meta_log
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# Converse with OpenAI GPT
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metadata = understand(q, api_key=constants.processor_config.conversation.openai_api_key, verbose=constants.verbose)
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if constants.verbose > 1:
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print(f'Understood: {get_from_dict(metadata, "intent")}')
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if get_from_dict(metadata, "intent", "memory-type") == "notes":
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query = get_from_dict(metadata, "intent", "query")
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result_list = search(query, n=1, t=SearchType.Org)
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collated_result = "\n".join([item["entry"] for item in result_list])
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if constants.verbose > 1:
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print(f'Semantically Similar Notes:\n{collated_result}')
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gpt_response = summarize(collated_result, summary_type="notes", user_query=q, api_key=constants.processor_config.conversation.openai_api_key)
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else:
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gpt_response = converse(q, chat_session, api_key=constants.processor_config.conversation.openai_api_key)
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# Update Conversation History
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constants.processor_config.conversation.chat_session = message_to_prompt(q, chat_session, gpt_message=gpt_response)
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constants.processor_config.conversation.meta_log['chat'] = message_to_log(q, metadata, gpt_response, meta_log.get('chat', []))
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return {'status': 'ok', 'response': gpt_response}
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@router.on_event('shutdown')
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def shutdown_event():
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# No need to create empty log file
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if not (constants.processor_config and constants.processor_config.conversation and constants.processor_config.conversation.meta_log):
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return
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elif constants.processor_config.conversation.verbose:
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print('INFO:\tSaving conversation logs to disk...')
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# Summarize Conversation Logs for this Session
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chat_session = constants.processor_config.conversation.chat_session
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openai_api_key = constants.processor_config.conversation.openai_api_key
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conversation_log = constants.processor_config.conversation.meta_log
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session = {
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"summary": summarize(chat_session, summary_type="chat", api_key=openai_api_key),
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"session-start": conversation_log.get("session", [{"session-end": 0}])[-1]["session-end"],
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"session-end": len(conversation_log["chat"])
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}
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if 'session' in conversation_log:
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conversation_log['session'].append(session)
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else:
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conversation_log['session'] = [session]
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# Save Conversation Metadata Logs to Disk
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conversation_logfile = get_absolute_path(constants.processor_config.conversation.conversation_logfile)
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with open(conversation_logfile, "w+", encoding='utf-8') as logfile:
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json.dump(conversation_log, logfile)
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print('INFO:\tConversation logs saved to disk.')
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