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https://github.com/khoaliber/khoj.git
synced 2026-03-06 05:39:12 +00:00
Put global state variables into separate state module
- Variables storing app, device state aren't constants. Do not mix with actual constants like empty_escape_sequence, web_directory
This commit is contained in:
Debanjum Singh Solanky
@@ -11,7 +11,7 @@ from src.processor.org_mode.org_to_jsonl import org_to_jsonl
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from src.search_type import image_search, text_search
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from src.utils.config import SearchType, SearchModels, ProcessorConfigModel, ConversationProcessorConfigModel
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from src.utils.cli import cli
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from src.utils import constants
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from src.utils import state
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from src.utils.helpers import get_absolute_path
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from src.utils.rawconfig import FullConfig
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@@ -21,19 +21,19 @@ def initialize_server(cmd_args):
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args = cli(cmd_args)
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# Stores the file path to the config file.
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constants.config_file = args.config_file
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state.config_file = args.config_file
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# Store the raw config data.
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constants.config = args.config
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state.config = args.config
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# Store the verbose flag
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constants.verbose = args.verbose
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state.verbose = args.verbose
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# Initialize the search model from Config
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constants.model = initialize_search(constants.model, args.config, args.regenerate, device=constants.device, verbose=constants.verbose)
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state.model = initialize_search(state.model, args.config, args.regenerate, device=state.device, verbose=state.verbose)
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# Initialize Processor from Config
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constants.processor_config = initialize_processor(args.config, verbose=constants.verbose)
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state.processor_config = initialize_processor(args.config, verbose=state.verbose)
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return args.host, args.port, args.socket
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@@ -9,9 +9,9 @@ from fastapi.staticfiles import StaticFiles
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from PyQt6 import QtCore, QtGui, QtWidgets
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# Internal Packages
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from src.utils import constants
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from src.configure import initialize_server
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from src.router import router
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from src.utils import constants
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# Initialize the Application Server
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@@ -20,7 +20,7 @@ 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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from src.utils import state, constants
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router = APIRouter()
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@@ -36,15 +36,15 @@ def config_page(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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return state.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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state.config = updated_config
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with open(state.config_file, 'w') as outfile:
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yaml.dump(yaml.safe_load(state.config.json(by_alias=True)), outfile)
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outfile.close()
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return constants.config
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return state.config
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@router.get('/search')
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@lru_cache(maxsize=100)
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@@ -57,10 +57,10 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
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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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if (t == SearchType.Org or t == None) and state.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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hits, entries = text_search.query(user_query, state.model.orgmode_search, rank_results=r, device=state.device, filters=[DateFilter(), ExplicitFilter()], verbose=state.verbose)
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query_end = time.time()
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# collate and return results
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@@ -68,10 +68,10 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
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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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if (t == SearchType.Music or t == None) and state.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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hits, entries = text_search.query(user_query, state.model.music_search, rank_results=r, device=state.device, filters=[DateFilter(), ExplicitFilter()], verbose=state.verbose)
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query_end = time.time()
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# collate and return results
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@@ -79,10 +79,10 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
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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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if (t == SearchType.Markdown or t == None) and state.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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hits, entries = text_search.query(user_query, state.model.markdown_search, rank_results=r, device=state.device, filters=[ExplicitFilter(), DateFilter()], verbose=state.verbose)
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query_end = time.time()
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# collate and return results
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@@ -90,10 +90,10 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
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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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if (t == SearchType.Ledger or t == None) and state.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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hits, entries = text_search.query(user_query, state.model.ledger_search, rank_results=r, device=state.device, filters=[ExplicitFilter(), DateFilter()], verbose=state.verbose)
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query_end = time.time()
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# collate and return results
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@@ -101,10 +101,10 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
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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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if (t == SearchType.Image or t == None) and state.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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hits = image_search.query(user_query, results_count, state.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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@@ -112,13 +112,13 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
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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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image_names=state.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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if state.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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@@ -127,20 +127,20 @@ def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Opti
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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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state.model = initialize_search(state.model, state.config, regenerate=False, t=t, device=state.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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state.model = initialize_search(state.model, state.config, regenerate=True, t=t, device=state.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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metadata = extract_search_type(q, api_key=state.processor_config.conversation.openai_api_key, verbose=state.verbose)
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search_type = get_from_dict(metadata, "search-type")
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# Search
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@@ -153,27 +153,27 @@ def search_beta(q: str, n: Optional[int] = 1):
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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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chat_session = state.processor_config.conversation.chat_session
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meta_log = state.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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metadata = understand(q, api_key=state.processor_config.conversation.openai_api_key, verbose=state.verbose)
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if state.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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if state.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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gpt_response = summarize(collated_result, summary_type="notes", user_query=q, api_key=state.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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gpt_response = converse(q, chat_session, api_key=state.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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state.processor_config.conversation.chat_session = message_to_prompt(q, chat_session, gpt_message=gpt_response)
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state.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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@@ -181,15 +181,15 @@ def chat(q: str):
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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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if not (state.processor_config and state.processor_config.conversation and state.processor_config.conversation.meta_log):
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return
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elif constants.processor_config.conversation.verbose:
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elif state.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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chat_session = state.processor_config.conversation.chat_session
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openai_api_key = state.processor_config.conversation.openai_api_key
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conversation_log = state.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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@@ -201,7 +201,7 @@ def shutdown_event():
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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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conversation_logfile = get_absolute_path(state.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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@@ -1,19 +1,4 @@
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# External Packages
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import torch
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from pathlib import Path
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# Internal Packages
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from src.utils.config import SearchModels, ProcessorConfigModel
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from src.utils.rawconfig import FullConfig
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# Application Global State
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config = FullConfig()
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model = SearchModels()
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processor_config = ProcessorConfigModel()
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config_file: Path = ""
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verbose: int = 0
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") # Set device to GPU if available
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# Other Constants
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web_directory = Path(__file__).parent.parent / 'interface/web/'
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empty_escape_sequences = r'\n|\r\t '
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15
src/utils/state.py
Normal file
15
src/utils/state.py
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@@ -0,0 +1,15 @@
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# External Packages
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import torch
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from pathlib import Path
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# Internal Packages
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from src.utils.config import SearchModels, ProcessorConfigModel
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from src.utils.rawconfig import FullConfig
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# Application Global State
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config = FullConfig()
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model = SearchModels()
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processor_config = ProcessorConfigModel()
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config_file: Path = ""
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verbose: int = 0
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") # Set device to GPU if available
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