mirror of
https://github.com/khoaliber/khoj.git
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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
95
src/configure.py
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95
src/configure.py
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@@ -0,0 +1,95 @@
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# Standard Packages
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import sys
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# External Packages
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import torch
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# Internal Packages
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from src.processor.ledger.beancount_to_jsonl import beancount_to_jsonl
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from src.processor.markdown.markdown_to_jsonl import markdown_to_jsonl
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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.helpers import get_absolute_path
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from src.utils.rawconfig import FullConfig
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def initialize_server(cmd_args):
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# Load config from CLI
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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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# Store the raw config data.
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constants.config = args.config
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# Store the verbose flag
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constants.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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# Initialize Processor from Config
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constants.processor_config = initialize_processor(args.config, verbose=constants.verbose)
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return args.host, args.port, args.socket
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def initialize_search(model: SearchModels, config: FullConfig, regenerate: bool, t: SearchType = None, device=torch.device("cpu"), verbose: int = 0):
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# Initialize Org Notes Search
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if (t == SearchType.Org or t == None) and config.content_type.org:
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# Extract Entries, Generate Notes Embeddings
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model.orgmode_search = text_search.setup(org_to_jsonl, config.content_type.org, search_config=config.search_type.asymmetric, regenerate=regenerate, device=device, verbose=verbose)
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# Initialize Org Music Search
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if (t == SearchType.Music or t == None) and config.content_type.music:
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# Extract Entries, Generate Music Embeddings
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model.music_search = text_search.setup(org_to_jsonl, config.content_type.music, search_config=config.search_type.asymmetric, regenerate=regenerate, device=device, verbose=verbose)
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# Initialize Markdown Search
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if (t == SearchType.Markdown or t == None) and config.content_type.markdown:
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# Extract Entries, Generate Markdown Embeddings
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model.markdown_search = text_search.setup(markdown_to_jsonl, config.content_type.markdown, search_config=config.search_type.asymmetric, regenerate=regenerate, device=device, verbose=verbose)
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# Initialize Ledger Search
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if (t == SearchType.Ledger or t == None) and config.content_type.ledger:
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# Extract Entries, Generate Ledger Embeddings
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model.ledger_search = text_search.setup(beancount_to_jsonl, config.content_type.ledger, search_config=config.search_type.symmetric, regenerate=regenerate, verbose=verbose)
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# Initialize Image Search
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if (t == SearchType.Image or t == None) and config.content_type.image:
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# Extract Entries, Generate Image Embeddings
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model.image_search = image_search.setup(config.content_type.image, search_config=config.search_type.image, regenerate=regenerate, verbose=verbose)
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return model
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def initialize_processor(config: FullConfig, verbose: int):
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if not config.processor:
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return
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processor_config = ProcessorConfigModel()
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# Initialize Conversation Processor
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processor_config.conversation = ConversationProcessorConfigModel(config.processor.conversation, verbose)
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conversation_logfile = processor_config.conversation.conversation_logfile
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if processor_config.conversation.verbose:
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print('INFO:\tLoading conversation logs from disk...')
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if conversation_logfile.expanduser().absolute().is_file():
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# Load Metadata Logs from Conversation Logfile
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with open(get_absolute_path(conversation_logfile), 'r') as f:
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processor_config.conversation.meta_log = json.load(f)
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print('INFO:\tConversation logs loaded from disk.')
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else:
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# Initialize Conversation Logs
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processor_config.conversation.meta_log = {}
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processor_config.conversation.chat_session = ""
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return processor_config
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319
src/main.py
319
src/main.py
@@ -1,325 +1,28 @@
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# Standard Packages
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import sys, json, yaml
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import time
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from typing import Optional
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from pathlib import Path
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from functools import lru_cache
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import sys
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import webbrowser
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# External Packages
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import uvicorn
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import torch
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from fastapi import FastAPI, Request
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from fastapi.responses import HTMLResponse, FileResponse
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from fastapi import FastAPI
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from fastapi.staticfiles import StaticFiles
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from fastapi.templating import Jinja2Templates
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from PyQt6 import QtCore, QtGui, QtWidgets
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# Internal Packages
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from src.search_type import image_search, text_search
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from src.processor.org_mode.org_to_jsonl import org_to_jsonl
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from src.processor.ledger.beancount_to_jsonl import beancount_to_jsonl
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from src.processor.markdown.markdown_to_jsonl import markdown_to_jsonl
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from src.utils.helpers import get_absolute_path, get_from_dict
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from src.utils.cli import cli
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from src.utils.config import SearchType, SearchModels, ProcessorConfigModel, ConversationProcessorConfigModel
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from src.utils.rawconfig import FullConfig
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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 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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# 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 = ""
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verbose = 0
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# Initialize the Application Server
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app = FastAPI()
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this_directory = Path(__file__).parent
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web_directory = this_directory / 'interface/web/'
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app.mount("/static", StaticFiles(directory=web_directory), name="static")
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templates = Jinja2Templates(directory=web_directory)
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# Controllers
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@app.get("/", response_class=FileResponse)
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def index():
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return FileResponse(web_directory / "index.html")
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@app.get('/config', response_class=HTMLResponse)
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def config(request: Request):
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return templates.TemplateResponse("config.html", context={'request': request})
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@app.get('/config/data', response_model=FullConfig)
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def config_data():
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return config
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@app.post('/config/data')
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async def config_data(updated_config: FullConfig):
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global config
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config = updated_config
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with open(config_file, 'w') as outfile:
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yaml.dump(yaml.safe_load(config.json(by_alias=True)), outfile)
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outfile.close()
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return config
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@app.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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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
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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 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, model.orgmode_search, rank_results=r, device=device, filters=[DateFilter(), ExplicitFilter()], verbose=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 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, model.music_search, rank_results=r, device=device, filters=[DateFilter(), ExplicitFilter()], verbose=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 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, model.markdown_search, rank_results=r, device=device, filters=[ExplicitFilter(), DateFilter()], verbose=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 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, model.ledger_search, rank_results=r, device=device, filters=[ExplicitFilter(), DateFilter()], verbose=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 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, model.image_search)
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output_directory = 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=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 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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@app.get('/reload')
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def reload(t: Optional[SearchType] = None):
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global model
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
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model = initialize_search(config, regenerate=False, t=t, device=device)
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return {'status': 'ok', 'message': 'reload completed'}
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@app.get('/regenerate')
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def regenerate(t: Optional[SearchType] = None):
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global model
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
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model = initialize_search(config, regenerate=True, t=t, device=device)
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return {'status': 'ok', 'message': 'regeneration completed'}
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@app.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=processor_config.conversation.openai_api_key, verbose=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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@app.get('/chat')
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def chat(q: str):
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# Load Conversation History
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chat_session = processor_config.conversation.chat_session
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meta_log = processor_config.conversation.meta_log
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# Converse with OpenAI GPT
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metadata = understand(q, api_key=processor_config.conversation.openai_api_key, verbose=verbose)
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if 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 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=processor_config.conversation.openai_api_key)
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else:
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gpt_response = converse(q, chat_session, api_key=processor_config.conversation.openai_api_key)
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# Update Conversation History
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processor_config.conversation.chat_session = message_to_prompt(q, chat_session, gpt_message=gpt_response)
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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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def initialize_search(config: FullConfig, regenerate: bool, t: SearchType = None, device=torch.device("cpu")):
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# Initialize Org Notes Search
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if (t == SearchType.Org or t == None) and config.content_type.org:
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# Extract Entries, Generate Notes Embeddings
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model.orgmode_search = text_search.setup(org_to_jsonl, config.content_type.org, search_config=config.search_type.asymmetric, regenerate=regenerate, device=device, verbose=verbose)
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# Initialize Org Music Search
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if (t == SearchType.Music or t == None) and config.content_type.music:
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# Extract Entries, Generate Music Embeddings
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model.music_search = text_search.setup(org_to_jsonl, config.content_type.music, search_config=config.search_type.asymmetric, regenerate=regenerate, device=device, verbose=verbose)
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# Initialize Markdown Search
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if (t == SearchType.Markdown or t == None) and config.content_type.markdown:
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# Extract Entries, Generate Markdown Embeddings
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model.markdown_search = text_search.setup(markdown_to_jsonl, config.content_type.markdown, search_config=config.search_type.asymmetric, regenerate=regenerate, device=device, verbose=verbose)
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# Initialize Ledger Search
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if (t == SearchType.Ledger or t == None) and config.content_type.ledger:
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# Extract Entries, Generate Ledger Embeddings
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model.ledger_search = text_search.setup(beancount_to_jsonl, config.content_type.ledger, search_config=config.search_type.symmetric, regenerate=regenerate, verbose=verbose)
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# Initialize Image Search
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if (t == SearchType.Image or t == None) and config.content_type.image:
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# Extract Entries, Generate Image Embeddings
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model.image_search = image_search.setup(config.content_type.image, search_config=config.search_type.image, regenerate=regenerate, verbose=verbose)
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return model
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def initialize_processor(config: FullConfig):
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if not config.processor:
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return
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processor_config = ProcessorConfigModel()
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# Initialize Conversation Processor
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processor_config.conversation = ConversationProcessorConfigModel(config.processor.conversation, verbose)
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conversation_logfile = processor_config.conversation.conversation_logfile
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if processor_config.conversation.verbose:
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print('INFO:\tLoading conversation logs from disk...')
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if conversation_logfile.expanduser().absolute().is_file():
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# Load Metadata Logs from Conversation Logfile
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with open(get_absolute_path(conversation_logfile), 'r') as f:
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processor_config.conversation.meta_log = json.load(f)
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print('INFO:\tConversation logs loaded from disk.')
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else:
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# Initialize Conversation Logs
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processor_config.conversation.meta_log = {}
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processor_config.conversation.chat_session = ""
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return processor_config
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@app.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 (processor_config and processor_config.conversation and processor_config.conversation.meta_log):
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return
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elif 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 = processor_config.conversation.chat_session
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openai_api_key = processor_config.conversation.openai_api_key
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conversation_log = 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(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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def setup_server():
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# Load config from CLI
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args = cli(sys.argv[1:])
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# Stores the file path to the config file.
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global config_file
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config_file = args.config_file
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# Store the raw config data.
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global config
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config = args.config
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# Store the verbose flag
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global verbose
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verbose = args.verbose
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# Set device to GPU if available
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
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# Initialize the search model from Config
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global model
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model = initialize_search(args.config, args.regenerate, device=device)
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# Initialize Processor from Config
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global processor_config
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processor_config = initialize_processor(args.config)
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return args.host, args.port, args.socket
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app.mount("/static", StaticFiles(directory=constants.web_directory), name="static")
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||||
app.include_router(router)
|
||||
|
||||
|
||||
def run():
|
||||
# Setup Application Server
|
||||
host, port, socket = setup_server()
|
||||
host, port, socket = initialize_server(sys.argv[1:])
|
||||
|
||||
# Setup GUI
|
||||
gui = QtWidgets.QApplication([])
|
||||
@@ -363,7 +66,7 @@ def create_system_tray():
|
||||
"""
|
||||
|
||||
# Create the system tray with icon
|
||||
icon_path = web_directory / 'assets/icons/favicon-144x144.png'
|
||||
icon_path = constants.web_directory / 'assets/icons/favicon-144x144.png'
|
||||
icon = QtGui.QIcon(f'{icon_path.absolute()}')
|
||||
tray = QtWidgets.QSystemTrayIcon(icon)
|
||||
tray.setVisible(True)
|
||||
|
||||
208
src/router.py
Normal file
208
src/router.py
Normal file
@@ -0,0 +1,208 @@
|
||||
# Standard Packages
|
||||
import yaml
|
||||
import json
|
||||
import time
|
||||
from typing import Optional
|
||||
from functools import lru_cache
|
||||
|
||||
# External Packages
|
||||
from fastapi import APIRouter
|
||||
from fastapi import Request
|
||||
from fastapi.responses import HTMLResponse, FileResponse
|
||||
from fastapi.templating import Jinja2Templates
|
||||
|
||||
# Internal Packages
|
||||
from src.configure import initialize_search
|
||||
from src.search_type import image_search, text_search
|
||||
from src.processor.conversation.gpt import converse, extract_search_type, message_to_log, message_to_prompt, understand, summarize
|
||||
from src.search_filter.explicit_filter import ExplicitFilter
|
||||
from src.search_filter.date_filter import DateFilter
|
||||
from src.utils.rawconfig import FullConfig
|
||||
from src.utils.config import SearchType
|
||||
from src.utils.helpers import get_absolute_path, get_from_dict
|
||||
from src.utils import constants
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
templates = Jinja2Templates(directory=constants.web_directory)
|
||||
|
||||
@router.get("/", response_class=FileResponse)
|
||||
def index():
|
||||
return FileResponse(constants.web_directory / "index.html")
|
||||
|
||||
@router.get('/config', response_class=HTMLResponse)
|
||||
def config_page(request: Request):
|
||||
return templates.TemplateResponse("config.html", context={'request': request})
|
||||
|
||||
@router.get('/config/data', response_model=FullConfig)
|
||||
def config_data():
|
||||
return constants.config
|
||||
|
||||
@router.post('/config/data')
|
||||
async def config_data(updated_config: FullConfig):
|
||||
constants.config = updated_config
|
||||
with open(constants.config_file, 'w') as outfile:
|
||||
yaml.dump(yaml.safe_load(constants.config.json(by_alias=True)), outfile)
|
||||
outfile.close()
|
||||
return constants.config
|
||||
|
||||
@router.get('/search')
|
||||
@lru_cache(maxsize=100)
|
||||
def search(q: str, n: Optional[int] = 5, t: Optional[SearchType] = None, r: Optional[bool] = False):
|
||||
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
|
||||
results = {}
|
||||
|
||||
if (t == SearchType.Org or t == None) and constants.model.orgmode_search:
|
||||
# query org-mode notes
|
||||
query_start = time.time()
|
||||
hits, entries = text_search.query(user_query, constants.model.orgmode_search, rank_results=r, device=constants.device, filters=[DateFilter(), ExplicitFilter()], verbose=constants.verbose)
|
||||
query_end = time.time()
|
||||
|
||||
# collate and return results
|
||||
collate_start = time.time()
|
||||
results = text_search.collate_results(hits, entries, results_count)
|
||||
collate_end = time.time()
|
||||
|
||||
if (t == SearchType.Music or t == None) and constants.model.music_search:
|
||||
# query music library
|
||||
query_start = time.time()
|
||||
hits, entries = text_search.query(user_query, constants.model.music_search, rank_results=r, device=constants.device, filters=[DateFilter(), ExplicitFilter()], verbose=constants.verbose)
|
||||
query_end = time.time()
|
||||
|
||||
# collate and return results
|
||||
collate_start = time.time()
|
||||
results = text_search.collate_results(hits, entries, results_count)
|
||||
collate_end = time.time()
|
||||
|
||||
if (t == SearchType.Markdown or t == None) and constants.model.orgmode_search:
|
||||
# query markdown files
|
||||
query_start = time.time()
|
||||
hits, entries = text_search.query(user_query, constants.model.markdown_search, rank_results=r, device=constants.device, filters=[ExplicitFilter(), DateFilter()], verbose=constants.verbose)
|
||||
query_end = time.time()
|
||||
|
||||
# collate and return results
|
||||
collate_start = time.time()
|
||||
results = text_search.collate_results(hits, entries, results_count)
|
||||
collate_end = time.time()
|
||||
|
||||
if (t == SearchType.Ledger or t == None) and constants.model.ledger_search:
|
||||
# query transactions
|
||||
query_start = time.time()
|
||||
hits, entries = text_search.query(user_query, constants.model.ledger_search, rank_results=r, device=constants.device, filters=[ExplicitFilter(), DateFilter()], verbose=constants.verbose)
|
||||
query_end = time.time()
|
||||
|
||||
# collate and return results
|
||||
collate_start = time.time()
|
||||
results = text_search.collate_results(hits, entries, results_count)
|
||||
collate_end = time.time()
|
||||
|
||||
if (t == SearchType.Image or t == None) and constants.model.image_search:
|
||||
# query images
|
||||
query_start = time.time()
|
||||
hits = image_search.query(user_query, results_count, constants.model.image_search)
|
||||
output_directory = constants.web_directory / 'images'
|
||||
query_end = time.time()
|
||||
|
||||
# collate and return results
|
||||
collate_start = time.time()
|
||||
results = image_search.collate_results(
|
||||
hits,
|
||||
image_names=constants.model.image_search.image_names,
|
||||
output_directory=output_directory,
|
||||
image_files_url='/static/images',
|
||||
count=results_count)
|
||||
collate_end = time.time()
|
||||
|
||||
if constants.verbose > 1:
|
||||
print(f"Query took {query_end - query_start:.3f} seconds")
|
||||
print(f"Collating results took {collate_end - collate_start:.3f} seconds")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
@router.get('/reload')
|
||||
def reload(t: Optional[SearchType] = None):
|
||||
constants.model = initialize_search(constants.model, constants.config, regenerate=False, t=t, device=constants.device)
|
||||
return {'status': 'ok', 'message': 'reload completed'}
|
||||
|
||||
|
||||
@router.get('/regenerate')
|
||||
def regenerate(t: Optional[SearchType] = None):
|
||||
constants.model = initialize_search(constants.model, constants.config, regenerate=True, t=t, device=constants.device)
|
||||
return {'status': 'ok', 'message': 'regeneration completed'}
|
||||
|
||||
|
||||
@router.get('/beta/search')
|
||||
def search_beta(q: str, n: Optional[int] = 1):
|
||||
# Extract Search Type using GPT
|
||||
metadata = extract_search_type(q, api_key=constants.processor_config.conversation.openai_api_key, verbose=constants.verbose)
|
||||
search_type = get_from_dict(metadata, "search-type")
|
||||
|
||||
# Search
|
||||
search_results = search(q, n=n, t=SearchType(search_type))
|
||||
|
||||
# Return response
|
||||
return {'status': 'ok', 'result': search_results, 'type': search_type}
|
||||
|
||||
|
||||
@router.get('/chat')
|
||||
def chat(q: str):
|
||||
# Load Conversation History
|
||||
chat_session = constants.processor_config.conversation.chat_session
|
||||
meta_log = constants.processor_config.conversation.meta_log
|
||||
|
||||
# Converse with OpenAI GPT
|
||||
metadata = understand(q, api_key=constants.processor_config.conversation.openai_api_key, verbose=constants.verbose)
|
||||
if constants.verbose > 1:
|
||||
print(f'Understood: {get_from_dict(metadata, "intent")}')
|
||||
|
||||
if get_from_dict(metadata, "intent", "memory-type") == "notes":
|
||||
query = get_from_dict(metadata, "intent", "query")
|
||||
result_list = search(query, n=1, t=SearchType.Org)
|
||||
collated_result = "\n".join([item["entry"] for item in result_list])
|
||||
if constants.verbose > 1:
|
||||
print(f'Semantically Similar Notes:\n{collated_result}')
|
||||
gpt_response = summarize(collated_result, summary_type="notes", user_query=q, api_key=constants.processor_config.conversation.openai_api_key)
|
||||
else:
|
||||
gpt_response = converse(q, chat_session, api_key=constants.processor_config.conversation.openai_api_key)
|
||||
|
||||
# Update Conversation History
|
||||
constants.processor_config.conversation.chat_session = message_to_prompt(q, chat_session, gpt_message=gpt_response)
|
||||
constants.processor_config.conversation.meta_log['chat'] = message_to_log(q, metadata, gpt_response, meta_log.get('chat', []))
|
||||
|
||||
return {'status': 'ok', 'response': gpt_response}
|
||||
|
||||
|
||||
@router.on_event('shutdown')
|
||||
def shutdown_event():
|
||||
# No need to create empty log file
|
||||
if not (constants.processor_config and constants.processor_config.conversation and constants.processor_config.conversation.meta_log):
|
||||
return
|
||||
elif constants.processor_config.conversation.verbose:
|
||||
print('INFO:\tSaving conversation logs to disk...')
|
||||
|
||||
# Summarize Conversation Logs for this Session
|
||||
chat_session = constants.processor_config.conversation.chat_session
|
||||
openai_api_key = constants.processor_config.conversation.openai_api_key
|
||||
conversation_log = constants.processor_config.conversation.meta_log
|
||||
session = {
|
||||
"summary": summarize(chat_session, summary_type="chat", api_key=openai_api_key),
|
||||
"session-start": conversation_log.get("session", [{"session-end": 0}])[-1]["session-end"],
|
||||
"session-end": len(conversation_log["chat"])
|
||||
}
|
||||
if 'session' in conversation_log:
|
||||
conversation_log['session'].append(session)
|
||||
else:
|
||||
conversation_log['session'] = [session]
|
||||
|
||||
# Save Conversation Metadata Logs to Disk
|
||||
conversation_logfile = get_absolute_path(constants.processor_config.conversation.conversation_logfile)
|
||||
with open(conversation_logfile, "w+", encoding='utf-8') as logfile:
|
||||
json.dump(conversation_log, logfile)
|
||||
|
||||
print('INFO:\tConversation logs saved to disk.')
|
||||
@@ -1 +1,19 @@
|
||||
empty_escape_sequences = r'\n|\r\t '
|
||||
# External Packages
|
||||
import torch
|
||||
from pathlib import Path
|
||||
|
||||
# Internal Packages
|
||||
from src.utils.config import SearchModels, ProcessorConfigModel
|
||||
from src.utils.rawconfig import FullConfig
|
||||
|
||||
# Application Global State
|
||||
config = FullConfig()
|
||||
model = SearchModels()
|
||||
processor_config = ProcessorConfigModel()
|
||||
config_file: Path = ""
|
||||
verbose: int = 0
|
||||
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") # Set device to GPU if available
|
||||
|
||||
# Other Constants
|
||||
web_directory = Path(__file__).parent.parent / 'interface/web/'
|
||||
empty_escape_sequences = r'\n|\r\t '
|
||||
|
||||
Reference in New Issue
Block a user