diff --git a/.gitattributes b/.gitattributes index d234b5e0..661ee711 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,2 +1,3 @@ *.min.lua linguist-vendored *documentation.html linguist-vendored +/static/swagger-ui/* linguist-vendored diff --git a/aiserver.py b/aiserver.py index 937cd14c..7adf0e0c 100644 --- a/aiserver.py +++ b/aiserver.py @@ -36,8 +36,10 @@ import itertools import bisect import functools import traceback +import inspect +import warnings from collections.abc import Iterable -from typing import Any, Callable, TypeVar, Tuple, Union, Dict, Set, List +from typing import Any, Callable, TypeVar, Tuple, Union, Dict, Set, List, Optional, Type import requests import html @@ -130,6 +132,7 @@ model_menu = { ["Nerys FSD 13B V2 (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys-v2", "32GB", False], ["Nerys FSD 13B (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB", False], ["Skein 6B", "KoboldAI/GPT-J-6B-Skein", "16GB", False], + ["OPT Nerys 6B V2", "KoboldAI/OPT-6B-nerys-v2", "16GB", False], ["Adventure 6B", "KoboldAI/GPT-J-6B-Adventure", "16GB", False], ["Nerys FSD 2.7B (Hybrid)", "KoboldAI/fairseq-dense-2.7B-Nerys", "8GB", False], ["Adventure 2.7B", "KoboldAI/GPT-Neo-2.7B-AID", "8GB", False], @@ -141,6 +144,7 @@ model_menu = { ["Nerys FSD 13B V2 (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys-v2", "32GB", False], ["Janeway FSD 13B", "KoboldAI/fairseq-dense-13B-Janeway", "32GB", False], ["Nerys FSD 13B (Hybrid)", "KoboldAI/fairseq-dense-13B-Nerys", "32GB", False], + ["OPT Nerys 6B V2", "KoboldAI/OPT-6B-nerys-v2", "16GB", False], ["Janeway FSD 6.7B", "KoboldAI/fairseq-dense-6.7B-Janeway", "16GB", False], ["Janeway Neo 6B", "KoboldAI/GPT-J-6B-Janeway", "16GB", False], ["Janeway Neo 2.7B", "KoboldAI/GPT-Neo-2.7B-Janeway", "8GB", False], @@ -211,10 +215,24 @@ model_menu = { ["GooseAI API (requires API key)", "GooseAI", "", False], ["OpenAI API (requires API key)", "OAI", "", False], ["InferKit API (requires API key)", "InferKit", "", False], - ["KoboldAI Server API (Old Google Colab)", "Colab", "", False], + # ["KoboldAI Server API (Old Google Colab)", "Colab", "", False], + ["KoboldAI API", "API", "", False], ["Return to Main Menu", "mainmenu", "", True], ] } + +class TokenStreamQueue: + def __init__(self): + self.probability_buffer = None + self.queue = [] + + def add_text(self, text): + self.queue.append({ + "decoded": text, + "probabilities": self.probability_buffer + }) + self.probability_buffer = None + # Variables class vars: lastact = "" # The last action received from the user @@ -351,8 +369,14 @@ class vars: lazy_load = True # Whether or not to use torch_lazy_loader.py for transformers models in order to reduce CPU memory usage use_colab_tpu = os.environ.get("COLAB_TPU_ADDR", "") != "" or os.environ.get("TPU_NAME", "") != "" # Whether or not we're in a Colab TPU instance or Kaggle TPU instance and are going to use the TPU rather than the CPU revision = None - output_streaming = False - token_stream_queue = [] # Queue for the token streaming + standalone = False + api_tokenizer_id = None + disable_set_aibusy = False + disable_input_formatting = False + disable_output_formatting = False + output_streaming = True + token_stream_queue = TokenStreamQueue() # Queue for the token streaming + show_probs = False # Whether or not to show token probabilities utils.vars = vars @@ -372,9 +396,11 @@ log.setLevel(logging.ERROR) # Start flask & SocketIO print("{0}Initializing Flask... {1}".format(colors.PURPLE, colors.END), end="") -from flask import Flask, render_template, Response, request, copy_current_request_context, send_from_directory, session -from flask_socketio import SocketIO, emit +from flask import Flask, render_template, Response, request, copy_current_request_context, send_from_directory, session, jsonify, abort, redirect +from flask_socketio import SocketIO +from flask_socketio import emit as _emit from flask_session import Session +from werkzeug.exceptions import HTTPException, NotFound, InternalServerError import secrets app = Flask(__name__, root_path=os.getcwd()) app.secret_key = secrets.token_hex() @@ -384,6 +410,208 @@ Session(app) socketio = SocketIO(app, async_method="eventlet") print("{0}OK!{1}".format(colors.GREEN, colors.END)) +old_socketio_on = socketio.on +def new_socketio_on(*a, **k): + decorator = old_socketio_on(*a, **k) + def new_decorator(f): + @functools.wraps(f) + def g(*a, **k): + if args.no_ui: + return + return f(*a, **k) + return decorator(g) + return new_decorator +socketio.on = new_socketio_on + +def emit(*args, **kwargs): + try: + return _emit(*args, **kwargs) + except AttributeError: + return socketio.emit(*args, **kwargs) + +# marshmallow/apispec setup +from apispec import APISpec +from apispec.ext.marshmallow import MarshmallowPlugin +from apispec.ext.marshmallow.field_converter import make_min_max_attributes +from apispec_webframeworks.flask import FlaskPlugin +from marshmallow import Schema, fields, validate, EXCLUDE +from marshmallow.exceptions import ValidationError + +class KoboldSchema(Schema): + pass + +def new_make_min_max_attributes(validators, min_attr, max_attr) -> dict: + # Patched apispec function that creates "exclusiveMinimum"/"exclusiveMaximum" OpenAPI attributes insteaed of "minimum"/"maximum" when using validators.Range or validators.Length with min_inclusive=False or max_inclusive=False + attributes = {} + min_list = [validator.min for validator in validators if validator.min is not None] + max_list = [validator.max for validator in validators if validator.max is not None] + min_inclusive_list = [getattr(validator, "min_inclusive", True) for validator in validators if validator.min is not None] + max_inclusive_list = [getattr(validator, "max_inclusive", True) for validator in validators if validator.max is not None] + if min_list: + if min_attr == "minimum" and not min_inclusive_list[max(range(len(min_list)), key=min_list.__getitem__)]: + min_attr = "exclusiveMinimum" + attributes[min_attr] = max(min_list) + if max_list: + if min_attr == "maximum" and not max_inclusive_list[min(range(len(max_list)), key=max_list.__getitem__)]: + min_attr = "exclusiveMaximum" + attributes[max_attr] = min(max_list) + return attributes +make_min_max_attributes.__code__ = new_make_min_max_attributes.__code__ + +def api_format_docstring(f): + f.__doc__ = eval('f"""{}"""'.format(f.__doc__.replace("\\", "\\\\"))) + return f + +def api_catch_out_of_memory_errors(f): + @functools.wraps(f) + def decorated(*args, **kwargs): + try: + return f(*args, **kwargs) + except Exception as e: + if any (s in traceback.format_exc().lower() for s in ("out of memory", "not enough memory")): + for line in reversed(traceback.format_exc().split("\n")): + if any(s in line.lower() for s in ("out of memory", "not enough memory")) and line.count(":"): + line = line.split(":", 1)[1] + line = re.sub(r"\[.+?\] +data\.", "", line).strip() + raise KoboldOutOfMemoryError("KoboldAI ran out of memory: " + line, type="out_of_memory.gpu.cuda" if "cuda out of memory" in line.lower() else "out_of_memory.gpu.hip" if "hip out of memory" in line.lower() else "out_of_memory.tpu.hbm" if "memory space hbm" in line.lower() else "out_of_memory.cpu.default_memory_allocator" if "defaultmemoryallocator" in line.lower() else "out_of_memory.unknown.unknown") + raise KoboldOutOfMemoryError(type="out_of_memory.unknown.unknown") + raise e + return decorated + +def api_schema_wrap(f): + try: + input_schema: Type[Schema] = next(iter(inspect.signature(f).parameters.values())).annotation + except: + HAS_SCHEMA = False + else: + HAS_SCHEMA = inspect.isclass(input_schema) and issubclass(input_schema, Schema) + f = api_format_docstring(f) + f = api_catch_out_of_memory_errors(f) + @functools.wraps(f) + def decorated(*args, **kwargs): + if HAS_SCHEMA: + body = request.get_json() + schema = input_schema.from_dict(input_schema().load(body)) + response = f(schema, *args, **kwargs) + else: + response = f(*args, **kwargs) + if not isinstance(response, Response): + response = jsonify(response) + return response + return decorated + +@app.errorhandler(HTTPException) +def handler(e): + if request.path != "/api" and not request.path.startswith("/api/"): + return e + resp = jsonify(detail={"msg": str(e), "type": "generic.error_" + str(e.code)}) + if e.code == 405 and e.valid_methods is not None: + resp.headers["Allow"] = ", ".join(e.valid_methods) + return resp, e.code + +class KoboldOutOfMemoryError(HTTPException): + code = 507 + description = "KoboldAI ran out of memory." + type = "out_of_memory.unknown.unknown" + def __init__(self, *args, type=None, **kwargs): + super().__init__(*args, **kwargs) + if type is not None: + self.type = type +@app.errorhandler(KoboldOutOfMemoryError) +def handler(e): + if request.path != "/api" and not request.path.startswith("/api/"): + return InternalServerError() + return jsonify(detail={"type": e.type, "msg": e.description}), e.code + +@app.errorhandler(ValidationError) +def handler(e): + if request.path != "/api" and not request.path.startswith("/api/"): + return InternalServerError() + return jsonify(detail=e.messages), 422 + +@app.errorhandler(NotImplementedError) +def handler(e): + if request.path != "/api" and not request.path.startswith("/api/"): + return InternalServerError() + return jsonify(detail={"type": "not_implemented", "msg": str(e).strip()}), 501 + +api_versions: List[str] = [] + +class KoboldAPISpec(APISpec): + class KoboldFlaskPlugin(FlaskPlugin): + def __init__(self, api: "KoboldAPISpec", *args, **kwargs): + self._kobold_api_spec = api + super().__init__(*args, **kwargs) + + def path_helper(self, *args, **kwargs): + return super().path_helper(*args, **kwargs)[len(self._kobold_api_spec._prefixes[0]):] + + def __init__(self, *args, title: str = "KoboldAI API", openapi_version: str = "3.0.3", version: str = "1.0.0", prefixes: List[str] = None, **kwargs): + plugins = [KoboldAPISpec.KoboldFlaskPlugin(self), MarshmallowPlugin()] + self._prefixes = prefixes if prefixes is not None else [""] + self._kobold_api_spec_version = version + api_versions.append(version) + api_versions.sort(key=lambda x: [int(e) for e in x.split(".")]) + super().__init__(*args, title=title, openapi_version=openapi_version, version=version, plugins=plugins, servers=[{"url": self._prefixes[0]}], **kwargs) + for prefix in self._prefixes: + app.route(prefix, endpoint="~KoboldAPISpec~" + prefix)(lambda: redirect(request.path + "/docs/")) + app.route(prefix + "/", endpoint="~KoboldAPISpec~" + prefix + "/")(lambda: redirect("docs/")) + app.route(prefix + "/docs", endpoint="~KoboldAPISpec~" + prefix + "/docs")(lambda: redirect("docs/")) + app.route(prefix + "/docs/", endpoint="~KoboldAPISpec~" + prefix + "/docs/")(lambda: render_template("swagger-ui.html", url=self._prefixes[0] + "/openapi.json")) + app.route(prefix + "/openapi.json", endpoint="~KoboldAPISpec~" + prefix + "/openapi.json")(lambda: jsonify(self.to_dict())) + + def route(self, rule: str, methods=["GET"], **kwargs): + __F = TypeVar("__F", bound=Callable[..., Any]) + if "strict_slashes" not in kwargs: + kwargs["strict_slashes"] = False + def new_decorator(f: __F) -> __F: + @functools.wraps(f) + def g(*args, **kwargs): + global api_version + api_version = self._kobold_api_spec_version + try: + return f(*args, **kwargs) + finally: + api_version = None + for prefix in self._prefixes: + g = app.route(prefix + rule, methods=methods, **kwargs)(g) + with app.test_request_context(): + self.path(view=g, **kwargs) + return g + return new_decorator + + def get(self, rule: str, **kwargs): + return self.route(rule, methods=["GET"], **kwargs) + + def post(self, rule: str, **kwargs): + return self.route(rule, methods=["POST"], **kwargs) + + def put(self, rule: str, **kwargs): + return self.route(rule, methods=["PUT"], **kwargs) + + def patch(self, rule: str, **kwargs): + return self.route(rule, methods=["PATCH"], **kwargs) + + def delete(self, rule: str, **kwargs): + return self.route(rule, methods=["DELETE"], **kwargs) + +tags = [ + {"name": "info", "description": "Metadata about this API"}, + {"name": "generate", "description": "Text generation endpoints"}, + {"name": "model", "description": "Information about the current text generation model"}, + {"name": "story", "description": "Endpoints for managing the story in the KoboldAI GUI"}, + {"name": "world_info", "description": "Endpoints for managing the world info in the KoboldAI GUI"}, + {"name": "config", "description": "Allows you to get/set various setting values"}, +] + +api_version = None # This gets set automatically so don't change this value + +api_v1 = KoboldAPISpec( + version="1.1.4", + prefixes=["/api/v1", "/api/latest"], + tags=tags, +) + #==================================================================# # Function to get model selection at startup #==================================================================# @@ -527,7 +755,10 @@ def device_config(config): global breakmodel, generator import breakmodel n_layers = utils.num_layers(config) - if(args.breakmodel_gpulayers is not None or (utils.HAS_ACCELERATE and args.breakmodel_disklayers is not None)): + if args.cpu: + breakmodel.gpu_blocks = [0]*n_layers + return + elif(args.breakmodel_gpulayers is not None or (utils.HAS_ACCELERATE and args.breakmodel_disklayers is not None)): try: if(not args.breakmodel_gpulayers): breakmodel.gpu_blocks = [] @@ -803,6 +1034,7 @@ def savesettings(): js["autosave"] = vars.autosave js["welcome"] = vars.welcome js["output_streaming"] = vars.output_streaming + js["show_probs"] = vars.show_probs if(vars.seed_specified): js["seed"] = vars.seed @@ -916,6 +1148,8 @@ def processsettings(js): vars.welcome = js["welcome"] if("output_streaming" in js): vars.output_streaming = js["output_streaming"] + if("show_probs" in js): + vars.show_probs = js["show_probs"] if("seed" in js): vars.seed = js["seed"] @@ -955,11 +1189,11 @@ def check_for_sp_change(): emit('from_server', {'cmd': 'spstatitems', 'data': {vars.spfilename: vars.spmeta} if vars.allowsp and len(vars.spfilename) else {}}, namespace=None, broadcast=True) vars.sp_changed = False - if(vars.output_streaming and vars.token_stream_queue): + if(vars.token_stream_queue.queue): # If emit blocks, waiting for it to complete before clearing could # introduce a race condition that drops tokens. - queued_tokens = list(vars.token_stream_queue) - vars.token_stream_queue.clear() + queued_tokens = list(vars.token_stream_queue.queue) + vars.token_stream_queue.queue.clear() socketio.emit("from_server", {"cmd": "streamtoken", "data": queued_tokens}, namespace=None, broadcast=True) socketio.start_background_task(check_for_sp_change) @@ -1058,6 +1292,7 @@ def general_startup(override_args=None): parser.add_argument("--lowmem", action='store_true', help="Extra Low Memory loading for the GPU, slower but memory does not peak to twice the usage") parser.add_argument("--savemodel", action='store_true', help="Saves the model to the models folder even if --colab is used (Allows you to save models to Google Drive)") parser.add_argument("--customsettings", help="Preloads arguements from json file. You only need to provide the location of the json file. Use customsettings.json template file. It can be renamed if you wish so that you can store multiple configurations. Leave any settings you want as default as null. Any values you wish to set need to be in double quotation marks") + parser.add_argument("--no_ui", action='store_true', default=False, help="Disables the GUI and Socket.IO server while leaving the API server running.") #args: argparse.Namespace = None if "pytest" in sys.modules and override_args is None: args = parser.parse_args([]) @@ -1078,6 +1313,12 @@ def general_startup(override_args=None): if importedsettings[items] is not None: setattr(args, items, importedsettings[items]) f.close() + + if args.no_ui: + def new_emit(*args, **kwargs): + return + old_emit = socketio.emit + socketio.emit = new_emit vars.model = args.model; vars.revision = args.revision @@ -1190,6 +1431,8 @@ def get_model_info(model, directory=""): url = True elif not utils.HAS_ACCELERATE and not torch.cuda.is_available(): pass + elif args.cpu: + pass else: layer_count = get_layer_count(model, directory=directory) if layer_count is None: @@ -1224,7 +1467,7 @@ def get_model_info(model, directory=""): def get_layer_count(model, directory=""): - if(model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]): + if(model not in ["InferKit", "Colab", "API", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ"]): if(vars.model == "GPT2Custom"): model_config = open(vars.custmodpth + "/config.json", "r") # Get the model_type from the config or assume a model type if it isn't present @@ -1492,6 +1735,9 @@ def patch_transformers(): self.regeneration_required = False self.halt = False + if(vars.standalone): + return scores + scores_shape = scores.shape scores_list = scores.tolist() vars.lua_koboldbridge.logits = vars.lua_state.table() @@ -1509,10 +1755,37 @@ def patch_transformers(): assert scores.shape == scores_shape return scores + + from torch.nn import functional as F + + class ProbabilityVisualizerLogitsProcessor(LogitsProcessor): + def __init__(self): + pass + + def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor: + assert scores.ndim == 2 + assert input_ids.ndim == 2 + + if vars.numseqs > 1 or not vars.show_probs: + return scores + + probs = F.softmax(scores, dim = -1).cpu().numpy()[0] + + token_prob_info = [] + for token_id, score in sorted(enumerate(probs), key=lambda x: x[1], reverse=True)[:8]: + token_prob_info.append({ + "tokenId": token_id, + "decoded": utils.decodenewlines(tokenizer.decode(token_id)), + "score": float(score), + }) + + vars.token_stream_queue.probability_buffer = token_prob_info + return scores def new_get_logits_processor(*args, **kwargs) -> LogitsProcessorList: processors = new_get_logits_processor.old_get_logits_processor(*args, **kwargs) processors.insert(0, LuaLogitsProcessor()) + processors.append(ProbabilityVisualizerLogitsProcessor()) return processors new_get_logits_processor.old_get_logits_processor = transformers.generation_utils.GenerationMixin._get_logits_processor transformers.generation_utils.GenerationMixin._get_logits_processor = new_get_logits_processor @@ -1568,12 +1841,14 @@ def patch_transformers(): **kwargs, ) -> bool: # Do not intermingle multiple generations' outputs! - if(vars.numseqs > 1): + if vars.numseqs > 1: + return False + + if not (vars.show_probs or vars.output_streaming): return False tokenizer_text = utils.decodenewlines(tokenizer.decode(input_ids[0, -1])) - - vars.token_stream_queue.append(tokenizer_text) + vars.token_stream_queue.add_text(tokenizer_text) return False @@ -1595,12 +1870,14 @@ def patch_transformers(): **kwargs, ) -> bool: vars.generated_tkns += 1 - if(vars.lua_koboldbridge.generated_cols and vars.generated_tkns != vars.lua_koboldbridge.generated_cols): + if(not vars.standalone and vars.lua_koboldbridge.generated_cols and vars.generated_tkns != vars.lua_koboldbridge.generated_cols): raise RuntimeError(f"Inconsistency detected between KoboldAI Python and Lua backends ({vars.generated_tkns} != {vars.lua_koboldbridge.generated_cols})") if(vars.abort or vars.generated_tkns >= vars.genamt): self.regeneration_required = False self.halt = False return True + if(vars.standalone): + return False assert input_ids.ndim == 2 assert len(self.excluded_world_info) == input_ids.shape[0] @@ -1637,6 +1914,30 @@ def patch_transformers(): return stopping_criteria transformers.generation_utils.GenerationMixin._get_stopping_criteria = new_get_stopping_criteria +def reset_model_settings(): + vars.socketio = socketio + vars.max_length = 1024 # Maximum number of tokens to submit per action + vars.ikmax = 3000 # Maximum number of characters to submit to InferKit + vars.genamt = 80 # Amount of text for each action to generate + vars.ikgen = 200 # Number of characters for InferKit to generate + vars.rep_pen = 1.1 # Default generator repetition_penalty + vars.rep_pen_slope = 0.7 # Default generator repetition penalty slope + vars.rep_pen_range = 1024 # Default generator repetition penalty range + vars.temp = 0.5 # Default generator temperature + vars.top_p = 0.9 # Default generator top_p + vars.top_k = 0 # Default generator top_k + vars.top_a = 0.0 # Default generator top-a + vars.tfs = 1.0 # Default generator tfs (tail-free sampling) + vars.typical = 1.0 # Default generator typical sampling threshold + vars.numseqs = 1 # Number of sequences to ask the generator to create + vars.generated_tkns = 0 # If using a backend that supports Lua generation modifiers, how many tokens have already been generated, otherwise 0 + vars.badwordsids = [] + vars.fp32_model = False # Whether or not the most recently loaded HF model was in fp32 format + vars.modeldim = -1 # Embedding dimension of your model (e.g. it's 4096 for GPT-J-6B and 2560 for GPT-Neo-2.7B) + vars.sampler_order = [0, 1, 2, 3, 4, 5] + vars.newlinemode = "n" + vars.revision = None + def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=False, online_model=""): global model global generator @@ -1644,6 +1945,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal global model_config global GPT2TokenizerFast global tokenizer + reset_model_settings() if not utils.HAS_ACCELERATE: disk_layers = None vars.noai = False @@ -1655,20 +1957,26 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal time.sleep(0.1) if gpu_layers is not None: args.breakmodel_gpulayers = gpu_layers + elif initial_load: + gpu_layers = args.breakmodel_gpulayers if disk_layers is not None: args.breakmodel_disklayers = int(disk_layers) + elif initial_load: + disk_layers = args.breakmodel_disklayers #We need to wipe out the existing model and refresh the cuda cache model = None generator = None model_config = None - for tensor in gc.get_objects(): - try: - if torch.is_tensor(tensor): - with torch.no_grad(): - tensor.set_(torch.tensor((), device=tensor.device, dtype=tensor.dtype)) - except: - pass + with torch.no_grad(): + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="torch.distributed.reduce_op is deprecated") + for tensor in gc.get_objects(): + try: + if torch.is_tensor(tensor): + tensor.set_(torch.tensor((), device=tensor.device, dtype=tensor.dtype)) + except: + pass gc.collect() try: torch.cuda.empty_cache() @@ -1706,7 +2014,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal # If transformers model was selected & GPU available, ask to use CPU or GPU - if(vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): + if(vars.model not in ["InferKit", "Colab", "API", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): vars.allowsp = True # Test for GPU support @@ -1745,7 +2053,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal print("WARNING: No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)") vars.model_type = "gpt_neo" - if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): + if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "API", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): loadmodelsettings() loadsettings() print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="") @@ -1765,41 +2073,19 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal else: print("{0}NOT FOUND!{1}".format(colors.YELLOW, colors.END)) - if args.model: - if(vars.hascuda): - genselected = True + if args.cpu: + vars.usegpu = False + gpu_layers = None + disk_layers = None + vars.breakmodel = False + elif vars.hascuda: + if(vars.bmsupported): + vars.usegpu = False + vars.breakmodel = True + else: + vars.breakmodel = False vars.usegpu = True - vars.breakmodel = utils.HAS_ACCELERATE - if(vars.bmsupported): - vars.usegpu = False - vars.breakmodel = True - if(args.cpu): - vars.usegpu = False - vars.breakmodel = utils.HAS_ACCELERATE - elif(vars.hascuda): - if(vars.bmsupported): - genselected = True - vars.usegpu = False - vars.breakmodel = True - else: - genselected = False - else: - genselected = False - if(vars.hascuda): - if(use_gpu): - if(vars.bmsupported): - vars.breakmodel = True - vars.usegpu = False - genselected = True - else: - vars.breakmodel = False - vars.usegpu = True - genselected = True - else: - vars.breakmodel = utils.HAS_ACCELERATE - vars.usegpu = False - genselected = True # Ask for API key if InferKit was selected if(vars.model == "InferKit"): @@ -1820,7 +2106,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal vars.noai = True # Start transformers and create pipeline - if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): + if(not vars.use_colab_tpu and vars.model not in ["InferKit", "Colab", "API", "OAI", "GooseAI" , "ReadOnly", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): if(not vars.noai): print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END)) for m in ("GPTJModel", "XGLMModel"): @@ -2018,7 +2304,8 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal # If we're using torch_lazy_loader, we need to get breakmodel config # early so that it knows where to load the individual model tensors - if(utils.HAS_ACCELERATE or vars.lazy_load and vars.hascuda and vars.breakmodel): + if (utils.HAS_ACCELERATE or vars.lazy_load and vars.hascuda and vars.breakmodel) and not vars.nobreakmodel: + print(1) device_config(model_config) # Download model from Huggingface if it does not exist, otherwise load locally @@ -2149,6 +2436,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel) vars.modeldim = get_hidden_size_from_model(model) if(not vars.lazy_load): + print(2) device_config(model.config) move_model_to_devices(model) elif(utils.HAS_ACCELERATE and __import__("breakmodel").disk_blocks > 0): @@ -2271,7 +2559,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal } # If we're running Colab or OAI, we still need a tokenizer. - if(vars.model == "Colab"): + if(vars.model in ("Colab", "API")): from transformers import GPT2TokenizerFast tokenizer = GPT2TokenizerFast.from_pretrained("EleutherAI/gpt-neo-2.7B", revision=vars.revision, cache_dir="cache") loadsettings() @@ -2328,16 +2616,24 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal @app.route('/') @app.route('/index') def index(): + if args.no_ui: + return redirect('/api/latest') if 'new_ui' in request.args: return render_template('index_new.html', hide_ai_menu=args.noaimenu) else: return render_template('index.html', hide_ai_menu=args.noaimenu, flaskwebgui=vars.flaskwebgui) +@app.route('/api', strict_slashes=False) +def api(): + return redirect('/api/latest') @app.route('/favicon.ico') def favicon(): return send_from_directory(app.root_path, 'koboldai.ico', mimetype='image/vnd.microsoft.icon') @app.route('/download') def download(): + if args.no_ui: + raise NotFound() + save_format = request.args.get("format", "json").strip().lower() if(save_format == "plaintext"): @@ -2735,7 +3031,8 @@ def lua_has_setting(setting): "rmspch", "adsnsp", "singleline", - "output_streaming" + "output_streaming", + "show_probs" ) #==================================================================# @@ -2768,6 +3065,7 @@ def lua_get_setting(setting): if(setting in ("frmtadsnsp", "adsnsp")): return vars.formatoptns["frmtadsnsp"] if(setting in ("frmtsingleline", "singleline")): return vars.formatoptns["singleline"] if(setting == "output_streaming"): return vars.output_streaming + if(setting == "show_probs"): return vars.show_probs #==================================================================# # Set the setting with the given name if it exists @@ -2805,6 +3103,7 @@ def lua_set_setting(setting, v): if(setting in ("frmtadsnsp", "adsnsp")): vars.formatoptns["frmtadsnsp"] = v if(setting in ("frmtsingleline", "singleline")): vars.formatoptns["singleline"] = v if(setting == "output_streaming"): vars.output_streaming = v + if(setting == "show_probs"): vars.show_probs = v #==================================================================# # Get contents of memory @@ -2906,7 +3205,7 @@ def lua_set_chunk(k, v): def lua_get_modeltype(): if(vars.noai): return "readonly" - if(vars.model in ("Colab", "OAI", "InferKit")): + if(vars.model in ("Colab", "API", "OAI", "InferKit")): return "api" if(not vars.use_colab_tpu and vars.model not in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX") and (vars.model in ("GPT2Custom", "NeoCustom") or vars.model_type in ("gpt2", "gpt_neo", "gptj"))): hidden_size = get_hidden_size_from_model(model) @@ -2935,7 +3234,7 @@ def lua_get_modeltype(): def lua_get_modelbackend(): if(vars.noai): return "readonly" - if(vars.model in ("Colab", "OAI", "InferKit")): + if(vars.model in ("Colab", "API", "OAI", "InferKit")): return "api" if(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): return "mtj" @@ -3384,7 +3683,7 @@ def get_message(msg): else: filename = "settings/{}.breakmodel".format(vars.model.replace('/', '_')) f = open(filename, "w") - f.write(msg['gpu_layers'] + '\n' + msg['disk_layers']) + f.write(str(msg['gpu_layers']) + '\n' + str(msg['disk_layers'])) f.close() vars.colaburl = msg['url'] + "/request" load_model(use_gpu=msg['use_gpu'], gpu_layers=msg['gpu_layers'], disk_layers=msg['disk_layers'], online_model=msg['online_model']) @@ -3521,6 +3820,10 @@ def get_message(msg): vars.output_streaming = msg['data'] settingschanged() refresh_settings() + elif(msg['cmd'] == 'setshowprobs'): + vars.show_probs = msg['data'] + settingschanged() + refresh_settings() elif(not vars.host and msg['cmd'] == 'importwi'): wiimportrequest() elif(msg['cmd'] == 'debug'): @@ -3528,6 +3831,40 @@ def get_message(msg): emit('from_server', {'cmd': 'set_debug', 'data': msg['data']}, broadcast=True) if vars.debug: send_debug() + elif(msg['cmd'] == 'getfieldbudget'): + unencoded = msg["data"]["unencoded"] + field = msg["data"]["field"] + + # Tokenizer may be undefined here when a model has not been chosen. + if "tokenizer" not in globals(): + # We don't have a tokenizer, just return nulls. + emit( + 'from_server', + {'cmd': 'showfieldbudget', 'data': {"length": None, "max": None, "field": field}}, + broadcast=True + ) + return + + header_length = len(tokenizer._koboldai_header) + max_tokens = vars.max_length - header_length - vars.sp_length - vars.genamt + + if not unencoded: + # Unencoded is empty, just return 0 + emit( + 'from_server', + {'cmd': 'showfieldbudget', 'data': {"length": 0, "max": max_tokens, "field": field}}, + broadcast=True + ) + else: + if field == "anoteinput": + unencoded = buildauthorsnote(unencoded, msg["data"]["anotetemplate"]) + tokens_length = len(tokenizer.encode(unencoded)) + + emit( + 'from_server', + {'cmd': 'showfieldbudget', 'data': {"length": tokens_length, "max": max_tokens, "field": field}}, + broadcast=True + ) #==================================================================# # Send userscripts list to client @@ -3606,7 +3943,7 @@ def check_for_backend_compilation(): break vars.checking = False -def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, disable_recentrng=False): +def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, disable_recentrng=False, no_generate=False): # Ignore new submissions if the AI is currently busy if(vars.aibusy): return @@ -3614,6 +3951,32 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, while(True): set_aibusy(1) + if(vars.model == "API"): + global tokenizer + tokenizer_id = requests.get( + vars.colaburl[:-8] + "/api/v1/model", + ).json()["result"] + if tokenizer_id != vars.api_tokenizer_id: + try: + if(os.path.isdir(tokenizer_id)): + try: + tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=vars.revision, cache_dir="cache") + except: + tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=vars.revision, cache_dir="cache", use_fast=False) + elif(os.path.isdir("models/{}".format(tokenizer_id.replace('/', '_')))): + try: + tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=vars.revision, cache_dir="cache") + except: + tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=vars.revision, cache_dir="cache", use_fast=False) + else: + try: + tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=vars.revision, cache_dir="cache") + except: + tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=vars.revision, cache_dir="cache", use_fast=False) + except: + print(f"WARNING: Unknown tokenizer {repr(tokenizer_id)}") + vars.api_tokenizer_id = tokenizer_id + if(disable_recentrng): vars.recentrng = vars.recentrngm = None @@ -3640,20 +4003,21 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, if(not vars.gamestarted): vars.submission = data - execute_inmod() + if(not no_generate): + execute_inmod() vars.submission = re.sub(r"[^\S\r\n]*([\r\n]*)$", r"\1", vars.submission) # Remove trailing whitespace, excluding newlines data = vars.submission if(not force_submit and len(data.strip()) == 0): assert False # Start the game vars.gamestarted = True - if(not vars.noai and vars.lua_koboldbridge.generating and (not vars.nopromptgen or force_prompt_gen)): + if(not no_generate and not vars.noai and vars.lua_koboldbridge.generating and (not vars.nopromptgen or force_prompt_gen)): # Save this first action as the prompt vars.prompt = data # Clear the startup text from game screen emit('from_server', {'cmd': 'updatescreen', 'gamestarted': False, 'data': 'Please wait, generating story...'}, broadcast=True) calcsubmit(data) # Run the first action through the generator - if(not vars.abort and vars.lua_koboldbridge.restart_sequence is not None and len(vars.genseqs) == 0): + if(not no_generate and not vars.abort and vars.lua_koboldbridge.restart_sequence is not None and len(vars.genseqs) == 0): data = "" force_submit = True disable_recentrng = True @@ -3665,7 +4029,8 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, vars.prompt = data if len(data) > 0 else '"' for i in range(vars.numseqs): vars.lua_koboldbridge.outputs[i+1] = "" - execute_outmod() + if(not no_generate): + execute_outmod() vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(vars.numseqs): @@ -3699,7 +4064,8 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, if(vars.actionmode == 0): data = applyinputformatting(data) vars.submission = data - execute_inmod() + if(not no_generate): + execute_inmod() vars.submission = re.sub(r"[^\S\r\n]*([\r\n]*)$", r"\1", vars.submission) # Remove trailing whitespace, excluding newlines data = vars.submission # Dont append submission if it's a blank/continue action @@ -3729,7 +4095,7 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, update_story_chunk('last') send_debug() - if(not vars.noai and vars.lua_koboldbridge.generating): + if(not no_generate and not vars.noai and vars.lua_koboldbridge.generating): # Off to the tokenizer! calcsubmit(data) if(not vars.abort and vars.lua_koboldbridge.restart_sequence is not None and len(vars.genseqs) == 0): @@ -3740,23 +4106,24 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True) break else: - for i in range(vars.numseqs): - vars.lua_koboldbridge.outputs[i+1] = "" - execute_outmod() - vars.lua_koboldbridge.regeneration_required = False + if(not no_generate): + for i in range(vars.numseqs): + vars.lua_koboldbridge.outputs[i+1] = "" + execute_outmod() + vars.lua_koboldbridge.regeneration_required = False genout = [] for i in range(vars.numseqs): - genout.append({"generated_text": vars.lua_koboldbridge.outputs[i+1]}) + genout.append({"generated_text": vars.lua_koboldbridge.outputs[i+1] if not no_generate else ""}) assert type(genout[-1]["generated_text"]) is str if(len(genout) == 1): genresult(genout[0]["generated_text"]) - if(not vars.abort and vars.lua_koboldbridge.restart_sequence is not None): + if(not no_generate and not vars.abort and vars.lua_koboldbridge.restart_sequence is not None): data = "" force_submit = True disable_recentrng = True continue else: - if(not vars.abort and vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): + if(not no_generate and not vars.abort and vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): genresult(genout[vars.lua_koboldbridge.restart_sequence-1]["generated_text"]) data = "" force_submit = True @@ -3767,6 +4134,130 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False, emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True) break +def apiactionsubmit_generate(txt, minimum, maximum): + vars.generated_tkns = 0 + + if not vars.quiet: + print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, utils.decodenewlines(tokenizer.decode(txt)), colors.END)) + + # Clear CUDA cache if using GPU + if(vars.hascuda and (vars.usegpu or vars.breakmodel)): + gc.collect() + torch.cuda.empty_cache() + + # Submit input text to generator + _genout, already_generated = tpool.execute(_generate, txt, minimum, maximum, set()) + + genout = [applyoutputformatting(utils.decodenewlines(tokenizer.decode(tokens[-already_generated:]))) for tokens in _genout] + + # Clear CUDA cache again if using GPU + if(vars.hascuda and (vars.usegpu or vars.breakmodel)): + del _genout + gc.collect() + torch.cuda.empty_cache() + + return genout + +def apiactionsubmit_tpumtjgenerate(txt, minimum, maximum): + vars.generated_tkns = 0 + + if(vars.full_determinism): + tpu_mtj_backend.set_rng_seed(vars.seed) + + if not vars.quiet: + print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, utils.decodenewlines(tokenizer.decode(txt)), colors.END)) + + vars._actions = vars.actions + vars._prompt = vars.prompt + if(vars.dynamicscan): + vars._actions = vars._actions.copy() + + # Submit input text to generator + soft_tokens = tpumtjgetsofttokens() + genout = tpool.execute( + tpu_mtj_backend.infer_static, + np.uint32(txt), + gen_len = maximum-minimum+1, + temp=vars.temp, + top_p=vars.top_p, + top_k=vars.top_k, + tfs=vars.tfs, + typical=vars.typical, + top_a=vars.top_a, + numseqs=vars.numseqs, + repetition_penalty=vars.rep_pen, + rpslope=vars.rep_pen_slope, + rprange=vars.rep_pen_range, + soft_embeddings=vars.sp, + soft_tokens=soft_tokens, + sampler_order=vars.sampler_order, + ) + genout = [applyoutputformatting(utils.decodenewlines(tokenizer.decode(txt))) for txt in genout] + + return genout + +def apiactionsubmit(data, use_memory=False, use_world_info=False, use_story=False, use_authors_note=False): + if(vars.model == "Colab"): + raise NotImplementedError("API generation is not supported in old Colab API mode.") + elif(vars.model == "API"): + raise NotImplementedError("API generation is not supported in API mode.") + elif(vars.model == "OAI"): + raise NotImplementedError("API generation is not supported in OpenAI/GooseAI mode.") + elif(vars.model == "ReadOnly"): + raise NotImplementedError("API generation is not supported in read-only mode; please load a model and then try again.") + + data = applyinputformatting(data) + + if(vars.memory != "" and vars.memory[-1] != "\n"): + mem = vars.memory + "\n" + else: + mem = vars.memory + if(use_authors_note and vars.authornote != ""): + anotetxt = ("\n" + vars.authornotetemplate + "\n").replace("<|>", vars.authornote) + else: + anotetxt = "" + MIN_STORY_TOKENS = 8 + story_tokens = [] + mem_tokens = [] + wi_tokens = [] + story_budget = lambda: vars.max_length - vars.sp_length - vars.genamt - len(tokenizer._koboldai_header) - len(story_tokens) - len(mem_tokens) - len(wi_tokens) + budget = lambda: story_budget() + MIN_STORY_TOKENS + if budget() < 0: + abort(Response(json.dumps({"detail": { + "msg": f"Your Max Tokens setting is too low for your current soft prompt and tokenizer to handle. It needs to be at least {vars.max_length - budget()}.", + "type": "token_overflow", + }}), mimetype="application/json", status=500)) + if use_memory: + mem_tokens = tokenizer.encode(utils.encodenewlines(mem))[-budget():] + if use_world_info: + world_info, _ = checkworldinfo(data, force_use_txt=True, scan_story=use_story) + wi_tokens = tokenizer.encode(utils.encodenewlines(world_info))[-budget():] + if use_story: + if vars.useprompt: + story_tokens = tokenizer.encode(utils.encodenewlines(vars.prompt))[-budget():] + story_tokens = tokenizer.encode(utils.encodenewlines(data))[-story_budget():] + story_tokens + if use_story: + for i, action in enumerate(reversed(vars.actions.values())): + if story_budget() <= 0: + assert story_budget() == 0 + break + story_tokens = tokenizer.encode(utils.encodenewlines(action))[-story_budget():] + story_tokens + if i == vars.andepth - 1: + story_tokens = tokenizer.encode(utils.encodenewlines(anotetxt))[-story_budget():] + story_tokens + if not vars.useprompt: + story_tokens = tokenizer.encode(utils.encodenewlines(vars.prompt))[-budget():] + story_tokens + tokens = tokenizer._koboldai_header + mem_tokens + wi_tokens + story_tokens + assert story_budget() >= 0 + minimum = len(tokens) + 1 + maximum = len(tokens) + vars.genamt + + if(not vars.use_colab_tpu and vars.model not in ["Colab", "API", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): + genout = apiactionsubmit_generate(tokens, minimum, maximum) + elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): + genout = apiactionsubmit_tpumtjgenerate(tokens, minimum, maximum) + + return genout + #==================================================================# # #==================================================================# @@ -3858,6 +4349,12 @@ def actionredo(): #==================================================================# # #==================================================================# +def buildauthorsnote(authorsnote, template): + # Build Author's Note if set + if authorsnote == "": + return "" + return ("\n" + template + "\n").replace("<|>", authorsnote) + def calcsubmitbudgetheader(txt, **kwargs): # Scan for WorldInfo matches winfo, found_entries = checkworldinfo(txt, **kwargs) @@ -3868,11 +4365,7 @@ def calcsubmitbudgetheader(txt, **kwargs): else: mem = vars.memory - # Build Author's Note if set - if(vars.authornote != ""): - anotetxt = ("\n" + vars.authornotetemplate + "\n").replace("<|>", vars.authornote) - else: - anotetxt = "" + anotetxt = buildauthorsnote(vars.authornote, vars.authornotetemplate) return winfo, mem, anotetxt, found_entries @@ -3926,7 +4419,7 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None, if(actionlen == 0): # First/Prompt action - tokens = tokenizer._koboldai_header + memtokens + witokens + anotetkns + prompttkns + tokens = (tokenizer._koboldai_header if vars.model not in ("Colab", "API", "OAI") else []) + memtokens + witokens + anotetkns + prompttkns assert len(tokens) <= vars.max_length - lnsp - vars.genamt - budget_deduction ln = len(tokens) + lnsp return tokens, ln+1, ln+vars.genamt @@ -3974,12 +4467,12 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None, # Did we get to add the A.N.? If not, do it here if(anotetxt != ""): if((not anoteadded) or forceanote): - tokens = tokenizer._koboldai_header + memtokens + witokens + anotetkns + prompttkns + tokens + tokens = (tokenizer._koboldai_header if vars.model not in ("Colab", "API", "OAI") else []) + memtokens + witokens + anotetkns + prompttkns + tokens else: - tokens = tokenizer._koboldai_header + memtokens + witokens + prompttkns + tokens + tokens = (tokenizer._koboldai_header if vars.model not in ("Colab", "API", "OAI") else []) + memtokens + witokens + prompttkns + tokens else: # Prepend Memory, WI, and Prompt before action tokens - tokens = tokenizer._koboldai_header + memtokens + witokens + prompttkns + tokens + tokens = (tokenizer._koboldai_header if vars.model not in ("Colab", "API", "OAI") else []) + memtokens + witokens + prompttkns + tokens # Send completed bundle to generator assert len(tokens) <= vars.max_length - lnsp - vars.genamt - budget_deduction @@ -4001,19 +4494,23 @@ def calcsubmit(txt): if(vars.model != "InferKit"): subtxt, min, max = calcsubmitbudget(actionlen, winfo, mem, anotetxt, vars.actions, submission=txt) if(actionlen == 0): - if(not vars.use_colab_tpu and vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): + if(not vars.use_colab_tpu and vars.model not in ["Colab", "API", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): generate(subtxt, min, max, found_entries=found_entries) elif(vars.model == "Colab"): sendtocolab(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) + elif(vars.model == "API"): + sendtoapi(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.model == "OAI"): oairequest(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): tpumtjgenerate(subtxt, min, max, found_entries=found_entries) else: - if(not vars.use_colab_tpu and vars.model not in ["Colab", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): + if(not vars.use_colab_tpu and vars.model not in ["Colab", "API", "OAI", "TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX"]): generate(subtxt, min, max, found_entries=found_entries) elif(vars.model == "Colab"): sendtocolab(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) + elif(vars.model == "API"): + sendtoapi(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.model == "OAI"): oairequest(utils.decodenewlines(tokenizer.decode(subtxt)), min, max) elif(vars.use_colab_tpu or vars.model in ("TPUMeshTransformerGPTJ", "TPUMeshTransformerGPTNeoX")): @@ -4419,6 +4916,81 @@ def sendtocolab(txt, min, max): emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) set_aibusy(0) + +#==================================================================# +# Send transformers-style request to KoboldAI API +#==================================================================# +def sendtoapi(txt, min, max): + # Log request to console + if not vars.quiet: + print("{0}Tokens:{1}, Txt:{2}{3}".format(colors.YELLOW, min-1, txt, colors.END)) + + # Store context in memory to use it for comparison with generated content + vars.lastctx = txt + + # Build request JSON data + reqdata = { + 'prompt': txt, + 'max_length': max - min + 1, + 'max_context_length': vars.max_length, + 'rep_pen': vars.rep_pen, + 'rep_pen_slope': vars.rep_pen_slope, + 'rep_pen_range': vars.rep_pen_range, + 'temperature': vars.temp, + 'top_p': vars.top_p, + 'top_k': vars.top_k, + 'top_a': vars.top_a, + 'tfs': vars.tfs, + 'typical': vars.typical, + 'n': vars.numseqs, + } + + # Create request + while True: + req = requests.post( + vars.colaburl[:-8] + "/api/v1/generate", + json=reqdata, + ) + if(req.status_code == 503): # Server is currently generating something else so poll until it's our turn + time.sleep(1) + continue + js = req.json() + if(req.status_code != 200): + errmsg = "KoboldAI API Error: Failed to get a reply from the server. Please check the console." + print("{0}{1}{2}".format(colors.RED, json.dumps(js, indent=2), colors.END)) + emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True) + set_aibusy(0) + return + + genout = [obj["text"] for obj in js["results"]] + + for i in range(vars.numseqs): + vars.lua_koboldbridge.outputs[i+1] = genout[i] + + execute_outmod() + if(vars.lua_koboldbridge.regeneration_required): + vars.lua_koboldbridge.regeneration_required = False + genout = [] + for i in range(vars.numseqs): + genout.append(vars.lua_koboldbridge.outputs[i+1]) + assert type(genout[-1]) is str + + if(len(genout) == 1): + genresult(genout[0]) + else: + # Convert torch output format to transformers + seqs = [] + for seq in genout: + seqs.append({"generated_text": seq}) + if(vars.lua_koboldbridge.restart_sequence is not None and vars.lua_koboldbridge.restart_sequence > 0): + genresult(genout[vars.lua_koboldbridge.restart_sequence-1]["generated_text"]) + else: + genselect(genout) + + set_aibusy(0) + return + + #==================================================================# # Send text to TPU mesh transformer backend #==================================================================# @@ -4719,6 +5291,7 @@ def refresh_settings(): emit('from_server', {'cmd': 'updatefrmtadsnsp', 'data': vars.formatoptns["frmtadsnsp"]}, broadcast=True) emit('from_server', {'cmd': 'updatesingleline', 'data': vars.formatoptns["singleline"]}, broadcast=True) emit('from_server', {'cmd': 'updateoutputstreaming', 'data': vars.output_streaming}, broadcast=True) + emit('from_server', {'cmd': 'updateshowprobs', 'data': vars.show_probs}, broadcast=True) # Allow toggle events again emit('from_server', {'cmd': 'allowtoggle', 'data': True}, broadcast=True) @@ -4727,6 +5300,8 @@ def refresh_settings(): # Sets the logical and display states for the AI Busy condition #==================================================================# def set_aibusy(state): + if(vars.disable_set_aibusy): + return if(state): vars.aibusy = True emit('from_server', {'cmd': 'setgamestate', 'data': 'wait'}, broadcast=True) @@ -4830,7 +5405,7 @@ def inlinedelete(chunk): "Previous Selection": True, "Edited": False}] + vars.actions_metadata[chunk-1]['Alternative Text'] vars.actions_metadata[chunk-1]['Selected Text'] = '' - vars.actions[chunk-1] = '' + del vars.actions[chunk-1] else: print(f"WARNING: Attempted to delete non-existent chunk {chunk}") setgamesaved(False) @@ -6420,6 +6995,2661 @@ def get_files_folders(starting_folder): socketio.emit("popup_breadcrumbs", breadcrumbs, broadcast=True) +class EmptySchema(KoboldSchema): + pass + +class BasicTextResultInnerSchema(KoboldSchema): + text: str = fields.String(required=True) + +class BasicTextResultSchema(KoboldSchema): + result: BasicTextResultInnerSchema = fields.Nested(BasicTextResultInnerSchema) + +class BasicResultInnerSchema(KoboldSchema): + result: str = fields.String(required=True) + +class BasicResultSchema(KoboldSchema): + result: BasicResultInnerSchema = fields.Nested(BasicResultInnerSchema, required=True) + +class BasicResultsSchema(KoboldSchema): + results: BasicResultInnerSchema = fields.List(fields.Nested(BasicResultInnerSchema), required=True) + +class BasicStringSchema(KoboldSchema): + value: str = fields.String(required=True) + +class BasicBooleanSchema(KoboldSchema): + value: bool = fields.Boolean(required=True) + +class BasicUIDSchema(KoboldSchema): + uid: str = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry/folder."}) + +class BasicErrorSchema(KoboldSchema): + msg: str = fields.String(required=True) + type: str = fields.String(required=True) + +class StoryEmptyErrorSchema(KoboldSchema): + detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) + +class StoryTooShortErrorSchema(KoboldSchema): + detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) + +class OutOfMemoryErrorSchema(KoboldSchema): + detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) + +class NotFoundErrorSchema(KoboldSchema): + detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) + +api_out_of_memory_response = """507: + description: Out of memory + content: + application/json: + schema: OutOfMemoryErrorSchema + examples: + gpu.cuda: + value: + detail: + msg: "KoboldAI ran out of memory: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.97 GiB already allocated; 0 bytes free; 2.99 GiB reserved in total by PyTorch)" + type: out_of_memory.gpu.cuda + gpu.hip: + value: + detail: + msg: "KoboldAI ran out of memory: HIP out of memory. Tried to allocate 20.00 MiB (GPU 0; 4.00 GiB total capacity; 2.97 GiB already allocated; 0 bytes free; 2.99 GiB reserved in total by PyTorch)" + type: out_of_memory.gpu.hip + tpu.hbm: + value: + detail: + msg: "KoboldAI ran out of memory: Compilation failed: Compilation failure: Ran out of memory in memory space hbm. Used 8.83G of 8.00G hbm. Exceeded hbm capacity by 848.88M." + type: out_of_memory.tpu.hbm + cpu.default_cpu_allocator: + value: + detail: + msg: "KoboldAI ran out of memory: DefaultCPUAllocator: not enough memory: you tried to allocate 209715200 bytes." + type: out_of_memory.cpu.default_cpu_allocator + unknown.unknown: + value: + detail: + msg: "KoboldAI ran out of memory." + type: out_of_memory.unknown.unknown""" + +class ValidationErrorSchema(KoboldSchema): + detail: Dict[str, List[str]] = fields.Dict(keys=fields.String(), values=fields.List(fields.String(), validate=validate.Length(min=1)), required=True) + +api_validation_error_response = """422: + description: Validation error + content: + application/json: + schema: ValidationErrorSchema""" + +class ServerBusyErrorSchema(KoboldSchema): + detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) + +api_server_busy_response = """503: + description: Server is busy + content: + application/json: + schema: ServerBusyErrorSchema + example: + detail: + msg: Server is busy; please try again later. + type: service_unavailable""" + +class NotImplementedErrorSchema(KoboldSchema): + detail: BasicErrorSchema = fields.Nested(BasicErrorSchema, required=True) + +api_not_implemented_response = """501: + description: Not implemented + content: + application/json: + schema: NotImplementedErrorSchema + example: + detail: + msg: API generation is not supported in read-only mode; please load a model and then try again. + type: not_implemented""" + +class SamplerSettingsSchema(KoboldSchema): + rep_pen: Optional[float] = fields.Float(validate=validate.Range(min=1), metadata={"description": "Base repetition penalty value."}) + rep_pen_range: Optional[int] = fields.Integer(validate=validate.Range(min=0), metadata={"description": "Repetition penalty range."}) + rep_pen_slope: Optional[float] = fields.Float(validate=validate.Range(min=0), metadata={"description": "Repetition penalty slope."}) + top_k: Optional[int] = fields.Integer(validate=validate.Range(min=0), metadata={"description": "Top-k sampling value."}) + top_a: Optional[float] = fields.Float(validate=validate.Range(min=0), metadata={"description": "Top-a sampling value."}) + top_p: Optional[float] = fields.Float(validate=validate.Range(min=0, max=1), metadata={"description": "Top-p sampling value."}) + tfs: Optional[float] = fields.Float(validate=validate.Range(min=0, max=1), metadata={"description": "Tail free sampling value."}) + typical: Optional[float] = fields.Float(validate=validate.Range(min=0, max=1), metadata={"description": "Typical sampling value."}) + temperature: Optional[float] = fields.Float(validate=validate.Range(min=0, min_inclusive=False), metadata={"description": "Temperature value."}) + +def soft_prompt_validator(soft_prompt: str): + if len(soft_prompt.strip()) == 0: + return + if not vars.allowsp: + raise ValidationError("Cannot use soft prompts with current backend.") + if any(q in soft_prompt for q in ("/", "\\")): + return + z, _, _, _, _ = fileops.checksp(soft_prompt.strip(), vars.modeldim) + if isinstance(z, int): + raise ValidationError("Must be a valid soft prompt name.") + z.close() + return True + +def story_load_validator(name: str): + if any(q in name for q in ("/", "\\")): + return + if len(name.strip()) == 0 or not os.path.isfile(fileops.storypath(name)): + raise ValidationError("Must be a valid story name.") + return True + +class GenerationInputSchema(SamplerSettingsSchema): + prompt: str = fields.String(required=True, metadata={"description": "This is the submission."}) + use_memory: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the memory from the KoboldAI GUI when generating text."}) + use_story: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the story from the KoboldAI GUI when generating text."}) + use_authors_note: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the author's note from the KoboldAI GUI when generating text. This has no effect unless `use_story` is also enabled."}) + use_world_info: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the world info from the KoboldAI GUI when generating text."}) + use_userscripts: bool = fields.Boolean(load_default=False, metadata={"description": "Whether or not to use the userscripts from the KoboldAI GUI when generating text."}) + soft_prompt: Optional[str] = fields.String(metadata={"description": "Soft prompt to use when generating. If set to the empty string or any other string containing no non-whitespace characters, uses no soft prompt."}, validate=[soft_prompt_validator, validate.Regexp(r"^[^/\\]*$")]) + max_length: int = fields.Integer(validate=validate.Range(min=1, max=512), metadata={"description": "Number of tokens to generate."}) + max_context_length: int = fields.Integer(validate=validate.Range(min=512, max=2048), metadata={"description": "Maximum number of tokens to send to the model."}) + n: int = fields.Integer(validate=validate.Range(min=1, max=5), metadata={"description": "Number of outputs to generate."}) + disable_output_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, all output formatting options default to `false` instead of the value in the KoboldAI GUI."}) + frmttriminc: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, removes some characters from the end of the output such that the output doesn't end in the middle of a sentence. If the output is less than one sentence long, does nothing.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) + frmtrmblln: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, replaces all occurrences of two or more consecutive newlines in the output with one newline.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) + frmtrmspch: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, removes `#/@%{}+=~|\^<>` from the output.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) + singleline: Optional[bool] = fields.Boolean(metadata={"description": "Output formatting option. When enabled, removes everything after the first line of the output, including the newline.\n\nIf `disable_output_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) + disable_input_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, all input formatting options default to `false` instead of the value in the KoboldAI GUI"}) + frmtadsnsp: Optional[bool] = fields.Boolean(metadata={"description": "Input formatting option. When enabled, adds a leading space to your input if there is no trailing whitespace at the end of the previous action.\n\nIf `disable_input_formatting` is `true`, this defaults to `false` instead of the value in the KoboldAI GUI."}) + +class GenerationResultSchema(KoboldSchema): + text: str = fields.String(required=True, metadata={"description": "Generated output as plain text."}) + +class GenerationOutputSchema(KoboldSchema): + results: List[GenerationResultSchema] = fields.List(fields.Nested(GenerationResultSchema), required=True, metadata={"description": "Array of generated outputs."}) + +class StoryNumsChunkSchema(KoboldSchema): + num: int = fields.Integer(required=True, metadata={"description": "Guaranteed to not equal the `num` of any other active story chunk. Equals 0 iff this is the first action of the story (the prompt)."}) + +class StoryChunkSchema(StoryNumsChunkSchema, KoboldSchema): + text: str = fields.String(required=True, metadata={"description": "The text inside this story chunk."}) + +class StorySchema(KoboldSchema): + results: List[StoryChunkSchema] = fields.List(fields.Nested(StoryChunkSchema), required=True, metadata={"description": "Array of story actions. The array is sorted such that actions closer to the end of this array are closer to the end of the story."}) + +class BasicBooleanSchema(KoboldSchema): + result: bool = fields.Boolean(required=True) + +class StoryNumsSchema(KoboldSchema): + results: List[int] = fields.List(fields.Integer(), required=True, metadata={"description": "Array of story action nums. The array is sorted such that actions closer to the end of this array are closer to the end of the story."}) + +class StoryChunkResultSchema(KoboldSchema): + result: StoryChunkSchema = fields.Nested(StoryChunkSchema, required=True) + +class StoryChunkNumSchema(KoboldSchema): + value: int = fields.Integer(required=True) + +class StoryChunkTextSchema(KoboldSchema): + value: str = fields.String(required=True) + +class StoryChunkSetTextSchema(KoboldSchema): + value: str = fields.String(required=True, validate=validate.Regexp(r"^(.|\n)*\S$")) + +class StoryLoadSchema(KoboldSchema): + name: str = fields.String(required=True, validate=[story_load_validator, validate.Regexp(r"^[^/\\]*$")]) + +class StorySaveSchema(KoboldSchema): + name: str = fields.String(required=True, validate=validate.Regexp(r"^(?=.*\S)(?!.*[/\\]).*$")) + +class WorldInfoEntrySchema(KoboldSchema): + uid: int = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry."}) + content: str = fields.String(required=True, metadata={"description": "The \"What To Remember\" for this entry."}) + key: str = fields.String(required=True, metadata={"description": "Comma-separated list of keys, or of primary keys if selective mode is enabled."}) + keysecondary: str = fields.String(metadata={"description": "Comma-separated list of secondary keys if selective mode is enabled."}) + selective: bool = fields.Boolean(required=True, metadata={"description": "Whether or not selective mode is enabled for this world info entry."}) + constant: bool = fields.Boolean(required=True, metadata={"description": "Whether or not constant mode is enabled for this world info entry."}) + comment: bool = fields.String(required=True, metadata={"description": "The comment/description/title for this world info entry."}) + +class WorldInfoEntryResultSchema(KoboldSchema): + result: WorldInfoEntrySchema = fields.Nested(WorldInfoEntrySchema, required=True) + +class WorldInfoFolderBasicSchema(KoboldSchema): + uid: int = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info folder."}) + name: str = fields.String(required=True, metadata={"description": "Name of this world info folder."}) + +class WorldInfoFolderSchema(WorldInfoFolderBasicSchema): + entries: List[WorldInfoEntrySchema] = fields.List(fields.Nested(WorldInfoEntrySchema), required=True) + +class WorldInfoFolderUIDsSchema(KoboldSchema): + uid: int = fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info folder."}) + entries: List[int] = fields.List(fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry."}), required=True) + +class WorldInfoEntriesSchema(KoboldSchema): + entries: List[WorldInfoEntrySchema] = fields.List(fields.Nested(WorldInfoEntrySchema), required=True) + +class WorldInfoFoldersSchema(KoboldSchema): + folders: List[WorldInfoFolderBasicSchema] = fields.List(fields.Nested(WorldInfoFolderBasicSchema), required=True) + +class WorldInfoSchema(WorldInfoEntriesSchema): + folders: List[WorldInfoFolderSchema] = fields.List(fields.Nested(WorldInfoFolderSchema), required=True) + +class WorldInfoEntriesUIDsSchema(KoboldSchema): + entries: List[int] = fields.List(fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info entry."}), required=True) + +class WorldInfoFoldersUIDsSchema(KoboldSchema): + folders: List[int] = fields.List(fields.Integer(required=True, validate=validate.Range(min=-2147483648, max=2147483647), metadata={"description": "32-bit signed integer unique to this world info folder."}), required=True) + +class WorldInfoUIDsSchema(WorldInfoEntriesUIDsSchema): + folders: List[WorldInfoFolderSchema] = fields.List(fields.Nested(WorldInfoFolderUIDsSchema), required=True) + +def _generate_text(body: GenerationInputSchema): + if vars.aibusy or vars.genseqs: + abort(Response(json.dumps({"detail": { + "msg": "Server is busy; please try again later.", + "type": "service_unavailable", + }}), mimetype="application/json", status=503)) + mapping = { + "disable_input_formatting": ("vars", "disable_input_formatting", None), + "disable_output_formatting": ("vars", "disable_output_formatting", None), + "rep_pen": ("vars", "rep_pen", None), + "rep_pen_range": ("vars", "rep_pen_range", None), + "rep_pen_slope": ("vars", "rep_pen_slope", None), + "top_k": ("vars", "top_k", None), + "top_a": ("vars", "top_a", None), + "top_p": ("vars", "top_p", None), + "tfs": ("vars", "tfs", None), + "typical": ("vars", "typical", None), + "temperature": ("vars", "temp", None), + "frmtadsnsp": ("vars.formatoptns", "@frmtadsnsp", "input"), + "frmttriminc": ("vars.formatoptns", "@frmttriminc", "output"), + "frmtrmblln": ("vars.formatoptns", "@frmtrmblln", "output"), + "frmtrmspch": ("vars.formatoptns", "@frmtrmspch", "output"), + "singleline": ("vars.formatoptns", "@singleline", "output"), + "max_length": ("vars", "genamt", None), + "max_context_length": ("vars", "max_length", None), + "n": ("vars", "numseqs", None), + } + saved_settings = {} + set_aibusy(1) + disable_set_aibusy = vars.disable_set_aibusy + vars.disable_set_aibusy = True + _standalone = vars.standalone + vars.standalone = True + show_probs = vars.show_probs + vars.show_probs = False + output_streaming = vars.output_streaming + vars.output_streaming = False + for key, entry in mapping.items(): + obj = {"vars": vars, "vars.formatoptns": vars.formatoptns}[entry[0]] + if entry[2] == "input" and vars.disable_input_formatting and not hasattr(body, key): + setattr(body, key, False) + if entry[2] == "output" and vars.disable_output_formatting and not hasattr(body, key): + setattr(body, key, False) + if getattr(body, key, None) is not None: + if entry[1].startswith("@"): + saved_settings[key] = obj[entry[1][1:]] + obj[entry[1][1:]] = getattr(body, key) + else: + saved_settings[key] = getattr(obj, entry[1]) + setattr(obj, entry[1], getattr(body, key)) + try: + if vars.allowsp and getattr(body, "soft_prompt", None) is not None: + if any(q in body.soft_prompt for q in ("/", "\\")): + raise RuntimeError + old_spfilename = vars.spfilename + spRequest(body.soft_prompt.strip()) + genout = apiactionsubmit(body.prompt, use_memory=body.use_memory, use_story=body.use_story, use_world_info=body.use_world_info, use_authors_note=body.use_authors_note) + output = {"results": [{"text": txt} for txt in genout]} + finally: + for key in saved_settings: + entry = mapping[key] + obj = {"vars": vars, "vars.formatoptns": vars.formatoptns}[entry[0]] + if getattr(body, key, None) is not None: + if entry[1].startswith("@"): + if obj[entry[1][1:]] == getattr(body, key): + obj[entry[1][1:]] = saved_settings[key] + else: + if getattr(obj, entry[1]) == getattr(body, key): + setattr(obj, entry[1], saved_settings[key]) + vars.disable_set_aibusy = disable_set_aibusy + vars.standalone = _standalone + vars.show_probs = show_probs + vars.output_streaming = output_streaming + if vars.allowsp and getattr(body, "soft_prompt", None) is not None: + spRequest(old_spfilename) + set_aibusy(0) + return output + + +@api_v1.get("/info/version") +@api_schema_wrap +def get_version(): + """--- + get: + summary: Current API version + tags: + - info + description: |-2 + Returns the version of the API that you are currently using. + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicResultSchema + example: + result: 1.0.0 + """ + return {"result": api_version} + + +@api_v1.get("/info/version/latest") +@api_schema_wrap +def get_version_latest(): + """--- + get: + summary: Latest API version + tags: + - info + description: |-2 + Returns the latest API version available. + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicResultSchema + example: + result: 1.0.0 + """ + return {"result": api_versions[-1]} + + +@api_v1.get("/info/version/list") +@api_schema_wrap +def get_version_list(): + """--- + get: + summary: List API versions + tags: + - info + description: |-2 + Returns a list of available API versions sorted in ascending order. + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicResultsSchema + example: + results: + - 1.0.0 + """ + return {"results": api_versions} + + +@api_v1.post("/generate") +@api_schema_wrap +def post_generate(body: GenerationInputSchema): + """--- + post: + summary: Generate text + tags: + - generate + description: |-2 + Generates text given a submission, sampler settings, soft prompt and number of return sequences. + + By default, the story, userscripts, memory, author's note and world info are disabled. + + Unless otherwise specified, optional values default to the values in the KoboldAI GUI. + requestBody: + required: true + content: + application/json: + schema: GenerationInputSchema + example: + prompt: |-2 + Explosions of suspicious origin occur at AMNAT satellite-receiver stations from Turkey to Labrador as three high-level Canadian defense ministers vanish and then a couple of days later are photographed at a Volgograd bistro hoisting shots of Stolichnaya with Slavic bimbos on their knee. + top_p: 0.9 + temperature: 0.5 + responses: + 200: + description: Successful request + content: + application/json: + schema: GenerationOutputSchema + example: + results: + - text: |-2 + It is later established that all of the cabinet members have died of old age. + MEGAMATRIX becomes involved in the growing number of mass abductions and kidnappings. Many disappearances occur along highways in western Canada, usually when traffic has come to a standstill because of a stalled truck or snowstorm. One or two abducted individuals will be released within a day or so but never + {api_validation_error_response} + {api_not_implemented_response} + {api_server_busy_response} + {api_out_of_memory_response} + """ + return _generate_text(body) + + +@api_v1.get("/model") +@api_schema_wrap +def get_model(): + """--- + get: + summary: Retrieve the current model string + description: |-2 + Gets the current model string, which is shown in the title of the KoboldAI GUI in parentheses, e.g. "KoboldAI Client (KoboldAI/fairseq-dense-13B-Nerys-v2)". + tags: + - model + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicResultSchema + example: + result: KoboldAI/fairseq-dense-13B-Nerys-v2 + """ + return {"result": vars.model} + + +def prompt_validator(prompt: str): + if len(prompt.strip()) == 0: + raise ValidationError("String does not match expected pattern.") + +class SubmissionInputSchema(KoboldSchema): + prompt: str = fields.String(required=True, validate=prompt_validator, metadata={"pattern": r"^.*\S.*$", "description": "This is the submission."}) + disable_input_formatting: bool = fields.Boolean(load_default=True, metadata={"description": "When enabled, disables all input formatting options, overriding their individual enabled/disabled states."}) + frmtadsnsp: Optional[bool] = fields.Boolean(metadata={"description": "Input formatting option. When enabled, adds a leading space to your input if there is no trailing whitespace at the end of the previous action."}) + +@api_v1.post("/story/end") +@api_schema_wrap +def post_story_end(body: SubmissionInputSchema): + """--- + post: + summary: Add an action to the end of the story + tags: + - story + description: |-2 + Inserts a single action at the end of the story in the KoboldAI GUI without generating text. + requestBody: + required: true + content: + application/json: + schema: SubmissionInputSchema + example: + prompt: |-2 + This is some text to put at the end of the story. + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + {api_validation_error_response} + {api_server_busy_response} + """ + if vars.aibusy or vars.genseqs: + abort(Response(json.dumps({"detail": { + "msg": "Server is busy; please try again later.", + "type": "service_unavailable", + }}), mimetype="application/json", status=503)) + set_aibusy(1) + disable_set_aibusy = vars.disable_set_aibusy + vars.disable_set_aibusy = True + _standalone = vars.standalone + vars.standalone = True + numseqs = vars.numseqs + vars.numseqs = 1 + try: + actionsubmit(body.prompt, force_submit=True, no_generate=True) + finally: + vars.disable_set_aibusy = disable_set_aibusy + vars.standalone = _standalone + vars.numseqs = numseqs + set_aibusy(0) + return {} + + +@api_v1.get("/story/end") +@api_schema_wrap +def get_story_end(): + """--- + get: + summary: Retrieve the last action of the story + tags: + - story + description: |-2 + Returns the last action of the story in the KoboldAI GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: StoryChunkResultSchema + 510: + description: Story is empty + content: + application/json: + schema: StoryEmptyErrorSchema + example: + detail: + msg: Could not retrieve the last action of the story because the story is empty. + type: story_empty + """ + if not vars.gamestarted: + abort(Response(json.dumps({"detail": { + "msg": "Could not retrieve the last action of the story because the story is empty.", + "type": "story_empty", + }}), mimetype="application/json", status=510)) + if len(vars.actions) == 0: + return {"result": {"text": vars.prompt, "num": 0}} + return {"result": {"text": vars.actions[vars.actions.get_last_key()], "num": vars.actions.get_last_key() + 1}} + + +@api_v1.get("/story/end/num") +@api_schema_wrap +def get_story_end_num(): + """--- + get: + summary: Retrieve the num of the last action of the story + tags: + - story + description: |-2 + Returns the `num` of the last action of the story in the KoboldAI GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: StoryChunkNumSchema + 510: + description: Story is empty + content: + application/json: + schema: StoryEmptyErrorSchema + example: + detail: + msg: Could not retrieve the last action of the story because the story is empty. + type: story_empty + """ + if not vars.gamestarted: + abort(Response(json.dumps({"detail": { + "msg": "Could not retrieve the last action of the story because the story is empty.", + "type": "story_empty", + }}), mimetype="application/json", status=510)) + if len(vars.actions) == 0: + return {"result": {"text": 0}} + return {"result": {"text": vars.actions.get_last_key() + 1}} + + +@api_v1.get("/story/end/text") +@api_schema_wrap +def get_story_end_text(): + """--- + get: + summary: Retrieve the text of the last action of the story + tags: + - story + description: |-2 + Returns the text of the last action of the story in the KoboldAI GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: StoryChunkTextSchema + 510: + description: Story is empty + content: + application/json: + schema: StoryEmptyErrorSchema + example: + detail: + msg: Could not retrieve the last action of the story because the story is empty. + type: story_empty + """ + if not vars.gamestarted: + abort(Response(json.dumps({"detail": { + "msg": "Could not retrieve the last action of the story because the story is empty.", + "type": "story_empty", + }}), mimetype="application/json", status=510)) + if len(vars.actions) == 0: + return {"result": {"text": vars.prompt}} + return {"result": {"text": vars.actions[vars.actions.get_last_key()]}} + + +@api_v1.put("/story/end/text") +@api_schema_wrap +def put_story_end_text(body: StoryChunkSetTextSchema): + """--- + put: + summary: Set the text of the last action of the story + tags: + - story + description: |-2 + Sets the text of the last action of the story in the KoboldAI GUI to the desired value. + requestBody: + required: true + content: + application/json: + schema: StoryChunkSetTextSchema + example: + value: string + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 510: + description: Story is empty + content: + application/json: + schema: StoryEmptyErrorSchema + example: + detail: + msg: Could not retrieve the last action of the story because the story is empty. + type: story_empty + {api_validation_error_response} + """ + if not vars.gamestarted: + abort(Response(json.dumps({"detail": { + "msg": "Could not retrieve the last action of the story because the story is empty.", + "type": "story_empty", + }}), mimetype="application/json", status=510)) + value = body.value.rstrip() + if len(vars.actions) == 0: + inlineedit(0, value) + else: + inlineedit(vars.actions.get_last_key() + 1, value) + return {} + + +@api_v1.post("/story/end/delete") +@api_schema_wrap +def post_story_end_delete(body: EmptySchema): + """--- + post: + summary: Remove the last action of the story + tags: + - story + description: |-2 + Removes the last action of the story in the KoboldAI GUI. + requestBody: + required: true + content: + application/json: + schema: EmptySchema + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 510: + description: Story too short + content: + application/json: + schema: StoryTooShortErrorSchema + example: + detail: + msg: Could not delete the last action of the story because the number of actions in the story is less than or equal to 1. + type: story_too_short + {api_validation_error_response} + {api_server_busy_response} + """ + if vars.aibusy or vars.genseqs: + abort(Response(json.dumps({"detail": { + "msg": "Server is busy; please try again later.", + "type": "service_unavailable", + }}), mimetype="application/json", status=503)) + if not vars.gamestarted or not len(vars.actions): + abort(Response(json.dumps({"detail": { + "msg": "Could not delete the last action of the story because the number of actions in the story is less than or equal to 1.", + "type": "story_too_short", + }}), mimetype="application/json", status=510)) + actionback() + return {} + + +@api_v1.get("/story") +@api_schema_wrap +def get_story(): + """--- + get: + summary: Retrieve the entire story + tags: + - story + description: |-2 + Returns the entire story currently shown in the KoboldAI GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: StorySchema + """ + chunks = [] + if vars.gamestarted: + chunks.append({"num": 0, "text": vars.prompt}) + for num, action in vars.actions.items(): + chunks.append({"num": num + 1, "text": action}) + return {"results": chunks} + + +@api_v1.get("/story/nums") +@api_schema_wrap +def get_story_nums(): + """--- + get: + summary: Retrieve a list of the nums of the chunks in the current story + tags: + - story + description: |-2 + Returns the `num`s of the story chunks currently shown in the KoboldAI GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: StorySchema + """ + chunks = [] + if vars.gamestarted: + chunks.append(0) + for num in vars.actions.keys(): + chunks.append(num + 1) + return {"results": chunks} + + +@api_v1.get("/story/nums/") +@api_schema_wrap +def get_story_nums_num(num: int): + """--- + get: + summary: Determine whether or not there is a story chunk with the given num + tags: + - story + parameters: + - name: num + in: path + description: |-2 + `num` of the desired story chunk. + schema: + type: integer + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicBooleanSchema + """ + if num == 0: + return {"result": vars.gamestarted} + return {"result": num - 1 in vars.actions} + + +@api_v1.get("/story/") +@api_schema_wrap +def get_story_num(num: int): + """--- + get: + summary: Retrieve a story chunk + tags: + - story + description: |-2 + Returns information about a story chunk given its `num`. + parameters: + - name: num + in: path + description: |-2 + `num` of the desired story chunk. + schema: + type: integer + responses: + 200: + description: Successful request + content: + application/json: + schema: StoryChunkResultSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No chunk with the given num exists. + type: key_error + """ + if num == 0: + if not vars.gamestarted: + abort(Response(json.dumps({"detail": { + "msg": "No chunk with the given num exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"result": {"text": vars.prompt, "num": num}} + if num - 1 not in vars.actions: + abort(Response(json.dumps({"detail": { + "msg": "No chunk with the given num exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"result": {"text": vars.actions[num - 1], "num": num}} + + +@api_v1.get("/story//text") +@api_schema_wrap +def get_story_num_text(num: int): + """--- + get: + summary: Retrieve the text of a story chunk + tags: + - story + description: |-2 + Returns the text inside a story chunk given its `num`. + parameters: + - name: num + in: path + description: |-2 + `num` of the desired story chunk. + schema: + type: integer + responses: + 200: + description: Successful request + content: + application/json: + schema: StoryChunkTextSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No chunk with the given num exists. + type: key_error + """ + if num == 0: + if not vars.gamestarted: + abort(Response(json.dumps({"detail": { + "msg": "No chunk with the given num exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"value": vars.prompt} + if num - 1 not in vars.actions: + abort(Response(json.dumps({"detail": { + "msg": "No chunk with the given num exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"value": vars.actions[num - 1]} + + +@api_v1.put("/story//text") +@api_schema_wrap +def put_story_num_text(body: StoryChunkSetTextSchema, num: int): + """--- + put: + summary: Set the text of a story chunk + tags: + - story + description: |-2 + Sets the text inside a story chunk given its `num`. + parameters: + - name: num + in: path + description: |-2 + `num` of the desired story chunk. + schema: + type: integer + requestBody: + required: true + content: + application/json: + schema: StoryChunkSetTextSchema + example: + value: string + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No chunk with the given num exists. + type: key_error + {api_validation_error_response} + """ + if num == 0: + if not vars.gamestarted: + abort(Response(json.dumps({"detail": { + "msg": "No chunk with the given num exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + inlineedit(0, body.value.rstrip()) + return {} + if num - 1 not in vars.actions: + abort(Response(json.dumps({"detail": { + "msg": "No chunk with the given num exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + inlineedit(num, body.value.rstrip()) + return {} + + +@api_v1.delete("/story/") +@api_schema_wrap +def post_story_num_delete(num: int): + """--- + delete: + summary: Remove a story chunk + tags: + - story + description: |-2 + Removes a story chunk from the story in the KoboldAI GUI given its `num`. Cannot be used to delete the first action (the prompt). + parameters: + - name: num + in: path + description: |-2 + `num` of the desired story chunk. Must be larger than or equal to 1. + schema: + type: integer + minimum: 1 + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No chunk with the given num exists. + type: key_error + {api_server_busy_response} + """ + if num < 1: + abort(Response(json.dumps({"detail": { + "num": ["Must be greater than or equal to 1."], + }}), mimetype="application/json", status=422)) + if num - 1 not in vars.actions: + abort(Response(json.dumps({"detail": { + "msg": "No chunk with the given num exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + if vars.aibusy or vars.genseqs: + abort(Response(json.dumps({"detail": { + "msg": "Server is busy; please try again later.", + "type": "service_unavailable", + }}), mimetype="application/json", status=503)) + inlinedelete(num) + return {} + + +@api_v1.delete("/story") +@api_schema_wrap +def delete_story(): + """--- + delete: + summary: Clear the story + tags: + - story + description: |-2 + Starts a new blank story. + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + {api_server_busy_response} + """ + if vars.aibusy or vars.genseqs: + abort(Response(json.dumps({"detail": { + "msg": "Server is busy; please try again later.", + "type": "service_unavailable", + }}), mimetype="application/json", status=503)) + newGameRequest() + return {} + + +@api_v1.put("/story/load") +@api_schema_wrap +def put_story_load(body: StoryLoadSchema): + """--- + put: + summary: Load a story + tags: + - story + description: |-2 + Loads a story given its filename (without the .json). + requestBody: + required: true + content: + application/json: + schema: StoryLoadSchema + example: + name: string + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + {api_validation_error_response} + {api_server_busy_response} + """ + if vars.aibusy or vars.genseqs: + abort(Response(json.dumps({"detail": { + "msg": "Server is busy; please try again later.", + "type": "service_unavailable", + }}), mimetype="application/json", status=503)) + loadRequest(fileops.storypath(body.name.strip())) + return {} + + +@api_v1.put("/story/save") +@api_schema_wrap +def put_story_save(body: StorySaveSchema): + """--- + put: + summary: Save the current story + tags: + - story + description: |-2 + Saves the current story given its destination filename (without the .json). + requestBody: + required: true + content: + application/json: + schema: StorySaveSchema + example: + name: string + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + {api_validation_error_response} + """ + saveRequest(fileops.storypath(body.name.strip())) + return {} + + +@api_v1.get("/world_info") +@api_schema_wrap +def get_world_info(): + """--- + get: + summary: Retrieve all world info entries + tags: + - world_info + description: |-2 + Returns all world info entries currently shown in the KoboldAI GUI. + + The `folders` are sorted in the same order as they are in the GUI and the `entries` within the folders and within the parent `result` object are all sorted in the same order as they are in their respective parts of the GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: WorldInfoSchema + """ + folders = [] + entries = [] + ln = len(vars.worldinfo) + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + folder: Optional[list] = None + if ln: + last_folder = ... + for wi in vars.worldinfo_i: + if wi["folder"] != last_folder: + folder = [] + if wi["folder"] is not None: + folders.append({"uid": wi["folder"], "name": vars.wifolders_d[wi["folder"]]["name"], "entries": folder}) + last_folder = wi["folder"] + (folder if wi["folder"] is not None else entries).append({k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")}) + return {"folders": folders, "entries": entries} + +@api_v1.get("/world_info/uids") +@api_schema_wrap +def get_world_info_uids(): + """--- + get: + summary: Retrieve the UIDs of all world info entries + tags: + - world_info + description: |-2 + Returns in a similar format as GET /world_info except only the `uid`s are returned. + responses: + 200: + description: Successful request + content: + application/json: + schema: WorldInfoUIDsSchema + """ + folders = [] + entries = [] + ln = len(vars.worldinfo) + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + folder: Optional[list] = None + if ln: + last_folder = ... + for wi in vars.worldinfo_i: + if wi["folder"] != last_folder: + folder = [] + if wi["folder"] is not None: + folders.append({"uid": wi["folder"], "entries": folder}) + last_folder = wi["folder"] + (folder if wi["folder"] is not None else entries).append(wi["uid"]) + return {"folders": folders, "entries": entries} + + +@api_v1.get("/world_info/uids/") +@api_schema_wrap +def get_world_info_uids_uid(uid: int): + """--- + get: + summary: Determine whether or not there is a world info entry with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicBooleanSchema + """ + return {"result": uid in vars.worldinfo_u and vars.worldinfo_u[uid]["init"]} + + +@api_v1.get("/world_info/folders") +@api_schema_wrap +def get_world_info_folders(): + """--- + get: + summary: Retrieve all world info folders + tags: + - world_info + description: |-2 + Returns details about all world info folders currently shown in the KoboldAI GUI. + + The `folders` are sorted in the same order as they are in the GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: WorldInfoFoldersSchema + """ + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + return {"folders": [{"uid": folder, **{k: v for k, v in vars.wifolders_d[folder].items() if k != "collapsed"}} for folder in vars.wifolders_l]} + + +@api_v1.get("/world_info/folders/uids") +@api_schema_wrap +def get_world_info_folders_uids(): + """--- + get: + summary: Retrieve the UIDs all world info folders + tags: + - world_info + description: |-2 + Returns the `uid`s of all world info folders currently shown in the KoboldAI GUI. + + The `folders` are sorted in the same order as they are in the GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: WorldInfoFoldersUIDsSchema + """ + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + return {"folders": vars.wifolders_l} + + +@api_v1.get("/world_info/folders/none") +@api_schema_wrap +def get_world_info_folders_none(): + """--- + get: + summary: Retrieve all world info entries not in a folder + tags: + - world_info + description: |-2 + Returns all world info entries that are not in a world info folder. + + The `entries` are sorted in the same order as they are in the KoboldAI GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: WorldInfoEntriesSchema + """ + entries = [] + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + for wi in reversed(vars.worldinfo_i): + if wi["folder"] is not None: + break + entries.append({k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")}) + return {"entries": list(reversed(entries))} + + +@api_v1.get("/world_info/folders/none/uids") +@api_schema_wrap +def get_world_info_folders_none_uids(): + """--- + get: + summary: Retrieve the UIDs of all world info entries not in a folder + tags: + - world_info + description: |-2 + Returns the `uid`s of all world info entries that are not in a world info folder. + + The `entries` are sorted in the same order as they are in the KoboldAI GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: WorldInfoEntriesUIDsSchema + """ + entries = [] + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + for wi in reversed(vars.worldinfo_i): + if wi["folder"] is not None: + break + entries.append(wi["uid"]) + return {"entries": list(reversed(entries))} + + +@api_v1.get("/world_info/folders/none/uids/") +@api_schema_wrap +def get_world_info_folders_none_uids_uid(uid: int): + """--- + get: + summary: Determine whether or not there is a world info entry with the given UID that is not in a world info folder + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicBooleanSchema + """ + return {"result": uid in vars.worldinfo_u and vars.worldinfo_u[uid]["folder"] is None and vars.worldinfo_u[uid]["init"]} + + +@api_v1.get("/world_info/folders/") +@api_schema_wrap +def get_world_info_folders_uid(uid: int): + """--- + get: + summary: Retrieve all world info entries in the given folder + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info folder. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + description: |-2 + Returns all world info entries that are in the world info folder with the given `uid`. + + The `entries` are sorted in the same order as they are in the KoboldAI GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: WorldInfoEntriesSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info folder with the given uid exists. + type: key_error + """ + if uid not in vars.wifolders_d: + abort(Response(json.dumps({"detail": { + "msg": "No world info folder with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + entries = [] + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + for wi in vars.wifolders_u[uid]: + if wi["init"]: + entries.append({k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")}) + return {"entries": entries} + + +@api_v1.get("/world_info/folders//uids") +@api_schema_wrap +def get_world_info_folders_uid_uids(uid: int): + """--- + get: + summary: Retrieve the UIDs of all world info entries in the given folder + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info folder. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + description: |-2 + Returns the `uid`s of all world info entries that are in the world info folder with the given `uid`. + + The `entries` are sorted in the same order as they are in the KoboldAI GUI. + responses: + 200: + description: Successful request + content: + application/json: + schema: WorldInfoEntriesUIDsSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info folder with the given uid exists. + type: key_error + """ + if uid not in vars.wifolders_d: + abort(Response(json.dumps({"detail": { + "msg": "No world info folder with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + entries = [] + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + for wi in vars.wifolders_u[uid]: + if wi["init"]: + entries.append(wi["uid"]) + return {"entries": entries} + + +@api_v1.get("/world_info/folders//uids/") +@api_schema_wrap +def get_world_info_folders_folder_uid_uids_entry_uid(folder_uid: int, entry_uid: int): + """--- + get: + summary: Determine whether or not there is a world info entry with the given UID in the world info folder with the given UID + tags: + - world_info + parameters: + - name: folder_uid + in: path + description: |-2 + `uid` of the desired world info folder. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + - name: entry_uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicBooleanSchema + """ + return {"result": entry_uid in vars.worldinfo_u and vars.worldinfo_u[entry_uid]["folder"] == folder_uid and vars.worldinfo_u[entry_uid]["init"]} + + +@api_v1.get("/world_info/folders//name") +@api_schema_wrap +def get_world_info_folders_uid_name(uid: int): + """--- + get: + summary: Retrieve the name of the world info folder with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info folder. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicStringSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info folder with the given uid exists. + type: key_error + """ + if uid not in vars.wifolders_d: + abort(Response(json.dumps({"detail": { + "msg": "No world info folder with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"value": vars.wifolders_d[uid]["name"]} + + +@api_v1.put("/world_info/folders//name") +@api_schema_wrap +def put_world_info_folders_uid_name(body: BasicStringSchema, uid: int): + """--- + put: + summary: Set the name of the world info folder with the given UID to the specified value + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info folder. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + requestBody: + required: true + content: + application/json: + schema: BasicStringSchema + example: + value: string + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info folder with the given uid exists. + type: key_error + {api_validation_error_response} + """ + if uid not in vars.wifolders_d: + abort(Response(json.dumps({"detail": { + "msg": "No world info folder with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + vars.wifolders_d[uid]["name"] = body.value + setgamesaved(False) + return {} + + +@api_v1.get("/world_info/") +@api_schema_wrap +def get_world_info_uid(uid: int): + """--- + get: + summary: Retrieve information about the world info entry with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: WorldInfoEntrySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + wi = vars.worldinfo_u[uid] + return {k: v for k, v in wi.items() if k not in ("init", "folder", "num") and (wi["selective"] or k != "keysecondary")} + + +@api_v1.get("/world_info//comment") +@api_schema_wrap +def get_world_info_uid_comment(uid: int): + """--- + get: + summary: Retrieve the comment of the world info entry with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicStringSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"value": vars.worldinfo_u[uid]["comment"]} + + +@api_v1.put("/world_info//comment") +@api_schema_wrap +def put_world_info_uid_comment(body: BasicStringSchema, uid: int): + """--- + put: + summary: Set the comment of the world info entry with the given UID to the specified value + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + requestBody: + required: true + content: + application/json: + schema: BasicStringSchema + example: + value: string + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + {api_validation_error_response} + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + vars.worldinfo_u[uid]["comment"] = body.value + setgamesaved(False) + return {} + + +@api_v1.get("/world_info//content") +@api_schema_wrap +def get_world_info_uid_content(uid: int): + """--- + get: + summary: Retrieve the content of the world info entry with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicStringSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"value": vars.worldinfo_u[uid]["content"]} + + +@api_v1.put("/world_info//content") +@api_schema_wrap +def put_world_info_uid_content(body: BasicStringSchema, uid: int): + """--- + put: + summary: Set the content of the world info entry with the given UID to the specified value + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + requestBody: + required: true + content: + application/json: + schema: BasicStringSchema + example: + value: string + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + {api_validation_error_response} + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + vars.worldinfo_u[uid]["content"] = body.value + setgamesaved(False) + return {} + + +@api_v1.get("/world_info//key") +@api_schema_wrap +def get_world_info_uid_key(uid: int): + """--- + get: + summary: Retrieve the keys or primary keys of the world info entry with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicStringSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"value": vars.worldinfo_u[uid]["key"]} + + +@api_v1.put("/world_info//key") +@api_schema_wrap +def put_world_info_uid_key(body: BasicStringSchema, uid: int): + """--- + put: + summary: Set the keys or primary keys of the world info entry with the given UID to the specified value + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + requestBody: + required: true + content: + application/json: + schema: BasicStringSchema + example: + value: string + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + {api_validation_error_response} + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + vars.worldinfo_u[uid]["key"] = body.value + setgamesaved(False) + return {} + + +@api_v1.get("/world_info//keysecondary") +@api_schema_wrap +def get_world_info_uid_keysecondary(uid: int): + """--- + get: + summary: Retrieve the secondary keys of the world info entry with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicStringSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"value": vars.worldinfo_u[uid]["keysecondary"]} + + +@api_v1.put("/world_info//keysecondary") +@api_schema_wrap +def put_world_info_uid_keysecondary(body: BasicStringSchema, uid: int): + """--- + put: + summary: Set the secondary keys of the world info entry with the given UID to the specified value + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + requestBody: + required: true + content: + application/json: + schema: BasicStringSchema + example: + value: string + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + {api_validation_error_response} + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + vars.worldinfo_u[uid]["keysecondary"] = body.value + setgamesaved(False) + return {} + + +@api_v1.get("/world_info//selective") +@api_schema_wrap +def get_world_info_uid_selective(uid: int): + """--- + get: + summary: Retrieve the selective mode state of the world info entry with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicBooleanSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"value": vars.worldinfo_u[uid]["selective"]} + + +@api_v1.put("/world_info//selective") +@api_schema_wrap +def put_world_info_uid_selective(body: BasicBooleanSchema, uid: int): + """--- + put: + summary: Set the selective mode state of the world info entry with the given UID to the specified value + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + requestBody: + required: true + content: + application/json: + schema: BasicBooleanSchema + example: + value: true + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + {api_validation_error_response} + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + vars.worldinfo_u[uid]["selective"] = body.value + setgamesaved(False) + return {} + + +@api_v1.get("/world_info//constant") +@api_schema_wrap +def get_world_info_uid_constant(uid: int): + """--- + get: + summary: Retrieve the constant mode state of the world info entry with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicBooleanSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + return {"value": vars.worldinfo_u[uid]["constant"]} + + +@api_v1.put("/world_info//constant") +@api_schema_wrap +def put_world_info_uid_constant(body: BasicBooleanSchema, uid: int): + """--- + put: + summary: Set the constant mode state of the world info entry with the given UID to the specified value + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + requestBody: + required: true + content: + application/json: + schema: BasicBooleanSchema + example: + value: true + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + {api_validation_error_response} + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + vars.worldinfo_u[uid]["constant"] = body.value + setgamesaved(False) + return {} + + +@api_v1.post("/world_info/folders/none") +@api_schema_wrap +def post_world_info_folders_none(body: EmptySchema): + """--- + post: + summary: Create a new world info entry outside of a world info folder, at the end of the world info + tags: + - world_info + requestBody: + required: true + content: + application/json: + schema: EmptySchema + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicUIDSchema + {api_validation_error_response} + """ + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + setgamesaved(False) + emit('from_server', {'cmd': 'wiexpand', 'data': vars.worldinfo[-1]["num"]}, broadcast=True) + vars.worldinfo[-1]["init"] = True + addwiitem(folder_uid=None) + return {"uid": vars.worldinfo[-2]["uid"]} + + +@api_v1.post("/world_info/folders/") +@api_schema_wrap +def post_world_info_folders_uid(body: EmptySchema, uid: int): + """--- + post: + summary: Create a new world info entry at the end of the world info folder with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info folder. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + requestBody: + required: true + content: + application/json: + schema: EmptySchema + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicUIDSchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info folder with the given uid exists. + type: key_error + {api_validation_error_response} + """ + if uid not in vars.wifolders_d: + abort(Response(json.dumps({"detail": { + "msg": "No world info folder with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + stablesortwi() + vars.worldinfo_i = [wi for wi in vars.worldinfo if wi["init"]] + setgamesaved(False) + emit('from_server', {'cmd': 'wiexpand', 'data': vars.wifolders_u[uid][-1]["num"]}, broadcast=True) + vars.wifolders_u[uid][-1]["init"] = True + addwiitem(folder_uid=uid) + return {"uid": vars.wifolders_u[uid][-2]["uid"]} + + +@api_v1.delete("/world_info/") +@api_schema_wrap +def delete_world_info_uid(uid: int): + """--- + delete: + summary: Delete the world info entry with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info entry. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info entry with the given uid exists. + type: key_error + """ + if uid not in vars.worldinfo_u: + abort(Response(json.dumps({"detail": { + "msg": "No world info entry with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + deletewi(uid) + return {} + + +@api_v1.post("/world_info/folders") +@api_schema_wrap +def post_world_info_folders(body: EmptySchema): + """--- + post: + summary: Create a new world info folder at the end of the world info + tags: + - world_info + requestBody: + required: true + content: + application/json: + schema: EmptySchema + responses: + 200: + description: Successful request + content: + application/json: + schema: BasicUIDSchema + {api_validation_error_response} + """ + addwifolder() + return {"uid": vars.wifolders_l[-1]} + + +@api_v1.delete("/world_info/folders/") +@api_schema_wrap +def delete_world_info_folders_uid(uid: int): + """--- + delete: + summary: Delete the world info folder with the given UID + tags: + - world_info + parameters: + - name: uid + in: path + description: |-2 + `uid` of the desired world info folder. + schema: + type: integer + minimum: -2147483648 + maximum: 2147483647 + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + 404: + description: Not found + content: + application/json: + schema: NotFoundErrorSchema + example: + detail: + msg: No world info folders with the given uid exists. + type: key_error + """ + if uid not in vars.wifolders_d: + abort(Response(json.dumps({"detail": { + "msg": "No world info folder with the given uid exists.", + "type": "key_error", + }}), mimetype="application/json", status=404)) + deletewifolder(uid) + return {} + + +def _make_f_get(obj, _var_name, _name, _schema, _example_yaml_value): + def f_get(): + """--- + get: + summary: Retrieve the current {} setting value + tags: + - config + responses: + 200: + description: Successful request + content: + application/json: + schema: {} + example: + value: {} + """ + _obj = {"vars": vars, "vars.formatoptns": vars.formatoptns}[obj] + if _var_name.startswith("@"): + return {"value": _obj[_var_name[1:]]} + else: + return {"value": getattr(_obj, _var_name)} + f_get.__doc__ = f_get.__doc__.format(_name, _schema, _example_yaml_value) + return f_get + +def _make_f_put(schema_class: Type[KoboldSchema], obj, _var_name, _name, _schema, _example_yaml_value): + def f_put(body: schema_class): + """--- + put: + summary: Set {} setting to specified value + tags: + - config + requestBody: + required: true + content: + application/json: + schema: {} + example: + value: {} + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + {api_validation_error_response} + """ + _obj = {"vars": vars, "vars.formatoptns": vars.formatoptns}[obj] + if _var_name.startswith("@"): + _obj[_var_name[1:]] = body.value + else: + setattr(_obj, _var_name, body.value) + settingschanged() + refresh_settings() + return {} + f_put.__doc__ = f_put.__doc__.format(_name, _schema, _example_yaml_value, api_validation_error_response=api_validation_error_response) + return f_put + +def create_config_endpoint(method="GET", schema="MemorySchema"): + _name = globals()[schema].KoboldMeta.name + _var_name = globals()[schema].KoboldMeta.var_name + _route_name = globals()[schema].KoboldMeta.route_name + _obj = globals()[schema].KoboldMeta.obj + _example_yaml_value = globals()[schema].KoboldMeta.example_yaml_value + _schema = schema + f = _make_f_get(_obj, _var_name, _name, _schema, _example_yaml_value) if method == "GET" else _make_f_put(globals()[schema], _obj, _var_name, _name, _schema, _example_yaml_value) + f.__name__ = f"{method.lower()}_config_{_name}" + f = api_schema_wrap(f) + for api in (api_v1,): + f = api.route(f"/config/{_route_name}", methods=[method])(f) + +class SoftPromptSettingSchema(KoboldSchema): + value: str = fields.String(required=True, validate=[soft_prompt_validator, validate.Regexp(r"^[^/\\]*$")], metadata={"description": "Soft prompt name, or a string containing only whitespace for no soft prompt. If using the GET method and no soft prompt is loaded, this will always be the empty string."}) + +@api_v1.get("/config/soft_prompt") +@api_schema_wrap +def get_config_soft_prompt(): + """--- + get: + summary: Retrieve the current soft prompt name + tags: + - config + responses: + 200: + description: Successful request + content: + application/json: + schema: SoftPromptSettingSchema + example: + value: "" + """ + return {"value": vars.spfilename.strip()} + +@api_v1.put("/config/soft_prompt") +@api_schema_wrap +def put_config_soft_prompt(body: SoftPromptSettingSchema): + """--- + put: + summary: Set soft prompt by name + tags: + - config + requestBody: + required: true + content: + application/json: + schema: SoftPromptSettingSchema + example: + value: "" + responses: + 200: + description: Successful request + content: + application/json: + schema: EmptySchema + {api_validation_error_response} + """ + if vars.allowsp: + spRequest(body.value) + settingschanged() + return {} + +config_endpoint_schemas: List[Type[KoboldSchema]] = [] + +def config_endpoint_schema(c: Type[KoboldSchema]): + config_endpoint_schemas.append(c) + return c + + +@config_endpoint_schema +class MemorySettingSchema(KoboldSchema): + value = fields.String(required=True) + class KoboldMeta: + route_name = "memory" + obj = "vars" + var_name = "memory" + name = "memory" + example_yaml_value = "Memory" + +@config_endpoint_schema +class AuthorsNoteSettingSchema(KoboldSchema): + value = fields.String(required=True) + class KoboldMeta: + route_name = "authors_note" + obj = "vars" + var_name = "authornote" + name = "author's note" + example_yaml_value = "''" + +@config_endpoint_schema +class AuthorsNoteTemplateSettingSchema(KoboldSchema): + value = fields.String(required=True) + class KoboldMeta: + route_name = "authors_note_template" + obj = "vars" + var_name = "authornotetemplate" + name = "author's note template" + example_yaml_value = "\"[Author's note: <|>]\"" + +@config_endpoint_schema +class TopKSamplingSettingSchema(KoboldSchema): + value = fields.Integer(validate=validate.Range(min=0), required=True) + class KoboldMeta: + route_name = "top_k" + obj = "vars" + var_name = "top_k" + name = "top-k sampling" + example_yaml_value = "0" + +@config_endpoint_schema +class TopASamplingSettingSchema(KoboldSchema): + value = fields.Float(validate=validate.Range(min=0), required=True) + class KoboldMeta: + route_name = "top_a" + obj = "vars" + var_name = "top_a" + name = "top-a sampling" + example_yaml_value = "0.0" + +@config_endpoint_schema +class TopPSamplingSettingSchema(KoboldSchema): + value = fields.Float(validate=validate.Range(min=0, max=1), required=True) + class KoboldMeta: + route_name = "top_p" + obj = "vars" + var_name = "top_p" + name = "top-p sampling" + example_yaml_value = "0.9" + +@config_endpoint_schema +class TailFreeSamplingSettingSchema(KoboldSchema): + value = fields.Float(validate=validate.Range(min=0, max=1), required=True) + class KoboldMeta: + route_name = "tfs" + obj = "vars" + var_name = "tfs" + name = "tail free sampling" + example_yaml_value = "1.0" + +@config_endpoint_schema +class TypicalSamplingSettingSchema(KoboldSchema): + value = fields.Float(validate=validate.Range(min=0, max=1), required=True) + class KoboldMeta: + route_name = "typical" + obj = "vars" + var_name = "typical" + name = "typical sampling" + example_yaml_value = "1.0" + +@config_endpoint_schema +class TemperatureSamplingSettingSchema(KoboldSchema): + value = fields.Float(validate=validate.Range(min=0, min_inclusive=False), required=True) + class KoboldMeta: + route_name = "temperature" + obj = "vars" + var_name = "temp" + name = "temperature" + example_yaml_value = "0.5" + +@config_endpoint_schema +class GensPerActionSettingSchema(KoboldSchema): + value = fields.Integer(validate=validate.Range(min=0, max=5), required=True) + class KoboldMeta: + route_name = "n" + obj = "vars" + var_name = "numseqs" + name = "Gens Per Action" + example_yaml_value = "1" + +@config_endpoint_schema +class MaxLengthSettingSchema(KoboldSchema): + value = fields.Integer(validate=validate.Range(min=1, max=512), required=True) + class KoboldMeta: + route_name = "max_length" + obj = "vars" + var_name = "genamt" + name = "max length" + example_yaml_value = "80" + +@config_endpoint_schema +class WorldInfoDepthSettingSchema(KoboldSchema): + value = fields.Integer(validate=validate.Range(min=1, max=5), required=True) + class KoboldMeta: + route_name = "world_info_depth" + obj = "vars" + var_name = "widepth" + name = "world info depth" + example_yaml_value = "3" + +@config_endpoint_schema +class AuthorsNoteDepthSettingSchema(KoboldSchema): + value = fields.Integer(validate=validate.Range(min=1, max=5), required=True) + class KoboldMeta: + route_name = "authors_note_depth" + obj = "vars" + var_name = "andepth" + name = "author's note depth" + example_yaml_value = "3" + +@config_endpoint_schema +class MaxContextLengthSettingSchema(KoboldSchema): + value = fields.Integer(validate=validate.Range(min=512, max=2048), required=True) + class KoboldMeta: + route_name = "max_context_length" + obj = "vars" + var_name = "max_length" + name = "max context length" + example_yaml_value = "2048" + +@config_endpoint_schema +class TrimIncompleteSentencesSettingsSchema(KoboldSchema): + value = fields.Boolean(required=True) + class KoboldMeta: + route_name = "frmttriminc" + obj = "vars.formatoptns" + var_name = "@frmttriminc" + name = "trim incomplete sentences (output formatting)" + example_yaml_value = "false" + +@config_endpoint_schema +class RemoveBlankLinesSettingsSchema(KoboldSchema): + value = fields.Boolean(required=True) + class KoboldMeta: + route_name = "frmtrmblln" + obj = "vars.formatoptns" + var_name = "@frmtrmblln" + name = "remove blank lines (output formatting)" + example_yaml_value = "false" + +@config_endpoint_schema +class RemoveSpecialCharactersSettingsSchema(KoboldSchema): + value = fields.Boolean(required=True) + class KoboldMeta: + route_name = "frmtrmspch" + obj = "vars.formatoptns" + var_name = "@frmtrmspch" + name = "remove special characters (output formatting)" + example_yaml_value = "false" + +@config_endpoint_schema +class SingleLineSettingsSchema(KoboldSchema): + value = fields.Boolean(required=True) + class KoboldMeta: + route_name = "singleline" + obj = "vars.formatoptns" + var_name = "@singleline" + name = "single line (output formatting)" + example_yaml_value = "false" + +@config_endpoint_schema +class AddSentenceSpacingSettingsSchema(KoboldSchema): + value = fields.Boolean(required=True) + class KoboldMeta: + route_name = "frmtadsnsp" + obj = "vars.formatoptns" + var_name = "@frmtadsnsp" + name = "add sentence spacing (input formatting)" + example_yaml_value = "false" + + + +for schema in config_endpoint_schemas: + create_config_endpoint(schema=schema.__name__, method="GET") + create_config_endpoint(schema=schema.__name__, method="PUT") + #==================================================================# # Final startup commands to launch Flask app @@ -6474,8 +9704,9 @@ if __name__ == "__main__": socketio.run(app, host='0.0.0.0', port=port) else: if args.unblock: - import webbrowser - webbrowser.open_new('http://localhost:{0}'.format(port)) + if not args.no_ui: + import webbrowser + webbrowser.open_new('http://localhost:{0}'.format(port)) print("{0}Server started!\nYou may now connect with a browser at http://127.0.0.1:{1}/{2}" .format(colors.GREEN, port, colors.END)) vars.serverstarted = True @@ -6487,9 +9718,9 @@ if __name__ == "__main__": vars.flaskwebgui = True FlaskUI(app, socketio=socketio, start_server="flask-socketio", maximized=True, close_server_on_exit=True).run() except: - pass - import webbrowser - webbrowser.open_new('http://localhost:{0}'.format(port)) + if not args.no_ui: + import webbrowser + webbrowser.open_new('http://localhost:{0}'.format(port)) print("{0}Server started!\nYou may now connect with a browser at http://127.0.0.1:{1}/{2}" .format(colors.GREEN, port, colors.END)) vars.serverstarted = True diff --git a/colab/GPU.ipynb b/colab/GPU.ipynb index 563b278e..f652d3bb 100644 --- a/colab/GPU.ipynb +++ b/colab/GPU.ipynb @@ -125,20 +125,20 @@ "# GPU Edition Model Descriptions\n", "| Model | Size | Style | Description |\n", "| --- | --- | --- | --- |\n", - "| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", + "| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", "| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", - "| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n", + "| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n", "| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n", - "| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n", + "| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n", "| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n", - "| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n", + "| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n", "| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n", "\n", "# [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb)\n", "\n", "| Model | Size | Style | Description |\n", "| --- | --- | --- | --- |\n", - "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", + "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", "| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", "| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n", "| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n", @@ -156,6 +156,7 @@ "| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. |\n", "| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. |\n", "\n", + "---\n", "# How to start KoboldAI in 7 simple steps\n", "Using KoboldAI on Google Colab is easy! Simply follow these steps to get started:\n", "1. Mobile phone? Tap the play button below next to \"<--- Tap this if you play on mobile\" to reveal an audio player, play the silent audio to keep the tab alive so Google will not shut you down when your using KoboldAI. If no audio player is revealed your phone browser does not support Google Colab in the mobile view, go to your browser menu and enable Desktop mode before you continue.\n", diff --git a/colab/TPU.ipynb b/colab/TPU.ipynb index d18bdad2..448880e7 100644 --- a/colab/TPU.ipynb +++ b/colab/TPU.ipynb @@ -148,7 +148,7 @@ "\n", "| Model | Size | Style | Description |\n", "| --- | --- | --- | --- |\n", - "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", + "| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", "| [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", "| [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. |\n", "| [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. |\n", @@ -163,13 +163,13 @@ "\n", "| Model | Size | Style | Description |\n", "| --- | --- | --- | --- |\n", - "| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", + "| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. |\n", "| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. |\n", - "| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n", + "| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. |\n", "| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. |\n", - "| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n", + "| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. |\n", "| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. |\n", - "| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n", + "| [Shinen 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. |\n", "| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. |\n", "\n", "| Style | Description |\n", diff --git a/colab/vscode.ipynb b/colab/vscode.ipynb deleted file mode 100644 index 6a8fe84c..00000000 --- a/colab/vscode.ipynb +++ /dev/null @@ -1,76 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "ColabKobold Code", - "provenance": [], - "authorship_tag": "ABX9TyOuIHmyxj4U9dipAib4hfIi", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "TPU" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# ColabKobold VSCode Edition\n", - "This is a special edition of ColabKobold aimed at developers, it will not start a KoboldAI instance for you to play KoboldAI and instead will launch a fully functional copy of VSCode for easy development.\n", - "\n", - "Few things of note:\n", - "1. Make sure the desired (or no) accelertor is selected on Colab, you do not want a TPU ban for not using it.\n", - "1. The Version can be replaced with your github URL and appended with -b for the branch for example \"https://github.com/henk717/koboldai -b united\" dependencies will automatically be installed from requirements.txt or requirements_mtj.txt.\n", - "1. With the args you can specify launch options for the KoboldAI Deployment Script, this way you can easily preinstall models to your development instance so you have a model to test with. To install TPU requirements specify the -m TPUMeshTransformerGPTJ argument.\n", - "1. You will need an Ngrok auth token which you can obtain here : https://dashboard.ngrok.com/get-started/your-authtoken\n", - "1. KoboldAI is installed in /content/koboldai-client opening this folder is enough to automatically get full git history and revision support. Also keep in mind that it mounts your Google Drive, be careful comitting directly from this instance.\n", - "1. With Ctrl + Shift + ` you can get a terminal to launch KoboldAI with your own parameters, launching with --colab is recommended.\n", - "\n", - "# [If you are not a developer and are looking to use KoboldAI click here](https://henk.tech/colabkobold)" - ], - "metadata": { - "id": "hMRnGz42Xsy3" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "40B1QvI3Xv02" - }, - "outputs": [], - "source": [ - "#@title VSCode Server\n", - "Version = \"United\" #@param [\"Official\", \"United\"] {allow-input: true}\n", - "Args = \"-m TPUMeshTransformerGPTJ -a https://api.wandb.ai/files/ve-forbryderne/skein/files/gpt-j-6b-skein-jax/aria2.txt\" #@param {type:\"string\"}\n", - "Authtoken = \"\" #@param {type:\"string\"}\n", - "\n", - "from google.colab import drive\n", - "drive.mount('/content/drive/')\n", - "\n", - "!wget https://henk.tech/ckds -O - | bash /dev/stdin -g $Version -i only $Args\n", - "\n", - "!pip install colabcode\n", - "!pip install 'flask>=2.1.0'\n", - "from colabcode import ColabCode\n", - "ColabCode(authtoken=Authtoken)" - ] - } - ] -} \ No newline at end of file diff --git a/environments/finetuneanon.yml b/environments/finetuneanon.yml index 5411d2ce..995bd8fa 100644 --- a/environments/finetuneanon.yml +++ b/environments/finetuneanon.yml @@ -16,6 +16,8 @@ dependencies: - bleach=4.1.0 - pip - git=2.35.1 + - marshmallow>=3.13 + - apispec-webframeworks - pip: - git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b - flask-cloudflared diff --git a/environments/huggingface.yml b/environments/huggingface.yml index f24c5336..2d9ace1d 100644 --- a/environments/huggingface.yml +++ b/environments/huggingface.yml @@ -17,9 +17,11 @@ dependencies: - git=2.35.1 - sentencepiece - protobuf + - marshmallow>=3.13 + - apispec-webframeworks - pip: - flask-cloudflared - flask-ngrok - lupa==1.10 - transformers>=4.20.1 - - accelerate \ No newline at end of file + - accelerate diff --git a/environments/rocm-finetune.yml b/environments/rocm-finetune.yml index 60b17d98..05d7a2c1 100644 --- a/environments/rocm-finetune.yml +++ b/environments/rocm-finetune.yml @@ -12,6 +12,8 @@ dependencies: - bleach=4.1.0 - pip - git=2.35.1 + - marshmallow>=3.13 + - apispec-webframeworks - pip: - --find-links https://download.pytorch.org/whl/rocm4.2/torch_stable.html - torch diff --git a/environments/rocm.yml b/environments/rocm.yml index 4778208d..142d4230 100644 --- a/environments/rocm.yml +++ b/environments/rocm.yml @@ -14,6 +14,8 @@ dependencies: - git=2.35.1 - sentencepiece - protobuf + - marshmallow>=3.13 + - apispec-webframeworks - pip: - --find-links https://download.pytorch.org/whl/rocm4.2/torch_stable.html - torch==1.10.* diff --git a/gensettings.py b/gensettings.py index 5ec022ef..3839ff4e 100644 --- a/gensettings.py +++ b/gensettings.py @@ -263,6 +263,17 @@ gensettingstf = [ "default": 0, "tooltip": "Shows outputs to you as they are made. Does not work with more than one gens per action." }, + { + "uitype": "toggle", + "unit": "bool", + "label": "Probability Viewer", + "id": "setshowprobs", + "min": 0, + "max": 1, + "step": 1, + "default": 0, + "tooltip": "Shows token selection probabilities. Does not work with more than one gens per action." + }, ] gensettingsik =[{ diff --git a/readme.md b/readme.md index ee0d601b..43b43046 100644 --- a/readme.md +++ b/readme.md @@ -8,7 +8,7 @@ Stories can be played like a Novel, a text adventure game or used as a chatbot w ### Adventure mode -By default KoboldAI will run in a generic mode optimized for writing, but with the right model you can play this like AI Dungeon without any issues. You can enable this in the settings and bring your own prompt, try generating a random prompt or download one of the prompts available at [prompts.aidg.club](https://prompts.aidg.club) . +By default KoboldAI will run in a generic mode optimized for writing, but with the right model you can play this like AI Dungeon without any issues. You can enable this in the settings and bring your own prompt, try generating a random prompt or download one of the prompts available at [/aids/ Prompts](https://aetherroom.club/). The gameplay will be slightly different than the gameplay in AI Dungeon because we adopted the Type of the Unleashed fork, giving you full control over all the characters because we do not automatically adapt your sentences behind the scenes. This means you can more reliably control characters that are not you. @@ -21,7 +21,7 @@ If you want to do this with your friends we advise using the main character as Y ### Writing assistant -If you want to use KoboldAI as a writing assistant this is best done in the regular mode with a model optimized for Novels. These models do not make the assumption that there is a You character and focus on Novel like writing. For writing these will often give you better results than Adventure or Generic models. That said, if you give it a good introduction to the story large generic models like 6B can be used if a more specific model is not available for what you wish to write. You can also try to use models that are not specific to what you wish to do, for example a NSFW Novel model for a SFW story if a SFW model is unavailable. This will mean you will have to correct the model more often because of its bias, but can still produce good enough results if it is familiar enough with your topic. +If you want to use KoboldAI as a writing assistant this is best done in the regular mode with a model optimized for Novels. These models do not make the assumption that there is a You character and focus on Novel like writing. For writing these will often give you better results than Adventure or Generic models. That said, if you give it a good introduction to the story large generic models like 13B can be used if a more specific model is not available for what you wish to write. You can also try to use models that are not specific to what you wish to do, for example a NSFW Novel model for a SFW story if a SFW model is unavailable. This will mean you will have to correct the model more often because of its bias, but can still produce good enough results if it is familiar enough with your topic. ### Chatbot Mode @@ -48,18 +48,16 @@ If you would like to play KoboldAI online for free on a powerful computer you ca Each edition features different models and requires different hardware to run, this means that if you are unable to obtain a TPU or a GPU you might still be able to use the other version. The models you can use are listed underneath the edition. To open a Colab click the big link featuring the editions name. -### [Click here for the TPU Edition Colab](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb) +## [TPU Edition Model Descriptions](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/TPU.ipynb) | Model | Size | Style | Description | | --- | --- | --- | --- | -| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. | +| [Nerys](https://huggingface.co/KoboldAI/fairseq-dense-13B-Nerys) by Mr Seeker | 13B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. | | [Janeway](https://huggingface.co/KoboldAI/fairseq-dense-13B-Janeway) by Mr Seeker | 13B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. | | [Shinen](https://huggingface.co/KoboldAI/fairseq-dense-13B-Shinen) by Mr Seeker | 13B | NSFW | Shinen is an NSFW model designed to be more explicit. Trained on a variety of stories from the website Sexstories it contains many different kinks. | | [Skein](https://huggingface.co/KoboldAI/GPT-J-6B-Skein) by VE\_FORBRYDERNE | 6B | Adventure | Skein is best used with Adventure mode enabled, it consists of a 4 times larger adventure dataset than the Adventure model making it excellent for text adventure gaming. On top of that it also consists of light novel training further expanding its knowledge and writing capabilities. It can be used with the You filter bias if you wish to write Novels with it, but dedicated Novel models can perform better for this task. | | [Adventure](https://huggingface.co/KoboldAI/GPT-J-6B-Adventure) by VE\_FORBRYDERNE | 6B | Adventure | Adventure is a 6B model designed to mimick the behavior of AI Dungeon. It is exclusively for Adventure Mode and can take you on the epic and wackey adventures that AI Dungeon players love. It also features the many tropes of AI Dungeon as it has been trained on very similar data. It must be used in second person (You). | | [Lit](https://huggingface.co/hakurei/lit-6B) by Haru | 6B | NSFW | Lit is a great NSFW model trained by Haru on both a large set of Literotica stories and high quality novels along with tagging support. Creating a high quality model for your NSFW stories. This model is exclusively a novel model and is best used in third person. | -| [Convo](https://huggingface.co/hitomi-team/convo-6B) by Hitomi Team | 6B | Chatbot | Convo-6B is a GPT-J 6B model fine-tuned on a collection of high quality open source datasets which amount to 6 million messages. The primary goal of the model is to provide improved performance and generalization when generating multi-turn dialogue for characters that were not present from within the fine tuning data. The prompted performance has especially improved over the predecessor model [C1-6B](https://huggingface.co/hakurei/c1-6B). | -| [C1](https://huggingface.co/hakurei/c1-6B) by Haru | 6B | Chatbot | C1 has been trained on various internet chatrooms, it makes the basis for an interesting chatbot model and has been optimized to be used in the Chatmode. | | Neo(X) by EleutherAI | 20B | Generic | NeoX is the largest EleutherAI model currently available, being a generic model it is not particularly trained towards anything and can do a variety of writing, Q&A and coding tasks. 20B's performance is closely compared to the 13B models and it is worth trying both especially if you have a task that does not involve english writing. Its behavior will be similar to the GPT-J-6B model since they are trained on the same dataset but with more sensitivity towards repetition penalty and with more knowledge. | | [Fairseq Dense](https://huggingface.co/KoboldAI/fairseq-dense-13B) | 13B | Generic | Trained by Facebook Researchers this model stems from the MOE research project within Fairseq. This particular version has been converted by us for use in KoboldAI. It is known to be on par with the larger 20B model from EleutherAI and considered as better for pop culture and language tasks. Because the model has never seen a new line (enter) it may perform worse on formatting and paragraphing. | | [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B) by EleutherAI | 6B | Generic | This model serves as the basis for most other 6B models (Some being based on Fairseq Dense instead). Being trained on the Pile and not biased towards anything in particular it is suitable for a variety of tasks such as writing, Q&A and coding tasks. You will likely get better result with larger generic models or finetuned models. | @@ -68,21 +66,23 @@ Each edition features different models and requires different hardware to run, t | Model | Size | Style | Description | | --- | --- | --- | --- | -| [Fairseq-Dense-2.7B-Nerys](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. | -| [GPT-Neo-2.7B-Janeway](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. | -| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. | -| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. | -| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. | +| [Nerys 2.7B](https://huggingface.co/KoboldAI/fairseq-dense-2.7B-Nerys) by Mr Seeker | 2.7B | Novel/Adventure | Nerys is a hybrid model based on Pike (A newer Janeway), on top of the Pike dataset you also get some Light Novels, Adventure mode support and a little bit of Shinen thrown in the mix. The end result is a very diverse model that is heavily biased towards SFW novel writing, but one that can go beyond its novel training and make for an excellent adventure model to. Adventure mode is best played from a second person perspective, but can be played in first or third person as well. Novel writing can be done best from the first or third person. | +| [Janeway 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Janeway) by Mr Seeker | 2.7B | Novel | Janeway is a model created from Picard's dataset combined with a brand new collection of ebooks. This model is trained on 20% more content than Picard and has been trained on literature from various genres. Although the model is mainly focussed on SFW, romantic scenes might involve a degree of nudity. | +| [Picard 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B | Novel | Picard is a model trained for SFW Novels based on Neo 2.7B. It is focused on Novel style writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. | +| [AID 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. | +| [Horni LN 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B | Novel | This model is based on Horni 2.7B and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. | +| [Horni 2.7B](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B | NSFW | This model is tuned on Literotica to produce a Novel style model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. | +| [Shinen 2.7B ](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you Shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. | +| [Neo 2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. | -| Style | Description | +### Styles + +| Type | Description | | --- | --- | | Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. | | NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. | -| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel style model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. | -| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. | +| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel Type model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. | +| Hybrid | Hybrid models are a blend between different Types, for example they are trained on both Novel stories and Adventure stories. These models are great variety models that you can use for multiple different playTypes and modes, but depending on your usage you may need to enable Adventure Mode or the You bias (in userscripts). | | Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. | ## Tips to get the most out of Google Colab @@ -94,28 +94,6 @@ Each edition features different models and requires different hardware to run, t * Done with KoboldAI? Go to the Runtime menu, click on Manage Sessions and terminate your open sessions that you no longer need. This trick can help you maintain higher priority towards getting a TPU. * Models stored on Google Drive typically load faster than models we need to download from the internet. -### [Click here for the GPU Edition Colab](https://colab.research.google.com/github/KoboldAI/KoboldAI-Client/blob/main/colab/GPU.ipynb) - -| Model | Size | Type | Description | -| --- | --- | --- | --- | -| [GPT-Neo-2.7B-Picard](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Picard) by Mr Seeker | 2.7B GPU | Novel | Picard is a model trained for SFW Novels based on GPT-Neo-2.7B. It is focused on Novel Type writing without the NSFW bias. While the name suggests a sci-fi model this model is designed for Novels of a variety of genre's. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B-AID](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-AID) by melastacho | 2.7B GPU | Adventure | Also know as Adventure 2.7B this is a clone of the AI Dungeon Classic model and is best known for the epic wackey adventures that AI Dungeon Classic players love. | -| [GPT-Neo-2.7B-Horni-LN](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni-LN) by finetune | 2.7B GPU | Novel | This model is based on GPT-Neo-2.7B-Horni and retains its NSFW knowledge, but was then further biased towards SFW novel stories. If you seek a balance between a SFW Novel model and a NSFW model this model should be a good choice. | -| [GPT-Neo-2.7B-Horni](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Horni) by finetune | 2.7B GPU | NSFW | This model is tuned on Literotica to produce a Novel Type model biased towards NSFW content. Can still be used for SFW stories but will have a bias towards NSFW content. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B-Shinen](https://huggingface.co/KoboldAI/GPT-Neo-2.7B-Shinen) by Mr Seeker | 2.7B GPU | NSFW | Shinen is an alternative to the Horni model designed to be more explicit. If Horni is to tame for you shinen might produce better results. While it is a Novel model it is unsuitable for SFW stories due to its heavy NSFW bias. Shinen will not hold back. It is meant to be used in KoboldAI's regular mode. | -| [GPT-Neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) by EleutherAI | 2.7B GPU | Generic | This is the base model for all the other 2.7B models, it is best used when you have a use case that we have no other models available for, such as writing blog articles or programming. It can also be a good basis for the experience of some of the softprompts if your softprompt is not about a subject the other models cover. | - -### Model Types - -| Type | Description | -| --- | --- | -| Novel | For regular story writing, not compatible with Adventure mode or other specialty modes. | -| NSFW | Indicates that the model is strongly biased towards NSFW content and is not suitable for children, work environments or livestreaming. Most NSFW models are also Novel models in nature. | -| Adventure | These models are excellent for people willing to play KoboldAI like a Text Adventure game and are meant to be used with Adventure mode enabled. Even if you wish to use it as a Novel Type model you should always have Adventure mode on and set it to story. These models typically have a strong bias towards the use of the word You and without Adventure mode enabled break the story flow and write actions on your behalf. | -| Chatbot | These models are specifically trained for chatting and are best used with the Chatmode enabled. Typically trained on either public chatrooms or private chats. | -| Hybrid | Hybrid models are a blend between different Types, for example they are trained on both Novel stories and Adventure stories. These models are great variety models that you can use for multiple different playTypes and modes, but depending on your usage you may need to enable Adventure Mode or the You bias (in userscripts). | -| Generic | Generic models are not trained towards anything specific, typically used as a basis for other tasks and models. They can do everything the other models can do, but require much more handholding to work properly. Generic models are an ideal basis for tasks that we have no specific model for, or for experiencing a softprompt in its raw form. | - ## Install KoboldAI on your own computer KoboldAI has a large number of dependencies you will need to install on your computer, unfortunately Python does not make it easy for us to provide instructions that work for everyone. The instructions below will work on most computers, but if you have multiple versions of Python installed conflicts can occur. @@ -195,11 +173,11 @@ If you get these errors you either did not select the correct folder for your cu Softprompts (also known as Modules in other products) are addons that can change the output of existing models. For example you may load a softprompt that biases the AI towards a certain subject and style like transcripts from your favorite TV show. -Since these softprompts are often based on existing franchises we currently do not bundle any of them with KoboldAI due to copyright concerns (We do not want to put the entire project at risk). Instead look at community resources like #softprompts on the [KoboldAI Discord](https://discord.gg/XuQWadgU9k) or the [community hosted mirror](https://storage.henk.tech/KoboldAI/softprompts/) . +Since these softprompts are often based on existing franchises we currently do not bundle any of them with KoboldAI due to copyright concerns (We do not want to put the entire project at risk). Instead look at community resources like #softprompts on the [KoboldAI Discord](https://discord.gg/XuQWadgU9k) or the [community hosted mirror](https://storage.henk.tech/KoboldAI/softprompts/). That way we are better protected from any DMCA claims as things can be taken down easier than directly on Github. If you have a copyright free softprompt that you made from scratch and is not based on existing IP that you would like to see officially bundled with KoboldAI issue a pull request with your softprompt. -Training softprompts can be done for free with the [mtj-softtuner colab](https://colab.research.google.com/github/VE-FORBRYDERNE/mtj-softtuner/blob/main/mtj-softtuner.ipynb) , in that case you can leave most of the settings default. Your source data needs to be a folder with text files that are UTF-8 formatted and contain Unix line endings. +Training softprompts can be done for free with the [Easy Softprompt Tuner](https://colab.research.google.com/gist/henk717/281fd57ebd2e88d852ef9dcc3f29bebf/easy-softprompt-tuner.ipynb#sandboxMode=true), in that case you can leave most of the settings default. Your source data needs to be a folder with text files that are UTF-8 formatted and contain Unix line endings. ## Userscripts diff --git a/requirements.txt b/requirements.txt index 6d6d1d64..5dd27bce 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,6 @@ bleach==4.1.0 sentencepiece protobuf accelerate -flask-session \ No newline at end of file +flask-session +marshmallow>=3.13 +apispec-webframeworks diff --git a/requirements_mtj.txt b/requirements_mtj.txt index eb7cf79c..67ff0e25 100644 --- a/requirements_mtj.txt +++ b/requirements_mtj.txt @@ -17,4 +17,6 @@ eventlet lupa==1.10 markdown bleach==4.1.0 -flask-session \ No newline at end of file +flask-session +marshmallow>=3.13 +apispec-webframeworks diff --git a/static/application.js b/static/application.js index b354f419..f74e874b 100644 --- a/static/application.js +++ b/static/application.js @@ -79,6 +79,7 @@ var rs_close; var seqselmenu; var seqselcontents; var stream_preview; +var token_prob_container; var storyname = null; var memorymode = false; @@ -511,6 +512,16 @@ function addWiLine(ob) { $(".wisortable-excluded-dynamic").removeClass("wisortable-excluded-dynamic"); $(this).parent().css("max-height", "").find(".wicomment").find(".form-control").css("max-height", ""); }); + + for (const wientry of document.getElementsByClassName("wientry")) { + // If we are uninitialized, skip. + if ($(wientry).closest(".wilistitem-uninitialized").length) continue; + + // add() will not add if the class is already present + wientry.classList.add("tokens-counted"); + } + + registerTokenCounters(); } function addWiFolder(uid, ob) { @@ -834,6 +845,7 @@ function exitMemoryMode() { button_actmem.html("Memory"); show([button_actback, button_actfwd, button_actretry, button_actwi]); input_text.val(""); + updateInputBudget(input_text[0]); // Hide Author's Note field anote_menu.slideUp("fast"); } @@ -890,7 +902,7 @@ function formatChunkInnerText(chunk) { } function dosubmit(disallow_abort) { - ignore_stream = false; + beginStream(); submit_start = Date.now(); var txt = input_text.val().replace(/\u00a0/g, " "); if((disallow_abort || gamestate !== "wait") && !memorymode && !gamestarted && ((!adventure || !action_mode) && txt.trim().length == 0)) { @@ -905,7 +917,7 @@ function dosubmit(disallow_abort) { } function _dosubmit() { - ignore_stream = false; + beginStream(); var txt = submit_throttle.txt; var disallow_abort = submit_throttle.disallow_abort; submit_throttle = null; @@ -1048,6 +1060,18 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) { if (breadcrumbs.length > 0) { $("#loadmodellistbreadcrumbs").append("
") } + //If we're in the custom load menu (we need to send the path data back in that case) + if(['NeoCustom', 'GPT2Custom'].includes(menu)) { + $("#loadmodel"+i).off("click").on("click", (function () { + return function () { + socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")}); + highlightLoadLine($(this)); + } + })(i)); + $("#custommodelname").removeClass("hidden"); + $("#custommodelname")[0].setAttribute("menu", menu); + } + for(i=0; i" } else { //this is a model - html = html + "
" + html = html + "
" } //now let's do the delete icon if applicable @@ -1083,6 +1107,7 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) { " loadmodelcontent.append(html); //If this is a menu + console.log(ar[i]); if(ar[i][3]) { $("#loadmodel"+i).off("click").on("click", (function () { return function () { @@ -1090,27 +1115,29 @@ function buildLoadModelList(ar, menu, breadcrumbs, showdelete) { disableButtons([load_model_accept]); } })(i)); - //If we're in the custom load menu (we need to send the path data back in that case) - } else if(['NeoCustom', 'GPT2Custom'].includes(menu)) { - $("#loadmodel"+i).off("click").on("click", (function () { - return function () { - socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")}); - highlightLoadLine($(this)); - } - })(i)); - $("#custommodelname").removeClass("hidden"); - $("#custommodelname")[0].setAttribute("menu", menu); //Normal load } else { - $("#loadmodel"+i).off("click").on("click", (function () { - return function () { - $("#use_gpu_div").addClass("hidden"); - $("#modelkey").addClass("hidden"); - $("#modellayers").addClass("hidden"); - socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name")}); - highlightLoadLine($(this)); - } - })(i)); + if (['NeoCustom', 'GPT2Custom'].includes(menu)) { + $("#loadmodel"+i).off("click").on("click", (function () { + return function () { + $("#use_gpu_div").addClass("hidden"); + $("#modelkey").addClass("hidden"); + $("#modellayers").addClass("hidden"); + socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name"), 'path': $(this).attr("pretty_name")}); + highlightLoadLine($(this)); + } + })(i)); + } else { + $("#loadmodel"+i).off("click").on("click", (function () { + return function () { + $("#use_gpu_div").addClass("hidden"); + $("#modelkey").addClass("hidden"); + $("#modellayers").addClass("hidden"); + socket.send({'cmd': 'selectmodel', 'data': $(this).attr("name")}); + highlightLoadLine($(this)); + } + })(i)); + } } } } @@ -2086,6 +2113,11 @@ function unbindGametext() { gametext_bound = false; } +function beginStream() { + ignore_stream = false; + token_prob_container[0].innerHTML = ""; +} + function endStream() { // Clear stream, the real text is about to be displayed. ignore_stream = true; @@ -2123,6 +2155,45 @@ function RemoveAllButFirstOption(selectElement) { } } +function interpolateRGB(color0, color1, t) { + return [ + color0[0] + ((color1[0] - color0[0]) * t), + color0[1] + ((color1[1] - color0[1]) * t), + color0[2] + ((color1[2] - color0[2]) * t), + ] +} + +function updateInputBudget(inputElement) { + let data = {"unencoded": inputElement.value, "field": inputElement.id}; + + if (inputElement.id === "anoteinput") { + data["anotetemplate"] = $("#anotetemplate").val(); + } + + socket.send({"cmd": "getfieldbudget", "data": data}); +} + +function registerTokenCounters() { + // Add token counters to all input containers with the class of "tokens-counted", + // if a token counter is not already a child of said container. + for (const el of document.getElementsByClassName("tokens-counted")) { + if (el.getElementsByClassName("input-token-usage").length) continue; + + let span = document.createElement("span"); + span.classList.add("input-token-usage"); + span.innerText = "?/? Tokens"; + el.appendChild(span); + + let inputElement = el.querySelector("input, textarea"); + + inputElement.addEventListener("input", function() { + updateInputBudget(this); + }); + + updateInputBudget(inputElement); + } +} + //=================================================================// // READY/RUNTIME //=================================================================// @@ -2214,6 +2285,8 @@ $(document).ready(function(){ rs_close = $("#btn_rsclose"); seqselmenu = $("#seqselmenu"); seqselcontents = $("#seqselcontents"); + token_prob_container = $("#token_prob_container"); + token_prob_menu = $("#token_prob_menu"); // Connect to SocketIO server socket = io.connect(window.document.origin, {transports: ['polling', 'websocket'], closeOnBeforeunload: false}); @@ -2277,14 +2350,68 @@ $(document).ready(function(){ // appearing after the output. To combat this, we only allow tokens // to be displayed after requesting and before recieving text. if (ignore_stream) return; - if (!$("#setoutputstreaming")[0].checked) return; - if (!stream_preview) { + let streamingEnabled = $("#setoutputstreaming")[0].checked; + let probabilitiesEnabled = $("#setshowprobs")[0].checked; + + if (!streamingEnabled && !probabilitiesEnabled) return; + + if (!stream_preview && streamingEnabled) { stream_preview = document.createElement("span"); game_text.append(stream_preview); } - stream_preview.innerText += msg.data.join(""); + for (const token of msg.data) { + if (streamingEnabled) stream_preview.innerText += token.decoded; + + if (probabilitiesEnabled) { + // Probability display + let probDiv = document.createElement("div"); + probDiv.classList.add("token-probs"); + + let probTokenSpan = document.createElement("span"); + probTokenSpan.classList.add("token-probs-header"); + probTokenSpan.innerText = token.decoded.replaceAll("\n", "\\n"); + probDiv.appendChild(probTokenSpan); + + let probTable = document.createElement("table"); + let probTBody = document.createElement("tbody"); + probTable.appendChild(probTBody); + + for (const probToken of token.probabilities) { + let tr = document.createElement("tr"); + let rgb = interpolateRGB( + [255, 255, 255], + [0, 255, 0], + probToken.score + ).map(Math.round); + let color = `rgb(${rgb.join(", ")})`; + + if (probToken.decoded === token.decoded) { + tr.classList.add("token-probs-final-token"); + } + + let tds = {}; + + for (const property of ["tokenId", "decoded", "score"]) { + let td = document.createElement("td"); + td.style.color = color; + tds[property] = td; + tr.appendChild(td); + } + + tds.tokenId.innerText = probToken.tokenId; + tds.decoded.innerText = probToken.decoded.toString().replaceAll("\n", "\\n"); + tds.score.innerText = (probToken.score * 100).toFixed(2) + "%"; + + probTBody.appendChild(tr); + } + + probDiv.appendChild(probTable); + token_prob_container.append(probDiv); + } + } + scrollToBottom(); } else if(msg.cmd == "updatescreen") { var _gamestarted = gamestarted; @@ -2316,6 +2443,7 @@ $(document).ready(function(){ scrollToBottom(); } else if(msg.cmd == "updatechunk") { hideMessage(); + game_text.attr('contenteditable', allowedit); if (typeof submit_start !== 'undefined') { $("#runtime")[0].innerHTML = `Generation time: ${Math.round((Date.now() - submit_start)/1000)} sec`; delete submit_start; @@ -2409,6 +2537,7 @@ $(document).ready(function(){ memorytext = msg.data; input_text.val(msg.data); } + updateInputBudget(input_text[0]); } else if(msg.cmd == "setmemory") { memorytext = msg.data; if(memorymode) { @@ -2530,6 +2659,7 @@ $(document).ready(function(){ } else if(msg.cmd == "setanote") { // Set contents of Author's Note field anote_input.val(msg.data); + updateInputBudget(anote_input[0]); } else if(msg.cmd == "setanotetemplate") { // Set contents of Author's Note Template field $("#anotetemplate").val(msg.data); @@ -2559,6 +2689,14 @@ $(document).ready(function(){ } else if(msg.cmd == "updateoutputstreaming") { // Update toggle state $("#setoutputstreaming").prop('checked', msg.data).change(); + } else if(msg.cmd == "updateshowprobs") { + $("#setshowprobs").prop('checked', msg.data).change(); + + if(msg.data) { + token_prob_menu.removeClass("hidden"); + } else { + token_prob_menu.addClass("hidden"); + } } else if(msg.cmd == "allowtoggle") { // Allow toggle change states to propagate allowtoggle = msg.data; @@ -2761,6 +2899,8 @@ $(document).ready(function(){ if (msg.key) { $("#modelkey").removeClass("hidden"); $("#modelkey")[0].value = msg.key_value; + //if we're in the API list, disable to load button until the model is selected (after the API Key is entered) + disableButtons([load_model_accept]); } else { $("#modelkey").addClass("hidden"); @@ -2798,6 +2938,7 @@ $(document).ready(function(){ } } else if(msg.cmd == 'oai_engines') { $("#oaimodel").removeClass("hidden") + enableButtons([load_model_accept]); selected_item = 0; length = $("#oaimodel")[0].options.length; for (let i = 0; i < length; i++) { @@ -2820,6 +2961,7 @@ $(document).ready(function(){ $("#showmodelnamecontainer").removeClass("hidden"); } else if(msg.cmd == 'hide_model_name') { $("#showmodelnamecontainer").addClass("hidden"); + location.reload(); //console.log("Closing window"); } else if(msg.cmd == 'model_load_status') { $("#showmodelnamecontent").html("
" + msg.data + "
"); @@ -2833,7 +2975,18 @@ $(document).ready(function(){ opt.innerHTML = engine[1]; $("#oaimodel")[0].appendChild(opt); } + } else if(msg.cmd == 'showfieldbudget') { + let inputElement = document.getElementById(msg.data.field); + let tokenBudgetElement = inputElement.parentNode.getElementsByClassName("input-token-usage")[0]; + if (msg.data.max === null) { + tokenBudgetElement.innerText = ""; + } else { + let tokenLength = msg.data.length ?? "?"; + let tokenMax = msg.data.max ?? "?"; + tokenBudgetElement.innerText = `${tokenLength}/${tokenMax} Tokens`; + } } + enableButtons([load_model_accept]); }); socket.on('disconnect', function() { @@ -2954,7 +3107,7 @@ $(document).ready(function(){ }); button_actretry.on("click", function(ev) { - ignore_stream = false; + beginStream(); hideMessage(); socket.send({'cmd': 'retry', 'chatname': chatmode ? chat_name.val() : undefined, 'data': ''}); hidegenseqs(); @@ -3201,7 +3354,7 @@ $(document).ready(function(){ }); rs_accept.on("click", function(ev) { - ignore_stream = false; + beginStream(); hideMessage(); socket.send({'cmd': 'rndgame', 'memory': $("#rngmemory").val(), 'data': topic.val()}); hideRandomStoryPopup(); @@ -3301,6 +3454,15 @@ $(document).ready(function(){ if (handled) ev.preventDefault(); }); + + $("#anotetemplate").on("input", function() { + updateInputBudget(anote_input[0]); + }) + + registerTokenCounters(); + + updateInputBudget(input_text[0]); + }); diff --git a/static/custom.css b/static/custom.css index 10ed2dc6..af238dc7 100644 --- a/static/custom.css +++ b/static/custom.css @@ -1647,4 +1647,78 @@ body.connected .popupfooter, .popupfooter.always-available { .breadcrumbitem:hover { cursor: pointer; background-color: #688f1f; +} + +#token_prob_menu { + color: white; + background-color: #262626; +} + +.token-probs { + display: inline-block; + text-align: center; + margin-right: 5px; +} + +.token-probs > table { + width: 100%; +} + +.token-probs > table > tbody > tr > td { + border: 1px solid #262626; + border-collapse: collapse; + padding: 2px 15px; +} + +.token-probs > table > tbody > tr { + background-color: #3e3e3e; +} + +.token-probs > table > tbody > tr:nth-child(2n) { + background-color: #575757; +} + +.token-probs-final-token { + font-weight: bold; + text-decoration: underline; +} + +.token-probs-final-token > td { + background: #5c8a5a; +} + +.token-probs-header { + display: block; +} + +#token_prob_container { + overflow-x: auto; + white-space: nowrap; +} + +.tokens-counted { + position: relative; +} + +.input-token-usage { + color: white; + position: absolute; + font-size: 10px; + bottom: 2px; + right: 5px; + + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +/* Override needed here due to the 10px right padding on inputrowleft; add 10 px. */ +#inputrowleft > .input-token-usage { + right: 15px; + bottom: 1px; +} + +.wientry > .input-token-usage { + bottom: 8px; } \ No newline at end of file diff --git a/static/swagger-ui/LICENSE b/static/swagger-ui/LICENSE new file mode 100644 index 00000000..d6456956 --- /dev/null +++ b/static/swagger-ui/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ + +a { color: #8c8cfa; } + +::-webkit-scrollbar-track-piece { background-color: rgba(255, 255, 255, .2) !important; } + +::-webkit-scrollbar-track { background-color: rgba(255, 255, 255, .3) !important; } + +::-webkit-scrollbar-thumb { background-color: rgba(255, 255, 255, .5) !important; } + +embed[type="application/pdf"] { filter: invert(90%); } + +html { + background: #1f1f1f !important; + box-sizing: border-box; + filter: contrast(100%) brightness(100%) saturate(100%); + overflow-y: scroll; +} + +body { + background: #1f1f1f; + background-color: #1f1f1f; + background-image: none !important; +} + +button, input, select, textarea { + background-color: #1f1f1f; + color: #bfbfbf; +} + +font, html { color: #bfbfbf; } + +.swagger-ui, .swagger-ui section h3 { color: #b5bac9; } + +.swagger-ui a { background-color: transparent; } + +.swagger-ui mark { + background-color: #664b00; + color: #bfbfbf; +} + +.swagger-ui legend { color: inherit; } + +.swagger-ui .debug * { outline: #e6da99 solid 1px; } + +.swagger-ui .debug-white * { outline: #fff solid 1px; } + +.swagger-ui .debug-black * { outline: #bfbfbf solid 1px; } + +.swagger-ui .debug-grid { background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAICAYAAADED76LAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAyhpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw/eHBhY2tldCBiZWdpbj0i77u/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8+IDx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IkFkb2JlIFhNUCBDb3JlIDUuNi1jMTExIDc5LjE1ODMyNSwgMjAxNS8wOS8xMC0wMToxMDoyMCAgICAgICAgIj4gPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4gPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIgeG1sbnM6eG1wTU09Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9tbS8iIHhtbG5zOnN0UmVmPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvc1R5cGUvUmVzb3VyY2VSZWYjIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iIHhtcE1NOkRvY3VtZW50SUQ9InhtcC5kaWQ6MTRDOTY4N0U2N0VFMTFFNjg2MzZDQjkwNkQ4MjgwMEIiIHhtcE1NOkluc3RhbmNlSUQ9InhtcC5paWQ6MTRDOTY4N0Q2N0VFMTFFNjg2MzZDQjkwNkQ4MjgwMEIiIHhtcDpDcmVhdG9yVG9vbD0iQWRvYmUgUGhvdG9zaG9wIENDIDIwMTUgKE1hY2ludG9zaCkiPiA8eG1wTU06RGVyaXZlZEZyb20gc3RSZWY6aW5zdGFuY2VJRD0ieG1wLmlpZDo3NjcyQkQ3NjY3QzUxMUU2QjJCQ0UyNDA4MTAwMjE3MSIgc3RSZWY6ZG9jdW1lbnRJRD0ieG1wLmRpZDo3NjcyQkQ3NzY3QzUxMUU2QjJCQ0UyNDA4MTAwMjE3MSIvPiA8L3JkZjpEZXNjcmlwdGlvbj4gPC9yZGY6UkRGPiA8L3g6eG1wbWV0YT4gPD94cGFja2V0IGVuZD0iciI/PsBS+GMAAAAjSURBVHjaYvz//z8DLsD4gcGXiYEAGBIKGBne//fFpwAgwAB98AaF2pjlUQAAAABJRU5ErkJggg==) 0 0; } + +.swagger-ui .debug-grid-16 { background: url(data:image/png;base64,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) 0 0; } + +.swagger-ui .debug-grid-8-solid { background: url(data:image/jpeg;base64,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) 0 0 #1c1c21; } + +.swagger-ui .debug-grid-16-solid { background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAIAAACQkWg2AAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAyhpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw/eHBhY2tldCBiZWdpbj0i77u/IiBpZD0iVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkIj8+IDx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IkFkb2JlIFhNUCBDb3JlIDUuNi1jMTExIDc5LjE1ODMyNSwgMjAxNS8wOS8xMC0wMToxMDoyMCAgICAgICAgIj4gPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4gPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIgeG1sbnM6eG1wPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvIiB4bWxuczp4bXBNTT0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL21tLyIgeG1sbnM6c3RSZWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZVJlZiMiIHhtcDpDcmVhdG9yVG9vbD0iQWRvYmUgUGhvdG9zaG9wIENDIDIwMTUgKE1hY2ludG9zaCkiIHhtcE1NOkluc3RhbmNlSUQ9InhtcC5paWQ6NzY3MkJEN0U2N0M1MTFFNkIyQkNFMjQwODEwMDIxNzEiIHhtcE1NOkRvY3VtZW50SUQ9InhtcC5kaWQ6NzY3MkJEN0Y2N0M1MTFFNkIyQkNFMjQwODEwMDIxNzEiPiA8eG1wTU06RGVyaXZlZEZyb20gc3RSZWY6aW5zdGFuY2VJRD0ieG1wLmlpZDo3NjcyQkQ3QzY3QzUxMUU2QjJCQ0UyNDA4MTAwMjE3MSIgc3RSZWY6ZG9jdW1lbnRJRD0ieG1wLmRpZDo3NjcyQkQ3RDY3QzUxMUU2QjJCQ0UyNDA4MTAwMjE3MSIvPiA8L3JkZjpEZXNjcmlwdGlvbj4gPC9yZGY6UkRGPiA8L3g6eG1wbWV0YT4gPD94cGFja2V0IGVuZD0iciI/Pve6J3kAAAAzSURBVHjaYvz//z8D0UDsMwMjSRoYP5Gq4SPNbRjVMEQ1fCRDg+in/6+J1AJUxsgAEGAA31BAJMS0GYEAAAAASUVORK5CYII=) 0 0 #1c1c21; } + +.swagger-ui .b--black { border-color: #000; } + +.swagger-ui .b--near-black { border-color: #121212; } + +.swagger-ui .b--dark-gray { border-color: #333; } + +.swagger-ui .b--mid-gray { border-color: #545454; } + +.swagger-ui .b--gray { border-color: #787878; } + +.swagger-ui .b--silver { border-color: #999; } + +.swagger-ui .b--light-silver { border-color: #6e6e6e; } + +.swagger-ui .b--moon-gray { border-color: #4d4d4d; } + +.swagger-ui .b--light-gray { border-color: #2b2b2b; } + +.swagger-ui .b--near-white { border-color: #242424; } + +.swagger-ui .b--white { border-color: #1c1c21; } + +.swagger-ui .b--white-90 { border-color: rgba(28, 28, 33, .9); } + +.swagger-ui .b--white-80 { border-color: rgba(28, 28, 33, .8); } + +.swagger-ui .b--white-70 { border-color: rgba(28, 28, 33, .7); } + +.swagger-ui .b--white-60 { border-color: rgba(28, 28, 33, .6); } + +.swagger-ui .b--white-50 { border-color: rgba(28, 28, 33, .5); } + +.swagger-ui .b--white-40 { border-color: rgba(28, 28, 33, .4); } + +.swagger-ui .b--white-30 { border-color: rgba(28, 28, 33, .3); } + +.swagger-ui .b--white-20 { border-color: rgba(28, 28, 33, .2); } + +.swagger-ui .b--white-10 { border-color: rgba(28, 28, 33, .1); } + +.swagger-ui .b--white-05 { border-color: rgba(28, 28, 33, .05); } + +.swagger-ui .b--white-025 { border-color: rgba(28, 28, 33, .024); } + +.swagger-ui .b--white-0125 { border-color: rgba(28, 28, 33, .01); } + +.swagger-ui .b--black-90 { border-color: rgba(0, 0, 0, .9); } + +.swagger-ui .b--black-80 { border-color: rgba(0, 0, 0, .8); } + +.swagger-ui .b--black-70 { border-color: rgba(0, 0, 0, .7); } + +.swagger-ui .b--black-60 { border-color: rgba(0, 0, 0, .6); } + +.swagger-ui .b--black-50 { border-color: rgba(0, 0, 0, .5); } + +.swagger-ui .b--black-40 { border-color: rgba(0, 0, 0, .4); } + +.swagger-ui .b--black-30 { border-color: rgba(0, 0, 0, .3); } + +.swagger-ui .b--black-20 { border-color: rgba(0, 0, 0, .2); } + +.swagger-ui .b--black-10 { border-color: rgba(0, 0, 0, .1); } + +.swagger-ui .b--black-05 { border-color: rgba(0, 0, 0, .05); } + +.swagger-ui .b--black-025 { border-color: rgba(0, 0, 0, .024); } + +.swagger-ui .b--black-0125 { border-color: rgba(0, 0, 0, .01); } + +.swagger-ui .b--dark-red { border-color: #bc2f36; } + +.swagger-ui .b--red { border-color: #c83932; } + +.swagger-ui .b--light-red { border-color: #ab3c2b; } + +.swagger-ui .b--orange { border-color: #cc6e33; } + +.swagger-ui .b--purple { border-color: #5e2ca5; } + +.swagger-ui .b--light-purple { border-color: #672caf; } + +.swagger-ui .b--dark-pink { border-color: #ab2b81; } + +.swagger-ui .b--hot-pink { border-color: #c03086; } + +.swagger-ui .b--pink { border-color: #8f2464; } + +.swagger-ui .b--light-pink { border-color: #721d4d; } + +.swagger-ui .b--dark-green { border-color: #1c6e50; } + +.swagger-ui .b--green { border-color: #279b70; } + +.swagger-ui .b--light-green { border-color: #228762; } + +.swagger-ui .b--navy { border-color: #0d1d35; } + +.swagger-ui .b--dark-blue { border-color: #20497e; } + +.swagger-ui .b--blue { border-color: #4380d0; } + +.swagger-ui .b--light-blue { border-color: #20517e; } + +.swagger-ui .b--lightest-blue { border-color: #143a52; } + +.swagger-ui .b--washed-blue { border-color: #0c312d; } + +.swagger-ui .b--washed-green { border-color: #0f3d2c; } + +.swagger-ui .b--washed-red { border-color: #411010; } + +.swagger-ui .b--transparent { border-color: transparent; } + +.swagger-ui .b--gold, .swagger-ui .b--light-yellow, .swagger-ui .b--washed-yellow, .swagger-ui .b--yellow { border-color: #664b00; } + +.swagger-ui .shadow-1 { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; } + +.swagger-ui .shadow-2 { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; } + +.swagger-ui .shadow-3 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; } + +.swagger-ui .shadow-4 { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; } + +.swagger-ui .shadow-5 { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; } + +@media screen and (min-width: 30em) { + .swagger-ui .shadow-1-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; } + + .swagger-ui .shadow-2-ns { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; } + + .swagger-ui .shadow-3-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; } + + .swagger-ui .shadow-4-ns { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; } + + .swagger-ui .shadow-5-ns { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; } +} + +@media screen and (max-width: 60em) and (min-width: 30em) { + .swagger-ui .shadow-1-m { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; } + + .swagger-ui .shadow-2-m { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; } + + .swagger-ui .shadow-3-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; } + + .swagger-ui .shadow-4-m { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; } + + .swagger-ui .shadow-5-m { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; } +} + +@media screen and (min-width: 60em) { + .swagger-ui .shadow-1-l { box-shadow: rgba(0, 0, 0, .2) 0 0 4px 2px; } + + .swagger-ui .shadow-2-l { box-shadow: rgba(0, 0, 0, .2) 0 0 8px 2px; } + + .swagger-ui .shadow-3-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 4px 2px; } + + .swagger-ui .shadow-4-l { box-shadow: rgba(0, 0, 0, .2) 2px 2px 8px 0; } + + .swagger-ui .shadow-5-l { box-shadow: rgba(0, 0, 0, .2) 4px 4px 8px 0; } +} + +.swagger-ui .black-05 { color: rgba(191, 191, 191, .05); } + +.swagger-ui .bg-black-05 { background-color: rgba(0, 0, 0, .05); } + +.swagger-ui .black-90, .swagger-ui .hover-black-90:focus, .swagger-ui .hover-black-90:hover { color: rgba(191, 191, 191, .9); } + +.swagger-ui .black-80, .swagger-ui .hover-black-80:focus, .swagger-ui .hover-black-80:hover { color: rgba(191, 191, 191, .8); } + +.swagger-ui .black-70, .swagger-ui .hover-black-70:focus, .swagger-ui .hover-black-70:hover { color: rgba(191, 191, 191, .7); } + +.swagger-ui .black-60, .swagger-ui .hover-black-60:focus, .swagger-ui .hover-black-60:hover { color: rgba(191, 191, 191, .6); } + +.swagger-ui .black-50, .swagger-ui .hover-black-50:focus, .swagger-ui .hover-black-50:hover { color: rgba(191, 191, 191, .5); } + +.swagger-ui .black-40, .swagger-ui .hover-black-40:focus, .swagger-ui .hover-black-40:hover { color: rgba(191, 191, 191, .4); } + +.swagger-ui .black-30, .swagger-ui .hover-black-30:focus, .swagger-ui .hover-black-30:hover { color: rgba(191, 191, 191, .3); } + +.swagger-ui .black-20, .swagger-ui .hover-black-20:focus, .swagger-ui .hover-black-20:hover { color: rgba(191, 191, 191, .2); } + +.swagger-ui .black-10, .swagger-ui .hover-black-10:focus, .swagger-ui .hover-black-10:hover { color: rgba(191, 191, 191, .1); } + +.swagger-ui .hover-white-90:focus, .swagger-ui .hover-white-90:hover, .swagger-ui .white-90 { color: rgba(255, 255, 255, .9); } + +.swagger-ui .hover-white-80:focus, .swagger-ui .hover-white-80:hover, .swagger-ui .white-80 { color: rgba(255, 255, 255, .8); } + +.swagger-ui .hover-white-70:focus, .swagger-ui .hover-white-70:hover, .swagger-ui .white-70 { color: rgba(255, 255, 255, .7); } + +.swagger-ui .hover-white-60:focus, .swagger-ui .hover-white-60:hover, .swagger-ui .white-60 { color: rgba(255, 255, 255, .6); } + +.swagger-ui .hover-white-50:focus, .swagger-ui .hover-white-50:hover, .swagger-ui .white-50 { color: rgba(255, 255, 255, .5); } + +.swagger-ui .hover-white-40:focus, .swagger-ui .hover-white-40:hover, .swagger-ui .white-40 { color: rgba(255, 255, 255, .4); } + +.swagger-ui .hover-white-30:focus, .swagger-ui .hover-white-30:hover, .swagger-ui .white-30 { color: rgba(255, 255, 255, .3); } + +.swagger-ui .hover-white-20:focus, .swagger-ui .hover-white-20:hover, .swagger-ui .white-20 { color: rgba(255, 255, 255, .2); } + +.swagger-ui .hover-white-10:focus, .swagger-ui .hover-white-10:hover, .swagger-ui .white-10 { color: rgba(255, 255, 255, .1); } + +.swagger-ui .hover-moon-gray:focus, .swagger-ui .hover-moon-gray:hover, .swagger-ui .moon-gray { color: #ccc; } + +.swagger-ui .hover-light-gray:focus, .swagger-ui .hover-light-gray:hover, .swagger-ui .light-gray { color: #ededed; } + +.swagger-ui .hover-near-white:focus, .swagger-ui .hover-near-white:hover, .swagger-ui .near-white { color: #f5f5f5; } + +.swagger-ui .dark-red, .swagger-ui .hover-dark-red:focus, .swagger-ui .hover-dark-red:hover { color: #e6999d; } + +.swagger-ui .hover-red:focus, .swagger-ui .hover-red:hover, .swagger-ui .red { color: #e69d99; } + +.swagger-ui .hover-light-red:focus, .swagger-ui .hover-light-red:hover, .swagger-ui .light-red { color: #e6a399; } + +.swagger-ui .hover-orange:focus, .swagger-ui .hover-orange:hover, .swagger-ui .orange { color: #e6b699; } + +.swagger-ui .gold, .swagger-ui .hover-gold:focus, .swagger-ui .hover-gold:hover { color: #e6d099; } + +.swagger-ui .hover-yellow:focus, .swagger-ui .hover-yellow:hover, .swagger-ui .yellow { color: #e6da99; } + +.swagger-ui .hover-light-yellow:focus, .swagger-ui .hover-light-yellow:hover, .swagger-ui .light-yellow { color: #ede6b6; } + +.swagger-ui .hover-purple:focus, .swagger-ui .hover-purple:hover, .swagger-ui .purple { color: #b99ae4; } + +.swagger-ui .hover-light-purple:focus, .swagger-ui .hover-light-purple:hover, .swagger-ui .light-purple { color: #bb99e6; } + +.swagger-ui .dark-pink, .swagger-ui .hover-dark-pink:focus, .swagger-ui .hover-dark-pink:hover { color: #e699cc; } + +.swagger-ui .hot-pink, .swagger-ui .hover-hot-pink:focus, .swagger-ui .hover-hot-pink:hover, .swagger-ui .hover-pink:focus, .swagger-ui .hover-pink:hover, .swagger-ui .pink { color: #e699c7; } + +.swagger-ui .hover-light-pink:focus, .swagger-ui .hover-light-pink:hover, .swagger-ui .light-pink { color: #edb6d5; } + +.swagger-ui .dark-green, .swagger-ui .green, .swagger-ui .hover-dark-green:focus, .swagger-ui .hover-dark-green:hover, .swagger-ui .hover-green:focus, .swagger-ui .hover-green:hover { color: #99e6c9; } + +.swagger-ui .hover-light-green:focus, .swagger-ui .hover-light-green:hover, .swagger-ui .light-green { color: #a1e8ce; } + +.swagger-ui .hover-navy:focus, .swagger-ui .hover-navy:hover, .swagger-ui .navy { color: #99b8e6; } + +.swagger-ui .blue, .swagger-ui .dark-blue, .swagger-ui .hover-blue:focus, .swagger-ui .hover-blue:hover, .swagger-ui .hover-dark-blue:focus, .swagger-ui .hover-dark-blue:hover { color: #99bae6; } + +.swagger-ui .hover-light-blue:focus, .swagger-ui .hover-light-blue:hover, .swagger-ui .light-blue { color: #a9cbea; } + +.swagger-ui .hover-lightest-blue:focus, .swagger-ui .hover-lightest-blue:hover, .swagger-ui .lightest-blue { color: #d6e9f5; } + +.swagger-ui .hover-washed-blue:focus, .swagger-ui .hover-washed-blue:hover, .swagger-ui .washed-blue { color: #f7fdfc; } + +.swagger-ui .hover-washed-green:focus, .swagger-ui .hover-washed-green:hover, .swagger-ui .washed-green { color: #ebfaf4; } + +.swagger-ui .hover-washed-yellow:focus, .swagger-ui .hover-washed-yellow:hover, .swagger-ui .washed-yellow { color: #fbf9ef; } + +.swagger-ui .hover-washed-red:focus, .swagger-ui .hover-washed-red:hover, .swagger-ui .washed-red { color: #f9e7e7; } + +.swagger-ui .color-inherit, .swagger-ui .hover-inherit:focus, .swagger-ui .hover-inherit:hover { color: inherit; } + +.swagger-ui .bg-black-90, .swagger-ui .hover-bg-black-90:focus, .swagger-ui .hover-bg-black-90:hover { background-color: rgba(0, 0, 0, .9); } + +.swagger-ui .bg-black-80, .swagger-ui .hover-bg-black-80:focus, .swagger-ui .hover-bg-black-80:hover { background-color: rgba(0, 0, 0, .8); } + +.swagger-ui .bg-black-70, .swagger-ui .hover-bg-black-70:focus, .swagger-ui .hover-bg-black-70:hover { background-color: rgba(0, 0, 0, .7); } + +.swagger-ui .bg-black-60, .swagger-ui .hover-bg-black-60:focus, .swagger-ui .hover-bg-black-60:hover { background-color: rgba(0, 0, 0, .6); } + +.swagger-ui .bg-black-50, .swagger-ui .hover-bg-black-50:focus, .swagger-ui .hover-bg-black-50:hover { background-color: rgba(0, 0, 0, .5); } + +.swagger-ui .bg-black-40, .swagger-ui .hover-bg-black-40:focus, .swagger-ui .hover-bg-black-40:hover { background-color: rgba(0, 0, 0, .4); } + +.swagger-ui .bg-black-30, .swagger-ui .hover-bg-black-30:focus, .swagger-ui .hover-bg-black-30:hover { background-color: rgba(0, 0, 0, .3); } + +.swagger-ui .bg-black-20, .swagger-ui .hover-bg-black-20:focus, .swagger-ui .hover-bg-black-20:hover { background-color: rgba(0, 0, 0, .2); } + +.swagger-ui .bg-white-90, .swagger-ui .hover-bg-white-90:focus, .swagger-ui .hover-bg-white-90:hover { background-color: rgba(28, 28, 33, .9); } + +.swagger-ui .bg-white-80, .swagger-ui .hover-bg-white-80:focus, .swagger-ui .hover-bg-white-80:hover { background-color: rgba(28, 28, 33, .8); } + +.swagger-ui .bg-white-70, .swagger-ui .hover-bg-white-70:focus, .swagger-ui .hover-bg-white-70:hover { background-color: rgba(28, 28, 33, .7); } + +.swagger-ui .bg-white-60, .swagger-ui .hover-bg-white-60:focus, .swagger-ui .hover-bg-white-60:hover { background-color: rgba(28, 28, 33, .6); } + +.swagger-ui .bg-white-50, .swagger-ui .hover-bg-white-50:focus, .swagger-ui .hover-bg-white-50:hover { background-color: rgba(28, 28, 33, .5); } + +.swagger-ui .bg-white-40, .swagger-ui .hover-bg-white-40:focus, .swagger-ui .hover-bg-white-40:hover { background-color: rgba(28, 28, 33, .4); } + +.swagger-ui .bg-white-30, .swagger-ui .hover-bg-white-30:focus, .swagger-ui .hover-bg-white-30:hover { background-color: rgba(28, 28, 33, .3); } + +.swagger-ui .bg-white-20, .swagger-ui .hover-bg-white-20:focus, .swagger-ui .hover-bg-white-20:hover { background-color: rgba(28, 28, 33, .2); } + +.swagger-ui .bg-black, .swagger-ui .hover-bg-black:focus, .swagger-ui .hover-bg-black:hover { background-color: #000; } + +.swagger-ui .bg-near-black, .swagger-ui .hover-bg-near-black:focus, .swagger-ui .hover-bg-near-black:hover { background-color: #121212; } + +.swagger-ui .bg-dark-gray, .swagger-ui .hover-bg-dark-gray:focus, .swagger-ui .hover-bg-dark-gray:hover { background-color: #333; } + +.swagger-ui .bg-mid-gray, .swagger-ui .hover-bg-mid-gray:focus, .swagger-ui .hover-bg-mid-gray:hover { background-color: #545454; } + +.swagger-ui .bg-gray, .swagger-ui .hover-bg-gray:focus, .swagger-ui .hover-bg-gray:hover { background-color: #787878; } + +.swagger-ui .bg-silver, .swagger-ui .hover-bg-silver:focus, .swagger-ui .hover-bg-silver:hover { background-color: #999; } + +.swagger-ui .bg-white, .swagger-ui .hover-bg-white:focus, .swagger-ui .hover-bg-white:hover { background-color: #1c1c21; } + +.swagger-ui .bg-transparent, .swagger-ui .hover-bg-transparent:focus, .swagger-ui .hover-bg-transparent:hover { background-color: transparent; } + +.swagger-ui .bg-dark-red, .swagger-ui .hover-bg-dark-red:focus, .swagger-ui .hover-bg-dark-red:hover { background-color: #bc2f36; } + +.swagger-ui .bg-red, .swagger-ui .hover-bg-red:focus, .swagger-ui .hover-bg-red:hover { background-color: #c83932; } + +.swagger-ui .bg-light-red, .swagger-ui .hover-bg-light-red:focus, .swagger-ui .hover-bg-light-red:hover { background-color: #ab3c2b; } + +.swagger-ui .bg-orange, .swagger-ui .hover-bg-orange:focus, .swagger-ui .hover-bg-orange:hover { background-color: #cc6e33; } + +.swagger-ui .bg-gold, .swagger-ui .bg-light-yellow, .swagger-ui .bg-washed-yellow, .swagger-ui .bg-yellow, .swagger-ui .hover-bg-gold:focus, .swagger-ui .hover-bg-gold:hover, .swagger-ui .hover-bg-light-yellow:focus, .swagger-ui .hover-bg-light-yellow:hover, .swagger-ui .hover-bg-washed-yellow:focus, .swagger-ui .hover-bg-washed-yellow:hover, .swagger-ui .hover-bg-yellow:focus, .swagger-ui .hover-bg-yellow:hover { background-color: #664b00; } + +.swagger-ui .bg-purple, .swagger-ui .hover-bg-purple:focus, .swagger-ui .hover-bg-purple:hover { background-color: #5e2ca5; } + +.swagger-ui .bg-light-purple, .swagger-ui .hover-bg-light-purple:focus, .swagger-ui .hover-bg-light-purple:hover { background-color: #672caf; } + +.swagger-ui .bg-dark-pink, .swagger-ui .hover-bg-dark-pink:focus, .swagger-ui .hover-bg-dark-pink:hover { background-color: #ab2b81; } + +.swagger-ui .bg-hot-pink, .swagger-ui .hover-bg-hot-pink:focus, .swagger-ui .hover-bg-hot-pink:hover { background-color: #c03086; } + +.swagger-ui .bg-pink, .swagger-ui .hover-bg-pink:focus, .swagger-ui .hover-bg-pink:hover { background-color: #8f2464; } + +.swagger-ui .bg-light-pink, .swagger-ui .hover-bg-light-pink:focus, .swagger-ui .hover-bg-light-pink:hover { background-color: #721d4d; } + +.swagger-ui .bg-dark-green, .swagger-ui .hover-bg-dark-green:focus, .swagger-ui .hover-bg-dark-green:hover { background-color: #1c6e50; } + +.swagger-ui .bg-green, .swagger-ui .hover-bg-green:focus, .swagger-ui .hover-bg-green:hover { background-color: #279b70; } + +.swagger-ui .bg-light-green, .swagger-ui .hover-bg-light-green:focus, .swagger-ui .hover-bg-light-green:hover { background-color: #228762; } + +.swagger-ui .bg-navy, .swagger-ui .hover-bg-navy:focus, .swagger-ui .hover-bg-navy:hover { background-color: #0d1d35; } + +.swagger-ui .bg-dark-blue, .swagger-ui .hover-bg-dark-blue:focus, .swagger-ui .hover-bg-dark-blue:hover { background-color: #20497e; } + +.swagger-ui .bg-blue, .swagger-ui .hover-bg-blue:focus, .swagger-ui .hover-bg-blue:hover { background-color: #4380d0; } + +.swagger-ui .bg-light-blue, .swagger-ui .hover-bg-light-blue:focus, .swagger-ui .hover-bg-light-blue:hover { background-color: #20517e; } + +.swagger-ui .bg-lightest-blue, .swagger-ui .hover-bg-lightest-blue:focus, .swagger-ui .hover-bg-lightest-blue:hover { background-color: #143a52; } + +.swagger-ui .bg-washed-blue, .swagger-ui .hover-bg-washed-blue:focus, .swagger-ui .hover-bg-washed-blue:hover { background-color: #0c312d; } + +.swagger-ui .bg-washed-green, .swagger-ui .hover-bg-washed-green:focus, .swagger-ui .hover-bg-washed-green:hover { background-color: #0f3d2c; } + +.swagger-ui .bg-washed-red, .swagger-ui .hover-bg-washed-red:focus, .swagger-ui .hover-bg-washed-red:hover { background-color: #411010; } + +.swagger-ui .bg-inherit, .swagger-ui .hover-bg-inherit:focus, .swagger-ui .hover-bg-inherit:hover { background-color: inherit; } + +.swagger-ui .shadow-hover { transition: all .5s cubic-bezier(.165, .84, .44, 1) 0s; } + +.swagger-ui .shadow-hover::after { + border-radius: inherit; + box-shadow: rgba(0, 0, 0, .2) 0 0 16px 2px; + content: ""; + height: 100%; + left: 0; + opacity: 0; + position: absolute; + top: 0; + transition: opacity .5s cubic-bezier(.165, .84, .44, 1) 0s; + width: 100%; + z-index: -1; +} + +.swagger-ui .bg-animate, .swagger-ui .bg-animate:focus, .swagger-ui .bg-animate:hover { transition: background-color .15s ease-in-out 0s; } + +.swagger-ui .nested-links a { + color: #99bae6; + transition: color .15s ease-in 0s; +} + +.swagger-ui .nested-links a:focus, .swagger-ui .nested-links a:hover { + color: #a9cbea; + transition: color .15s ease-in 0s; +} + +.swagger-ui .opblock-tag { + border-bottom: 1px solid rgba(58, 64, 80, .3); + color: #b5bac9; + transition: all .2s ease 0s; +} + +.swagger-ui .opblock-tag svg, .swagger-ui section.models h4 svg { transition: all .4s ease 0s; } + +.swagger-ui .opblock { + border: 1px solid #000; + border-radius: 4px; + box-shadow: rgba(0, 0, 0, .19) 0 0 3px; + margin: 0 0 15px; +} + +.swagger-ui .opblock .tab-header .tab-item.active h4 span::after { background: gray; } + +.swagger-ui .opblock.is-open .opblock-summary { border-bottom: 1px solid #000; } + +.swagger-ui .opblock .opblock-section-header { + background: rgba(28, 28, 33, .8); + box-shadow: rgba(0, 0, 0, .1) 0 1px 2px; +} + +.swagger-ui .opblock .opblock-section-header > label > span { padding: 0 10px 0 0; } + +.swagger-ui .opblock .opblock-summary-method { + background: #000; + color: #fff; + text-shadow: rgba(0, 0, 0, .1) 0 1px 0; +} + +.swagger-ui .opblock.opblock-post { + background: rgba(72, 203, 144, .1); + border-color: #48cb90; +} + +.swagger-ui .opblock.opblock-post .opblock-summary-method, .swagger-ui .opblock.opblock-post .tab-header .tab-item.active h4 span::after { background: #48cb90; } + +.swagger-ui .opblock.opblock-post .opblock-summary { border-color: #48cb90; } + +.swagger-ui .opblock.opblock-put { + background: rgba(213, 157, 88, .1); + border-color: #d59d58; +} + +.swagger-ui .opblock.opblock-put .opblock-summary-method, .swagger-ui .opblock.opblock-put .tab-header .tab-item.active h4 span::after { background: #d59d58; } + +.swagger-ui .opblock.opblock-put .opblock-summary { border-color: #d59d58; } + +.swagger-ui .opblock.opblock-delete { + background: rgba(200, 50, 50, .1); + border-color: #c83232; +} + +.swagger-ui .opblock.opblock-delete .opblock-summary-method, .swagger-ui .opblock.opblock-delete .tab-header .tab-item.active h4 span::after { background: #c83232; } + +.swagger-ui .opblock.opblock-delete .opblock-summary { border-color: #c83232; } + +.swagger-ui .opblock.opblock-get { + background: rgba(42, 105, 167, .1); + border-color: #2a69a7; +} + +.swagger-ui .opblock.opblock-get .opblock-summary-method, .swagger-ui .opblock.opblock-get .tab-header .tab-item.active h4 span::after { background: #2a69a7; } + +.swagger-ui .opblock.opblock-get .opblock-summary { border-color: #2a69a7; } + +.swagger-ui .opblock.opblock-patch { + background: rgba(92, 214, 188, .1); + border-color: #5cd6bc; +} + +.swagger-ui .opblock.opblock-patch .opblock-summary-method, .swagger-ui .opblock.opblock-patch .tab-header .tab-item.active h4 span::after { background: #5cd6bc; } + +.swagger-ui .opblock.opblock-patch .opblock-summary { border-color: #5cd6bc; } + +.swagger-ui .opblock.opblock-head { + background: rgba(140, 63, 207, .1); + border-color: #8c3fcf; +} + +.swagger-ui .opblock.opblock-head .opblock-summary-method, .swagger-ui .opblock.opblock-head .tab-header .tab-item.active h4 span::after { background: #8c3fcf; } + +.swagger-ui .opblock.opblock-head .opblock-summary { border-color: #8c3fcf; } + +.swagger-ui .opblock.opblock-options { + background: rgba(36, 89, 143, .1); + border-color: #24598f; +} + +.swagger-ui .opblock.opblock-options .opblock-summary-method, .swagger-ui .opblock.opblock-options .tab-header .tab-item.active h4 span::after { background: #24598f; } + +.swagger-ui .opblock.opblock-options .opblock-summary { border-color: #24598f; } + +.swagger-ui .opblock.opblock-deprecated { + background: rgba(46, 46, 46, .1); + border-color: #2e2e2e; + opacity: .6; +} + +.swagger-ui .opblock.opblock-deprecated .opblock-summary-method, .swagger-ui .opblock.opblock-deprecated .tab-header .tab-item.active h4 span::after { background: #2e2e2e; } + +.swagger-ui .opblock.opblock-deprecated .opblock-summary { border-color: #2e2e2e; } + +.swagger-ui .filter .operation-filter-input { border: 2px solid #2b3446; } + +.swagger-ui .tab li:first-of-type::after { background: rgba(0, 0, 0, .2); } + +.swagger-ui .download-contents { + background: #7c8192; + color: #fff; +} + +.swagger-ui .scheme-container { + background: #1c1c21; + box-shadow: rgba(0, 0, 0, .15) 0 1px 2px 0; +} + +.swagger-ui .loading-container .loading::before { + animation: 1s linear 0s infinite normal none running rotation, .5s ease 0s 1 normal none running opacity; + border-color: rgba(0, 0, 0, .6) rgba(84, 84, 84, .1) rgba(84, 84, 84, .1); +} + +.swagger-ui .response-control-media-type--accept-controller select { border-color: #196619; } + +.swagger-ui .response-control-media-type__accept-message { color: #99e699; } + +.swagger-ui .version-pragma__message code { background-color: #3b3b3b; } + +.swagger-ui .btn { + background: 0 0; + border: 2px solid gray; + box-shadow: rgba(0, 0, 0, .1) 0 1px 2px; + color: #b5bac9; +} + +.swagger-ui .btn:hover { box-shadow: rgba(0, 0, 0, .3) 0 0 5px; } + +.swagger-ui .btn.authorize, .swagger-ui .btn.cancel { + background-color: transparent; + border-color: #a72a2a; + color: #e69999; +} + +.swagger-ui .btn.authorize { + border-color: #48cb90; + color: #9ce3c3; +} + +.swagger-ui .btn.authorize svg { fill: #9ce3c3; } + +.swagger-ui .btn.execute { + background-color: #5892d5; + border-color: #5892d5; + color: #fff; +} + +.swagger-ui .copy-to-clipboard { background: #7c8192; } + +.swagger-ui .copy-to-clipboard button { background: url("data:image/svg+xml;charset=utf-8,") 50% center no-repeat; } + +.swagger-ui select { + background: url("data:image/svg+xml;charset=utf-8,") right 10px center/20px no-repeat #212121; + background: url(data:image/svg+xml;base64,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) right 10px center/20px no-repeat #1c1c21; + border: 2px solid #41444e; +} + +.swagger-ui select[multiple] { background: #212121; } + +.swagger-ui button.invalid, .swagger-ui input[type=email].invalid, .swagger-ui input[type=file].invalid, .swagger-ui input[type=password].invalid, .swagger-ui input[type=search].invalid, .swagger-ui input[type=text].invalid, .swagger-ui select.invalid, .swagger-ui textarea.invalid { + background: #390e0e; + border-color: #c83232; +} + +.swagger-ui input[type=email], .swagger-ui input[type=file], .swagger-ui input[type=password], .swagger-ui input[type=search], .swagger-ui input[type=text], .swagger-ui textarea { + background: #1c1c21; + border: 1px solid #404040; +} + +.swagger-ui textarea { + background: rgba(28, 28, 33, .8); + color: #b5bac9; +} + +.swagger-ui input[disabled], .swagger-ui select[disabled] { + background-color: #1f1f1f; + color: #bfbfbf; +} + +.swagger-ui textarea[disabled] { + background-color: #41444e; + color: #fff; +} + +.swagger-ui select[disabled] { border-color: #878787; } + +.swagger-ui textarea:focus { border: 2px solid #2a69a7; } + +.swagger-ui .checkbox input[type=checkbox] + label > .item { + background: #303030; + box-shadow: #303030 0 0 0 2px; +} + +.swagger-ui .checkbox input[type=checkbox]:checked + label > .item { background: url("data:image/svg+xml;charset=utf-8,") 50% center no-repeat #303030; } + +.swagger-ui .dialog-ux .backdrop-ux { background: rgba(0, 0, 0, .8); } + +.swagger-ui .dialog-ux .modal-ux { + background: #1c1c21; + border: 1px solid #2e2e2e; + box-shadow: rgba(0, 0, 0, .2) 0 10px 30px 0; +} + +.swagger-ui .dialog-ux .modal-ux-header .close-modal { background: 0 0; } + +.swagger-ui .model .deprecated span, .swagger-ui .model .deprecated td { color: #bfbfbf !important; } + +.swagger-ui .model-toggle::after { background: url("data:image/svg+xml;charset=utf-8,") 50% center/100% no-repeat; } + +.swagger-ui .model-hint { + background: rgba(0, 0, 0, .7); + color: #ebebeb; +} + +.swagger-ui section.models { border: 1px solid rgba(58, 64, 80, .3); } + +.swagger-ui section.models.is-open h4 { border-bottom: 1px solid rgba(58, 64, 80, .3); } + +.swagger-ui section.models .model-container { background: rgba(0, 0, 0, .05); } + +.swagger-ui section.models .model-container:hover { background: rgba(0, 0, 0, .07); } + +.swagger-ui .model-box { background: rgba(0, 0, 0, .1); } + +.swagger-ui .prop-type { color: #aaaad4; } + +.swagger-ui table thead tr td, .swagger-ui table thead tr th { + border-bottom: 1px solid rgba(58, 64, 80, .2); + color: #b5bac9; +} + +.swagger-ui .parameter__name.required::after { color: rgba(230, 153, 153, .6); } + +.swagger-ui .topbar .download-url-wrapper .select-label { color: #f0f0f0; } + +.swagger-ui .topbar .download-url-wrapper .download-url-button { + background: #63a040; + color: #fff; +} + +.swagger-ui .info .title small { background: #7c8492; } + +.swagger-ui .info .title small.version-stamp { background-color: #7a9b27; } + +.swagger-ui .auth-container .errors { + background-color: #350d0d; + color: #b5bac9; +} + +.swagger-ui .errors-wrapper { + background: rgba(200, 50, 50, .1); + border: 2px solid #c83232; +} + +.swagger-ui .markdown code, .swagger-ui .renderedmarkdown code { + background: rgba(0, 0, 0, .05); + color: #c299e6; +} + +.swagger-ui .model-toggle:after { background: url(data:image/svg+xml;base64,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) 50% no-repeat; } + +.swagger-ui .expand-operation svg, .swagger-ui section.models h4 svg { fill: #fff; } + +::-webkit-scrollbar-track { background-color: #646464 !important; } + +::-webkit-scrollbar-thumb { + background-color: #242424 !important; + border: 2px solid #3e4346 !important; +} + +::-webkit-scrollbar-button:vertical:start:decrement { + background: linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%), linear-gradient(230deg, #696969 40%, transparent 41%), linear-gradient(0deg, #696969 40%, transparent 31%); + background-color: #b6b6b6; +} + +::-webkit-scrollbar-button:vertical:end:increment { + background: linear-gradient(310deg, #696969 40%, transparent 41%), linear-gradient(50deg, #696969 40%, transparent 41%), linear-gradient(180deg, #696969 40%, transparent 31%); + background-color: #b6b6b6; +} + +::-webkit-scrollbar-button:horizontal:end:increment { + background: linear-gradient(210deg, #696969 40%, transparent 41%), linear-gradient(330deg, #696969 40%, transparent 41%), linear-gradient(90deg, #696969 30%, transparent 31%); + background-color: #b6b6b6; +} + +::-webkit-scrollbar-button:horizontal:start:decrement { + background: linear-gradient(30deg, #696969 40%, transparent 41%), linear-gradient(150deg, #696969 40%, transparent 41%), linear-gradient(270deg, #696969 30%, transparent 31%); + background-color: #b6b6b6; +} + +::-webkit-scrollbar-button, ::-webkit-scrollbar-track-piece { background-color: #3e4346 !important; } + +.swagger-ui .black, .swagger-ui .checkbox, .swagger-ui .dark-gray, .swagger-ui .download-url-wrapper .loading, .swagger-ui .errors-wrapper .errors small, .swagger-ui .fallback, .swagger-ui .filter .loading, .swagger-ui .gray, .swagger-ui .hover-black:focus, .swagger-ui .hover-black:hover, .swagger-ui .hover-dark-gray:focus, .swagger-ui .hover-dark-gray:hover, .swagger-ui .hover-gray:focus, .swagger-ui .hover-gray:hover, .swagger-ui .hover-light-silver:focus, .swagger-ui .hover-light-silver:hover, .swagger-ui .hover-mid-gray:focus, .swagger-ui .hover-mid-gray:hover, .swagger-ui .hover-near-black:focus, .swagger-ui .hover-near-black:hover, .swagger-ui .hover-silver:focus, .swagger-ui .hover-silver:hover, .swagger-ui .light-silver, .swagger-ui .markdown pre, .swagger-ui .mid-gray, .swagger-ui .model .property, .swagger-ui .model .property.primitive, .swagger-ui .model-title, .swagger-ui .near-black, .swagger-ui .parameter__extension, .swagger-ui .parameter__in, .swagger-ui .prop-format, .swagger-ui .renderedmarkdown pre, .swagger-ui .response-col_links .response-undocumented, .swagger-ui .response-col_status .response-undocumented, .swagger-ui .silver, .swagger-ui section.models h4, .swagger-ui section.models h5, .swagger-ui span.token-not-formatted, .swagger-ui span.token-string, .swagger-ui table.headers .header-example, .swagger-ui table.model tr.description, .swagger-ui table.model tr.extension { color: #bfbfbf; } + +.swagger-ui .hover-white:focus, .swagger-ui .hover-white:hover, .swagger-ui .info .title small pre, .swagger-ui .topbar a, .swagger-ui .white { color: #fff; } + +.swagger-ui .bg-black-10, .swagger-ui .hover-bg-black-10:focus, .swagger-ui .hover-bg-black-10:hover, .swagger-ui .stripe-dark:nth-child(2n + 1) { background-color: rgba(0, 0, 0, .1); } + +.swagger-ui .bg-white-10, .swagger-ui .hover-bg-white-10:focus, .swagger-ui .hover-bg-white-10:hover, .swagger-ui .stripe-light:nth-child(2n + 1) { background-color: rgba(28, 28, 33, .1); } + +.swagger-ui .bg-light-silver, .swagger-ui .hover-bg-light-silver:focus, .swagger-ui .hover-bg-light-silver:hover, .swagger-ui .striped--light-silver:nth-child(2n + 1) { background-color: #6e6e6e; } + +.swagger-ui .bg-moon-gray, .swagger-ui .hover-bg-moon-gray:focus, .swagger-ui .hover-bg-moon-gray:hover, .swagger-ui .striped--moon-gray:nth-child(2n + 1) { background-color: #4d4d4d; } + +.swagger-ui .bg-light-gray, .swagger-ui .hover-bg-light-gray:focus, .swagger-ui .hover-bg-light-gray:hover, .swagger-ui .striped--light-gray:nth-child(2n + 1) { background-color: #2b2b2b; } + +.swagger-ui .bg-near-white, .swagger-ui .hover-bg-near-white:focus, .swagger-ui .hover-bg-near-white:hover, .swagger-ui .striped--near-white:nth-child(2n + 1) { background-color: #242424; } + +.swagger-ui .opblock-tag:hover, .swagger-ui section.models h4:hover { background: rgba(0, 0, 0, .02); } + +.swagger-ui .checkbox p, .swagger-ui .dialog-ux .modal-ux-content h4, .swagger-ui .dialog-ux .modal-ux-content p, .swagger-ui .dialog-ux .modal-ux-header h3, .swagger-ui .errors-wrapper .errors h4, .swagger-ui .errors-wrapper hgroup h4, .swagger-ui .info .base-url, .swagger-ui .info .title, .swagger-ui .info h1, .swagger-ui .info h2, .swagger-ui .info h3, .swagger-ui .info h4, .swagger-ui .info h5, .swagger-ui .info li, .swagger-ui .info p, .swagger-ui .info table, .swagger-ui .loading-container .loading::after, .swagger-ui .model, .swagger-ui .opblock .opblock-section-header h4, .swagger-ui .opblock .opblock-section-header > label, .swagger-ui .opblock .opblock-summary-description, .swagger-ui .opblock .opblock-summary-operation-id, .swagger-ui .opblock .opblock-summary-path, .swagger-ui .opblock .opblock-summary-path__deprecated, .swagger-ui .opblock-description-wrapper, .swagger-ui .opblock-description-wrapper h4, .swagger-ui .opblock-description-wrapper p, .swagger-ui .opblock-external-docs-wrapper, .swagger-ui .opblock-external-docs-wrapper h4, .swagger-ui .opblock-external-docs-wrapper p, .swagger-ui .opblock-tag small, .swagger-ui .opblock-title_normal, .swagger-ui .opblock-title_normal h4, .swagger-ui .opblock-title_normal p, .swagger-ui .parameter__name, .swagger-ui .parameter__type, .swagger-ui .response-col_links, .swagger-ui .response-col_status, .swagger-ui .responses-inner h4, .swagger-ui .responses-inner h5, .swagger-ui .scheme-container .schemes > label, .swagger-ui .scopes h2, .swagger-ui .servers > label, .swagger-ui .tab li, .swagger-ui label, .swagger-ui select, .swagger-ui table.headers td { color: #b5bac9; } + +.swagger-ui .download-url-wrapper .failed, .swagger-ui .filter .failed, .swagger-ui .model-deprecated-warning, .swagger-ui .parameter__deprecated, .swagger-ui .parameter__name.required span, .swagger-ui table.model tr.property-row .star { color: #e69999; } + +.swagger-ui .opblock-body pre.microlight, .swagger-ui textarea.curl { + background: #41444e; + border-radius: 4px; + color: #fff; +} + +.swagger-ui .expand-methods svg, .swagger-ui .expand-methods:hover svg { fill: #bfbfbf; } + +.swagger-ui .auth-container, .swagger-ui .dialog-ux .modal-ux-header { border-bottom: 1px solid #2e2e2e; } + +.swagger-ui .topbar .download-url-wrapper .select-label select, .swagger-ui .topbar .download-url-wrapper input[type=text] { border: 2px solid #63a040; } + +.swagger-ui .info a, .swagger-ui .info a:hover, .swagger-ui .scopes h2 a { color: #99bde6; } + +/* Dark Scrollbar */ +::-webkit-scrollbar { + width: 14px; + height: 14px; +} + +::-webkit-scrollbar-button { + background-color: #3e4346 !important; +} + +::-webkit-scrollbar-track { + background-color: #646464 !important; +} + +::-webkit-scrollbar-track-piece { + background-color: #3e4346 !important; +} + +::-webkit-scrollbar-thumb { + height: 50px; + background-color: #242424 !important; + border: 2px solid #3e4346 !important; +} + +::-webkit-scrollbar-corner {} + +::-webkit-resizer {} + +::-webkit-scrollbar-button:vertical:start:decrement { + background: + linear-gradient(130deg, #696969 40%, rgba(255, 0, 0, 0) 41%), + linear-gradient(230deg, #696969 40%, rgba(0, 0, 0, 0) 41%), + linear-gradient(0deg, #696969 40%, rgba(0, 0, 0, 0) 31%); + background-color: #b6b6b6; +} + +::-webkit-scrollbar-button:vertical:end:increment { + background: + linear-gradient(310deg, #696969 40%, rgba(0, 0, 0, 0) 41%), + linear-gradient(50deg, #696969 40%, rgba(0, 0, 0, 0) 41%), + linear-gradient(180deg, #696969 40%, rgba(0, 0, 0, 0) 31%); + background-color: #b6b6b6; +} + +::-webkit-scrollbar-button:horizontal:end:increment { + background: + linear-gradient(210deg, #696969 40%, rgba(0, 0, 0, 0) 41%), + linear-gradient(330deg, #696969 40%, rgba(0, 0, 0, 0) 41%), + linear-gradient(90deg, #696969 30%, rgba(0, 0, 0, 0) 31%); + background-color: #b6b6b6; +} + +::-webkit-scrollbar-button:horizontal:start:decrement { + background: + linear-gradient(30deg, #696969 40%, rgba(0, 0, 0, 0) 41%), + linear-gradient(150deg, #696969 40%, rgba(0, 0, 0, 0) 41%), + linear-gradient(270deg, #696969 30%, rgba(0, 0, 0, 0) 31%); + background-color: #b6b6b6; +} diff --git a/static/swagger-ui/index.css b/static/swagger-ui/index.css new file mode 100644 index 00000000..0aa7e9be --- /dev/null +++ b/static/swagger-ui/index.css @@ -0,0 +1,17 @@ +/*! Swagger UI 4.13.2 | https://swagger.io/tools/swagger-ui/ | Apache License 2.0 (license file can be found at ./LICENSE) */ +html { + box-sizing: border-box; + overflow: -moz-scrollbars-vertical; + overflow-y: scroll; +} + +*, +*:before, +*:after { + box-sizing: inherit; +} + +body { + margin: 0; + background: #fafafa; +} diff --git a/static/swagger-ui/oauth2-redirect.html b/static/swagger-ui/oauth2-redirect.html new file mode 100644 index 00000000..9a25c9e8 --- /dev/null +++ b/static/swagger-ui/oauth2-redirect.html @@ -0,0 +1,79 @@ + + + + Swagger UI: OAuth2 Redirect + + + + + diff --git a/static/swagger-ui/swagger-ui-bundle.js b/static/swagger-ui/swagger-ui-bundle.js new file mode 100644 index 00000000..0f5ced83 --- /dev/null +++ b/static/swagger-ui/swagger-ui-bundle.js @@ -0,0 +1,2 @@ +/*! Swagger UI 4.13.2 | https://swagger.io/tools/swagger-ui/ | Apache License 2.0 (license file can be found at ./LICENSE) */ +!function(e,t){"object"==typeof exports&&"object"==typeof module?module.exports=t():"function"==typeof define&&define.amd?define([],t):"object"==typeof exports?exports.SwaggerUIBundle=t():e.SwaggerUIBundle=t()}(this,(function(){return(()=>{var e={17967:(e,t)=>{"use strict";t.N=void 0;var r=/^([^\w]*)(javascript|data|vbscript)/im,n=/&#(\w+)(^\w|;)?/g,o=/[\u0000-\u001F\u007F-\u009F\u2000-\u200D\uFEFF]/gim,a=/^([^:]+):/gm,i=[".","/"];t.N=function(e){var t,s=(t=e||"",t.replace(n,(function(e,t){return String.fromCharCode(t)}))).replace(o,"").trim();if(!s)return"about:blank";if(function(e){return i.indexOf(e[0])>-1}(s))return s;var l=s.match(a);if(!l)return s;var u=l[0];return r.test(u)?"about:blank":s}},53795:(e,t,r)=>{"use strict";r.d(t,{Z:()=>P});var n=r(23101),o=r.n(n),a=r(61125),i=r.n(a),s=r(11882),l=r.n(s),u=r(97606),c=r.n(u),p=r(67294),f=r(43393);function 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