from pathlib import Path from decouple import config from theflow.settings.default import * # noqa user_cache_dir = Path("./cache") user_cache_dir.mkdir(parents=True, exist_ok=True) COHERE_API_KEY = config("COHERE_API_KEY", default="") KH_MODE = "dev" KH_FEATURE_USER_MANAGEMENT = False KH_FEATURE_USER_MANAGEMENT_ADMIN = str( config("KH_FEATURE_USER_MANAGEMENT_ADMIN", default="admin") ) KH_FEATURE_USER_MANAGEMENT_PASSWORD = str( config("KH_FEATURE_USER_MANAGEMENT_PASSWORD", default="Abc@123") ) KH_ENABLE_ALEMBIC = False KH_DATABASE = f"sqlite:///{user_cache_dir / 'sql.db'}" KH_FILESTORAGE_PATH = str(user_cache_dir / "files") KH_DOCSTORE = { "__type__": "kotaemon.storages.SimpleFileDocumentStore", "path": str(user_cache_dir / "docstore"), } KH_VECTORSTORE = { "__type__": "kotaemon.storages.ChromaVectorStore", "path": str(user_cache_dir / "vectorstore"), } KH_LLMS = {} KH_EMBEDDINGS = {} # populate options from config if config("AZURE_OPENAI_API_KEY", default="") and config( "AZURE_OPENAI_ENDPOINT", default="" ): if config("AZURE_OPENAI_CHAT_DEPLOYMENT", default=""): KH_LLMS["azure"] = { "spec": { "__type__": "kotaemon.llms.AzureChatOpenAI", "temperature": 0, "azure_endpoint": config("AZURE_OPENAI_ENDPOINT", default=""), "api_key": config("AZURE_OPENAI_API_KEY", default=""), "api_version": config("OPENAI_API_VERSION", default="") or "2024-02-15-preview", "azure_deployment": config("AZURE_OPENAI_CHAT_DEPLOYMENT", default=""), "timeout": 20, }, "default": False, "accuracy": 5, "cost": 5, } if config("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT", default=""): KH_EMBEDDINGS["azure"] = { "spec": { "__type__": "kotaemon.embeddings.AzureOpenAIEmbeddings", "azure_endpoint": config("AZURE_OPENAI_ENDPOINT", default=""), "api_key": config("AZURE_OPENAI_API_KEY", default=""), "api_version": config("OPENAI_API_VERSION", default="") or "2024-02-15-preview", "azure_deployment": config( "AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT", default="" ), "timeout": 10, }, "default": False, } if config("OPENAI_API_KEY", default=""): KH_LLMS["openai"] = { "spec": { "__type__": "kotaemon.llms.ChatOpenAI", "temperature": 0, "base_url": config("OPENAI_API_BASE", default="") or "https://api.openai.com/v1", "api_key": config("OPENAI_API_KEY", default=""), "model": config("OPENAI_CHAT_MODEL", default="") or "gpt-3.5-turbo", "timeout": 10, }, "default": False, } if len(KH_EMBEDDINGS) < 1: KH_EMBEDDINGS["openai"] = { "spec": { "__type__": "kotaemon.embeddings.OpenAIEmbeddings", "base_url": config("OPENAI_API_BASE", default="") or "https://api.openai.com/v1", "api_key": config("OPENAI_API_KEY", default=""), "model": config( "OPENAI_EMBEDDINGS_MODEL", default="text-embedding-ada-002" ) or "text-embedding-ada-002", "timeout": 10, }, "default": False, } if config("LOCAL_MODEL", default=""): KH_LLMS["local"] = { "spec": { "__type__": "kotaemon.llms.EndpointChatLLM", "endpoint_url": "http://localhost:31415/v1/chat/completions", }, "default": False, "cost": 0, } if len(KH_EMBEDDINGS) < 1: KH_EMBEDDINGS["local"] = { "spec": { "__type__": "kotaemon.embeddings.EndpointEmbeddings", "endpoint_url": "http://localhost:31415/v1/embeddings", }, "default": False, "cost": 0, } if len(KH_EMBEDDINGS) < 1: KH_EMBEDDINGS["local-mxbai-large-v1"] = { "spec": { "__type__": "kotaemon.embeddings.FastEmbedEmbeddings", "model_name": "mixedbread-ai/mxbai-embed-large-v1", }, "default": True, } KH_REASONINGS = ["ktem.reasoning.simple.FullQAPipeline"] KH_VLM_ENDPOINT = "{0}/openai/deployments/{1}/chat/completions?api-version={2}".format( config("AZURE_OPENAI_ENDPOINT", default=""), config("OPENAI_VISION_DEPLOYMENT_NAME", default="gpt-4-vision"), config("OPENAI_API_VERSION", default=""), ) SETTINGS_APP = { "lang": { "name": "Language", "value": "en", "choices": [("English", "en"), ("Japanese", "ja")], "component": "dropdown", } } SETTINGS_REASONING = { "use": { "name": "Reasoning options", "value": None, "choices": [], "component": "radio", }, "lang": { "name": "Language", "value": "en", "choices": [("English", "en"), ("Japanese", "ja")], "component": "dropdown", }, } KH_INDEX_TYPES = ["ktem.index.file.FileIndex"] KH_INDICES = [ { "name": "File", "config": {}, "index_type": "ktem.index.file.FileIndex", }, ]