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import os
from inspect import currentframe, getframeinfo
from pathlib import Path
from decouple import config
from theflow.settings.default import * # noqa
cur_frame = currentframe()
if cur_frame is None:
raise ValueError("Cannot get the current frame.")
this_file = getframeinfo(cur_frame).filename
this_dir = Path(this_file).parent
# App can be ran from anywhere and it's not trivial to decide where to store app data.
# So let's use the same directory as the flowsetting.py file.
KH_APP_DATA_DIR = this_dir / "ktem_app_data"
KH_APP_DATA_DIR.mkdir(parents=True, exist_ok=True)
# User data directory
KH_USER_DATA_DIR = KH_APP_DATA_DIR / "user_data"
KH_USER_DATA_DIR.mkdir(parents=True, exist_ok=True)
# HF models can be big, let's store them in the app data directory so that it's easier
# for users to manage their storage.
# ref: https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache
# os.environ["HF_HOME"] = str(KH_APP_DATA_DIR / "huggingface")
# os.environ["HF_HUB_CACHE"] = str(KH_APP_DATA_DIR / "huggingface")
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="XsdMbe8zKP8KdeE@")
)
KH_ENABLE_ALEMBIC = False
KH_DATABASE = f"sqlite:///{KH_USER_DATA_DIR / 'sql.db'}"
KH_FILESTORAGE_PATH = str(KH_USER_DATA_DIR / "files")
KH_DOCSTORE = {
"__type__": "kotaemon.storages.SimpleFileDocumentStore",
"path": str(KH_USER_DATA_DIR / "docstore"),
}
KH_VECTORSTORE = {
"__type__": "kotaemon.storages.ChromaVectorStore",
"path": str(KH_USER_DATA_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-bge-base-en-v1.5"] = {
"spec": {
"__type__": "kotaemon.embeddings.FastEmbedEmbeddings",
"model_name": "BAAI/bge-base-en-v1.5",
},
"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=""),
)
# KH_LLMS["qwen_local"] = {
# "spec": {
# "__type__": "kotaemon.llms.LlamaCppChat",
# "repo_id": "Qwen/Qwen1.5-0.5B-Chat-GGUF",
# "filename": "qwen1_5-0_5b-chat-q5_k_m.gguf",
# },
# "default": False,
# "cost": 0,
# }
# KH_LLMS["qwen1.5"] = {
# "spec": {
# "__type__": "kotaemon.llms.LlamaCppChat",
# "repo_id": "Qwen/Qwen1.5-1.8B-Chat-GGUF",
# "filename": "qwen1_5-1_8b-chat-q5_k_m.gguf",
# "chat_format": "qwen",
# },
# "default": False,
# "cost": 0,
# }
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",
},
]
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