mirror of
https://github.com/Cinnamon/kotaemon.git
synced 2026-07-09 20:09:10 +02:00
406 lines
13 KiB
Python
406 lines
13 KiB
Python
import os
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from importlib.metadata import version
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from inspect import currentframe, getframeinfo
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from pathlib import Path
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from typing import Any
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from decouple import config
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from ktem.utils.lang import SUPPORTED_LANGUAGE_MAP
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from theflow.settings.default import * # noqa
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cur_frame = currentframe()
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if cur_frame is None:
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raise ValueError("Cannot get the current frame.")
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this_file = getframeinfo(cur_frame).filename
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this_dir = Path(this_file).parent
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# change this if your app use a different name
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KH_PACKAGE_NAME = "kotaemon_app"
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KH_APP_VERSION = config("KH_APP_VERSION", None)
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if not KH_APP_VERSION:
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try:
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# Caution: This might produce the wrong version
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# https://stackoverflow.com/a/59533071
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KH_APP_VERSION = version(KH_PACKAGE_NAME)
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except Exception:
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KH_APP_VERSION = "local"
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KH_GRADIO_SHARE = config("KH_GRADIO_SHARE", default=False, cast=bool)
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KH_ENABLE_FIRST_SETUP = config("KH_ENABLE_FIRST_SETUP", default=True, cast=bool)
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KH_DEMO_MODE = config("KH_DEMO_MODE", default=False, cast=bool)
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KH_OLLAMA_URL = config("KH_OLLAMA_URL", default="http://localhost:11434/v1/")
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# App can be ran from anywhere and it's not trivial to decide where to store app data.
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# So let's use the same directory as the flowsetting.py file.
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KH_APP_DATA_DIR = this_dir / "ktem_app_data"
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KH_APP_DATA_EXISTS = KH_APP_DATA_DIR.exists()
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KH_APP_DATA_DIR.mkdir(parents=True, exist_ok=True)
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# User data directory
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KH_USER_DATA_DIR = KH_APP_DATA_DIR / "user_data"
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KH_USER_DATA_DIR.mkdir(parents=True, exist_ok=True)
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# markdown output directory
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KH_MARKDOWN_OUTPUT_DIR = KH_APP_DATA_DIR / "markdown_cache_dir"
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KH_MARKDOWN_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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# chunks output directory
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KH_CHUNKS_OUTPUT_DIR = KH_APP_DATA_DIR / "chunks_cache_dir"
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KH_CHUNKS_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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# zip output directory
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KH_ZIP_OUTPUT_DIR = KH_APP_DATA_DIR / "zip_cache_dir"
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KH_ZIP_OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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# zip input directory
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KH_ZIP_INPUT_DIR = KH_APP_DATA_DIR / "zip_cache_dir_in"
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KH_ZIP_INPUT_DIR.mkdir(parents=True, exist_ok=True)
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# HF models can be big, let's store them in the app data directory so that it's easier
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# for users to manage their storage.
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# ref: https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache
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os.environ["HF_HOME"] = str(KH_APP_DATA_DIR / "huggingface")
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os.environ["HF_HUB_CACHE"] = str(KH_APP_DATA_DIR / "huggingface")
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# doc directory
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KH_DOC_DIR = this_dir / "docs"
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KH_MODE = "dev"
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KH_SSO_ENABLED = config("KH_SSO_ENABLED", default=False, cast=bool)
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KH_FEATURE_CHAT_SUGGESTION = config(
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"KH_FEATURE_CHAT_SUGGESTION", default=False, cast=bool
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)
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KH_FEATURE_USER_MANAGEMENT = config(
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"KH_FEATURE_USER_MANAGEMENT", default=True, cast=bool
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)
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KH_USER_CAN_SEE_PUBLIC = None
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KH_FEATURE_USER_MANAGEMENT_ADMIN = str(
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config("KH_FEATURE_USER_MANAGEMENT_ADMIN", default="admin")
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)
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KH_FEATURE_USER_MANAGEMENT_PASSWORD = str(
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config("KH_FEATURE_USER_MANAGEMENT_PASSWORD", default="admin")
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)
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KH_ENABLE_ALEMBIC = False
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KH_DATABASE = f"sqlite:///{KH_USER_DATA_DIR / 'sql.db'}"
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KH_FILESTORAGE_PATH = str(KH_USER_DATA_DIR / "files")
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KH_WEB_SEARCH_BACKEND = (
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"kotaemon.indices.retrievers.tavily_web_search.WebSearch"
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# "kotaemon.indices.retrievers.jina_web_search.WebSearch"
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)
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KH_DOCSTORE = {
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# "__type__": "kotaemon.storages.ElasticsearchDocumentStore",
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# "__type__": "kotaemon.storages.SimpleFileDocumentStore",
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"__type__": "kotaemon.storages.LanceDBDocumentStore",
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"path": str(KH_USER_DATA_DIR / "docstore"),
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}
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KH_VECTORSTORE = {
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# "__type__": "kotaemon.storages.LanceDBVectorStore",
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"__type__": "kotaemon.storages.ChromaVectorStore",
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# "__type__": "kotaemon.storages.MilvusVectorStore",
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# "__type__": "kotaemon.storages.QdrantVectorStore",
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"path": str(KH_USER_DATA_DIR / "vectorstore"),
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}
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KH_LLMS = {}
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KH_EMBEDDINGS = {}
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KH_RERANKINGS = {}
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# populate options from config
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if config("AZURE_OPENAI_API_KEY", default="") and config(
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"AZURE_OPENAI_ENDPOINT", default=""
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):
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if config("AZURE_OPENAI_CHAT_DEPLOYMENT", default=""):
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KH_LLMS["azure"] = {
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"spec": {
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"__type__": "kotaemon.llms.AzureChatOpenAI",
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"temperature": 0,
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"azure_endpoint": config("AZURE_OPENAI_ENDPOINT", default=""),
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"api_key": config("AZURE_OPENAI_API_KEY", default=""),
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"api_version": config("OPENAI_API_VERSION", default="")
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or "2024-02-15-preview",
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"azure_deployment": config("AZURE_OPENAI_CHAT_DEPLOYMENT", default=""),
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"timeout": 20,
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},
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"default": False,
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}
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if config("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT", default=""):
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KH_EMBEDDINGS["azure"] = {
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"spec": {
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"__type__": "kotaemon.embeddings.AzureOpenAIEmbeddings",
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"azure_endpoint": config("AZURE_OPENAI_ENDPOINT", default=""),
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"api_key": config("AZURE_OPENAI_API_KEY", default=""),
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"api_version": config("OPENAI_API_VERSION", default="")
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or "2024-02-15-preview",
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"azure_deployment": config(
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"AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT", default=""
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),
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"timeout": 10,
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},
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"default": False,
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}
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OPENAI_DEFAULT = "<YOUR_OPENAI_KEY>"
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OPENAI_API_KEY = config("OPENAI_API_KEY", default=OPENAI_DEFAULT)
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GOOGLE_API_KEY = config("GOOGLE_API_KEY", default="your-key")
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IS_OPENAI_DEFAULT = len(OPENAI_API_KEY) > 0 and OPENAI_API_KEY != OPENAI_DEFAULT
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if OPENAI_API_KEY:
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KH_LLMS["openai"] = {
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"spec": {
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"__type__": "kotaemon.llms.ChatOpenAI",
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"temperature": 0,
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"base_url": config("OPENAI_API_BASE", default="")
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or "https://api.openai.com/v1",
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"api_key": OPENAI_API_KEY,
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"model": config("OPENAI_CHAT_MODEL", default="gpt-4o-mini"),
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"timeout": 20,
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},
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"default": IS_OPENAI_DEFAULT,
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}
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KH_EMBEDDINGS["openai"] = {
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"spec": {
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"__type__": "kotaemon.embeddings.OpenAIEmbeddings",
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"base_url": config("OPENAI_API_BASE", default="https://api.openai.com/v1"),
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"api_key": OPENAI_API_KEY,
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"model": config(
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"OPENAI_EMBEDDINGS_MODEL", default="text-embedding-3-large"
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),
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"timeout": 10,
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"context_length": 8191,
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},
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"default": IS_OPENAI_DEFAULT,
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}
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VOYAGE_API_KEY = config("VOYAGE_API_KEY", default="")
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if VOYAGE_API_KEY:
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KH_EMBEDDINGS["voyageai"] = {
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"spec": {
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"__type__": "kotaemon.embeddings.VoyageAIEmbeddings",
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"api_key": VOYAGE_API_KEY,
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"model": config("VOYAGE_EMBEDDINGS_MODEL", default="voyage-3-large"),
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},
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"default": False,
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}
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KH_RERANKINGS["voyageai"] = {
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"spec": {
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"__type__": "kotaemon.rerankings.VoyageAIReranking",
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"model_name": "rerank-2",
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"api_key": VOYAGE_API_KEY,
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},
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"default": False,
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}
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if config("LOCAL_MODEL", default=""):
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KH_LLMS["ollama"] = {
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"spec": {
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"__type__": "kotaemon.llms.ChatOpenAI",
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"base_url": KH_OLLAMA_URL,
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"model": config("LOCAL_MODEL", default="qwen2.5:7b"),
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"api_key": "ollama",
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},
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"default": False,
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}
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KH_LLMS["ollama-long-context"] = {
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"spec": {
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"__type__": "kotaemon.llms.LCOllamaChat",
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"base_url": KH_OLLAMA_URL.replace("v1/", ""),
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"model": config("LOCAL_MODEL", default="qwen2.5:7b"),
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"num_ctx": 8192,
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},
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"default": False,
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}
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KH_EMBEDDINGS["ollama"] = {
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"spec": {
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"__type__": "kotaemon.embeddings.OpenAIEmbeddings",
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"base_url": KH_OLLAMA_URL,
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"model": config("LOCAL_MODEL_EMBEDDINGS", default="nomic-embed-text"),
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"api_key": "ollama",
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},
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"default": False,
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}
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KH_EMBEDDINGS["fast_embed"] = {
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"spec": {
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"__type__": "kotaemon.embeddings.FastEmbedEmbeddings",
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"model_name": "BAAI/bge-base-en-v1.5",
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},
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"default": False,
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}
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# additional LLM configurations
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KH_LLMS["claude"] = {
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"spec": {
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"__type__": "kotaemon.llms.chats.LCAnthropicChat",
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"model_name": "claude-3-5-sonnet-20240620",
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"api_key": "your-key",
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},
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"default": False,
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}
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KH_LLMS["google"] = {
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"spec": {
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"__type__": "kotaemon.llms.chats.LCGeminiChat",
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"model_name": "gemini-1.5-flash",
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"api_key": GOOGLE_API_KEY,
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},
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"default": not IS_OPENAI_DEFAULT,
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}
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KH_LLMS["groq"] = {
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"spec": {
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"__type__": "kotaemon.llms.ChatOpenAI",
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"base_url": "https://api.groq.com/openai/v1",
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"model": "llama-3.1-8b-instant",
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"api_key": "your-key",
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},
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"default": False,
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}
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KH_LLMS["cohere"] = {
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"spec": {
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"__type__": "kotaemon.llms.chats.LCCohereChat",
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"model_name": "command-r-plus-08-2024",
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"api_key": config("COHERE_API_KEY", default="your-key"),
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},
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"default": False,
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}
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KH_LLMS["mistral"] = {
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"spec": {
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"__type__": "kotaemon.llms.ChatOpenAI",
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"base_url": "https://api.mistral.ai/v1",
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"model": "ministral-8b-latest",
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"api_key": config("MISTRAL_API_KEY", default="your-key"),
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},
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"default": False,
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}
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# additional embeddings configurations
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KH_EMBEDDINGS["cohere"] = {
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"spec": {
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"__type__": "kotaemon.embeddings.LCCohereEmbeddings",
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"model": "embed-multilingual-v3.0",
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"cohere_api_key": config("COHERE_API_KEY", default="your-key"),
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"user_agent": "default",
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},
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"default": False,
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}
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KH_EMBEDDINGS["google"] = {
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"spec": {
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"__type__": "kotaemon.embeddings.LCGoogleEmbeddings",
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"model": "models/text-embedding-004",
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"google_api_key": GOOGLE_API_KEY,
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},
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"default": not IS_OPENAI_DEFAULT,
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}
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KH_EMBEDDINGS["mistral"] = {
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"spec": {
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"__type__": "kotaemon.embeddings.LCMistralEmbeddings",
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"model": "mistral-embed",
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"api_key": config("MISTRAL_API_KEY", default="your-key"),
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},
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"default": False,
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}
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# KH_EMBEDDINGS["huggingface"] = {
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# "spec": {
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# "__type__": "kotaemon.embeddings.LCHuggingFaceEmbeddings",
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# "model_name": "sentence-transformers/all-mpnet-base-v2",
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# },
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# "default": False,
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# }
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# default reranking models
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KH_RERANKINGS["cohere"] = {
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"spec": {
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"__type__": "kotaemon.rerankings.CohereReranking",
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"model_name": "rerank-multilingual-v2.0",
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"cohere_api_key": config("COHERE_API_KEY", default=""),
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},
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"default": True,
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}
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KH_REASONINGS = [
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"ktem.reasoning.simple.FullQAPipeline",
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"ktem.reasoning.simple.FullDecomposeQAPipeline",
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"ktem.reasoning.react.ReactAgentPipeline",
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"ktem.reasoning.rewoo.RewooAgentPipeline",
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]
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KH_REASONINGS_USE_MULTIMODAL = config("USE_MULTIMODAL", default=False, cast=bool)
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KH_VLM_ENDPOINT = "{0}/openai/deployments/{1}/chat/completions?api-version={2}".format(
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config("AZURE_OPENAI_ENDPOINT", default=""),
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config("OPENAI_VISION_DEPLOYMENT_NAME", default="gpt-4o"),
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config("OPENAI_API_VERSION", default=""),
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)
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SETTINGS_APP: dict[str, dict[str, Any]] = {}
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SETTINGS_REASONING = {
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"use": {
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"name": "Reasoning options",
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"value": None,
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"choices": [],
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"component": "radio",
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},
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"lang": {
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"name": "Language",
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"value": "en",
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"choices": [(lang, code) for code, lang in SUPPORTED_LANGUAGE_MAP.items()],
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"component": "dropdown",
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},
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"max_context_length": {
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"name": "Max context length (LLM)",
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"value": 32000,
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"component": "number",
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},
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}
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USE_GLOBAL_GRAPHRAG = config("USE_GLOBAL_GRAPHRAG", default=True, cast=bool)
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USE_NANO_GRAPHRAG = config("USE_NANO_GRAPHRAG", default=False, cast=bool)
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USE_LIGHTRAG = config("USE_LIGHTRAG", default=True, cast=bool)
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USE_MS_GRAPHRAG = config("USE_MS_GRAPHRAG", default=True, cast=bool)
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GRAPHRAG_INDEX_TYPES = []
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if USE_MS_GRAPHRAG:
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GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.GraphRAGIndex")
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if USE_NANO_GRAPHRAG:
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GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.NanoGraphRAGIndex")
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if USE_LIGHTRAG:
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GRAPHRAG_INDEX_TYPES.append("ktem.index.file.graph.LightRAGIndex")
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KH_INDEX_TYPES = [
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"ktem.index.file.FileIndex",
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*GRAPHRAG_INDEX_TYPES,
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]
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GRAPHRAG_INDICES = [
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{
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"name": graph_type.split(".")[-1].replace("Index", "")
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+ " Collection", # get last name
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"config": {
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"supported_file_types": (
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".png, .jpeg, .jpg, .tiff, .tif, .pdf, .xls, .xlsx, .doc, .docx, "
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".pptx, .csv, .html, .mhtml, .txt, .md, .zip"
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),
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"private": True,
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},
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"index_type": graph_type,
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}
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for graph_type in GRAPHRAG_INDEX_TYPES
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]
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KH_INDICES = [
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{
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"name": "File Collection",
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"config": {
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"supported_file_types": (
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".png, .jpeg, .jpg, .tiff, .tif, .pdf, .xls, .xlsx, .doc, .docx, "
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".pptx, .csv, .html, .mhtml, .txt, .md, .zip"
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),
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"private": True,
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},
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"index_type": "ktem.index.file.FileIndex",
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},
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*GRAPHRAG_INDICES,
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]
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