195 lines
5.6 KiB
Python
195 lines
5.6 KiB
Python
import os
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import chromadb
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from chromadb import Settings
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from secrets import token_bytes
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from base64 import b64encode
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from constants import ERROR_MESSAGES
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from pathlib import Path
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import json
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try:
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from dotenv import load_dotenv, find_dotenv
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load_dotenv(find_dotenv("../.env"))
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except ImportError:
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print("dotenv not installed, skipping...")
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####################################
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# ENV (dev,test,prod)
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####################################
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ENV = os.environ.get("ENV", "dev")
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####################################
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# DATA/FRONTEND BUILD DIR
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####################################
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DATA_DIR = str(Path(os.getenv("DATA_DIR", "./data")).resolve())
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FRONTEND_BUILD_DIR = str(Path(os.getenv("FRONTEND_BUILD_DIR", "../build")))
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try:
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with open(f"{DATA_DIR}/config.json", "r") as f:
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CONFIG_DATA = json.load(f)
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except:
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CONFIG_DATA = {}
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####################################
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# File Upload DIR
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####################################
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UPLOAD_DIR = f"{DATA_DIR}/uploads"
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Path(UPLOAD_DIR).mkdir(parents=True, exist_ok=True)
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####################################
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# Cache DIR
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####################################
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CACHE_DIR = f"{DATA_DIR}/cache"
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Path(CACHE_DIR).mkdir(parents=True, exist_ok=True)
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####################################
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# Docs DIR
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####################################
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DOCS_DIR = f"{DATA_DIR}/docs"
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Path(DOCS_DIR).mkdir(parents=True, exist_ok=True)
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####################################
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# OLLAMA_API_BASE_URL
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####################################
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OLLAMA_API_BASE_URL = os.environ.get(
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"OLLAMA_API_BASE_URL", "http://localhost:11434/api"
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)
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if ENV == "prod":
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if OLLAMA_API_BASE_URL == "/ollama/api":
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OLLAMA_API_BASE_URL = "http://host.docker.internal:11434/api"
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####################################
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# OPENAI_API
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####################################
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OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "")
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OPENAI_API_BASE_URL = os.environ.get("OPENAI_API_BASE_URL", "")
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if OPENAI_API_BASE_URL == "":
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OPENAI_API_BASE_URL = "https://api.openai.com/v1"
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####################################
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# WEBUI
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####################################
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ENABLE_SIGNUP = os.environ.get("ENABLE_SIGNUP", True)
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DEFAULT_MODELS = os.environ.get("DEFAULT_MODELS", None)
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DEFAULT_PROMPT_SUGGESTIONS = (
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CONFIG_DATA["ui"]["prompt_suggestions"]
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if "ui" in CONFIG_DATA
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and "prompt_suggestions" in CONFIG_DATA["ui"]
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and type(CONFIG_DATA["ui"]["prompt_suggestions"]) is list
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else [
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{
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"title": ["Help me study", "vocabulary for a college entrance exam"],
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"content": "Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option.",
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},
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{
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"title": ["Give me ideas", "for what to do with my kids' art"],
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"content": "What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter.",
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},
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{
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"title": ["Tell me a fun fact", "about the Roman Empire"],
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"content": "Tell me a random fun fact about the Roman Empire",
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},
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{
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"title": ["Show me a code snippet", "of a website's sticky header"],
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"content": "Show me a code snippet of a website's sticky header in CSS and JavaScript.",
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},
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]
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)
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DEFAULT_USER_ROLE = "pending"
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USER_PERMISSIONS = {"chat": {"deletion": True}}
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####################################
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# WEBUI_VERSION
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####################################
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WEBUI_VERSION = os.environ.get("WEBUI_VERSION", "v1.0.0-alpha.100")
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####################################
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# WEBUI_AUTH (Required for security)
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####################################
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WEBUI_AUTH = True
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####################################
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# WEBUI_SECRET_KEY
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####################################
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WEBUI_SECRET_KEY = os.environ.get(
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"WEBUI_SECRET_KEY",
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os.environ.get(
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"WEBUI_JWT_SECRET_KEY", "t0p-s3cr3t"
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), # DEPRECATED: remove at next major version
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)
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if WEBUI_AUTH and WEBUI_SECRET_KEY == "":
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raise ValueError(ERROR_MESSAGES.ENV_VAR_NOT_FOUND)
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####################################
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# RAG
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####################################
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CHROMA_DATA_PATH = f"{DATA_DIR}/vector_db"
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# this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (all-MiniLM-L6-v2)
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RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
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# device type ebbeding models - "cpu" (default), "cuda" (nvidia gpu required) or "mps" (apple silicon) - choosing this right can lead to better performance
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RAG_EMBEDDING_MODEL_DEVICE_TYPE = os.environ.get(
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"RAG_EMBEDDING_MODEL_DEVICE_TYPE", "cpu"
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)
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CHROMA_CLIENT = chromadb.PersistentClient(
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path=CHROMA_DATA_PATH,
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settings=Settings(allow_reset=True, anonymized_telemetry=False),
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)
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CHUNK_SIZE = 1500
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CHUNK_OVERLAP = 100
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RAG_TEMPLATE = """Use the following context as your learned knowledge, inside <context></context> XML tags.
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<context>
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[context]
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</context>
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When answer to user:
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- If you don't know, just say that you don't know.
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- If you don't know when you are not sure, ask for clarification.
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Avoid mentioning that you obtained the information from the context.
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And answer according to the language of the user's question.
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Given the context information, answer the query.
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Query: [query]"""
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####################################
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# Transcribe
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####################################
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WHISPER_MODEL = os.getenv("WHISPER_MODEL", "base")
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WHISPER_MODEL_DIR = os.getenv("WHISPER_MODEL_DIR", f"{CACHE_DIR}/whisper/models")
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####################################
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# Images
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####################################
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AUTOMATIC1111_BASE_URL = os.getenv("AUTOMATIC1111_BASE_URL", "")
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