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https://github.com/open-webui/open-webui
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enh: tiktoken/token splitter support
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@ -11,6 +11,10 @@ ARG USE_CUDA_VER=cu121
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# IMPORTANT: If you change the embedding model (sentence-transformers/all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
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ARG USE_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
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ARG USE_RERANKING_MODEL=""
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# Tiktoken encoding name; models to use can be found at https://huggingface.co/models?library=tiktoken
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ARG USE_TIKTOKEN_ENCODING_NAME="cl100k_base"
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ARG BUILD_HASH=dev-build
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# Override at your own risk - non-root configurations are untested
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ARG UID=0
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@ -72,6 +76,10 @@ ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \
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RAG_RERANKING_MODEL="$USE_RERANKING_MODEL_DOCKER" \
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SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
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## Tiktoken model settings ##
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ENV TIKTOKEN_ENCODING_NAME="$USE_TIKTOKEN_ENCODING_NAME" \
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TIKTOKEN_CACHE_DIR="/app/backend/data/cache/tiktoken"
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## Hugging Face download cache ##
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ENV HF_HOME="/app/backend/data/cache/embedding/models"
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@ -131,11 +139,13 @@ RUN pip3 install uv && \
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uv pip install --system -r requirements.txt --no-cache-dir && \
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python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
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python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
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python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
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else \
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir && \
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uv pip install --system -r requirements.txt --no-cache-dir && \
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python -c "import os; from sentence_transformers import SentenceTransformer; SentenceTransformer(os.environ['RAG_EMBEDDING_MODEL'], device='cpu')" && \
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python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"; \
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python -c "import os; import tiktoken; tiktoken.get_encoding(os.environ['TIKTOKEN_ENCODING_NAME'])"; \
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fi; \
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chown -R $UID:$GID /app/backend/data/
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@ -47,6 +47,8 @@ from open_webui.apps.retrieval.utils import (
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from open_webui.apps.webui.models.files import Files
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from open_webui.config import (
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BRAVE_SEARCH_API_KEY,
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TIKTOKEN_ENCODING_NAME,
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RAG_TEXT_SPLITTER,
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CHUNK_OVERLAP,
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CHUNK_SIZE,
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CONTENT_EXTRACTION_ENGINE,
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@ -102,7 +104,7 @@ from open_webui.utils.misc import (
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)
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from open_webui.utils.utils import get_admin_user, get_verified_user
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter
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from langchain_community.document_loaders import (
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YoutubeLoader,
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)
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@ -129,6 +131,9 @@ app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
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app.state.config.CONTENT_EXTRACTION_ENGINE = CONTENT_EXTRACTION_ENGINE
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app.state.config.TIKA_SERVER_URL = TIKA_SERVER_URL
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app.state.config.TEXT_SPLITTER = RAG_TEXT_SPLITTER
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app.state.config.TIKTOKEN_ENCODING_NAME = TIKTOKEN_ENCODING_NAME
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app.state.config.CHUNK_SIZE = CHUNK_SIZE
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app.state.config.CHUNK_OVERLAP = CHUNK_OVERLAP
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@ -648,11 +653,22 @@ def save_docs_to_vector_db(
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raise ValueError(ERROR_MESSAGES.DUPLICATE_CONTENT)
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if split:
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=app.state.config.CHUNK_SIZE,
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chunk_overlap=app.state.config.CHUNK_OVERLAP,
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add_start_index=True,
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)
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if app.state.config.TEXT_SPLITTER in ["", "character"]:
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=app.state.config.CHUNK_SIZE,
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chunk_overlap=app.state.config.CHUNK_OVERLAP,
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add_start_index=True,
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)
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elif app.state.config.TEXT_SPLITTER == "token":
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text_splitter = TokenTextSplitter(
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encoding_name=app.state.config.TIKTOKEN_ENCODING_NAME,
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chunk_size=app.state.config.CHUNK_SIZE,
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chunk_overlap=app.state.config.CHUNK_OVERLAP,
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add_start_index=True,
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)
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else:
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raise ValueError(ERROR_MESSAGES.DEFAULT("Invalid text splitter"))
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docs = text_splitter.split_documents(docs)
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if len(docs) == 0:
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@ -1014,6 +1014,22 @@ RAG_RERANKING_MODEL_TRUST_REMOTE_CODE = (
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os.environ.get("RAG_RERANKING_MODEL_TRUST_REMOTE_CODE", "").lower() == "true"
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)
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RAG_TEXT_SPLITTER = PersistentConfig(
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"RAG_TEXT_SPLITTER",
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"rag.text_splitter",
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os.environ.get("RAG_TEXT_SPLITTER", ""),
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)
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TIKTOKEN_CACHE_DIR = os.environ.get("TIKTOKEN_CACHE_DIR", f"{CACHE_DIR}/tiktoken")
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TIKTOKEN_ENCODING_NAME = PersistentConfig(
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"TIKTOKEN_ENCODING_NAME",
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"rag.tiktoken_encoding_name",
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os.environ.get("TIKTOKEN_ENCODING_NAME", "cl100k_base"),
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)
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CHUNK_SIZE = PersistentConfig(
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"CHUNK_SIZE", "rag.chunk_size", int(os.environ.get("CHUNK_SIZE", "1000"))
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)
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@ -20,7 +20,7 @@ class ERROR_MESSAGES(str, Enum):
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def __str__(self) -> str:
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return super().__str__()
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DEFAULT = lambda err="": f"Something went wrong :/\n{err if err else ''}"
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DEFAULT = lambda err="": f"Something went wrong :/\n[ERROR: {err if err else ''}]"
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ENV_VAR_NOT_FOUND = "Required environment variable not found. Terminating now."
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CREATE_USER_ERROR = "Oops! Something went wrong while creating your account. Please try again later. If the issue persists, contact support for assistance."
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DELETE_USER_ERROR = "Oops! Something went wrong. We encountered an issue while trying to delete the user. Please give it another shot."
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