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@ -13,7 +13,7 @@ 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_MODEL_NAME="cl100k_base"
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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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@ -77,7 +77,7 @@ ENV RAG_EMBEDDING_MODEL="$USE_EMBEDDING_MODEL_DOCKER" \
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SENTENCE_TRANSFORMERS_HOME="/app/backend/data/cache/embedding/models"
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## Tiktoken model settings ##
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ENV TIKTOKEN_ENCODING_MODEL_NAME="$USE_TIKTOKEN_ENCODING_MODEL_NAME" \
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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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@ -139,13 +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_MODEL_NAME'])"; \
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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_MODEL_NAME'])"; \
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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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@ -14,6 +14,7 @@ from typing import Iterator, Optional, Sequence, Union
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from fastapi import Depends, FastAPI, File, Form, HTTPException, UploadFile, status
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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import tiktoken
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from open_webui.storage.provider import Storage
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@ -50,7 +51,7 @@ 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_MODEL_NAME,
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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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@ -135,7 +136,7 @@ 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_MODEL_NAME = TIKTOKEN_ENCODING_MODEL_NAME
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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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@ -666,8 +667,9 @@ def save_docs_to_vector_db(
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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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model_name=app.state.config.TIKTOKEN_ENCODING_MODEL_NAME,
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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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@ -1074,10 +1074,10 @@ RAG_TEXT_SPLITTER = PersistentConfig(
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TIKTOKEN_CACHE_DIR = os.environ.get("TIKTOKEN_CACHE_DIR", f"{CACHE_DIR}/tiktoken")
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TIKTOKEN_ENCODING_MODEL_NAME = PersistentConfig(
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"TIKTOKEN_ENCODING_MODEL_NAME",
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"rag.tiktoken_encoding_model_name",
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os.environ.get("TIKTOKEN_ENCODING_MODEL_NAME", "cl100k_base"),
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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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