forked from open-webui/open-webui
Improve embedding model update & resolve network dependency
* Add config variable RAG_EMBEDDING_MODEL_AUTO_UPDATE to control update behavior * Add RAG utils embedding_model_get_path() function to output the filesystem path in addition to update of the model using huggingface_hub * Update and utilize existing RAG functions in main: get_embedding_model() & update_embedding_model() * Add GUI setting to execute manual update process
This commit is contained in:
Self Denial
parent
62392aa88a
commit
3b66aa55c0
5 changed files with 218 additions and 19 deletions
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@ -13,7 +13,6 @@ import os, shutil, logging, re
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from pathlib import Path
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from typing import List
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from sentence_transformers import SentenceTransformer
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from chromadb.utils import embedding_functions
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from langchain_community.document_loaders import (
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@ -45,7 +44,7 @@ from apps.web.models.documents import (
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DocumentResponse,
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)
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from apps.rag.utils import query_doc, query_collection
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from apps.rag.utils import query_doc, query_collection, embedding_model_get_path
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from utils.misc import (
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calculate_sha256,
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@ -60,6 +59,7 @@ from config import (
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DOCS_DIR,
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RAG_EMBEDDING_MODEL,
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RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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RAG_EMBEDDING_MODEL_AUTO_UPDATE,
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CHROMA_CLIENT,
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CHUNK_SIZE,
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CHUNK_OVERLAP,
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@ -71,15 +71,6 @@ from constants import ERROR_MESSAGES
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log = logging.getLogger(__name__)
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log.setLevel(SRC_LOG_LEVELS["RAG"])
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#
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# if RAG_EMBEDDING_MODEL:
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# sentence_transformer_ef = SentenceTransformer(
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# model_name_or_path=RAG_EMBEDDING_MODEL,
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# cache_folder=RAG_EMBEDDING_MODEL_DIR,
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# device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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# )
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app = FastAPI()
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app.state.PDF_EXTRACT_IMAGES = False
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@ -87,11 +78,12 @@ app.state.CHUNK_SIZE = CHUNK_SIZE
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app.state.CHUNK_OVERLAP = CHUNK_OVERLAP
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app.state.RAG_TEMPLATE = RAG_TEMPLATE
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app.state.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
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app.state.RAG_EMBEDDING_MODEL_PATH = embedding_model_get_path(app.state.RAG_EMBEDDING_MODEL, RAG_EMBEDDING_MODEL_AUTO_UPDATE)
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app.state.TOP_K = 4
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app.state.sentence_transformer_ef = (
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embedding_functions.SentenceTransformerEmbeddingFunction(
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model_name=app.state.RAG_EMBEDDING_MODEL,
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model_name=app.state.RAG_EMBEDDING_MODEL_PATH,
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device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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)
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)
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@ -132,6 +124,7 @@ async def get_embedding_model(user=Depends(get_admin_user)):
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return {
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"status": True,
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"embedding_model": app.state.RAG_EMBEDDING_MODEL,
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"embedding_model_path": app.state.RAG_EMBEDDING_MODEL_PATH,
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}
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@ -143,17 +136,39 @@ class EmbeddingModelUpdateForm(BaseModel):
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async def update_embedding_model(
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form_data: EmbeddingModelUpdateForm, user=Depends(get_admin_user)
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):
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status = True
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old_model_path = app.state.RAG_EMBEDDING_MODEL_PATH
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app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
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log.debug(f"form_data.embedding_model: {form_data.embedding_model}")
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log.info(f"Updating embedding model: {app.state.RAG_EMBEDDING_MODEL} to {form_data.embedding_model}")
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try:
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app.state.RAG_EMBEDDING_MODEL_PATH = embedding_model_get_path(app.state.RAG_EMBEDDING_MODEL, True)
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app.state.sentence_transformer_ef = (
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embedding_functions.SentenceTransformerEmbeddingFunction(
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model_name=app.state.RAG_EMBEDDING_MODEL,
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model_name=app.state.RAG_EMBEDDING_MODEL_PATH,
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device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
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)
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)
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except Exception as e:
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log.exception(f"Problem updating embedding model: {e}")
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=e,
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)
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if app.state.RAG_EMBEDDING_MODEL_PATH == old_model_path:
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status = False
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log.debug(f"app.state.RAG_EMBEDDING_MODEL_PATH: {app.state.RAG_EMBEDDING_MODEL_PATH}")
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log.debug(f"old_model_path: {old_model_path}")
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log.debug(f"status: {status}")
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return {
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"status": True,
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"status": status,
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"embedding_model": app.state.RAG_EMBEDDING_MODEL,
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"embedding_model_path": app.state.RAG_EMBEDDING_MODEL_PATH,
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}
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@ -1,6 +1,8 @@
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import os
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import re
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import logging
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from typing import List
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from huggingface_hub import snapshot_download
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from config import SRC_LOG_LEVELS, CHROMA_CLIENT
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@ -188,3 +190,36 @@ def rag_messages(docs, messages, template, k, embedding_function):
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messages[last_user_message_idx] = new_user_message
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return messages
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def embedding_model_get_path(embedding_model: str, update_embedding_model: bool = False):
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# Construct huggingface_hub kwargs with local_files_only to return the snapshot path
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cache_dir = os.getenv("SENTENCE_TRANSFORMERS_HOME")
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local_files_only = not update_embedding_model
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snapshot_kwargs = {
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"cache_dir": cache_dir,
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"local_files_only": local_files_only,
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}
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log.debug(f"SENTENCE_TRANSFORMERS_HOME cache_dir: {cache_dir}")
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log.debug(f"embedding_model: {embedding_model}")
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log.debug(f"update_embedding_model: {update_embedding_model}")
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log.debug(f"local_files_only: {local_files_only}")
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# Inspiration from upstream sentence_transformers
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if (os.path.exists(embedding_model) or ("\\" in embedding_model or embedding_model.count("/") > 1) and local_files_only):
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# If fully qualified path exists, return input, else set repo_id
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return embedding_model
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elif "/" not in embedding_model:
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# Set valid repo_id for model short-name
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embedding_model = "sentence-transformers" + "/" + embedding_model
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snapshot_kwargs["repo_id"] = embedding_model
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# Attempt to query the huggingface_hub library to determine the local path and/or to update
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try:
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embedding_model_repo_path = snapshot_download(**snapshot_kwargs)
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log.debug(f"embedding_model_repo_path: {embedding_model_repo_path}")
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return embedding_model_repo_path
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except Exception as e:
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log.exception(f"Cannot determine embedding model snapshot path: {e}")
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return embedding_model
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@ -395,6 +395,9 @@ RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
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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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RAG_EMBEDDING_MODEL_AUTO_UPDATE = False
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if os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true":
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RAG_EMBEDDING_MODEL_AUTO_UPDATE = True
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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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@ -345,3 +345,64 @@ export const resetVectorDB = async (token: string) => {
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return res;
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};
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export const getEmbeddingModel = async (token: string) => {
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let error = null;
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const res = await fetch(`${RAG_API_BASE_URL}/embedding/model`, {
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method: 'GET',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${token}`
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}
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})
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.then(async (res) => {
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if (!res.ok) throw await res.json();
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return res.json();
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})
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.catch((err) => {
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console.log(err);
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error = err.detail;
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return null;
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});
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if (error) {
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throw error;
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}
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return res;
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};
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type EmbeddingModelUpdateForm = {
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embedding_model: string;
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};
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export const updateEmbeddingModel = async (token: string, payload: EmbeddingModelUpdateForm) => {
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let error = null;
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const res = await fetch(`${RAG_API_BASE_URL}/embedding/model/update`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${token}`
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},
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body: JSON.stringify({
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...payload
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})
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})
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.then(async (res) => {
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if (!res.ok) throw await res.json();
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return res.json();
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})
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.catch((err) => {
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console.log(err);
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error = err.detail;
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return null;
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});
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if (error) {
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throw error;
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}
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return res;
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};
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@ -6,7 +6,9 @@
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getQuerySettings,
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scanDocs,
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updateQuerySettings,
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resetVectorDB
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resetVectorDB,
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getEmbeddingModel,
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updateEmbeddingModel
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} from '$lib/apis/rag';
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import { documents } from '$lib/stores';
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@ -18,6 +20,7 @@
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export let saveHandler: Function;
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let loading = false;
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let loading1 = false;
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let showResetConfirm = false;
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@ -30,6 +33,10 @@
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k: 4
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};
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let embeddingModel = {
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embedding_model: '',
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};
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const scanHandler = async () => {
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loading = true;
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const res = await scanDocs(localStorage.token);
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@ -41,6 +48,21 @@
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}
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};
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const embeddingModelUpdateHandler = async () => {
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loading1 = true;
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const res = await updateEmbeddingModel(localStorage.token, embeddingModel);
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loading1 = false;
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if (res) {
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console.log('embeddingModelUpdateHandler:', res);
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if (res.status == true) {
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toast.success($i18n.t('Model {{embedding_model}} update complete!', res));
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} else {
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toast.error($i18n.t('Model {{embedding_model}} update failed or not required!', res));
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}
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}
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};
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const submitHandler = async () => {
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const res = await updateRAGConfig(localStorage.token, {
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pdf_extract_images: pdfExtractImages,
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@ -62,6 +84,8 @@
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chunkOverlap = res.chunk.chunk_overlap;
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}
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embeddingModel = await getEmbeddingModel(localStorage.token);
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querySettings = await getQuerySettings(localStorage.token);
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});
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</script>
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@ -137,6 +161,67 @@
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{/if}
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</button>
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</div>
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<div class=" flex w-full justify-between">
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<div class=" self-center text-xs font-medium">
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{$i18n.t('Update embedding model {{embedding_model}}', embeddingModel)}
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</div>
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<button
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class=" self-center text-xs p-1 px-3 bg-gray-100 dark:bg-gray-800 dark:hover:bg-gray-700 rounded flex flex-row space-x-1 items-center {loading1
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? ' cursor-not-allowed'
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: ''}"
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on:click={() => {
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embeddingModelUpdateHandler(embeddingModel);
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console.log('Update embedding model:', embeddingModel.embedding_model);
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}}
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type="button"
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disabled={loading1}
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>
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<div class="self-center font-medium">{$i18n.t('Update')}</div>
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<!-- <svg
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xmlns="http://www.w3.org/2000/svg"
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viewBox="0 0 16 16"
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fill="currentColor"
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class="w-3 h-3"
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>
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<path
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fill-rule="evenodd"
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d="M13.836 2.477a.75.75 0 0 1 .75.75v3.182a.75.75 0 0 1-.75.75h-3.182a.75.75 0 0 1 0-1.5h1.37l-.84-.841a4.5 4.5 0 0 0-7.08.932.75.75 0 0 1-1.3-.75 6 6 0 0 1 9.44-1.242l.842.84V3.227a.75.75 0 0 1 .75-.75Zm-.911 7.5A.75.75 0 0 1 13.199 11a6 6 0 0 1-9.44 1.241l-.84-.84v1.371a.75.75 0 0 1-1.5 0V9.591a.75.75 0 0 1 .75-.75H5.35a.75.75 0 0 1 0 1.5H3.98l.841.841a4.5 4.5 0 0 0 7.08-.932.75.75 0 0 1 1.025-.273Z"
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clip-rule="evenodd"
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/>
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</svg> -->
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{#if loading1}
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<div class="ml-3 self-center">
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<svg
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class=" w-3 h-3"
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viewBox="0 0 24 24"
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fill="currentColor"
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xmlns="http://www.w3.org/2000/svg"
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><style>
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.spinner_ajPY {
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transform-origin: center;
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animation: spinner_AtaB 0.75s infinite linear;
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}
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@keyframes spinner_AtaB {
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100% {
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transform: rotate(360deg);
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}
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}
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</style><path
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d="M12,1A11,11,0,1,0,23,12,11,11,0,0,0,12,1Zm0,19a8,8,0,1,1,8-8A8,8,0,0,1,12,20Z"
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opacity=".25"
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/><path
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d="M10.14,1.16a11,11,0,0,0-9,8.92A1.59,1.59,0,0,0,2.46,12,1.52,1.52,0,0,0,4.11,10.7a8,8,0,0,1,6.66-6.61A1.42,1.42,0,0,0,12,2.69h0A1.57,1.57,0,0,0,10.14,1.16Z"
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class="spinner_ajPY"
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/></svg
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>
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</div>
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{/if}
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</button>
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</div>
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</div>
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<hr class=" dark:border-gray-700" />
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