forked from open-webui/open-webui
Merge pull request #1419 from lainedfles/embedding-model-fix-and-manual-update
feat: improve embedding model update & resolve network dependency
This commit is contained in:
Timothy Jaeryang Baek
GitHub
commit
b9cadff16b
6 changed files with 438 additions and 210 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 chromadb.utils.batch_utils import create_batches
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@ -46,7 +45,7 @@ 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, get_embedding_model_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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UPLOAD_DIR,
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DOCS_DIR,
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RAG_EMBEDDING_MODEL,
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RAG_EMBEDDING_MODEL_AUTO_UPDATE,
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DEVICE_TYPE,
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CHROMA_CLIENT,
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CHUNK_SIZE,
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@ -78,12 +78,18 @@ app.state.PDF_EXTRACT_IMAGES = False
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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.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=get_embedding_model_path(
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app.state.RAG_EMBEDDING_MODEL, RAG_EMBEDDING_MODEL_AUTO_UPDATE
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),
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device=DEVICE_TYPE,
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)
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)
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@ -135,18 +141,34 @@ 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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app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
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app.state.sentence_transformer_ef = (
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log.info(
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f"Updating embedding model: {app.state.RAG_EMBEDDING_MODEL} to {form_data.embedding_model}"
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)
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try:
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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=get_embedding_model_path(form_data.embedding_model, True),
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device=DEVICE_TYPE,
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)
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)
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app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
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app.state.sentence_transformer_ef = sentence_transformer_ef
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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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}
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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=ERROR_MESSAGES.DEFAULT(e),
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)
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@app.get("/config")
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async def get_rag_config(user=Depends(get_admin_user)):
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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 +190,43 @@ 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 get_embedding_model_path(
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embedding_model: str, update_embedding_model: bool = False
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):
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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"embedding_model: {embedding_model}")
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log.debug(f"snapshot_kwargs: {snapshot_kwargs}")
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# Inspiration from upstream sentence_transformers
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if (
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os.path.exists(embedding_model)
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or ("\\" in embedding_model or embedding_model.count("/") > 1)
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and local_files_only
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):
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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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@ -403,6 +403,12 @@ 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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log.info(f"Embedding model set: {RAG_EMBEDDING_MODEL}"),
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RAG_EMBEDDING_MODEL_AUTO_UPDATE = (
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os.environ.get("RAG_EMBEDDING_MODEL_AUTO_UPDATE", "").lower() == "true"
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)
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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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USE_CUDA = os.environ.get("USE_CUDA_DOCKER", "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 ${token}`
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},
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body: JSON.stringify({
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...payload
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})
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})
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.then(async (res) => {
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if (!res.ok) throw await res.json();
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return res.json();
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})
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.catch((err) => {
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console.log(err);
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error = err.detail;
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return null;
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});
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if (error) {
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throw error;
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}
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return res;
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};
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@ -6,18 +6,23 @@
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getQuerySettings,
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scanDocs,
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updateQuerySettings,
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resetVectorDB
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resetVectorDB,
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getEmbeddingModel,
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updateEmbeddingModel
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} from '$lib/apis/rag';
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import { documents } from '$lib/stores';
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import { onMount, getContext } from 'svelte';
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import { toast } from 'svelte-sonner';
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import Tooltip from '$lib/components/common/Tooltip.svelte';
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const i18n = getContext('i18n');
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export let saveHandler: Function;
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let loading = false;
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let scanDirLoading = false;
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let updateEmbeddingModelLoading = false;
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let showResetConfirm = false;
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@ -30,10 +35,12 @@
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k: 4
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};
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let embeddingModel = '';
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const scanHandler = async () => {
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loading = true;
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scanDirLoading = true;
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const res = await scanDocs(localStorage.token);
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loading = false;
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scanDirLoading = false;
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if (res) {
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await documents.set(await getDocs(localStorage.token));
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@ -41,6 +48,38 @@
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}
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};
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const embeddingModelUpdateHandler = async () => {
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if (embeddingModel.split('/').length - 1 > 1) {
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toast.error(
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$i18n.t(
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'Model filesystem path detected. Model shortname is required for update, cannot continue.'
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)
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);
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return;
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}
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console.log('Update embedding model attempt:', embeddingModel);
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updateEmbeddingModelLoading = true;
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const res = await updateEmbeddingModel(localStorage.token, {
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embedding_model: embeddingModel
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}).catch(async (error) => {
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toast.error(error);
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embeddingModel = (await getEmbeddingModel(localStorage.token)).embedding_model;
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return null;
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});
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updateEmbeddingModelLoading = false;
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if (res) {
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console.log('embeddingModelUpdateHandler:', res);
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if (res.status === true) {
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toast.success($i18n.t('Model {{embedding_model}} update complete!', res), {
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duration: 1000 * 10
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});
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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 +101,8 @@
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chunkOverlap = res.chunk.chunk_overlap;
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}
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embeddingModel = (await getEmbeddingModel(localStorage.token)).embedding_model;
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querySettings = await getQuerySettings(localStorage.token);
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});
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</script>
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@ -73,7 +114,7 @@
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saveHandler();
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}}
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>
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<div class=" space-y-3 pr-1.5 overflow-y-scroll max-h-80">
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<div class=" space-y-3 pr-1.5 overflow-y-scroll max-h-[22rem]">
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<div>
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<div class=" mb-2 text-sm font-medium">{$i18n.t('General Settings')}</div>
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@ -83,7 +124,7 @@
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</div>
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<button
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class=" self-center text-xs p-1 px-3 bg-gray-100 dark:bg-gray-800 dark:hover:bg-gray-700 rounded flex flex-row space-x-1 items-center {loading
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class=" self-center text-xs p-1 px-3 bg-gray-100 dark:bg-gray-800 dark:hover:bg-gray-700 rounded-lg flex flex-row space-x-1 items-center {scanDirLoading
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? ' cursor-not-allowed'
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: ''}"
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on:click={() => {
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@ -91,24 +132,11 @@
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console.log('check');
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}}
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type="button"
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disabled={loading}
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disabled={scanDirLoading}
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>
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<div class="self-center font-medium">{$i18n.t('Scan')}</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 loading}
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{#if scanDirLoading}
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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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@ -141,6 +169,78 @@
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<hr class=" dark:border-gray-700" />
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<div class="space-y-2">
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<div>
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<div class=" mb-2 text-sm font-medium">{$i18n.t('Update Embedding Model')}</div>
|
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<div class="flex w-full">
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<div class="flex-1 mr-2">
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<input
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class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
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placeholder={$i18n.t('Update embedding model (e.g. {{model}})', {
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model: embeddingModel.slice(-40)
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})}
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bind:value={embeddingModel}
|
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/>
|
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</div>
|
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<button
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class="px-2.5 bg-gray-100 hover:bg-gray-200 text-gray-800 dark:bg-gray-850 dark:hover:bg-gray-800 dark:text-gray-100 rounded-lg transition"
|
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on:click={() => {
|
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embeddingModelUpdateHandler();
|
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}}
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disabled={updateEmbeddingModelLoading}
|
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>
|
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{#if updateEmbeddingModelLoading}
|
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<div class="self-center">
|
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<svg
|
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class=" w-4 h-4"
|
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viewBox="0 0 24 24"
|
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fill="currentColor"
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
><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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</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"
|
||||
/><path
|
||||
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"
|
||||
/></svg
|
||||
>
|
||||
</div>
|
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{:else}
|
||||
<svg
|
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xmlns="http://www.w3.org/2000/svg"
|
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viewBox="0 0 16 16"
|
||||
fill="currentColor"
|
||||
class="w-4 h-4"
|
||||
>
|
||||
<path
|
||||
d="M8.75 2.75a.75.75 0 0 0-1.5 0v5.69L5.03 6.22a.75.75 0 0 0-1.06 1.06l3.5 3.5a.75.75 0 0 0 1.06 0l3.5-3.5a.75.75 0 0 0-1.06-1.06L8.75 8.44V2.75Z"
|
||||
/>
|
||||
<path
|
||||
d="M3.5 9.75a.75.75 0 0 0-1.5 0v1.5A2.75 2.75 0 0 0 4.75 14h6.5A2.75 2.75 0 0 0 14 11.25v-1.5a.75.75 0 0 0-1.5 0v1.5c0 .69-.56 1.25-1.25 1.25h-6.5c-.69 0-1.25-.56-1.25-1.25v-1.5Z"
|
||||
/>
|
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</svg>
|
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{/if}
|
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</button>
|
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</div>
|
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|
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<div class="mt-2 mb-1 text-xs text-gray-400 dark:text-gray-500">
|
||||
{$i18n.t(
|
||||
'Warning: If you update or change your embedding model, you will need to re-import all documents.'
|
||||
)}
|
||||
</div>
|
||||
|
||||
<hr class=" dark:border-gray-700 my-3" />
|
||||
|
||||
<div class=" ">
|
||||
<div class=" text-sm font-medium">{$i18n.t('Chunk Params')}</div>
|
||||
|
||||
|
|
@ -150,7 +250,7 @@
|
|||
|
||||
<div class="self-center p-3">
|
||||
<input
|
||||
class=" w-full rounded py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-800 outline-none border border-gray-100 dark:border-gray-600"
|
||||
class=" w-full rounded-lg py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
|
||||
type="number"
|
||||
placeholder={$i18n.t('Enter Chunk Size')}
|
||||
bind:value={chunkSize}
|
||||
|
|
@ -161,11 +261,13 @@
|
|||
</div>
|
||||
|
||||
<div class="flex w-full">
|
||||
<div class=" self-center text-xs font-medium min-w-fit">{$i18n.t('Chunk Overlap')}</div>
|
||||
<div class=" self-center text-xs font-medium min-w-fit">
|
||||
{$i18n.t('Chunk Overlap')}
|
||||
</div>
|
||||
|
||||
<div class="self-center p-3">
|
||||
<input
|
||||
class="w-full rounded py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-800 outline-none border border-gray-100 dark:border-gray-600"
|
||||
class="w-full rounded-lg py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
|
||||
type="number"
|
||||
placeholder={$i18n.t('Enter Chunk Overlap')}
|
||||
bind:value={chunkOverlap}
|
||||
|
|
@ -176,7 +278,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<div class="pr-2">
|
||||
<div class="flex justify-between items-center text-xs">
|
||||
<div class=" text-xs font-medium">{$i18n.t('PDF Extract Images (OCR)')}</div>
|
||||
|
||||
|
|
@ -191,6 +293,8 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<hr class=" dark:border-gray-700 my-3" />
|
||||
|
||||
<div>
|
||||
<div class=" text-sm font-medium">{$i18n.t('Query Params')}</div>
|
||||
|
||||
|
|
@ -200,7 +304,7 @@
|
|||
|
||||
<div class="self-center p-3">
|
||||
<input
|
||||
class=" w-full rounded py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-800 outline-none border border-gray-100 dark:border-gray-600"
|
||||
class=" w-full rounded-lg py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
|
||||
type="number"
|
||||
placeholder={$i18n.t('Enter Top K')}
|
||||
bind:value={querySettings.k}
|
||||
|
|
@ -209,34 +313,19 @@
|
|||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- <div class="flex w-full">
|
||||
<div class=" self-center text-xs font-medium min-w-fit">Chunk Overlap</div>
|
||||
|
||||
<div class="self-center p-3">
|
||||
<input
|
||||
class="w-full rounded py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-800 outline-none border border-gray-100 dark:border-gray-600"
|
||||
type="number"
|
||||
placeholder="Enter Chunk Overlap"
|
||||
bind:value={chunkOverlap}
|
||||
autocomplete="off"
|
||||
min="0"
|
||||
/>
|
||||
</div>
|
||||
</div> -->
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<div class=" mb-2.5 text-sm font-medium">{$i18n.t('RAG Template')}</div>
|
||||
<textarea
|
||||
bind:value={querySettings.template}
|
||||
class="w-full rounded p-4 text-sm dark:text-gray-300 dark:bg-gray-800 outline-none resize-none"
|
||||
class="w-full rounded-lg px-4 py-3 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none resize-none"
|
||||
rows="4"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<hr class=" dark:border-gray-700" />
|
||||
<hr class=" dark:border-gray-700 my-3" />
|
||||
|
||||
{#if showResetConfirm}
|
||||
<div class="flex justify-between rounded-md items-center py-2 px-3.5 w-full transition">
|
||||
|
|
@ -330,7 +419,8 @@
|
|||
</button>
|
||||
{/if}
|
||||
</div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<div class="flex justify-end pt-3 text-sm font-medium">
|
||||
<button
|
||||
class=" px-4 py-2 bg-emerald-600 hover:bg-emerald-700 text-gray-100 transition rounded"
|
||||
|
|
|
|||
|
|
@ -120,6 +120,7 @@
|
|||
"Edit Doc": "",
|
||||
"Edit User": "",
|
||||
"Email": "",
|
||||
"Embedding model: {{embedding_model}}": "",
|
||||
"Enable Chat History": "",
|
||||
"Enable New Sign Ups": "",
|
||||
"Enabled": "",
|
||||
|
|
@ -194,8 +195,11 @@
|
|||
"MMMM DD, YYYY": "",
|
||||
"Model '{{modelName}}' has been successfully downloaded.": "",
|
||||
"Model '{{modelTag}}' is already in queue for downloading.": "",
|
||||
"Model {{embedding_model}} update complete!": "",
|
||||
"Model {{embedding_model}} update failed or not required!": "",
|
||||
"Model {{modelId}} not found": "",
|
||||
"Model {{modelName}} already exists.": "",
|
||||
"Model filesystem path detected. Model shortname is required for update, cannot continue.": "",
|
||||
"Model Name": "",
|
||||
"Model not selected": "",
|
||||
"Model Tag Name": "",
|
||||
|
|
@ -333,7 +337,10 @@
|
|||
"TTS Settings": "",
|
||||
"Type Hugging Face Resolve (Download) URL": "",
|
||||
"Uh-oh! There was an issue connecting to {{provider}}.": "",
|
||||
"Understand that updating or changing your embedding model requires reset of the vector database and re-import of all documents. You have been warned!": "",
|
||||
"Unknown File Type '{{file_type}}', but accepting and treating as plain text": "",
|
||||
"Update": "",
|
||||
"Update embedding model {{embedding_model}}": "",
|
||||
"Update password": "",
|
||||
"Upload a GGUF model": "",
|
||||
"Upload files": "",
|
||||
|
|
|
|||
Loading…
Reference in a new issue