merged from main and added new translation keys

CHANGELOG.md

@@ -5,6 +5,24 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.1.111] - 2024-03-10
+
+### Added
+
+- 🛡️ **Model Whitelisting**: Admins now have the ability to whitelist models for users with the 'user' role.
+- 🔄 **Update All Models**: Added a convenient button to update all models at once.
+- 📄 **Toggle PDF OCR**: Users can now toggle PDF OCR option for improved parsing performance.
+- 🎨 **DALL-E Integration**: Introduced DALL-E integration for image generation alongside automatic1111.
+- 🛠️ **RAG API Refactoring**: Refactored RAG logic and exposed its API, with additional documentation to follow.
+
+### Fixed
+
+- 🔒 **Max Token Settings**: Added max token settings for anthropic/claude-3-sonnet-20240229 (Issue #1094).
+- 🔧 **Misalignment Issue**: Corrected misalignment of Edit and Delete Icons when Chat Title is Empty (Issue #1104).
+- 🔄 **Context Loss Fix**: Resolved RAG losing context on model response regeneration with Groq models via API key (Issue #1105).
+- 📁 **File Handling Bug**: Addressed File Not Found Notification when Dropping a Conversation Element (Issue #1098).
+- 🖱️ **Dragged File Styling**: Fixed dragged file layover styling issue.
+
 ## [0.1.110] - 2024-03-06
 
 ### Added

backend/apps/openai/main.py

@@ -179,20 +179,26 @@ def merge_models_lists(model_lists):
 
 async def get_all_models():
     print("get_all_models")
-    tasks = [
-        fetch_url(f"{url}/models", app.state.OPENAI_API_KEYS[idx])
-        for idx, url in enumerate(app.state.OPENAI_API_BASE_URLS)
-    ]
-    responses = await asyncio.gather(*tasks)
-    responses = list(filter(lambda x: x is not None and "error" not in x, responses))
-    models = {
-        "data": merge_models_lists(
-            list(map(lambda response: response["data"], responses))
-        )
-    }
-    app.state.MODELS = {model["id"]: model for model in models["data"]}
 
-    return models
+    if len(app.state.OPENAI_API_KEYS) == 1 and app.state.OPENAI_API_KEYS[0] == "":
+        models = {"data": []}
+    else:
+        tasks = [
+            fetch_url(f"{url}/models", app.state.OPENAI_API_KEYS[idx])
+            for idx, url in enumerate(app.state.OPENAI_API_BASE_URLS)
+        ]
+        responses = await asyncio.gather(*tasks)
+        responses = list(
+            filter(lambda x: x is not None and "error" not in x, responses)
+        )
+        models = {
+            "data": merge_models_lists(
+                list(map(lambda response: response["data"], responses))
+            )
+        }
+        app.state.MODELS = {model["id"]: model for model in models["data"]}
+
+        return models
 
 
 @app.get("/models")

backend/apps/rag/main.py

@@ -77,6 +77,7 @@ from constants import ERROR_MESSAGES
 
 app = FastAPI()
 
+app.state.PDF_EXTRACT_IMAGES = False
 app.state.CHUNK_SIZE = CHUNK_SIZE
 app.state.CHUNK_OVERLAP = CHUNK_OVERLAP
 app.state.RAG_TEMPLATE = RAG_TEMPLATE
@@ -184,12 +185,15 @@ async def update_embedding_model(
     }
 
 
-@app.get("/chunk")
+@app.get("/config")
-async def get_chunk_params(user=Depends(get_admin_user)):
+async def get_rag_config(user=Depends(get_admin_user)):
     return {
         "status": True,
-        "chunk_size": app.state.CHUNK_SIZE,
+        "pdf_extract_images": app.state.PDF_EXTRACT_IMAGES,
-        "chunk_overlap": app.state.CHUNK_OVERLAP,
+        "chunk": {
+            "chunk_size": app.state.CHUNK_SIZE,
+            "chunk_overlap": app.state.CHUNK_OVERLAP,
+        },
     }
 
 
@@ -198,17 +202,24 @@ class ChunkParamUpdateForm(BaseModel):
     chunk_overlap: int
 
 
-@app.post("/chunk/update")
+class ConfigUpdateForm(BaseModel):
-async def update_chunk_params(
+    pdf_extract_images: bool
-    form_data: ChunkParamUpdateForm, user=Depends(get_admin_user)
+    chunk: ChunkParamUpdateForm
-):
+
-    app.state.CHUNK_SIZE = form_data.chunk_size
+
-    app.state.CHUNK_OVERLAP = form_data.chunk_overlap
+@app.post("/config/update")
+async def update_rag_config(form_data: ConfigUpdateForm, user=Depends(get_admin_user)):
+    app.state.PDF_EXTRACT_IMAGES = form_data.pdf_extract_images
+    app.state.CHUNK_SIZE = form_data.chunk.chunk_size
+    app.state.CHUNK_OVERLAP = form_data.chunk.chunk_overlap
 
     return {
         "status": True,
-        "chunk_size": app.state.CHUNK_SIZE,
+        "pdf_extract_images": app.state.PDF_EXTRACT_IMAGES,
-        "chunk_overlap": app.state.CHUNK_OVERLAP,
+        "chunk": {
+            "chunk_size": app.state.CHUNK_SIZE,
+            "chunk_overlap": app.state.CHUNK_OVERLAP,
+        },
     }
 
 
@@ -364,7 +375,7 @@ def get_loader(filename: str, file_content_type: str, file_path: str):
     ]
 
     if file_ext == "pdf":
-        loader = PyPDFLoader(file_path, extract_images=True)
+        loader = PyPDFLoader(file_path, extract_images=app.state.PDF_EXTRACT_IMAGES)
     elif file_ext == "csv":
         loader = CSVLoader(file_path)
     elif file_ext == "rst":

backend/apps/rag/utils.py

@@ -95,3 +95,89 @@ def rag_template(template: str, context: str, query: str):
     template = re.sub(r"\[query\]", query, template)
 
     return template
+
+
+def rag_messages(docs, messages, template, k, embedding_function):
+    print(docs)
+
+    last_user_message_idx = None
+    for i in range(len(messages) - 1, -1, -1):
+        if messages[i]["role"] == "user":
+            last_user_message_idx = i
+            break
+
+    user_message = messages[last_user_message_idx]
+
+    if isinstance(user_message["content"], list):
+        # Handle list content input
+        content_type = "list"
+        query = ""
+        for content_item in user_message["content"]:
+            if content_item["type"] == "text":
+                query = content_item["text"]
+                break
+    elif isinstance(user_message["content"], str):
+        # Handle text content input
+        content_type = "text"
+        query = user_message["content"]
+    else:
+        # Fallback in case the input does not match expected types
+        content_type = None
+        query = ""
+
+    relevant_contexts = []
+
+    for doc in docs:
+        context = None
+
+        try:
+            if doc["type"] == "collection":
+                context = query_collection(
+                    collection_names=doc["collection_names"],
+                    query=query,
+                    k=k,
+                    embedding_function=embedding_function,
+                )
+            else:
+                context = query_doc(
+                    collection_name=doc["collection_name"],
+                    query=query,
+                    k=k,
+                    embedding_function=embedding_function,
+                )
+        except Exception as e:
+            print(e)
+            context = None
+
+        relevant_contexts.append(context)
+
+    context_string = ""
+    for context in relevant_contexts:
+        if context:
+            context_string += " ".join(context["documents"][0]) + "\n"
+
+    ra_content = rag_template(
+        template=template,
+        context=context_string,
+        query=query,
+    )
+
+    if content_type == "list":
+        new_content = []
+        for content_item in user_message["content"]:
+            if content_item["type"] == "text":
+                # Update the text item's content with ra_content
+                new_content.append({"type": "text", "text": ra_content})
+            else:
+                # Keep other types of content as they are
+                new_content.append(content_item)
+        new_user_message = {**user_message, "content": new_content}
+    else:
+        new_user_message = {
+            **user_message,
+            "content": ra_content,
+        }
+
+    messages[last_user_message_idx] = new_user_message
+
+    return messages

backend/config.py

@@ -209,10 +209,6 @@ OLLAMA_API_BASE_URL = os.environ.get(
 
 OLLAMA_BASE_URL = os.environ.get("OLLAMA_BASE_URL", "")
 
-if ENV == "prod":
-    if OLLAMA_BASE_URL == "/ollama":
-        OLLAMA_BASE_URL = "http://host.docker.internal:11434"
-
 
 if OLLAMA_BASE_URL == "" and OLLAMA_API_BASE_URL != "":
     OLLAMA_BASE_URL = (
@@ -221,6 +217,11 @@ if OLLAMA_BASE_URL == "" and OLLAMA_API_BASE_URL != "":
         else OLLAMA_API_BASE_URL
     )
 
+if ENV == "prod":
+    if OLLAMA_BASE_URL == "/ollama":
+        OLLAMA_BASE_URL = "http://host.docker.internal:11434"
+
+
 OLLAMA_BASE_URLS = os.environ.get("OLLAMA_BASE_URLS", "")
 OLLAMA_BASE_URLS = OLLAMA_BASE_URLS if OLLAMA_BASE_URLS != "" else OLLAMA_BASE_URL
 
@@ -234,8 +235,6 @@ OLLAMA_BASE_URLS = [url.strip() for url in OLLAMA_BASE_URLS.split(";")]
 OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "")
 OPENAI_API_BASE_URL = os.environ.get("OPENAI_API_BASE_URL", "")
 
-if OPENAI_API_KEY == "":
-    OPENAI_API_KEY = "none"
 
 if OPENAI_API_BASE_URL == "":
     OPENAI_API_BASE_URL = "https://api.openai.com/v1"

backend/data/config.json

@@ -1,4 +1,5 @@
 {
+    "version": "0.0.1",
     "ui": {
         "prompt_suggestions": [
             {

backend/main.py

@@ -28,7 +28,7 @@ from typing import List
 
 
 from utils.utils import get_admin_user
-from apps.rag.utils import query_doc, query_collection, rag_template
+from apps.rag.utils import rag_messages
 
 from config import (
     WEBUI_NAME,
@@ -60,19 +60,6 @@ app.state.MODEL_FILTER_LIST = MODEL_FILTER_LIST
 
 origins = ["*"]
 
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.on_event("startup")
-async def on_startup():
-    await litellm_app_startup()
-
 
 class RAGMiddleware(BaseHTTPMiddleware):
     async def dispatch(self, request: Request, call_next):
@@ -91,98 +78,33 @@ class RAGMiddleware(BaseHTTPMiddleware):
             # Example: Add a new key-value pair or modify existing ones
             # data["modified"] = True  # Example modification
             if "docs" in data:
-                docs = data["docs"]
-                print(docs)
 
-                last_user_message_idx = None
+                data = {**data}
-                for i in range(len(data["messages"]) - 1, -1, -1):
+                data["messages"] = rag_messages(
-                    if data["messages"][i]["role"] == "user":
+                    data["docs"],
-                        last_user_message_idx = i
+                    data["messages"],
-                        break
+                    rag_app.state.RAG_TEMPLATE,
-
+                    rag_app.state.TOP_K,
-                user_message = data["messages"][last_user_message_idx]
+                    rag_app.state.sentence_transformer_ef,
-
-                if isinstance(user_message["content"], list):
-                    # Handle list content input
-                    content_type = "list"
-                    query = ""
-                    for content_item in user_message["content"]:
-                        if content_item["type"] == "text":
-                            query = content_item["text"]
-                            break
-                elif isinstance(user_message["content"], str):
-                    # Handle text content input
-                    content_type = "text"
-                    query = user_message["content"]
-                else:
-                    # Fallback in case the input does not match expected types
-                    content_type = None
-                    query = ""
-
-                relevant_contexts = []
-
-                for doc in docs:
-                    context = None
-
-                    try:
-                        if doc["type"] == "collection":
-                            context = query_collection(
-                                collection_names=doc["collection_names"],
-                                query=query,
-                                k=rag_app.state.TOP_K,
-                                embedding_function=rag_app.state.sentence_transformer_ef,
-                            )
-                        else:
-                            context = query_doc(
-                                collection_name=doc["collection_name"],
-                                query=query,
-                                k=rag_app.state.TOP_K,
-                                embedding_function=rag_app.state.sentence_transformer_ef,
-                            )
-                    except Exception as e:
-                        print(e)
-                        context = None
-
-                    relevant_contexts.append(context)
-
-                context_string = ""
-                for context in relevant_contexts:
-                    if context:
-                        context_string += " ".join(context["documents"][0]) + "\n"
-
-                ra_content = rag_template(
-                    template=rag_app.state.RAG_TEMPLATE,
-                    context=context_string,
-                    query=query,
                 )
-
-                if content_type == "list":
-                    new_content = []
-                    for content_item in user_message["content"]:
-                        if content_item["type"] == "text":
-                            # Update the text item's content with ra_content
-                            new_content.append({"type": "text", "text": ra_content})
-                        else:
-                            # Keep other types of content as they are
-                            new_content.append(content_item)
-                    new_user_message = {**user_message, "content": new_content}
-                else:
-                    new_user_message = {
-                        **user_message,
-                        "content": ra_content,
-                    }
-
-                data["messages"][last_user_message_idx] = new_user_message
                 del data["docs"]
 
                 print(data["messages"])
 
             modified_body_bytes = json.dumps(data).encode("utf-8")
 
-            # Create a new request with the modified body
+            # Replace the request body with the modified one
-            scope = request.scope
+            request._body = modified_body_bytes
-            scope["body"] = modified_body_bytes
+
-            request = Request(scope, receive=lambda: self._receive(modified_body_bytes))
+            # Set custom header to ensure content-length matches new body length
+            request.headers.__dict__["_list"] = [
+                (b"content-length", str(len(modified_body_bytes)).encode("utf-8")),
+                *[
+                    (k, v)
+                    for k, v in request.headers.raw
+                    if k.lower() != b"content-length"
+                ],
+            ]
 
         response = await call_next(request)
         return response
@@ -194,6 +116,15 @@ class RAGMiddleware(BaseHTTPMiddleware):
 app.add_middleware(RAGMiddleware)
 
 
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
 @app.middleware("http")
 async def check_url(request: Request, call_next):
     start_time = int(time.time())
@@ -204,6 +135,11 @@ async def check_url(request: Request, call_next):
     return response
 
 
+@app.on_event("startup")
+async def on_startup():
+    await litellm_app_startup()
+
+
 app.mount("/api/v1", webui_app)
 app.mount("/litellm/api", litellm_app)
 

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "open-webui",
-	"version": "0.1.110",
+	"version": "0.1.111",
 	"private": true,
 	"scripts": {
 		"dev": "vite dev --host",

src/lib/apis/rag/index.ts

@@ -1,9 +1,9 @@
 import { RAG_API_BASE_URL } from '$lib/constants';
 
-export const getChunkParams = async (token: string) => {
+export const getRAGConfig = async (token: string) => {
 	let error = null;
 
-	const res = await fetch(`${RAG_API_BASE_URL}/chunk`, {
+	const res = await fetch(`${RAG_API_BASE_URL}/config`, {
 		method: 'GET',
 		headers: {
 			'Content-Type': 'application/json',
@@ -27,18 +27,27 @@ export const getChunkParams = async (token: string) => {
 	return res;
 };
 
-export const updateChunkParams = async (token: string, size: number, overlap: number) => {
+type ChunkConfigForm = {
+	chunk_size: number;
+	chunk_overlap: number;
+};
+
+type RAGConfigForm = {
+	pdf_extract_images: boolean;
+	chunk: ChunkConfigForm;
+};
+
+export const updateRAGConfig = async (token: string, payload: RAGConfigForm) => {
 	let error = null;
 
-	const res = await fetch(`${RAG_API_BASE_URL}/chunk/update`, {
+	const res = await fetch(`${RAG_API_BASE_URL}/config/update`, {
 		method: 'POST',
 		headers: {
 			'Content-Type': 'application/json',
 			Authorization: `Bearer ${token}`
 		},
 		body: JSON.stringify({
-			chunk_size: size,
+			...payload
-			chunk_overlap: overlap
 		})
 	})
 		.then(async (res) => {

src/lib/components/chat/Settings/Models.svelte

@@ -14,6 +14,7 @@
 	import { splitStream } from '$lib/utils';
 	import { onMount, getContext } from 'svelte';
 	import { addLiteLLMModel, deleteLiteLLMModel, getLiteLLMModelInfo } from '$lib/apis/litellm';
+	import Tooltip from '$lib/components/common/Tooltip.svelte';
 
 	const i18n = getContext('i18n');
 
@@ -39,6 +40,10 @@
 
 	let OLLAMA_URLS = [];
 	let selectedOllamaUrlIdx: string | null = null;
+
+	let updateModelId = null;
+	let updateProgress = null;
+
 	let showExperimentalOllama = false;
 	let ollamaVersion = '';
 	const MAX_PARALLEL_DOWNLOADS = 3;
@@ -63,6 +68,71 @@
 
 	let deleteModelTag = '';
 
+	const updateModelsHandler = async () => {
+		for (const model of $models.filter(
+			(m) =>
+				m.size != null &&
+				(selectedOllamaUrlIdx === null ? true : (m?.urls ?? []).includes(selectedOllamaUrlIdx))
+		)) {
+			console.log(model);
+
+			updateModelId = model.id;
+			const res = await pullModel(localStorage.token, model.id, selectedOllamaUrlIdx).catch(
+				(error) => {
+					toast.error(error);
+					return null;
+				}
+			);
+
+			if (res) {
+				const reader = res.body
+					.pipeThrough(new TextDecoderStream())
+					.pipeThrough(splitStream('\n'))
+					.getReader();
+
+				while (true) {
+					try {
+						const { value, done } = await reader.read();
+						if (done) break;
+
+						let lines = value.split('\n');
+
+						for (const line of lines) {
+							if (line !== '') {
+								let data = JSON.parse(line);
+
+								console.log(data);
+								if (data.error) {
+									throw data.error;
+								}
+								if (data.detail) {
+									throw data.detail;
+								}
+								if (data.status) {
+									if (data.digest) {
+										updateProgress = 0;
+										if (data.completed) {
+											updateProgress = Math.round((data.completed / data.total) * 1000) / 10;
+										} else {
+											updateProgress = 100;
+										}
+									} else {
+										toast.success(data.status);
+									}
+								}
+							}
+						}
+					} catch (error) {
+						console.log(error);
+					}
+				}
+			}
+		}
+
+		updateModelId = null;
+		updateProgress = null;
+	};
+
 	const pullModelHandler = async () => {
 		const sanitizedModelTag = modelTag.trim();
 		if (modelDownloadStatus[sanitizedModelTag]) {
@@ -389,7 +459,7 @@
 			return [];
 		});
 
-		if (OLLAMA_URLS.length > 1) {
+		if (OLLAMA_URLS.length > 0) {
 			selectedOllamaUrlIdx = 0;
 		}
 
@@ -404,18 +474,51 @@
 			<div class="space-y-2 pr-1.5">
 				<div class="text-sm font-medium">{$i18n.t('Manage Ollama Models')}</div>
 
-				{#if OLLAMA_URLS.length > 1}
+				{#if OLLAMA_URLS.length > 0}
-					<div class="flex-1 pb-1">
+					<div class="flex gap-2">
-						<select
+						<div class="flex-1 pb-1">
-							class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
+							<select
-							bind:value={selectedOllamaUrlIdx}
+								class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
-							placeholder={$i18n.t('Select an Ollama instance')}
+								bind:value={selectedOllamaUrlIdx}
-						>
+								placeholder="Select an Ollama instance"
-							{#each OLLAMA_URLS as url, idx}
+							>
-								<option value={idx} class="bg-gray-100 dark:bg-gray-700">{url}</option>
+								{#each OLLAMA_URLS as url, idx}
-							{/each}
+									<option value={idx} class="bg-gray-100 dark:bg-gray-700">{url}</option>
-						</select>
+								{/each}
+							</select>
+						</div>
+
+						<div>
+							<div class="flex w-full justify-end">
+								<Tooltip content="Update All Models" placement="top">
+									<button
+										class="p-2.5 flex gap-2 items-center bg-gray-100 hover:bg-gray-200 text-gray-800 dark:bg-gray-850 dark:hover:bg-gray-800 dark:text-gray-100 rounded-lg transition"
+										on:click={() => {
+											updateModelsHandler();
+										}}
+									>
+										<svg
+											xmlns="http://www.w3.org/2000/svg"
+											viewBox="0 0 16 16"
+											fill="currentColor"
+											class="w-4 h-4"
+										>
+											<path
+												d="M7 1a.75.75 0 0 1 .75.75V6h-1.5V1.75A.75.75 0 0 1 7 1ZM6.25 6v2.94L5.03 7.72a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l2.5-2.5a.75.75 0 1 0-1.06-1.06L7.75 8.94V6H10a2 2 0 0 1 2 2v3a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h2.25Z"
+											/>
+											<path
+												d="M4.268 14A2 2 0 0 0 6 15h6a2 2 0 0 0 2-2v-3a2 2 0 0 0-1-1.732V11a3 3 0 0 1-3 3H4.268Z"
+											/>
+										</svg>
+									</button>
+								</Tooltip>
+							</div>
+						</div>
 					</div>
+
+					{#if updateModelId}
+						Updating "{updateModelId}" {updateProgress ? `(${updateProgress}%)` : ''}
+					{/if}
 				{/if}
 
 				<div class="space-y-2">

src/lib/components/documents/Settings/General.svelte

@@ -1,10 +1,10 @@
 <script lang="ts">
 	import { getDocs } from '$lib/apis/documents';
 	import {
-		getChunkParams,
+		getRAGConfig,
+		updateRAGConfig,
 		getQuerySettings,
 		scanDocs,
-		updateChunkParams,
 		updateQuerySettings
 	} from '$lib/apis/rag';
 	import { documents } from '$lib/stores';
@@ -19,6 +19,7 @@
 
 	let chunkSize = 0;
 	let chunkOverlap = 0;
+	let pdfExtractImages = true;
 
 	let querySettings = {
 		template: '',
@@ -37,16 +38,24 @@
 	};
 
 	const submitHandler = async () => {
-		const res = await updateChunkParams(localStorage.token, chunkSize, chunkOverlap);
+		const res = await updateRAGConfig(localStorage.token, {
+			pdf_extract_images: pdfExtractImages,
+			chunk: {
+				chunk_overlap: chunkOverlap,
+				chunk_size: chunkSize
+			}
+		});
 		querySettings = await updateQuerySettings(localStorage.token, querySettings);
 	};
 
 	onMount(async () => {
-		const res = await getChunkParams(localStorage.token);
+		const res = await getRAGConfig(localStorage.token);
 
 		if (res) {
-			chunkSize = res.chunk_size;
+			pdfExtractImages = res.pdf_extract_images;
-			chunkOverlap = res.chunk_overlap;
+
+			chunkSize = res.chunk.chunk_size;
+			chunkOverlap = res.chunk.chunk_overlap;
 		}
 
 		querySettings = await getQuerySettings(localStorage.token);
@@ -163,6 +172,22 @@
 				</div>
 			</div>
 
+			<div>
+				<div class="flex justify-between items-center text-xs">
+					<div class=" text-xs font-medium">{$i18n.t('PDF Extract Images (OCR)')}</div>
+
+					<button
+						class=" text-xs font-medium text-gray-500"
+						type="button"
+						on:click={() => {
+							pdfExtractImages = !pdfExtractImages;
+						}}>{pdfExtractImages ? $i18n.t('On') : $i18n.t('Off')}</button
+					>
+				</div>
+			</div>
+		</div>
+
+		<div>
 			<div class=" text-sm font-medium">{$i18n.t('Query Params')}</div>
 
 			<div class=" flex">
@@ -182,19 +207,19 @@
 				</div>
 
 				<!-- <div class="flex w-full">
-					<div class=" self-center text-xs font-medium min-w-fit">Chunk Overlap</div>
+						<div class=" self-center text-xs font-medium min-w-fit">Chunk Overlap</div>
 	
-					<div class="self-center p-3">
+						<div class="self-center p-3">
-						<input
+							<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 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"
+								type="number"
-							placeholder="Enter Chunk Overlap"
+								placeholder="Enter Chunk Overlap"
-							bind:value={chunkOverlap}
+								bind:value={chunkOverlap}
-							autocomplete="off"
+								autocomplete="off"
-							min="0"
+								min="0"
-						/>
+							/>
-					</div>
+						</div>
-				</div> -->
+					</div> -->
 			</div>
 
 			<div>

src/lib/i18n/locales/de/translation.json

@@ -236,7 +236,7 @@
 	"Prompts": "Prompts",
 	"Pull a model from Ollama.com": "Ein Modell von Ollama.com abrufen",
 	"Pull Progress": "Fortschritt abrufen",
-	"Query Params": "",
+	"Query Params": "Query Parameter",
 	"RAG Template": "RAG-Vorlage",
 	"Raw Format": "Rohformat",
 	"Record voice": "Stimme aufnehmen",
@@ -346,5 +346,6 @@
 	"Write a summary in 50 words that summarizes [topic or keyword].": "Schreibe eine kurze Zusammenfassung in 50 Wörtern, die [Thema oder Schlüsselwort] zusammenfasst.",
 	"You": "Du",
 	"You're a helpful assistant.": "Du bist ein hilfreicher Assistent.",
-	"You're now logged in.": "Du bist nun eingeloggt."
+	"You're now logged in.": "Du bist nun eingeloggt.",
+	"PDF Extract Images (OCR)": "Text von Bilder aus PDFs extrahieren (OCR)"
 }

src/lib/i18n/locales/en/translation.json

@@ -236,7 +236,7 @@
 	"Prompts": "Prompts",
 	"Pull a model from Ollama.com": "Pull a model from Ollama.com",
 	"Pull Progress": "Pull Progress",
-	"Query Params": "",
+	"Query Params": "Query Params",
 	"RAG Template": "RAG Template",
 	"Raw Format": "Raw Format",
 	"Record voice": "Record voice",
@@ -346,5 +346,6 @@
 	"Write a summary in 50 words that summarizes [topic or keyword].": "Write a summary in 50 words that summarizes [topic or keyword].",
 	"You": "You",
 	"You're a helpful assistant.": "You're a helpful assistant.",
-	"You're now logged in.": "You're now logged in."
+	"You're now logged in.": "You're now logged in.",
+	"PDF Extract Images (OCR)": "PDF Extract Images (OCR)"
 }