Merge pull request #1107 from open-webui/dev

0.1.111

.github/workflows/build-release.yml

@@ -29,11 +29,11 @@ jobs:
     - name: Extract latest CHANGELOG entry
       id: changelog
       run: |
-        CHANGELOG_CONTENT=$(awk '/^## \[/{n++} n==1' CHANGELOG.md)
-        echo "CHANGELOG_CONTENT<<EOF" 
-        echo "$CHANGELOG_CONTENT"
-        echo "EOF" 
-        echo "::set-output name=content::${CHANGELOG_CONTENT}"
+        CHANGELOG_CONTENT=$(awk 'BEGIN {print_section=0;} /^## \[/ {if (print_section == 0) {print_section=1;} else {exit;}} print_section {print;}' CHANGELOG.md)
+        CHANGELOG_ESCAPED=$(echo "$CHANGELOG_CONTENT" | sed ':a;N;$!ba;s/\n/%0A/g')
+        echo "Extracted latest release notes from CHANGELOG.md:" 
+        echo -e "$CHANGELOG_CONTENT" 
+        echo "::set-output name=content::$CHANGELOG_ESCAPED"
 
     - name: Create GitHub release
       uses: actions/github-script@v5

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

Dockerfile

@@ -41,7 +41,7 @@ ENV WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
 # for better persormance and multilangauge support use "intfloat/multilingual-e5-large" (~2.5GB) or "intfloat/multilingual-e5-base" (~1.5GB)
 # IMPORTANT: If you change the default model (all-MiniLM-L6-v2) and vice versa, you aren't able to use RAG Chat with your previous documents loaded in the WebUI! You need to re-embed them.
 ENV RAG_EMBEDDING_MODEL="all-MiniLM-L6-v2"
-# device type for whisper tts and ebbeding models - "cpu" (default), "cuda" (nvidia gpu and CUDA required) or "mps" (apple silicon) - choosing this right can lead to better performance
+# device type for whisper tts and embbeding models - "cpu" (default), "cuda" (nvidia gpu and CUDA required) or "mps" (apple silicon) - choosing this right can lead to better performance
 ENV RAG_EMBEDDING_MODEL_DEVICE_TYPE="cpu"
 ENV RAG_EMBEDDING_MODEL_DIR="/app/backend/data/cache/embedding/models"
 ENV SENTENCE_TRANSFORMERS_HOME $RAG_EMBEDDING_MODEL_DIR
@@ -81,4 +81,4 @@ COPY --from=build /app/package.json /app/package.json
 # copy backend files
 COPY ./backend .
 
-CMD [ "bash", "start.sh"]
+CMD [ "bash", "start.sh"]
\ No newline at end of file

README.md

@@ -53,8 +53,6 @@ User-friendly WebUI for LLMs, supported LLM runners include Ollama and OpenAI-co
 
 - 💬 **Collaborative Chat**: Harness the collective intelligence of multiple models by seamlessly orchestrating group conversations. Use the `@` command to specify the model, enabling dynamic and diverse dialogues within your chat interface. Immerse yourself in the collective intelligence woven into your chat environment.
 
-- 🤝 **OpenAI API Integration**: Effortlessly integrate OpenAI-compatible API for versatile conversations alongside Ollama models. Customize the API Base URL to link with **LMStudio, Mistral, OpenRouter, and more**.
-
 - 🔄 **Regeneration History Access**: Easily revisit and explore your entire regeneration history.
 
 - 📜 **Chat History**: Effortlessly access and manage your conversation history.
@@ -65,8 +63,18 @@ User-friendly WebUI for LLMs, supported LLM runners include Ollama and OpenAI-co
 
 - ⚙️ **Fine-Tuned Control with Advanced Parameters**: Gain a deeper level of control by adjusting parameters such as temperature and defining your system prompts to tailor the conversation to your specific preferences and needs.
 
+- 🎨🤖 **Image Generation Integration**: Seamlessly incorporate image generation capabilities using AUTOMATIC1111 API (local) and DALL-E, enriching your chat experience with dynamic visual content.
+
+- 🤝 **OpenAI API Integration**: Effortlessly integrate OpenAI-compatible API for versatile conversations alongside Ollama models. Customize the API Base URL to link with **LMStudio, Mistral, OpenRouter, and more**.
+
+- ✨ **Multiple OpenAI-Compatible API Support**: Seamlessly integrate and customize various OpenAI-compatible APIs, enhancing the versatility of your chat interactions.
+
 - 🔗 **External Ollama Server Connection**: Seamlessly link to an external Ollama server hosted on a different address by configuring the environment variable.
 
+- 🔀 **Multiple Ollama Instance Load Balancing**: Effortlessly distribute chat requests across multiple Ollama instances for enhanced performance and reliability.
+
+- 👥 **Multi-User Management**: Easily oversee and administer users via our intuitive admin panel, streamlining user management processes.
+
 - 🔐 **Role-Based Access Control (RBAC)**: Ensure secure access with restricted permissions; only authorized individuals can access your Ollama, and exclusive model creation/pulling rights are reserved for administrators.
 
 - 🔒 **Backend Reverse Proxy Support**: Bolster security through direct communication between Open WebUI backend and Ollama. This key feature eliminates the need to expose Ollama over LAN. Requests made to the '/ollama/api' route from the web UI are seamlessly redirected to Ollama from the backend, enhancing overall system security.

backend/apps/images/main.py

@@ -21,7 +21,16 @@ from utils.utils import (
 from utils.misc import calculate_sha256
 from typing import Optional
 from pydantic import BaseModel
-from config import AUTOMATIC1111_BASE_URL
+from pathlib import Path
+import uuid
+import base64
+import json
+
+from config import CACHE_DIR, AUTOMATIC1111_BASE_URL
+
+
+IMAGE_CACHE_DIR = Path(CACHE_DIR).joinpath("./image/generations/")
+IMAGE_CACHE_DIR.mkdir(parents=True, exist_ok=True)
 
 app = FastAPI()
 app.add_middleware(
@@ -32,25 +41,34 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+app.state.ENGINE = ""
+app.state.ENABLED = False
+
+app.state.OPENAI_API_KEY = ""
+app.state.MODEL = ""
+
+
 app.state.AUTOMATIC1111_BASE_URL = AUTOMATIC1111_BASE_URL
-app.state.ENABLED = app.state.AUTOMATIC1111_BASE_URL != ""
+
 app.state.IMAGE_SIZE = "512x512"
 app.state.IMAGE_STEPS = 50
 
 
-@app.get("/enabled", response_model=bool)
-async def get_enable_status(request: Request, user=Depends(get_admin_user)):
-    return app.state.ENABLED
+@app.get("/config")
+async def get_config(request: Request, user=Depends(get_admin_user)):
+    return {"engine": app.state.ENGINE, "enabled": app.state.ENABLED}
 
 
-@app.get("/enabled/toggle", response_model=bool)
-async def toggle_enabled(request: Request, user=Depends(get_admin_user)):
-    try:
-        r = requests.head(app.state.AUTOMATIC1111_BASE_URL)
-        app.state.ENABLED = not app.state.ENABLED
-        return app.state.ENABLED
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(e))
+class ConfigUpdateForm(BaseModel):
+    engine: str
+    enabled: bool
+
+
+@app.post("/config/update")
+async def update_config(form_data: ConfigUpdateForm, user=Depends(get_admin_user)):
+    app.state.ENGINE = form_data.engine
+    app.state.ENABLED = form_data.enabled
+    return {"engine": app.state.ENGINE, "enabled": app.state.ENABLED}
 
 
 class UrlUpdateForm(BaseModel):
@@ -58,17 +76,24 @@ class UrlUpdateForm(BaseModel):
 
 
 @app.get("/url")
-async def get_openai_url(user=Depends(get_admin_user)):
+async def get_automatic1111_url(user=Depends(get_admin_user)):
     return {"AUTOMATIC1111_BASE_URL": app.state.AUTOMATIC1111_BASE_URL}
 
 
 @app.post("/url/update")
-async def update_openai_url(form_data: UrlUpdateForm, user=Depends(get_admin_user)):
+async def update_automatic1111_url(
+    form_data: UrlUpdateForm, user=Depends(get_admin_user)
+):
 
     if form_data.url == "":
         app.state.AUTOMATIC1111_BASE_URL = AUTOMATIC1111_BASE_URL
     else:
-        app.state.AUTOMATIC1111_BASE_URL = form_data.url.strip("/")
+        url = form_data.url.strip("/")
+        try:
+            r = requests.head(url)
+            app.state.AUTOMATIC1111_BASE_URL = url
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(e))
 
     return {
         "AUTOMATIC1111_BASE_URL": app.state.AUTOMATIC1111_BASE_URL,
@@ -76,6 +101,30 @@ async def update_openai_url(form_data: UrlUpdateForm, user=Depends(get_admin_use
     }
 
 
+class OpenAIKeyUpdateForm(BaseModel):
+    key: str
+
+
+@app.get("/key")
+async def get_openai_key(user=Depends(get_admin_user)):
+    return {"OPENAI_API_KEY": app.state.OPENAI_API_KEY}
+
+
+@app.post("/key/update")
+async def update_openai_key(
+    form_data: OpenAIKeyUpdateForm, user=Depends(get_admin_user)
+):
+
+    if form_data.key == "":
+        raise HTTPException(status_code=400, detail=ERROR_MESSAGES.API_KEY_NOT_FOUND)
+
+    app.state.OPENAI_API_KEY = form_data.key
+    return {
+        "OPENAI_API_KEY": app.state.OPENAI_API_KEY,
+        "status": True,
+    }
+
+
 class ImageSizeUpdateForm(BaseModel):
     size: str
 
@@ -132,9 +181,22 @@ async def update_image_size(
 @app.get("/models")
 def get_models(user=Depends(get_current_user)):
     try:
-        r = requests.get(url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/sd-models")
-        models = r.json()
-        return models
+        if app.state.ENGINE == "openai":
+            return [
+                {"id": "dall-e-2", "name": "DALL·E 2"},
+                {"id": "dall-e-3", "name": "DALL·E 3"},
+            ]
+        else:
+            r = requests.get(
+                url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/sd-models"
+            )
+            models = r.json()
+            return list(
+                map(
+                    lambda model: {"id": model["title"], "name": model["model_name"]},
+                    models,
+                )
+            )
     except Exception as e:
         app.state.ENABLED = False
         raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(e))
@@ -143,10 +205,12 @@ def get_models(user=Depends(get_current_user)):
 @app.get("/models/default")
 async def get_default_model(user=Depends(get_admin_user)):
     try:
-        r = requests.get(url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/options")
-        options = r.json()
-
-        return {"model": options["sd_model_checkpoint"]}
+        if app.state.ENGINE == "openai":
+            return {"model": app.state.MODEL if app.state.MODEL else "dall-e-2"}
+        else:
+            r = requests.get(url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/options")
+            options = r.json()
+            return {"model": options["sd_model_checkpoint"]}
     except Exception as e:
         app.state.ENABLED = False
         raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(e))
@@ -157,16 +221,21 @@ class UpdateModelForm(BaseModel):
 
 
 def set_model_handler(model: str):
-    r = requests.get(url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/options")
-    options = r.json()
 
-    if model != options["sd_model_checkpoint"]:
-        options["sd_model_checkpoint"] = model
-        r = requests.post(
-            url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/options", json=options
-        )
+    if app.state.ENGINE == "openai":
+        app.state.MODEL = model
+        return app.state.MODEL
+    else:
+        r = requests.get(url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/options")
+        options = r.json()
+
+        if model != options["sd_model_checkpoint"]:
+            options["sd_model_checkpoint"] = model
+            r = requests.post(
+                url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/options", json=options
+            )
 
-    return options
+        return options
 
 
 @app.post("/models/default/update")
@@ -181,45 +250,113 @@ class GenerateImageForm(BaseModel):
     model: Optional[str] = None
     prompt: str
     n: int = 1
-    size: str = "512x512"
+    size: Optional[str] = None
     negative_prompt: Optional[str] = None
 
 
+def save_b64_image(b64_str):
+    image_id = str(uuid.uuid4())
+    file_path = IMAGE_CACHE_DIR.joinpath(f"{image_id}.png")
+
+    try:
+        # Split the base64 string to get the actual image data
+        img_data = base64.b64decode(b64_str)
+
+        # Write the image data to a file
+        with open(file_path, "wb") as f:
+            f.write(img_data)
+
+        return image_id
+    except Exception as e:
+        print(f"Error saving image: {e}")
+        return None
+
+
 @app.post("/generations")
 def generate_image(
     form_data: GenerateImageForm,
     user=Depends(get_current_user),
 ):
 
-    print(form_data)
-
+    r = None
     try:
-        if form_data.model:
-            set_model_handler(form_data.model)
+        if app.state.ENGINE == "openai":
 
-        width, height = tuple(map(int, app.state.IMAGE_SIZE.split("x")))
+            headers = {}
+            headers["Authorization"] = f"Bearer {app.state.OPENAI_API_KEY}"
+            headers["Content-Type"] = "application/json"
 
-        data = {
-            "prompt": form_data.prompt,
-            "batch_size": form_data.n,
-            "width": width,
-            "height": height,
-        }
+            data = {
+                "model": app.state.MODEL if app.state.MODEL != "" else "dall-e-2",
+                "prompt": form_data.prompt,
+                "n": form_data.n,
+                "size": form_data.size if form_data.size else app.state.IMAGE_SIZE,
+                "response_format": "b64_json",
+            }
+            r = requests.post(
+                url=f"https://api.openai.com/v1/images/generations",
+                json=data,
+                headers=headers,
+            )
 
-        if app.state.IMAGE_STEPS != None:
-            data["steps"] = app.state.IMAGE_STEPS
+            r.raise_for_status()
 
-        if form_data.negative_prompt != None:
-            data["negative_prompt"] = form_data.negative_prompt
+            res = r.json()
 
-        print(data)
+            images = []
 
-        r = requests.post(
-            url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/txt2img",
-            json=data,
-        )
+            for image in res["data"]:
+                image_id = save_b64_image(image["b64_json"])
+                images.append({"url": f"/cache/image/generations/{image_id}.png"})
+                file_body_path = IMAGE_CACHE_DIR.joinpath(f"{image_id}.json")
+
+                with open(file_body_path, "w") as f:
+                    json.dump(data, f)
+
+            return images
+
+        else:
+            if form_data.model:
+                set_model_handler(form_data.model)
+
+            width, height = tuple(map(int, app.state.IMAGE_SIZE.split("x")))
+
+            data = {
+                "prompt": form_data.prompt,
+                "batch_size": form_data.n,
+                "width": width,
+                "height": height,
+            }
+
+            if app.state.IMAGE_STEPS != None:
+                data["steps"] = app.state.IMAGE_STEPS
+
+            if form_data.negative_prompt != None:
+                data["negative_prompt"] = form_data.negative_prompt
+
+            r = requests.post(
+                url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/txt2img",
+                json=data,
+            )
+
+            res = r.json()
+
+            print(res)
+
+            images = []
+
+            for image in res["images"]:
+                image_id = save_b64_image(image)
+                images.append({"url": f"/cache/image/generations/{image_id}.png"})
+                file_body_path = IMAGE_CACHE_DIR.joinpath(f"{image_id}.json")
+
+                with open(file_body_path, "w") as f:
+                    json.dump({**data, "info": res["info"]}, f)
+
+            return images
 
-        return r.json()
     except Exception as e:
         print(e)
+        if r:
+            print(r.json())
         raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(e))

backend/apps/litellm/main.py

+from litellm.proxy.proxy_server import ProxyConfig, initialize
+from litellm.proxy.proxy_server import app
+
+from fastapi import FastAPI, Request, Depends, status
+from fastapi.responses import JSONResponse
+from utils.utils import get_http_authorization_cred, get_current_user
+from config import ENV
+
+proxy_config = ProxyConfig()
+
+
+async def config():
+    router, model_list, general_settings = await proxy_config.load_config(
+        router=None, config_file_path="./data/litellm/config.yaml"
+    )
+
+    await initialize(config="./data/litellm/config.yaml", telemetry=False)
+
+
+async def startup():
+    await config()
+
+
+@app.on_event("startup")
+async def on_startup():
+    await startup()
+
+
+@app.middleware("http")
+async def auth_middleware(request: Request, call_next):
+    auth_header = request.headers.get("Authorization", "")
+
+    if ENV != "dev":
+        try:
+            user = get_current_user(get_http_authorization_cred(auth_header))
+            print(user)
+        except Exception as e:
+            return JSONResponse(status_code=400, content={"detail": str(e)})
+
+    response = await call_next(request)
+    return response

backend/apps/ollama/main.py

@@ -15,7 +15,7 @@ import asyncio
 from apps.web.models.users import Users
 from constants import ERROR_MESSAGES
 from utils.utils import decode_token, get_current_user, get_admin_user
-from config import OLLAMA_BASE_URLS
+from config import OLLAMA_BASE_URLS, MODEL_FILTER_ENABLED, MODEL_FILTER_LIST
 
 from typing import Optional, List, Union
 
@@ -29,6 +29,10 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+
+app.state.MODEL_FILTER_ENABLED = MODEL_FILTER_ENABLED
+app.state.MODEL_FILTER_LIST = MODEL_FILTER_LIST
+
 app.state.OLLAMA_BASE_URLS = OLLAMA_BASE_URLS
 app.state.MODELS = {}
 
@@ -129,9 +133,19 @@ async def get_all_models():
 async def get_ollama_tags(
     url_idx: Optional[int] = None, user=Depends(get_current_user)
 ):
-
     if url_idx == None:
-        return await get_all_models()
+        models = await get_all_models()
+
+        if app.state.MODEL_FILTER_ENABLED:
+            if user.role == "user":
+                models["models"] = list(
+                    filter(
+                        lambda model: model["name"] in app.state.MODEL_FILTER_LIST,
+                        models["models"],
+                    )
+                )
+                return models
+        return models
     else:
         url = app.state.OLLAMA_BASE_URLS[url_idx]
         try:

backend/apps/openai/main.py

@@ -18,7 +18,13 @@ from utils.utils import (
     get_verified_user,
     get_admin_user,
 )
-from config import OPENAI_API_BASE_URLS, OPENAI_API_KEYS, CACHE_DIR
+from config import (
+    OPENAI_API_BASE_URLS,
+    OPENAI_API_KEYS,
+    CACHE_DIR,
+    MODEL_FILTER_ENABLED,
+    MODEL_FILTER_LIST,
+)
 from typing import List, Optional
 
 
@@ -34,6 +40,9 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+app.state.MODEL_FILTER_ENABLED = MODEL_FILTER_ENABLED
+app.state.MODEL_FILTER_LIST = MODEL_FILTER_LIST
+
 app.state.OPENAI_API_BASE_URLS = OPENAI_API_BASE_URLS
 app.state.OPENAI_API_KEYS = OPENAI_API_KEYS
 
@@ -186,12 +195,21 @@ async def get_all_models():
     return models
 
 
-# , user=Depends(get_current_user)
 @app.get("/models")
 @app.get("/models/{url_idx}")
-async def get_models(url_idx: Optional[int] = None):
+async def get_models(url_idx: Optional[int] = None, user=Depends(get_current_user)):
     if url_idx == None:
-        return await get_all_models()
+        models = await get_all_models()
+        if app.state.MODEL_FILTER_ENABLED:
+            if user.role == "user":
+                models["data"] = list(
+                    filter(
+                        lambda model: model["id"] in app.state.MODEL_FILTER_LIST,
+                        models["data"],
+                    )
+                )
+                return models
+        return models
     else:
         url = app.state.OPENAI_API_BASE_URLS[url_idx]
         try:

backend/apps/rag/main.py

@@ -44,6 +44,8 @@ from apps.web.models.documents import (
     DocumentResponse,
 )
 
+from apps.rag.utils import query_doc, query_collection
+
 from utils.misc import (
     calculate_sha256,
     calculate_sha256_string,
@@ -75,6 +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
@@ -182,12 +185,15 @@ async def update_embedding_model(
     }
 
 
-@app.get("/chunk")
-async def get_chunk_params(user=Depends(get_admin_user)):
+@app.get("/config")
+async def get_rag_config(user=Depends(get_admin_user)):
     return {
         "status": True,
-        "chunk_size": app.state.CHUNK_SIZE,
-        "chunk_overlap": app.state.CHUNK_OVERLAP,
+        "pdf_extract_images": app.state.PDF_EXTRACT_IMAGES,
+        "chunk": {
+            "chunk_size": app.state.CHUNK_SIZE,
+            "chunk_overlap": app.state.CHUNK_OVERLAP,
+        },
     }
 
 
@@ -196,17 +202,24 @@ class ChunkParamUpdateForm(BaseModel):
     chunk_overlap: int
 
 
-@app.post("/chunk/update")
-async def update_chunk_params(
-    form_data: ChunkParamUpdateForm, user=Depends(get_admin_user)
-):
-    app.state.CHUNK_SIZE = form_data.chunk_size
-    app.state.CHUNK_OVERLAP = form_data.chunk_overlap
+class ConfigUpdateForm(BaseModel):
+    pdf_extract_images: bool
+    chunk: ChunkParamUpdateForm
+
+
+@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,
-        "chunk_overlap": app.state.CHUNK_OVERLAP,
+        "pdf_extract_images": app.state.PDF_EXTRACT_IMAGES,
+        "chunk": {
+            "chunk_size": app.state.CHUNK_SIZE,
+            "chunk_overlap": app.state.CHUNK_OVERLAP,
+        },
     }
 
 
@@ -248,21 +261,18 @@ class QueryDocForm(BaseModel):
 
 
 @app.post("/query/doc")
-def query_doc(
+def query_doc_handler(
     form_data: QueryDocForm,
     user=Depends(get_current_user),
 ):
+
     try:
-        # if you use docker use the model from the environment variable
-        collection = CHROMA_CLIENT.get_collection(
-            name=form_data.collection_name,
+        return query_doc(
+            collection_name=form_data.collection_name,
+            query=form_data.query,
+            k=form_data.k if form_data.k else app.state.TOP_K,
             embedding_function=app.state.sentence_transformer_ef,
         )
-        result = collection.query(
-            query_texts=[form_data.query],
-            n_results=form_data.k if form_data.k else app.state.TOP_K,
-        )
-        return result
     except Exception as e:
         print(e)
         raise HTTPException(
@@ -277,76 +287,16 @@ class QueryCollectionsForm(BaseModel):
     k: Optional[int] = None
 
 
-def merge_and_sort_query_results(query_results, k):
-    # Initialize lists to store combined data
-    combined_ids = []
-    combined_distances = []
-    combined_metadatas = []
-    combined_documents = []
-
-    # Combine data from each dictionary
-    for data in query_results:
-        combined_ids.extend(data["ids"][0])
-        combined_distances.extend(data["distances"][0])
-        combined_metadatas.extend(data["metadatas"][0])
-        combined_documents.extend(data["documents"][0])
-
-    # Create a list of tuples (distance, id, metadata, document)
-    combined = list(
-        zip(combined_distances, combined_ids, combined_metadatas, combined_documents)
-    )
-
-    # Sort the list based on distances
-    combined.sort(key=lambda x: x[0])
-
-    # Unzip the sorted list
-    sorted_distances, sorted_ids, sorted_metadatas, sorted_documents = zip(*combined)
-
-    # Slicing the lists to include only k elements
-    sorted_distances = list(sorted_distances)[:k]
-    sorted_ids = list(sorted_ids)[:k]
-    sorted_metadatas = list(sorted_metadatas)[:k]
-    sorted_documents = list(sorted_documents)[:k]
-
-    # Create the output dictionary
-    merged_query_results = {
-        "ids": [sorted_ids],
-        "distances": [sorted_distances],
-        "metadatas": [sorted_metadatas],
-        "documents": [sorted_documents],
-        "embeddings": None,
-        "uris": None,
-        "data": None,
-    }
-
-    return merged_query_results
-
-
 @app.post("/query/collection")
-def query_collection(
+def query_collection_handler(
     form_data: QueryCollectionsForm,
     user=Depends(get_current_user),
 ):
-    results = []
-
-    for collection_name in form_data.collection_names:
-        try:
-            # if you use docker use the model from the environment variable
-            collection = CHROMA_CLIENT.get_collection(
-                name=collection_name,
-                embedding_function=app.state.sentence_transformer_ef,
-            )
-
-            result = collection.query(
-                query_texts=[form_data.query],
-                n_results=form_data.k if form_data.k else app.state.TOP_K,
-            )
-            results.append(result)
-        except:
-            pass
-
-    return merge_and_sort_query_results(
-        results, form_data.k if form_data.k else app.state.TOP_K
+    return query_collection(
+        collection_names=form_data.collection_names,
+        query=form_data.query,
+        k=form_data.k if form_data.k else app.state.TOP_K,
+        embedding_function=app.state.sentence_transformer_ef,
     )
 
 
@@ -425,7 +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

+import re
+from typing import List
+
+from config import CHROMA_CLIENT
+
+
+def query_doc(collection_name: str, query: str, k: int, embedding_function):
+    try:
+        # if you use docker use the model from the environment variable
+        collection = CHROMA_CLIENT.get_collection(
+            name=collection_name,
+            embedding_function=embedding_function,
+        )
+        result = collection.query(
+            query_texts=[query],
+            n_results=k,
+        )
+        return result
+    except Exception as e:
+        raise e
+
+
+def merge_and_sort_query_results(query_results, k):
+    # Initialize lists to store combined data
+    combined_ids = []
+    combined_distances = []
+    combined_metadatas = []
+    combined_documents = []
+
+    # Combine data from each dictionary
+    for data in query_results:
+        combined_ids.extend(data["ids"][0])
+        combined_distances.extend(data["distances"][0])
+        combined_metadatas.extend(data["metadatas"][0])
+        combined_documents.extend(data["documents"][0])
+
+    # Create a list of tuples (distance, id, metadata, document)
+    combined = list(
+        zip(combined_distances, combined_ids, combined_metadatas, combined_documents)
+    )
+
+    # Sort the list based on distances
+    combined.sort(key=lambda x: x[0])
+
+    # Unzip the sorted list
+    sorted_distances, sorted_ids, sorted_metadatas, sorted_documents = zip(*combined)
+
+    # Slicing the lists to include only k elements
+    sorted_distances = list(sorted_distances)[:k]
+    sorted_ids = list(sorted_ids)[:k]
+    sorted_metadatas = list(sorted_metadatas)[:k]
+    sorted_documents = list(sorted_documents)[:k]
+
+    # Create the output dictionary
+    merged_query_results = {
+        "ids": [sorted_ids],
+        "distances": [sorted_distances],
+        "metadatas": [sorted_metadatas],
+        "documents": [sorted_documents],
+        "embeddings": None,
+        "uris": None,
+        "data": None,
+    }
+
+    return merged_query_results
+
+
+def query_collection(
+    collection_names: List[str], query: str, k: int, embedding_function
+):
+
+    results = []
+
+    for collection_name in collection_names:
+        try:
+            # if you use docker use the model from the environment variable
+            collection = CHROMA_CLIENT.get_collection(
+                name=collection_name,
+                embedding_function=embedding_function,
+            )
+
+            result = collection.query(
+                query_texts=[query],
+                n_results=k,
+            )
+            results.append(result)
+        except:
+            pass
+
+    return merge_and_sort_query_results(results, k)
+
+
+def rag_template(template: str, context: str, query: str):
+    template = re.sub(r"\[context\]", context, template)
+    template = re.sub(r"\[query\]", query, template)
+
+    return template

backend/config.py

@@ -251,7 +251,7 @@ OPENAI_API_BASE_URLS = (
     OPENAI_API_BASE_URLS if OPENAI_API_BASE_URLS != "" else OPENAI_API_BASE_URL
 )
 
-OPENAI_API_BASE_URLS = [url.strip() for url in OPENAI_API_BASE_URL.split(";")]
+OPENAI_API_BASE_URLS = [url.strip() for url in OPENAI_API_BASE_URLS.split(";")]
 
 
 ####################################
@@ -292,6 +292,11 @@ DEFAULT_USER_ROLE = os.getenv("DEFAULT_USER_ROLE", "pending")
 USER_PERMISSIONS = {"chat": {"deletion": True}}
 
 
+MODEL_FILTER_ENABLED = os.environ.get("MODEL_FILTER_ENABLED", False)
+MODEL_FILTER_LIST = os.environ.get("MODEL_FILTER_LIST", "")
+MODEL_FILTER_LIST = [model.strip() for model in MODEL_FILTER_LIST.split(";")]
+
+
 ####################################
 # WEBUI_VERSION
 ####################################

backend/data/config.json

 {
+    "version": "0.0.1",
     "ui": {
         "prompt_suggestions": [
             {

backend/main.py

@@ -9,27 +9,37 @@ import requests
 from fastapi import FastAPI, Request, Depends, status
 from fastapi.staticfiles import StaticFiles
 from fastapi import HTTPException
-from fastapi.responses import JSONResponse
 from fastapi.middleware.wsgi import WSGIMiddleware
 from fastapi.middleware.cors import CORSMiddleware
 from starlette.exceptions import HTTPException as StarletteHTTPException
+from starlette.middleware.base import BaseHTTPMiddleware
 
 
-from litellm.proxy.proxy_server import ProxyConfig, initialize
-from litellm.proxy.proxy_server import app as litellm_app
-
 from apps.ollama.main import app as ollama_app
 from apps.openai.main import app as openai_app
+from apps.litellm.main import app as litellm_app, startup as litellm_app_startup
 from apps.audio.main import app as audio_app
 from apps.images.main import app as images_app
 from apps.rag.main import app as rag_app
 from apps.web.main import app as webui_app
 
+from pydantic import BaseModel
+from typing import List
 
-from config import WEBUI_NAME, ENV, VERSION, CHANGELOG, FRONTEND_BUILD_DIR
-from constants import ERROR_MESSAGES
 
-from utils.utils import get_http_authorization_cred, get_current_user
+from utils.utils import get_admin_user
+from apps.rag.utils import query_doc, query_collection, rag_template
+
+from config import (
+    WEBUI_NAME,
+    ENV,
+    VERSION,
+    CHANGELOG,
+    FRONTEND_BUILD_DIR,
+    MODEL_FILTER_ENABLED,
+    MODEL_FILTER_LIST,
+)
+from constants import ERROR_MESSAGES
 
 
 class SPAStaticFiles(StaticFiles):
@@ -43,23 +53,11 @@ class SPAStaticFiles(StaticFiles):
                 raise ex
 
 
-proxy_config = ProxyConfig()
-
-
-async def config():
-    router, model_list, general_settings = await proxy_config.load_config(
-        router=None, config_file_path="./data/litellm/config.yaml"
-    )
-
-    await initialize(config="./data/litellm/config.yaml", telemetry=False)
-
-
-async def startup():
-    await config()
-
-
 app = FastAPI(docs_url="/docs" if ENV == "dev" else None, redoc_url=None)
 
+app.state.MODEL_FILTER_ENABLED = MODEL_FILTER_ENABLED
+app.state.MODEL_FILTER_LIST = MODEL_FILTER_LIST
+
 origins = ["*"]
 
 app.add_middleware(
@@ -73,7 +71,127 @@ app.add_middleware(
 
 @app.on_event("startup")
 async def on_startup():
-    await startup()
+    await litellm_app_startup()
+
+
+class RAGMiddleware(BaseHTTPMiddleware):
+    async def dispatch(self, request: Request, call_next):
+        if request.method == "POST" and (
+            "/api/chat" in request.url.path or "/chat/completions" in request.url.path
+        ):
+            print(request.url.path)
+
+            # Read the original request body
+            body = await request.body()
+            # Decode body to string
+            body_str = body.decode("utf-8")
+            # Parse string to JSON
+            data = json.loads(body_str) if body_str else {}
+
+            # 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
+                for i in range(len(data["messages"]) - 1, -1, -1):
+                    if data["messages"][i]["role"] == "user":
+                        last_user_message_idx = i
+                        break
+
+                user_message = data["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=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
+            scope = request.scope
+            scope["body"] = modified_body_bytes
+            request = Request(scope, receive=lambda: self._receive(modified_body_bytes))
+
+        response = await call_next(request)
+        return response
+
+    async def _receive(self, body: bytes):
+        return {"type": "http.request", "body": body, "more_body": False}
+
+
+app.add_middleware(RAGMiddleware)
 
 
 @app.middleware("http")
@@ -86,21 +204,6 @@ async def check_url(request: Request, call_next):
     return response
 
 
-@litellm_app.middleware("http")
-async def auth_middleware(request: Request, call_next):
-    auth_header = request.headers.get("Authorization", "")
-
-    if ENV != "dev":
-        try:
-            user = get_current_user(get_http_authorization_cred(auth_header))
-            print(user)
-        except Exception as e:
-            return JSONResponse(status_code=400, content={"detail": str(e)})
-
-    response = await call_next(request)
-    return response
-
-
 app.mount("/api/v1", webui_app)
 app.mount("/litellm/api", litellm_app)
 
@@ -125,6 +228,39 @@ async def get_app_config():
     }
 
 
+@app.get("/api/config/model/filter")
+async def get_model_filter_config(user=Depends(get_admin_user)):
+    return {
+        "enabled": app.state.MODEL_FILTER_ENABLED,
+        "models": app.state.MODEL_FILTER_LIST,
+    }
+
+
+class ModelFilterConfigForm(BaseModel):
+    enabled: bool
+    models: List[str]
+
+
+@app.post("/api/config/model/filter")
+async def get_model_filter_config(
+    form_data: ModelFilterConfigForm, user=Depends(get_admin_user)
+):
+
+    app.state.MODEL_FILTER_ENABLED = form_data.enabled
+    app.state.MODEL_FILTER_LIST = form_data.models
+
+    ollama_app.state.MODEL_FILTER_ENABLED = app.state.MODEL_FILTER_ENABLED
+    ollama_app.state.MODEL_FILTER_LIST = app.state.MODEL_FILTER_LIST
+
+    openai_app.state.MODEL_FILTER_ENABLED = app.state.MODEL_FILTER_ENABLED
+    openai_app.state.MODEL_FILTER_LIST = app.state.MODEL_FILTER_LIST
+
+    return {
+        "enabled": app.state.MODEL_FILTER_ENABLED,
+        "models": app.state.MODEL_FILTER_LIST,
+    }
+
+
 @app.get("/api/version")
 async def get_app_config():
 
@@ -156,6 +292,7 @@ async def get_app_latest_release_version():
 
 
 app.mount("/static", StaticFiles(directory="static"), name="static")
+app.mount("/cache", StaticFiles(directory="data/cache"), name="cache")
 
 
 app.mount(

backend/requirements.txt

@@ -16,7 +16,8 @@ aiohttp
 peewee
 bcrypt
 
-litellm
+litellm==1.30.7
+argon2-cffi
 apscheduler
 google-generativeai
 

package.json

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

src/lib/apis/images/index.ts

 import { IMAGES_API_BASE_URL } from '$lib/constants';
 
-export const getImageGenerationEnabledStatus = async (token: string = '') => {
+export const getImageGenerationConfig = async (token: string = '') => {
 	let error = null;
 
-	const res = await fetch(`${IMAGES_API_BASE_URL}/enabled`, {
+	const res = await fetch(`${IMAGES_API_BASE_URL}/config`, {
 		method: 'GET',
 		headers: {
 			Accept: 'application/json',
@@ -32,10 +32,50 @@ export const getImageGenerationEnabledStatus = async (token: string = '') => {
 	return res;
 };
 
-export const toggleImageGenerationEnabledStatus = async (token: string = '') => {
+export const updateImageGenerationConfig = async (
+	token: string = '',
+	engine: string,
+	enabled: boolean
+) => {
 	let error = null;
 
-	const res = await fetch(`${IMAGES_API_BASE_URL}/enabled/toggle`, {
+	const res = await fetch(`${IMAGES_API_BASE_URL}/config/update`, {
+		method: 'POST',
+		headers: {
+			Accept: 'application/json',
+			'Content-Type': 'application/json',
+			...(token && { authorization: `Bearer ${token}` })
+		},
+		body: JSON.stringify({
+			engine,
+			enabled
+		})
+	})
+		.then(async (res) => {
+			if (!res.ok) throw await res.json();
+			return res.json();
+		})
+		.catch((err) => {
+			console.log(err);
+			if ('detail' in err) {
+				error = err.detail;
+			} else {
+				error = 'Server connection failed';
+			}
+			return null;
+		});
+
+	if (error) {
+		throw error;
+	}
+
+	return res;
+};
+
+export const getOpenAIKey = async (token: string = '') => {
+	let error = null;
+
+	const res = await fetch(`${IMAGES_API_BASE_URL}/key`, {
 		method: 'GET',
 		headers: {
 			Accept: 'application/json',
@@ -61,7 +101,42 @@ export const toggleImageGenerationEnabledStatus = async (token: string = '') =>
 		throw error;
 	}
 
-	return res;
+	return res.OPENAI_API_KEY;
+};
+
+export const updateOpenAIKey = async (token: string = '', key: string) => {
+	let error = null;
+
+	const res = await fetch(`${IMAGES_API_BASE_URL}/key/update`, {
+		method: 'POST',
+		headers: {
+			Accept: 'application/json',
+			'Content-Type': 'application/json',
+			...(token && { authorization: `Bearer ${token}` })
+		},
+		body: JSON.stringify({
+			key: key
+		})
+	})
+		.then(async (res) => {
+			if (!res.ok) throw await res.json();
+			return res.json();
+		})
+		.catch((err) => {
+			console.log(err);
+			if ('detail' in err) {
+				error = err.detail;
+			} else {
+				error = 'Server connection failed';
+			}
+			return null;
+		});
+
+	if (error) {
+		throw error;
+	}
+
+	return res.OPENAI_API_KEY;
 };
 
 export const getAUTOMATIC1111Url = async (token: string = '') => {
@@ -263,7 +338,7 @@ export const updateImageSteps = async (token: string = '', steps: number) => {
 	return res.IMAGE_STEPS;
 };
 
-export const getDiffusionModels = async (token: string = '') => {
+export const getImageGenerationModels = async (token: string = '') => {
 	let error = null;
 
 	const res = await fetch(`${IMAGES_API_BASE_URL}/models`, {
@@ -295,7 +370,7 @@ export const getDiffusionModels = async (token: string = '') => {
 	return res;
 };
 
-export const getDefaultDiffusionModel = async (token: string = '') => {
+export const getDefaultImageGenerationModel = async (token: string = '') => {
 	let error = null;
 
 	const res = await fetch(`${IMAGES_API_BASE_URL}/models/default`, {
@@ -327,7 +402,7 @@ export const getDefaultDiffusionModel = async (token: string = '') => {
 	return res.model;
 };
 
-export const updateDefaultDiffusionModel = async (token: string = '', model: string) => {
+export const updateDefaultImageGenerationModel = async (token: string = '', model: string) => {
 	let error = null;
 
 	const res = await fetch(`${IMAGES_API_BASE_URL}/models/default/update`, {

src/lib/apis/index.ts

@@ -77,3 +77,65 @@ export const getVersionUpdates = async () => {
 
 	return res;
 };
+
+export const getModelFilterConfig = async (token: string) => {
+	let error = null;
+
+	const res = await fetch(`${WEBUI_BASE_URL}/api/config/model/filter`, {
+		method: 'GET',
+		headers: {
+			'Content-Type': 'application/json',
+			Authorization: `Bearer ${token}`
+		}
+	})
+		.then(async (res) => {
+			if (!res.ok) throw await res.json();
+			return res.json();
+		})
+		.catch((err) => {
+			console.log(err);
+			error = err;
+			return null;
+		});
+
+	if (error) {
+		throw error;
+	}
+
+	return res;
+};
+
+export const updateModelFilterConfig = async (
+	token: string,
+	enabled: boolean,
+	models: string[]
+) => {
+	let error = null;
+
+	const res = await fetch(`${WEBUI_BASE_URL}/api/config/model/filter`, {
+		method: 'POST',
+		headers: {
+			'Content-Type': 'application/json',
+			Authorization: `Bearer ${token}`
+		},
+		body: JSON.stringify({
+			enabled: enabled,
+			models: models
+		})
+	})
+		.then(async (res) => {
+			if (!res.ok) throw await res.json();
+			return res.json();
+		})
+		.catch((err) => {
+			console.log(err);
+			error = err;
+			return null;
+		});
+
+	if (error) {
+		throw error;
+	}
+
+	return res;
+};

src/lib/apis/litellm/index.ts

@@ -77,6 +77,7 @@ type AddLiteLLMModelForm = {
 	api_base: string;
 	api_key: string;
 	rpm: string;
+	max_tokens: string;
 };
 
 export const addLiteLLMModel = async (token: string = '', payload: AddLiteLLMModelForm) => {
@@ -95,7 +96,8 @@ export const addLiteLLMModel = async (token: string = '', payload: AddLiteLLMMod
 				model: payload.model,
 				...(payload.api_base === '' ? {} : { api_base: payload.api_base }),
 				...(payload.api_key === '' ? {} : { api_key: payload.api_key }),
-				...(isNaN(parseInt(payload.rpm)) ? {} : { rpm: parseInt(payload.rpm) })
+				...(isNaN(parseInt(payload.rpm)) ? {} : { rpm: parseInt(payload.rpm) }),
+				...(payload.max_tokens === '' ? {} : { max_tokens: payload.max_tokens })
 			}
 		})
 	})

src/lib/apis/rag/index.ts

 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,
-			chunk_overlap: overlap
+			...payload
 		})
 	})
 		.then(async (res) => {
@@ -252,7 +261,7 @@ export const queryCollection = async (
 	token: string,
 	collection_names: string,
 	query: string,
-	k: number
+	k: number | null = null
 ) => {
 	let error = null;
 

src/lib/components/admin/Settings/Users.svelte

 <script lang="ts">
+	import { getModelFilterConfig, updateModelFilterConfig } from '$lib/apis';
 	import { getSignUpEnabledStatus, toggleSignUpEnabledStatus } from '$lib/apis/auths';
 	import { getUserPermissions, updateUserPermissions } from '$lib/apis/users';
+	import { models } from '$lib/stores';
 	import { onMount } from 'svelte';
 
 	export let saveHandler: Function;
 
+	let whitelistEnabled = false;
+	let whitelistModels = [''];
 	let permissions = {
 		chat: {
 			deletion: true
@@ -13,6 +17,13 @@
 
 	onMount(async () => {
 		permissions = await getUserPermissions(localStorage.token);
+
+		const res = await getModelFilterConfig(localStorage.token);
+		if (res) {
+			whitelistEnabled = res.enabled;
+
+			whitelistModels = res.models.length > 0 ? res.models : [''];
+		}
 	});
 </script>
 
@@ -21,6 +32,8 @@
 	on:submit|preventDefault={async () => {
 		// console.log('submit');
 		await updateUserPermissions(localStorage.token, permissions);
+
+		await updateModelFilterConfig(localStorage.token, whitelistEnabled, whitelistModels);
 		saveHandler();
 	}}
 >
@@ -69,6 +82,106 @@
 				</button>
 			</div>
 		</div>
+
+		<hr class=" dark:border-gray-700 my-2" />
+
+		<div class="mt-2 space-y-3 pr-1.5">
+			<div>
+				<div class="mb-2">
+					<div class="flex justify-between items-center text-xs">
+						<div class=" text-sm font-medium">Manage Models</div>
+					</div>
+				</div>
+
+				<div class=" space-y-3">
+					<div>
+						<div class="flex justify-between items-center text-xs">
+							<div class=" text-xs font-medium">Model Whitelisting</div>
+
+							<button
+								class=" text-xs font-medium text-gray-500"
+								type="button"
+								on:click={() => {
+									whitelistEnabled = !whitelistEnabled;
+								}}>{whitelistEnabled ? 'On' : 'Off'}</button
+							>
+						</div>
+					</div>
+
+					{#if whitelistEnabled}
+						<div>
+							<div class=" space-y-1.5">
+								{#each whitelistModels as modelId, modelIdx}
+									<div class="flex w-full">
+										<div class="flex-1 mr-2">
+											<select
+												class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
+												bind:value={modelId}
+												placeholder="Select a model"
+											>
+												<option value="" disabled selected>Select a model</option>
+												{#each $models.filter((model) => model.id) as model}
+													<option value={model.id} class="bg-gray-100 dark:bg-gray-700"
+														>{model.name}</option
+													>
+												{/each}
+											</select>
+										</div>
+
+										{#if modelIdx === 0}
+											<button
+												class="px-2.5 bg-gray-100 hover:bg-gray-200 text-gray-800 dark:bg-gray-900 dark:text-white rounded-lg transition"
+												type="button"
+												on:click={() => {
+													if (whitelistModels.at(-1) !== '') {
+														whitelistModels = [...whitelistModels, ''];
+													}
+												}}
+											>
+												<svg
+													xmlns="http://www.w3.org/2000/svg"
+													viewBox="0 0 16 16"
+													fill="currentColor"
+													class="w-4 h-4"
+												>
+													<path
+														d="M8.75 3.75a.75.75 0 0 0-1.5 0v3.5h-3.5a.75.75 0 0 0 0 1.5h3.5v3.5a.75.75 0 0 0 1.5 0v-3.5h3.5a.75.75 0 0 0 0-1.5h-3.5v-3.5Z"
+													/>
+												</svg>
+											</button>
+										{:else}
+											<button
+												class="px-2.5 bg-gray-100 hover:bg-gray-200 text-gray-800 dark:bg-gray-900 dark:text-white rounded-lg transition"
+												type="button"
+												on:click={() => {
+													whitelistModels.splice(modelIdx, 1);
+													whitelistModels = whitelistModels;
+												}}
+											>
+												<svg
+													xmlns="http://www.w3.org/2000/svg"
+													viewBox="0 0 16 16"
+													fill="currentColor"
+													class="w-4 h-4"
+												>
+													<path d="M3.75 7.25a.75.75 0 0 0 0 1.5h8.5a.75.75 0 0 0 0-1.5h-8.5Z" />
+												</svg>
+											</button>
+										{/if}
+									</div>
+								{/each}
+							</div>
+
+							<div class="flex justify-end items-center text-xs mt-1.5 text-right">
+								<div class=" text-xs font-medium">
+									{whitelistModels.length} Model(s) Whitelisted
+								</div>
+							</div>
+						</div>
+					{/if}
+				</div>
+			</div>
+		</div>
 	</div>
 
 	<div class="flex justify-end pt-3 text-sm font-medium">