Merge branch 'main' into feat/cancel-model-download

# Conflicts:
#	src/lib/components/chat/Settings/Models.svelte

.dockerignore

@@ -7,7 +7,6 @@ node_modules
 /package
 .env
 .env.*
-!.env.example
 vite.config.js.timestamp-*
 vite.config.ts.timestamp-*
 __pycache__

example.env → .env.example

 # Ollama URL for the backend to connect
-# The path '/ollama/api' will be redirected to the specified backend URL
-OLLAMA_API_BASE_URL='http://localhost:11434/api'
+# The path '/ollama' will be redirected to the specified backend URL
+OLLAMA_BASE_URL='http://localhost:11434'
 
 OPENAI_API_BASE_URL=''
 OPENAI_API_KEY=''
 
+# AUTOMATIC1111_BASE_URL="http://localhost:7860"
+
 # DO NOT TRACK
 SCARF_NO_ANALYTICS=true
 DO_NOT_TRACK=true
\ No newline at end of file

.github/ISSUE_TEMPLATE/bug_report.md

@@ -32,7 +32,7 @@ assignees: ''
 **Confirmation:**
 
 - [ ] I have read and followed all the instructions provided in the README.md.
-- [ ] I have reviewed the troubleshooting.md document.
+- [ ] I am on the latest version of both Open WebUI and Ollama.
 - [ ] I have included the browser console logs.
 - [ ] I have included the Docker container logs.
 

.github/pull_request_template.md

+## Pull Request Checklist
+
+- [ ] **Description:** Briefly describe the changes in this pull request.
+- [ ] **Changelog:** Ensure a changelog entry following the format of [Keep a Changelog](https://keepachangelog.com/) is added at the bottom of the PR description.
+- [ ] **Documentation:** Have you updated relevant documentation?
+- [ ] **Dependencies:** Are there any new dependencies? Have you updated the dependency versions in the documentation?
+
+---
+
+## Description
+
+[Insert a brief description of the changes made in this pull request]
+
+---
+
+### Changelog Entry
+
+### Added
+
+- [List any new features or additions]
+
+### Fixed
+
+- [List any fixes or corrections]
+
+### Changed
+
+- [List any changes or updates]
+
+### Removed
+
+- [List any removed features or files]

.github/workflows/build-release.yml

+name: Release
+
+on:
+  push:
+    branches:
+      - main # or whatever branch you want to use
+
+jobs:
+  release:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v2
+
+    - name: Check for changes in package.json
+      run: |
+        git diff --cached --diff-filter=d package.json || {
+          echo "No changes to package.json"
+          exit 1
+        }
+    
+    - name: Get version number from package.json
+      id: get_version
+      run: |
+        VERSION=$(jq -r '.version' package.json)
+        echo "::set-output name=version::$VERSION"
+
+    - name: Extract latest CHANGELOG entry
+      id: changelog
+      run: |
+        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
+      with:
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        script: |
+          const changelog = `${{ steps.changelog.outputs.content }}`;
+          const release = await github.rest.repos.createRelease({
+            owner: context.repo.owner,
+            repo: context.repo.repo,
+            tag_name: `v${{ steps.get_version.outputs.version }}`,
+            name: `v${{ steps.get_version.outputs.version }}`,
+            body: changelog,
+          })
+          console.log(`Created release ${release.data.html_url}`)
+
+    - name: Upload package to GitHub release
+      uses: actions/upload-artifact@v3
+      with:
+        name: package
+        path: .
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/docker-build.yaml

@@ -52,6 +52,7 @@ jobs:
             type=ref,event=tag
             type=sha,prefix=git-
             type=semver,pattern={{version}}
+            type=semver,pattern={{major}}.{{minor}}
           flavor: |
             latest=${{ github.ref == 'refs/heads/main' }}
 

CHANGELOG.md

+# Changelog
+
+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).
+
+## [0.1.114] - 2024-03-20
+
+### Added
+
+- **🔗 Webhook Integration**: Now you can subscribe to new user sign-up events via webhook. Simply navigate to the admin panel > admin settings > webhook URL.
+- **🛡️ Enhanced Model Filtering**: Alongside Ollama, OpenAI proxy model whitelisting, we've added model filtering functionality for LiteLLM proxy.
+- **🌍 Expanded Language Support**: Spanish, Catalan, and Vietnamese languages are now available, with improvements made to others.
+
+### Fixed
+
+- **🔧 Input Field Spelling**: Resolved issue with spelling mistakes in input fields.
+- **🖊️ Light Mode Styling**: Fixed styling issue with light mode in document adding.
+
+### Changed
+
+- **🔄 Language Sorting**: Languages are now sorted alphabetically by their code for improved organization.
+
+## [0.1.113] - 2024-03-18
+
+### Added
+
+- 🌍 **Localization**: You can now change the UI language in Settings > General. We support Ukrainian, German, Farsi (Persian), Traditional and Simplified Chinese and French translations. You can help us to translate the UI into your language! More info in our [CONTRIBUTION.md](https://github.com/open-webui/open-webui/blob/main/docs/CONTRIBUTING.md#-translations-and-internationalization).
+- 🎨 **System-wide Theme**: Introducing a new system-wide theme for enhanced visual experience.
+
+### Fixed
+
+- 🌑 **Dark Background on Select Fields**: Improved readability by adding a dark background to select fields, addressing issues on certain browsers/devices.
+- **Multiple OPENAI_API_BASE_URLS Issue**: Resolved issue where multiple base URLs caused conflicts when one wasn't functioning.
+- **RAG Encoding Issue**: Fixed encoding problem in RAG.
+- **npm Audit Fix**: Addressed npm audit findings.
+- **Reduced Scroll Threshold**: Improved auto-scroll experience by reducing the scroll threshold from 50px to 5px.
+
+### Changed
+
+- 🔄 **Sidebar UI Update**: Updated sidebar UI to feature a chat menu dropdown, replacing two icons for improved navigation.
+
+## [0.1.112] - 2024-03-15
+
+### Fixed
+
+- 🗨️ Resolved chat malfunction after image generation.
+- 🎨 Fixed various RAG issues.
+- 🧪 Rectified experimental broken GGUF upload logic.
+
+## [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
+
+- **🌐 Multiple OpenAI Servers Support**: Enjoy seamless integration with multiple OpenAI-compatible APIs, now supported natively.
+
+### Fixed
+
+- **🔍 OCR Issue**: Resolved PDF parsing issue caused by OCR malfunction.
+- **🚫 RAG Issue**: Fixed the RAG functionality, ensuring it operates smoothly.
+- **📄 "Add Docs" Model Button**: Addressed the non-functional behavior of the "Add Docs" model button.
+
+## [0.1.109] - 2024-03-06
+
+### Added
+
+- **🔄 Multiple Ollama Servers Support**: Enjoy enhanced scalability and performance with support for multiple Ollama servers in a single WebUI. Load balancing features are now available, providing improved efficiency (#788, #278).
+- **🔧 Support for Claude 3 and Gemini**: Responding to user requests, we've expanded our toolset to include Claude 3 and Gemini, offering a wider range of functionalities within our platform (#1064).
+- **🔍 OCR Functionality for PDF Loader**: We've augmented our PDF loader with Optical Character Recognition (OCR) capabilities. Now, extract text from scanned documents and images within PDFs, broadening the scope of content processing (#1050).
+
+### Fixed
+
+- **🛠️ RAG Collection**: Implemented a dynamic mechanism to recreate RAG collections, ensuring users have up-to-date and accurate data (#1031).
+- **📝 User Agent Headers**: Fixed issue of RAG web requests being sent with empty user_agent headers, reducing rejections from certain websites. Realistic headers are now utilized for these requests (#1024).
+- **⏹️ Playground Cancel Functionality**: Introducing a new "Cancel" option for stopping Ollama generation in the Playground, enhancing user control and usability (#1006).
+- **🔤 Typographical Error in 'ASSISTANT' Field**: Corrected a typographical error in the 'ASSISTANT' field within the GGUF model upload template for accuracy and consistency (#1061).
+
+### Changed
+
+- **🔄 Refactored Message Deletion Logic**: Streamlined message deletion process for improved efficiency and user experience, simplifying interactions within the platform (#1004).
+- **⚠️ Deprecation of `OLLAMA_API_BASE_URL`**: Deprecated `OLLAMA_API_BASE_URL` environment variable; recommend using `OLLAMA_BASE_URL` instead. Refer to our documentation for further details.
+
+## [0.1.108] - 2024-03-02
+
+### Added
+
+- **🎮 Playground Feature (Beta)**: Explore the full potential of the raw API through an intuitive UI with our new playground feature, accessible to admins. Simply click on the bottom name area of the sidebar to access it. The playground feature offers two modes text completion (notebook) and chat completion. As it's in beta, please report any issues you encounter.
+- **🛠️ Direct Database Download for Admins**: Admins can now download the database directly from the WebUI via the admin settings.
+- **🎨 Additional RAG Settings**: Customize your RAG process with the ability to edit the TOP K value. Navigate to Documents > Settings > General to make changes.
+- **🖥️ UI Improvements**: Tooltips now available in the input area and sidebar handle. More tooltips will be added across other parts of the UI.
+
+### Fixed
+
+- Resolved input autofocus issue on mobile when the sidebar is open, making it easier to use.
+- Corrected numbered list display issue in Safari (#963).
+- Restricted user ability to delete chats without proper permissions (#993).
+
+### Changed
+
+- **Simplified Ollama Settings**: Ollama settings now don't require the `/api` suffix. You can now utilize the Ollama base URL directly, e.g., `http://localhost:11434`. Also, an `OLLAMA_BASE_URL` environment variable has been added.
+- **Database Renaming**: Starting from this release, `ollama.db` will be automatically renamed to `webui.db`.
+
+## [0.1.107] - 2024-03-01
+
+### Added
+
+- **🚀 Makefile and LLM Update Script**: Included Makefile and a script for LLM updates in the repository.
+
+### Fixed
+
+- Corrected issue where links in the settings modal didn't appear clickable (#960).
+- Fixed problem with web UI port not taking effect due to incorrect environment variable name in run-compose.sh (#996).
+- Enhanced user experience by displaying chat in browser title and enabling automatic scrolling to the bottom (#992).
+
+### Changed
+
+- Upgraded toast library from `svelte-french-toast` to `svelte-sonner` for a more polished UI.
+- Enhanced accessibility with the addition of dark mode on the authentication page.
+
+## [0.1.106] - 2024-02-27
+
+### Added
+
+- **🎯 Auto-focus Feature**: The input area now automatically focuses when initiating or opening a chat conversation.
+
+### Fixed
+
+- Corrected typo from "HuggingFace" to "Hugging Face" (Issue #924).
+- Resolved bug causing errors in chat completion API calls to OpenAI due to missing "num_ctx" parameter (Issue #927).
+- Fixed issues preventing text editing, selection, and cursor retention in the input field (Issue #940).
+- Fixed a bug where defining an OpenAI-compatible API server using 'OPENAI_API_BASE_URL' containing 'openai' string resulted in hiding models not containing 'gpt' string from the model menu. (Issue #930)
+
+## [0.1.105] - 2024-02-25
+
+### Added
+
+- **📄 Document Selection**: Now you can select and delete multiple documents at once for easier management.
+
+### Changed
+
+- **🏷️ Document Pre-tagging**: Simply click the "+" button at the top, enter tag names in the popup window, or select from a list of existing tags. Then, upload files with the added tags for streamlined organization.
+
+## [0.1.104] - 2024-02-25
+
+### Added
+
+- **🔄 Check for Updates**: Keep your system current by checking for updates conveniently located in Settings > About.
+- **🗑️ Automatic Tag Deletion**: Unused tags on the sidebar will now be deleted automatically with just a click.
+
+### Changed
+
+- **🎨 Modernized Styling**: Enjoy a refreshed look with updated styling for a more contemporary experience.
+
+## [0.1.103] - 2024-02-25
+
+### Added
+
+- **🔗 Built-in LiteLLM Proxy**: Now includes LiteLLM proxy within Open WebUI for enhanced functionality.
+
+  - Easily integrate existing LiteLLM configurations using `-v /path/to/config.yaml:/app/backend/data/litellm/config.yaml` flag.
+  - When utilizing Docker container to run Open WebUI, ensure connections to localhost use `host.docker.internal`.
+
+- **🖼️ Image Generation Enhancements**: Introducing Advanced Settings with Image Preview Feature.
+  - Customize image generation by setting the number of steps; defaults to A1111 value.
+
+### Fixed
+
+- Resolved issue with RAG scan halting document loading upon encountering unsupported MIME types or exceptions (Issue #866).
+
+### Changed
+
+- Ollama is no longer required to run Open WebUI.
+- Access our comprehensive documentation at [Open WebUI Documentation](https://docs.openwebui.com/).
+
+## [0.1.102] - 2024-02-22
+
+### Added
+
+- **🖼️ Image Generation**: Generate Images using the AUTOMATIC1111/stable-diffusion-webui API. You can set this up in Settings > Images.
+- **📝 Change title generation prompt**: Change the prompt used to generate titles for your chats. You can set this up in the Settings > Interface.
+- **🤖 Change embedding model**: Change the embedding model used to generate embeddings for your chats in the Dockerfile. Use any sentence transformer model from huggingface.co.
+- **📢 CHANGELOG.md/Popup**: This popup will show you the latest changes.
+
+## [0.1.101] - 2024-02-22
+
+### Fixed
+
+- LaTex output formatting issue (#828)
+
+### Changed
+
+- Instead of having the previous 1.0.0-alpha.101, we switched to semantic versioning as a way to respect global conventions.

Dockerfile

@@ -20,7 +20,7 @@ FROM python:3.11-slim-bookworm as base
 ENV ENV=prod
 ENV PORT ""
 
-ENV OLLAMA_API_BASE_URL "/ollama/api"
+ENV OLLAMA_BASE_URL "/ollama"
 
 ENV OPENAI_API_BASE_URL ""
 ENV OPENAI_API_KEY ""
@@ -30,15 +30,31 @@ ENV WEBUI_SECRET_KEY ""
 ENV SCARF_NO_ANALYTICS true
 ENV DO_NOT_TRACK true
 
-#Whisper TTS Settings
+######## Preloaded models ########
+# whisper TTS Settings
 ENV WHISPER_MODEL="base"
 ENV WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
 
+# RAG Embedding Model Settings
+# any sentence transformer model; models to use can be found at https://huggingface.co/models?library=sentence-transformers
+# Leaderboard: https://huggingface.co/spaces/mteb/leaderboard 
+# 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 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
+
+######## Preloaded models ########
+
 WORKDIR /app/backend
 
 # install python dependencies
 COPY ./backend/requirements.txt ./requirements.txt
 
+RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
+
 RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu --no-cache-dir
 RUN pip3 install -r requirements.txt --no-cache-dir
 
@@ -48,9 +64,10 @@ RUN apt-get update \
     && apt-get install -y pandoc netcat-openbsd \
     && rm -rf /var/lib/apt/lists/*
 
-# RUN python -c "from sentence_transformers import SentenceTransformer; model = SentenceTransformer('all-MiniLM-L6-v2')"
-RUN python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='cpu', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"
-
+# preload embedding model
+RUN python -c "import os; from chromadb.utils import embedding_functions; sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=os.environ['RAG_EMBEDDING_MODEL'], device=os.environ['RAG_EMBEDDING_MODEL_DEVICE_TYPE'])"
+# preload tts model
+RUN python -c "import os; from faster_whisper import WhisperModel; WhisperModel(os.environ['WHISPER_MODEL'], device='auto', compute_type='int8', download_root=os.environ['WHISPER_MODEL_DIR'])"
 
 # copy embedding weight from build
 RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2
@@ -58,6 +75,8 @@ COPY --from=build /app/onnx /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onn
 
 # copy built frontend files
 COPY --from=build /app/build /app/build
+COPY --from=build /app/CHANGELOG.md /app/CHANGELOG.md
+COPY --from=build /app/package.json /app/package.json
 
 # copy backend files
 COPY ./backend .

Makefile

+install:
+	@docker-compose up -d
+
+remove:
+	@chmod +x confirm_remove.sh
+	@./confirm_remove.sh
+
+
+start:
+	@docker-compose start
+
+stop:
+	@docker-compose stop
+
+update:
+	# Calls the LLM update script
+	chmod +x update_ollama_models.sh
+	@./update_ollama_models.sh
+	@git pull
+	@docker-compose down
+	# Make sure the ollama-webui container is stopped before rebuilding
+	@docker stop open-webui || true
+	@docker-compose up --build -d
+	@docker-compose start
+

TROUBLESHOOTING.md

@@ -4,7 +4,7 @@
 
 The Open WebUI system is designed to streamline interactions between the client (your browser) and the Ollama API. At the heart of this design is a backend reverse proxy, enhancing security and resolving CORS issues.
 
-- **How it Works**: The Open WebUI is designed to interact with the Ollama API through a specific route. When a request is made from the WebUI to Ollama, it is not directly sent to the Ollama API. Initially, the request is sent to the Open WebUI backend via `/ollama/api` route. From there, the backend is responsible for forwarding the request to the Ollama API. This forwarding is accomplished by using the route specified in the `OLLAMA_API_BASE_URL` environment variable. Therefore, a request made to `/ollama/api` in the WebUI is effectively the same as making a request to `OLLAMA_API_BASE_URL` in the backend. For instance, a request to `/ollama/api/tags` in the WebUI is equivalent to `OLLAMA_API_BASE_URL/tags` in the backend.
+- **How it Works**: The Open WebUI is designed to interact with the Ollama API through a specific route. When a request is made from the WebUI to Ollama, it is not directly sent to the Ollama API. Initially, the request is sent to the Open WebUI backend via `/ollama` route. From there, the backend is responsible for forwarding the request to the Ollama API. This forwarding is accomplished by using the route specified in the `OLLAMA_BASE_URL` environment variable. Therefore, a request made to `/ollama` in the WebUI is effectively the same as making a request to `OLLAMA_BASE_URL` in the backend. For instance, a request to `/ollama/api/tags` in the WebUI is equivalent to `OLLAMA_BASE_URL/api/tags` in the backend.
 
 - **Security Benefits**: This design prevents direct exposure of the Ollama API to the frontend, safeguarding against potential CORS (Cross-Origin Resource Sharing) issues and unauthorized access. Requiring authentication to access the Ollama API further enhances this security layer.
 
@@ -15,7 +15,7 @@ If you're experiencing connection issues, it’s often due to the WebUI docker c
 **Example Docker Command**:
 
 ```bash
-docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_API_BASE_URL=http://127.0.0.1:11434/api --name open-webui --restart always ghcr.io/open-webui/open-webui:main
+docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://127.0.0.1:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main
 ```
 
 ### General Connection Errors
@@ -25,8 +25,8 @@ docker run -d --network=host -v open-webui:/app/backend/data -e OLLAMA_API_BASE_
 **Troubleshooting Steps**:
 
 1. **Verify Ollama URL Format**:
-   - When running the Web UI container, ensure the `OLLAMA_API_BASE_URL` is correctly set, including the `/api` suffix. (e.g., `http://192.168.1.1:11434/api` for different host setups).
+   - When running the Web UI container, ensure the `OLLAMA_BASE_URL` is correctly set. (e.g., `http://192.168.1.1:11434` for different host setups).
    - In the Open WebUI, navigate to "Settings" > "General".
-   - Confirm that the Ollama Server URL is correctly set to `[OLLAMA URL]/api` (e.g., `http://localhost:11434/api`), including the `/api` suffix.
+   - Confirm that the Ollama Server URL is correctly set to `[OLLAMA URL]` (e.g., `http://localhost:11434`).
 
 By following these enhanced troubleshooting steps, connection issues should be effectively resolved. For further assistance or queries, feel free to reach out to us on our community Discord.

backend/.dockerignore

@@ -5,3 +5,10 @@ uploads
 .ipynb_checkpoints
 *.db
 _test
+!/data
+/data/*
+!/data/litellm
+/data/litellm/*
+!data/litellm/config.yaml
+
+!data/config.json
\ No newline at end of file

backend/.gitignore

@@ -6,5 +6,11 @@ uploads
 *.db
 _test
 Pipfile
-data/*
+!/data
+/data/*
+!/data/litellm
+/data/litellm/*
+!data/litellm/config.yaml
+
+!data/config.json
 .webui_secret_key
\ No newline at end of file

backend/apps/audio/main.py

@@ -56,7 +56,7 @@ def transcribe(
 
         model = WhisperModel(
             WHISPER_MODEL,
-            device="cpu",
+            device="auto",
             compute_type="int8",
             download_root=WHISPER_MODEL_DIR,
         )

backend/apps/images/main.py

+import re
+import requests
+from fastapi import (
+    FastAPI,
+    Request,
+    Depends,
+    HTTPException,
+    status,
+    UploadFile,
+    File,
+    Form,
+)
+from fastapi.middleware.cors import CORSMiddleware
+from faster_whisper import WhisperModel
+
+from constants import ERROR_MESSAGES
+from utils.utils import (
+    get_current_user,
+    get_admin_user,
+)
+from utils.misc import calculate_sha256
+from typing import Optional
+from pydantic import BaseModel
+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(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    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.IMAGE_SIZE = "512x512"
+app.state.IMAGE_STEPS = 50
+
+
+@app.get("/config")
+async def get_config(request: Request, user=Depends(get_admin_user)):
+    return {"engine": app.state.ENGINE, "enabled": app.state.ENABLED}
+
+
+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):
+    url: str
+
+
+@app.get("/url")
+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_automatic1111_url(
+    form_data: UrlUpdateForm, user=Depends(get_admin_user)
+):
+
+    if form_data.url == "":
+        app.state.AUTOMATIC1111_BASE_URL = AUTOMATIC1111_BASE_URL
+    else:
+        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,
+        "status": True,
+    }
+
+
+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
+
+
+@app.get("/size")
+async def get_image_size(user=Depends(get_admin_user)):
+    return {"IMAGE_SIZE": app.state.IMAGE_SIZE}
+
+
+@app.post("/size/update")
+async def update_image_size(
+    form_data: ImageSizeUpdateForm, user=Depends(get_admin_user)
+):
+    pattern = r"^\d+x\d+$"  # Regular expression pattern
+    if re.match(pattern, form_data.size):
+        app.state.IMAGE_SIZE = form_data.size
+        return {
+            "IMAGE_SIZE": app.state.IMAGE_SIZE,
+            "status": True,
+        }
+    else:
+        raise HTTPException(
+            status_code=400,
+            detail=ERROR_MESSAGES.INCORRECT_FORMAT("  (e.g., 512x512)."),
+        )
+
+
+class ImageStepsUpdateForm(BaseModel):
+    steps: int
+
+
+@app.get("/steps")
+async def get_image_size(user=Depends(get_admin_user)):
+    return {"IMAGE_STEPS": app.state.IMAGE_STEPS}
+
+
+@app.post("/steps/update")
+async def update_image_size(
+    form_data: ImageStepsUpdateForm, user=Depends(get_admin_user)
+):
+    if form_data.steps >= 0:
+        app.state.IMAGE_STEPS = form_data.steps
+        return {
+            "IMAGE_STEPS": app.state.IMAGE_STEPS,
+            "status": True,
+        }
+    else:
+        raise HTTPException(
+            status_code=400,
+            detail=ERROR_MESSAGES.INCORRECT_FORMAT("  (e.g., 50)."),
+        )
+
+
+@app.get("/models")
+def get_models(user=Depends(get_current_user)):
+    try:
+        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))
+
+
+@app.get("/models/default")
+async def get_default_model(user=Depends(get_admin_user)):
+    try:
+        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))
+
+
+class UpdateModelForm(BaseModel):
+    model: str
+
+
+def set_model_handler(model: str):
+
+    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
+
+
+@app.post("/models/default/update")
+def update_default_model(
+    form_data: UpdateModelForm,
+    user=Depends(get_current_user),
+):
+    return set_model_handler(form_data.model)
+
+
+class GenerateImageForm(BaseModel):
+    model: Optional[str] = None
+    prompt: str
+    n: int = 1
+    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),
+):
+
+    r = None
+    try:
+        if app.state.ENGINE == "openai":
+
+            headers = {}
+            headers["Authorization"] = f"Bearer {app.state.OPENAI_API_KEY}"
+            headers["Content-Type"] = "application/json"
+
+            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,
+            )
+
+            r.raise_for_status()
+            res = r.json()
+
+            images = []
+
+            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
+
+    except Exception as e:
+        error = e
+
+        if r != None:
+            data = r.json()
+            if "error" in data:
+                error = data["error"]["message"]
+        raise HTTPException(status_code=400, detail=ERROR_MESSAGES.DEFAULT(error))

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, Response
+from fastapi.responses import JSONResponse
+
+from starlette.middleware.base import BaseHTTPMiddleware, RequestResponseEndpoint
+from starlette.responses import StreamingResponse
+import json
+
+from utils.utils import get_http_authorization_cred, get_current_user
+from config import ENV
+
+
+from config import (
+    MODEL_FILTER_ENABLED,
+    MODEL_FILTER_LIST,
+)
+
+
+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.state.MODEL_FILTER_ENABLED = MODEL_FILTER_ENABLED
+app.state.MODEL_FILTER_LIST = MODEL_FILTER_LIST
+
+
+@app.middleware("http")
+async def auth_middleware(request: Request, call_next):
+    auth_header = request.headers.get("Authorization", "")
+    request.state.user = None
+
+    try:
+        user = get_current_user(get_http_authorization_cred(auth_header))
+        print(user)
+        request.state.user = user
+    except Exception as e:
+        return JSONResponse(status_code=400, content={"detail": str(e)})
+
+    response = await call_next(request)
+    return response
+
+
+class ModifyModelsResponseMiddleware(BaseHTTPMiddleware):
+    async def dispatch(
+        self, request: Request, call_next: RequestResponseEndpoint
+    ) -> Response:
+
+        response = await call_next(request)
+        user = request.state.user
+
+        if "/models" in request.url.path:
+            if isinstance(response, StreamingResponse):
+                # Read the content of the streaming response
+                body = b""
+                async for chunk in response.body_iterator:
+                    body += chunk
+
+                data = json.loads(body.decode("utf-8"))
+
+                if app.state.MODEL_FILTER_ENABLED:
+                    if user and user.role == "user":
+                        data["data"] = list(
+                            filter(
+                                lambda model: model["id"]
+                                in app.state.MODEL_FILTER_LIST,
+                                data["data"],
+                            )
+                        )
+
+                # Modified Flag
+                data["modified"] = True
+                return JSONResponse(content=data)
+
+        return response
+
+
+app.add_middleware(ModifyModelsResponseMiddleware)

backend/apps/ollama/old_main.py

-from fastapi import FastAPI, Request, Response, HTTPException, Depends
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse
-
-import requests
-import json
-from pydantic import BaseModel
-
-from apps.web.models.users import Users
-from constants import ERROR_MESSAGES
-from utils.utils import decode_token, get_current_user
-from config import OLLAMA_API_BASE_URL, WEBUI_AUTH
-
-import aiohttp
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-app.state.OLLAMA_API_BASE_URL = OLLAMA_API_BASE_URL
-
-# TARGET_SERVER_URL = OLLAMA_API_BASE_URL
-
-
-@app.get("/url")
-async def get_ollama_api_url(user=Depends(get_current_user)):
-    if user and user.role == "admin":
-        return {"OLLAMA_API_BASE_URL": app.state.OLLAMA_API_BASE_URL}
-    else:
-        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
-
-
-class UrlUpdateForm(BaseModel):
-    url: str
-
-
-@app.post("/url/update")
-async def update_ollama_api_url(
-    form_data: UrlUpdateForm, user=Depends(get_current_user)
-):
-    if user and user.role == "admin":
-        app.state.OLLAMA_API_BASE_URL = form_data.url
-        return {"OLLAMA_API_BASE_URL": app.state.OLLAMA_API_BASE_URL}
-    else:
-        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
-
-
-# async def fetch_sse(method, target_url, body, headers):
-#     async with aiohttp.ClientSession() as session:
-#         try:
-#             async with session.request(
-#                 method, target_url, data=body, headers=headers
-#             ) as response:
-#                 print(response.status)
-#                 async for line in response.content:
-#                     yield line
-#         except Exception as e:
-#             print(e)
-#             error_detail = "Open WebUI: Server Connection Error"
-#             yield json.dumps({"error": error_detail, "message": str(e)}).encode()
-
-
-@app.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE"])
-async def proxy(path: str, request: Request, user=Depends(get_current_user)):
-    target_url = f"{app.state.OLLAMA_API_BASE_URL}/{path}"
-    print(target_url)
-
-    body = await request.body()
-    headers = dict(request.headers)
-
-    if user.role in ["user", "admin"]:
-        if path in ["pull", "delete", "push", "copy", "create"]:
-            if user.role != "admin":
-                raise HTTPException(
-                    status_code=401, detail=ERROR_MESSAGES.ACCESS_PROHIBITED
-                )
-    else:
-        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
-
-    headers.pop("Host", None)
-    headers.pop("Authorization", None)
-    headers.pop("Origin", None)
-    headers.pop("Referer", None)
-
-    session = aiohttp.ClientSession()
-    response = None
-    try:
-        response = await session.request(
-            request.method, target_url, data=body, headers=headers
-        )
-
-        print(response)
-        if not response.ok:
-            data = await response.json()
-            print(data)
-            response.raise_for_status()
-
-        async def generate():
-            async for line in response.content:
-                print(line)
-                yield line
-            await session.close()
-
-        return StreamingResponse(generate(), response.status)
-
-    except Exception as e:
-        print(e)
-        error_detail = "Open WebUI: Server Connection Error"
-
-        if response is not None:
-            try:
-                res = await response.json()
-                if "error" in res:
-                    error_detail = f"Ollama: {res['error']}"
-            except:
-                error_detail = f"Ollama: {e}"
-
-        await session.close()
-        raise HTTPException(
-            status_code=response.status if response else 500,
-            detail=error_detail,
-        )

backend/apps/openai/main.py

@@ -3,7 +3,10 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
 
 import requests
+import aiohttp
+import asyncio
 import json
+
 from pydantic import BaseModel
 
 
@@ -15,7 +18,15 @@ from utils.utils import (
     get_verified_user,
     get_admin_user,
 )
-from config import OPENAI_API_BASE_URL, OPENAI_API_KEY, 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
+
 
 import hashlib
 from pathlib import Path
@@ -29,49 +40,62 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-app.state.OPENAI_API_BASE_URL = OPENAI_API_BASE_URL
-app.state.OPENAI_API_KEY = OPENAI_API_KEY
+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
 
-class UrlUpdateForm(BaseModel):
-    url: str
+app.state.MODELS = {}
 
 
-class KeyUpdateForm(BaseModel):
-    key: str
+@app.middleware("http")
+async def check_url(request: Request, call_next):
+    if len(app.state.MODELS) == 0:
+        await get_all_models()
+    else:
+        pass
 
+    response = await call_next(request)
+    return response
 
-@app.get("/url")
-async def get_openai_url(user=Depends(get_admin_user)):
-    return {"OPENAI_API_BASE_URL": app.state.OPENAI_API_BASE_URL}
 
+class UrlsUpdateForm(BaseModel):
+    urls: List[str]
 
-@app.post("/url/update")
-async def update_openai_url(form_data: UrlUpdateForm, user=Depends(get_admin_user)):
-    app.state.OPENAI_API_BASE_URL = form_data.url
-    return {"OPENAI_API_BASE_URL": app.state.OPENAI_API_BASE_URL}
 
+class KeysUpdateForm(BaseModel):
+    keys: List[str]
 
-@app.get("/key")
-async def get_openai_key(user=Depends(get_admin_user)):
-    return {"OPENAI_API_KEY": app.state.OPENAI_API_KEY}
 
+@app.get("/urls")
+async def get_openai_urls(user=Depends(get_admin_user)):
+    return {"OPENAI_API_BASE_URLS": app.state.OPENAI_API_BASE_URLS}
 
-@app.post("/key/update")
-async def update_openai_key(form_data: KeyUpdateForm, user=Depends(get_admin_user)):
-    app.state.OPENAI_API_KEY = form_data.key
-    return {"OPENAI_API_KEY": app.state.OPENAI_API_KEY}
 
+@app.post("/urls/update")
+async def update_openai_urls(form_data: UrlsUpdateForm, user=Depends(get_admin_user)):
+    app.state.OPENAI_API_BASE_URLS = form_data.urls
+    return {"OPENAI_API_BASE_URLS": app.state.OPENAI_API_BASE_URLS}
 
-@app.post("/audio/speech")
-async def speech(request: Request, user=Depends(get_verified_user)):
-    target_url = f"{app.state.OPENAI_API_BASE_URL}/audio/speech"
 
-    if app.state.OPENAI_API_KEY == "":
-        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.API_KEY_NOT_FOUND)
+@app.get("/keys")
+async def get_openai_keys(user=Depends(get_admin_user)):
+    return {"OPENAI_API_KEYS": app.state.OPENAI_API_KEYS}
+
+
+@app.post("/keys/update")
+async def update_openai_key(form_data: KeysUpdateForm, user=Depends(get_admin_user)):
+    app.state.OPENAI_API_KEYS = form_data.keys
+    return {"OPENAI_API_KEYS": app.state.OPENAI_API_KEYS}
 
-    body = await request.body()
 
+@app.post("/audio/speech")
+async def speech(request: Request, user=Depends(get_verified_user)):
+    idx = None
+    try:
+        idx = app.state.OPENAI_API_BASE_URLS.index("https://api.openai.com/v1")
+        body = await request.body()
         name = hashlib.sha256(body).hexdigest()
 
         SPEECH_CACHE_DIR = Path(CACHE_DIR).joinpath("./audio/speech/")
@@ -84,13 +108,13 @@ async def speech(request: Request, user=Depends(get_verified_user)):
             return FileResponse(file_path)
 
         headers = {}
-    headers["Authorization"] = f"Bearer {app.state.OPENAI_API_KEY}"
+        headers["Authorization"] = f"Bearer {app.state.OPENAI_API_KEYS[idx]}"
         headers["Content-Type"] = "application/json"
 
+        r = None
         try:
-        print("openai")
             r = requests.post(
-            url=target_url,
+                url=f"{app.state.OPENAI_API_BASE_URLS[idx]}/audio/speech",
                 data=body,
                 headers=headers,
                 stream=True,
@@ -120,25 +144,137 @@ async def speech(request: Request, user=Depends(get_verified_user)):
                 except:
                     error_detail = f"External: {e}"
 
-        raise HTTPException(status_code=r.status_code, detail=error_detail)
+            raise HTTPException(
+                status_code=r.status_code if r else 500, detail=error_detail
+            )
+
+    except ValueError:
+        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.OPENAI_NOT_FOUND)
+
+
+async def fetch_url(url, key):
+    try:
+        headers = {"Authorization": f"Bearer {key}"}
+        async with aiohttp.ClientSession() as session:
+            async with session.get(url, headers=headers) as response:
+                return await response.json()
+    except Exception as e:
+        # Handle connection error here
+        print(f"Connection error: {e}")
+        return None
+
+
+def merge_models_lists(model_lists):
+    merged_list = []
+
+    for idx, models in enumerate(model_lists):
+        if models is not None and "error" not in models:
+            merged_list.extend(
+                [
+                    {**model, "urlIdx": idx}
+                    for model in models
+                    if "api.openai.com" not in app.state.OPENAI_API_BASE_URLS[idx]
+                    or "gpt" in model["id"]
+                ]
+            )
+
+    return merged_list
+
+
+async def get_all_models():
+    print("get_all_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)
+        models = {
+            "data": merge_models_lists(
+                list(
+                    map(
+                        lambda response: (
+                            response["data"]
+                            if response and "data" in response
+                            else None
+                        ),
+                        responses,
+                    )
+                )
+            )
+        }
+
+        print(models)
+        app.state.MODELS = {model["id"]: model for model in models["data"]}
+
+        return models
+
+
+@app.get("/models")
+@app.get("/models/{url_idx}")
+async def get_models(url_idx: Optional[int] = None, user=Depends(get_current_user)):
+    if url_idx == None:
+        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]
+
+        r = None
+
+        try:
+            r = requests.request(method="GET", url=f"{url}/models")
+            r.raise_for_status()
+
+            response_data = r.json()
+            if "api.openai.com" in url:
+                response_data["data"] = list(
+                    filter(lambda model: "gpt" in model["id"], response_data["data"])
+                )
+
+            return response_data
+        except Exception as e:
+            print(e)
+            error_detail = "Open WebUI: Server Connection Error"
+            if r is not None:
+                try:
+                    res = r.json()
+                    if "error" in res:
+                        error_detail = f"External: {res['error']}"
+                except:
+                    error_detail = f"External: {e}"
+
+            raise HTTPException(
+                status_code=r.status_code if r else 500,
+                detail=error_detail,
+            )
 
 
 @app.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE"])
 async def proxy(path: str, request: Request, user=Depends(get_verified_user)):
-    target_url = f"{app.state.OPENAI_API_BASE_URL}/{path}"
-    print(target_url, app.state.OPENAI_API_KEY)
-
-    if app.state.OPENAI_API_KEY == "":
-        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.API_KEY_NOT_FOUND)
+    idx = 0
 
     body = await request.body()
-
     # TODO: Remove below after gpt-4-vision fix from Open AI
     # Try to decode the body of the request from bytes to a UTF-8 string (Require add max_token to fix gpt-4-vision)
     try:
         body = body.decode("utf-8")
         body = json.loads(body)
 
+        idx = app.state.MODELS[body.get("model")]["urlIdx"]
+
         # Check if the model is "gpt-4-vision-preview" and set "max_tokens" to 4000
         # This is a workaround until OpenAI fixes the issue with this model
         if body.get("model") == "gpt-4-vision-preview":
@@ -146,15 +282,32 @@ async def proxy(path: str, request: Request, user=Depends(get_verified_user)):
                 body["max_tokens"] = 4000
             print("Modified body_dict:", body)
 
+        # Fix for ChatGPT calls failing because the num_ctx key is in body
+        if "num_ctx" in body:
+            # If 'num_ctx' is in the dictionary, delete it
+            # Leaving it there generates an error with the
+            # OpenAI API (Feb 2024)
+            del body["num_ctx"]
+
         # Convert the modified body back to JSON
         body = json.dumps(body)
     except json.JSONDecodeError as e:
         print("Error loading request body into a dictionary:", e)
 
+    url = app.state.OPENAI_API_BASE_URLS[idx]
+    key = app.state.OPENAI_API_KEYS[idx]
+
+    target_url = f"{url}/{path}"
+
+    if key == "":
+        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.API_KEY_NOT_FOUND)
+
     headers = {}
-    headers["Authorization"] = f"Bearer {app.state.OPENAI_API_KEY}"
+    headers["Authorization"] = f"Bearer {key}"
     headers["Content-Type"] = "application/json"
 
+    r = None
+
     try:
         r = requests.request(
             method=request.method,
@@ -174,21 +327,7 @@ async def proxy(path: str, request: Request, user=Depends(get_verified_user)):
                 headers=dict(r.headers),
             )
         else:
-            # For non-SSE, read the response and return it
-            # response_data = (
-            #     r.json()
-            #     if r.headers.get("Content-Type", "")
-            #     == "application/json"
-            #     else r.text
-            # )
-
             response_data = r.json()
-
-            if "openai" in app.state.OPENAI_API_BASE_URL and path == "models":
-                response_data["data"] = list(
-                    filter(lambda model: "gpt" in model["id"], response_data["data"])
-                )
-
             return response_data
     except Exception as e:
         print(e)
@@ -201,4 +340,6 @@ async def proxy(path: str, request: Request, user=Depends(get_verified_user)):
             except:
                 error_detail = f"External: {e}"
 
-        raise HTTPException(status_code=r.status_code, detail=error_detail)
+        raise HTTPException(
+            status_code=r.status_code if r else 500, detail=error_detail
+        )

backend/apps/rag/main.py

 from fastapi import (
     FastAPI,
-    Request,
     Depends,
     HTTPException,
     status,
@@ -10,9 +9,12 @@ from fastapi import (
 )
 from fastapi.middleware.cors import CORSMiddleware
 import os, shutil
+
+from pathlib import Path
 from typing import List
 
-# from chromadb.utils import embedding_functions
+from sentence_transformers import SentenceTransformer
+from chromadb.utils import embedding_functions
 
 from langchain_community.document_loaders import (
     WebBaseLoader,
@@ -28,27 +30,68 @@ from langchain_community.document_loaders import (
     UnstructuredExcelLoader,
 )
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-from langchain.chains import RetrievalQA
-
 
 from pydantic import BaseModel
 from typing import Optional
-
+import mimetypes
 import uuid
-import time
+import json
+
+
+from apps.web.models.documents import (
+    Documents,
+    DocumentForm,
+    DocumentResponse,
+)
+
+from apps.rag.utils import query_doc, query_collection
 
-from utils.misc import calculate_sha256, calculate_sha256_string
+from utils.misc import (
+    calculate_sha256,
+    calculate_sha256_string,
+    sanitize_filename,
+    extract_folders_after_data_docs,
+)
 from utils.utils import get_current_user, get_admin_user
-from config import UPLOAD_DIR, EMBED_MODEL, CHROMA_CLIENT, CHUNK_SIZE, CHUNK_OVERLAP
+from config import (
+    UPLOAD_DIR,
+    DOCS_DIR,
+    RAG_EMBEDDING_MODEL,
+    RAG_EMBEDDING_MODEL_DEVICE_TYPE,
+    CHROMA_CLIENT,
+    CHUNK_SIZE,
+    CHUNK_OVERLAP,
+    RAG_TEMPLATE,
+)
+
 from constants import ERROR_MESSAGES
 
-# EMBEDDING_FUNC = embedding_functions.SentenceTransformerEmbeddingFunction(
-#     model_name=EMBED_MODEL
+#
+# if RAG_EMBEDDING_MODEL:
+#    sentence_transformer_ef = SentenceTransformer(
+#        model_name_or_path=RAG_EMBEDDING_MODEL,
+#        cache_folder=RAG_EMBEDDING_MODEL_DIR,
+#        device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
 #    )
 
+
 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
+app.state.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
+app.state.TOP_K = 4
+
+app.state.sentence_transformer_ef = (
+    embedding_functions.SentenceTransformerEmbeddingFunction(
+        model_name=app.state.RAG_EMBEDDING_MODEL,
+        device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
+    )
+)
+
+
 origins = ["*"]
 
 app.add_middleware(
@@ -68,9 +111,9 @@ class StoreWebForm(CollectionNameForm):
     url: str
 
 
-def store_data_in_vector_db(data, collection_name) -> bool:
+def store_data_in_vector_db(data, collection_name, overwrite: bool = False) -> bool:
     text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP
+        chunk_size=app.state.CHUNK_SIZE, chunk_overlap=app.state.CHUNK_OVERLAP
     )
     docs = text_splitter.split_documents(data)
 
@@ -78,7 +121,16 @@ def store_data_in_vector_db(data, collection_name) -> bool:
     metadatas = [doc.metadata for doc in docs]
 
     try:
-        collection = CHROMA_CLIENT.create_collection(name=collection_name)
+        if overwrite:
+            for collection in CHROMA_CLIENT.list_collections():
+                if collection_name == collection.name:
+                    print(f"deleting existing collection {collection_name}")
+                    CHROMA_CLIENT.delete_collection(name=collection_name)
+
+        collection = CHROMA_CLIENT.create_collection(
+            name=collection_name,
+            embedding_function=app.state.sentence_transformer_ef,
+        )
 
         collection.add(
             documents=texts, metadatas=metadatas, ids=[str(uuid.uuid1()) for _ in texts]
@@ -94,26 +146,133 @@ def store_data_in_vector_db(data, collection_name) -> bool:
 
 @app.get("/")
 async def get_status():
-    return {"status": True}
+    return {
+        "status": True,
+        "chunk_size": app.state.CHUNK_SIZE,
+        "chunk_overlap": app.state.CHUNK_OVERLAP,
+        "template": app.state.RAG_TEMPLATE,
+        "embedding_model": app.state.RAG_EMBEDDING_MODEL,
+    }
+
+
+@app.get("/embedding/model")
+async def get_embedding_model(user=Depends(get_admin_user)):
+    return {
+        "status": True,
+        "embedding_model": app.state.RAG_EMBEDDING_MODEL,
+    }
+
+
+class EmbeddingModelUpdateForm(BaseModel):
+    embedding_model: str
+
+
+@app.post("/embedding/model/update")
+async def update_embedding_model(
+    form_data: EmbeddingModelUpdateForm, user=Depends(get_admin_user)
+):
+    app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
+    app.state.sentence_transformer_ef = (
+        embedding_functions.SentenceTransformerEmbeddingFunction(
+            model_name=app.state.RAG_EMBEDDING_MODEL,
+            device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
+        )
+    )
+
+    return {
+        "status": True,
+        "embedding_model": app.state.RAG_EMBEDDING_MODEL,
+    }
+
+
+@app.get("/config")
+async def get_rag_config(user=Depends(get_admin_user)):
+    return {
+        "status": True,
+        "pdf_extract_images": app.state.PDF_EXTRACT_IMAGES,
+        "chunk": {
+            "chunk_size": app.state.CHUNK_SIZE,
+            "chunk_overlap": app.state.CHUNK_OVERLAP,
+        },
+    }
+
+
+class ChunkParamUpdateForm(BaseModel):
+    chunk_size: int
+    chunk_overlap: int
+
+
+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,
+        "pdf_extract_images": app.state.PDF_EXTRACT_IMAGES,
+        "chunk": {
+            "chunk_size": app.state.CHUNK_SIZE,
+            "chunk_overlap": app.state.CHUNK_OVERLAP,
+        },
+    }
+
+
+@app.get("/template")
+async def get_rag_template(user=Depends(get_current_user)):
+    return {
+        "status": True,
+        "template": app.state.RAG_TEMPLATE,
+    }
+
+
+@app.get("/query/settings")
+async def get_query_settings(user=Depends(get_admin_user)):
+    return {
+        "status": True,
+        "template": app.state.RAG_TEMPLATE,
+        "k": app.state.TOP_K,
+    }
+
+
+class QuerySettingsForm(BaseModel):
+    k: Optional[int] = None
+    template: Optional[str] = None
+
+
+@app.post("/query/settings/update")
+async def update_query_settings(
+    form_data: QuerySettingsForm, user=Depends(get_admin_user)
+):
+    app.state.RAG_TEMPLATE = form_data.template if form_data.template else RAG_TEMPLATE
+    app.state.TOP_K = form_data.k if form_data.k else 4
+    return {"status": True, "template": app.state.RAG_TEMPLATE}
 
 
 class QueryDocForm(BaseModel):
     collection_name: str
     query: str
-    k: Optional[int] = 4
+    k: Optional[int] = None
 
 
 @app.post("/query/doc")
-def query_doc(
+def query_doc_handler(
     form_data: QueryDocForm,
     user=Depends(get_current_user),
 ):
+
     try:
-        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)
-        return result
     except Exception as e:
         print(e)
         raise HTTPException(
@@ -125,74 +284,20 @@ def query_doc(
 class QueryCollectionsForm(BaseModel):
     collection_names: List[str]
     query: str
-    k: Optional[int] = 4
-
-
-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
+    k: Optional[int] = None
 
 
 @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:
-            collection = CHROMA_CLIENT.get_collection(
-                name=collection_name,
+    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,
     )
-            result = collection.query(
-                query_texts=[form_data.query], n_results=form_data.k
-            )
-            results.append(result)
-        except:
-            pass
-
-    return merge_and_sort_query_results(results, form_data.k)
 
 
 @app.post("/web")
@@ -206,7 +311,7 @@ def store_web(form_data: StoreWebForm, user=Depends(get_current_user)):
         if collection_name == "":
             collection_name = calculate_sha256_string(form_data.url)[:63]
 
-        store_data_in_vector_db(data, collection_name)
+        store_data_in_vector_db(data, collection_name, overwrite=True)
         return {
             "status": True,
             "collection_name": collection_name,
@@ -220,8 +325,8 @@ def store_web(form_data: StoreWebForm, user=Depends(get_current_user)):
         )
 
 
-def get_loader(file, file_path):
-    file_ext = file.filename.split(".")[-1].lower()
+def get_loader(filename: str, file_content_type: str, file_path: str):
+    file_ext = filename.split(".")[-1].lower()
     known_type = True
 
     known_source_ext = [
@@ -270,7 +375,7 @@ def get_loader(file, file_path):
     ]
 
     if file_ext == "pdf":
-        loader = PyPDFLoader(file_path)
+        loader = PyPDFLoader(file_path, extract_images=app.state.PDF_EXTRACT_IMAGES)
     elif file_ext == "csv":
         loader = CSVLoader(file_path)
     elif file_ext == "rst":
@@ -279,23 +384,25 @@ def get_loader(file, file_path):
         loader = UnstructuredXMLLoader(file_path)
     elif file_ext == "md":
         loader = UnstructuredMarkdownLoader(file_path)
-    elif file.content_type == "application/epub+zip":
+    elif file_content_type == "application/epub+zip":
         loader = UnstructuredEPubLoader(file_path)
     elif (
-        file.content_type
+        file_content_type
         == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
         or file_ext in ["doc", "docx"]
     ):
         loader = Docx2txtLoader(file_path)
-    elif file.content_type in [
+    elif file_content_type in [
         "application/vnd.ms-excel",
         "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
     ] or file_ext in ["xls", "xlsx"]:
         loader = UnstructuredExcelLoader(file_path)
-    elif file_ext in known_source_ext or file.content_type.find("text/") >= 0:
-        loader = TextLoader(file_path)
+    elif file_ext in known_source_ext or (
+        file_content_type and file_content_type.find("text/") >= 0
+    ):
+        loader = TextLoader(file_path, autodetect_encoding=True)
     else:
-        loader = TextLoader(file_path)
+        loader = TextLoader(file_path, autodetect_encoding=True)
         known_type = False
 
     return loader, known_type
@@ -323,7 +430,7 @@ def store_doc(
             collection_name = calculate_sha256(f)[:63]
         f.close()
 
-        loader, known_type = get_loader(file, file_path)
+        loader, known_type = get_loader(file.filename, file.content_type, file_path)
         data = loader.load()
         result = store_data_in_vector_db(data, collection_name)
 
@@ -353,6 +460,63 @@ def store_doc(
             )
 
 
+@app.get("/scan")
+def scan_docs_dir(user=Depends(get_admin_user)):
+    for path in Path(DOCS_DIR).rglob("./**/*"):
+        try:
+            if path.is_file() and not path.name.startswith("."):
+                tags = extract_folders_after_data_docs(path)
+                filename = path.name
+                file_content_type = mimetypes.guess_type(path)
+
+                f = open(path, "rb")
+                collection_name = calculate_sha256(f)[:63]
+                f.close()
+
+                loader, known_type = get_loader(
+                    filename, file_content_type[0], str(path)
+                )
+                data = loader.load()
+
+                result = store_data_in_vector_db(data, collection_name)
+
+                if result:
+                    sanitized_filename = sanitize_filename(filename)
+                    doc = Documents.get_doc_by_name(sanitized_filename)
+
+                    if doc == None:
+                        doc = Documents.insert_new_doc(
+                            user.id,
+                            DocumentForm(
+                                **{
+                                    "name": sanitized_filename,
+                                    "title": filename,
+                                    "collection_name": collection_name,
+                                    "filename": filename,
+                                    "content": (
+                                        json.dumps(
+                                            {
+                                                "tags": list(
+                                                    map(
+                                                        lambda name: {"name": name},
+                                                        tags,
+                                                    )
+                                                )
+                                            }
+                                        )
+                                        if len(tags)
+                                        else "{}"
+                                    ),
+                                }
+                            ),
+                        )
+
+        except Exception as e:
+            print(e)
+
+    return True
+
+
 @app.get("/reset/db")
 def reset_vector_db(user=Depends(get_admin_user)):
     CHROMA_CLIENT.reset()