Merge pull request #897 from open-webui/main

dev

.dockerignore

+.github
 .DS_Store
+docs
+kubernetes
 node_modules
 /.svelte-kit
 /package

example.env → .env.example

@@ -4,3 +4,9 @@ OLLAMA_API_BASE_URL='http://localhost:11434/api'
 
 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

.gitattributes

+*.sh text eol=lf
\ No newline at end of file

.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: Create GitHub release
+      uses: actions/github-script@v5
+      with:
+        github-token: ${{ secrets.GITHUB_TOKEN }}
+        script: |
+          const release = await github.rest.repos.createRelease({
+            owner: context.repo.owner,
+            repo: context.repo.repo,
+            tag_name: `v${{ steps.get_version.outputs.version }}`,
+            name: `v${{ steps.get_version.outputs.version }}`,
+            body: 'Automatically created new release',
+          })
+          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

@@ -40,15 +40,21 @@ jobs:
           registry: ${{ env.REGISTRY }}
           username: ${{ github.actor }}
           password: ${{ secrets.GITHUB_TOKEN }}
-      # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels.
-      - name: Extract metadata (tags, labels) for Docker
+
+      - name: Extract metadata for Docker images
         id: meta
         uses: docker/metadata-action@v5
         with:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-      # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages.
-      # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see "[Usage](https://github.com/docker/build-push-action#usage)" in the README of the `docker/build-push-action` repository.
-      # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step.
+          # This configuration dynamically generates tags based on the branch, tag, commit, and custom suffix for lite version.
+          tags: |
+            type=ref,event=branch
+            type=ref,event=tag
+            type=sha,prefix=git-
+            type=semver,pattern={{version}}
+          flavor: |
+            latest=${{ github.ref == 'refs/heads/main' }}
+
       - name: Build and push Docker image
         uses: docker/build-push-action@v5
         with:

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).
+
+## [0.1.102] - 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

@@ -5,7 +5,8 @@ FROM node:alpine as build
 WORKDIR /app
 
 # wget embedding model weight from alpine (does not exist from slim-buster)
-RUN wget "https://chroma-onnx-models.s3.amazonaws.com/all-MiniLM-L6-v2/onnx.tar.gz"
+RUN wget "https://chroma-onnx-models.s3.amazonaws.com/all-MiniLM-L6-v2/onnx.tar.gz" -O - | \
+    tar -xzf - -C /app
 
 COPY package.json package-lock.json ./
 RUN npm ci
@@ -17,35 +18,65 @@ RUN npm run build
 FROM python:3.11-slim-bookworm as base
 
 ENV ENV=prod
+ENV PORT ""
 
 ENV OLLAMA_API_BASE_URL "/ollama/api"
 
 ENV OPENAI_API_BASE_URL ""
 ENV OPENAI_API_KEY ""
 
-ENV WEBUI_JWT_SECRET_KEY "SECRET_KEY"
+ENV WEBUI_SECRET_KEY ""
 
-WORKDIR /app
+ENV SCARF_NO_ANALYTICS true
+ENV DO_NOT_TRACK true
 
-# copy embedding weight from build
-RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2
-COPY --from=build /app/onnx.tar.gz /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2
+######## Preloaded models ########
+# whisper TTS Settings
+ENV WHISPER_MODEL="base"
+ENV WHISPER_MODEL_DIR="/app/backend/data/cache/whisper/models"
 
-RUN cd /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2 &&\
-    tar -xzf onnx.tar.gz
+# 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 ebbeding 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
 
-# copy built frontend files
-COPY --from=build /app/build /app/build
+######## Preloaded models ########
 
 WORKDIR /app/backend
 
+# install python dependencies
 COPY ./backend/requirements.txt ./requirements.txt
 
-RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
-RUN pip3 install -r requirements.txt
+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
+
+# Install pandoc and netcat
+# RUN python -c "import pypandoc; pypandoc.download_pandoc()"
+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')"
+# 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
+COPY --from=build /app/onnx /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx
+
+# 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 .
 
-CMD [ "sh", "start.sh"]
+CMD [ "bash", "start.sh"]
\ No newline at end of file

INSTALLATION.md

-### Installing Both Ollama and Ollama Web UI Using Kustomize
+### Installing Both Ollama and Open WebUI Using Kustomize
 
 For cpu-only pod
 
@@ -12,7 +12,7 @@ For gpu-enabled pod
 kubectl apply -k ./kubernetes/manifest
 ```
 
-### Installing Both Ollama and Ollama Web UI Using Helm
+### Installing Both Ollama and Open WebUI Using Helm
 
 Package Helm file first
 

TROUBLESHOOTING.md

-# Ollama Web UI Troubleshooting Guide
+# Open WebUI Troubleshooting Guide
 
-## Understanding the Ollama WebUI Architecture
+## Understanding the Open WebUI Architecture
 
-The Ollama 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.
+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 Ollama 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 Ollama 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/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.
 
 - **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.
 
-## Ollama WebUI: Server Connection Error
+## Open WebUI: Server Connection Error
 
 If you're experiencing connection issues, it’s often due to the WebUI docker container not being able to reach the Ollama server at 127.0.0.1:11434 (host.docker.internal:11434) inside the container . Use the `--network=host` flag in your docker command to resolve this. Note that the port changes from 3000 to 8080, resulting in the link: `http://localhost:8080`.
 
 **Example Docker Command**:
 
 ```bash
-docker run -d --network=host -v ollama-webui:/app/backend/data -e OLLAMA_API_BASE_URL=http://127.0.0.1:11434/api --name ollama-webui --restart always ghcr.io/ollama-webui/ollama-webui:main
+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
 ```
 
 ### General Connection Errors
 
-**Ensure Ollama Version is Up-to-Date**: Always start by checking that you have the latest version of Ollama. Visit [Ollama's official site](https://ollama.ai/) for the latest updates.
+**Ensure Ollama Version is Up-to-Date**: Always start by checking that you have the latest version of Ollama. Visit [Ollama's official site](https://ollama.com/) for the latest updates.
 
 **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).
-   - In the Ollama WebUI, navigate to "Settings" > "General".
+   - 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.
 
 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/.gitignore

@@ -7,3 +7,5 @@ uploads
 _test
 Pipfile
 data/*
+!data/config.json
+.webui_secret_key
\ No newline at end of file

backend/apps/audio/main.py

+import os
+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 (
+    decode_token,
+    get_current_user,
+    get_verified_user,
+    get_admin_user,
+)
+from utils.misc import calculate_sha256
+
+from config import CACHE_DIR, UPLOAD_DIR, WHISPER_MODEL, WHISPER_MODEL_DIR
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.post("/transcribe")
+def transcribe(
+    file: UploadFile = File(...),
+    user=Depends(get_current_user),
+):
+    print(file.content_type)
+
+    if file.content_type not in ["audio/mpeg", "audio/wav"]:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.FILE_NOT_SUPPORTED,
+        )
+
+    try:
+        filename = file.filename
+        file_path = f"{UPLOAD_DIR}/{filename}"
+        contents = file.file.read()
+        with open(file_path, "wb") as f:
+            f.write(contents)
+            f.close()
+
+        model = WhisperModel(
+            WHISPER_MODEL,
+            device="auto",
+            compute_type="int8",
+            download_root=WHISPER_MODEL_DIR,
+        )
+
+        segments, info = model.transcribe(file_path, beam_size=5)
+        print(
+            "Detected language '%s' with probability %f"
+            % (info.language, info.language_probability)
+        )
+
+        transcript = "".join([segment.text for segment in list(segments)])
+
+        return {"text": transcript.strip()}
+
+    except Exception as e:
+        print(e)
+
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ERROR_MESSAGES.DEFAULT(e),
+        )

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 config import AUTOMATIC1111_BASE_URL
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.state.AUTOMATIC1111_BASE_URL = AUTOMATIC1111_BASE_URL
+app.state.ENABLED = app.state.AUTOMATIC1111_BASE_URL != ""
+app.state.IMAGE_SIZE = "512x512"
+
+
+@app.get("/enabled", response_model=bool)
+async def get_enable_status(request: Request, user=Depends(get_admin_user)):
+    return 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=r.status_code, detail=ERROR_MESSAGES.DEFAULT(e))
+
+
+class UrlUpdateForm(BaseModel):
+    url: str
+
+
+@app.get("/url")
+async def get_openai_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)):
+
+    if form_data.url == "":
+        app.state.AUTOMATIC1111_BASE_URL = AUTOMATIC1111_BASE_URL
+    else:
+        app.state.AUTOMATIC1111_BASE_URL = form_data.url.strip("/")
+
+    return {
+        "AUTOMATIC1111_BASE_URL": app.state.AUTOMATIC1111_BASE_URL,
+        "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)."),
+        )
+
+
+@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
+    except Exception as e:
+        raise HTTPException(status_code=r.status_code, detail=ERROR_MESSAGES.DEFAULT(e))
+
+
+@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"]}
+    except Exception as e:
+        raise HTTPException(status_code=r.status_code, detail=ERROR_MESSAGES.DEFAULT(e))
+
+
+class UpdateModelForm(BaseModel):
+    model: str
+
+
+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
+        )
+
+    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: str = "512x512"
+    negative_prompt: Optional[str] = None
+
+
+@app.post("/generations")
+def generate_image(
+    form_data: GenerateImageForm,
+    user=Depends(get_current_user),
+):
+
+    print(form_data)
+
+    try:
+        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 form_data.negative_prompt != None:
+            data["negative_prompt"] = form_data.negative_prompt
+
+        print(data)
+
+        r = requests.post(
+            url=f"{app.state.AUTOMATIC1111_BASE_URL}/sdapi/v1/txt2img",
+            json=data,
+        )
+
+        return r.json()
+    except Exception as e:
+        print(e)
+        raise HTTPException(status_code=r.status_code, detail=ERROR_MESSAGES.DEFAULT(e))

backend/apps/ollama/main.py

-from fastapi import FastAPI, Request, Response, HTTPException, Depends
+from fastapi import FastAPI, Request, Response, HTTPException, Depends, status
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import StreamingResponse
 from fastapi.concurrency import run_in_threadpool
 
 import requests
 import json
+import uuid
 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 utils.utils import decode_token, get_current_user, get_admin_user
 from config import OLLAMA_API_BASE_URL, WEBUI_AUTH
 
 app = FastAPI()
@@ -26,12 +27,12 @@ app.state.OLLAMA_API_BASE_URL = OLLAMA_API_BASE_URL
 # TARGET_SERVER_URL = OLLAMA_API_BASE_URL
 
 
+REQUEST_POOL = []
+
+
 @app.get("/url")
-async def get_ollama_api_url(user=Depends(get_current_user)):
-    if user and user.role == "admin":
+async def get_ollama_api_url(user=Depends(get_admin_user)):
     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):
@@ -39,12 +40,17 @@ class UrlUpdateForm(BaseModel):
 
 
 @app.post("/url/update")
-async def update_ollama_api_url(
-    form_data: UrlUpdateForm, user=Depends(get_current_user)
-):
-    if user and user.role == "admin":
+async def update_ollama_api_url(form_data: UrlUpdateForm, user=Depends(get_admin_user)):
     app.state.OLLAMA_API_BASE_URL = form_data.url
     return {"OLLAMA_API_BASE_URL": app.state.OLLAMA_API_BASE_URL}
+
+
+@app.get("/cancel/{request_id}")
+async def cancel_ollama_request(request_id: str, user=Depends(get_current_user)):
+    if user:
+        if request_id in REQUEST_POOL:
+            REQUEST_POOL.remove(request_id)
+        return True
     else:
         raise HTTPException(status_code=401, detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
 
@@ -60,21 +66,45 @@ async def proxy(path: str, request: Request, user=Depends(get_current_user)):
         if path in ["pull", "delete", "push", "copy", "create"]:
             if user.role != "admin":
                 raise HTTPException(
-                    status_code=401, detail=ERROR_MESSAGES.ACCESS_PROHIBITED
+                    status_code=status.HTTP_401_UNAUTHORIZED,
+                    detail=ERROR_MESSAGES.ACCESS_PROHIBITED,
                 )
     else:
-        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail=ERROR_MESSAGES.ACCESS_PROHIBITED,
+        )
 
-    headers.pop("Host", None)
-    headers.pop("Authorization", None)
-    headers.pop("Origin", None)
-    headers.pop("Referer", None)
+    headers.pop("host", None)
+    headers.pop("authorization", None)
+    headers.pop("origin", None)
+    headers.pop("referer", None)
 
     r = None
 
     def get_request():
         nonlocal r
+
+        request_id = str(uuid.uuid4())
+        try:
+            REQUEST_POOL.append(request_id)
+
+            def stream_content():
                 try:
+                    if path in ["chat"]:
+                        yield json.dumps({"id": request_id, "done": False}) + "\n"
+
+                    for chunk in r.iter_content(chunk_size=8192):
+                        if request_id in REQUEST_POOL:
+                            yield chunk
+                        else:
+                            print("User: canceled request")
+                            break
+                finally:
+                    if hasattr(r, "close"):
+                        r.close()
+                        REQUEST_POOL.remove(request_id)
+
             r = requests.request(
                 method=request.method,
                 url=target_url,
@@ -85,8 +115,10 @@ async def proxy(path: str, request: Request, user=Depends(get_current_user)):
 
             r.raise_for_status()
 
+            # r.close()
+
             return StreamingResponse(
-                r.iter_content(chunk_size=8192),
+                stream_content(),
                 status_code=r.status_code,
                 headers=dict(r.headers),
             )
@@ -96,7 +128,7 @@ async def proxy(path: str, request: Request, user=Depends(get_current_user)):
     try:
         return await run_in_threadpool(get_request)
     except Exception as e:
-        error_detail = "Ollama WebUI: Server Connection Error"
+        error_detail = "Open WebUI: Server Connection Error"
         if r is not None:
             try:
                 res = r.json()

backend/apps/ollama/old_main.py

@@ -61,7 +61,7 @@ async def update_ollama_api_url(
 #                     yield line
 #         except Exception as e:
 #             print(e)
-#             error_detail = "Ollama WebUI: Server Connection Error"
+#             error_detail = "Open WebUI: Server Connection Error"
 #             yield json.dumps({"error": error_detail, "message": str(e)}).encode()
 
 
@@ -110,7 +110,7 @@ async def proxy(path: str, request: Request, user=Depends(get_current_user)):
 
     except Exception as e:
         print(e)
-        error_detail = "Ollama WebUI: Server Connection Error"
+        error_detail = "Open WebUI: Server Connection Error"
 
         if response is not None:
             try:

backend/apps/openai/main.py

 from fastapi import FastAPI, Request, Response, HTTPException, Depends
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import StreamingResponse, JSONResponse
+from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
 
 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 OPENAI_API_BASE_URL, OPENAI_API_KEY
+from utils.utils import (
+    decode_token,
+    get_current_user,
+    get_verified_user,
+    get_admin_user,
+)
+from config import OPENAI_API_BASE_URL, OPENAI_API_KEY, CACHE_DIR
+
+import hashlib
+from pathlib import Path
 
 app = FastAPI()
 app.add_middleware(
@@ -33,60 +42,114 @@ class KeyUpdateForm(BaseModel):
 
 
 @app.get("/url")
-async def get_openai_url(user=Depends(get_current_user)):
-    if user and user.role == "admin":
+async def get_openai_url(user=Depends(get_admin_user)):
     return {"OPENAI_API_BASE_URL": app.state.OPENAI_API_BASE_URL}
-    else:
-        raise HTTPException(status_code=401,
-                            detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
 
 
 @app.post("/url/update")
-async def update_openai_url(form_data: UrlUpdateForm,
-                            user=Depends(get_current_user)):
-    if user and user.role == "admin":
+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}
-    else:
-        raise HTTPException(status_code=401,
-                            detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
 
 
 @app.get("/key")
-async def get_openai_key(user=Depends(get_current_user)):
-    if user and user.role == "admin":
+async def get_openai_key(user=Depends(get_admin_user)):
     return {"OPENAI_API_KEY": app.state.OPENAI_API_KEY}
-    else:
-        raise HTTPException(status_code=401,
-                            detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
 
 
 @app.post("/key/update")
-async def update_openai_key(form_data: KeyUpdateForm,
-                            user=Depends(get_current_user)):
-    if user and user.role == "admin":
+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}
-    else:
-        raise HTTPException(status_code=401,
-                            detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
+
+
+@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)
+
+    body = await request.body()
+
+    name = hashlib.sha256(body).hexdigest()
+
+    SPEECH_CACHE_DIR = Path(CACHE_DIR).joinpath("./audio/speech/")
+    SPEECH_CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    file_path = SPEECH_CACHE_DIR.joinpath(f"{name}.mp3")
+    file_body_path = SPEECH_CACHE_DIR.joinpath(f"{name}.json")
+
+    # Check if the file already exists in the cache
+    if file_path.is_file():
+        return FileResponse(file_path)
+
+    headers = {}
+    headers["Authorization"] = f"Bearer {app.state.OPENAI_API_KEY}"
+    headers["Content-Type"] = "application/json"
+
+    try:
+        print("openai")
+        r = requests.post(
+            url=target_url,
+            data=body,
+            headers=headers,
+            stream=True,
+        )
+
+        r.raise_for_status()
+
+        # Save the streaming content to a file
+        with open(file_path, "wb") as f:
+            for chunk in r.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+        with open(file_body_path, "w") as f:
+            json.dump(json.loads(body.decode("utf-8")), f)
+
+        # Return the saved file
+        return FileResponse(file_path)
+
+    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, detail=error_detail)
 
 
 @app.api_route("/{path:path}", methods=["GET", "POST", "PUT", "DELETE"])
-async def proxy(path: str, request: Request, user=Depends(get_current_user)):
+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 user.role not in ["user", "admin"]:
-        raise HTTPException(status_code=401,
-                            detail=ERROR_MESSAGES.ACCESS_PROHIBITED)
     if app.state.OPENAI_API_KEY == "":
-        raise HTTPException(status_code=401,
-                            detail=ERROR_MESSAGES.API_KEY_NOT_FOUND)
+        raise HTTPException(status_code=401, detail=ERROR_MESSAGES.API_KEY_NOT_FOUND)
 
     body = await request.body()
-    # headers = dict(request.headers)
-    # print(headers)
+
+    # 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)
+
+        # 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":
+            if "max_tokens" not in body:
+                body["max_tokens"] = 4000
+            print("Modified body_dict:", body)
+
+        # 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)
 
     headers = {}
     headers["Authorization"] = f"Bearer {app.state.OPENAI_API_KEY}"
@@ -121,17 +184,15 @@ async def proxy(path: str, request: Request, user=Depends(get_current_user)):
 
             response_data = r.json()
 
-            print(type(response_data))
-
             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"]))
+                    filter(lambda model: "gpt" in model["id"], response_data["data"])
+                )
 
             return response_data
     except Exception as e:
         print(e)
-        error_detail = "Ollama WebUI: Server Connection Error"
+        error_detail = "Open WebUI: Server Connection Error"
         if r is not None:
             try:
                 res = r.json()

backend/apps/rag/main.py

 from fastapi import (
     FastAPI,
-    Request,
     Depends,
     HTTPException,
     status,
@@ -11,7 +10,11 @@ from fastapi import (
 from fastapi.middleware.cors import CORSMiddleware
 import os, shutil
 
-# from chromadb.utils import embedding_functions
+from pathlib import Path
+from typing import List
+
+from sentence_transformers import SentenceTransformer
+from chromadb.utils import embedding_functions
 
 from langchain_community.document_loaders import (
     WebBaseLoader,
@@ -19,29 +22,71 @@ from langchain_community.document_loaders import (
     PyPDFLoader,
     CSVLoader,
     Docx2txtLoader,
+    UnstructuredEPubLoader,
+    UnstructuredWordDocumentLoader,
+    UnstructuredMarkdownLoader,
+    UnstructuredXMLLoader,
+    UnstructuredRSTLoader,
+    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 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,
+    DOCS_DIR,
+    RAG_EMBEDDING_MODEL,
+    RAG_EMBEDDING_MODEL_DEVICE_TYPE,
+    CHROMA_CLIENT,
+    CHUNK_SIZE,
+    CHUNK_OVERLAP,
+    RAG_TEMPLATE,
+)
 
-from utils.misc import calculate_sha256
-from utils.utils import get_current_user
-from config import UPLOAD_DIR, EMBED_MODEL, CHROMA_CLIENT, CHUNK_SIZE, CHUNK_OVERLAP
 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.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.sentence_transformer_ef = (
+    embedding_functions.SentenceTransformerEmbeddingFunction(
+        model_name=app.state.RAG_EMBEDDING_MODEL,
+        device=RAG_EMBEDDING_MODEL_DEVICE_TYPE,
+    )
+)
+
+
 origins = ["*"]
 
 app.add_middleware(
@@ -63,7 +108,7 @@ class StoreWebForm(CollectionNameForm):
 
 def store_data_in_vector_db(data, collection_name) -> 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)
 
@@ -71,7 +116,10 @@ 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)
+        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]
@@ -87,22 +135,112 @@ 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("/query/{collection_name}")
-def query_collection(
-    collection_name: str,
-    query: str,
-    k: Optional[int] = 4,
+@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("/chunk")
+async def get_chunk_params(user=Depends(get_admin_user)):
+    return {
+        "status": True,
+        "chunk_size": app.state.CHUNK_SIZE,
+        "chunk_overlap": app.state.CHUNK_OVERLAP,
+    }
+
+
+class ChunkParamUpdateForm(BaseModel):
+    chunk_size: int
+    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
+
+    return {
+        "status": True,
+        "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,
+    }
+
+
+class RAGTemplateForm(BaseModel):
+    template: str
+
+
+@app.post("/template/update")
+async def update_rag_template(form_data: RAGTemplateForm, user=Depends(get_admin_user)):
+    # TODO: check template requirements
+    app.state.RAG_TEMPLATE = (
+        form_data.template if form_data.template != "" else RAG_TEMPLATE
+    )
+    return {"status": True, "template": app.state.RAG_TEMPLATE}
+
+
+class QueryDocForm(BaseModel):
+    collection_name: str
+    query: str
+    k: Optional[int] = 4
+
+
+@app.post("/query/doc")
+def query_doc(
+    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=collection_name,
+            name=form_data.collection_name,
+            embedding_function=app.state.sentence_transformer_ef,
         )
-        result = collection.query(query_texts=[query], n_results=k)
-
+        result = collection.query(query_texts=[form_data.query], n_results=form_data.k)
         return result
     except Exception as e:
         print(e)
@@ -112,14 +250,99 @@ def query_collection(
         )
 
 
+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
+
+
+@app.post("/query/collection")
+def query_collection(
+    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
+            )
+            results.append(result)
+        except:
+            pass
+
+    return merge_and_sort_query_results(results, form_data.k)
+
+
 @app.post("/web")
 def store_web(form_data: StoreWebForm, user=Depends(get_current_user)):
     # "https://www.gutenberg.org/files/1727/1727-h/1727-h.htm"
     try:
         loader = WebBaseLoader(form_data.url)
         data = loader.load()
-        store_data_in_vector_db(data, form_data.collection_name)
-        return {"status": True, "collection_name": form_data.collection_name}
+
+        collection_name = form_data.collection_name
+        if collection_name == "":
+            collection_name = calculate_sha256_string(form_data.url)[:63]
+
+        store_data_in_vector_db(data, collection_name)
+        return {
+            "status": True,
+            "collection_name": collection_name,
+            "filename": form_data.url,
+        }
     except Exception as e:
         print(e)
         raise HTTPException(
@@ -128,6 +351,87 @@ def store_web(form_data: StoreWebForm, user=Depends(get_current_user)):
         )
 
 
+def get_loader(filename: str, file_content_type: str, file_path: str):
+    file_ext = filename.split(".")[-1].lower()
+    known_type = True
+
+    known_source_ext = [
+        "go",
+        "py",
+        "java",
+        "sh",
+        "bat",
+        "ps1",
+        "cmd",
+        "js",
+        "ts",
+        "css",
+        "cpp",
+        "hpp",
+        "h",
+        "c",
+        "cs",
+        "sql",
+        "log",
+        "ini",
+        "pl",
+        "pm",
+        "r",
+        "dart",
+        "dockerfile",
+        "env",
+        "php",
+        "hs",
+        "hsc",
+        "lua",
+        "nginxconf",
+        "conf",
+        "m",
+        "mm",
+        "plsql",
+        "perl",
+        "rb",
+        "rs",
+        "db2",
+        "scala",
+        "bash",
+        "swift",
+        "vue",
+        "svelte",
+    ]
+
+    if file_ext == "pdf":
+        loader = PyPDFLoader(file_path)
+    elif file_ext == "csv":
+        loader = CSVLoader(file_path)
+    elif file_ext == "rst":
+        loader = UnstructuredRSTLoader(file_path, mode="elements")
+    elif file_ext == "xml":
+        loader = UnstructuredXMLLoader(file_path)
+    elif file_ext == "md":
+        loader = UnstructuredMarkdownLoader(file_path)
+    elif file_content_type == "application/epub+zip":
+        loader = UnstructuredEPubLoader(file_path)
+    elif (
+        file_content_type
+        == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+        or file_ext in ["doc", "docx"]
+    ):
+        loader = Docx2txtLoader(file_path)
+    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 and file_content_type.find("text/") >= 0):
+        loader = TextLoader(file_path)
+    else:
+        loader = TextLoader(file_path)
+        known_type = False
+
+    return loader, known_type
+
+
 @app.post("/doc")
 def store_doc(
     collection_name: Optional[str] = Form(None),
@@ -136,17 +440,7 @@ def store_doc(
 ):
     # "https://www.gutenberg.org/files/1727/1727-h/1727-h.htm"
 
-    if file.content_type not in [
-        "application/pdf",
-        "text/plain",
-        "text/csv",
-        "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-    ]:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail=ERROR_MESSAGES.FILE_NOT_SUPPORTED,
-        )
-
+    print(file.content_type)
     try:
         filename = file.filename
         file_path = f"{UPLOAD_DIR}/{filename}"
@@ -160,23 +454,17 @@ def store_doc(
             collection_name = calculate_sha256(f)[:63]
         f.close()
 
-        if file.content_type == "application/pdf":
-            loader = PyPDFLoader(file_path)
-        elif (
-            file.content_type
-            == "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-        ):
-            loader = Docx2txtLoader(file_path)
-        elif file.content_type == "text/plain":
-            loader = TextLoader(file_path)
-        elif file.content_type == "text/csv":
-            loader = CSVLoader(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)
 
         if result:
-            return {"status": True, "collection_name": collection_name}
+            return {
+                "status": True,
+                "collection_name": collection_name,
+                "filename": filename,
+                "known_type": known_type,
+            }
         else:
             raise HTTPException(
                 status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
@@ -184,26 +472,82 @@ def store_doc(
             )
     except Exception as e:
         print(e)
+        if "No pandoc was found" in str(e):
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=ERROR_MESSAGES.PANDOC_NOT_INSTALLED,
+            )
+        else:
             raise HTTPException(
                 status_code=status.HTTP_400_BAD_REQUEST,
                 detail=ERROR_MESSAGES.DEFAULT(e),
             )
 
 
+@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_current_user)):
-    if user.role == "admin":
+def reset_vector_db(user=Depends(get_admin_user)):
     CHROMA_CLIENT.reset()
-    else:
-        raise HTTPException(
-            status_code=status.HTTP_403_FORBIDDEN,
-            detail=ERROR_MESSAGES.ACCESS_PROHIBITED,
-        )
 
 
 @app.get("/reset")
-def reset(user=Depends(get_current_user)) -> bool:
-    if user.role == "admin":
+def reset(user=Depends(get_admin_user)) -> bool:
     folder = f"{UPLOAD_DIR}"
     for filename in os.listdir(folder):
         file_path = os.path.join(folder, filename)
@@ -221,8 +565,3 @@ def reset(user=Depends(get_current_user)) -> bool:
         print(e)
 
     return True
-    else:
-        raise HTTPException(
-            status_code=status.HTTP_403_FORBIDDEN,
-            detail=ERROR_MESSAGES.ACCESS_PROHIBITED,
-        )

backend/apps/web/internal/db.py

 from peewee import *
+from config import DATA_DIR
 
-DB = SqliteDatabase("./data/ollama.db")
+
+DB = SqliteDatabase(f"{DATA_DIR}/ollama.db")
 DB.connect()

backend/apps/web/main.py

 from fastapi import FastAPI, Depends
 from fastapi.routing import APIRoute
 from fastapi.middleware.cors import CORSMiddleware
-from apps.web.routers import auths, users, chats, modelfiles, prompts, configs, utils
-from config import WEBUI_VERSION, WEBUI_AUTH
+from apps.web.routers import (
+    auths,
+    users,
+    chats,
+    documents,
+    modelfiles,
+    prompts,
+    configs,
+    utils,
+)
+from config import (
+    WEBUI_VERSION,
+    WEBUI_AUTH,
+    DEFAULT_MODELS,
+    DEFAULT_PROMPT_SUGGESTIONS,
+    DEFAULT_USER_ROLE,
+    ENABLE_SIGNUP,
+    USER_PERMISSIONS,
+)
 
 app = FastAPI()
 
 origins = ["*"]
 
-app.state.ENABLE_SIGNUP = True
-app.state.DEFAULT_MODELS = None
+app.state.ENABLE_SIGNUP = ENABLE_SIGNUP
+app.state.JWT_EXPIRES_IN = "-1"
+
+app.state.DEFAULT_MODELS = DEFAULT_MODELS
+app.state.DEFAULT_PROMPT_SUGGESTIONS = DEFAULT_PROMPT_SUGGESTIONS
+app.state.DEFAULT_USER_ROLE = DEFAULT_USER_ROLE
+app.state.USER_PERMISSIONS = USER_PERMISSIONS
+
 
 app.add_middleware(
     CORSMiddleware,
@@ -22,9 +45,8 @@ app.add_middleware(
 app.include_router(auths.router, prefix="/auths", tags=["auths"])
 app.include_router(users.router, prefix="/users", tags=["users"])
 app.include_router(chats.router, prefix="/chats", tags=["chats"])
-app.include_router(modelfiles.router,
-                   prefix="/modelfiles",
-                   tags=["modelfiles"])
+app.include_router(documents.router, prefix="/documents", tags=["documents"])
+app.include_router(modelfiles.router, prefix="/modelfiles", tags=["modelfiles"])
 app.include_router(prompts.router, prefix="/prompts", tags=["prompts"])
 
 app.include_router(configs.router, prefix="/configs", tags=["configs"])
@@ -35,7 +57,7 @@ app.include_router(utils.router, prefix="/utils", tags=["utils"])
 async def get_status():
     return {
         "status": True,
-        "version": WEBUI_VERSION,
         "auth": WEBUI_AUTH,
         "default_models": app.state.DEFAULT_MODELS,
+        "default_prompt_suggestions": app.state.DEFAULT_PROMPT_SUGGESTIONS,
     }