[GH-ISSUE #12228] feat: uploading files without backend processing #16516

New Issue

GiteaMirror · 2026-04-19T22:24:58-05:00

GiteaMirror commented

2026-04-19 22:24:58 -05:00

Originally created by @wangjiyang on GitHub (Mar 31, 2025).
Original GitHub issue: https://github.com/open-webui/open-webui/issues/12228

Originally assigned to: @tjbck on GitHub.

Check Existing Issues

I have searched the existing issues and discussions.

Problem Description

I am trying to use open webui as a frontend webui, and trying to process user reuqest via chating ui, which open webui has a great user experience and works stable. Thanks for your great work.

We are trying to add more features to make good use of our self-hosted aigc backend. This feature is something like: user upload some files and ask some question", and then our backend (which is driven by pipeline) get original file and processing it and send sse events to pipeline and then emit it to open webui front end. RAG feature or OCR feature(which i don't know) is always trying to extract data into text, which is not a must for our use case. This takes a lot of time and affect user experience. Also, it seems open webui always trying to send raw text to open webui backend, it's also not required by our feature and affect user experience.

So I am requesting one feature that:

bypass content extraction feature completely (I already check "bypass vector database" inside document tab, but seems not disabled completely)
pipeline can access user uploaded file via raw url(of course respecting cors setting), so it can do more stuff inside.

Could you please have a discussion of this feature, thanks.

Desired Solution you'd like

bypass content extraction feature completely (I already check "bypass vector database" inside document tab, but seems not disabled completely)
pipeline can access user uploaded file via raw url(of course respecting cors setting), so it can do more stuff inside.

Alternatives Considered

No response

Additional Context

No response

Originally created by @wangjiyang on GitHub (Mar 31, 2025). Original GitHub issue: https://github.com/open-webui/open-webui/issues/12228 Originally assigned to: @tjbck on GitHub. ### Check Existing Issues - [x] I have searched the existing issues and discussions. ### Problem Description I am trying to use open webui as a frontend webui, and trying to process user reuqest via chating ui, which open webui has a great user experience and works stable. Thanks for your great work. We are trying to add more features to make good use of our self-hosted aigc backend. This feature is something like: user upload some files and ask some question", and then our backend (which is driven by pipeline) get original file and processing it and send sse events to pipeline and then emit it to open webui front end. RAG feature or OCR feature(which i don't know) is always trying to extract data into text, which is not a must for our use case. This takes a lot of time and affect user experience. Also, it seems open webui always trying to send raw text to open webui backend, it's also not required by our feature and affect user experience. So I am requesting one feature that: 1. bypass content extraction feature completely (I already check "bypass vector database" inside document tab, but seems not disabled completely) 2. pipeline can access user uploaded file via raw url(of course respecting cors setting), so it can do more stuff inside. Could you please have a discussion of this feature, thanks. ### Desired Solution you'd like 1. bypass content extraction feature completely (I already check "bypass vector database" inside document tab, but seems not disabled completely) 2. pipeline can access user uploaded file via raw url(of course respecting cors setting), so it can do more stuff inside. ### Alternatives Considered _No response_ ### Additional Context _No response_

GiteaMirror commented

2026-04-19 22:25:00 -05:00

@harrywang commented on GitHub (Apr 15, 2025):

Yes. This is needed. Bypass Embedding and Retrieval option does not work for this purpose - I think the chunking, embedding happens when the file is uploaded.

@harrywang commented on GitHub (Apr 15, 2025): Yes. This is needed. Bypass Embedding and Retrieval option does not work for this purpose - I think the chunking, embedding happens when the file is uploaded.

GiteaMirror commented

2026-04-19 22:25:00 -05:00

@roeizavida commented on GitHub (Apr 15, 2025):

It would be great if that file can be passed to the LLM using the files API, similar to OpenAI.
This way you can pass text files, images, PDFs directly to the LLM.

@roeizavida commented on GitHub (Apr 15, 2025): It would be great if that file can be passed to the LLM using the files API, similar to OpenAI. This way you can pass text files, images, PDFs directly to the LLM.

GiteaMirror commented

2026-04-19 22:25:01 -05:00

@tjbck commented on GitHub (Apr 19, 2025):

@harrywang Chunking, and embedding does NOT happen when the file is uploaded if you have Bypass Embedding and Retrieval enabled.

@tjbck commented on GitHub (Apr 19, 2025): @harrywang Chunking, and embedding does NOT happen when the file is uploaded if you have `Bypass Embedding and Retrieval` enabled.

GiteaMirror commented

2026-04-19 22:25:02 -05:00

@Classic298 commented on GitHub (Apr 19, 2025):

A great usecase for this would also be to upload audio files directly to the AI model

For example: upload a audio file directly to a gemini model, so that it can differentiate the different people speaking in the audio and create a comprehensive transcription, complete with person identification.

If this would be possible that'd be cool

@Classic298 commented on GitHub (Apr 19, 2025): A great usecase for this would also be to upload audio files directly to the AI model For example: upload a audio file directly to a gemini model, so that it can differentiate the different people speaking in the audio and create a comprehensive transcription, complete with person identification. If this would be possible that'd be cool

GiteaMirror commented

2026-04-19 22:25:04 -05:00

@tremlin commented on GitHub (Apr 19, 2025):

We've been trying to implement models in Open WebUI that use tools to operate on files (instead of passing files directly to the AI model). These tools could be a code execution sandbox, a custom backend for file processing, or a remote API where we want the AI to upload the files to (like creating a Jira issue with attachment). If I understand this issue correctly, this is more or less about the same feature.

Ideal for us would be an option that:

Makes the name or ids of uploaded files available in the model prompt so that the model can operate on the "name" level.
Makes the content available to tools (already works for built-in Tools because they have access to storage, but not for Tool Servers).
Does not pass the contents of the file to the model in order to avoid confusing the model.
And in particular, it does not treat images differently. At the moment, images are distinct from files and don't have a filename, for instance.
Configurable per model because we want to host "normal" models that do pass files to the API on the same Open WebUI instance.

We've already tried a few workarounds with Tools and Functions, but these are not powerful enough.

@tremlin commented on GitHub (Apr 19, 2025): We've been trying to implement models in Open WebUI that use tools to operate on files (instead of passing files directly to the AI model). These tools could be a code execution sandbox, a custom backend for file processing, or a remote API where we want the AI to upload the files to (like creating a Jira issue with attachment). If I understand this issue correctly, this is more or less about the same feature. Ideal for us would be an option that: - Makes the name or ids of uploaded files available in the model prompt so that the model can operate on the "name" level. - Makes the content available to tools (already works for built-in Tools because they have access to storage, but not for Tool Servers). - Does not pass the contents of the file to the model in order to avoid confusing the model. - And in particular, it does not treat images differently. At the moment, images are distinct from files and don't have a filename, for instance. - Configurable per model because we want to host "normal" models that do pass files to the API on the same Open WebUI instance. We've already tried a few workarounds with Tools and Functions, but these are not powerful enough.

GiteaMirror commented

2026-04-19 22:25:06 -05:00

@harrywang commented on GitHub (Apr 19, 2025):

@harrywang Chunking, and embedding does NOT happen when the file is uploaded if you have Bypass Embedding and Retrieval enabled.

You are right, the following is AI's analysis of the code base for what happens once a file is uploaded in chat. I hope we can disable file upload based on file type for each model.

📁 File Upload Process Flow (Open WebUI)

1. Frontend Initiates Upload

When a user uploads a file in the chat interface, the frontend component (typically MessageInput.svelte) calls the uploadFileHandler function.
This function uses the uploadFile API from $lib/apis/files.
A FormData object is created, the file is appended, and a POST request is sent to /files/ with the user's authentication token.

2. Backend Receives the File

The request is handled by the upload_file endpoint in /backend/open_webui/routers/files.py.
The file is received as an UploadFile object via FastAPI.

3. File Processing and Storage

A unique ID (UUID) is generated for the file.
The original filename is preserved but prefixed with the UUID.
The file is stored using one of several storage providers:
- LocalStorageProvider – local filesystem
- S3StorageProvider – Amazon S3
- GCSStorageProvider – Google Cloud Storage
- AzureStorageProvider – Azure Blob Storage

4. Database Entry Creation

A database entry is created with metadata:
- File ID, name, path
- Content type
- File size
- Uploading user ID

5. Content Extraction and Processing

If process=true, the system attempts to extract content:
- Audio files (.mp3, .wav, .ogg, .m4a): Transcribed to text
- Other files (excluding images): Processed via a content extraction engine
Extracted text is stored in the file’s data field.

6. Vector Database Integration

Unless BYPASS_EMBEDDING_AND_RETRIEVAL is enabled:
- Extracted content is converted into embeddings.
- Embeddings are stored in a vector database.
- Each file gets its own collection named: file-{file_id}.

7. Response to Frontend

The backend returns:
- File ID and metadata
- Extracted content (if applicable)
- Any processing errors

8. Frontend Updates UI

The frontend receives the response and:
- Updates the UI to display the uploaded file.
- Enables the file to be referenced in chat or used for RAG (retrieval-augmented generation).

🔍 Special File Type Handling

File Type	Behavior
Audio files	Automatically transcribed to text
Images	Stored, not processed for text extraction
Documents/PDFs	Text extracted via engine (Tika, Docling, etc.)

This process enables rich file handling and integration into AI interactions, especially for RAG-based workflows.

@harrywang commented on GitHub (Apr 19, 2025): > [@harrywang](https://github.com/harrywang) Chunking, and embedding does NOT happen when the file is uploaded if you have `Bypass Embedding and Retrieval` enabled. You are right, the following is AI's analysis of the code base for what happens once a file is uploaded in chat. I hope we can disable file upload based on file type for each model. # 📁 File Upload Process Flow (Open WebUI) ## 1. Frontend Initiates Upload - When a user uploads a file in the chat interface, the frontend component (typically `MessageInput.svelte`) calls the `uploadFileHandler` function. - This function uses the `uploadFile` API from `$lib/apis/files`. - A `FormData` object is created, the file is appended, and a `POST` request is sent to `/files/` with the user's authentication token. --- ## 2. Backend Receives the File - The request is handled by the `upload_file` endpoint in `/backend/open_webui/routers/files.py`. - The file is received as an `UploadFile` object via **FastAPI**. --- ## 3. File Processing and Storage - A unique ID (UUID) is generated for the file. - The original filename is preserved but prefixed with the UUID. - The file is stored using one of several storage providers: - `LocalStorageProvider` – local filesystem - `S3StorageProvider` – Amazon S3 - `GCSStorageProvider` – Google Cloud Storage - `AzureStorageProvider` – Azure Blob Storage --- ## 4. Database Entry Creation - A database entry is created with metadata: - File ID, name, path - Content type - File size - Uploading user ID --- ## 5. Content Extraction and Processing - If `process=true`, the system attempts to extract content: - **Audio files** (`.mp3`, `.wav`, `.ogg`, `.m4a`): Transcribed to text - **Other files** (excluding images): Processed via a content extraction engine - Extracted text is stored in the file’s `data` field. --- ## 6. Vector Database Integration - Unless `BYPASS_EMBEDDING_AND_RETRIEVAL` is enabled: - Extracted content is converted into **embeddings**. - Embeddings are stored in a **vector database**. - Each file gets its own collection named: `file-{file_id}`. --- ## 7. Response to Frontend - The backend returns: - File ID and metadata - Extracted content (if applicable) - Any processing errors --- ## 8. Frontend Updates UI - The frontend receives the response and: - Updates the UI to display the uploaded file. - Enables the file to be referenced in chat or used for RAG (retrieval-augmented generation). --- ## 🔍 Special File Type Handling | File Type | Behavior | |----------------|-----------------------------------------------| | Audio files | Automatically transcribed to text | | Images | Stored, not processed for text extraction | | Documents/PDFs | Text extracted via engine (Tika, Docling, etc.) | --- This process enables rich file handling and integration into AI interactions, especially for RAG-based workflows.

GiteaMirror commented

2026-04-19 22:25:06 -05:00

@harrywang commented on GitHub (Apr 19, 2025):

We've been trying to implement models in Open WebUI that use tools to operate on files (instead of passing files directly to the AI model). These tools could be a code execution sandbox, a custom backend for file processing, or a remote API where we want the AI to upload the files to (like creating a Jira issue with attachment). If I understand this issue correctly, this is more or less about the same feature.

Ideal for us would be an option that:

Makes the name or ids of uploaded files available in the model prompt so that the model can operate on the "name" level.

Makes the content available to tools (already works for built-in Tools because they have access to storage, but not for Tool Servers).

Does not pass the contents of the file to the model in order to avoid confusing the model.

And in particular, it does not treat images differently. At the moment, images are distinct from files and don't have a filename, for instance.

Configurable per model because we want to host "normal" models that do pass files to the API on the same Open WebUI instance.

We've already tried a few workarounds with Tools and Functions, but these are not powerful enough.

Yes. See my comments above. We are hardcoding some conditions to treat images and other files (txt, csv) differently but it would be nice if this can be configured via Admin UI.

@harrywang commented on GitHub (Apr 19, 2025): > We've been trying to implement models in Open WebUI that use tools to operate on files (instead of passing files directly to the AI model). These tools could be a code execution sandbox, a custom backend for file processing, or a remote API where we want the AI to upload the files to (like creating a Jira issue with attachment). If I understand this issue correctly, this is more or less about the same feature. > > Ideal for us would be an option that: > > * Makes the name or ids of uploaded files available in the model prompt so that the model can operate on the "name" level. > * Makes the content available to tools (already works for built-in Tools because they have access to storage, but not for Tool Servers). > * Does not pass the contents of the file to the model in order to avoid confusing the model. > * And in particular, it does not treat images differently. At the moment, images are distinct from files and don't have a filename, for instance. > * Configurable per model because we want to host "normal" models that do pass files to the API on the same Open WebUI instance. > > We've already tried a few workarounds with Tools and Functions, but these are not powerful enough. Yes. See my comments above. We are hardcoding some conditions to treat images and other files (txt, csv) differently but it would be nice if this can be configured via Admin UI.

GiteaMirror commented

2026-04-19 22:25:07 -05:00

@mjp0 commented on GitHub (Apr 20, 2025):

Yes. See my comments above. We are hardcoding some conditions to treat images and other files (txt, csv) differently but it would be nice if this can be configured via Admin UI.

I'd suggest making this a provider/model switch instead of a global switch. With less capable models, the existing processing is great, but with Gemini I want to pass all files everything as-is.

@mjp0 commented on GitHub (Apr 20, 2025): > Yes. See my comments above. We are hardcoding some conditions to treat images and other files (txt, csv) differently but it would be nice if this can be configured via Admin UI. I'd suggest making this a provider/model switch instead of a global switch. With less capable models, the existing processing is great, but with Gemini I want to pass all files everything as-is.

GiteaMirror commented

2026-04-19 22:25:09 -05:00

@wangjiyang commented on GitHub (Apr 24, 2025):

Yes. See my comments above. We are hardcoding some conditions to treat images and other files (txt, csv) differently but it would be nice if this can be configured via Admin UI.

I'd suggest making this a provider/model switch instead of a global switch. With less capable models, the existing processing is great, but with Gemini I want to pass all files everything as-is.

yes, introducing a webui configuration for different file types is a good idea, and this switch can be configured per model would be better.

@wangjiyang commented on GitHub (Apr 24, 2025): > > Yes. See my comments above. We are hardcoding some conditions to treat images and other files (txt, csv) differently but it would be nice if this can be configured via Admin UI. > > I'd suggest making this a provider/model switch instead of a global switch. With less capable models, the existing processing is great, but with Gemini I want to pass all files everything as-is. yes, introducing a webui configuration for different file types is a good idea, and this switch can be configured per model would be better.

GiteaMirror commented

2026-04-19 22:25:10 -05:00

@matjbru commented on GitHub (May 7, 2025):

The option of a complete bypass of any processing on a per model (or per chat) basis would be great. For example, I would like to upload PDFs as-is to Gemini 2.5 via API, due to its excellent PDF processing capability.

@matjbru commented on GitHub (May 7, 2025): The option of a complete bypass of any processing on a per model (or per chat) basis would be great. For example, I would like to upload PDFs as-is to Gemini 2.5 via API, due to its excellent PDF processing capability.

GiteaMirror commented

2026-04-19 22:25:10 -05:00

@istranic commented on GitHub (May 29, 2025):

For those looking to process files using a custom pipeline, you can fetch the file in the pipe method from the OpenWebUI backend using the code below. The awkward part of is that you have to intercept the file and add it to the message history before passing it to your model. There is no record of the file in the conversation history maintained by OpenWebUI, which makes it impossible to build an agentic system that uses the internal conversation history and can reason on top of files that were uploaded earlier in the conversation. It works very well for files pass in the latest invokation of the pipe method.... i.e. the last user message.

TLDR: Please record the presence of files to the conversation history, not just in the body, because the body only contains information for the latest file that was uploaded, and not for the files that were uploaded earlier in that conversation.


async def inlet(self, body: dict, user: dict) -> dict:
    body["file_contents"] = body.get("files", [])

def pipe(...........)

....
            if body["file_contents"]:
                last_message_content = messages[-1]["content"]
                if type(last_message_content) == str:
                    last_message_content = [
                        {"type": "text", "text": last_message_content}
                    ]

                for f in body["file_contents"]:

                    url = self.config.OPENWEBUI_URL + f["url"] + "/content"
                    print(f"fetching files from {url}")
                    response = requests.get(
                        url,
                        headers={
                            "Authorization": f"Bearer {self.config.OPENWEBUI_API_KEY}"
                        },
                    )

                    if type(response.content) == bytes and response.status_code == 200:
                        last_message_content.append(
                            {
                                "type": "file",
                                "file": {
                                    "filename": f["file"]["filename"],
                                    "file_data": f"data:{f['file']['meta']['content_type']};base64,{base64.b64encode(response.content).decode('utf-8')}",
                                },
                            }
                        )
                    else:
                        print("Response from fetching files did not return bytes")

                messages[-1]["content"] = last_message_content

@istranic commented on GitHub (May 29, 2025): For those looking to process files using a custom pipeline, you can fetch the file in the `pipe` method from the OpenWebUI backend using the code below. The awkward part of is that you have to intercept the file and add it to the message history before passing it to your model. There is no record of the file in the conversation history maintained by OpenWebUI, which makes it impossible to build an agentic system that uses the internal conversation history and can reason on top of files that were uploaded earlier in the conversation. It works very well for files pass in the latest invokation of the `pipe` method.... i.e. the last user message. TLDR: Please record the presence of files to the conversation history, not just in the body, because the body only contains information for the latest file that was uploaded, and not for the files that were uploaded earlier in that conversation. ``` async def inlet(self, body: dict, user: dict) -> dict: body["file_contents"] = body.get("files", []) def pipe(...........) .... if body["file_contents"]: last_message_content = messages[-1]["content"] if type(last_message_content) == str: last_message_content = [ {"type": "text", "text": last_message_content} ] for f in body["file_contents"]: url = self.config.OPENWEBUI_URL + f["url"] + "/content" print(f"fetching files from {url}") response = requests.get( url, headers={ "Authorization": f"Bearer {self.config.OPENWEBUI_API_KEY}" }, ) if type(response.content) == bytes and response.status_code == 200: last_message_content.append( { "type": "file", "file": { "filename": f["file"]["filename"], "file_data": f"data:{f['file']['meta']['content_type']};base64,{base64.b64encode(response.content).decode('utf-8')}", }, } ) else: print("Response from fetching files did not return bytes") messages[-1]["content"] = last_message_content ```

GiteaMirror commented

2026-04-19 22:25:11 -05:00

@MonsieurBibo commented on GitHub (Jul 3, 2025):

What is the status of this issue ? Is it scheduled to be implemented ? Is help needed ?

@MonsieurBibo commented on GitHub (Jul 3, 2025): What is the status of this issue ? Is it scheduled to be implemented ? Is help needed ?

GiteaMirror commented

2026-04-19 22:25:11 -05:00

@TigerAI-TW commented on GitHub (Jul 6, 2025):

Even turn on the Bypass Embedding and Retrieval, but it still works. and parsing pdf(uploading) to text into system prompt.

@istranic can you kindly provide this code to pipe markerplace. can it support when uploading file to openwebui then store the file into local folder.

Below is ChatGPT recommended
import os
import time
import json
import base64
import requests
from typing import Optional, Callable, Awaitable, Any, Dict
from pydantic import BaseModel, Field

Define a simple config-like class to store environment variables

class Config:
OPENWEBUI_URL: str = os.getenv("OPENWEBUI_URL", "http://localhost:3000")
OPENWEBUI_API_KEY: str = os.getenv("OPENWEBUI_API_KEY", "")
SAVE_FOLDER: str = os.getenv("UPLOAD_SAVE_DIR", "data/uploads")

class Pipe:
class Valves(BaseModel):
ENABLE_STATUS_INDICATOR: bool = Field(default=True)
EMIT_INTERVAL: float = Field(default=2.0)

def __init__(self):
    self.name = "OpenWebUI Save Local Agent"
    self.valves = self.Valves()
    self.last_emit_time = 0
    self.config = Config()

async def emit_status(
    self,
    __event_emitter__: Callable[[dict], Awaitable[None]],
    level: str,
    message: str,
    done: bool,
):
    current_time = time.time()
    if (
        __event_emitter__
        and self.valves.ENABLE_STATUS_INDICATOR
        and (
            current_time - self.last_emit_time >= self.valves.EMIT_INTERVAL or done
        )
    ):
        await __event_emitter__({
            "type": "status",
            "data": {
                "status": "complete" if done else "in_progress",
                "level": level,
                "description": message,
                "done": done,
            },
        })
        self.last_emit_time = current_time

async def pipe(
    self,
    body: dict,
    __user__: Optional[dict] = None,
    __event_emitter__: Optional[Callable[[dict], Awaitable[None]]] = None,
    __event_call__: Optional[Callable[[dict], Awaitable[dict]]] = None,
) -> Optional[dict]:

    messages = body.get("messages", [])
    if not messages:
        await self.emit_status(__event_emitter__, "error", "No messages found", True)
        return {"error": "No messages found in request body"}

    body["file_contents"] = body.get("files", [])
    saved_files = []

    if body["file_contents"]:
        last_message_content = messages[-1]["content"]
        if isinstance(last_message_content, str):
            last_message_content = [{"type": "text", "text": last_message_content}]

        for f in body["file_contents"]:
            url = self.config.OPENWEBUI_URL + f["url"] + "/content"
            print(f"Fetching files from {url}")
            response = requests.get(
                url,
                headers={"Authorization": f"Bearer {self.config.OPENWEBUI_API_KEY}"},
            )

            if isinstance(response.content, bytes) and response.status_code == 200:
                file_name = f["file"]["filename"]
                content_type = f["file"]["meta"]["content_type"]

                save_path = os.path.join(self.config.SAVE_FOLDER, file_name)
                os.makedirs(os.path.dirname(save_path), exist_ok=True)

                with open(save_path, "wb") as fp:
                    fp.write(response.content)

                print(f"Saved file to {save_path}")
                saved_files.append({"filename": file_name, "path": save_path})
            else:
                print("Failed to fetch file or non-bytes content")

        messages[-1]["content"] = last_message_content

    await self.emit_status(__event_emitter__, "info", "File saved locally", True)
    return {"saved_files": saved_files}

@TigerAI-TW commented on GitHub (Jul 6, 2025): <img width="1288" height="42" alt="Image" src="https://github.com/user-attachments/assets/dc9a56f6-6aab-45e6-83bc-2c5bdaea2ab7" /> Even turn on the Bypass Embedding and Retrieval, but it still works. and parsing pdf(uploading) to text into system prompt. @istranic can you kindly provide this code to pipe markerplace. can it support when uploading file to openwebui then store the file into local folder. Below is ChatGPT recommended import os import time import json import base64 import requests from typing import Optional, Callable, Awaitable, Any, Dict from pydantic import BaseModel, Field # Define a simple config-like class to store environment variables class Config: OPENWEBUI_URL: str = os.getenv("OPENWEBUI_URL", "http://localhost:3000") OPENWEBUI_API_KEY: str = os.getenv("OPENWEBUI_API_KEY", "") SAVE_FOLDER: str = os.getenv("UPLOAD_SAVE_DIR", "data/uploads") class Pipe: class Valves(BaseModel): ENABLE_STATUS_INDICATOR: bool = Field(default=True) EMIT_INTERVAL: float = Field(default=2.0) def __init__(self): self.name = "OpenWebUI Save Local Agent" self.valves = self.Valves() self.last_emit_time = 0 self.config = Config() async def emit_status( self, __event_emitter__: Callable[[dict], Awaitable[None]], level: str, message: str, done: bool, ): current_time = time.time() if ( __event_emitter__ and self.valves.ENABLE_STATUS_INDICATOR and ( current_time - self.last_emit_time >= self.valves.EMIT_INTERVAL or done ) ): await __event_emitter__({ "type": "status", "data": { "status": "complete" if done else "in_progress", "level": level, "description": message, "done": done, }, }) self.last_emit_time = current_time async def pipe( self, body: dict, __user__: Optional[dict] = None, __event_emitter__: Optional[Callable[[dict], Awaitable[None]]] = None, __event_call__: Optional[Callable[[dict], Awaitable[dict]]] = None, ) -> Optional[dict]: messages = body.get("messages", []) if not messages: await self.emit_status(__event_emitter__, "error", "No messages found", True) return {"error": "No messages found in request body"} body["file_contents"] = body.get("files", []) saved_files = [] if body["file_contents"]: last_message_content = messages[-1]["content"] if isinstance(last_message_content, str): last_message_content = [{"type": "text", "text": last_message_content}] for f in body["file_contents"]: url = self.config.OPENWEBUI_URL + f["url"] + "/content" print(f"Fetching files from {url}") response = requests.get( url, headers={"Authorization": f"Bearer {self.config.OPENWEBUI_API_KEY}"}, ) if isinstance(response.content, bytes) and response.status_code == 200: file_name = f["file"]["filename"] content_type = f["file"]["meta"]["content_type"] save_path = os.path.join(self.config.SAVE_FOLDER, file_name) os.makedirs(os.path.dirname(save_path), exist_ok=True) with open(save_path, "wb") as fp: fp.write(response.content) print(f"Saved file to {save_path}") saved_files.append({"filename": file_name, "path": save_path}) else: print("Failed to fetch file or non-bytes content") messages[-1]["content"] = last_message_content await self.emit_status(__event_emitter__, "info", "File saved locally", True) return {"saved_files": saved_files}

GiteaMirror commented

2026-04-19 22:25:12 -05:00

@TigerAI-TW commented on GitHub (Jul 6, 2025):

found the work around solution,
when file uploads, it store into OpenWebUI/uploads
We can parse the information to other tool, and execute the following process.

@TigerAI-TW commented on GitHub (Jul 6, 2025): found the work around solution, when file uploads, it store into OpenWebUI/uploads We can parse the information to other tool, and execute the following process.

GiteaMirror commented

2026-04-19 22:25:12 -05:00

@thinkrivan commented on GitHub (Jul 7, 2025):

the problem is openwebui still tried to inject the file contents with the RAG prompt and it doesnt disable it completely.

@thinkrivan commented on GitHub (Jul 7, 2025): the problem is openwebui still tried to inject the file contents with the RAG prompt and it doesnt disable it completely.

GiteaMirror commented

2026-04-19 22:25:13 -05:00

@JonasWild commented on GitHub (Jul 14, 2025):

+1

@JonasWild commented on GitHub (Jul 14, 2025): +1

GiteaMirror commented

2026-04-19 22:25:13 -05:00

@GmodCentral commented on GitHub (Jul 15, 2025):

I think there should be an option to pass the file name and path to the LLM in context.
Tools like python-docx could easily take advantage of this. I had to make a convoluted work around for my template editor that uses the API to retrieve the proper file names, and build the path to the document. It would have been way easier if this was automatically passed to the llm when it was attached.

https://gitlab.com/gmod-central/owui-word-doc-template-editor

@GmodCentral commented on GitHub (Jul 15, 2025): I think there should be an option to pass the file name and path to the LLM in context. Tools like python-docx could easily take advantage of this. I had to make a convoluted work around for my template editor that uses the API to retrieve the proper file names, and build the path to the document. It would have been way easier if this was automatically passed to the llm when it was attached. https://gitlab.com/gmod-central/owui-word-doc-template-editor

GiteaMirror commented

2026-04-19 22:25:14 -05:00

@kevinxschulz commented on GitHub (Aug 29, 2025):

thanks guys, I would also love this feature! We use custom APIs as Tool Servers and some endpoints expect a binary file as parameter.
What is the status of this issue ?

@kevinxschulz commented on GitHub (Aug 29, 2025): thanks guys, I would also love this feature! We use custom APIs as Tool Servers and some endpoints expect a binary file as parameter. What is the status of this issue ?

GiteaMirror commented

2026-04-19 22:25:14 -05:00

@parthraut45 commented on GitHub (Oct 24, 2025):

Hey is there any update on this , i want to disable embeddings just for csv and excel files , and keep for other file types , Bypass embeddings prevents it from being uploaded completely , saying 401: unsupported file type , is there any workaround to do so

@parthraut45 commented on GitHub (Oct 24, 2025): Hey is there any update on this , i want to disable embeddings just for csv and excel files , and keep for other file types , Bypass embeddings prevents it from being uploaded completely , saying 401: unsupported file type , is there any workaround to do so

GiteaMirror commented

2026-04-19 22:25:15 -05:00

@adhusch commented on GitHub (Feb 18, 2026):

I think this is becoming highly relevant now as skills are now available :-) , e.g. for the case of xlsx files described by @parthraut45 one could now use a skill to process the xlsx files, but for that upload of the raw files is needed.

@adhusch commented on GitHub (Feb 18, 2026): I think this is becoming highly relevant now as skills are now available :-) , e.g. for the case of xlsx files described by @parthraut45 one could now use a skill to process the xlsx files, but for that upload of the raw files is needed.

GiteaMirror commented

2026-04-19 22:25:15 -05:00

@Atrocraz commented on GitHub (Mar 6, 2026):

Why is this feat being ignored for almost a year already? This is just common sense.
Here is basic example of why it is needed - random user just casually dropped 30k rows x 20 columns excel file (that is below max file size setting, cause excel almost always much more compact, than PDF for example) in chat, which caused massive lag for other users, paralyzed ollama embeddings endpoint and after 5 minutes failed to process file.

If i was able to catch it with pipeline\filter and process via pandas and\or openpyxl there wouldnt not be problem at all.
This is almost critical issue, imho and really needs to be addressed.

@tjbck is there any fundamental problem with that? give us at least some kind of response, please, this feat request is unanswered for too long

@Atrocraz commented on GitHub (Mar 6, 2026): Why is this feat being ignored for almost a year already? This is just common sense. Here is basic example of why it is needed - random user just casually dropped 30k rows x 20 columns excel file (that is below max file size setting, cause excel almost always much more compact, than PDF for example) in chat, which caused massive lag for other users, paralyzed ollama embeddings endpoint and after 5 minutes failed to process file. If i was able to catch it with pipeline\filter and process via pandas and\or openpyxl there wouldnt not be problem at all. This is almost critical issue, imho and really needs to be addressed. @tjbck is there any fundamental problem with that? give us at least some kind of response, please, this feat request is unanswered for too long

GiteaMirror commented

2026-04-19 22:25:16 -05:00

@rgaricano commented on GitHub (Mar 6, 2026):

@Atrocraz,
we can process the files as External Content Extraction Engine, I putted a sample: https://github.com/open-webui/open-webui/discussions/17621
also I did another versions using other libs, as pandas, but I left its half done.

@rgaricano commented on GitHub (Mar 6, 2026): @Atrocraz, we can process the files as External Content Extraction Engine, I putted a sample: https://github.com/open-webui/open-webui/discussions/17621 also I did another versions using other libs, as pandas, but I left its half done.

GiteaMirror commented

2026-04-19 22:25:16 -05:00

@Atrocraz commented on GitHub (Mar 10, 2026):

@rgaricano the thing is we don't need OCR and RAG for excel at all, it's very bad idea for processing excel tables that way. Absolutely unnecessary.

I guess for now i'll need to modify OWUI core and rebuild container, i don't see any other workaround.

@Atrocraz commented on GitHub (Mar 10, 2026): @rgaricano the thing is we don't need OCR and RAG for excel at all, it's very bad idea for processing excel tables that way. Absolutely unnecessary. I guess for now i'll need to modify OWUI core and rebuild container, i don't see any other workaround.

GiteaMirror commented

2026-04-19 22:25:17 -05:00

@rgaricano commented on GitHub (Mar 10, 2026):

yes @Atrocraz, it's not necessary, but in this way (using External Content Extraction Engine) you can specify the parser used for each doc type, in my sample I used OCR is for images and pdf files...
e.g. of xls parser using openpyxl lib:

...
    def process_xlsx(self, file_path: str, filename: str) -> List[Dict[str, Any]]:
        """Process Excel files."""
        if not openpyxl:
            return [self._create_error_document(filename, "openpyxl library not available")]

        try:
            workbook = openpyxl.load_workbook(file_path, data_only=True)
            documents = []

            for sheet_name in workbook.sheetnames:
                sheet = workbook[sheet_name]
                sheet_content = []

                for row in sheet.iter_rows(values_only=True):
                    row_text = '\t'.join([str(cell) if cell is not None else '' for cell in row])
                    if row_text.strip():
                        sheet_content.append(row_text)

                if sheet_content:
                    documents.append({
                        "page_content": '\n'.join(sheet_content),
                        "metadata": {
                            "source": filename,
                            "sheet_name": sheet_name,
                            "extraction_method": "xlsx_parser",
                            "rows": len(sheet_content)
                        }
                    })

            return documents if documents else [self._create_error_document(filename, "No content found")]

        except Exception as e:
            return [self._create_error_document(filename, f"Excel processing error: {e}")]
...

@rgaricano commented on GitHub (Mar 10, 2026): yes @Atrocraz, it's not necessary, but in this way (using External Content Extraction Engine) you can specify the parser used for each doc type, in my sample I used OCR is for images and pdf files... e.g. of xls parser using `openpyxl` lib: ``` ... def process_xlsx(self, file_path: str, filename: str) -> List[Dict[str, Any]]: """Process Excel files.""" if not openpyxl: return [self._create_error_document(filename, "openpyxl library not available")] try: workbook = openpyxl.load_workbook(file_path, data_only=True) documents = [] for sheet_name in workbook.sheetnames: sheet = workbook[sheet_name] sheet_content = [] for row in sheet.iter_rows(values_only=True): row_text = '\t'.join([str(cell) if cell is not None else '' for cell in row]) if row_text.strip(): sheet_content.append(row_text) if sheet_content: documents.append({ "page_content": '\n'.join(sheet_content), "metadata": { "source": filename, "sheet_name": sheet_name, "extraction_method": "xlsx_parser", "rows": len(sheet_content) } }) return documents if documents else [self._create_error_document(filename, "No content found")] except Exception as e: return [self._create_error_document(filename, f"Excel processing error: {e}")] ... ```

GiteaMirror commented

2026-04-19 22:25:17 -05:00

@Atrocraz commented on GitHub (Mar 10, 2026):

yes @Atrocraz, it's not necessary, but in this way (using External Content Extraction Engine) you can specify the parser used for each doc type, in my sample I used OCR is for images and pdf files... e.g. of xls parser using openpyxl lib:

...
    def process_xlsx(self, file_path: str, filename: str) -> List[Dict[str, Any]]:
        """Process Excel files."""
        if not openpyxl:
            return [self._create_error_document(filename, "openpyxl library not available")]

        try:
            workbook = openpyxl.load_workbook(file_path, data_only=True)
            documents = []

            for sheet_name in workbook.sheetnames:
                sheet = workbook[sheet_name]
                sheet_content = []

                for row in sheet.iter_rows(values_only=True):
                    row_text = '\t'.join([str(cell) if cell is not None else '' for cell in row])
                    if row_text.strip():
                        sheet_content.append(row_text)

                if sheet_content:
                    documents.append({
                        "page_content": '\n'.join(sheet_content),
                        "metadata": {
                            "source": filename,
                            "sheet_name": sheet_name,
                            "extraction_method": "xlsx_parser",
                            "rows": len(sheet_content)
                        }
                    })

            return documents if documents else [self._create_error_document(filename, "No content found")]

        except Exception as e:
            return [self._create_error_document(filename, f"Excel processing error: {e}")]
...

sorry, i misunderstood at first.
yeah, it's a possible workaround, thank you for sharing, gonna test it soon.
still confused, why devs dont implement it for so long

however if i understand correctly, how OWUI works, excel still will be loaded in RAG even with custom content extraction engine, so it doesnt fully solve issue

@Atrocraz commented on GitHub (Mar 10, 2026): > yes [@Atrocraz](https://github.com/Atrocraz), it's not necessary, but in this way (using External Content Extraction Engine) you can specify the parser used for each doc type, in my sample I used OCR is for images and pdf files... e.g. of xls parser using `openpyxl` lib: > > ``` > ... > def process_xlsx(self, file_path: str, filename: str) -> List[Dict[str, Any]]: > """Process Excel files.""" > if not openpyxl: > return [self._create_error_document(filename, "openpyxl library not available")] > > try: > workbook = openpyxl.load_workbook(file_path, data_only=True) > documents = [] > > for sheet_name in workbook.sheetnames: > sheet = workbook[sheet_name] > sheet_content = [] > > for row in sheet.iter_rows(values_only=True): > row_text = '\t'.join([str(cell) if cell is not None else '' for cell in row]) > if row_text.strip(): > sheet_content.append(row_text) > > if sheet_content: > documents.append({ > "page_content": '\n'.join(sheet_content), > "metadata": { > "source": filename, > "sheet_name": sheet_name, > "extraction_method": "xlsx_parser", > "rows": len(sheet_content) > } > }) > > return documents if documents else [self._create_error_document(filename, "No content found")] > > except Exception as e: > return [self._create_error_document(filename, f"Excel processing error: {e}")] > ... > ``` sorry, i misunderstood at first. yeah, it's a possible workaround, thank you for sharing, gonna test it soon. still confused, why devs dont implement it for so long however if i understand correctly, how OWUI works, excel still will be loaded in RAG even with custom content extraction engine, so it doesnt fully solve issue

GiteaMirror commented

2026-04-19 22:25:18 -05:00

@bitsofinfo commented on GitHub (Mar 15, 2026):

please just provide a clear, single toggle option, to default disable all this pre-processing, just let us paste/upload files, b64 encode them and send to the backend model

@bitsofinfo commented on GitHub (Mar 15, 2026): please just provide a clear, single toggle option, to default disable all this pre-processing, just let us paste/upload files, b64 encode them and send to the backend model

GiteaMirror commented

2026-04-19 22:25:18 -05:00

@Message00 commented on GitHub (Mar 17, 2026):

I think it is a vital issue that needs to be addressed. Are there any updates? Alternatively, is there a temporary workaround available for now? I need to upload the raw files directly to the API provider.

@Message00 commented on GitHub (Mar 17, 2026): I think it is a vital issue that needs to be addressed. Are there any updates? Alternatively, is there a temporary workaround available for now? I need to upload the **raw** files directly to the API provider.

GiteaMirror commented

2026-04-19 22:25:19 -05:00

@rgaricano commented on GitHub (Mar 17, 2026):

Until then, a filter function can be useful: Raw_FileHandler_Filter_Function_v0.1.1.py

It intercepts the request before it goes to the model, processes files, converts them to base64 format and embedded directly in the message content (the original files array is removed to prevent duplicate processing)

Installation Steps

Navigate to Admin Settings → Functions
Click New Function
Paste the code above
Set the function ID (e.g., raw_file_handler)
Save and enable the function

Configuration

The filter includes configurable valves:

Priority: Controls execution order (higher = earlier)
Max File Size: Limits file size to prevent memory issues
User Enabled: Per-user toggle for the feature

@rgaricano commented on GitHub (Mar 17, 2026): Until then, a filter function can be useful: [Raw_FileHandler_Filter_Function_v0.1.1.py](https://github.com/user-attachments/files/26066834/Raw_FileHandler_Filter_Function_v0.1.1.py) It intercepts the request before it goes to the model, processes files, converts them to base64 format and embedded directly in the message content (the original files array is removed to prevent duplicate processing) ### Installation Steps 1. Navigate to **Admin Settings → Functions** 2. Click **New Function** 3. Paste the code above 4. Set the function ID (e.g., `raw_file_handler`) 5. Save and enable the function ### Configuration The filter includes configurable valves: - **Priority**: Controls execution order (higher = earlier) - **Max File Size**: Limits file size to prevent memory issues - **User Enabled**: Per-user toggle for the feature

GiteaMirror commented

2026-04-19 22:25:19 -05:00

@xec-abailey commented on GitHub (Mar 17, 2026):

I'm going to go the external content extraction engine route for now, but being able to specify to skip processing for certain files BUT keep the file accessible in the chat context is a critical feature for any files with tabular data

For anyone who comes after, here is a diagram as i understand (from researching the docs) how the content extraction engine fits:

flowchart TD
    Upload["User uploads .xlsx file"] --> Store["Stage 1: File stored in OWUI DB + storage\n(file_id assigned, raw bytes saved)"]
    Store --> Process["Stage 2: process_file() triggered"]
    Process --> Loader["Loader checks CONTENT_EXTRACTION_ENGINE"]
    
    Loader -->|"engine = default"| Builtin["UnstructuredExcelLoader\n⚠️ Iterates every cell in every sheet\n⚠️ Hangs on large workbooks (5MB+)"]
    Loader -->|"engine = external"| Ext["POST raw bytes to\nEXTERNAL_DOCUMENT_LOADER_URL"]
    
    Ext --> Custom["Custom Extraction Service"]
    Custom -->|"MIME = xlsx/xls"| Preview["Extract lightweight preview:\n• Sheet names + dimensions\n• Column headers\n• First N data rows\n• Inferred column types"]
    Custom -->|"Other MIME types"| Passthrough["Handle normally\n(text decode, PDF parse, etc.)"]
    
    Preview --> SmallDoc["Returns 1 doc per sheet\n(~50–100 lines total)"]
    Passthrough --> NormalDocs["Returns extracted documents"]
    Builtin --> HangOrDocs["Returns docs\n(or hangs indefinitely ⚠️)"]
    
    SmallDoc --> Embed["Stage 2b: Chunk + embed into vector DB"]
    NormalDocs --> Embed
    HangOrDocs --> Embed
    
    Embed --> Chat["Stage 3: Chat completion\n__files__ populated from Stage 1\n(independent of Stage 2)"]
    
    Chat --> Tools["Tools (e.g. openpyxl-based reader)\naccess raw file bytes via __files__\n— unaffected by extraction method"]

    style Builtin fill:#fee,stroke:#c00
    style Preview fill:#efe,stroke:#0a0
    style Custom fill:#efe,stroke:#0a0

@xec-abailey commented on GitHub (Mar 17, 2026): I'm going to go the external content extraction engine route for now, but being able to specify to skip processing for certain files BUT keep the file accessible in the chat context is a critical feature for any files with tabular data For anyone who comes after, here is a diagram as i understand (from researching the docs) how the content extraction engine fits: ```mermaid flowchart TD Upload["User uploads .xlsx file"] --> Store["Stage 1: File stored in OWUI DB + storage\n(file_id assigned, raw bytes saved)"] Store --> Process["Stage 2: process_file() triggered"] Process --> Loader["Loader checks CONTENT_EXTRACTION_ENGINE"] Loader -->|"engine = default"| Builtin["UnstructuredExcelLoader\n⚠️ Iterates every cell in every sheet\n⚠️ Hangs on large workbooks (5MB+)"] Loader -->|"engine = external"| Ext["POST raw bytes to\nEXTERNAL_DOCUMENT_LOADER_URL"] Ext --> Custom["Custom Extraction Service"] Custom -->|"MIME = xlsx/xls"| Preview["Extract lightweight preview:\n• Sheet names + dimensions\n• Column headers\n• First N data rows\n• Inferred column types"] Custom -->|"Other MIME types"| Passthrough["Handle normally\n(text decode, PDF parse, etc.)"] Preview --> SmallDoc["Returns 1 doc per sheet\n(~50–100 lines total)"] Passthrough --> NormalDocs["Returns extracted documents"] Builtin --> HangOrDocs["Returns docs\n(or hangs indefinitely ⚠️)"] SmallDoc --> Embed["Stage 2b: Chunk + embed into vector DB"] NormalDocs --> Embed HangOrDocs --> Embed Embed --> Chat["Stage 3: Chat completion\n__files__ populated from Stage 1\n(independent of Stage 2)"] Chat --> Tools["Tools (e.g. openpyxl-based reader)\naccess raw file bytes via __files__\n— unaffected by extraction method"] style Builtin fill:#fee,stroke:#c00 style Preview fill:#efe,stroke:#0a0 style Custom fill:#efe,stroke:#0a0 ```

GiteaMirror commented

2026-04-19 22:25:20 -05:00

@bitsofinfo commented on GitHub (Apr 7, 2026):

@rgaricano trying your filter, getting error ('UserValves' object has no attribute 'get'). Just uploading a pdf in the chat dialog, no additional text, hit send. v0.8.10

@bitsofinfo commented on GitHub (Apr 7, 2026): @rgaricano trying your filter, getting error (`'UserValves' object has no attribute 'get'`). Just uploading a pdf in the chat dialog, no additional text, hit send. v0.8.10

GiteaMirror commented

2026-04-19 22:25:21 -05:00

@lionelvoser commented on GitHub (Apr 7, 2026):

Adding another use case for this: we use Gemini models that support native PDF input, meaning the raw file can be passed directly to the model API. The current pipeline defeats the purpose of native file handling entirely.

The ideal behavior for us would be model-level configuration of how uploaded files are handled:

Native passthrough; raw file bytes sent directly to the model API (for models that support it, like Gemini and Claude)
Full context injection; extracted text injected in full when the document fits within the model's configured context window
RAG fallback; chunking and retrieval kicks in only when the document exceeds that threshold

Happy to help! What would be most useful?

@lionelvoser commented on GitHub (Apr 7, 2026): Adding another use case for this: we use Gemini models that support native PDF input, meaning the raw file can be passed directly to the model API. The current pipeline defeats the purpose of native file handling entirely. The ideal behavior for us would be model-level configuration of how uploaded files are handled: - Native passthrough; raw file bytes sent directly to the model API (for models that support it, like Gemini and Claude) - Full context injection; extracted text injected in full when the document fits within the model's configured context window - RAG fallback; chunking and retrieval kicks in only when the document exceeds that threshold Happy to help! What would be most useful?

GiteaMirror commented

2026-04-19 22:25:22 -05:00

@bitsofinfo commented on GitHub (Apr 7, 2026):

would be great to just have that toggle to stop all the background processing, just send the docs (just like it currently does w/ images) to the backend like this

{
  "role": "user",
  "content": [
    { "type": "text", "text": "any user text" },
    {
      "type": "file",
      "file": {
        "file_data": "data:application/pdf;base64,JVBERi0xLjM...",  
        "filename": "document.pdf"     
      }
    }
  ]
}

@bitsofinfo commented on GitHub (Apr 7, 2026): would be great to just have that toggle to stop all the background processing, just send the docs (just like it currently does w/ images) to the backend like this ``` { "role": "user", "content": [ { "type": "text", "text": "any user text" }, { "type": "file", "file": { "file_data": "data:application/pdf;base64,JVBERi0xLjM...", "filename": "document.pdf" } } ] } ```

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2026-04-19 23:50:16 -05:00

[GH-ISSUE #16516] feat: Add support for selecting multiple items at once for move/delete in the side bar #17937

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[GH-ISSUE #16516] feat: Add support for selecting multiple items at once for move/delete in the side bar #33466

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2026-05-05 19:47:08 -05:00

[GH-ISSUE #16516] feat: Add support for selecting multiple items at once for move/delete in the side bar #56603

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2026-05-13 04:36:56 -05:00

[GH-ISSUE #16516] feat: Add support for selecting multiple items at once for move/delete in the side bar #72541