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[GH-ISSUE #11620] issue: The model returned the following errors: too many total text bytes #70902

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opened 2026-05-13 02:53:10 -05:00 by GiteaMirror · 2 comments
GiteaMirror commented 2026-05-13 02:53:10 -05:00
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Originally created by @hongbo-miao on GitHub (Mar 13, 2025).
Original GitHub issue: https://github.com/open-webui/open-webui/issues/11620

Check Existing Issues

  • I have searched the existing issues and discussions.
  • I am using the latest version of Open WebUI.

Installation Method

Docker

Open WebUI Version

v0.5.19

Ollama Version (if applicable)

No response

Operating System

Ubuntu 22.04

Browser (if applicable)

No response

Confirmation

  • I have read and followed all instructions in README.md.
  • I am using the latest version of both Open WebUI and Ollama.
  • I have included the browser console logs.
  • I have included the Docker container logs.
  • I have listed steps to reproduce the bug in detail.

Expected Behavior

The model can do Q&A for any knowledge collection.

Actual Behavior

When the knowledge collection is large, when full context mode enabled, it will fail as it is over model input limitation.

Steps to Reproduce

Add a lot of markdown files in a knowledge collection and do Q&A.
Full context mode in Documents settings is enabled

This is my deployment code

---
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
  name: production-hm-open-webui
  namespace: production-hm-argo-cd
  labels:
    app.kubernetes.io/name: hm-open-webui
spec:
  project: production-hm
  source:
    repoURL: https://helm.openwebui.com
    # https://artifacthub.io/packages/helm/open-webui/open-webui
    targetRevision: 5.20.0
    chart: open-webui
    helm:
      releaseName: hm-open-webui
      values: |
        # https://github.com/open-webui/helm-charts/blob/main/charts/open-webui/values.yaml
        ---
        image:
          repository: ghcr.io/open-webui/open-webui
          # https://github.com/open-webui/open-webui/releases
          tag: v0.5.19
        ollama:
          enabled: false
        pipelines:
          enabled: false
        persistence:
          enabled: true
          existingClaim: hm-open-webui-persistent-volume-claim
        serviceAccount:
          enable: false
        # https://docs.openwebui.com/getting-started/env-configuration/
        # https://github.com/open-webui/open-webui/blob/main/backend/open_webui/config.py
        extraEnvVars:
          - name: ENV
            value: prod
          - name: WEBUI_NAME
            value: AI Chat
          - name: WEBUI_URL
            value: https://open-webui.hongbomiao.com
          - name: OPENAI_API_BASE_URLS
            value: http://hm-litellm-service.production-hm-litellm.svc:80;http://hm-open-webui-pipelines.production-hm-open-webui-pipelines.svc:9099
          - name: OPENAI_API_KEYS
            valueFrom:
              secretKeyRef:
                name: hm-open-webui-secret
                key: OPENAI_API_KEYS
          - name: DEFAULT_MODELS
            value: claude-3-7-sonnet
          - name: PDF_EXTRACT_IMAGES
            value: "true"
          - name: RAG_FULL_CONTEXT
            value: "true"
  destination:
    namespace: production-hm-open-webui
    server: https://kubernetes.default.svc
  syncPolicy:
    syncOptions:
      - ServerSideApply=true
    automated:
      prune: true

Logs & Screenshots

It is stuck there with no response:

Image

Browser log:

Image

Open WebUI log:

2025-03-13 06:49:53.107 | INFO     | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "POST /api/v1/users/user/info/update HTTP/1.1" 200 - {}
2025-03-13 06:49:53.196 | INFO     | open_webui.routers.openai:get_all_models:379 - get_all_models() - {}
2025-03-13 06:49:55.005 | INFO     | open_webui.retrieval.utils:get_doc:95 - query_doc:result [['bdb39b62-8958-4d1c-8bff-e071f13e3623', 'dcfa5a28-3559-4703-b8b2-28a7a615588d', '59fb53a5-ce47-42a2-b774-eb448c3627e2', '753b5958-52f0-4722-88f3-10d4bd42c006', 'b4b268f0-7d0a-4ad2-b2f4-6a34064b2699', '2d82fdf4-a10a-4787-bdb4-f6f11b8f7b4a', '5b52e424-14e2-4aa7-91de-88870dcc74ff', '763bc17d-8166-434e-ac72-684682ef937b', '2dd13392-fed7-432b-ba1e-d926e497e82d', 'bc596773-5fa1-4f34-b334-84a2f4e25e76', '95b17f1f-2dfe-49af-8a51-40835eb274e9', '9aecf69a-d5f2-4b4a-93ab-c2949d756b22', '2d902a59-ab30-4e39-a148-371bbe8ecb68', '3ab659f5-44f7-400f-a736-19d36da28fb7', 'dba04f1f-88fe-4e07-86e8-c96f9b27d903', '173b1d28-4b52-4f4d-93d2-d14801f6c20e', '6dbdf6a3-f06a-4d0c-bf96-8f8a30074779', '10092801-b49a-4e3a-a7a2-cc3ea52c2364', '18f0d429-c0d1-42e0-899e-0917d401dd27', '63449ecc-d2ba-48a1-8c02-a3951d381fe3', 'f2d946f0-a717-4212-b2bb-2d0c86ffa30c', '7605baac-e253-4141-9b45-74cd3bb8cb8f', 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'22d59eec-a796-4aac-97e7-fa3f8d515345', '7ce57094-8ac5-4a47-9f03-eec0bc047f79', '16903a41-a6bd-4f9e-823a-31571717e05c', 'b6abcda1-332e-4b12-b64b-eb13cc88f3a7', 'ef65bdb7-6485-4241-8f8f-ada300a05c07', 'b4ca6a64-ef8d-418b-aa0b-f60accc1d9cf', '8acdf6d2-6657-45dd-b46f-fe136125890a', 'e9be9ff5-e452-4b70-98cf-8cb66f976456']] [[{'created_by': 'da770326-38ba-40f7-8180-a1f0cc222260', 'embedding_config': '{"engine": "", "model": "sentence-transformers/all-MiniLM-L6-v2"}', 'file_id': '0f2a92fe-73e7-437e-8f9f-f98f62748030', 'hash': 'f565bf587d34d44eeeb79dcaa24b7f23e1f1988a68ccea1161ee3d1c981db917', 'name': 'xxx', 'source': 'xxx', 'start_index': 0}]] - {}
filter_functions=[]
2025-03-13 06:49:56.456 | INFO     | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "POST /api/chat/completions HTTP/1.1" 200 - {}
2025-03-13 06:49:56.792 | INFO     | open_webui.routers.openai:get_all_models:379 - get_all_models() - {}
2025-03-13 06:49:56.803 | INFO     | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "GET /api/v1/chats/?page=1 HTTP/1.1" 200 - {}
2025-03-13 06:49:57.039 | INFO     | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "POST /api/chat/completed HTTP/1.1" 200 - {}
2025-03-13 06:49:58.779 | INFO     | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "POST /api/v1/chats/3f9a5562-1347-4e2c-8cdb-92a99c76b63f HTTP/1.1" 200 - {}
2025-03-13 06:49:59.441 | INFO     | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "GET /api/v1/chats/?page=1 HTTP/1.1" 200 - {}

LiteLLM log:

INFO:     172.31.156.135:33904 - "POST /chat/completions HTTP/1.1" 200 OK
07:08:16 - LiteLLM Proxy:ERROR: proxy_server.py:3039 - litellm.proxy.proxy_server.async_data_generator(): Exception occured - litellm.BadRequestError: BedrockException - validationException {"message":"The model returned the following errors: too many total text bytes: 15020337 > 9000000"}
Traceback (most recent call last):
  File "/usr/lib/python3.13/site-packages/litellm/litellm_core_utils/streaming_handler.py", line 1596, in __anext__
    async for chunk in self.completion_stream:
    ...<50 lines>...
        return processed_chunk
  File "/usr/lib/python3.13/site-packages/litellm/llms/bedrock/chat/invoke_handler.py", line 1482, in aiter_bytes
    message = self._parse_message_from_event(event)
  File "/usr/lib/python3.13/site-packages/litellm/llms/bedrock/chat/invoke_handler.py", line 1501, in _parse_message_from_event
    raise BedrockError(
    ...<6 lines>...
    )
litellm.llms.bedrock.common_utils.BedrockError: validationException {"message":"The model returned the following errors: too many total text bytes: 15020337 > 9000000"}

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/lib/python3.13/site-packages/litellm/proxy/proxy_server.py", line 3018, in async_data_generator
    async for chunk in response:
    ...<14 lines>...
            yield f"data: {str(e)}\n\n"
  File "/usr/lib/python3.13/site-packages/litellm/litellm_core_utils/streaming_handler.py", line 1751, in __anext__
    raise exception_type(
          ~~~~~~~~~~~~~~^
        model=self.model,
        ^^^^^^^^^^^^^^^^^
    ...<3 lines>...
        extra_kwargs={},
        ^^^^^^^^^^^^^^^^
    )
    ^
  File "/usr/lib/python3.13/site-packages/litellm/litellm_core_utils/exception_mapping_utils.py", line 2202, in exception_type
    raise e
  File "/usr/lib/python3.13/site-packages/litellm/litellm_core_utils/exception_mapping_utils.py", line 909, in exception_type
    raise BadRequestError(
    ...<4 lines>...
    )
litellm.exceptions.BadRequestError: litellm.BadRequestError: BedrockException - validationException {"message":"The model returned the following errors: too many total text bytes: 15020337 > 9000000"}

Additional Information

I checked LiteLLM log, I can see Open WebUI accessed LiteLLM's /models endpoint which returns

{
  "data": [
    {
      "id": "claude-3-7-sonnet",
      "object": "model",
      "created": 1677610602,
      "owned_by": "openai"
    }
  ],
  "object": "list"
}

However, Open WebUI never tried to access LiteLLM /model/info endpoint which include token limitation info which may be related with this issue.

{
  "data": [
    {
      "model_name": "claude-3-7-sonnet",
      "litellm_params": {
        "aws_region_name": "us-west-2",
        "use_in_pass_through": false,
        "model": "bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0"
      },
      "model_info": {
        "id": "09760fb8d7c845875a86710f1aa0c5afcc1f1bd1f0a439d4ed67bf1b1d1173f9",
        "db_model": false,
        "key": "anthropic.claude-3-7-sonnet-20250219-v1:0",
        "max_tokens": 8192,
        "max_input_tokens": 200000,
        "max_output_tokens": 8192,
        "input_cost_per_token": 0.000003,
        "cache_creation_input_token_cost": null,
        "cache_read_input_token_cost": null,
        "input_cost_per_character": null,
        "input_cost_per_token_above_128k_tokens": null,
        "input_cost_per_query": null,
        "input_cost_per_second": null,
        "input_cost_per_audio_token": null,
        "output_cost_per_token": 0.000015,
        "output_cost_per_audio_token": null,
        "output_cost_per_character": null,
        "output_cost_per_token_above_128k_tokens": null,
        "output_cost_per_character_above_128k_tokens": null,
        "output_cost_per_second": null,
        "output_cost_per_image": null,
        "output_vector_size": null,
        "litellm_provider": "bedrock_converse",
        "mode": "chat",
        "supports_system_messages": null,
        "supports_response_schema": true,
        "supports_vision": true,
        "supports_function_calling": true,
        "supports_tool_choice": true,
        "supports_assistant_prefill": true,
        "supports_prompt_caching": true,
        "supports_audio_input": false,
        "supports_audio_output": false,
        "supports_pdf_input": false,
        "supports_embedding_image_input": false,
        "supports_native_streaming": null,
        "tpm": null,
        "rpm": null,
        "supported_openai_params": [
          "max_tokens",
          "max_completion_tokens",
          "stream",
          "stream_options",
          "stop",
          "temperature",
          "top_p",
          "extra_headers",
          "response_format",
          "tools",
          "thinking"
        ]
      }
    }
  ]
}
Originally created by @hongbo-miao on GitHub (Mar 13, 2025). Original GitHub issue: https://github.com/open-webui/open-webui/issues/11620 ### Check Existing Issues - [x] I have searched the existing issues and discussions. - [x] I am using the latest version of Open WebUI. ### Installation Method Docker ### Open WebUI Version v0.5.19 ### Ollama Version (if applicable) _No response_ ### Operating System Ubuntu 22.04 ### Browser (if applicable) _No response_ ### Confirmation - [x] I have read and followed all instructions in `README.md`. - [x] I am using the latest version of **both** Open WebUI and Ollama. - [x] I have included the browser console logs. - [x] I have included the Docker container logs. - [x] I have listed steps to reproduce the bug in detail. ### Expected Behavior The model can do Q&A for any knowledge collection. ### Actual Behavior When the knowledge collection is large, when full context mode enabled, it will fail as it is over model input limitation. ### Steps to Reproduce Add a lot of markdown files in a knowledge collection and do Q&A. Full context mode in Documents settings is enabled This is my deployment code ```yaml --- apiVersion: argoproj.io/v1alpha1 kind: Application metadata: name: production-hm-open-webui namespace: production-hm-argo-cd labels: app.kubernetes.io/name: hm-open-webui spec: project: production-hm source: repoURL: https://helm.openwebui.com # https://artifacthub.io/packages/helm/open-webui/open-webui targetRevision: 5.20.0 chart: open-webui helm: releaseName: hm-open-webui values: | # https://github.com/open-webui/helm-charts/blob/main/charts/open-webui/values.yaml --- image: repository: ghcr.io/open-webui/open-webui # https://github.com/open-webui/open-webui/releases tag: v0.5.19 ollama: enabled: false pipelines: enabled: false persistence: enabled: true existingClaim: hm-open-webui-persistent-volume-claim serviceAccount: enable: false # https://docs.openwebui.com/getting-started/env-configuration/ # https://github.com/open-webui/open-webui/blob/main/backend/open_webui/config.py extraEnvVars: - name: ENV value: prod - name: WEBUI_NAME value: AI Chat - name: WEBUI_URL value: https://open-webui.hongbomiao.com - name: OPENAI_API_BASE_URLS value: http://hm-litellm-service.production-hm-litellm.svc:80;http://hm-open-webui-pipelines.production-hm-open-webui-pipelines.svc:9099 - name: OPENAI_API_KEYS valueFrom: secretKeyRef: name: hm-open-webui-secret key: OPENAI_API_KEYS - name: DEFAULT_MODELS value: claude-3-7-sonnet - name: PDF_EXTRACT_IMAGES value: "true" - name: RAG_FULL_CONTEXT value: "true" destination: namespace: production-hm-open-webui server: https://kubernetes.default.svc syncPolicy: syncOptions: - ServerSideApply=true automated: prune: true ``` ### Logs & Screenshots It is stuck there with no response: ![Image](https://github.com/user-attachments/assets/688182c9-dc2d-4f0c-b8d6-b6f885cbafba) Browser log: ![Image](https://github.com/user-attachments/assets/19e3a74e-dd82-40fb-a954-2e0adca5ce6c) Open WebUI log: ```sh 2025-03-13 06:49:53.107 | INFO | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "POST /api/v1/users/user/info/update HTTP/1.1" 200 - {} 2025-03-13 06:49:53.196 | INFO | open_webui.routers.openai:get_all_models:379 - get_all_models() - {} 2025-03-13 06:49:55.005 | INFO | open_webui.retrieval.utils:get_doc:95 - query_doc:result [['bdb39b62-8958-4d1c-8bff-e071f13e3623', 'dcfa5a28-3559-4703-b8b2-28a7a615588d', '59fb53a5-ce47-42a2-b774-eb448c3627e2', '753b5958-52f0-4722-88f3-10d4bd42c006', 'b4b268f0-7d0a-4ad2-b2f4-6a34064b2699', '2d82fdf4-a10a-4787-bdb4-f6f11b8f7b4a', '5b52e424-14e2-4aa7-91de-88870dcc74ff', '763bc17d-8166-434e-ac72-684682ef937b', '2dd13392-fed7-432b-ba1e-d926e497e82d', 'bc596773-5fa1-4f34-b334-84a2f4e25e76', '95b17f1f-2dfe-49af-8a51-40835eb274e9', '9aecf69a-d5f2-4b4a-93ab-c2949d756b22', '2d902a59-ab30-4e39-a148-371bbe8ecb68', '3ab659f5-44f7-400f-a736-19d36da28fb7', 'dba04f1f-88fe-4e07-86e8-c96f9b27d903', '173b1d28-4b52-4f4d-93d2-d14801f6c20e', '6dbdf6a3-f06a-4d0c-bf96-8f8a30074779', '10092801-b49a-4e3a-a7a2-cc3ea52c2364', '18f0d429-c0d1-42e0-899e-0917d401dd27', '63449ecc-d2ba-48a1-8c02-a3951d381fe3', 'f2d946f0-a717-4212-b2bb-2d0c86ffa30c', '7605baac-e253-4141-9b45-74cd3bb8cb8f', 'e099580c-0525-48df-affe-e32e320b6e13', 'a519d366-d72a-4c47-ab05-057a384849c4', 'c851eb04-642c-4fbf-b05f-9d9072016ac8', '681fa3de-acf0-4393-9737-4485bbd9dfb8', 'fd097201-d48a-4613-841c-fbe2aa006ece', '81781eb7-2a71-4d82-a807-78a1d3fadae7', 'b85ed5e8-9bb5-4f9b-b555-342f2e6b80f1', '35d2dc98-b1f9-4e40-9c8b-e56e4b078a39', '39a18145-946d-46af-9831-e58df918a6db', '09e37d1a-8ff0-493d-a871-7a62bdfb398b', '0f0452be-65d4-45e4-a1af-b27aef42b3ff', '6d976d98-1cb1-496a-bdc5-8b0c694b99c5', '69e6d9c9-7612-4084-8d86-26833f636266', '70bbe91b-6dce-40c3-b276-ff02363745b4', '0e921372-46e4-460b-9ee5-5076e068f419', 'ed88bfff-5ca0-4562-9452-2e02dce71396', 'd01eafd0-06d8-40d2-ad2c-15d4cde69b9b', '880ecfc3-7550-4625-8d33-3cfa6e9ac05f', '937453c9-3ab7-4dc9-9dd5-d6dd8f32a94c', '6b0a0e89-8d8a-4234-a60a-716da5558326', 'f9351087-a269-4de5-afbf-2efb9064592b', '2974e1e8-5da9-476d-9013-3c8bd56f9d38', 'd04abc73-b446-4a79-b419-6c6a50cfb1ba', '9625896f-c12e-4efd-b7a6-5d28fbd4af28', '05ded1f9-584d-421e-b9ee-5179b5318f25', '039047ed-f6f9-472f-9f49-3e247633fc73', '5929d10e-7a71-4886-a512-e82e050674ab', '96b416b9-4129-4240-a907-90fca0379570', '65dbff47-0c25-4c0e-9343-af685ab21f0d', '593d3336-09cd-4dc3-8e7e-7e465aa3060e', '797c41fb-77cd-465e-aaa1-ff614b737e3a', 'ade37ef7-30a2-4a33-b51f-d5218871ad73', 'ccf66f3d-ab5e-4612-937b-c3585572fe19', 'ebd78c5d-fee6-4655-a002-5f13fa76906d', '97d93a25-bed5-476f-86bc-8e4fb6e9fbf5', '5bc4e8bb-94e9-4d32-bac8-3621de20e837', '954d7046-3dd8-403a-9a8a-a0c0650c3e0c', '0a9be566-3fb2-4008-a360-a0c2512d4907', '4bc40cbe-ab53-4fd4-9bf1-a524fcce8585', '93b9b2a9-316a-4eda-9046-a9ca0cb6c1d6', '6e04ef02-6f43-4a4f-9179-6bcd9b20c0fd', '91814445-294d-4563-a46b-98fb273e7f34', 'bc8f5440-f38b-4137-b1ab-370736fafda9', '1d6ef5ad-f61a-46c2-8d95-8102b543a316', 'd482f245-9245-446f-94a5-31c8a6592f9d', 'b48c2480-e989-457f-a2f3-38586a9cc1f7', '6480adcd-2aa8-4fc7-82df-3e3e0e163714', '63182db5-33dd-452b-8552-456baa9f6b50', '99bbcadb-d3d6-407f-bb68-003e1f976613', 'c0c50806-7eef-446b-9276-74c18cc52130', 'c7d8c0a1-1efe-4fa7-8f84-444b6513697f', '6fc237b5-5e74-499d-922b-2ea0d0cf139c', 'babfdffc-25a7-4f98-be6b-d40949e77b55', '0244ef28-6414-4b15-ab33-e186d1c44ded', '7af5a3eb-1779-4da8-997a-347b78246e5b', 'dc1a12d0-fedb-4f57-8fe9-20fc98e040e6', '69cebacd-8c37-473c-abcf-0304513e23a4', 'edfdcfd0-72c0-481f-b494-a24bd11ff2a2', '70074609-1916-4d91-b640-84beb6a839a2', '735b9042-eb31-437d-8d8d-c04a7c69e482', 'f4ba1b36-373c-4fe3-b7c7-0ca90fb72027', '0781db66-fb57-4497-9ad4-47edaf1ec7bd', '826dd6a2-75c9-4e3d-83e8-530b9c73d721', '56eb382b-a433-4290-974e-763cc470a0f8', '7da89289-e139-495b-8441-746ee6d4f2b4', '6f6ed635-8c1d-4c20-89c2-52064adfb1d4', '8d174c78-89d1-486e-8d89-19705c7c8a03', '82f30e55-20a0-46ab-86e6-a2fa6e6dd429', '2f8254d6-f565-495c-a430-b0435c9a7a94', '73eb6607-9144-4c94-842f-adbb855ae4b3', 'bd7bf4d2-08a0-4bf5-a072-2a3f6e75e234', 'ac9fee5f-5d90-4d43-9d6e-877200e10a1a', 'fad5ab42-f3a2-4d59-8a20-01421728e63c', 'ed1579ba-f476-4ed8-930a-f54935a48529', 'ff08a956-4a19-46c6-b65d-6bdd63faef23', '30368cab-2893-41ee-939f-ab0d90ddc892', '70472980-8b5e-420a-8ba4-61c1032b4078', '5c87fc02-3779-461d-a545-6906d887154e', 'e57d70d2-8d2c-4356-a007-d1d85fd66f36', '236159e5-f7f3-4b57-901e-0d56ef11b5b3', '441d6e38-6c8f-4ac8-b146-7db558f6e365', '2d4ee691-2556-48e6-a761-8ed8f88ec40a', '5965d187-8420-48d9-b82d-d7c6f257db7b', 'c7967126-0f49-4668-86a0-8d9f600e155a', 'f1464941-74c4-4d0f-9f21-78134dcb4beb', 'a83eb0ae-d93b-43f6-9ac3-ca1202f229d7', 'f504bf2b-1567-4915-b313-03fc29049e45', '7fbabfe5-00cd-4971-9e78-a6c543812d5f', '7c24a660-f2be-444d-a44e-bd290815b35c', 'd6517476-31d7-49a3-bd47-e6c1825af018', '5b626b46-424a-4ae6-b709-0bf489d1542c', '2206456c-b29b-422c-8303-c7cee7257350', '7b24fadb-a84f-48ef-8551-bb2ac561e69d', 'e42ba8ff-e45b-4a00-a6d4-59e1596568c1', '926b117e-2f03-4741-b3dd-22731830d461', '2d2d4fc0-68f5-43be-93f4-0ff2a485e7a1', '732a2b88-be67-4973-968d-106bcf505a0f', '21197af8-c6bc-4264-9cc4-59ff748b9c64', '9710bd33-1b27-4e50-b7ea-268d65e1601e', '83dd1f2a-a310-42be-901c-768df2277fef', 'f8378ff5-9af4-4818-a375-6236bb776581', '2530c137-eb6d-4d25-85ba-cfe0d54ccc71', '4ba73a9a-608f-42ae-8621-2a00c88652ab', 'e017a4c5-0e26-4a78-8fb7-d4931d292154', '1c25e0f4-b48f-4fb6-aeef-cf759894d472', '9097f61e-1c17-4aed-a06e-6b84e09eaef4', 'c5922fdb-6ce5-42c3-8c51-66c427624584', '4ea8d05f-d508-4932-ba47-c76a435d9016', '27c24ed0-7e88-44f0-a7a5-af8b2a8cb9ec', 'e69ac32d-ccab-472c-bb56-a1e34811ea35', '68e376d4-12a9-446c-91dc-cef3e35626de', '3a9b025e-7e76-4bb8-974a-9095e522f680', '8db7a226-99d5-4084-85a9-15b8cde62dbd', '8e0636fd-2bd2-4d38-b0af-23ab15d595f9', '8e3cb8cb-7167-4457-890c-0d8953047351', 'a993b6b5-00cf-4ea6-ba31-cd5ed7c1d024', 'c6a84a3c-43d1-46e6-a7ba-1299fd04588d', 'fde21dc8-1e79-405c-9c69-5a90b0e5b040', '05cbb8f4-edb0-49f5-b670-5fcee71d14eb', 'f7be6112-771e-4a36-9508-ebff4a379a57', 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'1d5afc20-e908-4df2-9251-5e21e7e94295', '0a0f3e3d-7f51-407c-a5f3-4dccbe7b560f', '9e2807d7-52c1-4f7f-b9e7-09de978e2654', 'bc4878cf-a17f-4df9-9738-1e9c94efc0c0', 'c2956e48-1738-4e55-ba7c-b22efcc6e0ae', 'c04ef0ca-3d0a-45c6-8725-e9a942e20841', '1879d441-b1d2-43ec-b1a5-464b8733be7b', '4c9651ed-ddb5-40cc-932b-1d2086763235', '1e76da77-cd9d-4760-acf0-251c63c3e3cb', '6bd0f2e9-c755-429c-96f9-40565907fc64', '70f32007-ad3f-4395-9aed-5d9da651ea00', 'ef441cbf-7f68-4fcc-aa1d-fb0d1052822c', 'a9310665-a760-4048-8fdf-0f691d1222ee', 'd8e73d69-a810-47aa-8acd-5990ae1276a5', '00452b6b-97b9-40c8-b3f9-7cdef7d284a4', '017ffa91-a7de-40d0-9b3b-0aaafa1e8073', '4f891d9c-f2a2-418e-88de-aa19527398e6', '5613427a-984f-4195-b73b-5cb65d4f8389', '0f96ec1f-f67f-44e6-857b-eb02067f0bef', 'f4e77cc9-0dc4-4f50-b9d4-b37994e5fe9c', 'f1a97b57-ff67-4a17-b6a5-57932f2e9f41', '08152445-de9a-4925-89b5-18b606ee62df', '0f3cc23c-9736-4b30-85f5-dbc1d404a681', '29d99125-b27a-4541-b716-7eecb7de6b2c', '9fe9bb34-77ac-43f0-b368-03a353e80dd2', '62df3836-2a05-48d6-8837-d16875a8ad66', '1fef0434-2196-46db-afe3-c47d8bb79837', 'f2f2ff40-3987-443b-9ff0-92818cba713a', '192dd069-db9b-4e18-886c-8f62b6868c44', '22e12ff4-64be-4b64-9dcf-02b2df3c19de', '22d59eec-a796-4aac-97e7-fa3f8d515345', '7ce57094-8ac5-4a47-9f03-eec0bc047f79', '16903a41-a6bd-4f9e-823a-31571717e05c', 'b6abcda1-332e-4b12-b64b-eb13cc88f3a7', 'ef65bdb7-6485-4241-8f8f-ada300a05c07', 'b4ca6a64-ef8d-418b-aa0b-f60accc1d9cf', '8acdf6d2-6657-45dd-b46f-fe136125890a', 'e9be9ff5-e452-4b70-98cf-8cb66f976456']] [[{'created_by': 'da770326-38ba-40f7-8180-a1f0cc222260', 'embedding_config': '{"engine": "", "model": "sentence-transformers/all-MiniLM-L6-v2"}', 'file_id': '0f2a92fe-73e7-437e-8f9f-f98f62748030', 'hash': 'f565bf587d34d44eeeb79dcaa24b7f23e1f1988a68ccea1161ee3d1c981db917', 'name': 'xxx', 'source': 'xxx', 'start_index': 0}]] - {} filter_functions=[] 2025-03-13 06:49:56.456 | INFO | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "POST /api/chat/completions HTTP/1.1" 200 - {} 2025-03-13 06:49:56.792 | INFO | open_webui.routers.openai:get_all_models:379 - get_all_models() - {} 2025-03-13 06:49:56.803 | INFO | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "GET /api/v1/chats/?page=1 HTTP/1.1" 200 - {} 2025-03-13 06:49:57.039 | INFO | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "POST /api/chat/completed HTTP/1.1" 200 - {} 2025-03-13 06:49:58.779 | INFO | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "POST /api/v1/chats/3f9a5562-1347-4e2c-8cdb-92a99c76b63f HTTP/1.1" 200 - {} 2025-03-13 06:49:59.441 | INFO | uvicorn.protocols.http.httptools_impl:send:476 - 172.31.187.16:0 - "GET /api/v1/chats/?page=1 HTTP/1.1" 200 - {} ``` LiteLLM log: ```sh INFO: 172.31.156.135:33904 - "POST /chat/completions HTTP/1.1" 200 OK 07:08:16 - LiteLLM Proxy:ERROR: proxy_server.py:3039 - litellm.proxy.proxy_server.async_data_generator(): Exception occured - litellm.BadRequestError: BedrockException - validationException {"message":"The model returned the following errors: too many total text bytes: 15020337 > 9000000"} Traceback (most recent call last): File "/usr/lib/python3.13/site-packages/litellm/litellm_core_utils/streaming_handler.py", line 1596, in __anext__ async for chunk in self.completion_stream: ...<50 lines>... return processed_chunk File "/usr/lib/python3.13/site-packages/litellm/llms/bedrock/chat/invoke_handler.py", line 1482, in aiter_bytes message = self._parse_message_from_event(event) File "/usr/lib/python3.13/site-packages/litellm/llms/bedrock/chat/invoke_handler.py", line 1501, in _parse_message_from_event raise BedrockError( ...<6 lines>... ) litellm.llms.bedrock.common_utils.BedrockError: validationException {"message":"The model returned the following errors: too many total text bytes: 15020337 > 9000000"} During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/lib/python3.13/site-packages/litellm/proxy/proxy_server.py", line 3018, in async_data_generator async for chunk in response: ...<14 lines>... yield f"data: {str(e)}\n\n" File "/usr/lib/python3.13/site-packages/litellm/litellm_core_utils/streaming_handler.py", line 1751, in __anext__ raise exception_type( ~~~~~~~~~~~~~~^ model=self.model, ^^^^^^^^^^^^^^^^^ ...<3 lines>... extra_kwargs={}, ^^^^^^^^^^^^^^^^ ) ^ File "/usr/lib/python3.13/site-packages/litellm/litellm_core_utils/exception_mapping_utils.py", line 2202, in exception_type raise e File "/usr/lib/python3.13/site-packages/litellm/litellm_core_utils/exception_mapping_utils.py", line 909, in exception_type raise BadRequestError( ...<4 lines>... ) litellm.exceptions.BadRequestError: litellm.BadRequestError: BedrockException - validationException {"message":"The model returned the following errors: too many total text bytes: 15020337 > 9000000"} ``` ### Additional Information I checked LiteLLM log, I can see Open WebUI accessed LiteLLM's `/models` endpoint which returns ```json { "data": [ { "id": "claude-3-7-sonnet", "object": "model", "created": 1677610602, "owned_by": "openai" } ], "object": "list" } ``` However, Open WebUI never tried to access LiteLLM `/model/info` endpoint which include token limitation info which may be related with this issue. ```json { "data": [ { "model_name": "claude-3-7-sonnet", "litellm_params": { "aws_region_name": "us-west-2", "use_in_pass_through": false, "model": "bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0" }, "model_info": { "id": "09760fb8d7c845875a86710f1aa0c5afcc1f1bd1f0a439d4ed67bf1b1d1173f9", "db_model": false, "key": "anthropic.claude-3-7-sonnet-20250219-v1:0", "max_tokens": 8192, "max_input_tokens": 200000, "max_output_tokens": 8192, "input_cost_per_token": 0.000003, "cache_creation_input_token_cost": null, "cache_read_input_token_cost": null, "input_cost_per_character": null, "input_cost_per_token_above_128k_tokens": null, "input_cost_per_query": null, "input_cost_per_second": null, "input_cost_per_audio_token": null, "output_cost_per_token": 0.000015, "output_cost_per_audio_token": null, "output_cost_per_character": null, "output_cost_per_token_above_128k_tokens": null, "output_cost_per_character_above_128k_tokens": null, "output_cost_per_second": null, "output_cost_per_image": null, "output_vector_size": null, "litellm_provider": "bedrock_converse", "mode": "chat", "supports_system_messages": null, "supports_response_schema": true, "supports_vision": true, "supports_function_calling": true, "supports_tool_choice": true, "supports_assistant_prefill": true, "supports_prompt_caching": true, "supports_audio_input": false, "supports_audio_output": false, "supports_pdf_input": false, "supports_embedding_image_input": false, "supports_native_streaming": null, "tpm": null, "rpm": null, "supported_openai_params": [ "max_tokens", "max_completion_tokens", "stream", "stream_options", "stop", "temperature", "top_p", "extra_headers", "response_format", "tools", "thinking" ] } } ] } ```
GiteaMirror added the bug label 2026-05-13 02:53:10 -05:00
GiteaMirror commented 2026-05-13 02:53:13 -05:00
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@Classic298 commented on GitHub (Mar 13, 2025):

Expected Behavior
The model can do Q&A for any knowledge collection.

Actual Behavior
When the knowledge collection is large, when full context mode enabled, it will fail as it is over model input limitation.

Well ok this makes sense and is expected, isn't it?
I don't see how this is a bug

<!-- gh-comment-id:2720301341 --> @Classic298 commented on GitHub (Mar 13, 2025): > Expected Behavior > The model can do Q&A for any knowledge collection. > > Actual Behavior > When the knowledge collection is large, when full context mode enabled, it will fail as it is over model input limitation. Well ok this makes sense and is expected, isn't it? I don't see how this is a bug
GiteaMirror commented 2026-05-13 02:53:14 -05:00
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@hongbo-miao commented on GitHub (Mar 13, 2025):

I agree from an engineering standpoint, the failure is expected.
However, from a user’s perspective, the Q&A should never fail or stuck under any circumstances.

Just brainstorm:

  • For example, maybe somehow set a limit on the knowledge collection size to prevent additional document uploads once the limit is reached if know model input limitation in advance. But it is hard as well, as it can have different models. Maybe giving admin hints is enough?
  • the system could fall back to a partial-context mode with a warning logged or a notification to user that answer quality may have dropped (?).
<!-- gh-comment-id:2720372578 --> @hongbo-miao commented on GitHub (Mar 13, 2025): I agree from an engineering standpoint, the failure is expected. However, from a user’s perspective, the Q&A should never fail or stuck under any circumstances. Just brainstorm: - For example, maybe somehow set a limit on the knowledge collection size to prevent additional document uploads once the limit is reached if know model input limitation in advance. But it is hard as well, as it can have different models. Maybe giving admin hints is enough? - the system could fall back to a partial-context mode with a warning logged or a notification to user that answer quality may have dropped (?).
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Reference: github-starred/open-webui#70902
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