Revert "Main"

docs/my-website/docs/providers/openai/videos.md

@@ -135,81 +135,6 @@ curl --location --request POST 'http://localhost:4000/v1/videos/video_id/remix'
 }'
 ```
 
-### Character, Edit, and Extension Routes
-
-OpenAI video routes supported by LiteLLM proxy:
-
-- `POST /v1/videos/characters`
-- `GET /v1/videos/characters/{character_id}`
-- `POST /v1/videos/edits`
-- `POST /v1/videos/extensions`
-
-#### `target_model_names` support on character creation
-
-`POST /v1/videos/characters` supports `target_model_names` for model-based routing (same behavior as video create).
-
-```bash
-curl --location 'http://localhost:4000/v1/videos/characters' \
---header 'Authorization: Bearer sk-1234' \
--F 'name=hero' \
--F 'target_model_names=gpt-4' \
--F 'video=@/path/to/character.mp4'
-```
-
-When `target_model_names` is used, LiteLLM returns an encoded character ID:
-
-```json
-{
-  "id": "character_...",
-  "object": "character",
-  "created_at": 1712697600,
-  "name": "hero"
-}
-```
-
-Use that encoded ID directly on get:
-
-```bash
-curl --location 'http://localhost:4000/v1/videos/characters/character_...' \
---header 'Authorization: Bearer sk-1234'
-```
-
-#### Encoded and non-encoded video IDs for edit/extension
-
-Both routes accept either plain or encoded `video.id`:
-
-- `POST /v1/videos/edits`
-- `POST /v1/videos/extensions`
-
-```bash
-curl --location 'http://localhost:4000/v1/videos/edits' \
---header 'Authorization: Bearer sk-1234' \
---header 'Content-Type: application/json' \
---data '{
-  "prompt": "Make this brighter",
-  "video": { "id": "video_..." }
-}'
-```
-
-```bash
-curl --location 'http://localhost:4000/v1/videos/extensions' \
---header 'Authorization: Bearer sk-1234' \
---header 'Content-Type: application/json' \
---data '{
-  "prompt": "Continue this scene",
-  "seconds": "4",
-  "video": { "id": "video_..." }
-}'
-```
-
-#### `custom_llm_provider` input sources
-
-For these routes, `custom_llm_provider` may be supplied via:
-
-- header: `custom-llm-provider`
-- query: `?custom_llm_provider=...`
-- body: `custom_llm_provider` (and `extra_body.custom_llm_provider` where supported)
-
 Test OpenAI video generation request
 
 ```bash

docs/my-website/docs/videos.md

@@ -290,82 +290,6 @@ curl --location 'http://localhost:4000/v1/videos' \
 --header 'custom-llm-provider: azure'
 ```
 
-### Character, Edit, and Extension Endpoints
-
-LiteLLM proxy also supports these OpenAI-compatible video routes:
-
-- `POST /v1/videos/characters`
-- `GET /v1/videos/characters/{character_id}`
-- `POST /v1/videos/edits`
-- `POST /v1/videos/extensions`
-
-#### Routing Behavior (`target_model_names`, encoded IDs, and provider overrides)
-
-- `POST /v1/videos/characters` supports `target_model_names` like `POST /v1/videos`.
-- When `target_model_names` is provided on character creation, LiteLLM encodes the returned `character_id` with routing metadata.
-- `GET /v1/videos/characters/{character_id}` accepts encoded character IDs directly. LiteLLM decodes the ID internally and routes with the correct model/provider metadata.
-- `POST /v1/videos/edits` and `POST /v1/videos/extensions` support both:
-  - plain `video.id`
-  - encoded `video.id` values returned by LiteLLM
-- `custom_llm_provider` can be supplied using the same patterns as other proxy endpoints:
-  - header: `custom-llm-provider`
-  - query: `?custom_llm_provider=...`
-  - body: `custom_llm_provider` (or `extra_body.custom_llm_provider` where applicable)
-
-#### Character create with `target_model_names`
-
-```bash
-curl --location 'http://localhost:4000/v1/videos/characters' \
---header 'Authorization: Bearer sk-1234' \
--F 'name=hero' \
--F 'target_model_names=gpt-4' \
--F 'video=@/path/to/character.mp4'
-```
-
-Example response (encoded `id`):
-
-```json
-{
-  "id": "character_...",
-  "object": "character",
-  "created_at": 1712697600,
-  "name": "hero"
-}
-```
-
-#### Get character using encoded `character_id`
-
-```bash
-curl --location 'http://localhost:4000/v1/videos/characters/character_...' \
---header 'Authorization: Bearer sk-1234'
-```
-
-#### Video edit with encoded `video.id`
-
-```bash
-curl --location 'http://localhost:4000/v1/videos/edits' \
---header 'Authorization: Bearer sk-1234' \
---header 'Content-Type: application/json' \
---data '{
-  "prompt": "Make this brighter",
-  "video": { "id": "video_..." }
-}'
-```
-
-#### Video extension with provider override from `extra_body`
-
-```bash
-curl --location 'http://localhost:4000/v1/videos/extensions' \
---header 'Authorization: Bearer sk-1234' \
---header 'Content-Type: application/json' \
---data '{
-  "prompt": "Continue this scene",
-  "seconds": "4",
-  "video": { "id": "video_..." },
-  "extra_body": { "custom_llm_provider": "openai" }
-}'
-```
-
 Test Azure video generation request
 
 ```bash

enterprise/litellm_enterprise/proxy/hooks/managed_files.py

@@ -26,7 +26,6 @@
     get_batch_id_from_unified_batch_id,
     get_content_type_from_file_object,
     get_model_id_from_unified_batch_id,
-    get_models_from_unified_file_id,
     normalize_mime_type_for_provider,
 )
 from litellm.types.llms.openai import (
@@ -905,21 +904,6 @@ async def async_post_call_success_hook(
             )  # managed batch id
             model_id = cast(Optional[str], response._hidden_params.get("model_id"))
             model_name = cast(Optional[str], response._hidden_params.get("model_name"))
-            resolved_model_name = model_name
-
-            # Some providers (e.g. Vertex batch retrieve) do not set model_name on
-            # the response. In that case, recover target_model_names from the input
-            # managed file metadata so unified output IDs preserve routing metadata.
-            if not resolved_model_name and isinstance(unified_file_id, str):
-                decoded_unified_file_id = (
-                    _is_base64_encoded_unified_file_id(unified_file_id)
-                    or unified_file_id
-                )
-                target_model_names = get_models_from_unified_file_id(
-                    decoded_unified_file_id
-                )
-                if target_model_names:
-                    resolved_model_name = ",".join(target_model_names)
             original_response_id = response.id
 
             if (unified_batch_id or unified_file_id) and model_id:
@@ -935,7 +919,7 @@ async def async_post_call_success_hook(
                         unified_file_id = self.get_unified_output_file_id(
                             output_file_id=original_file_id,
                             model_id=model_id,
-                            model_name=resolved_model_name,
+                            model_name=model_name,
                         )
                         setattr(response, file_attr, unified_file_id)
 

litellm/anthropic_beta_headers_manager.py

@@ -367,42 +367,6 @@ def update_headers_with_filtered_beta(
     return headers
 
 
-def update_request_with_filtered_beta(
-    headers: dict,
-    request_data: dict,
-    provider: str,
-) -> tuple[dict, dict]:
-    """
-    Update both headers and request body beta fields based on provider support.
-    Modifies both dicts in place and returns them.
-
-    Args:
-        headers: Request headers dict (will be modified in place)
-        request_data: Request body dict (will be modified in place)
-        provider: Provider name
-
-    Returns:
-        Tuple of (updated headers, updated request_data)
-    """
-    headers = update_headers_with_filtered_beta(headers=headers, provider=provider)
-
-    existing_body_betas = request_data.get("anthropic_beta")
-    if not existing_body_betas:
-        return headers, request_data
-
-    filtered_body_betas = filter_and_transform_beta_headers(
-        beta_headers=existing_body_betas,
-        provider=provider,
-    )
-
-    if filtered_body_betas:
-        request_data["anthropic_beta"] = filtered_body_betas
-    else:
-        request_data.pop("anthropic_beta", None)
-
-    return headers, request_data
-
-
 def get_unsupported_headers(provider: str) -> List[str]:
     """
     Get all beta headers that are unsupported by a provider (have null values in mapping).

litellm/blog_posts.json

@@ -1,10 +1,10 @@
 {
   "posts": [
     {
-      "title": "Realtime WebRTC HTTP Endpoints",
-      "description": "Use the LiteLLM proxy to route OpenAI-style WebRTC realtime via HTTP: client_secrets and SDP exchange.",
-      "date": "2026-03-12",
-      "url": "https://docs.litellm.ai/blog/realtime_webrtc_http_endpoints"
+      "title": "Incident Report: SERVER_ROOT_PATH regression broke UI routing",
+      "description": "How a single line removal caused UI 404s for all deployments using SERVER_ROOT_PATH, and the tests we added to prevent it from happening again.",
+      "date": "2026-02-21",
+      "url": "https://docs.litellm.ai/blog/server-root-path-incident"
     }
   ]
 }