fix(vertex_ai): enable context-1m-2025-08-07 beta header (#21870)

* server root path regression doc

* fixing syntax

* fix: replace Zapier webhook with Google Form for survey submission (#21621)

* Replace Zapier webhook with Google Form for survey submission

* Add back error logging for survey submission debugging

---------

Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>

* Revert "Merge pull request #21140 from BerriAI/litellm_perf_user_api_key_auth"

This reverts commit 0e1db3f7e4, reversing
changes made to 7e2d6f2355.

* test_vertex_ai_gemini_2_5_pro_streaming

* UI new build

* fix rendering

* ui new build

* docs fix

* docs fix

* docs fix

* docs fix

* docs fix

* docs fix

* docs fix

* docs fix

* release note docs

* docs

* adding image

* fix(vertex_ai): enable context-1m-2025-08-07 beta header

The `context-1m-2025-08-07` Anthropic beta header was set to `null` for vertex_ai,
causing it to be filtered out when users set `extra_headers: {anthropic-beta: context-1m-2025-08-07}`.

This prevented using Claude's 1M context window feature via Vertex AI, resulting in
`prompt is too long: 460500 tokens > 200000 maximum` errors.

Fixes #21861

---------

Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>

docs/my-website/blog/server_root_path/index.md

@@ -0,0 +1,154 @@
+---
+slug: server-root-path-incident
+title: "Incident Report: SERVER_ROOT_PATH regression broke UI routing"
+date: 2026-02-21T10:00:00
+authors:
+  - name: Yuneng Jiang
+    title: SWE @ LiteLLM (Full Stack)
+    url: https://www.linkedin.com/in/yunengjiang/
+  - name: Ishaan Jaff
+    title: "CTO, LiteLLM"
+    url: https://www.linkedin.com/in/reffajnaahsi/
+    image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+  - name: Krrish Dholakia
+    title: "CEO, LiteLLM"
+    url: https://www.linkedin.com/in/krish-d/
+    image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+tags: [incident-report, ui, stability]
+hide_table_of_contents: false
+---
+
+**Date:** January 22, 2026
+**Duration:** ~4 days (until fix merged January 26, 2026)
+**Severity:** High
+**Status:** Resolved
+
+> **Note:** This fix is available starting from LiteLLM `v1.81.3.rc.6` or higher.
+
+## Summary
+
+A PR ([`#19467`](https://github.com/BerriAI/litellm/pull/19467)) accidentally removed the `root_path=server_root_path` parameter from the FastAPI app initialization in `proxy_server.py`. This caused the proxy to ignore the `SERVER_ROOT_PATH` environment variable when serving the UI. Users who deploy LiteLLM behind a reverse proxy with a path prefix (e.g., `/api/v1` or `/llmproxy`) found that all UI pages returned 404 Not Found.
+
+- **LLM API calls:** No impact. API routing was unaffected.
+- **UI pages:** All UI pages returned 404 for deployments using `SERVER_ROOT_PATH`.
+- **Swagger/OpenAPI docs:** Broken when accessed through the configured root path.
+
+{/* truncate */}
+
+---
+
+## Background
+
+Many LiteLLM deployments run behind a reverse proxy (e.g., Nginx, Traefik, AWS ALB) that routes traffic to LiteLLM under a path prefix. FastAPI's `root_path` parameter tells the application about this prefix so it can correctly serve static files, generate URLs, and handle routing.
+
+```mermaid
+sequenceDiagram
+    participant User as User Browser
+    participant RP as Reverse Proxy
+    participant LP as LiteLLM Proxy
+
+    User->>RP: GET /llmproxy/ui/
+    RP->>LP: GET /ui/ (X-Forwarded-Prefix: /llmproxy)
+
+    Note over LP: Before regression:<br/>FastAPI root_path="/llmproxy"<br/>→ Serves UI correctly
+
+    Note over LP: After regression:<br/>FastAPI root_path=""<br/>→ UI assets resolve to wrong paths<br/>→ 404 Not Found
+```
+
+The `root_path` parameter was present in `proxy_server.py` since early versions of LiteLLM. It was removed as a side effect of PR [#19467](https://github.com/BerriAI/litellm/pull/19467), which was intended to fix a different UI 404 issue.
+
+---
+
+## Root cause
+
+PR [#19467](https://github.com/BerriAI/litellm/pull/19467) (`73d49f8`) removed the `root_path=server_root_path` line from the `FastAPI()` constructor in `proxy_server.py`:
+
+```diff
+ app = FastAPI(
+     docs_url=_get_docs_url(),
+     redoc_url=_get_redoc_url(),
+     title=_title,
+     description=_description,
+     version=version,
+-    root_path=server_root_path,
+     lifespan=proxy_startup_event,
+ )
+```
+
+Without `root_path`, FastAPI treated all requests as if the application was mounted at `/`, causing path mismatches for any deployment using `SERVER_ROOT_PATH`.
+
+The regression went undetected because:
+
+1. **No automated test** verified that `root_path` was set on the FastAPI app.
+2. **No manual test procedure** existed for `SERVER_ROOT_PATH` functionality.
+3. **Default deployments** (without `SERVER_ROOT_PATH`) were unaffected, so most CI tests passed.
+
+---
+
+## Remediation
+
+| #   | Action                                                                                            | Status  | Code                                                                                                                       |
+| --- | ------------------------------------------------------------------------------------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------- |
+| 1   | Restore `root_path=server_root_path` in FastAPI app initialization                                | ✅ Done | [`#19790`](https://github.com/BerriAI/litellm/pull/19790) (`5426b3c`)                                                      |
+| 2   | Add unit tests for `get_server_root_path()` and FastAPI app initialization                        | ✅ Done | [`test_server_root_path.py`](https://github.com/BerriAI/litellm/blob/main/tests/proxy_unit_tests/test_server_root_path.py) |
+| 3   | Add CI workflow that builds Docker image and tests UI routing with `SERVER_ROOT_PATH` on every PR | ✅ Done | [`test_server_root_path.yml`](https://github.com/BerriAI/litellm/blob/main/.github/workflows/test_server_root_path.yml)    |
+| 4   | Document manual test procedure for `SERVER_ROOT_PATH`                                             | ✅ Done | [Discussion #8495](https://github.com/BerriAI/litellm/discussions/8495)                                                    |
+
+---
+
+## CI workflow details
+
+The new [`test_server_root_path.yml`](https://github.com/BerriAI/litellm/blob/main/.github/workflows/test_server_root_path.yml) workflow runs on every PR against `main`. It:
+
+1. Builds the LiteLLM Docker image
+2. Starts a container with `SERVER_ROOT_PATH` set (tests both `/api/v1` and `/llmproxy`)
+3. Verifies the UI returns valid HTML at `{ROOT_PATH}/ui/`
+4. Fails the workflow if the UI is unreachable
+
+```mermaid
+flowchart TD
+    A["PR opened/updated"] --> B["Build Docker image"]
+    B --> C["Start container with SERVER_ROOT_PATH=/api/v1"]
+    B --> D["Start container with SERVER_ROOT_PATH=/llmproxy"]
+    C --> E["curl {ROOT_PATH}/ui/ → expect HTML"]
+    D --> F["curl {ROOT_PATH}/ui/ → expect HTML"]
+    E -->|"HTML found"| G["✅ Pass"]
+    E -->|"404 or no HTML"| H["❌ Fail Workflow"]
+    F -->|"HTML found"| G
+    F -->|"404 or no HTML"| H
+
+    style G fill:#d4edda,stroke:#28a745
+    style H fill:#f8d7da,stroke:#dc3545
+```
+
+This prevents future regressions where changes to `proxy_server.py` accidentally break `SERVER_ROOT_PATH` support.
+
+---
+
+## Timeline
+
+| Time (UTC)         | Event                                                                                                                                                        |
+| ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| Jan 22, 2026 04:20 | PR [#19467](https://github.com/BerriAI/litellm/pull/19467) merged, removing `root_path=server_root_path`                                                     |
+| Jan 22–26          | Users on nightly builds report UI 404 errors when using `SERVER_ROOT_PATH`                                                                                   |
+| Jan 26, 2026 17:48 | Fix PR [#19790](https://github.com/BerriAI/litellm/pull/19790) merged, restoring `root_path=server_root_path`                                                |
+| Feb 18, 2026       | CI workflow [`test_server_root_path.yml`](https://github.com/BerriAI/litellm/blob/main/.github/workflows/test_server_root_path.yml) added to run on every PR |
+
+---
+
+## Resolution steps for users
+
+For users still experiencing issues, update to the latest LiteLLM version:
+
+```bash
+pip install --upgrade litellm
+```
+
+Verify your `SERVER_ROOT_PATH` is correctly set:
+
+```bash
+# In your environment or docker-compose.yml
+SERVER_ROOT_PATH="/your-prefix"
+```
+
+Then confirm the UI is accessible at `http://your-host:4000/your-prefix/ui/`.

docs/my-website/docs/proxy/config_settings.md

@@ -758,7 +758,7 @@ router_settings:
 | LITELLM_CLI_JWT_EXPIRATION_HOURS | Expiration time in hours for CLI-generated JWT tokens. Default is 24 hours
 | LITELLM_DD_AGENT_HOST | Hostname or IP of DataDog agent for LiteLLM-specific logging. When set, logs are sent to agent instead of direct API
 | LITELLM_DEPLOYMENT_ENVIRONMENT | Environment name for the deployment (e.g., "production", "staging"). Used as a fallback when OTEL_ENVIRONMENT_NAME is not set. Sets the `environment` tag in telemetry data
-| LITELLM_DETAILED_TIMING | When true, adds detailed per-phase timing headers to responses (x-litellm-timing-{pre-processing,llm-api,post-processing,message-copy}-ms). Default is false. See [latency overhead docs](../troubleshoot/latency_overhead.md)
+| LITELLM_DETAILED_TIMING | When true, adds detailed per-phase timing headers to responses (`x-litellm-timing-{pre-processing,llm-api,post-processing,message-copy}-ms`). Default is false. See [latency overhead docs](../troubleshoot/latency_overhead.md)
 | LITELLM_DD_AGENT_PORT | Port of DataDog agent for LiteLLM-specific log intake. Default is 10518
 | LITELLM_DD_LLM_OBS_PORT | Port for Datadog LLM Observability agent. Default is 8126
 | LITELLM_DONT_SHOW_FEEDBACK_BOX | Flag to hide feedback box in LiteLLM UI

docs/my-website/docs/proxy/ui_store_model_db_setting.md

@@ -0,0 +1,92 @@
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Store Model in DB Settings
+
+Enable or disable storing model definitions in the database directly from the Admin UI—no config file edits or proxy restart required. This is especially useful for cloud deployments where updating the config is difficult or requires a long release process.
+
+## Overview
+
+Previously, the `store_model_in_db` setting had to be configured in `proxy_config.yaml` under `general_settings`. Changing it required editing the config and restarting the proxy, which was problematic for cloud users who don't have direct access to the config file or who want to avoid the downtime caused by restarts.
+
+<Image img={require('../../img/ui_store_model_in_db.png')} />
+
+**Store Model in DB Settings** lets you:
+
+- **Enable or disable storing models in the database** – Control whether model definitions are cached in your database (useful for reducing config file size and improving scalability)
+- **Apply changes immediately** – No proxy restart needed; settings take effect for new model operations as soon as you save
+
+:::warning UI overrides config
+Settings changed in the UI **override** the values in your config file. For example, if `store_model_in_db` is set to `false` in `general_settings`, enabling it in the UI will still persist model definitions to the database. Use the UI when you want runtime control without redeploying.
+:::
+
+## How Store Model in DB Works
+
+When `store_model_in_db` is enabled, the LiteLLM proxy stores model definitions in the database instead of relying solely on your `proxy_config.yaml`. This provides several benefits:
+
+- **Reduced config size** – Move model definitions out of YAML for easier maintenance
+- **Scalability** – Database storage scales better than large YAML files
+- **Dynamic updates** – Models can be added or updated without editing config files
+- **Persistence** – Model definitions persist across proxy instances and restarts
+
+The setting applies to all new model operations from the moment you save it.
+
+## How to Configure Store Model in DB in the UI
+
+### 1. Access Models + Endpoints Settings
+
+Navigate to the Admin UI (e.g. `http://localhost:4000/ui` or your `PROXY_BASE_URL/ui`) and go to the **Models + Endpoints** page.
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-02-22/55bc71f5-730f-4b2c-8539-8a4f46b8bd10/ascreenshot_0f7ba8f1c2694e94938996fd1b4adfcc_text_export.jpeg)
+
+### 2. Open Settings
+
+Click **Models + Endpoints** from the navigation menu.
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-02-22/55bc71f5-730f-4b2c-8539-8a4f46b8bd10/ascreenshot_fc2b9e4812a9480087f4eb350fa0a792_text_export.jpeg)
+
+### 3. Click the Settings Icon
+
+Look for the settings (gear) icon on the Models + Endpoints page to open the configuration panel.
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-02-22/7b394364-c281-4db8-8cad-ee322c76c935/ascreenshot_d7c8a6b234bc4e4d92aa7f09aefb13d3_text_export.jpeg)
+
+### 4. Enable or Disable Store Model in DB
+
+Toggle the **Store Model in DB** setting based on your preference:
+
+- **Enabled**: Model definitions will be stored in the database
+- **Disabled**: Models are read from the config file only
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-02-22/54a263ec-ad67-4b16-ba9f-2be57c3e4cb8/ascreenshot_501abda2a6c847f79d085efce814265d_text_export.jpeg)
+
+### 5. Save Settings
+
+Click **Save Settings** to apply the change. No proxy restart is required; the new setting takes effect immediately for subsequent model operations.
+
+![](https://colony-recorder.s3.amazonaws.com/files/2026-02-22/7d13559a-d4e4-41f7-993b-cb20fbfa1f6e/ascreenshot_3245f3c5bd0d43cb96c5f5ff0ccb461d_text_export.jpeg)
+
+## Use Cases
+
+### Cloud and Managed Deployments
+
+When the proxy runs in a managed or cloud environment, config may be in a separate repo, require a long release cycle, or be controlled by another team. Using the UI lets you change the `store_model_in_db` setting without going through a deployment process.
+
+### Reducing Configuration Complexity
+
+For large deployments with hundreds of models, storing model definitions in the database reduces the size and complexity of your `proxy_config.yaml`, making it easier to maintain and version control.
+
+### Dynamic Model Management
+
+Enable `store_model_in_db` to support dynamic model additions and updates without editing your config file. Teams can manage models through the UI or API without needing to redeploy the proxy.
+
+### Zero-Downtime Updates
+
+Change the setting from the UI and have it take effect immediately—perfect for production environments where downtime must be minimized.
+
+## Related Documentation
+
+- [Admin UI Overview](./ui_overview.md) – General guide to the LiteLLM Admin UI
+- [Models and Endpoints](./models_and_endpoints.md) – Managing models and API endpoints
+- [Config Settings](./config_settings.md) – `store_model_in_db` in `general_settings`

docs/my-website/release_notes/v1.81.14.md

@@ -1,5 +1,5 @@
 ---
-title: "v1.81.14 - Claude Sonnet 4.6, Guardrail Garden & Major Performance Improvements"
+title: "[Preview] v1.81.14 - New Gateway Level Guardrails & Compliance Playground"
 slug: "v1-81-14"
 date: 2026-02-21T00:00:00
 authors:
@@ -42,36 +42,57 @@ pip install litellm==1.81.14
 
 ## Key Highlights
 
-- **Guardrail Model Garden** - [Easily browse built-in and partner guardrails by use case — competitor blockers, topic filters, keyword lists, GDPR PII, EU AI Act, prompt injection, and more](../../docs/proxy/guardrails/policy_templates)
-- **3 new built-in guardrails** - [Competitor name blocker, topic blocker, and insults/keyword filter — all run at the gateway level, no external API, configurable per-team or key](../../docs/proxy/guardrails)
-- **Compliance Playground** - [Run your guardrails against your own datasets or pre-built ones to measure performance before going live](../../docs/proxy/guardrails/policy_templates)
-- **Claude Sonnet 4.6 — day 0** - [Full support across Anthropic and Vertex AI: reasoning, computer use, prompt caching, 200K context](../../docs/providers/anthropic)
-- **Major performance batch** - 20+ targeted optimizations across router algorithms, logging overhead, cost calculator, and connection management — meaningfully lower latency and CPU overhead on every request
+- **Guardrail Garden** — [Browse built-in and partner guardrails by use case — competitor blocking, topic filtering, GDPR, prompt injection, and more. Pick a template, customize it, attach it to a team or key.](../../docs/proxy/guardrails/policy_templates)
+- **Compliance Playground** — [Test any guardrail policy against your own traffic before it goes live. See precision, recall, and false positive rate — so you know how it'll behave in production.](../../docs/proxy/guardrails/policy_templates)
+- **3 new zero-cost built-in guardrails** — [Competitor name blocker, topic blocker, and insults filter — all gateway-level, <0.1ms latency, no external API, configurable per-team or key](../../docs/proxy/guardrails)
+- **Store Model in DB Settings via UI** - [Configure model storage directly in the Admin UI without editing config files or restarting the proxy—perfect for cloud deployments](../../docs/proxy/ui_store_model_db_setting)
+- **Claude Sonnet 4.6 — day 0** — [Full support across Anthropic and Vertex AI: reasoning, computer use, prompt caching, 200K context](../../docs/providers/anthropic)
+- **20+ performance optimizations** — Faster routing, lower logging overhead, reduced cost-calculator latency, and connection pool fixes — meaningfully less CPU and latency on every request
 
 ---
 
-## Guardrail Model Garden
 
-Browse built-in and partner guardrails organized by use case — competitor blocking, topic filtering, keyword lists, GDPR/EU AI Act compliance, prompt injection detection, and more. Pick a template, customize the parameters (keyword lists, blocked topics, score thresholds), and attach it to a team or key.
+### Guardrail Garden
 
-## 3 New Built-in Guardrails
+AI Platform Admins can now browse built-in and partner guardrails from the Guardrail Garden. Guardrails are organized by use case — blocking financial advice, filtering insults, detecting competitor mentions, and more — so you can find the right one and deploy it in a few clicks.
 
-A competitor name blocker, a topic blocker (keyword and embedding-based), and an insults/keyword filter. All run at the gateway level with no external API call. They're configurable per-team or per-key, and you can swap in AWS Bedrock Guardrails or Azure Content Safety on the same endpoint without changing your application code.
+![Guardrail Garden](../img/release_notes/guardrail_garden.png)
 
-## Compliance Playground
+### 3 New Built-in Guardrails
 
-Upload your own dataset or use a pre-built one to measure how a guardrail policy performs before it goes live. See precision, recall, and false positive rate on your actual traffic patterns — so you know how the policy will behave in production before you deploy it.
+This release brings 3 new built-in guardrails that run directly on the gateway. This is great for AI Gateway Admins who need low latency, zero cost guardrails for their scenarios.
 
-### Eval results
+- **Denied Financial Advice** — detects requests for personalized financial advice, investment recommendations, or financial planning
+- **Denied Insults** — detects insults, name-calling, and personal attacks directed at the chatbot, staff, or other people
+- **Competitor Name Blocker** — detects mentions of competitor brands in responses
 
-We benchmark every built-in guardrail against labeled datasets before shipping. Results for the two policies most relevant to topic and keyword blocking (207 investment-question cases, 299 insult cases):
+These guardrails are built for production and on our benchmarks had a 100% Recall and Precision.
+
+### Store Model in DB Settings via UI
+
+Previously, the `store_model_in_db` setting could only be configured in `proxy_config.yaml` under `general_settings`, requiring a proxy restart to take effect. Now you can enable or disable this setting directly from the Admin UI without any restarts. This is especially useful for cloud deployments where you don't have direct access to config files or want to avoid downtime. Enable `store_model_in_db` to move model definitions from your YAML into the database—reducing config complexity, improving scalability, and enabling dynamic model management across multiple proxy instances.
+
+![Store model in DB Setting](../img/ui_store_model_in_db.png)
+
+
+#### Eval results
+
+We benchmarked our new built-in guardrails against labeled datasets before shipping. You can see the results for Denied Financial Advice (207 cases) and Denied Insults (299 cases):
 
 | Guardrail | Precision | Recall | F1 | Latency p50 | Cost/req |
 |-----------|-----------|--------|----|-------------|----------|
-| Block investment questions | 100% | 100% | 100% | <0.1ms | $0 |
-| Block insults / keywords | 100% | 100% | 100% | <0.1ms | $0 |
+| Denied Financial Advice | 100% | 100% | 100% | <0.1ms | $0 |
+| Denied Insults | 100% | 100% | 100% | <0.1ms | $0 |
+
+100% precision means zero false positives — no legitimate messages were incorrectly blocked. 100% recall means zero false negatives — every message that should have been blocked was caught.
+
+
+### Compliance Playground
+
+The Compliance Playground lets you test any guardrail against our pre-built eval datasets or your own custom datasets, so you can see precision, recall, and false positive rate before rolling it out to production.
+
+![Compliance Playground](../img/release_notes/compliance_playground.png)
 
-For reference, ONNX embedding approaches on the same eval set hit 95–98% precision at 2–20ms latency and require additional dependencies. The built-in content filter uses no ML model — just structured YAML rules with layered matching — so there's nothing to download, no API key needed, and latency is effectively zero.
 
 ---
 

docs/my-website/sidebars.js

@@ -334,6 +334,7 @@ const sidebars = {
                 "proxy/ui_credentials",
                 "proxy/ai_hub",
                 "proxy/model_compare_ui",
+                "proxy/ui_store_model_db_setting",
               ]
             },
             {

litellm/anthropic_beta_headers_config.json

@@ -127,7 +127,7 @@
     "compact-2026-01-12": null,
     "computer-use-2025-01-24": "computer-use-2025-01-24",
     "computer-use-2025-11-24": "computer-use-2025-11-24",
-    "context-1m-2025-08-07": null,
+    "context-1m-2025-08-07": "context-1m-2025-08-07",
     "context-management-2025-06-27": "context-management-2025-06-27",
     "effort-2025-11-24": null,
     "fast-mode-2026-02-01": null,

litellm/proxy/_experimental/out/__next.__PAGE__.txt

@@ -1,31 +1,31 @@
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