From a2a506c4dc6d52de4430f7cea1b4d68dd75ab854 Mon Sep 17 00:00:00 2001
From: Chesars <cesarponce19544@gmail.com>
Date: Sat, 25 Apr 2026 17:10:41 -0300
Subject: [PATCH] Revert "Merge pull request #16590 from
 Chesars/refactor/remove-backup-file-dry-principle"

This reverts commit 62df6325b1aca611df6756fd701027f5a4862be8, reversing
changes made to a6462143be7bdd656ff7a0701efdd781564c842a.
---
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diff --git a/.gitignore b/.gitignore
index ce164a15fb..76cf6fdba2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -101,5 +101,4 @@ STABILIZATION_TODO.md
 **/test-results
 **/playwright-report
 **/*.storageState.json
-**/coverage
-litellm/model_prices_and_context_window.json
+**/coverage
\ No newline at end of file
diff --git a/ci_cd/check_files_match.py b/ci_cd/check_files_match.py
new file mode 100644
index 0000000000..18b6cf792a
--- /dev/null
+++ b/ci_cd/check_files_match.py
@@ -0,0 +1,32 @@
+import sys
+import filecmp
+import shutil
+
+
+def main(argv=None):
+    print(
+        "Comparing model_prices_and_context_window and litellm/model_prices_and_context_window_backup.json files... checking if they match."
+    )
+
+    file1 = "model_prices_and_context_window.json"
+    file2 = "litellm/model_prices_and_context_window_backup.json"
+
+    cmp_result = filecmp.cmp(file1, file2, shallow=False)
+
+    if cmp_result:
+        print(f"Passed! Files {file1} and {file2} match.")
+        return 0
+    else:
+        print(
+            f"Failed! Files {file1} and {file2} do not match. Copying content from {file1} to {file2}."
+        )
+        copy_content(file1, file2)
+        return 1
+
+
+def copy_content(source, destination):
+    shutil.copy2(source, destination)
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/litellm/litellm_core_utils/get_model_cost_map.py b/litellm/litellm_core_utils/get_model_cost_map.py
index b24ffa8678..7679358bbc 100644
--- a/litellm/litellm_core_utils/get_model_cost_map.py
+++ b/litellm/litellm_core_utils/get_model_cost_map.py
@@ -10,8 +10,6 @@ export LITELLM_LOCAL_MODEL_COST_MAP=True
 
 import json
 import os
-from pathlib import Path
-
 from importlib.resources import files
 from typing import Dict, List, Optional
 
@@ -28,48 +26,30 @@ class GetModelCostMap:
     """
     Handles fetching, validating, and loading the model cost map.
 
-    Only the local model *count* is cached (a single int). The full
-    local dict is never held in memory — it is only parsed when it
+    Only the backup model *count* is cached (a single int). The full
+    backup dict is never held in memory — it is only parsed when it
     needs to be *returned* as a fallback.
     """
 
-    _local_model_count: int = -1  # -1 = not yet loaded
+    _backup_model_count: int = -1  # -1 = not yet loaded
 
     @staticmethod
     def load_local_model_cost_map() -> dict:
-        """Load the local model cost map.
-
-        Tries to load from:
-        1. Package resources (production, after pip install)
-        2. Project root (development)
-        """
-        try:
-            content = json.loads(
-                files("litellm")
-                .joinpath("model_prices_and_context_window.json")
-                .read_text(encoding="utf-8")
-            )
-            return content
-        except FileNotFoundError:
-            pass
-        except ModuleNotFoundError:
-            verbose_logger.warning(
-                "LiteLLM: Could not load model cost map from package resources. "
-                "Falling back to project root."
-            )
-
-        current_dir = Path(__file__).parent.parent.parent
-        model_cost_map_path = current_dir / "model_prices_and_context_window.json"
-        with open(model_cost_map_path, "r") as f:
-            return json.load(f)
+        """Load the local backup model cost map bundled with the package."""
+        content = json.loads(
+            files("litellm")
+            .joinpath("model_prices_and_context_window_backup.json")
+            .read_text(encoding="utf-8")
+        )
+        return content
 
     @classmethod
-    def _get_local_model_count(cls) -> int:
-        """Return the number of models in the local model cost map (cached int)."""
-        if cls._local_model_count < 0:
-            local = cls.load_local_model_cost_map()
-            cls._local_model_count = len(local)
-        return cls._local_model_count
+    def _get_backup_model_count(cls) -> int:
+        """Return the number of models in the local backup (cached int)."""
+        if cls._backup_model_count < 0:
+            backup = cls.load_local_model_cost_map()
+            cls._backup_model_count = len(backup)
+        return cls._backup_model_count
 
     @staticmethod
     def _check_is_valid_dict(fetched_map: dict) -> bool:
@@ -77,7 +57,7 @@ class GetModelCostMap:
         if not isinstance(fetched_map, dict):
             verbose_logger.warning(
                 "LiteLLM: Fetched model cost map is not a dict (type=%s). "
-                "Falling back to local model cost map.",
+                "Falling back to local backup.",
                 type(fetched_map).__name__,
             )
             return False
@@ -85,7 +65,7 @@ class GetModelCostMap:
         if len(fetched_map) == 0:
             verbose_logger.warning(
                 "LiteLLM: Fetched model cost map is empty. "
-                "Falling back to local model cost map.",
+                "Falling back to local backup.",
             )
             return False
 
@@ -95,34 +75,34 @@ class GetModelCostMap:
     def _check_model_count_not_reduced(
         cls,
         fetched_map: dict,
-        local_model_count: int,
+        backup_model_count: int,
         min_model_count: int = MODEL_COST_MAP_MIN_MODEL_COUNT,
         max_shrink_ratio: float = MODEL_COST_MAP_MAX_SHRINK_RATIO,
     ) -> bool:
-        """Check 2: model count has not reduced significantly vs local."""
+        """Check 2: model count has not reduced significantly vs backup."""
         fetched_count = len(fetched_map)
 
         if fetched_count < min_model_count:
             verbose_logger.warning(
                 "LiteLLM: Fetched model cost map has only %d models (minimum=%d). "
                 "This may indicate a corrupted upstream file. "
-                "Falling back to local model cost map.",
+                "Falling back to local backup.",
                 fetched_count,
                 min_model_count,
             )
             return False
 
         if (
-            local_model_count > 0
-            and fetched_count < local_model_count * max_shrink_ratio
+            backup_model_count > 0
+            and fetched_count < backup_model_count * max_shrink_ratio
         ):
             verbose_logger.warning(
                 "LiteLLM: Fetched model cost map shrank significantly "
-                "(fetched=%d, local=%d, threshold=%.0f%%). "
+                "(fetched=%d, backup=%d, threshold=%.0f%%). "
                 "This may indicate a corrupted upstream file. "
-                "Falling back to local model cost map.",
+                "Falling back to local backup.",
                 fetched_count,
-                local_model_count,
+                backup_model_count,
                 max_shrink_ratio * 100,
             )
             return False
@@ -133,7 +113,7 @@ class GetModelCostMap:
     def validate_model_cost_map(
         cls,
         fetched_map: dict,
-        local_model_count: int,
+        backup_model_count: int,
         min_model_count: int = MODEL_COST_MAP_MIN_MODEL_COUNT,
         max_shrink_ratio: float = MODEL_COST_MAP_MAX_SHRINK_RATIO,
     ) -> bool:
@@ -145,7 +125,7 @@ class GetModelCostMap:
         Checks:
         1. ``_check_is_valid_dict`` -- fetched map is a non-empty dict.
         2. ``_check_model_count_not_reduced`` -- model count meets minimum
-           and has not shrunk >``max_shrink_ratio`` vs local.
+           and has not shrunk >``max_shrink_ratio`` vs backup.
 
         Returns True if all checks pass, False otherwise.
         """
@@ -154,7 +134,7 @@ class GetModelCostMap:
 
         if not cls._check_model_count_not_reduced(
             fetched_map=fetched_map,
-            local_model_count=local_model_count,
+            backup_model_count=backup_model_count,
             min_model_count=min_model_count,
             max_shrink_ratio=max_shrink_ratio,
         ):
@@ -265,12 +245,12 @@ def get_model_cost_map(url: str) -> dict:
     """
     Public entry point — returns the model cost map dict.
 
-    1. If ``LITELLM_LOCAL_MODEL_COST_MAP`` is set, uses the local model cost map only.
+    1. If ``LITELLM_LOCAL_MODEL_COST_MAP`` is set, uses the local backup only.
     2. Otherwise fetches from ``url``, validates integrity, and falls back
-       to the local model cost map on any failure.
+       to the local backup on any failure.
 
-    Only the local model count is cached (a single int) for validation.
-    The full local dict is only parsed when it must be *returned* as a
+    Only the backup model count is cached (a single int) for validation.
+    The full backup dict is only parsed when it must be *returned* as a
     fallback — it is never held in memory long-term.
     """
     # Note: can't use get_secret_bool here — this runs during litellm.__init__
@@ -290,7 +270,7 @@ def get_model_cost_map(url: str) -> dict:
     except Exception as e:
         verbose_logger.warning(
             "LiteLLM: Failed to fetch remote model cost map from %s: %s. "
-            "Falling back to local model cost map.",
+            "Falling back to local backup.",
             url,
             str(e),
         )
@@ -301,11 +281,11 @@ def get_model_cost_map(url: str) -> dict:
     # Validate using cached count (cheap int comparison, no file I/O)
     if not GetModelCostMap.validate_model_cost_map(
         fetched_map=content,
-        local_model_count=GetModelCostMap._get_local_model_count(),
+        backup_model_count=GetModelCostMap._get_backup_model_count(),
     ):
         verbose_logger.warning(
             "LiteLLM: Fetched model cost map failed integrity check. "
-            "Using local model cost map instead. url=%s",
+            "Using local backup instead. url=%s",
             url,
         )
         _cost_map_source_info.source = "local"
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
new file mode 100644
index 0000000000..c53ee943c5
--- /dev/null
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -0,0 +1,37398 @@
+{
+    "sample_spec": {
+        "code_interpreter_cost_per_session": 0.0,
+        "computer_use_input_cost_per_1k_tokens": 0.0,
+        "computer_use_output_cost_per_1k_tokens": 0.0,
+        "deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD",
+        "file_search_cost_per_1k_calls": 0.0,
+        "file_search_cost_per_gb_per_day": 0.0,
+        "input_cost_per_audio_token": 0.0,
+        "input_cost_per_token": 0.0,
+        "litellm_provider": "one of https://docs.litellm.ai/docs/providers",
+        "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens",
+        "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens",
+        "max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.",
+        "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank, search",
+        "output_cost_per_reasoning_token": 0.0,
+        "output_cost_per_token": 0.0,
+        "search_context_cost_per_query": {
+            "search_context_size_high": 0.0,
+            "search_context_size_low": 0.0,
+            "search_context_size_medium": 0.0
+        },
+        "supported_regions": [
+            "global",
+            "us-west-2",
+            "eu-west-1",
+            "ap-southeast-1",
+            "ap-northeast-1"
+        ],
+        "supports_audio_input": true,
+        "supports_audio_output": true,
+        "supports_function_calling": true,
+        "supports_parallel_function_calling": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_vision": true,
+        "supports_web_search": true,
+        "vector_store_cost_per_gb_per_day": 0.0
+    },
+    "1024-x-1024/50-steps/bedrock/amazon.nova-canvas-v1:0": {
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 2600,
+        "mode": "image_generation",
+        "output_cost_per_image": 0.06
+    },
+    "1024-x-1024/50-steps/stability.stable-diffusion-xl-v1": {
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 77,
+        "max_tokens": 77,
+        "mode": "image_generation",
+        "output_cost_per_image": 0.04
+    },
+    "1024-x-1024/dall-e-2": {
+        "input_cost_per_pixel": 1.9e-08,
+        "litellm_provider": "openai",
+        "mode": "image_generation",
+        "output_cost_per_pixel": 0.0
+    },
+    "1024-x-1024/max-steps/stability.stable-diffusion-xl-v1": {
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 77,
+        "max_tokens": 77,
+        "mode": "image_generation",
+        "output_cost_per_image": 0.08
+    },
+    "256-x-256/dall-e-2": {
+        "input_cost_per_pixel": 2.4414e-07,
+        "litellm_provider": "openai",
+        "mode": "image_generation",
+        "output_cost_per_pixel": 0.0
+    },
+    "512-x-512/50-steps/stability.stable-diffusion-xl-v0": {
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 77,
+        "max_tokens": 77,
+        "mode": "image_generation",
+        "output_cost_per_image": 0.018
+    },
+    "512-x-512/dall-e-2": {
+        "input_cost_per_pixel": 6.86e-08,
+        "litellm_provider": "openai",
+        "mode": "image_generation",
+        "output_cost_per_pixel": 0.0
+    },
+    "512-x-512/max-steps/stability.stable-diffusion-xl-v0": {
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 77,
+        "max_tokens": 77,
+        "mode": "image_generation",
+        "output_cost_per_image": 0.036
+    },
+    "ai21.j2-mid-v1": {
+        "input_cost_per_token": 1.25e-05,
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 8191,
+        "max_output_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "chat",
+        "output_cost_per_token": 1.25e-05
+    },
+    "ai21.j2-ultra-v1": {
+        "input_cost_per_token": 1.88e-05,
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 8191,
+        "max_output_tokens": 8191,
+        "max_tokens": 8191,
+        "mode": "chat",
+        "output_cost_per_token": 1.88e-05
+    },
+    "ai21.jamba-1-5-large-v1:0": {
+        "input_cost_per_token": 2e-06,
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 256000,
+        "max_output_tokens": 256000,
+        "max_tokens": 256000,
+        "mode": "chat",
+        "output_cost_per_token": 8e-06
+    },
+    "ai21.jamba-1-5-mini-v1:0": {
+        "input_cost_per_token": 2e-07,
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 256000,
+        "max_output_tokens": 256000,
+        "max_tokens": 256000,
+        "mode": "chat",
+        "output_cost_per_token": 4e-07
+    },
+    "ai21.jamba-instruct-v1:0": {
+        "input_cost_per_token": 5e-07,
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 70000,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096,
+        "mode": "chat",
+        "output_cost_per_token": 7e-07,
+        "supports_system_messages": true
+    },
+    "aiml/dall-e-2": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "DALL-E 2 via AI/ML API - Reliable text-to-image generation"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.026,
+        "source": "https://docs.aimlapi.com/",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/dall-e-3": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "DALL-E 3 via AI/ML API - High-quality text-to-image generation"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.052,
+        "source": "https://docs.aimlapi.com/",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/flux-pro": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "Flux Dev - Development version optimized for experimentation"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.065,
+        "source": "https://docs.aimlapi.com/",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/flux-pro/v1.1": {
+        "litellm_provider": "aiml",
+        "mode": "image_generation",
+        "output_cost_per_image": 0.052,
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/flux-pro/v1.1-ultra": {
+        "litellm_provider": "aiml",
+        "mode": "image_generation",
+        "output_cost_per_image": 0.063,
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/flux-realism": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "Flux Pro - Professional-grade image generation model"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.046,
+        "source": "https://docs.aimlapi.com/",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/flux/dev": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "Flux Dev - Development version optimized for experimentation"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.033,
+        "source": "https://docs.aimlapi.com/",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/flux/kontext-max/text-to-image": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "Flux Pro v1.1 - Enhanced version with improved capabilities and 6x faster inference speed"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.104,
+        "source": "https://docs.aimlapi.com/",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/flux/kontext-pro/text-to-image": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "Flux Pro v1.1 - Enhanced version with improved capabilities and 6x faster inference speed"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.052,
+        "source": "https://docs.aimlapi.com/",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/flux/schnell": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "Flux Schnell - Fast generation model optimized for speed"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.004,
+        "source": "https://docs.aimlapi.com/",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/google/imagen-4.0-ultra-generate-001": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "Imagen 4.0 Ultra Generate API - Photorealistic image generation with precise text rendering"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.078,
+        "source": "https://docs.aimlapi.com/api-references/image-models/google/imagen-4-ultra-generate",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "aiml/google/nano-banana-pro": {
+        "litellm_provider": "aiml",
+        "metadata": {
+            "notes": "Gemini 3 Pro Image (Nano Banana Pro) - Advanced text-to-image generation with reasoning and 4K resolution support"
+        },
+        "mode": "image_generation",
+        "output_cost_per_image": 0.195,
+        "source": "https://docs.aimlapi.com/api-references/image-models/google/gemini-3-pro-image-preview",
+        "supported_endpoints": [
+            "/v1/images/generations"
+        ]
+    },
+    "amazon.nova-canvas-v1:0": {
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 2600,
+        "mode": "image_generation",
+        "output_cost_per_image": 0.06
+    },
+    "us.writer.palmyra-x4-v1:0": {
+        "input_cost_per_token": 2.5e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 1e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true
+    },
+    "us.writer.palmyra-x5-v1:0": {
+        "input_cost_per_token": 6e-07,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 6e-06,
+        "supports_function_calling": true,
+        "supports_pdf_input": true
+    },
+    "writer.palmyra-x4-v1:0": {
+        "input_cost_per_token": 2.5e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 1e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true
+    },
+    "writer.palmyra-x5-v1:0": {
+        "input_cost_per_token": 6e-07,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 6e-06,
+        "supports_function_calling": true,
+        "supports_pdf_input": true
+    },
+    "amazon.nova-lite-v1:0": {
+        "input_cost_per_token": 6e-08,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 300000,
+        "max_output_tokens": 10000,
+        "max_tokens": 10000,
+        "mode": "chat",
+        "output_cost_per_token": 2.4e-07,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_response_schema": true,
+        "supports_vision": true
+    },
+    "amazon.nova-2-lite-v1:0": {
+        "cache_read_input_token_cost": 7.5e-08,
+        "input_cost_per_token": 3e-07,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 2.5e-06,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
+    "amazon.nova-2-pro-preview-20251202-v1:0": {
+        "cache_read_input_token_cost": 5.46875e-07,
+        "input_cost_per_token": 2.1875e-06,
+        "input_cost_per_image_token": 2.1875e-06,
+        "input_cost_per_audio_token": 2.1875e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 1.75e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
+    "apac.amazon.nova-2-lite-v1:0": {
+        "cache_read_input_token_cost": 8.25e-08,
+        "input_cost_per_token": 3.3e-07,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 2.75e-06,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
+    "apac.amazon.nova-2-pro-preview-20251202-v1:0": {
+        "cache_read_input_token_cost": 5.46875e-07,
+        "input_cost_per_token": 2.1875e-06,
+        "input_cost_per_image_token": 2.1875e-06,
+        "input_cost_per_audio_token": 2.1875e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 1.75e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
+    "eu.amazon.nova-2-lite-v1:0": {
+        "cache_read_input_token_cost": 8.25e-08,
+        "input_cost_per_token": 3.3e-07,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 2.75e-06,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
+    "eu.amazon.nova-2-pro-preview-20251202-v1:0": {
+        "cache_read_input_token_cost": 5.46875e-07,
+        "input_cost_per_token": 2.1875e-06,
+        "input_cost_per_image_token": 2.1875e-06,
+        "input_cost_per_audio_token": 2.1875e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 1.75e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
+    "us.amazon.nova-2-lite-v1:0": {
+        "cache_read_input_token_cost": 8.25e-08,
+        "input_cost_per_token": 3.3e-07,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 2.75e-06,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
+    "us.amazon.nova-2-pro-preview-20251202-v1:0": {
+        "cache_read_input_token_cost": 5.46875e-07,
+        "input_cost_per_token": 2.1875e-06,
+        "input_cost_per_image_token": 2.1875e-06,
+        "input_cost_per_audio_token": 2.1875e-06,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "mode": "chat",
+        "output_cost_per_token": 1.75e-05,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_response_schema": true,
+        "supports_video_input": true,
+        "supports_vision": true
+    },
+    "amazon.nova-2-multimodal-embeddings-v1:0": {
+        "litellm_provider": "bedrock",
+        "max_input_tokens": 8172,
+        "max_tokens": 8172,
+        "mode": "embedding",
+        "input_cost_per_token": 1.35e-07,
+        "input_cost_per_image": 6e-05,
+        "input_cost_per_video_per_second": 0.0007,
+        "input_cost_per_audio_per_second": 0.00014,
+        "output_cost_per_token": 0.0,
+        "output_vector_size": 3072,
+        "source": "https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/model-catalog/serverless/amazon.nova-2-multimodal-embeddings-v1:0",
+        "supports_embedding_image_input": true,
+        "supports_image_input": true,
+        "supports_video_input": true,
+        "supports_audio_input": true
+    },
+    "amazon.nova-micro-v1:0": {
+        "input_cost_per_token": 3.5e-08,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 10000,
+        "max_tokens": 10000,
+        "mode": "chat",
+        "output_cost_per_token": 1.4e-07,
+        "supports_function_calling": true,
+        "supports_prompt_caching": true,
+        "supports_response_schema": true
+    },
+    "amazon.nova-pro-v1:0": {
+        "input_cost_per_token": 8e-07,
+        "litellm_provider": "bedrock_converse",
+        "max_input_tokens": 300000,
+        "max_output_tokens": 10000,
+        "max_tokens": 10000,
+        "mode": "chat",
+        "output_cost_per_token": 3.2e-06,
+        "supports_function_calling": true,
+        "supports_pdf_input": true,
+        "supports_prompt_caching": true,
+        "supports_response_schema": true,
+        "supports_vision": true
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+        "max_tokens": 65536,
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+        "max_tokens": 8192,
+        "mode": "chat",
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+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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+        "output_cost_per_token": 1.5000020000000002e-05,
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+        "max_tokens": 64000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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+        "max_tokens": 32000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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+        "supports_function_calling": true,
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+        "supports_tool_choice": true
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+        "max_tokens": 64000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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+        "mode": "chat",
+        "output_cost_per_token": 2.5000010000000002e-05,
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+        "supports_function_calling": true,
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+        "max_tokens": 64000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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+        "mode": "chat",
+        "output_cost_per_token": 1.5000020000000002e-05,
+        "output_dbu_cost_per_token": 0.000214286,
+        "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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+        "supports_function_calling": true,
+        "supports_reasoning": true,
+        "supports_tool_choice": true
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+    "databricks/databricks-claude-sonnet-4-1": {
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+        "max_output_tokens": 64000,
+        "max_tokens": 64000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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+        "mode": "chat",
+        "output_cost_per_token": 1.5000020000000002e-05,
+        "output_dbu_cost_per_token": 0.000214286,
+        "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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+        "supports_function_calling": true,
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+        "max_tokens": 64000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
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+        "mode": "chat",
+        "output_cost_per_token": 1.5000020000000002e-05,
+        "output_dbu_cost_per_token": 0.000214286,
+        "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
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+        "supports_function_calling": true,
+        "supports_reasoning": true,
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+        "max_tokens": 65535,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 2.49998e-06,
+        "output_dbu_cost_per_token": 3.5714e-05,
+        "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
+    "databricks/databricks-gemini-2-5-pro": {
+        "input_cost_per_token": 1.24999e-06,
+        "input_dbu_cost_per_token": 1.7857e-05,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 1048576,
+        "max_output_tokens": 65536,
+        "max_tokens": 65536,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 9.999990000000002e-06,
+        "output_dbu_cost_per_token": 0.000142857,
+        "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving",
+        "supports_function_calling": true,
+        "supports_tool_choice": true
+    },
+    "databricks/databricks-gemma-3-12b": {
+        "input_cost_per_token": 1.5000999999999998e-07,
+        "input_dbu_cost_per_token": 2.1429999999999996e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 32000,
+        "max_tokens": 32000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 5.0001e-07,
+        "output_dbu_cost_per_token": 7.143e-06,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+    },
+    "databricks/databricks-gpt-5": {
+        "input_cost_per_token": 1.24999e-06,
+        "input_dbu_cost_per_token": 1.7857e-05,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 272000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 9.999990000000002e-06,
+        "output_dbu_cost_per_token": 0.000142857,
+        "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving"
+    },
+    "databricks/databricks-gpt-5-1": {
+        "input_cost_per_token": 1.24999e-06,
+        "input_dbu_cost_per_token": 1.7857e-05,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 272000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 9.999990000000002e-06,
+        "output_dbu_cost_per_token": 0.000142857,
+        "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving"
+    },
+    "databricks/databricks-gpt-5-mini": {
+        "input_cost_per_token": 2.4997000000000006e-07,
+        "input_dbu_cost_per_token": 3.571e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 272000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 1.9999700000000004e-06,
+        "output_dbu_cost_per_token": 2.8571e-05,
+        "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving"
+    },
+    "databricks/databricks-gpt-5-nano": {
+        "input_cost_per_token": 4.998e-08,
+        "input_dbu_cost_per_token": 7.14e-07,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 272000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 3.9998000000000007e-07,
+        "output_dbu_cost_per_token": 5.714000000000001e-06,
+        "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving"
+    },
+    "databricks/databricks-gpt-oss-120b": {
+        "input_cost_per_token": 1.5000999999999998e-07,
+        "input_dbu_cost_per_token": 2.1429999999999996e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "max_tokens": 131072,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 5.9997e-07,
+        "output_dbu_cost_per_token": 8.571e-06,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+    },
+    "databricks/databricks-gpt-oss-20b": {
+        "input_cost_per_token": 7e-08,
+        "input_dbu_cost_per_token": 1e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 131072,
+        "max_tokens": 131072,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 3.0001999999999996e-07,
+        "output_dbu_cost_per_token": 4.285999999999999e-06,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+    },
+    "databricks/databricks-gte-large-en": {
+        "input_cost_per_token": 1.2999000000000001e-07,
+        "input_dbu_cost_per_token": 1.857e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 8192,
+        "max_tokens": 8192,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "embedding",
+        "output_cost_per_token": 0.0,
+        "output_dbu_cost_per_token": 0.0,
+        "output_vector_size": 1024,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+    },
+    "databricks/databricks-llama-2-70b-chat": {
+        "input_cost_per_token": 5.0001e-07,
+        "input_dbu_cost_per_token": 7.143e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 4096,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 1.5000300000000002e-06,
+        "output_dbu_cost_per_token": 2.1429e-05,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "supports_tool_choice": true
+    },
+    "databricks/databricks-llama-4-maverick": {
+        "input_cost_per_token": 5.0001e-07,
+        "input_dbu_cost_per_token": 7.143e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "metadata": {
+            "notes": "Databricks documentation now provides both DBU costs (_dbu_cost_per_token) and dollar costs(_cost_per_token)."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 1.5000300000000002e-06,
+        "output_dbu_cost_per_token": 2.1429e-05,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "supports_tool_choice": true
+    },
+    "databricks/databricks-meta-llama-3-1-405b-instruct": {
+        "input_cost_per_token": 5.00003e-06,
+        "input_dbu_cost_per_token": 7.1429e-05,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 1.5000020000000002e-05,
+        "output_dbu_cost_per_token": 0.000214286,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "supports_tool_choice": true
+    },
+    "databricks/databricks-meta-llama-3-1-8b-instruct": {
+        "input_cost_per_token": 1.5000999999999998e-07,
+        "input_dbu_cost_per_token": 2.1429999999999996e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 200000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 4.5003000000000007e-07,
+        "output_dbu_cost_per_token": 6.429000000000001e-06,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving"
+    },
+    "databricks/databricks-meta-llama-3-3-70b-instruct": {
+        "input_cost_per_token": 5.0001e-07,
+        "input_dbu_cost_per_token": 7.143e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 1.5000300000000002e-06,
+        "output_dbu_cost_per_token": 2.1429e-05,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "supports_tool_choice": true
+    },
+    "databricks/databricks-meta-llama-3-70b-instruct": {
+        "input_cost_per_token": 1.00002e-06,
+        "input_dbu_cost_per_token": 1.4286e-05,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 128000,
+        "max_output_tokens": 128000,
+        "max_tokens": 128000,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 2.9999900000000002e-06,
+        "output_dbu_cost_per_token": 4.2857e-05,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "supports_tool_choice": true
+    },
+    "databricks/databricks-mixtral-8x7b-instruct": {
+        "input_cost_per_token": 5.0001e-07,
+        "input_dbu_cost_per_token": 7.143e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 4096,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 1.00002e-06,
+        "output_dbu_cost_per_token": 1.4286e-05,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "supports_tool_choice": true
+    },
+    "databricks/databricks-mpt-30b-instruct": {
+        "input_cost_per_token": 1.00002e-06,
+        "input_dbu_cost_per_token": 1.4286e-05,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 8192,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 1.00002e-06,
+        "output_dbu_cost_per_token": 1.4286e-05,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "supports_tool_choice": true
+    },
+    "databricks/databricks-mpt-7b-instruct": {
+        "input_cost_per_token": 5.0001e-07,
+        "input_dbu_cost_per_token": 7.143e-06,
+        "litellm_provider": "databricks",
+        "max_input_tokens": 8192,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "metadata": {
+            "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation."
+        },
+        "mode": "chat",
+        "output_cost_per_token": 0.0,
+        "output_dbu_cost_per_token": 0.0,
+        "source": "https://www.databricks.com/product/pricing/foundation-model-serving",
+        "supports_tool_choice": true
+    },
+    "dataforseo/search": {
+        "input_cost_per_query": 0.003,
+        "litellm_provider": "dataforseo",
+        "mode": "search"
+    },
+    "davinci-002": {
+        "input_cost_per_token": 2e-06,
+        "litellm_provider": "text-completion-openai",
+        "max_input_tokens": 16384,
+        "max_output_tokens": 4096,
+        "max_tokens": 4096,
+        "mode": "completion",
+        "output_cost_per_token": 2e-06
+    },
+    "deepgram/base": {
+        "input_cost_per_second": 0.00020833,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+            "original_pricing_per_minute": 0.0125
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/base-conversationalai": {
+        "input_cost_per_second": 0.00020833,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+            "original_pricing_per_minute": 0.0125
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/base-finance": {
+        "input_cost_per_second": 0.00020833,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+            "original_pricing_per_minute": 0.0125
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/base-general": {
+        "input_cost_per_second": 0.00020833,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+            "original_pricing_per_minute": 0.0125
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/base-meeting": {
+        "input_cost_per_second": 0.00020833,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+            "original_pricing_per_minute": 0.0125
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/base-phonecall": {
+        "input_cost_per_second": 0.00020833,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+            "original_pricing_per_minute": 0.0125
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/base-video": {
+        "input_cost_per_second": 0.00020833,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+            "original_pricing_per_minute": 0.0125
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/base-voicemail": {
+        "input_cost_per_second": 0.00020833,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0125/60 seconds = $0.00020833 per second",
+            "original_pricing_per_minute": 0.0125
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/enhanced": {
+        "input_cost_per_second": 0.00024167,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+            "original_pricing_per_minute": 0.0145
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/enhanced-finance": {
+        "input_cost_per_second": 0.00024167,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+            "original_pricing_per_minute": 0.0145
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/enhanced-general": {
+        "input_cost_per_second": 0.00024167,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+            "original_pricing_per_minute": 0.0145
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/enhanced-meeting": {
+        "input_cost_per_second": 0.00024167,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+            "original_pricing_per_minute": 0.0145
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/enhanced-phonecall": {
+        "input_cost_per_second": 0.00024167,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0145/60 seconds = $0.00024167 per second",
+            "original_pricing_per_minute": 0.0145
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/nova": {
+        "input_cost_per_second": 7.167e-05,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0043/60 seconds = $0.00007167 per second",
+            "original_pricing_per_minute": 0.0043
+        },
+        "mode": "audio_transcription",
+        "output_cost_per_second": 0.0,
+        "source": "https://deepgram.com/pricing",
+        "supported_endpoints": [
+            "/v1/audio/transcriptions"
+        ]
+    },
+    "deepgram/nova-2": {
+        "input_cost_per_second": 7.167e-05,
+        "litellm_provider": "deepgram",
+        "metadata": {
+            "calculation": "$0.0043/60 seconds = $0.00007167 per second",
+            "original_pricing_per_minute": 0.0043
+        },
+        "mode": "audio_transcription",
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+        "supports_tool_choice": true,
+        "supports_url_context": true,
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+        "max_tokens": 65535,
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+        "supported_output_modalities": [
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+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
+        "supports_web_search": true,
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+        "input_cost_per_token_above_128k_tokens": 0,
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+        "max_pdf_size_mb": 30,
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+        "max_video_length": 1,
+        "max_videos_per_prompt": 10,
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+        "supports_response_schema": true,
+        "supports_system_messages": true,
+        "supports_tool_choice": true,
+        "supports_vision": true,
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+        "input_cost_per_token_above_128k_tokens": 0,
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+        "supports_system_messages": true,
+        "supports_tool_choice": true,
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+        "tpm": 4000000
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+        "max_tokens": 8192,
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+        "supports_tool_choice": true,
+        "supports_vision": true,
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+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
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+        "supports_tool_choice": true,
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+        "litellm_provider": "gemini",
+        "mode": "image_generation",
+        "output_cost_per_image": 0.04,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
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+        "litellm_provider": "gemini",
+        "mode": "image_generation",
+        "output_cost_per_image": 0.02,
+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
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+    "gemini/imagen-4.0-generate-001": {
+        "litellm_provider": "gemini",
+        "mode": "image_generation",
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+        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing"
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+        "mode": "image_generation",
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+        "supported_output_modalities": [
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+        "supported_output_modalities": [
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+        "max_tokens": 1024,
+        "mode": "video_generation",
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+        "supported_modalities": [
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+        ],
+        "supported_output_modalities": [
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+    },
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+        "max_tokens": 16000,
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new file mode 100644
index 0000000000..98694f8d9e
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/06ebe9b0e9cdf241.js
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Admin UI."})]}),(0,t.jsx)(s.Modal,{title:"Public Model Hub",width:600,open:O,footer:null,onOk:ek,onCancel:e_,children:(0,t.jsxs)("div",{className:"pt-5 pb-5",children:[(0,t.jsxs)("div",{className:"flex justify-between mb-4",children:[(0,t.jsx)(o.Text,{className:"text-base mr-2",children:"Shareable Link:"}),(0,t.jsx)(o.Text,{className:"max-w-sm ml-2 bg-gray-200 pr-2 pl-2 pt-1 pb-1 text-center rounded",children:`${(0,x.getProxyBaseUrl)()}/ui/model_hub_table`})]}),(0,t.jsx)("div",{className:"flex justify-end",children:(0,t.jsx)(N.Button,{onClick:()=>{eC.replace(`/model_hub_table?key=${e}`)},children:"See Page"})})]})}),(0,t.jsx)(s.Modal,{title:H?.model_group||"Model Details",width:1e3,open:z,footer:null,onOk:ek,onCancel:e_,children:H&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Model Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Model Group:"}),(0,t.jsx)(o.Text,{children:H.model_group})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Mode:"}),(0,t.jsx)(o.Text,{children:H.mode||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Providers:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:H.providers.map(e=>(0,t.jsx)(m.Badge,{color:"blue",children:e},e))})]})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Token & Cost Information"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Max Input Tokens:"}),(0,t.jsx)(o.Text,{children:H.max_input_tokens?.toLocaleString()||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Max Output Tokens:"}),(0,t.jsx)(o.Text,{children:H.max_output_tokens?.toLocaleString()||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Input Cost per 1M Tokens:"}),(0,t.jsx)(o.Text,{children:H.input_cost_per_token?eI(H.input_cost_per_token):"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Output Cost per 1M Tokens:"}),(0,t.jsx)(o.Text,{children:H.output_cost_per_token?eI(H.output_cost_per_token):"Not specified"})]})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Capabilities"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:(c=Object.entries(H).filter(([e,t])=>e.startsWith("supports_")&&!0===t).map(([e])=>e),h=["green","blue","purple","orange","red","yellow"],0===c.length?(0,t.jsx)(o.Text,{className:"text-gray-500",children:"No special capabilities listed"}):c.map((e,l)=>(0,t.jsx)(m.Badge,{color:h[l%h.length],children:e.replace(/^supports_/,"").split("_").map(e=>e.charAt(0).toUpperCase()+e.slice(1)).join(" ")},e)))})]}),(H.tpm||H.rpm)&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Rate Limits"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[H.tpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Tokens per Minute:"}),(0,t.jsx)(o.Text,{children:H.tpm.toLocaleString()})]}),H.rpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Requests per Minute:"}),(0,t.jsx)(o.Text,{children:H.rpm.toLocaleString()})]})]})]}),H.supported_openai_params&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Supported OpenAI Parameters"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:H.supported_openai_params.map(e=>(0,t.jsx)(m.Badge,{color:"green",children:e},e))})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Usage Example"}),(0,t.jsx)(el.Prism,{language:"python",className:"text-sm",children:`import openai
+
+client = openai.OpenAI(
+    api_key="your_api_key",
+    base_url="${(0,x.getProxyBaseUrl)()}"  # Your LiteLLM Proxy URL
+)
+
+response = client.chat.completions.create(
+    model="${H.model_group}",
+    messages=[
+        {
+            "role": "user",
+            "content": "Hello, how are you?"
+        }
+    ]
+)
+
+print(response.choices[0].message.content)`})]})]})}),(0,t.jsx)(s.Modal,{title:em?.name||"Agent Details",width:1e3,open:eu,footer:null,onOk:ek,onCancel:e_,children:em&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Agent Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Name:"}),(0,t.jsx)(o.Text,{children:em.name})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Version:"}),(0,t.jsxs)(m.Badge,{color:"blue",children:["v",em.version]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Protocol Version:"}),(0,t.jsx)(o.Text,{children:em.protocolVersion})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"URL:"}),(0,t.jsxs)("div",{className:"flex items-center 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+import asyncio
+
+# Standard MCP configuration
+config = {
+    "mcpServers": {
+        "${ef.server_name}": {
+            "url": "${(0,x.getProxyBaseUrl)()}/${ef.server_name}/mcp",
+            "headers": {
+                "x-litellm-api-key": "Bearer sk-1234"
+            }
+        }
+    }
+}
+
+# Create a client that connects to the server
+client = Client(config)
+
+async def main():
+    async with client:
+        # List available tools
+        tools = await client.list_tools()
+        print(f"Available tools: {[tool.name for tool in tools]}")
+
+        # Call a tool
+        response = await client.call_tool(
+            name="tool_name",
+            arguments={"arg": "value"}
+        )
+        print(f"Response: {response}")
+
+if __name__ == "__main__":
+    asyncio.run(main())`})]})]})}),(0,t.jsx)(y,{visible:R,onClose:()=>V(!1),accessToken:e||"",modelHubData:_||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.modelHubCall)(e);I(t.data)}catch(e){console.error("Error refreshing model data:",e)}})()}}),(0,t.jsx)(p,{visible:er,onClose:()=>ec(!1),accessToken:e||"",agentHubData:Y||[],onSuccess:()=>{e&&(async()=>{try{let t=(await (0,x.getAgentsList)(e)).agents.map(e=>({agent_id:e.agent_id,...e.agent_card_params,is_public:e.is_public}));en(t)}catch(e){console.error("Error refreshing agent data:",e)}})()}}),(0,t.jsx)(b,{visible:eS,onClose:()=>e$(!1),accessToken:e||"",mcpHubData:ep||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.fetchMCPServers)(e);eg(t)}catch(e){console.error("Error refreshing MCP server data:",e)}})()}})]})}],934879)}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/07758720cf3d40ba.js b/litellm/proxy/_experimental/out/_next/static/chunks/07758720cf3d40ba.js
deleted file mode 100644
index 3e82330a48..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/07758720cf3d40ba.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0cb257e73482dfdb.js b/litellm/proxy/_experimental/out/_next/static/chunks/0cb257e73482dfdb.js
deleted file mode 100644
index 945dcadce6..0000000000
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+        & > ${n}-affix-wrapper,
+        & > ${n}-number-affix-wrapper,
+        & > ${o}-picker-range
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+      & > ${o}-cascader-picker ${n},
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+        > ${t},
+        ${t}-affix-wrapper`]:{"&:hover, &:focus, &:active":{zIndex:2}},[`> ${t}-affix-wrapper-focused`]:{zIndex:2}}}}})(t),(e=>{let{componentCls:t}=e;return{[`${t}-out-of-range`]:{[`&, & input, & textarea, ${t}-show-count-suffix, ${t}-data-count`]:{color:e.colorError}}}})(t),(0,n.genCompactItemStyle)(t)]},l,{resetFont:!1});e.s(["default",0,T,"genBasicInputStyle",0,j,"genInputGroupStyle",0,O,"genInputSmallStyle",0,x,"genPlaceholderStyle",0,E,"useSharedStyle",0,k],349942)},831357,e=>{"use strict";var 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+        ${o}-appear
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+      ${o}-appear
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+        &${n}-allow-clear ${n}-selector
+      `]:{paddingInlineEnd:e.calc(e.fontSizeIcon).add(e.controlPaddingHorizontal).equal()}}};return[((e,r)=>{let{componentCls:n,INTERNAL_FIXED_ITEM_MARGIN:o}=e,a=`${n}-selection-overflow`,i=e.multipleSelectItemHeight,l=(e=>{let{multipleSelectItemHeight:t,selectHeight:r,lineWidth:n}=e;return e.calc(r).sub(t).div(2).sub(n).equal()})(e),c=r?`${n}-${r}`:"",u=(e=>{let{multipleSelectItemHeight:t,paddingXXS:r,lineWidth:n,INTERNAL_FIXED_ITEM_MARGIN:o}=e,a=e.max(e.calc(r).sub(n).equal(),0),i=e.max(e.calc(a).sub(o).equal(),0);return{basePadding:a,containerPadding:i,itemHeight:(0,s.unit)(t),itemLineHeight:(0,s.unit)(e.calc(t).sub(e.calc(e.lineWidth).mul(2)).equal())}})(e);return{[`${n}-multiple${c}`]:Object.assign(Object.assign({},(e=>{let{componentCls:r,iconCls:n,borderRadiusSM:o,motionDurationSlow:a,paddingXS:i,multipleItemColorDisabled:l,multipleItemBorderColorDisabled:s,colorIcon:c,colorIconHover:u,INTERNAL_FIXED_ITEM_MARGIN:d}=e;return{[`${r}-selection-overflow`]:{position:"relative",display:"flex",flex:"auto",flexWrap:"wrap",maxWidth:"100%","&-item":{flex:"none",alignSelf:"center",maxWidth:"calc(100% - 4px)",display:"inline-flex"},[`${r}-selection-item`]:{display:"flex",alignSelf:"center",flex:"none",boxSizing:"border-box",maxWidth:"100%",marginBlock:d,borderRadius:o,cursor:"default",transition:`font-size ${a}, line-height ${a}, height ${a}`,marginInlineEnd:e.calc(d).mul(2).equal(),paddingInlineStart:i,paddingInlineEnd:e.calc(i).div(2).equal(),[`${r}-disabled&`]:{color:l,borderColor:s,cursor:"not-allowed"},"&-content":{display:"inline-block",marginInlineEnd:e.calc(i).div(2).equal(),overflow:"hidden",whiteSpace:"pre",textOverflow:"ellipsis"},"&-remove":Object.assign(Object.assign({},(0,t.resetIcon)()),{display:"inline-flex",alignItems:"center",color:c,fontWeight:"bold",fontSize:10,lineHeight:"inherit",cursor:"pointer",[`> ${n}`]:{verticalAlign:"-0.2em"},"&:hover":{color:u}})}}}})(e)),{[`${n}-selector`]:{display:"flex",alignItems:"center",width:"100%",height:"100%",paddingInline:u.basePadding,paddingBlock:u.containerPadding,borderRadius:e.borderRadius,[`${n}-disabled&`]:{background:e.multipleSelectorBgDisabled,cursor:"not-allowed"},"&:after":{display:"inline-block",width:0,margin:`${(0,s.unit)(o)} 0`,lineHeight:(0,s.unit)(i),visibility:"hidden",content:'"\\a0"'}},[`${n}-selection-item`]:{height:u.itemHeight,lineHeight:(0,s.unit)(u.itemLineHeight)},[`${n}-selection-wrap`]:{alignSelf:"flex-start","&:after":{lineHeight:(0,s.unit)(i),marginBlock:o}},[`${n}-prefix`]:{marginInlineStart:e.calc(e.inputPaddingHorizontalBase).sub(u.basePadding).equal()},[`${a}-item + ${a}-item,
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+          &-mirror
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+          ${n}-selection-item,
+          ${n}-selection-placeholder
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+        &${n}-show-arrow ${n}-selection-item,
+        &${n}-show-arrow ${n}-selection-search,
+        &${n}-show-arrow ${n}-selection-placeholder
+      `]:{paddingInlineEnd:e.showArrowPaddingInlineEnd},[`&${n}-open ${n}-selection-item`]:{color:e.colorTextPlaceholder},[`&:not(${n}-customize-input)`]:{[`${n}-selector`]:{width:"100%",height:"100%",alignItems:"center",padding:`0 ${(0,s.unit)(o)}`,[`${n}-selection-search-input`]:{height:i,fontSize:e.fontSize},"&:after":{lineHeight:(0,s.unit)(i)}}},[`&${n}-customize-input`]:{[`${n}-selector`]:{"&:after":{display:"none"},[`${n}-selection-search`]:{position:"static",width:"100%"},[`${n}-selection-placeholder`]:{position:"absolute",insetInlineStart:0,insetInlineEnd:0,padding:`0 ${(0,s.unit)(o)}`,"&:after":{display:"none"}}}}}}}let d=(e,t)=>{let{componentCls:r,antCls:n,controlOutlineWidth:o}=e;return{[`&:not(${r}-customize-input) ${r}-selector`]:{border:`${(0,s.unit)(e.lineWidth)} ${e.lineType} ${t.borderColor}`,background:e.selectorBg},[`&:not(${r}-disabled):not(${r}-customize-input):not(${n}-pagination-size-changer)`]:{[`&:hover 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+          ${c}${d}bottomLeft
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+          ${c}${d}topLeft,
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+          ${c}${d}topRight
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+          ${u}${d}topRight
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0ea9112947894f26.js b/litellm/proxy/_experimental/out/_next/static/chunks/0ea9112947894f26.js
new file mode 100644
index 0000000000..4813818903
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/0ea9112947894f26.js
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Member",1!==e.length?"s":""]}),(0,t.jsx)(o.Table,{columns:k,dataSource:e,rowKey:e=>e.user_id??e.user_email??JSON.stringify(e),pagination:!1,size:"small",scroll:{x:"max-content"},locale:w?{emptyText:w}:void 0}),f&&u&&(0,t.jsx)(s.Button,{icon:(0,t.jsx)(r.UserAddOutlined,{}),type:"primary",onClick:f,children:"Add Member"})]})}e.s(["default",()=>h])}]);
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5fd77ee53c52d9e7.js b/litellm/proxy/_experimental/out/_next/static/chunks/11362340846735c3.js
similarity index 56%
rename from litellm/proxy/_experimental/out/_next/static/chunks/5fd77ee53c52d9e7.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/11362340846735c3.js
index 7154651e86..15dc8cc860 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/5fd77ee53c52d9e7.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/11362340846735c3.js
@@ -1,16 +1,16 @@
-(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,166406,e=>{"use strict";var t=e.i(190144);e.s(["CopyOutlined",()=>t.default])},755151,e=>{"use strict";var t=e.i(247153);e.s(["DownOutlined",()=>t.default])},818581,(e,t,r)=>{"use strict";Object.defineProperty(r,"__esModule",{value:!0}),Object.defineProperty(r,"useMergedRef",{enumerable:!0,get:function(){return o}});let a=e.r(271645);function o(e,t){let r=(0,a.useRef)(null),o=(0,a.useRef)(null);return(0,a.useCallback)(a=>{if(null===a){let e=r.current;e&&(r.current=null,e());let t=o.current;t&&(o.current=null,t())}else e&&(r.current=i(e,a)),t&&(o.current=i(t,a))},[e,t])}function i(e,t){if("function"!=typeof e)return e.current=t,()=>{e.current=null};{let r=e(t);return"function"==typeof r?r:()=>e(null)}}("function"==typeof r.default||"object"==typeof r.default&&null!==r.default)&&void 0===r.default.__esModule&&(Object.defineProperty(r.default,"__esModule",{value:!0}),Object.assign(r.default,r),t.exports=r.default)},62478,e=>{"use strict";var t=e.i(764205);let r=async e=>{if(!e)return null;try{return await (0,t.getProxyUISettings)(e)}catch(e){return console.error("Error fetching proxy settings:",e),null}};e.s(["fetchProxySettings",0,r])},602073,e=>{"use strict";e.i(247167);var t=e.i(931067),r=e.i(271645);let a={icon:{tag:"svg",attrs:{viewBox:"0 0 1024 1024",focusable:"false"},children:[{tag:"path",attrs:{d:"M512 64L128 192v384c0 212.1 171.9 384 384 384s384-171.9 384-384V192L512 64zm312 512c0 172.3-139.7 312-312 312S200 748.3 200 576V246l312-110 312 110v330z"}},{tag:"path",attrs:{d:"M378.4 475.1a35.91 35.91 0 00-50.9 0 35.91 35.91 0 000 50.9l129.4 129.4 2.1 2.1a33.98 33.98 0 0048.1 0L730.6 434a33.98 33.98 0 000-48.1l-2.8-2.8a33.98 33.98 0 00-48.1 0L483 579.7 378.4 475.1z"}}]},name:"safety",theme:"outlined"};var o=e.i(9583),i=r.forwardRef(function(e,i){return r.createElement(o.default,(0,t.default)({},e,{ref:i,icon:a}))});e.s(["SafetyOutlined",0,i],602073)},190272,785913,e=>{"use strict";var t,r,a=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),o=((r={}).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents",r.MCP="mcp",r.REALTIME="realtime",r);let i={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>o,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=i[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:r,accessToken:a,apiKey:i,inputMessage:n,chatHistory:s,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:u,selectedMCPServers:g,mcpServers:m,mcpServerToolRestrictions:p,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:b,proxySettings:v}=e,A="session"===r?a:i,I=window.location.origin,C=v?.LITELLM_UI_API_DOC_BASE_URL;C&&C.trim()?I=C:v?.PROXY_BASE_URL&&(I=v.PROXY_BASE_URL);let w=n||"Your prompt here",x=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),E=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),y={};l.length>0&&(y.tags=l),c.length>0&&(y.vector_stores=c),d.length>0&&(y.guardrails=d),u.length>0&&(y.policies=u);let O=_||"your-model-name",k="azure"===b?`import openai
+(globalThis.TURBOPACK||(globalThis.TURBOPACK=[])).push(["object"==typeof document?document.currentScript:void 0,166406,e=>{"use strict";var t=e.i(190144);e.s(["CopyOutlined",()=>t.default])},755151,e=>{"use strict";var t=e.i(247153);e.s(["DownOutlined",()=>t.default])},602073,e=>{"use strict";e.i(247167);var t=e.i(931067),r=e.i(271645);let a={icon:{tag:"svg",attrs:{viewBox:"0 0 1024 1024",focusable:"false"},children:[{tag:"path",attrs:{d:"M512 64L128 192v384c0 212.1 171.9 384 384 384s384-171.9 384-384V192L512 64zm312 512c0 172.3-139.7 312-312 312S200 748.3 200 576V246l312-110 312 110v330z"}},{tag:"path",attrs:{d:"M378.4 475.1a35.91 35.91 0 00-50.9 0 35.91 35.91 0 000 50.9l129.4 129.4 2.1 2.1a33.98 33.98 0 0048.1 0L730.6 434a33.98 33.98 0 000-48.1l-2.8-2.8a33.98 33.98 0 00-48.1 0L483 579.7 378.4 475.1z"}}]},name:"safety",theme:"outlined"};var o=e.i(9583),i=r.forwardRef(function(e,i){return r.createElement(o.default,(0,t.default)({},e,{ref:i,icon:a}))});e.s(["SafetyOutlined",0,i],602073)},818581,(e,t,r)=>{"use strict";Object.defineProperty(r,"__esModule",{value:!0}),Object.defineProperty(r,"useMergedRef",{enumerable:!0,get:function(){return o}});let a=e.r(271645);function o(e,t){let r=(0,a.useRef)(null),o=(0,a.useRef)(null);return(0,a.useCallback)(a=>{if(null===a){let e=r.current;e&&(r.current=null,e());let t=o.current;t&&(o.current=null,t())}else e&&(r.current=i(e,a)),t&&(o.current=i(t,a))},[e,t])}function i(e,t){if("function"!=typeof e)return e.current=t,()=>{e.current=null};{let r=e(t);return"function"==typeof r?r:()=>e(null)}}("function"==typeof r.default||"object"==typeof r.default&&null!==r.default)&&void 0===r.default.__esModule&&(Object.defineProperty(r.default,"__esModule",{value:!0}),Object.assign(r.default,r),t.exports=r.default)},62478,e=>{"use strict";var t=e.i(764205);let r=async e=>{if(!e)return null;try{return await (0,t.getProxyUISettings)(e)}catch(e){return console.error("Error fetching proxy settings:",e),null}};e.s(["fetchProxySettings",0,r])},190272,785913,e=>{"use strict";var t,r,a=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),o=((r={}).IMAGE="image",r.VIDEO="video",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",r.EMBEDDINGS="embeddings",r.SPEECH="speech",r.TRANSCRIPTION="transcription",r.A2A_AGENTS="a2a_agents",r.MCP="mcp",r.REALTIME="realtime",r);let i={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>o,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=i[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:r,accessToken:a,apiKey:i,inputMessage:n,chatHistory:s,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:u,selectedMCPServers:g,mcpServers:p,mcpServerToolRestrictions:m,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:b,proxySettings:A}=e,v="session"===r?a:i,I=window.location.origin,x=A?.LITELLM_UI_API_DOC_BASE_URL;x&&x.trim()?I=x:A?.PROXY_BASE_URL&&(I=A.PROXY_BASE_URL);let C=n||"Your prompt here",w=C.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),E=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),y={};l.length>0&&(y.tags=l),c.length>0&&(y.vector_stores=c),d.length>0&&(y.guardrails=d),u.length>0&&(y.policies=u);let O=_||"your-model-name",T="azure"===b?`import openai
 
 client = openai.AzureOpenAI(
-	api_key="${A||"YOUR_LITELLM_API_KEY"}",
+	api_key="${v||"YOUR_LITELLM_API_KEY"}",
 	azure_endpoint="${I}",
 	api_version="2024-02-01"
 )`:`import openai
 
 client = openai.OpenAI(
-	api_key="${A||"YOUR_LITELLM_API_KEY"}",
+	api_key="${v||"YOUR_LITELLM_API_KEY"}",
 	base_url="${I}"
 )`;switch(h){case o.CHAT:{let e=Object.keys(y).length>0,r="";if(e){let e=JSON.stringify({metadata:y},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();r=`,
-    extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:w}];t=`
+    extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:C}];t=`
 import base64
 
 # Helper function to encode images to base64
@@ -36,7 +36,7 @@ print(response)
 #             "content": [
 #                 {
 #                     "type": "text",
-#                     "text": "${x}"
+#                     "text": "${w}"
 #                 },
 #                 {
 #                     "type": "image_url",
@@ -50,7 +50,7 @@ print(response)
 # )
 # print(response_with_file)
 `;break}case o.RESPONSES:{let e=Object.keys(y).length>0,r="";if(e){let e=JSON.stringify({metadata:y},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();r=`,
-    extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:w}];t=`
+    extra_body=${e}`}let a=E.length>0?E:[{role:"user",content:C}];t=`
 import base64
 
 # Helper function to encode images to base64
@@ -74,7 +74,7 @@ print(response.output_text)
 #         {
 #             "role": "user",
 #             "content": [
-#                 {"type": "input_text", "text": "${x}"},
+#                 {"type": "input_text", "text": "${w}"},
 #                 {
 #                     "type": "input_image",
 #                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
@@ -151,7 +151,7 @@ def create_file(image_path):
 	return f"file_{os.path.basename(image_path).replace('.', '_')}"
 
 # The prompt entered by the user
-prompt = "${x}"
+prompt = "${w}"
 
 # Encode images to base64
 base64_image1 = encode_image("body-lotion.png")
@@ -229,7 +229,7 @@ def encode_image(image_path):
 			return base64.b64encode(image_file.read()).decode('utf-8')
 
 # The prompt entered by the user
-prompt = "${x}"
+prompt = "${w}"
 
 # Encode images to base64
 base64_image1 = encode_image("body-lotion.png")
@@ -310,7 +310,7 @@ def create_file(image_path):
 	return f"file_{os.path.basename(image_path).replace('.', '_')}"
 
 # The prompt entered by the user
-prompt = "${x}"
+prompt = "${w}"
 
 # Encode images to base64
 base64_image1 = encode_image("body-lotion.png")
@@ -416,5 +416,5 @@ print(f"Audio saved to {output_filename}")
 #     speed=1.0  # Range: 0.25 to 4.0
 # )
 # response.stream_to_file("output_speech.mp3")
-`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${k}
-${t}`}],190272)},916925,e=>{"use strict";var t,r=((t={}).A2A_Agent="A2A Agent",t.AI21="Ai21",t.AI21_CHAT="Ai21 Chat",t.AIML="AI/ML API",t.AIOHTTP_OPENAI="Aiohttp Openai",t.Anthropic="Anthropic",t.ANTHROPIC_TEXT="Anthropic Text",t.AssemblyAI="AssemblyAI",t.AUTO_ROUTER="Auto Router",t.Bedrock="Amazon Bedrock",t.BedrockMantle="Amazon Bedrock Mantle",t.SageMaker="AWS SageMaker",t.Azure="Azure",t.Azure_AI_Studio="Azure AI Foundry (Studio)",t.AZURE_TEXT="Azure Text",t.BASETEN="Baseten",t.BYTEZ="Bytez",t.Cerebras="Cerebras",t.CLARIFAI="Clarifai",t.CLOUDFLARE="Cloudflare",t.CODESTRAL="Codestral",t.Cohere="Cohere",t.COHERE_CHAT="Cohere Chat",t.COMETAPI="Cometapi",t.COMPACTIFAI="Compactifai",t.Cursor="Cursor",t.Dashscope="Dashscope",t.Databricks="Databricks (Qwen API)",t.DATAROBOT="Datarobot",t.DeepInfra="DeepInfra",t.Deepgram="Deepgram",t.Deepseek="Deepseek",t.DOCKER_MODEL_RUNNER="Docker Model Runner",t.DOTPROMPT="Dotprompt",t.ElevenLabs="ElevenLabs",t.EMPOWER="Empower",t.FalAI="Fal AI",t.FEATHERLESS_AI="Featherless Ai",t.FireworksAI="Fireworks AI",t.FRIENDLIAI="Friendliai",t.GALADRIEL="Galadriel",t.GITHUB_COPILOT="Github Copilot",t.Google_AI_Studio="Google AI Studio",t.GradientAI="GradientAI",t.Groq="Groq",t.HEROKU="Heroku",t.Hosted_Vllm="vllm",t.HUGGINGFACE="Huggingface",t.HYPERBOLIC="Hyperbolic",t.Infinity="Infinity",t.JinaAI="Jina AI",t.LAMBDA_AI="Lambda Ai",t.LEMONADE="Lemonade",t.LLAMAFILE="Llamafile",t.LM_STUDIO="Lm Studio",t.LLAMA="Meta Llama",t.MARITALK="Maritalk",t.MiniMax="MiniMax",t.MistralAI="Mistral AI",t.MOONSHOT="Moonshot",t.MORPH="Morph",t.NEBIUS="Nebius",t.NLP_CLOUD="Nlp Cloud",t.NOVITA="Novita",t.NSCALE="Nscale",t.NVIDIA_NIM="Nvidia Nim",t.Ollama="Ollama",t.OLLAMA_CHAT="Ollama Chat",t.OOBABOOGA="Oobabooga",t.OpenAI="OpenAI",t.OPENAI_LIKE="Openai Like",t.OpenAI_Compatible="OpenAI-Compatible Endpoints (Together AI, etc.)",t.OpenAI_Text="OpenAI Text Completion",t.OpenAI_Text_Compatible="OpenAI-Compatible Text Completion Models (Together AI, 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/84dd260c7412819c.js b/litellm/proxy/_experimental/out/_next/static/chunks/184161a27f806cd4.js
similarity index 70%
rename from litellm/proxy/_experimental/out/_next/static/chunks/84dd260c7412819c.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/184161a27f806cd4.js
index 769b56a087..fd9670e381 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/84dd260c7412819c.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/184161a27f806cd4.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5400ee883dfa8c43.js b/litellm/proxy/_experimental/out/_next/static/chunks/1a04d31843c96649.js
similarity index 97%
rename from litellm/proxy/_experimental/out/_next/static/chunks/5400ee883dfa8c43.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/1a04d31843c96649.js
index 2337c72cb6..a9a583efa3 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/5400ee883dfa8c43.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/1a04d31843c96649.js
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deleted file mode 100644
index 1defc85623..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/1ace641bf6de38f0.js
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new file mode 100644
index 0000000000..5ea6f73f34
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1da362a651d209bd.js b/litellm/proxy/_experimental/out/_next/static/chunks/1da362a651d209bd.js
deleted file mode 100644
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new file mode 100644
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md:table-cell"}},{header:"Details",id:"details",enableSorting:!1,cell:({row:l})=>{let i=l.original;return(0,t.jsxs)(N.Button,{size:"xs",variant:"secondary",onClick:()=>e(i),icon:C.InfoCircleOutlined,children:[(0,t.jsx)("span",{className:"hidden lg:inline",children:"Details"}),(0,t.jsx)("span",{className:"lg:hidden",children:"Info"})]})}}])(e=>{ev(e),eN(!0)},eM,a),data:ep||[],isLoading:eb,defaultSorting:[{id:"server_name",desc:!1}]})]}),(0,t.jsx)("div",{className:"mt-4 text-center space-y-2",children:(0,t.jsxs)(o.Text,{className:"text-sm text-gray-600",children:["Showing ",ep?.length||0," MCP server",ep?.length!==1?"s":""]})})]}),(0,t.jsx)(X.TabPanel,{children:(0,t.jsx)(Q,{publicPage:a})})]})]})]}):(0,t.jsxs)(j.Card,{className:"mx-auto max-w-xl mt-10",children:[(0,t.jsx)(o.Text,{className:"text-xl text-center mb-2 text-black",children:"Public Model Hub not enabled."}),(0,t.jsx)("p",{className:"text-base text-center text-slate-800",children:"Ask your proxy admin to enable this on their Admin UI."})]}),(0,t.jsx)(s.Modal,{title:"Public Model Hub",width:600,open:A,footer:null,onOk:ew,onCancel:e_,children:(0,t.jsxs)("div",{className:"pt-5 pb-5",children:[(0,t.jsxs)("div",{className:"flex justify-between mb-4",children:[(0,t.jsx)(o.Text,{className:"text-base mr-2",children:"Shareable Link:"}),(0,t.jsx)(o.Text,{className:"max-w-sm ml-2 bg-gray-200 pr-2 pl-2 pt-1 pb-1 text-center rounded",children:`${(0,x.getProxyBaseUrl)()}/ui/model_hub_table`})]}),(0,t.jsx)("div",{className:"flex justify-end",children:(0,t.jsx)(N.Button,{onClick:()=>{eT.replace(`/model_hub_table?key=${e}`)},children:"See Page"})})]})}),(0,t.jsx)(s.Modal,{title:H?.model_group||"Model Details",width:1e3,open:z,footer:null,onOk:ew,onCancel:e_,children:H&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Model Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Model Group:"}),(0,t.jsx)(o.Text,{children:H.model_group})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Mode:"}),(0,t.jsx)(o.Text,{children:H.mode||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Providers:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:H.providers.map(e=>(0,t.jsx)(m.Badge,{color:"blue",children:e},e))})]})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Token & Cost Information"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Max Input Tokens:"}),(0,t.jsx)(o.Text,{children:H.max_input_tokens?.toLocaleString()||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Max Output Tokens:"}),(0,t.jsx)(o.Text,{children:H.max_output_tokens?.toLocaleString()||"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Input Cost per 1M Tokens:"}),(0,t.jsx)(o.Text,{children:H.input_cost_per_token?eI(H.input_cost_per_token):"Not specified"})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Output Cost per 1M Tokens:"}),(0,t.jsx)(o.Text,{children:H.output_cost_per_token?eI(H.output_cost_per_token):"Not specified"})]})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Capabilities"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:(c=Object.entries(H).filter(([e,t])=>e.startsWith("supports_")&&!0===t).map(([e])=>e),h=["green","blue","purple","orange","red","yellow"],0===c.length?(0,t.jsx)(o.Text,{className:"text-gray-500",children:"No special capabilities listed"}):c.map((e,l)=>(0,t.jsx)(m.Badge,{color:h[l%h.length],children:e.replace(/^supports_/,"").split("_").map(e=>e.charAt(0).toUpperCase()+e.slice(1)).join(" ")},e)))})]}),(H.tpm||H.rpm)&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Rate Limits"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[H.tpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Tokens per Minute:"}),(0,t.jsx)(o.Text,{children:H.tpm.toLocaleString()})]}),H.rpm&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Requests per Minute:"}),(0,t.jsx)(o.Text,{children:H.rpm.toLocaleString()})]})]})]}),H.supported_openai_params&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Supported OpenAI Parameters"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:H.supported_openai_params.map(e=>(0,t.jsx)(m.Badge,{color:"green",children:e},e))})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Usage Example"}),(0,t.jsx)(el.Prism,{language:"python",className:"text-sm",children:`import openai
+
+client = openai.OpenAI(
+    api_key="your_api_key",
+    base_url="${(0,x.getProxyBaseUrl)()}"  # Your LiteLLM Proxy URL
+)
+
+response = client.chat.completions.create(
+    model="${H.model_group}",
+    messages=[
+        {
+            "role": "user",
+            "content": "Hello, how are you?"
+        }
+    ]
+)
+
+print(response.choices[0].message.content)`})]})]})}),(0,t.jsx)(s.Modal,{title:em?.name||"Agent Details",width:1e3,open:eu,footer:null,onOk:ew,onCancel:e_,children:em&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Agent Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Name:"}),(0,t.jsx)(o.Text,{children:em.name})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Version:"}),(0,t.jsxs)(m.Badge,{color:"blue",children:["v",em.version]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Protocol Version:"}),(0,t.jsx)(o.Text,{children:em.protocolVersion})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"URL:"}),(0,t.jsxs)("div",{className:"flex items-center 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Modes:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:em.defaultInputModes?.map(e=>(0,t.jsx)(m.Badge,{color:"blue",children:e},e))||(0,t.jsx)(o.Text,{children:"Not specified"})})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Output Modes:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:em.defaultOutputModes?.map(e=>(0,t.jsx)(m.Badge,{color:"purple",children:e},e))||(0,t.jsx)(o.Text,{children:"Not specified"})})]})]})]}),em.skills&&em.skills.length>0&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Skills"}),(0,t.jsx)("div",{className:"space-y-4",children:em.skills.map(e=>(0,t.jsxs)("div",{className:"border border-gray-200 rounded p-4",children:[(0,t.jsxs)("div",{className:"flex justify-between items-start mb-2",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium text-base",children:e.name}),(0,t.jsxs)(o.Text,{className:"text-xs text-gray-500",children:["ID: ",e.id]})]}),e.tags&&e.tags.length>0&&(0,t.jsx)("div",{className:"flex flex-wrap gap-1",children:e.tags.map(e=>(0,t.jsx)(m.Badge,{color:"purple",size:"xs",children:e},e))})]}),(0,t.jsx)(o.Text,{className:"text-sm mb-2",children:e.description}),e.examples&&e.examples.length>0&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-xs font-medium text-gray-700",children:"Examples:"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-1 mt-1",children:e.examples.map((e,l)=>(0,t.jsx)(m.Badge,{color:"gray",size:"xs",children:e},l))})]})]},e.id))})]}),em.supportsAuthenticatedExtendedCard&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Additional Features"}),(0,t.jsx)(m.Badge,{color:"green",children:"Supports Authenticated Extended Card"})]})]})}),(0,t.jsx)(s.Modal,{title:ef?.server_name||"MCP Server Details",width:1e3,open:ey,footer:null,onOk:ew,onCancel:e_,children:ef&&(0,t.jsxs)("div",{className:"space-y-6",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Server Overview"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4 mb-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Server Name:"}),(0,t.jsx)(o.Text,{children:ef.server_name})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Server ID:"}),(0,t.jsxs)("div",{className:"flex items-center space-x-2",children:[(0,t.jsx)(o.Text,{className:"text-xs truncate",children:ef.server_id}),(0,t.jsx)(T.CopyOutlined,{onClick:()=>eM(ef.server_id),className:"cursor-pointer text-gray-500 hover:text-blue-500"})]})]}),ef.alias&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Alias:"}),(0,t.jsx)(o.Text,{children:ef.alias})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Transport:"}),(0,t.jsx)(m.Badge,{color:"blue",children:ef.transport})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Auth Type:"}),(0,t.jsx)(m.Badge,{color:"none"===ef.auth_type?"gray":"green",children:ef.auth_type})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Status:"}),(0,t.jsx)(m.Badge,{color:"active"===ef.status||"healthy"===ef.status?"green":"inactive"===ef.status||"unhealthy"===ef.status?"red":"gray",children:ef.status||"unknown"})]})]}),ef.description&&(0,t.jsxs)("div",{className:"mt-2",children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Description:"}),(0,t.jsx)(o.Text,{className:"mt-1",children:ef.description})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Connection Details"}),(0,t.jsxs)("div",{className:"space-y-2",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"URL:"}),(0,t.jsxs)("div",{className:"flex items-center space-x-2 mt-1",children:[(0,t.jsx)(o.Text,{className:"text-sm break-all bg-gray-100 p-2 rounded 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gap-2",children:ef.teams.map((e,l)=>(0,t.jsx)(m.Badge,{color:"blue",children:e},l))})]}),ef.mcp_access_groups&&ef.mcp_access_groups.length>0&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Access Groups"}),(0,t.jsx)("div",{className:"flex flex-wrap gap-2",children:ef.mcp_access_groups.map((e,l)=>(0,t.jsx)(m.Badge,{color:"green",children:e},l))})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Metadata"}),(0,t.jsxs)("div",{className:"grid grid-cols-2 gap-4",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Created By:"}),(0,t.jsx)(o.Text,{children:ef.created_by})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Updated By:"}),(0,t.jsx)(o.Text,{children:ef.updated_by})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Created At:"}),(0,t.jsx)(o.Text,{className:"text-sm",children:new Date(ef.created_at).toLocaleString()})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Updated At:"}),(0,t.jsx)(o.Text,{className:"text-sm",children:new Date(ef.updated_at).toLocaleString()})]}),ef.last_health_check&&(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"font-medium",children:"Last Health Check:"}),(0,t.jsx)(o.Text,{className:"text-sm",children:new Date(ef.last_health_check).toLocaleString()})]})]}),ef.health_check_error&&(0,t.jsxs)("div",{className:"mt-2 p-2 bg-red-50 rounded",children:[(0,t.jsx)(o.Text,{className:"font-medium text-red-700",children:"Health Check Error:"}),(0,t.jsx)(o.Text,{className:"text-sm text-red-600 mt-1",children:ef.health_check_error})]})]}),(0,t.jsxs)("div",{children:[(0,t.jsx)(o.Text,{className:"text-lg font-semibold mb-4",children:"Usage Example"}),(0,t.jsx)(el.Prism,{language:"python",className:"text-sm",children:`from fastmcp import Client
+import asyncio
+
+# Standard MCP configuration
+config = {
+    "mcpServers": {
+        "${ef.server_name}": {
+            "url": "${(0,x.getProxyBaseUrl)()}/${ef.server_name}/mcp",
+            "headers": {
+                "x-litellm-api-key": "Bearer sk-1234"
+            }
+        }
+    }
+}
+
+# Create a client that connects to the server
+client = Client(config)
+
+async def main():
+    async with client:
+        # List available tools
+        tools = await client.list_tools()
+        print(f"Available tools: {[tool.name for tool in tools]}")
+
+        # Call a tool
+        response = await client.call_tool(
+            name="tool_name",
+            arguments={"arg": "value"}
+        )
+        print(f"Response: {response}")
+
+if __name__ == "__main__":
+    asyncio.run(main())`})]})]})}),(0,t.jsx)(y,{visible:R,onClose:()=>V(!1),accessToken:e||"",modelHubData:_||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.modelHubCall)(e);I(t.data)}catch(e){console.error("Error refreshing model data:",e)}})()}}),(0,t.jsx)(p,{visible:er,onClose:()=>ec(!1),accessToken:e||"",agentHubData:Y||[],onSuccess:()=>{e&&(async()=>{try{let t=(await (0,x.getAgentsList)(e)).agents.map(e=>({agent_id:e.agent_id,...e.agent_card_params,is_public:e.is_public}));en(t)}catch(e){console.error("Error refreshing agent data:",e)}})()}}),(0,t.jsx)(b,{visible:eS,onClose:()=>e$(!1),accessToken:e||"",mcpHubData:ep||[],onSuccess:()=>{e&&(async()=>{try{let t=await (0,x.fetchMCPServers)(e);eg(t)}catch(e){console.error("Error refreshing MCP server data:",e)}})()}})]})}],934879)}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/1f6df7977860dc7b.js b/litellm/proxy/_experimental/out/_next/static/chunks/1f6df7977860dc7b.js
new file mode 100644
index 0000000000..f10573a30c
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/1f6df7977860dc7b.js
@@ -0,0 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/203dde2108f3f1ac.js b/litellm/proxy/_experimental/out/_next/static/chunks/203dde2108f3f1ac.js
deleted file mode 100644
index c94ad050b0..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/203dde2108f3f1ac.js
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+    <!DOCTYPE html>
+    <html>
+    <head>
+      <title>Multi-Model Cost Estimate Report</title>
+      <style>
+        body {
+          font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
+          padding: 40px;
+          max-width: 900px;
+          margin: 0 auto;
+          color: #333;
+        }
+        h1 {
+          color: #1a1a1a;
+          border-bottom: 2px solid #1890ff;
+          padding-bottom: 10px;
+          margin-bottom: 30px;
+        }
+        h2 {
+          color: #444;
+          margin-top: 30px;
+          margin-bottom: 15px;
+        }
+        h3 {
+          color: #555;
+          margin-top: 25px;
+          margin-bottom: 10px;
+          padding-bottom: 5px;
+          border-bottom: 1px solid #eee;
+        }
+        .provider {
+          font-weight: normal;
+          color: #1890ff;
+          font-size: 14px;
+        }
+        .meta {
+          background: #f5f5f5;
+          padding: 12px 15px;
+          border-radius: 8px;
+          margin-bottom: 15px;
+          font-size: 13px;
+        }
+        .meta p {
+          margin: 4px 0;
+        }
+        table {
+          width: 100%;
+          border-collapse: collapse;
+          margin-bottom: 20px;
+        }
+        th, td {
+          padding: 10px 12px;
+          text-align: left;
+          border-bottom: 1px solid #ddd;
+        }
+        th {
+          background: #f8f9fa;
+          font-weight: 600;
+          font-size: 13px;
+        }
+        .cost-value {
+          font-family: monospace;
+          font-size: 13px;
+        }
+        .total-row {
+          font-weight: bold;
+          background: #e6f7ff;
+        }
+        .summary-box {
+          background: linear-gradient(135deg, #e6f7ff 0%, #f9f0ff 100%);
+          border: 1px solid #91d5ff;
+          border-radius: 8px;
+          padding: 20px;
+          margin-bottom: 30px;
+        }
+        .summary-box h2 {
+          margin-top: 0;
+          color: #1890ff;
+        }
+        .summary-grid {
+          display: grid;
+          grid-template-columns: repeat(3, 1fr);
+          gap: 20px;
+          margin-top: 15px;
+        }
+        .summary-item {
+          text-align: center;
+        }
+        .summary-item .label {
+          font-size: 12px;
+          color: #666;
+          margin-bottom: 5px;
+        }
+        .summary-item .value {
+          font-size: 20px;
+          font-weight: bold;
+          font-family: monospace;
+        }
+        .summary-item .value.blue { color: #1890ff; }
+        .summary-item .value.green { color: #52c41a; }
+        .summary-item .value.purple { color: #722ed1; }
+        .model-section {
+          margin-bottom: 30px;
+          page-break-inside: avoid;
+        }
+        .footer {
+          margin-top: 40px;
+          padding-top: 20px;
+          border-top: 1px solid #ddd;
+          font-size: 12px;
+          color: #666;
+        }
+        @media print {
+          body { padding: 20px; }
+          .model-section { page-break-inside: avoid; }
+        }
+      </style>
+    </head>
+    <body>
+      <h1>LLM Cost Estimate Report</h1>
+      <p style="color: #666; margin-top: -20px; margin-bottom: 30px;">${a} model${1!==a?"s":""} configured</p>
+      
+      <div class="summary-box">
+        <h2>Combined Totals</h2>
+        <div class="summary-grid">
+          <div class="summary-item">
+            <div class="label">Total Per Request</div>
+            <div class="value blue">${tM(e.totals.cost_per_request)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Total Daily</div>
+            <div class="value green">${tM(e.totals.daily_cost)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Total Monthly</div>
+            <div class="value purple">${tM(e.totals.monthly_cost)}</div>
+          </div>
+        </div>
+        ${e.totals.margin_per_request>0?`
+        <div class="summary-grid" style="margin-top: 15px; padding-top: 15px; border-top: 1px solid #ddd;">
+          <div class="summary-item">
+            <div class="label">Margin/Request</div>
+            <div class="value" style="color: #faad14;">${tM(e.totals.margin_per_request)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Daily Margin</div>
+            <div class="value" style="color: #faad14;">${tM(e.totals.daily_margin)}</div>
+          </div>
+          <div class="summary-item">
+            <div class="label">Monthly Margin</div>
+            <div class="value" style="color: #faad14;">${tM(e.totals.monthly_margin)}</div>
+          </div>
+        </div>
+        `:""}
+      </div>
+
+      <h2>Model Breakdown</h2>
+      ${s.map(e=>{let t;return t=e.result,`
+    <div class="model-section">
+      <h3>${t.model} ${t.provider?`<span class="provider">(${t.provider})</span>`:""}</h3>
+      
+      <div class="meta">
+        <p><strong>Input Tokens per Request:</strong> ${tD(t.input_tokens)}</p>
+        <p><strong>Output Tokens per Request:</strong> ${tD(t.output_tokens)}</p>
+        ${t.num_requests_per_day?`<p><strong>Requests per Day:</strong> ${tD(t.num_requests_per_day)}</p>`:""}
+        ${t.num_requests_per_month?`<p><strong>Requests per Month:</strong> ${tD(t.num_requests_per_month)}</p>`:""}
+      </div>
+
+      <table>
+        <tr>
+          <th>Cost Type</th>
+          <th>Per Request</th>
+          ${null!==t.daily_cost?"<th>Daily</th>":""}
+          ${null!==t.monthly_cost?"<th>Monthly</th>":""}
+        </tr>
+        <tr>
+          <td>Input Cost</td>
+          <td class="cost-value">${tM(t.input_cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tM(t.daily_input_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tM(t.monthly_input_cost)}</td>`:""}
+        </tr>
+        <tr>
+          <td>Output Cost</td>
+          <td class="cost-value">${tM(t.output_cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tM(t.daily_output_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tM(t.monthly_output_cost)}</td>`:""}
+        </tr>
+        <tr>
+          <td>Margin/Fee</td>
+          <td class="cost-value">${tM(t.margin_cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tM(t.daily_margin_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tM(t.monthly_margin_cost)}</td>`:""}
+        </tr>
+        <tr class="total-row">
+          <td>Total</td>
+          <td class="cost-value">${tM(t.cost_per_request)}</td>
+          ${null!==t.daily_cost?`<td class="cost-value">${tM(t.daily_cost)}</td>`:""}
+          ${null!==t.monthly_cost?`<td class="cost-value">${tM(t.monthly_cost)}</td>`:""}
+        </tr>
+      </table>
+    </div>
+  `}).join("")}
+
+      <div class="footer">
+        <p>Generated by LiteLLM Pricing Calculator on ${new Date().toLocaleString()}</p>
+      </div>
+    </body>
+    </html>
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2c21eeb7a235384a.js b/litellm/proxy/_experimental/out/_next/static/chunks/2c21eeb7a235384a.js
new file mode 100644
index 0000000000..28cc92c17e
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/2c21eeb7a235384a.js
@@ -0,0 +1 @@
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-curl -X POST --location '<your_proxy_base_url>/end_user/new' \\
-
--H 'Authorization: Bearer <your-master-key>' \\
-
--H 'Content-Type: application/json' \\
-
--d '{"user_id": "my-customer-id', "budget_id": "<BUDGET_ID>"}' # 👈 KEY CHANGE
-
-`,M=`
-curl -X POST --location '<your_proxy_base_url>/chat/completions' \\
-
--H 'Authorization: Bearer <your-master-key>' \\
-
--H 'Content-Type: application/json' \\
-
--d '{
-  "model": "gpt-3.5-turbo',
-  "messages":[{"role": "user", "content": "Hey, how's it going?"}],
-  "user": "my-customer-id"
-}' # 👈 KEY CHANGE
-
-`,P=`from openai import OpenAI
-client = OpenAI(
-  base_url="<your_proxy_base_url>",
-  api_key="<your_proxy_key>"
-)
-
-completion = client.chat.completions.create(
-  model="gpt-3.5-turbo",
-  messages=[
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "Hello!"}
-  ],
-  user="my-customer-id"
-)
-
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/ee5f9a39a526e423.js b/litellm/proxy/_experimental/out/_next/static/chunks/2d471965761a22ff.js
similarity index 97%
rename from litellm/proxy/_experimental/out/_next/static/chunks/ee5f9a39a526e423.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/2d471965761a22ff.js
index 277b07a43c..0c4a26f605 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/ee5f9a39a526e423.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/2d471965761a22ff.js
@@ -1 +1 @@
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deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/31e02a31dea7d5d2.js b/litellm/proxy/_experimental/out/_next/static/chunks/31e02a31dea7d5d2.js
new file mode 100644
index 0000000000..d392a68c99
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/31e02a31dea7d5d2.js
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new file mode 100644
index 0000000000..5ee3928112
--- /dev/null
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+        & > ${n}-affix-wrapper,
+        & > ${n}-number-affix-wrapper,
+        & > ${o}-picker-range
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+      & > ${o}-select-auto-complete ${n},
+      & > ${o}-cascader-picker ${n},
+      & > ${n}-group-wrapper ${n}`]:{borderInlineEndWidth:e.lineWidth,borderRadius:0,"&:hover, &:focus":{zIndex:1}},[`& > ${o}-select-focused`]:{zIndex:1},[`& > ${o}-select > ${o}-select-arrow`]:{zIndex:1},[`& > *:first-child,
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+      & > ${o}-select-auto-complete:first-child ${n},
+      & > ${o}-cascader-picker:first-child ${n}`]:{borderStartStartRadius:e.borderRadius,borderEndStartRadius:e.borderRadius},[`& > *:last-child,
+      & > ${o}-select:last-child > ${o}-select-selector,
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/06550e57530ff75d.js b/litellm/proxy/_experimental/out/_next/static/chunks/39768ec0eebd2554.js
similarity index 86%
rename from litellm/proxy/_experimental/out/_next/static/chunks/06550e57530ff75d.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/39768ec0eebd2554.js
index a4a83fb159..d95f5a3ef8 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/06550e57530ff75d.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/39768ec0eebd2554.js
@@ -1 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/39bdd72c165f9ec0.js b/litellm/proxy/_experimental/out/_next/static/chunks/39bdd72c165f9ec0.js
deleted file mode 100644
index 18c0548127..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/39bdd72c165f9ec0.js
+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5963ae3163ecd9b6.js b/litellm/proxy/_experimental/out/_next/static/chunks/3b3c0b070b14da06.js
similarity index 92%
rename from litellm/proxy/_experimental/out/_next/static/chunks/5963ae3163ecd9b6.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/3b3c0b070b14da06.js
index a2486f051b..786780e51d 100644
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/3b3c0b070b14da06.js
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new file mode 100644
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/40f766ecc87dbf9a.js
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deleted file mode 100644
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eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let 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JSON: ${t}`);eN(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tL(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tL(e){}let t$={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tU={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tD extends tw{constructor(){super(...arguments),this.batches=new tA(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tU&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tU[r.model]}
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Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=t$[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tM.createMessage(this,e,t)}countTokens(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:t_([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tD.Batches=tA;class tB extends tw{constructor(){super(...arguments),this.models=new tC(this._client),this.messages=new tD(this._client),this.files=new tE(this._client)}}tB.Models=tC,tB.Messages=tD,tB.Files=tE;class tq extends tw{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tz="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tW{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),H.set(this,!1),W.set(this,void 0),F.set(this,void 0),V.set(this,e=>{if(e_(this,q,!0,"f"),eE(e)&&(e=new eO),e instanceof eO)return e_(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eT)return this._emit("error",e);if(e instanceof Error){let t=new eT(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eT(String(e)))}),e_(this,R,new Promise((e,t)=>{e_(this,I,e,"f"),e_(this,M,t,"f")}),"f"),e_(this,L,new Promise((e,t)=>{e_(this,$,e,"f"),e_(this,U,t,"f")}),"f"),eN(this,R,"f").catch(()=>{}),eN(this,L,"f").catch(()=>{})}get response(){return eN(this,W,"f")}get request_id(){return eN(this,F,"f")}async withResponse(){let e=await eN(this,R,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tW;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tW;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eN(this,V,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eN(this,O,"m",K).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eN(this,O,"m",X).call(this,e);if(n.controller.signal?.aborted)throw new eO;eN(this,O,"m",Y).call(this)}_connected(e){this.ended||(e_(this,W,e,"f"),e_(this,F,e?.headers.get("request-id"),"f"),eN(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return eN(this,B,"f")}get errored(){return eN(this,q,"f")}get aborted(){return eN(this,z,"f")}abort(){this.controller.abort()}on(e,t){return(eN(this,D,"f")[e]||(eN(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eN(this,D,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eN(this,D,"f")[e]||(eN(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{e_(this,H,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){e_(this,H,!0,"f"),await eN(this,L,"f")}get currentMessage(){return eN(this,P,"f")}async finalMessage(){return await this.done(),eN(this,O,"m",J).call(this)}async finalText(){return await this.done(),eN(this,O,"m",G).call(this)}_emit(e,...t){if(eN(this,B,"f"))return;"end"===e&&(e_(this,B,!0,"f"),eN(this,$,"f").call(this));let s=eN(this,D,"f")[e];if(s&&(eN(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eN(this,H,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eN(this,H,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eN(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eN(this,O,"m",K).call(this),this._connected(null);let r=te.fromReadableStream(e,this.controller);for await(let e of r)eN(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eO;eN(this,O,"m",Y).call(this)}[(P=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,H=new WeakMap,W=new WeakMap,F=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eT("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eT("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eT("stream ended without producing a content 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eT("stream has ended, this shouldn't happen");let e=eN(this,P,"f");if(!e)throw new eT("request ended without sending any chunks");return e_(this,P,void 0,"f"),e},Q=function(e){let t=eN(this,P,"f");if("message_start"===e.type){if(t)throw new eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tH(s)){let t=s[tz]||"";Object.defineProperty(s,tz,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tP(t))}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tF(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tF(e){}class tJ extends tw{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tk`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",tc,{query:e,...t})}delete(e,t){return this._client.delete(tk`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tk`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new eT(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:t_([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tT.fromResponse(t.response,t.controller))}}class tG extends tw{constructor(){super(...arguments),this.batches=new tJ(this._client)}create(e,t){e.model in tV&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tV[e.model]}
-Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=t$[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,stream:e.stream??!1})}stream(e,t){return tW.createMessage(this,e,t)}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let tV={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};tG.Batches=tJ;class tK extends tw{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(tk`/v1/models/${e}`,{...s,headers:t_([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",tc,{query:r,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}let tX=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()??void 0:void 0!==globalThis.Deno?globalThis.Deno.env?.get?.(e)?.trim():void 0;class tY{constructor({baseURL:e=tX("ANTHROPIC_BASE_URL"),apiKey:t=tX("ANTHROPIC_API_KEY")??null,authToken:s=tX("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){Z.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new eT("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??tQ.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=eJ(a.logLevel,"ClientOptions.logLevel",this)??eJ(tX("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),e_(this,Z,e6,"f"),this._options=a,this.apiKey=t,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/dd8143b7cc961427.js b/litellm/proxy/_experimental/out/_next/static/chunks/4472ece1be7379b3.js
similarity index 63%
rename from litellm/proxy/_experimental/out/_next/static/chunks/dd8143b7cc961427.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/4472ece1be7379b3.js
index 0c9ac471fe..6fa196b647 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/dd8143b7cc961427.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/4472ece1be7379b3.js
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/4e0ee3124dcdc85b.js b/litellm/proxy/_experimental/out/_next/static/chunks/4e0ee3124dcdc85b.js
deleted file mode 100644
index 2465c158b2..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/4e0ee3124dcdc85b.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/f3e0cbc0e84e0a5d.js b/litellm/proxy/_experimental/out/_next/static/chunks/54e29148cb2f2582.js
similarity index 97%
rename from litellm/proxy/_experimental/out/_next/static/chunks/f3e0cbc0e84e0a5d.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/54e29148cb2f2582.js
index 351467a1d4..b71eb9b21f 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/f3e0cbc0e84e0a5d.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/54e29148cb2f2582.js
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index 0000000000..9b0e7c6f6d
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/591e3b6fbe6e4d4a.js b/litellm/proxy/_experimental/out/_next/static/chunks/591e3b6fbe6e4d4a.js
new file mode 100644
index 0000000000..e79c30fd92
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/591e3b6fbe6e4d4a.js
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deleted file mode 100644
index 31634f9781..0000000000
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/5b3ae9c60eff5343.js b/litellm/proxy/_experimental/out/_next/static/chunks/5b3ae9c60eff5343.js
deleted file mode 100644
index 86a0d3bd52..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/5b3ae9c60eff5343.js
+++ /dev/null
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this._client.delete(tC`/v1/messages/batches/${e}?beta=true`,{...s,headers:tk([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}cancel(e,t={},s){let{betas:r}=t??{};return this._client.post(tC`/v1/messages/batches/${e}/cancel?beta=true`,{...s,headers:tk([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}async results(e,t={},s){let r=await this.retrieve(e);if(!r.results_url)throw new eO(`No batch \`results_url\`; Has it finished processing? ${r.processing_status} - ${r.id}`);let{betas:a}=t??{};return this._client.get(r.results_url,{...s,headers:tk([{"anthropic-beta":[...a??[],"message-batches-2024-09-24"].toString(),Accept:"application/binary"},s?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tO.fromResponse(t.response,t.controller))}}let tR=e=>{if(0===e.length)return e;let t=e[e.length-1];switch(t.type){case"separator":return tR(e=e.slice(0,e.length-1));case"number":let 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AbortController,u.set(this,void 0),h.set(this,()=>{}),m.set(this,()=>{}),p.set(this,void 0),f.set(this,()=>{}),g.set(this,()=>{}),y.set(this,{}),x.set(this,!1),b.set(this,!1),v.set(this,!1),w.set(this,!1),j.set(this,void 0),S.set(this,void 0),k.set(this,e=>{if(ek(this,b,!0,"f"),eT(e)&&(e=new eR),e instanceof eR)return ek(this,v,!0,"f"),this._emit("abort",e);if(e instanceof eO)return this._emit("error",e);if(e instanceof Error){let t=new eO(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eO(String(e)))}),ek(this,u,new Promise((e,t)=>{ek(this,h,e,"f"),ek(this,m,t,"f")}),"f"),ek(this,p,new Promise((e,t)=>{ek(this,f,e,"f"),ek(this,g,t,"f")}),"f"),eE(this,u,"f").catch(()=>{}),eE(this,p,"f").catch(()=>{})}get response(){return eE(this,j,"f")}get request_id(){return eE(this,S,"f")}async withResponse(){let e=await eE(this,u,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new t$;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new t$;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eE(this,k,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eE(this,c,"m",E).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eE(this,c,"m",C).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,c,"m",T).call(this)}_connected(e){this.ended||(ek(this,j,e,"f"),ek(this,S,e?.headers.get("request-id"),"f"),eE(this,h,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,x,"f")}get errored(){return eE(this,b,"f")}get aborted(){return eE(this,v,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,y,"f")[e]||(eE(this,y,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(this,y,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eE(this,y,"f")[e]||(eE(this,y,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,w,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,w,!0,"f"),await eE(this,p,"f")}get currentMessage(){return eE(this,d,"f")}async finalMessage(){return await this.done(),eE(this,c,"m",_).call(this)}async finalText(){return await 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eR;eE(this,c,"m",T).call(this)}[(d=new WeakMap,u=new WeakMap,h=new WeakMap,m=new WeakMap,p=new WeakMap,f=new WeakMap,g=new WeakMap,y=new WeakMap,x=new WeakMap,b=new WeakMap,v=new WeakMap,w=new WeakMap,j=new WeakMap,S=new WeakMap,k=new WeakMap,c=new WeakSet,_=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eO("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||ek(this,d,void 0,"f")},C=function(e){if(this.ended)return;let t=eE(this,c,"m",A).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tL(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tU(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":ek(this,d,t,"f")}},T=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,d,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,d,void 0,"f"),e},A=function(e){let t=eE(this,d,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tL(s)){let t=s[tM]||"";if(Object.defineProperty(s,tM,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tI(t)}catch(s){let e=new eO(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${s}. JSON: ${t}`);eE(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tU(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tU(e){}let tD={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tB={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tq extends tS{constructor(){super(...arguments),this.batches=new tP(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tB&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tB[r.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=tD[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return t$.createMessage(this,e,t)}countTokens(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:tk([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tq.Batches=tP;class tz extends tS{constructor(){super(...arguments),this.models=new tA(this._client),this.messages=new tq(this._client),this.files=new tT(this._client)}}tz.Models=tA,tz.Messages=tq,tz.Files=tT;class tW extends tS{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tF="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tJ{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),W.set(this,!1),F.set(this,void 0),H.set(this,void 0),V.set(this,e=>{if(ek(this,q,!0,"f"),eT(e)&&(e=new eR),e instanceof eR)return ek(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eO)return this._emit("error",e);if(e instanceof Error){let t=new eO(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eO(String(e)))}),ek(this,R,new Promise((e,t)=>{ek(this,I,e,"f"),ek(this,M,t,"f")}),"f"),ek(this,L,new Promise((e,t)=>{ek(this,$,e,"f"),ek(this,U,t,"f")}),"f"),eE(this,R,"f").catch(()=>{}),eE(this,L,"f").catch(()=>{})}get response(){return eE(this,F,"f")}get request_id(){return eE(this,H,"f")}async withResponse(){let e=await eE(this,R,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tJ;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tJ;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eE(this,V,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eE(this,O,"m",K).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eE(this,O,"m",X).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}_connected(e){this.ended||(ek(this,F,e,"f"),ek(this,H,e?.headers.get("request-id"),"f"),eE(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,B,"f")}get errored(){return eE(this,q,"f")}get aborted(){return eE(this,z,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(this,D,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,W,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,W,!0,"f"),await eE(this,L,"f")}get currentMessage(){return eE(this,P,"f")}async finalMessage(){return await this.done(),eE(this,O,"m",J).call(this)}async finalText(){return await this.done(),eE(this,O,"m",G).call(this)}_emit(e,...t){if(eE(this,B,"f"))return;"end"===e&&(ek(this,B,!0,"f"),eE(this,$,"f").call(this));let s=eE(this,D,"f")[e];if(s&&(eE(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eE(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eE(this,O,"m",K).call(this),this._connected(null);let r=ts.fromReadableStream(e,this.controller);for await(let e of r)eE(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}[(P=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,W=new WeakMap,F=new WeakMap,H=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eO("stream ended without producing a content block with type=text");return e.join(" ")},K=function(){this.ended||ek(this,P,void 0,"f")},X=function(e){if(this.ended)return;let t=eE(this,O,"m",Q).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tH(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tG(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":ek(this,P,t,"f")}},Y=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,P,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,P,void 0,"f"),e},Q=function(e){let t=eE(this,P,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tH(s)){let t=s[tF]||"";Object.defineProperty(s,tF,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tI(t))}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tG(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tG(e){}class 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deleted file mode 100644
index 100e952895..0000000000
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n=e||"#fff",r=t||"#000";return{colorBgBase:n,colorTextBase:r,colorText:d(r,.88),colorTextSecondary:d(r,.65),colorTextTertiary:d(r,.45),colorTextQuaternary:d(r,.25),colorFill:d(r,.15),colorFillSecondary:d(r,.06),colorFillTertiary:d(r,.04),colorFillQuaternary:d(r,.02),colorBgSolid:d(r,1),colorBgSolidHover:d(r,.75),colorBgSolidActive:d(r,.95),colorBgLayout:p(n,4),colorBgContainer:p(n,0),colorBgElevated:p(n,0),colorBgSpotlight:d(r,.85),colorBgBlur:"transparent",colorBorder:p(n,15),colorBorderSecondary:p(n,6)}};function v(e){r.presetPrimaryColors.pink=r.presetPrimaryColors.magenta,r.presetPalettes.pink=r.presetPalettes.magenta;let t=Object.keys(o).map(t=>{let o=e[t]===r.presetPrimaryColors[t]?r.presetPalettes[t]:(0,n.generate)(e[t]);return Array.from({length:10},()=>1).reduce((e,n,r)=>(e[`${t}-${r+1}`]=o[r],e[`${t}${r+1}`]=o[r],e),{})}).reduce((e,t)=>e=Object.assign(Object.assign({},e),t),{});return 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/26542a70b9512f71.js b/litellm/proxy/_experimental/out/_next/static/chunks/66ef9d81cc17cfa8.js
similarity index 80%
rename from litellm/proxy/_experimental/out/_next/static/chunks/26542a70b9512f71.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/66ef9d81cc17cfa8.js
index e1674f2607..4bc34c56b1 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/26542a70b9512f71.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/66ef9d81cc17cfa8.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/99109c78121231a0.js b/litellm/proxy/_experimental/out/_next/static/chunks/67ddb5107368a659.js
similarity index 74%
rename from litellm/proxy/_experimental/out/_next/static/chunks/99109c78121231a0.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/67ddb5107368a659.js
index c5ff8a170d..56a8ade942 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/99109c78121231a0.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/caf98722823e1b40.js b/litellm/proxy/_experimental/out/_next/static/chunks/6a167cef4b09b496.js
similarity index 72%
rename from litellm/proxy/_experimental/out/_next/static/chunks/caf98722823e1b40.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/6a167cef4b09b496.js
index 29972b2ccd..5aa2036c62 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/caf98722823e1b40.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/6a167cef4b09b496.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/6b2bc4046c4cbfc8.js b/litellm/proxy/_experimental/out/_next/static/chunks/6b2bc4046c4cbfc8.js
deleted file mode 100644
index 94b6c36a34..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/6b2bc4046c4cbfc8.js
+++ /dev/null
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new file mode 100644
index 0000000000..15e9dca449
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-curl -X POST --location '<your_proxy_base_url>/end_user/new' \\
-
--H 'Authorization: Bearer <your-master-key>' \\
-
--H 'Content-Type: application/json' \\
-
--d '{"user_id": "my-customer-id', "budget_id": "<BUDGET_ID>"}' # 👈 KEY CHANGE
-
-`,O=`
-curl -X POST --location '<your_proxy_base_url>/chat/completions' \\
-
--H 'Authorization: Bearer <your-master-key>' \\
-
--H 'Content-Type: application/json' \\
-
--d '{
-  "model": "gpt-3.5-turbo',
-  "messages":[{"role": "user", "content": "Hey, how's it going?"}],
-  "user": "my-customer-id"
-}' # 👈 KEY CHANGE
-
-`,B=`from openai import OpenAI
-client = OpenAI(
-  base_url="<your_proxy_base_url>",
-  api_key="<your_proxy_key>"
-)
-
-completion = client.chat.completions.create(
-  model="gpt-3.5-turbo",
-  messages=[
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "Hello!"}
-  ],
-  user="my-customer-id"
-)
-
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/ca2a9c0a5c5dd869.js b/litellm/proxy/_experimental/out/_next/static/chunks/7174130ddef406dd.js
similarity index 65%
rename from litellm/proxy/_experimental/out/_next/static/chunks/ca2a9c0a5c5dd869.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/7174130ddef406dd.js
index fa7a6d11d9..21cdd1b50a 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/ca2a9c0a5c5dd869.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/7174130ddef406dd.js
@@ -1,8 +1,8 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/7b9ef931d44e410f.js b/litellm/proxy/_experimental/out/_next/static/chunks/7b9ef931d44e410f.js
new file mode 100644
index 0000000000..4b04ad1abe
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/7b9ef931d44e410f.js
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new file mode 100644
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+++ b/litellm/proxy/_experimental/out/_next/static/chunks/7d82a1cebfdb679c.js
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t,a,i=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),n=((a={}).IMAGE="image",a.VIDEO="video",a.CHAT="chat",a.RESPONSES="responses",a.IMAGE_EDITS="image_edits",a.ANTHROPIC_MESSAGES="anthropic_messages",a.EMBEDDINGS="embeddings",a.SPEECH="speech",a.TRANSCRIPTION="transcription",a.A2A_AGENTS="a2a_agents",a.MCP="mcp",a.REALTIME="realtime",a);let r={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>n,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let t=r[e];return console.log("endpointType:",t),t}return"chat"}],785913),e.s(["generateCodeSnippet",0,e=>{let t,{apiKeySource:a,accessToken:i,apiKey:r,inputMessage:o,chatHistory:s,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:u,selectedMCPServers:m,mcpServers:g,mcpServerToolRestrictions:p,selectedVoice:f,endpointType:h,selectedModel:_,selectedSdk:b,proxySettings:v}=e,w="session"===a?i:r,x=window.location.origin,y=v?.LITELLM_UI_API_DOC_BASE_URL;y&&y.trim()?x=y:v?.PROXY_BASE_URL&&(x=v.PROXY_BASE_URL);let E=o||"Your prompt here",$=E.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),j=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),C={};l.length>0&&(C.tags=l),c.length>0&&(C.vector_stores=c),d.length>0&&(C.guardrails=d),u.length>0&&(C.policies=u);let k=_||"your-model-name",O="azure"===b?`import openai
+
+client = openai.AzureOpenAI(
+	api_key="${w||"YOUR_LITELLM_API_KEY"}",
+	azure_endpoint="${x}",
+	api_version="2024-02-01"
+)`:`import openai
+
+client = openai.OpenAI(
+	api_key="${w||"YOUR_LITELLM_API_KEY"}",
+	base_url="${x}"
+)`;switch(h){case n.CHAT:{let e=Object.keys(C).length>0,a="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let i=j.length>0?j:[{role:"user",content:E}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.chat.completions.create(
+    model="${k}",
+    messages=${JSON.stringify(i,null,4)}${a}
+)
+
+print(response)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.chat.completions.create(
+#     model="${k}",
+#     messages=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {
+#                     "type": "text",
+#                     "text": "${$}"
+#                 },
+#                 {
+#                     "type": "image_url",
+#                     "image_url": {
+#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
+#                     }
+#                 }
+#             ]
+#         }
+#     ]${a}
+# )
+# print(response_with_file)
+`;break}case n.RESPONSES:{let e=Object.keys(C).length>0,a="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
+    extra_body=${e}`}let i=j.length>0?j:[{role:"user",content:E}];t=`
+import base64
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Example with text only
+response = client.responses.create(
+    model="${k}",
+    input=${JSON.stringify(i,null,4)}${a}
+)
+
+print(response.output_text)
+
+# Example with image or PDF (uncomment and provide file path to use)
+# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
+# response_with_file = client.responses.create(
+#     model="${k}",
+#     input=[
+#         {
+#             "role": "user",
+#             "content": [
+#                 {"type": "input_text", "text": "${$}"},
+#                 {
+#                     "type": "input_image",
+#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
+#                 },
+#             ],
+#         }
+#     ]${a}
+# )
+# print(response_with_file.output_text)
+`;break}case n.IMAGE:t="azure"===b?`
+# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
+# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
+# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
+import os
+import requests
+import json
+import time
+from PIL import Image
+
+result = client.images.generate(
+	model="${k}",
+	prompt="${o}",
+	n=1
+)
+
+json_response = json.loads(result.model_dump_json())
+
+# Set the directory for the stored image
+image_dir = os.path.join(os.curdir, 'images')
+
+# If the directory doesn't exist, create it
+if not os.path.isdir(image_dir):
+	os.mkdir(image_dir)
+
+# Initialize the image path
+image_filename = f"generated_image_{int(time.time())}.png"
+image_path = os.path.join(image_dir, image_filename)
+
+try:
+	# Retrieve the generated image
+	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
+			image_url = json_response["data"][0]["url"]
+			generated_image = requests.get(image_url).content
+			with open(image_path, "wb") as image_file:
+					image_file.write(generated_image)
+
+			print(f"Image saved to {image_path}")
+			# Display the image
+			image = Image.open(image_path)
+			image.show()
+	else:
+			print("Could not find image URL in response.")
+			print("Full response:", json_response)
+except Exception as e:
+	print(f"An error occurred: {e}")
+	print("Full response:", json_response)
+`:`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${$}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${k}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case n.IMAGE_EDITS:t="azure"===b?`
+import base64
+import os
+import time
+import json
+from PIL import Image
+import requests
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# The prompt entered by the user
+prompt = "${$}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${k}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`:`
+import base64
+import os
+import time
+
+# Helper function to encode images to base64
+def encode_image(image_path):
+	with open(image_path, "rb") as image_file:
+			return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Helper function to create a file (simplified for this example)
+def create_file(image_path):
+	# In a real implementation, this would upload the file to OpenAI
+	# For this example, we'll just return a placeholder ID
+	return f"file_{os.path.basename(image_path).replace('.', '_')}"
+
+# The prompt entered by the user
+prompt = "${$}"
+
+# Encode images to base64
+base64_image1 = encode_image("body-lotion.png")
+base64_image2 = encode_image("soap.png")
+
+# Create file IDs
+file_id1 = create_file("body-lotion.png")
+file_id2 = create_file("incense-kit.png")
+
+response = client.responses.create(
+	model="${k}",
+	input=[
+			{
+					"role": "user",
+					"content": [
+							{"type": "input_text", "text": prompt},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image1}",
+							},
+							{
+									"type": "input_image",
+									"image_url": f"data:image/jpeg;base64,{base64_image2}",
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id1,
+							},
+							{
+									"type": "input_image",
+									"file_id": file_id2,
+							}
+					],
+			}
+	],
+	tools=[{"type": "image_generation"}],
+)
+
+# Process the response
+image_generation_calls = [
+	output
+	for output in response.output
+	if output.type == "image_generation_call"
+]
+
+image_data = [output.result for output in image_generation_calls]
+
+if image_data:
+	image_base64 = image_data[0]
+	image_filename = f"edited_image_{int(time.time())}.png"
+	with open(image_filename, "wb") as f:
+			f.write(base64.b64decode(image_base64))
+	print(f"Image saved to {image_filename}")
+else:
+	# If no image is generated, there might be a text response with an explanation
+	text_response = [output.text for output in response.output if hasattr(output, 'text')]
+	if text_response:
+			print("No image generated. Model response:")
+			print("\\n".join(text_response))
+	else:
+			print("No image data found in response.")
+	print("Full response for debugging:")
+	print(response)
+`;break;case n.EMBEDDINGS:t=`
+response = client.embeddings.create(
+	input="${o||"Your string here"}",
+	model="${k}",
+	encoding_format="base64" # or "float"
+)
+
+print(response.data[0].embedding)
+`;break;case n.TRANSCRIPTION:t=`
+# Open the audio file
+audio_file = open("path/to/your/audio/file.mp3", "rb")
+
+# Make the transcription request
+response = client.audio.transcriptions.create(
+	model="${k}",
+	file=audio_file${o?`,
+	prompt="${o.replace(/"/g,'\\"')}"`:""}
+)
+
+print(response.text)
+`;break;case n.SPEECH:t=`
+# Make the text-to-speech request
+response = client.audio.speech.create(
+	model="${k}",
+	input="${o||"Your text to convert to speech here"}",
+	voice="${f}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
+)
+
+# Save the audio to a file
+output_filename = "output_speech.mp3"
+response.stream_to_file(output_filename)
+print(f"Audio saved to {output_filename}")
+
+# Optional: Customize response format and speed
+# response = client.audio.speech.create(
+#     model="${k}",
+#     input="${o||"Your text to convert to speech here"}",
+#     voice="alloy",
+#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
+#     speed=1.0  # Range: 0.25 to 4.0
+# )
+# response.stream_to_file("output_speech.mp3")
+`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${O}
+${t}`}],190272)},735049,e=>{"use strict";var t=e.i(654310),a=function(e){if((0,t.default)()&&window.document.documentElement){var a=Array.isArray(e)?e:[e],i=window.document.documentElement;return a.some(function(e){return e in i.style})}return!1},i=function(e,t){if(!a(e))return!1;var i=document.createElement("div"),n=i.style[e];return i.style[e]=t,i.style[e]!==n};function n(e,t){return Array.isArray(e)||void 0===t?a(e):i(e,t)}e.s(["isStyleSupport",()=>n])},190144,e=>{"use strict";e.i(247167);var t=e.i(931067),a=e.i(271645);let i={icon:{tag:"svg",attrs:{viewBox:"64 64 896 896",focusable:"false"},children:[{tag:"path",attrs:{d:"M832 64H296c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h496v688c0 4.4 3.6 8 8 8h56c4.4 0 8-3.6 8-8V96c0-17.7-14.3-32-32-32zM704 192H192c-17.7 0-32 14.3-32 32v530.7c0 8.5 3.4 16.6 9.4 22.6l173.3 173.3c2.2 2.2 4.7 4 7.4 5.5v1.9h4.2c3.5 1.3 7.2 2 11 2H704c17.7 0 32-14.3 32-32V224c0-17.7-14.3-32-32-32zM350 856.2L263.9 770H350v86.2zM664 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new file mode 100644
index 0000000000..9b8726f6ac
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/82c0757c56d48c63.js b/litellm/proxy/_experimental/out/_next/static/chunks/82c0757c56d48c63.js
deleted file mode 100644
index d42b1f35e1..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/82c0757c56d48c63.js
+++ /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8a6de9a16d49b44f.js b/litellm/proxy/_experimental/out/_next/static/chunks/8a6de9a16d49b44f.js
deleted file mode 100644
index 004fd5ec5d..0000000000
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8ae157c8a223fdc3.js b/litellm/proxy/_experimental/out/_next/static/chunks/8ae157c8a223fdc3.js
new file mode 100644
index 0000000000..e173e7e526
--- /dev/null
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8ce3b5a1ebec3185.js b/litellm/proxy/_experimental/out/_next/static/chunks/8ce3b5a1ebec3185.js
deleted file mode 100644
index 3fdf577aac..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/8ce3b5a1ebec3185.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/21151511c0b21d94.js b/litellm/proxy/_experimental/out/_next/static/chunks/8dda507c226082ca.js
similarity index 51%
rename from litellm/proxy/_experimental/out/_next/static/chunks/21151511c0b21d94.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/8dda507c226082ca.js
index 11f5bcd0f1..d4c14ab125 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/21151511c0b21d94.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/8dda507c226082ca.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/82bc4bb51160556f.js b/litellm/proxy/_experimental/out/_next/static/chunks/8dfde809dc4ad794.js
similarity index 70%
rename from litellm/proxy/_experimental/out/_next/static/chunks/82bc4bb51160556f.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/8dfde809dc4ad794.js
index e1783caf15..e805a98151 100644
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eq=e.i(269200),eJ=e.i(942232),eW=e.i(977572),eU=e.i(427612),eV=e.i(64848),eY=e.i(496020),eZ=e.i(752978),eQ=e.i(994388),eX=e.i(68155),e0=e.i(94629),e1=e.i(360820),e2=e.i(871943),e4=e.i(389083),e5=e.i(152990),e6=e.i(682830),e8=e.i(779241);let{Title:e3,Text:e7}=d.Typography,{Option:e9}=n.Select,te=({visible:e,onClose:t,accessToken:a,onSuccess:o,guardrailId:d,initialValues:c})=>{let[g]=r.Form.useForm(),[x,h]=(0,m.useState)(!1),[f,y]=(0,m.useState)(c?.provider||null),[j,_]=(0,m.useState)(null),[b,v]=(0,m.useState)([]),[C,N]=(0,m.useState)({});(0,m.useEffect)(()=>{(async()=>{try{if(!a)return;let e=await (0,p.getGuardrailUISettings)(a);_(e)}catch(e){console.error("Error fetching guardrail settings:",e),u.default.fromBackend("Failed to load guardrail settings")}})()},[a]),(0,m.useEffect)(()=>{c?.pii_entities_config&&Object.keys(c.pii_entities_config).length>0&&(v(Object.keys(c.pii_entities_config)),N(c.pii_entities_config))},[c]);let 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/8fd0d92f10755035.js b/litellm/proxy/_experimental/out/_next/static/chunks/8fd0d92f10755035.js
deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/91ea7c8381830ae6.js b/litellm/proxy/_experimental/out/_next/static/chunks/91ea7c8381830ae6.js
deleted file mode 100644
index 2dd284cc92..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/91ea7c8381830ae6.js
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deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/f9c24d6e7ec43046.js b/litellm/proxy/_experimental/out/_next/static/chunks/9d6e5aad99b19216.js
similarity index 98%
rename from litellm/proxy/_experimental/out/_next/static/chunks/f9c24d6e7ec43046.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/9d6e5aad99b19216.js
index 75813d4261..ec62235582 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/f9c24d6e7ec43046.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/9d6e5aad99b19216.js
@@ -1 +1 @@
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0be054dbc84bd8be.js b/litellm/proxy/_experimental/out/_next/static/chunks/9dd55e1f36a7225c.js
similarity index 95%
rename from litellm/proxy/_experimental/out/_next/static/chunks/0be054dbc84bd8be.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/9dd55e1f36a7225c.js
index de1555a6d9..930122a85f 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/0be054dbc84bd8be.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/9dd55e1f36a7225c.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/c1f8dc096ddb32ef.js b/litellm/proxy/_experimental/out/_next/static/chunks/a6c7f80b3968f639.js
similarity index 96%
rename from litellm/proxy/_experimental/out/_next/static/chunks/c1f8dc096ddb32ef.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/a6c7f80b3968f639.js
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 # )
 # response.stream_to_file("output_speech.mp3")
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new file mode 100644
index 0000000000..a9efeb7a86
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bg-white/80 px-3.5 py-2 text-sm font-medium text-zinc-700 shadow-sm","hover:bg-white focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-blue-500 active:translate-y-[0.5px]",l),children:[(0,t.jsx)("span",{children:"API Reference Docs"}),(0,t.jsx)(d.default,{"aria-hidden":!0,className:"h-4 w-4 opacity-80"}),(0,t.jsx)("span",{className:"sr-only",children:"(opens in a new tab)"})]});e.s(["default",0,({proxySettings:e})=>{let d="<your_proxy_base_url>",m=e?.LITELLM_UI_API_DOC_BASE_URL;return m&&m.trim()?d=m:e?.PROXY_BASE_URL&&(d=e.PROXY_BASE_URL),(0,t.jsx)(t.Fragment,{children:(0,t.jsx)(o.Grid,{className:"gap-2 p-8 h-[80vh] w-full mt-2",children:(0,t.jsxs)("div",{className:"mb-5",children:[(0,t.jsxs)("div",{className:"flex items-center justify-between",children:[(0,t.jsx)("p",{className:"text-2xl text-tremor-content-strong dark:text-dark-tremor-content-strong font-semibold",children:"OpenAI Compatible Proxy: API Reference"}),(0,t.jsx)(u,{className:"ml-3 shrink-0",href:"https://docs.litellm.ai/docs/proxy/user_keys"})]}),(0,t.jsxs)(l.Text,{className:"mt-2 mb-2",children:["LiteLLM is OpenAI Compatible. This means your API Key works with the OpenAI SDK. Just replace the base_url to point to your litellm proxy. Example Below"," "]}),(0,t.jsxs)(s.TabGroup,{children:[(0,t.jsxs)(r.TabList,{children:[(0,t.jsx)(a.Tab,{children:"OpenAI Python SDK"}),(0,t.jsx)(a.Tab,{children:"LlamaIndex"}),(0,t.jsx)(a.Tab,{children:"Langchain Py"})]}),(0,t.jsxs)(n.TabPanels,{children:[(0,t.jsx)(i.TabPanel,{children:(0,t.jsx)(c.default,{language:"python",code:`import openai
+client = openai.OpenAI(
+    api_key="your_api_key",
+    base_url="${d}" # LiteLLM Proxy is OpenAI compatible, Read More: https://docs.litellm.ai/docs/proxy/user_keys
+)
+
+response = client.chat.completions.create(
+    model="gpt-3.5-turbo", # model to send to the proxy
+    messages = [
+        {
+            "role": "user",
+            "content": "this is a test request, write a short poem"
+        }
+    ]
+)
+
+print(response)`})}),(0,t.jsx)(i.TabPanel,{children:(0,t.jsx)(c.default,{language:"python",code:`import os, dotenv
+
+from llama_index.llms import AzureOpenAI
+from llama_index.embeddings import AzureOpenAIEmbedding
+from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
+
+llm = AzureOpenAI(
+    engine="azure-gpt-3.5",               # model_name on litellm proxy
+    temperature=0.0,
+    azure_endpoint="${d}", # litellm proxy endpoint
+    api_key="sk-1234",                    # litellm proxy API Key
+    api_version="2023-07-01-preview",
+)
+
+embed_model = AzureOpenAIEmbedding(
+    deployment_name="azure-embedding-model",
+    azure_endpoint="${d}",
+    api_key="sk-1234",
+    api_version="2023-07-01-preview",
+)
+
+documents = SimpleDirectoryReader("llama_index_data").load_data()
+service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)
+index = VectorStoreIndex.from_documents(documents, service_context=service_context)
+
+query_engine = index.as_query_engine()
+response = query_engine.query("What did the author do growing up?")
+print(response)`})}),(0,t.jsx)(i.TabPanel,{children:(0,t.jsx)(c.default,{language:"python",code:`from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    SystemMessagePromptTemplate,
+)
+from langchain.schema import HumanMessage, SystemMessage
+
+chat = ChatOpenAI(
+    openai_api_base="${d}",
+    model = "gpt-3.5-turbo",
+    temperature=0.1
+)
+
+messages = [
+    SystemMessage(
+        content="You are a helpful assistant that im using to make a test request to."
+    ),
+    HumanMessage(
+        content="test from litellm. tell me why it's amazing in 1 sentence"
+    ),
+]
+response = chat(messages)
+
+print(response)`})})]})]})]})})})}],794357)},584578,e=>{"use strict";var t=e.i(764205);let l=async(e,l,a,s,r)=>{let i;i="Admin"!=a&&"Admin Viewer"!=a?await (0,t.teamListCall)(e,s?.organization_id||null,l):await (0,t.teamListCall)(e,s?.organization_id||null),console.log(`givenTeams: ${i}`),r(i)};e.s(["fetchTeams",0,l])},747871,e=>{"use strict";var t=e.i(843476),l=e.i(271645),a=e.i(269200),s=e.i(942232),r=e.i(977572),i=e.i(427612),n=e.i(64848),o=e.i(496020),c=e.i(304967),d=e.i(994388),u=e.i(599724),m=e.i(389083),h=e.i(764205),g=e.i(727749);e.s(["default",0,({accessToken:e,userID:x})=>{let[p,f]=(0,l.useState)([]);(0,l.useEffect)(()=>{(async()=>{if(e&&x)try{let t=await (0,h.availableTeamListCall)(e);f(t)}catch(e){console.error("Error fetching available teams:",e)}})()},[e,x]);let b=async t=>{if(e&&x)try{await (0,h.teamMemberAddCall)(e,t,{user_id:x,role:"user"}),g.default.success("Successfully joined team"),f(e=>e.filter(e=>e.team_id!==t))}catch(e){console.error("Error joining team:",e),g.default.fromBackend("Failed to join team")}};return(0,t.jsx)(c.Card,{className:"w-full mx-auto flex-auto overflow-y-auto max-h-[50vh]",children:(0,t.jsxs)(a.Table,{children:[(0,t.jsx)(i.TableHead,{children:(0,t.jsxs)(o.TableRow,{children:[(0,t.jsx)(n.TableHeaderCell,{children:"Team Name"}),(0,t.jsx)(n.TableHeaderCell,{children:"Description"}),(0,t.jsx)(n.TableHeaderCell,{children:"Members"}),(0,t.jsx)(n.TableHeaderCell,{children:"Models"}),(0,t.jsx)(n.TableHeaderCell,{children:"Actions"})]})}),(0,t.jsxs)(s.TableBody,{children:[p.map(e=>(0,t.jsxs)(o.TableRow,{children:[(0,t.jsx)(r.TableCell,{children:(0,t.jsx)(u.Text,{children:e.team_alias})}),(0,t.jsx)(r.TableCell,{children:(0,t.jsx)(u.Text,{children:e.description||"No description available"})}),(0,t.jsx)(r.TableCell,{children:(0,t.jsxs)(u.Text,{children:[e.members_with_roles.length," members"]})}),(0,t.jsx)(r.TableCell,{children:(0,t.jsx)("div",{className:"flex flex-col",children:e.models&&0!==e.models.length?e.models.map((e,l)=>(0,t.jsx)(m.Badge,{size:"xs",className:"mb-1",color:"blue",children:(0,t.jsx)(u.Text,{children:e.length>30?`${e.slice(0,30)}...`:e})},l)):(0,t.jsx)(m.Badge,{size:"xs",color:"red",children:(0,t.jsx)(u.Text,{children:"All Proxy Models"})})})}),(0,t.jsx)(r.TableCell,{children:(0,t.jsx)(d.Button,{size:"xs",variant:"secondary",onClick:()=>b(e.team_id),children:"Join Team"})})]},e.team_id)),0===p.length&&(0,t.jsx)(o.TableRow,{children:(0,t.jsx)(r.TableCell,{colSpan:5,className:"text-center",children:(0,t.jsxs)(u.Text,{children:["No available teams to join. 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+curl -X POST --location '<your_proxy_base_url>/end_user/new' \\
+
+-H 'Authorization: Bearer <your-master-key>' \\
+
+-H 'Content-Type: application/json' \\
+
+-d '{"user_id": "my-customer-id', "budget_id": "<BUDGET_ID>"}' # 👈 KEY CHANGE
+
+`,D=`
+curl -X POST --location '<your_proxy_base_url>/chat/completions' \\
+
+-H 'Authorization: Bearer <your-master-key>' \\
+
+-H 'Content-Type: application/json' \\
+
+-d '{
+  "model": "gpt-3.5-turbo',
+  "messages":[{"role": "user", "content": "Hey, how's it going?"}],
+  "user": "my-customer-id"
+}' # 👈 KEY CHANGE
+
+`,M=`from openai import OpenAI
+client = OpenAI(
+  base_url="<your_proxy_base_url>",
+  api_key="<your_proxy_key>"
+)
+
+completion = client.chat.completions.create(
+  model="gpt-3.5-turbo",
+  messages=[
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Hello!"}
+  ],
+  user="my-customer-id"
+)
+
+print(completion.choices[0].message)`;e.s(["default",0,({accessToken:e})=>{let[w,_]=(0,p.useState)(!1),[N,k]=(0,p.useState)(!1),[C,S]=(0,p.useState)(null),[T,I]=(0,p.useState)([]),[E,B]=(0,p.useState)(!1),[R,L]=(0,p.useState)(!1);(0,p.useEffect)(()=>{e&&(0,v.getBudgetList)(e).then(e=>{I(e)})},[e]);let F=async t=>{null!=e&&(S(t),k(!0))},z=async()=>{if(C&&null!=e){B(!0);try{await (0,v.budgetDeleteCall)(e,C.budget_id),j.default.success("Budget deleted."),await H()}catch(e){console.error("Error deleting budget:",e),"function"==typeof j.default.fromBackend?j.default.fromBackend("Failed to delete budget"):j.default.info("Failed to delete budget")}finally{B(!1),L(!1),S(null)}}},H=async()=>{null!=e&&(0,v.getBudgetList)(e).then(e=>{I(e)})};return(0,t.jsxs)("div",{className:"w-full mx-auto flex-auto overflow-y-auto m-8 p-2",children:[(0,t.jsx)(l.Button,{size:"sm",variant:"primary",className:"mb-2",onClick:()=>_(!0),children:"+ Create Budget"}),(0,t.jsxs)(r.TabGroup,{children:[(0,t.jsxs)(m.TabList,{children:[(0,t.jsx)(s.Tab,{children:"Budgets"}),(0,t.jsx)(s.Tab,{children:"Examples"})]}),(0,t.jsxs)(g.TabPanels,{children:[(0,t.jsx)(h.TabPanel,{children:(0,t.jsxs)("div",{className:"mt-6",children:[(0,t.jsx)(A,{accessToken:e,isModalVisible:w,setIsModalVisible:_,setBudgetList:I}),C&&(0,t.jsx)(P,{accessToken:e,isModalVisible:N,setIsModalVisible:k,setBudgetList:I,existingBudget:C,handleUpdateCall:H}),(0,t.jsxs)(a.Card,{children:[(0,t.jsx)(x.Text,{children:"Create a budget to assign to customers."}),(0,t.jsxs)(i.Table,{children:[(0,t.jsx)(c.TableHead,{children:(0,t.jsxs)(u.TableRow,{children:[(0,t.jsx)(d.TableHeaderCell,{children:"Budget ID"}),(0,t.jsx)(d.TableHeaderCell,{children:"Max Budget"}),(0,t.jsx)(d.TableHeaderCell,{children:"TPM"}),(0,t.jsx)(d.TableHeaderCell,{children:"RPM"})]})}),(0,t.jsx)(n.TableBody,{children:T.slice().sort((e,t)=>new Date(t.updated_at).getTime()-new Date(e.updated_at).getTime()).map((e,l)=>(0,t.jsxs)(u.TableRow,{children:[(0,t.jsx)(o.TableCell,{children:e.budget_id}),(0,t.jsx)(o.TableCell,{children:e.max_budget?e.max_budget:"n/a"}),(0,t.jsx)(o.TableCell,{children:e.tpm_limit?e.tpm_limit:"n/a"}),(0,t.jsx)(o.TableCell,{children:e.rpm_limit?e.rpm_limit:"n/a"}),(0,t.jsx)(y.default,{variant:"Edit",tooltipText:"Edit budget",onClick:()=>F(e),dataTestId:"edit-budget-button"}),(0,t.jsx)(y.default,{variant:"Delete",tooltipText:"Delete budget",onClick:()=>{S(e),L(!0)},dataTestId:"delete-budget-button"})]},l))})]})]}),(0,t.jsx)(b.default,{isOpen:R,title:"Delete Budget?",message:"Are you sure you want to delete this budget? 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/0922b7d1606dd8f3.js b/litellm/proxy/_experimental/out/_next/static/chunks/ac9e96d21c200b48.js
similarity index 82%
rename from litellm/proxy/_experimental/out/_next/static/chunks/0922b7d1606dd8f3.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/ac9e96d21c200b48.js
index c1571fc412..24c5b8edd1 100644
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new file mode 100644
index 0000000000..f459f4ec5b
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new file mode 100644
index 0000000000..2dd4d62b54
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/49e9dce7df902771.js b/litellm/proxy/_experimental/out/_next/static/chunks/ae9cf43b8c0c76aa.js
similarity index 99%
rename from litellm/proxy/_experimental/out/_next/static/chunks/49e9dce7df902771.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/ae9cf43b8c0c76aa.js
index 63822604ea..320b5d3c0f 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/49e9dce7df902771.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/ae9cf43b8c0c76aa.js
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deleted file mode 100644
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t,a,i=((t={}).AUDIO_SPEECH="audio_speech",t.AUDIO_TRANSCRIPTION="audio_transcription",t.IMAGE_GENERATION="image_generation",t.VIDEO_GENERATION="video_generation",t.CHAT="chat",t.RESPONSES="responses",t.IMAGE_EDITS="image_edits",t.ANTHROPIC_MESSAGES="anthropic_messages",t.EMBEDDING="embedding",t),r=((a={}).IMAGE="image",a.VIDEO="video",a.CHAT="chat",a.RESPONSES="responses",a.IMAGE_EDITS="image_edits",a.ANTHROPIC_MESSAGES="anthropic_messages",a.EMBEDDINGS="embeddings",a.SPEECH="speech",a.TRANSCRIPTION="transcription",a.A2A_AGENTS="a2a_agents",a.MCP="mcp",a.REALTIME="realtime",a);let n={image_generation:"image",video_generation:"video",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages",audio_speech:"speech",audio_transcription:"transcription",embedding:"embeddings"};e.s(["EndpointType",()=>r,"getEndpointType",0,e=>{if(console.log("getEndpointType:",e),Object.values(i).includes(e)){let t=n[e];return 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-
-client = openai.AzureOpenAI(
-	api_key="${w||"YOUR_LITELLM_API_KEY"}",
-	azure_endpoint="${x}",
-	api_version="2024-02-01"
-)`:`import openai
-
-client = openai.OpenAI(
-	api_key="${w||"YOUR_LITELLM_API_KEY"}",
-	base_url="${x}"
-)`;switch(h){case r.CHAT:{let e=Object.keys(C).length>0,a="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
-    extra_body=${e}`}let i=$.length>0?$:[{role:"user",content:E}];t=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.chat.completions.create(
-    model="${k}",
-    messages=${JSON.stringify(i,null,4)}${a}
-)
-
-print(response)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
-# response_with_file = client.chat.completions.create(
-#     model="${k}",
-#     messages=[
-#         {
-#             "role": "user",
-#             "content": [
-#                 {
-#                     "type": "text",
-#                     "text": "${j}"
-#                 },
-#                 {
-#                     "type": "image_url",
-#                     "image_url": {
-#                         "url": f"data:image/jpeg;base64,{base64_file}"  # or data:application/pdf;base64,{base64_file}
-#                     }
-#                 }
-#             ]
-#         }
-#     ]${a}
-# )
-# print(response_with_file)
-`;break}case r.RESPONSES:{let e=Object.keys(C).length>0,a="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();a=`,
-    extra_body=${e}`}let i=$.length>0?$:[{role:"user",content:E}];t=`
-import base64
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-    with open(image_path, "rb") as image_file:
-        return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Example with text only
-response = client.responses.create(
-    model="${k}",
-    input=${JSON.stringify(i,null,4)}${a}
-)
-
-print(response.output_text)
-
-# Example with image or PDF (uncomment and provide file path to use)
-# base64_file = encode_image("path/to/your/file.jpg")  # or .pdf
-# response_with_file = client.responses.create(
-#     model="${k}",
-#     input=[
-#         {
-#             "role": "user",
-#             "content": [
-#                 {"type": "input_text", "text": "${j}"},
-#                 {
-#                     "type": "input_image",
-#                     "image_url": f"data:image/jpeg;base64,{base64_file}",  # or data:application/pdf;base64,{base64_file}
-#                 },
-#             ],
-#         }
-#     ]${a}
-# )
-# print(response_with_file.output_text)
-`;break}case r.IMAGE:t="azure"===b?`
-# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.
-# This snippet uses 'client.images.generate' and will create a new image based on your prompt.
-# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.
-import os
-import requests
-import json
-import time
-from PIL import Image
-
-result = client.images.generate(
-	model="${k}",
-	prompt="${l}",
-	n=1
-)
-
-json_response = json.loads(result.model_dump_json())
-
-# Set the directory for the stored image
-image_dir = os.path.join(os.curdir, 'images')
-
-# If the directory doesn't exist, create it
-if not os.path.isdir(image_dir):
-	os.mkdir(image_dir)
-
-# Initialize the image path
-image_filename = f"generated_image_{int(time.time())}.png"
-image_path = os.path.join(image_dir, image_filename)
-
-try:
-	# Retrieve the generated image
-	if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):
-			image_url = json_response["data"][0]["url"]
-			generated_image = requests.get(image_url).content
-			with open(image_path, "wb") as image_file:
-					image_file.write(generated_image)
-
-			print(f"Image saved to {image_path}")
-			# Display the image
-			image = Image.open(image_path)
-			image.show()
-	else:
-			print("Could not find image URL in response.")
-			print("Full response:", json_response)
-except Exception as e:
-	print(f"An error occurred: {e}")
-	print("Full response:", json_response)
-`:`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-	with open(image_path, "rb") as image_file:
-			return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
-	# In a real implementation, this would upload the file to OpenAI
-	# For this example, we'll just return a placeholder ID
-	return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${j}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${k}",
-	input=[
-			{
-					"role": "user",
-					"content": [
-							{"type": "input_text", "text": prompt},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image1}",
-							},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image2}",
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id1,
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id2,
-							}
-					],
-			}
-	],
-	tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
-	output
-	for output in response.output
-	if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
-	image_base64 = image_data[0]
-	image_filename = f"edited_image_{int(time.time())}.png"
-	with open(image_filename, "wb") as f:
-			f.write(base64.b64decode(image_base64))
-	print(f"Image saved to {image_filename}")
-else:
-	# If no image is generated, there might be a text response with an explanation
-	text_response = [output.text for output in response.output if hasattr(output, 'text')]
-	if text_response:
-			print("No image generated. Model response:")
-			print("\\n".join(text_response))
-	else:
-			print("No image data found in response.")
-	print("Full response for debugging:")
-	print(response)
-`;break;case r.IMAGE_EDITS:t="azure"===b?`
-import base64
-import os
-import time
-import json
-from PIL import Image
-import requests
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-	with open(image_path, "rb") as image_file:
-			return base64.b64encode(image_file.read()).decode('utf-8')
-
-# The prompt entered by the user
-prompt = "${j}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${k}",
-	input=[
-			{
-					"role": "user",
-					"content": [
-							{"type": "input_text", "text": prompt},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image1}",
-							},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image2}",
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id1,
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id2,
-							}
-					],
-			}
-	],
-	tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
-	output
-	for output in response.output
-	if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
-	image_base64 = image_data[0]
-	image_filename = f"edited_image_{int(time.time())}.png"
-	with open(image_filename, "wb") as f:
-			f.write(base64.b64decode(image_base64))
-	print(f"Image saved to {image_filename}")
-else:
-	# If no image is generated, there might be a text response with an explanation
-	text_response = [output.text for output in response.output if hasattr(output, 'text')]
-	if text_response:
-			print("No image generated. Model response:")
-			print("\\n".join(text_response))
-	else:
-			print("No image data found in response.")
-	print("Full response for debugging:")
-	print(response)
-`:`
-import base64
-import os
-import time
-
-# Helper function to encode images to base64
-def encode_image(image_path):
-	with open(image_path, "rb") as image_file:
-			return base64.b64encode(image_file.read()).decode('utf-8')
-
-# Helper function to create a file (simplified for this example)
-def create_file(image_path):
-	# In a real implementation, this would upload the file to OpenAI
-	# For this example, we'll just return a placeholder ID
-	return f"file_{os.path.basename(image_path).replace('.', '_')}"
-
-# The prompt entered by the user
-prompt = "${j}"
-
-# Encode images to base64
-base64_image1 = encode_image("body-lotion.png")
-base64_image2 = encode_image("soap.png")
-
-# Create file IDs
-file_id1 = create_file("body-lotion.png")
-file_id2 = create_file("incense-kit.png")
-
-response = client.responses.create(
-	model="${k}",
-	input=[
-			{
-					"role": "user",
-					"content": [
-							{"type": "input_text", "text": prompt},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image1}",
-							},
-							{
-									"type": "input_image",
-									"image_url": f"data:image/jpeg;base64,{base64_image2}",
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id1,
-							},
-							{
-									"type": "input_image",
-									"file_id": file_id2,
-							}
-					],
-			}
-	],
-	tools=[{"type": "image_generation"}],
-)
-
-# Process the response
-image_generation_calls = [
-	output
-	for output in response.output
-	if output.type == "image_generation_call"
-]
-
-image_data = [output.result for output in image_generation_calls]
-
-if image_data:
-	image_base64 = image_data[0]
-	image_filename = f"edited_image_{int(time.time())}.png"
-	with open(image_filename, "wb") as f:
-			f.write(base64.b64decode(image_base64))
-	print(f"Image saved to {image_filename}")
-else:
-	# If no image is generated, there might be a text response with an explanation
-	text_response = [output.text for output in response.output if hasattr(output, 'text')]
-	if text_response:
-			print("No image generated. Model response:")
-			print("\\n".join(text_response))
-	else:
-			print("No image data found in response.")
-	print("Full response for debugging:")
-	print(response)
-`;break;case r.EMBEDDINGS:t=`
-response = client.embeddings.create(
-	input="${l||"Your string here"}",
-	model="${k}",
-	encoding_format="base64" # or "float"
-)
-
-print(response.data[0].embedding)
-`;break;case r.TRANSCRIPTION:t=`
-# Open the audio file
-audio_file = open("path/to/your/audio/file.mp3", "rb")
-
-# Make the transcription request
-response = client.audio.transcriptions.create(
-	model="${k}",
-	file=audio_file${l?`,
-	prompt="${l.replace(/"/g,'\\"')}"`:""}
-)
-
-print(response.text)
-`;break;case r.SPEECH:t=`
-# Make the text-to-speech request
-response = client.audio.speech.create(
-	model="${k}",
-	input="${l||"Your text to convert to speech here"}",
-	voice="${f}"  # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer
-)
-
-# Save the audio to a file
-output_filename = "output_speech.mp3"
-response.stream_to_file(output_filename)
-print(f"Audio saved to {output_filename}")
-
-# Optional: Customize response format and speed
-# response = client.audio.speech.create(
-#     model="${k}",
-#     input="${l||"Your text to convert to speech here"}",
-#     voice="alloy",
-#     response_format="mp3",  # Options: mp3, opus, aac, flac, wav, pcm
-#     speed=1.0  # Range: 0.25 to 4.0
-# )
-# response.stream_to_file("output_speech.mp3")
-`;break;default:t="\n# Code generation for this endpoint is not implemented yet."}return`${O}
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deleted file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/c74f3813068add76.js b/litellm/proxy/_experimental/out/_next/static/chunks/c74f3813068add76.js
deleted file mode 100644
index a115dc2a0f..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/c74f3813068add76.js
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this._client.delete(tk`/v1/messages/batches/${e}?beta=true`,{...s,headers:t_([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}cancel(e,t={},s){let{betas:r}=t??{};return this._client.post(tk`/v1/messages/batches/${e}/cancel?beta=true`,{...s,headers:t_([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}async results(e,t={},s){let r=await this.retrieve(e);if(!r.results_url)throw new eT(`No batch \`results_url\`; Has it finished processing? ${r.processing_status} - ${r.id}`);let{betas:a}=t??{};return this._client.get(r.results_url,{...s,headers:t_([{"anthropic-beta":[...a??[],"message-batches-2024-09-24"].toString(),Accept:"application/binary"},s?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tT.fromResponse(t.response,t.controller))}}let tO=e=>{if(0===e.length)return e;let t=e[e.length-1];switch(t.type){case"separator":return tO(e=e.slice(0,e.length-1));case"number":let 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AbortController,u.set(this,void 0),h.set(this,()=>{}),m.set(this,()=>{}),p.set(this,void 0),f.set(this,()=>{}),g.set(this,()=>{}),y.set(this,{}),x.set(this,!1),b.set(this,!1),v.set(this,!1),w.set(this,!1),j.set(this,void 0),S.set(this,void 0),k.set(this,e=>{if(e_(this,b,!0,"f"),eE(e)&&(e=new eO),e instanceof eO)return e_(this,v,!0,"f"),this._emit("abort",e);if(e instanceof eT)return this._emit("error",e);if(e instanceof Error){let t=new eT(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eT(String(e)))}),e_(this,u,new Promise((e,t)=>{e_(this,h,e,"f"),e_(this,m,t,"f")}),"f"),e_(this,p,new Promise((e,t)=>{e_(this,f,e,"f"),e_(this,g,t,"f")}),"f"),eN(this,u,"f").catch(()=>{}),eN(this,p,"f").catch(()=>{})}get response(){return eN(this,j,"f")}get request_id(){return eN(this,S,"f")}async withResponse(){let e=await eN(this,u,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tM;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tM;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eN(this,k,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eN(this,c,"m",E).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eN(this,c,"m",C).call(this,e);if(n.controller.signal?.aborted)throw new eO;eN(this,c,"m",T).call(this)}_connected(e){this.ended||(e_(this,j,e,"f"),e_(this,S,e?.headers.get("request-id"),"f"),eN(this,h,"f").call(this,e),this._emit("connect"))}get ended(){return eN(this,x,"f")}get errored(){return eN(this,b,"f")}get aborted(){return eN(this,v,"f")}abort(){this.controller.abort()}on(e,t){return(eN(this,y,"f")[e]||(eN(this,y,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eN(this,y,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eN(this,y,"f")[e]||(eN(this,y,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{e_(this,w,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){e_(this,w,!0,"f"),await eN(this,p,"f")}get currentMessage(){return eN(this,d,"f")}async finalMessage(){return await this.done(),eN(this,c,"m",_).call(this)}async finalText(){return await 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eO;eN(this,c,"m",T).call(this)}[(d=new WeakMap,u=new WeakMap,h=new WeakMap,m=new WeakMap,p=new WeakMap,f=new WeakMap,g=new WeakMap,y=new WeakMap,x=new WeakMap,b=new WeakMap,v=new WeakMap,w=new WeakMap,j=new WeakMap,S=new WeakMap,k=new WeakMap,c=new WeakSet,_=function(){if(0===this.receivedMessages.length)throw new eT("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eT("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eT("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||e_(this,d,void 0,"f")},C=function(e){if(this.ended)return;let t=eN(this,c,"m",A).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tI(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tL(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":e_(this,d,t,"f")}},T=function(){if(this.ended)throw new eT("stream has ended, this shouldn't happen");let e=eN(this,d,"f");if(!e)throw new eT("request ended without sending any chunks");return e_(this,d,void 0,"f"),e},A=function(e){let t=eN(this,d,"f");if("message_start"===e.type){if(t)throw new eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tI(s)){let t=s[tR]||"";if(Object.defineProperty(s,tR,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tP(t)}catch(s){let e=new eT(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${s}. JSON: ${t}`);eN(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tL(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tL(e){}let t$={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tU={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tD extends tw{constructor(){super(...arguments),this.batches=new tA(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tU&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tU[r.model]}
-Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=t$[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return tM.createMessage(this,e,t)}countTokens(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:t_([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tD.Batches=tA;class tB extends tw{constructor(){super(...arguments),this.models=new tC(this._client),this.messages=new tD(this._client),this.files=new tE(this._client)}}tB.Models=tC,tB.Messages=tD,tB.Files=tE;class tq extends tw{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:t_([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tz="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tW{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),H.set(this,!1),W.set(this,void 0),F.set(this,void 0),V.set(this,e=>{if(e_(this,q,!0,"f"),eE(e)&&(e=new eO),e instanceof eO)return e_(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eT)return this._emit("error",e);if(e instanceof Error){let t=new eT(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eT(String(e)))}),e_(this,R,new 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Promise((t,s)=>{e_(this,H,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){e_(this,H,!0,"f"),await eN(this,L,"f")}get currentMessage(){return eN(this,P,"f")}async finalMessage(){return await this.done(),eN(this,O,"m",J).call(this)}async finalText(){return await this.done(),eN(this,O,"m",G).call(this)}_emit(e,...t){if(eN(this,B,"f"))return;"end"===e&&(e_(this,B,!0,"f"),eN(this,$,"f").call(this));let s=eN(this,D,"f")[e];if(s&&(eN(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eN(this,H,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eN(this,H,"f")||s?.length||Promise.reject(e),eN(this,M,"f").call(this,e),eN(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eN(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let 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eT("stream has ended, this shouldn't happen");let e=eN(this,P,"f");if(!e)throw new eT("request ended without sending any chunks");return e_(this,P,void 0,"f"),e},Q=function(e){let t=eN(this,P,"f");if("message_start"===e.type){if(t)throw new eT(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eT(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tH(s)){let t=s[tz]||"";Object.defineProperty(s,tz,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t&&(s.input=tP(t))}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tF(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new te(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tF(e){}class tJ extends tw{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(tk`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",tc,{query:e,...t})}delete(e,t){return this._client.delete(tk`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(tk`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new eT(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:t_([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tT.fromResponse(t.response,t.controller))}}class tG extends tw{constructor(){super(...arguments),this.batches=new tJ(this._client)}create(e,t){e.model in tV&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${tV[e.model]}
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new file mode 100644
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this._client.delete(tC`/v1/messages/batches/${e}?beta=true`,{...s,headers:tk([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}cancel(e,t={},s){let{betas:r}=t??{};return this._client.post(tC`/v1/messages/batches/${e}/cancel?beta=true`,{...s,headers:tk([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}async results(e,t={},s){let r=await this.retrieve(e);if(!r.results_url)throw new eO(`No batch \`results_url\`; Has it finished processing? ${r.processing_status} - ${r.id}`);let{betas:a}=t??{};return this._client.get(r.results_url,{...s,headers:tk([{"anthropic-beta":[...a??[],"message-batches-2024-09-24"].toString(),Accept:"application/binary"},s?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>tO.fromResponse(t.response,t.controller))}}let tR=e=>{if(0===e.length)return e;let t=e[e.length-1];switch(t.type){case"separator":return tR(e=e.slice(0,e.length-1));case"number":let 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fromReadableStream(e){let t=new t$;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new t$;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eE(this,k,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eE(this,c,"m",E).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eE(this,c,"m",C).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,c,"m",T).call(this)}_connected(e){this.ended||(ek(this,j,e,"f"),ek(this,S,e?.headers.get("request-id"),"f"),eE(this,h,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,x,"f")}get errored(){return eE(this,b,"f")}get aborted(){return eE(this,v,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,y,"f")[e]||(eE(this,y,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(this,y,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eE(this,y,"f")[e]||(eE(this,y,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,w,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,w,!0,"f"),await eE(this,p,"f")}get currentMessage(){return eE(this,d,"f")}async finalMessage(){return await this.done(),eE(this,c,"m",_).call(this)}async finalText(){return await 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eR;eE(this,c,"m",T).call(this)}[(d=new WeakMap,u=new WeakMap,h=new WeakMap,m=new WeakMap,p=new WeakMap,f=new WeakMap,g=new WeakMap,y=new WeakMap,x=new WeakMap,b=new WeakMap,v=new WeakMap,w=new WeakMap,j=new WeakMap,S=new WeakMap,k=new WeakMap,c=new WeakSet,_=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},N=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eO("stream ended without producing a content block with type=text");return e.join(" ")},E=function(){this.ended||ek(this,d,void 0,"f")},C=function(e){if(this.ended)return;let t=eE(this,c,"m",A).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tL(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tU(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":ek(this,d,t,"f")}},T=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,d,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,d,void 0,"f"),e},A=function(e){let t=eE(this,d,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.container=e.delta.container,t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push(e.content_block),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(s.text+=e.delta.text);break;case"citations_delta":s?.type==="text"&&(s.citations??(s.citations=[]),s.citations.push(e.delta.citation));break;case"input_json_delta":if(s&&tL(s)){let t=s[tM]||"";if(Object.defineProperty(s,tM,{value:t+=e.delta.partial_json,enumerable:!1,writable:!0}),t)try{s.input=tI(t)}catch(s){let e=new eO(`Unable to parse tool parameter JSON from model. Please retry your request or adjust your prompt. Error: ${s}. JSON: ${t}`);eE(this,k,"f").call(this,e)}}break;case"thinking_delta":s?.type==="thinking"&&(s.thinking+=e.delta.thinking);break;case"signature_delta":s?.type==="thinking"&&(s.signature=e.delta.signature);break;default:tU(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new ts(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function tU(e){}let tD={"claude-opus-4-20250514":8192,"claude-opus-4-0":8192,"claude-4-opus-20250514":8192,"anthropic.claude-opus-4-20250514-v1:0":8192,"claude-opus-4@20250514":8192},tB={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025"};class tq extends tS{constructor(){super(...arguments),this.batches=new tP(this._client)}create(e,t){let{betas:s,...r}=e;r.model in tB&&console.warn(`The model '${r.model}' is deprecated and will reach end-of-life on ${tB[r.model]}
+Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`);let a=this._client._options.timeout;if(!r.stream&&null==a){let e=tD[r.model]??void 0;a=this._client.calculateNonstreamingTimeout(r.max_tokens,e)}return this._client.post("/v1/messages?beta=true",{body:r,timeout:a??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}stream(e,t){return t$.createMessage(this,e,t)}countTokens(e,t){let{betas:s,...r}=e;return this._client.post("/v1/messages/count_tokens?beta=true",{body:r,...t,headers:tk([{"anthropic-beta":[...s??[],"token-counting-2024-11-01"].toString()},t?.headers])})}}tq.Batches=tP;class tz extends tS{constructor(){super(...arguments),this.models=new tA(this._client),this.messages=new tq(this._client),this.files=new tT(this._client)}}tz.Models=tA,tz.Messages=tq,tz.Files=tT;class tW extends tS{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:tk([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let tF="__json_buf";function tH(e){return"tool_use"===e.type||"server_tool_use"===e.type}class tJ{constructor(){O.add(this),this.messages=[],this.receivedMessages=[],P.set(this,void 0),this.controller=new AbortController,R.set(this,void 0),I.set(this,()=>{}),M.set(this,()=>{}),L.set(this,void 0),$.set(this,()=>{}),U.set(this,()=>{}),D.set(this,{}),B.set(this,!1),q.set(this,!1),z.set(this,!1),W.set(this,!1),F.set(this,void 0),H.set(this,void 0),V.set(this,e=>{if(ek(this,q,!0,"f"),eT(e)&&(e=new eR),e instanceof eR)return ek(this,z,!0,"f"),this._emit("abort",e);if(e instanceof eO)return this._emit("error",e);if(e instanceof Error){let t=new eO(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new eO(String(e)))}),ek(this,R,new Promise((e,t)=>{ek(this,I,e,"f"),ek(this,M,t,"f")}),"f"),ek(this,L,new Promise((e,t)=>{ek(this,$,e,"f"),ek(this,U,t,"f")}),"f"),eE(this,R,"f").catch(()=>{}),eE(this,L,"f").catch(()=>{})}get response(){return eE(this,F,"f")}get request_id(){return eE(this,H,"f")}async withResponse(){let e=await eE(this,R,"f");if(!e)throw Error("Could not resolve a `Response` object");return{data:this,response:e,request_id:e.headers.get("request-id")}}static fromReadableStream(e){let t=new tJ;return t._run(()=>t._fromReadableStream(e)),t}static createMessage(e,t,s){let r=new tJ;for(let e of t.messages)r._addMessageParam(e);return r._run(()=>r._createMessage(e,{...t,stream:!0},{...s,headers:{...s?.headers,"X-Stainless-Helper-Method":"stream"}})),r}_run(e){e().then(()=>{this._emitFinal(),this._emit("end")},eE(this,V,"f"))}_addMessageParam(e){this.messages.push(e)}_addMessage(e,t=!0){this.receivedMessages.push(e),t&&this._emit("message",e)}async _createMessage(e,t,s){let r=s?.signal;r&&(r.aborted&&this.controller.abort(),r.addEventListener("abort",()=>this.controller.abort())),eE(this,O,"m",K).call(this);let{response:a,data:n}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(a),n))eE(this,O,"m",X).call(this,e);if(n.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}_connected(e){this.ended||(ek(this,F,e,"f"),ek(this,H,e?.headers.get("request-id"),"f"),eE(this,I,"f").call(this,e),this._emit("connect"))}get ended(){return eE(this,B,"f")}get errored(){return eE(this,q,"f")}get aborted(){return eE(this,z,"f")}abort(){this.controller.abort()}on(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=eE(this,D,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(eE(this,D,"f")[e]||(eE(this,D,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{ek(this,W,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){ek(this,W,!0,"f"),await eE(this,L,"f")}get currentMessage(){return eE(this,P,"f")}async finalMessage(){return await this.done(),eE(this,O,"m",J).call(this)}async finalText(){return await this.done(),eE(this,O,"m",G).call(this)}_emit(e,...t){if(eE(this,B,"f"))return;"end"===e&&(ek(this,B,!0,"f"),eE(this,$,"f").call(this));let s=eE(this,D,"f")[e];if(s&&(eE(this,D,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];eE(this,W,"f")||s?.length||Promise.reject(e),eE(this,M,"f").call(this,e),eE(this,U,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",eE(this,O,"m",J).call(this))}async _fromReadableStream(e,t){let s=t?.signal;s&&(s.aborted&&this.controller.abort(),s.addEventListener("abort",()=>this.controller.abort())),eE(this,O,"m",K).call(this),this._connected(null);let r=ts.fromReadableStream(e,this.controller);for await(let e of r)eE(this,O,"m",X).call(this,e);if(r.controller.signal?.aborted)throw new eR;eE(this,O,"m",Y).call(this)}[(P=new WeakMap,R=new WeakMap,I=new WeakMap,M=new WeakMap,L=new WeakMap,$=new WeakMap,U=new WeakMap,D=new WeakMap,B=new WeakMap,q=new WeakMap,z=new WeakMap,W=new WeakMap,F=new WeakMap,H=new WeakMap,V=new WeakMap,O=new WeakSet,J=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},G=function(){if(0===this.receivedMessages.length)throw new eO("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new eO("stream ended without producing a content block with type=text");return e.join(" ")},K=function(){this.ended||ek(this,P,void 0,"f")},X=function(e){if(this.ended)return;let t=eE(this,O,"m",Q).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":tH(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:tG(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(t,!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":ek(this,P,t,"f")}},Y=function(){if(this.ended)throw new eO("stream has ended, this shouldn't happen");let e=eE(this,P,"f");if(!e)throw new eO("request ended without sending any chunks");return ek(this,P,void 0,"f"),e},Q=function(e){let t=eE(this,P,"f");if("message_start"===e.type){if(t)throw new eO(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new eO(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return 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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/cac89fc12fb6ef7e.js b/litellm/proxy/_experimental/out/_next/static/chunks/cac89fc12fb6ef7e.js
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\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/cc754afff0e2dd87.js b/litellm/proxy/_experimental/out/_next/static/chunks/cc754afff0e2dd87.js
deleted file mode 100644
index de1fdf75f8..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/cc754afff0e2dd87.js
+++ /dev/null
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-          font-size: 12px;
-          color: #666;
-          margin-bottom: 5px;
-        }
-        .summary-item .value {
-          font-size: 20px;
-          font-weight: bold;
-          font-family: monospace;
-        }
-        .summary-item .value.blue { color: #1890ff; }
-        .summary-item .value.green { color: #52c41a; }
-        .summary-item .value.purple { color: #722ed1; }
-        .model-section {
-          margin-bottom: 30px;
-          page-break-inside: avoid;
-        }
-        .footer {
-          margin-top: 40px;
-          padding-top: 20px;
-          border-top: 1px solid #ddd;
-          font-size: 12px;
-          color: #666;
-        }
-        @media print {
-          body { padding: 20px; }
-          .model-section { page-break-inside: avoid; }
-        }
-      </style>
-    </head>
-    <body>
-      <h1>LLM Cost Estimate Report</h1>
-      <p style="color: #666; margin-top: -20px; margin-bottom: 30px;">${a} model${1!==a?"s":""} configured</p>
-      
-      <div class="summary-box">
-        <h2>Combined Totals</h2>
-        <div class="summary-grid">
-          <div class="summary-item">
-            <div class="label">Total Per Request</div>
-            <div class="value blue">${tP(e.totals.cost_per_request)}</div>
-          </div>
-          <div class="summary-item">
-            <div class="label">Total Daily</div>
-            <div class="value green">${tP(e.totals.daily_cost)}</div>
-          </div>
-          <div class="summary-item">
-            <div class="label">Total Monthly</div>
-            <div class="value purple">${tP(e.totals.monthly_cost)}</div>
-          </div>
-        </div>
-        ${e.totals.margin_per_request>0?`
-        <div class="summary-grid" style="margin-top: 15px; padding-top: 15px; border-top: 1px solid #ddd;">
-          <div class="summary-item">
-            <div class="label">Margin/Request</div>
-            <div class="value" style="color: #faad14;">${tP(e.totals.margin_per_request)}</div>
-          </div>
-          <div class="summary-item">
-            <div class="label">Daily Margin</div>
-            <div class="value" style="color: #faad14;">${tP(e.totals.daily_margin)}</div>
-          </div>
-          <div class="summary-item">
-            <div class="label">Monthly Margin</div>
-            <div class="value" style="color: #faad14;">${tP(e.totals.monthly_margin)}</div>
-          </div>
-        </div>
-        `:""}
-      </div>
-
-      <h2>Model Breakdown</h2>
-      ${s.map(e=>{let t;return t=e.result,`
-    <div class="model-section">
-      <h3>${t.model} ${t.provider?`<span class="provider">(${t.provider})</span>`:""}</h3>
-      
-      <div class="meta">
-        <p><strong>Input Tokens per Request:</strong> ${tM(t.input_tokens)}</p>
-        <p><strong>Output Tokens per Request:</strong> ${tM(t.output_tokens)}</p>
-        ${t.num_requests_per_day?`<p><strong>Requests per Day:</strong> ${tM(t.num_requests_per_day)}</p>`:""}
-        ${t.num_requests_per_month?`<p><strong>Requests per Month:</strong> ${tM(t.num_requests_per_month)}</p>`:""}
-      </div>
-
-      <table>
-        <tr>
-          <th>Cost Type</th>
-          <th>Per Request</th>
-          ${null!==t.daily_cost?"<th>Daily</th>":""}
-          ${null!==t.monthly_cost?"<th>Monthly</th>":""}
-        </tr>
-        <tr>
-          <td>Input Cost</td>
-          <td class="cost-value">${tP(t.input_cost_per_request)}</td>
-          ${null!==t.daily_cost?`<td class="cost-value">${tP(t.daily_input_cost)}</td>`:""}
-          ${null!==t.monthly_cost?`<td class="cost-value">${tP(t.monthly_input_cost)}</td>`:""}
-        </tr>
-        <tr>
-          <td>Output Cost</td>
-          <td class="cost-value">${tP(t.output_cost_per_request)}</td>
-          ${null!==t.daily_cost?`<td class="cost-value">${tP(t.daily_output_cost)}</td>`:""}
-          ${null!==t.monthly_cost?`<td class="cost-value">${tP(t.monthly_output_cost)}</td>`:""}
-        </tr>
-        <tr>
-          <td>Margin/Fee</td>
-          <td class="cost-value">${tP(t.margin_cost_per_request)}</td>
-          ${null!==t.daily_cost?`<td class="cost-value">${tP(t.daily_margin_cost)}</td>`:""}
-          ${null!==t.monthly_cost?`<td class="cost-value">${tP(t.monthly_margin_cost)}</td>`:""}
-        </tr>
-        <tr class="total-row">
-          <td>Total</td>
-          <td class="cost-value">${tP(t.cost_per_request)}</td>
-          ${null!==t.daily_cost?`<td class="cost-value">${tP(t.daily_cost)}</td>`:""}
-          ${null!==t.monthly_cost?`<td class="cost-value">${tP(t.monthly_cost)}</td>`:""}
-        </tr>
-      </table>
-    </div>
-  `}).join("")}
-
-      <div class="footer">
-        <p>Generated by LiteLLM Pricing Calculator on ${new Date().toLocaleString()}</p>
-      </div>
-    </body>
-    </html>
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t;return{color:e.colorTextDisabled,backgroundColor:e.colorBgContainerDisabled,borderColor:e.colorBorder,boxShadow:"none",cursor:"not-allowed",opacity:1,"input[disabled], textarea[disabled]":{cursor:"not-allowed"},"&:hover:not([disabled])":Object.assign({},{borderColor:(t=(0,a.mergeToken)(e,{hoverBorderColor:e.colorBorder,hoverBg:e.colorBgContainerDisabled})).hoverBorderColor,backgroundColor:t.hoverBg})}},c=(e,t)=>({background:e.colorBgContainer,borderWidth:e.lineWidth,borderStyle:e.lineType,borderColor:t.borderColor,"&:hover":{borderColor:t.hoverBorderColor,backgroundColor:e.hoverBg},"&:focus, &:focus-within":{borderColor:t.activeBorderColor,boxShadow:t.activeShadow,outline:0,backgroundColor:e.activeBg}}),u=(e,t)=>({[`&${e.componentCls}-status-${t.status}:not(${e.componentCls}-disabled)`]:Object.assign(Object.assign({},c(e,t)),{[`${e.componentCls}-prefix, 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textarea[disabled]":{cursor:"not-allowed"}}),$(e,{status:"error",borderColor:e.colorError,hoverBorderColor:e.colorErrorBorderHover,activeBorderColor:e.colorError,activeShadow:e.errorActiveShadow,affixColor:e.colorError})),$(e,{status:"warning",borderColor:e.colorWarning,hoverBorderColor:e.colorWarningBorderHover,activeBorderColor:e.colorWarning,activeShadow:e.warningActiveShadow,affixColor:e.colorWarning})),t)});e.s(["genBaseOutlinedStyle",0,c,"genBorderlessStyle",0,m,"genDisabledStyle",0,s,"genFilledGroupStyle",0,b,"genFilledStyle",0,v,"genOutlinedGroupStyle",0,p,"genOutlinedStyle",0,d,"genUnderlinedStyle",0,C],889943);let x=e=>({"&::-moz-placeholder":{opacity:1},"&::placeholder":{color:e,userSelect:"none"},"&:placeholder-shown":{textOverflow:"ellipsis"}}),E=e=>{let{paddingBlockLG:r,lineHeightLG:o,borderRadiusLG:n,paddingInlineLG:a}=e;return{padding:`${(0,t.unit)(r)} ${(0,t.unit)(a)}`,fontSize:e.inputFontSizeLG,lineHeight:o,borderRadius:n}},S=e=>({padding:`${(0,t.unit)(e.paddingBlockSM)} ${(0,t.unit)(e.paddingInlineSM)}`,fontSize:e.inputFontSizeSM,borderRadius:e.borderRadiusSM}),k=e=>Object.assign(Object.assign({position:"relative",display:"inline-block",width:"100%",minWidth:0,padding:`${(0,t.unit)(e.paddingBlock)} ${(0,t.unit)(e.paddingInline)}`,color:e.colorText,fontSize:e.inputFontSize,lineHeight:e.lineHeight,borderRadius:e.borderRadius,transition:`all ${e.motionDurationMid}`},x(e.colorTextPlaceholder)),{"&-lg":Object.assign({},E(e)),"&-sm":Object.assign({},S(e)),"&-rtl, &-textarea-rtl":{direction:"rtl"}}),j=e=>{let{componentCls:o,antCls:n}=e;return{position:"relative",display:"table",width:"100%",borderCollapse:"separate",borderSpacing:0,"&[class*='col-']":{paddingInlineEnd:e.paddingXS,"&:last-child":{paddingInlineEnd:0}},[`&-lg ${o}, &-lg > ${o}-group-addon`]:Object.assign({},E(e)),[`&-sm ${o}, &-sm > 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t=(0,a.mergeToken)(e,i(e));return[(e=>{let{componentCls:t,borderRadiusLG:o,borderRadiusSM:n}=e;return{[`${t}-group`]:Object.assign(Object.assign(Object.assign({},(0,r.resetComponent)(e)),j(e)),{"&-rtl":{direction:"rtl"},"&-wrapper":Object.assign(Object.assign(Object.assign({display:"inline-block",width:"100%",textAlign:"start",verticalAlign:"top","&-rtl":{direction:"rtl"},"&-lg":{[`${t}-group-addon`]:{borderRadius:o,fontSize:e.inputFontSizeLG}},"&-sm":{[`${t}-group-addon`]:{borderRadius:n}}},p(e)),b(e)),{[`&:not(${t}-compact-first-item):not(${t}-compact-last-item)${t}-compact-item`]:{[`${t}, ${t}-group-addon`]:{borderRadius:0}},[`&:not(${t}-compact-last-item)${t}-compact-first-item`]:{[`${t}, ${t}-group-addon`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`&:not(${t}-compact-first-item)${t}-compact-last-item`]:{[`${t}, ${t}-group-addon`]:{borderStartStartRadius:0,borderEndStartRadius:0}},[`&:not(${t}-compact-last-item)${t}-compact-item`]:{[`${t}-affix-wrapper`]:{borderStartEndRadius:0,borderEndEndRadius:0}},[`&:not(${t}-compact-first-item)${t}-compact-item`]:{[`${t}-affix-wrapper`]:{borderStartStartRadius:0,borderEndStartRadius:0}}})})}})(t),(e=>{let{componentCls:t,antCls:r}=e,o=`${t}-search`;return{[o]:{[t]:{"&:not([disabled]):hover, &:not([disabled]):focus":{[`+ ${t}-group-addon ${o}-button:not(${r}-btn-color-primary):not(${r}-btn-variant-text)`]:{borderInlineStartColor:e.colorPrimaryHover}}},[`${t}-affix-wrapper`]:{height:e.controlHeight,borderRadius:0},[`${t}-lg`]:{lineHeight:e.calc(e.lineHeightLG).sub(2e-4).equal()},[`> ${t}-group`]:{[`> 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-        > ${t},
-        ${t}-affix-wrapper`]:{"&:hover, &:focus, &:active":{zIndex:2}},[`> ${t}-affix-wrapper-focused`]:{zIndex:2}}}}})(t),(e=>{let{componentCls:t}=e;return{[`${t}-out-of-range`]:{[`&, & input, & textarea, ${t}-show-count-suffix, ${t}-data-count`]:{color:e.colorError}}}})(t),(0,o.genCompactItemStyle)(t)]},l,{resetFont:!1});e.s(["default",0,T,"genBasicInputStyle",0,k,"genInputGroupStyle",0,j,"genInputSmallStyle",0,S,"genPlaceholderStyle",0,x,"useSharedStyle",0,O],349942)},831357,e=>{"use strict";var 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deleted file mode 100644
index df50ed3fb6..0000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/f69f708a0c17dacc.js b/litellm/proxy/_experimental/out/_next/static/chunks/d069df5baead6d90.js
similarity index 65%
rename from litellm/proxy/_experimental/out/_next/static/chunks/f69f708a0c17dacc.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/d069df5baead6d90.js
index 2b046f2435..66dcf73b6a 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/f69f708a0c17dacc.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/d069df5baead6d90.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/02158aed2f4518e2.js b/litellm/proxy/_experimental/out/_next/static/chunks/d2e3b7dd6499c245.js
similarity index 95%
rename from litellm/proxy/_experimental/out/_next/static/chunks/02158aed2f4518e2.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/d2e3b7dd6499c245.js
index fe9c184450..579d11a648 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/02158aed2f4518e2.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/d2e3b7dd6499c245.js
@@ -1 +1 @@
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Remove some to add more.`})]}),(0,t.jsx)("div",{className:"space-y-2 min-h-[100px]",children:0===e.fallbackModels.length?(0,t.jsxs)("div",{className:"h-32 border-2 border-dashed border-gray-300 rounded-lg flex flex-col items-center justify-center text-gray-400",children:[(0,t.jsx)("span",{className:"text-sm",children:"No fallback models selected"}),(0,t.jsx)("span",{className:"text-xs mt-1",children:"Add models from the dropdown above"})]}):e.fallbackModels.map((a,l)=>(0,t.jsxs)("div",{className:"group flex items-center justify-between p-3 bg-white rounded-lg border border-gray-200 hover:border-indigo-300 hover:shadow-sm transition-all",children:[(0,t.jsxs)("div",{className:"flex items-center gap-3",children:[(0,t.jsx)("div",{className:"flex items-center justify-center w-6 h-6 rounded bg-gray-100 text-gray-400 group-hover:text-indigo-500 group-hover:bg-indigo-50",children:(0,t.jsx)("span",{className:"text-xs font-bold",children:l+1})}),(0,t.jsx)("div",{children:(0,t.jsx)("span",{className:"font-medium text-gray-800",children:a})})]}),(0,t.jsx)("button",{type:"button",onClick:()=>{let t;return t=e.fallbackModels.filter((e,t)=>t!==l),void r({...e,fallbackModels:t})},className:"opacity-0 group-hover:opacity-100 transition-opacity text-gray-400 hover:text-red-500 p-1",children:(0,t.jsx)(b.X,{className:"w-4 h-4"})})]},`${a}-${l}`))})]})]})]})}function v({groups:e,onGroupsChange:r,availableModels:a,maxFallbacks:l=10,maxGroups:s=5}){let[n,i]=(0,g.useState)(e.length>0?e[0].id:"1");(0,g.useEffect)(()=>{e.length>0?e.some(e=>e.id===n)||i(e[0].id):i("1")},[e]);let o=()=>{if(e.length>=s)return;let t=Date.now().toString();r([...e,{id:t,primaryModel:null,fallbackModels:[]}]),i(t)},c=t=>{r(e.map(e=>e.id===t.id?t:e))},p=e.map((r,s)=>{let n=r.primaryModel?r.primaryModel:`Group ${s+1}`;return{key:r.id,label:n,closable:e.length>1,children:(0,t.jsx)(y,{group:r,onChange:c,availableModels:a,maxFallbacks:l})}});return 0===e.length?(0,t.jsxs)("div",{className:"text-center py-12 bg-gray-50 rounded-lg border border-dashed border-gray-300",children:[(0,t.jsx)("p",{className:"text-gray-500 mb-4",children:"No fallback groups configured"}),(0,t.jsx)(d.Button,{variant:"primary",onClick:o,icon:()=>(0,t.jsx)(m.Plus,{className:"w-4 h-4"}),children:"Create First Group"})]}):(0,t.jsx)(u.Tabs,{type:"editable-card",activeKey:n,onChange:i,onEdit:(t,a)=>{"add"===a?o():"remove"===a&&e.length>1&&(t=>{if(1===e.length)return f.default.warning("At least one group is required");let a=e.filter(e=>e.id!==t);r(a),n===t&&a.length>0&&i(a[a.length-1].id)})(t)},items:p,className:"fallback-tabs",tabBarStyle:{marginBottom:0},hideAdd:e.length>=s})}e.s(["FallbackSelectionForm",()=>v],419470)}]);
\ No newline at end of file
diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/d63044bdf28324dd.js b/litellm/proxy/_experimental/out/_next/static/chunks/d63044bdf28324dd.js
new file mode 100644
index 0000000000..f30581888a
--- /dev/null
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/d63044bdf28324dd.js
@@ -0,0 +1,38 @@
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Budget",name:"budget_duration",children:(0,t.jsxs)(E.Select,{defaultValue:null,placeholder:"n/a",children:[(0,t.jsx)(E.Select.Option,{value:"24h",children:"daily"}),(0,t.jsx)(E.Select.Option,{value:"7d",children:"weekly"}),(0,t.jsx)(E.Select.Option,{value:"30d",children:"monthly"})]})})]})]})]}),(0,t.jsx)("div",{style:{textAlign:"right",marginTop:"10px"},children:(0,t.jsx)(k.Button,{htmlType:"submit",children:"Save"})})]})})},N=`
+curl -X POST --location '<your_proxy_base_url>/end_user/new' \\
+
+-H 'Authorization: Bearer <your-master-key>' \\
+
+-H 'Content-Type: application/json' \\
+
+-d '{"user_id": "my-customer-id', "budget_id": "<BUDGET_ID>"}' # 👈 KEY CHANGE
+
+`,P=`
+curl -X POST --location '<your_proxy_base_url>/chat/completions' \\
+
+-H 'Authorization: Bearer <your-master-key>' \\
+
+-H 'Content-Type: application/json' \\
+
+-d '{
+  "model": "gpt-3.5-turbo',
+  "messages":[{"role": "user", "content": "Hey, how's it going?"}],
+  "user": "my-customer-id"
+}' # 👈 KEY CHANGE
+
+`,M=`from openai import OpenAI
+client = OpenAI(
+  base_url="<your_proxy_base_url>",
+  api_key="<your_proxy_key>"
+)
+
+completion = client.chat.completions.create(
+  model="gpt-3.5-turbo",
+  messages=[
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Hello!"}
+  ],
+  user="my-customer-id"
+)
+
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new file mode 100644
index 0000000000..51d3d1541c
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/92c3c06057498511.js b/litellm/proxy/_experimental/out/_next/static/chunks/dc8a270fee94ced6.js
similarity index 97%
rename from litellm/proxy/_experimental/out/_next/static/chunks/92c3c06057498511.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/dc8a270fee94ced6.js
index 6197c15438..397aec370e 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/92c3c06057498511.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/dc8a270fee94ced6.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/58461a445becf104.js b/litellm/proxy/_experimental/out/_next/static/chunks/df6546cd8a44d3b3.js
similarity index 70%
rename from litellm/proxy/_experimental/out/_next/static/chunks/58461a445becf104.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/df6546cd8a44d3b3.js
index ba3f4511b3..cf9da859aa 100644
--- a/litellm/proxy/_experimental/out/_next/static/chunks/58461a445becf104.js
+++ b/litellm/proxy/_experimental/out/_next/static/chunks/df6546cd8a44d3b3.js
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/673d847ad9c91666.js b/litellm/proxy/_experimental/out/_next/static/chunks/e0e37187792c3754.js
similarity index 88%
rename from litellm/proxy/_experimental/out/_next/static/chunks/673d847ad9c91666.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/e0e37187792c3754.js
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new file mode 100644
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new file mode 100644
index 0000000000..e66a19e1aa
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new file mode 100644
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/2a468361c849b2ff.js b/litellm/proxy/_experimental/out/_next/static/chunks/ea0f22bd4b3393bd.js
similarity index 78%
rename from litellm/proxy/_experimental/out/_next/static/chunks/2a468361c849b2ff.js
rename to litellm/proxy/_experimental/out/_next/static/chunks/ea0f22bd4b3393bd.js
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deleted file mode 100644
index d52c7ed905..0000000000
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diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/f213ca9417c86686.js b/litellm/proxy/_experimental/out/_next/static/chunks/f213ca9417c86686.js
deleted file mode 100644
index ecb957692c..0000000000
--- a/litellm/proxy/_experimental/out/_next/static/chunks/f213ca9417c86686.js
+++ /dev/null
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new file mode 100644
index 0000000000..5958d9e9d2
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new file mode 100644
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similarity index 98%
rename from litellm/proxy/_experimental/out/_next/static/chunks/turbopack-d1b22f5e0bd58c57.js
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diff --git a/litellm/proxy/_experimental/out/_not-found.txt b/litellm/proxy/_experimental/out/_not-found.txt
index 426df19262..5c6a19560f 100644
--- a/litellm/proxy/_experimental/out/_not-found.txt
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diff --git a/litellm/proxy/_experimental/out/_not-found.html b/litellm/proxy/_experimental/out/_not-found/index.html
similarity index 88%
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diff --git a/litellm/proxy/_experimental/out/settings/router-settings/__next.!KGRhc2hib2FyZCk.settings.router-settings.txt b/litellm/proxy/_experimental/out/settings/router-settings/__next.!KGRhc2hib2FyZCk.settings.router-settings.txt
index 1aca68f178..e52b3c68ff 100644
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index b14e96df78..abade69980 100644
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diff --git a/tests/local_testing/test_get_model_file.py b/tests/local_testing/test_get_model_file.py
index e36cf0c24c..17bd2d7cef 100644
--- a/tests/local_testing/test_get_model_file.py
+++ b/tests/local_testing/test_get_model_file.py
@@ -16,10 +16,10 @@ def test_get_model_cost_map():
         pytest.fail(f"An exception occurred: {e}")
 
 
-def test_get_local_model_cost_map():
-    """Test that load_local_model_cost_map returns a valid non-empty dict."""
-    from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap
-
-    content = GetModelCostMap.load_local_model_cost_map()
-    assert isinstance(content, dict)
-    assert len(content) > 0
+def test_get_backup_model_cost_map():
+    with importlib.resources.open_text(
+        "litellm", "model_prices_and_context_window_backup.json"
+    ) as f:
+        print("inside backup")
+        content = json.load(f)
+        print("content", content)
diff --git a/tests/test_litellm/test_deepseek_model_metadata.py b/tests/test_litellm/test_deepseek_model_metadata.py
index c6aae7ca53..4900af5d97 100644
--- a/tests/test_litellm/test_deepseek_model_metadata.py
+++ b/tests/test_litellm/test_deepseek_model_metadata.py
@@ -29,45 +29,48 @@ from litellm.utils import (
 # ---------------------------------------------------------------------------
 
 
-def _load_model_cost_json() -> dict:
-    """Load the model cost JSON via the production loading path."""
-    from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap
-
-    return GetModelCostMap.load_local_model_cost_map()
+def _load_backup_json() -> dict:
+    """Load the backup JSON directly from disk."""
+    backup_path = os.path.join(
+        os.path.dirname(litellm.__file__),
+        "model_prices_and_context_window_backup.json",
+    )
+    with open(backup_path, encoding="utf-8") as f:
+        return json.load(f)
 
 
 class TestDeepSeekModelCostEntries:
     """Verify that provider-prefixed DeepSeek entries contain the same
     capability flags as their bare-name counterparts in the JSON files."""
 
-    def test_deepseek_chat_supports_response_schema(self):
-        data = _load_model_cost_json()
+    def test_deepseek_chat_supports_response_schema_in_backup(self):
+        data = _load_backup_json()
         entry = data.get("deepseek/deepseek-chat", {})
         assert entry.get("supports_response_schema") is True
 
-    def test_deepseek_reasoner_supports_response_schema(self):
-        data = _load_model_cost_json()
+    def test_deepseek_reasoner_supports_response_schema_in_backup(self):
+        data = _load_backup_json()
         entry = data.get("deepseek/deepseek-reasoner", {})
         assert entry.get("supports_response_schema") is True
 
-    def test_deepseek_chat_supports_system_messages(self):
-        data = _load_model_cost_json()
+    def test_deepseek_chat_supports_system_messages_in_backup(self):
+        data = _load_backup_json()
         entry = data.get("deepseek/deepseek-chat", {})
         assert entry.get("supports_system_messages") is True
 
-    def test_deepseek_reasoner_supports_system_messages(self):
-        data = _load_model_cost_json()
+    def test_deepseek_reasoner_supports_system_messages_in_backup(self):
+        data = _load_backup_json()
         entry = data.get("deepseek/deepseek-reasoner", {})
         assert entry.get("supports_system_messages") is True
 
-    def test_deepseek_chat_max_input_tokens_matches_bare(self):
-        data = _load_model_cost_json()
+    def test_deepseek_chat_max_input_tokens_matches_bare_in_backup(self):
+        data = _load_backup_json()
         bare = data.get("deepseek-chat", {})
         prefixed = data.get("deepseek/deepseek-chat", {})
         assert prefixed.get("max_input_tokens") == bare.get("max_input_tokens")
 
-    def test_deepseek_reasoner_max_output_tokens_matches_bare(self):
-        data = _load_model_cost_json()
+    def test_deepseek_reasoner_max_output_tokens_matches_bare_in_backup(self):
+        data = _load_backup_json()
         bare = data.get("deepseek-reasoner", {})
         prefixed = data.get("deepseek/deepseek-reasoner", {})
         assert prefixed.get("max_output_tokens") == bare.get("max_output_tokens")