From 5e03ef73820f0ef64d472bd0f36186f5b91bf1c7 Mon Sep 17 00:00:00 2001 From: = Date: Sat, 4 Oct 2025 14:32:15 -0700 Subject: [PATCH 01/22] fixes bloated key alias network calls with lean endpoint --- .../key_management_endpoints.py | 75 ++++++++++++++++++- .../test_key_generate_prisma.py | 54 ++++++++++++- .../key_team_helpers/filter_helpers.ts | 42 ++--------- .../src/components/networking.tsx | 35 +++++++++ 4 files changed, 168 insertions(+), 38 deletions(-) diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index 007c0164be4..54e409bba79 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -15,7 +15,7 @@ import json import secrets import traceback from datetime import datetime, timedelta, timezone -from typing import List, Literal, Optional, Tuple, cast +from typing import List, Literal, Optional, Tuple, cast, Set import fastapi from fastapi import APIRouter, Depends, Header, HTTPException, Query, Request, status @@ -2614,6 +2614,79 @@ async def list_keys( code=status.HTTP_500_INTERNAL_SERVER_ERROR, ) +@router.get( + "/key/aliases", + tags=["key management"], + dependencies=[Depends(user_api_key_auth)], +) +@management_endpoint_wrapper +async def key_aliases() -> Dict[str, List[str]]: + """ + Lists all key aliases + + Returns: + { + "aliases": List[str] + } + """ + try: + from litellm.proxy.proxy_server import prisma_client + + verbose_proxy_logger.debug("Entering key_aliases function") + + if prisma_client is None: + verbose_proxy_logger.error("Database not connected") + raise Exception("Database not connected") + + where: Dict[str, Any] = {} + try: + where.update(_get_condition_to_filter_out_ui_session_tokens()) + except NameError: + # Helper may not exist in some builds; ignore if missing + pass + + rows = await prisma_client.db.litellm_verificationtoken.find_many( + where=where, + order=[{"key_alias": "asc"}], + ) + + seen = set() + aliases: List[str] = [] + for row in rows: + alias = getattr(row, "key_alias", None) + if alias is None and isinstance(row, dict): + alias = row.get("key_alias") + + if not alias: + continue + + alias_str = str(alias).strip() + if alias_str and alias_str not in seen: + seen.add(alias_str) + aliases.append(alias_str) + + verbose_proxy_logger.debug(f"Returning {len(aliases)} key aliases") + + return {"aliases": aliases} + + except Exception as e: + verbose_proxy_logger.exception(f"Error in key_aliases: {e}") + if isinstance(e, HTTPException): + raise ProxyException( + message=getattr(e, "detail", f"error({str(e)})"), + type=ProxyErrorTypes.internal_server_error, + param=getattr(e, "param", "None"), + code=getattr(e, "status_code", status.HTTP_500_INTERNAL_SERVER_ERROR), + ) + elif isinstance(e, ProxyException): + raise e + raise ProxyException( + message="Authentication Error, " + str(e), + type=ProxyErrorTypes.internal_server_error, + param=getattr(e, "param", "None"), + code=status.HTTP_500_INTERNAL_SERVER_ERROR, + ) + def _validate_sort_params( sort_by: Optional[str], sort_order: str diff --git a/tests/proxy_unit_tests/test_key_generate_prisma.py b/tests/proxy_unit_tests/test_key_generate_prisma.py index 6eb4efa7d63..a8df57283ca 100644 --- a/tests/proxy_unit_tests/test_key_generate_prisma.py +++ b/tests/proxy_unit_tests/test_key_generate_prisma.py @@ -60,7 +60,7 @@ from litellm.proxy.management_endpoints.key_management_endpoints import ( info_key_fn, list_keys, regenerate_key_fn, - update_key_fn, + update_key_fn, key_aliases, ) from litellm.proxy.management_endpoints.team_endpoints import ( new_team, @@ -3528,6 +3528,58 @@ async def test_list_keys(prisma_client): assert _key in response["keys"] +@pytest.mark.asyncio +async def test_key_aliases(prisma_client): + """ + Test the key_aliases function: + - Returns a list + - Includes alias from a newly created key + - Aliases are unique and sorted + """ + import asyncio + import uuid + import litellm + from litellm.proxy._types import LitellmUserRoles + + # Wire up test prisma client + setattr(litellm.proxy.proxy_server, "prisma_client", prisma_client) + setattr(litellm.proxy.proxy_server, "master_key", "sk-1234") + await litellm.proxy.proxy_server.prisma_client.connect() + + # Basic call + response = await key_aliases() + assert "aliases" in response + assert isinstance(response["aliases"], list) + + # Create a new user (and key) with a unique alias + unique_id = str(uuid.uuid4()) + test_alias = f"key-aliases-test-{unique_id}" + test_user_id = f"key-aliases-user-{unique_id}" + + await new_user( + data=NewUserRequest( + user_id=test_user_id, + user_role=LitellmUserRoles.INTERNAL_USER, + key_alias=test_alias, + ), + user_api_key_dict=UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN), + ) + + # Allow async DB writes to settle + await asyncio.sleep(2) + + # Call again and validate + response_after = await key_aliases() + aliases = response_after["aliases"] + + # Contains the new alias + assert test_alias in aliases + + # Unique & sorted (endpoint dedupes and orders ascending) + assert len(aliases) == len(set(aliases)) + assert aliases == sorted(aliases) + + @pytest.mark.asyncio async def test_auth_vertex_ai_route(prisma_client): """ diff --git a/ui/litellm-dashboard/src/components/key_team_helpers/filter_helpers.ts b/ui/litellm-dashboard/src/components/key_team_helpers/filter_helpers.ts index 887815596db..a46666cddc9 100644 --- a/ui/litellm-dashboard/src/components/key_team_helpers/filter_helpers.ts +++ b/ui/litellm-dashboard/src/components/key_team_helpers/filter_helpers.ts @@ -1,9 +1,9 @@ -import { keyListCall, teamListCall, organizationListCall } from "../networking"; +import { teamListCall, organizationListCall, keyAliasesCall } from "../networking" import { Team } from "./key_list"; import { Organization } from "../networking"; /** - * Fetches all key aliases across all pages + * Fetches all key aliases via the dedicated /key/aliases endpoint * @param accessToken The access token for API authentication * @returns Array of all unique key aliases */ @@ -13,46 +13,16 @@ export const fetchAllKeyAliases = async ( if (!accessToken) return []; try { - // Fetch all pages of keys to extract aliases - let allAliases: string[] = []; - let currentPage = 1; - let hasMorePages = true; - - while (hasMorePages) { - const response = await keyListCall( - accessToken, - null, // organization_id - "", // team_id - null, // selectedKeyAlias - null, // user_id - null, // key_hash - currentPage, - 100 // larger page size to reduce number of requests - ); - - // Extract aliases from this page - const pageAliases = response.keys - .map((key: any) => key.key_alias) - .filter(Boolean) as string[]; - - allAliases = [...allAliases, ...pageAliases]; - - // Check if there are more pages - if (currentPage < response.total_pages) { - currentPage++; - } else { - hasMorePages = false; - } - } - - // Remove duplicates - return Array.from(new Set(allAliases)); + const { aliases } = await keyAliasesCall(accessToken as unknown as String); + // Defensive dedupe & null-guard + return Array.from(new Set((aliases || []).filter(Boolean))); } catch (error) { console.error("Error fetching all key aliases:", error); return []; } }; + /** * Fetches all teams across all pages * @param accessToken The access token for API authentication diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index 6b8c7671a10..f674df444a5 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -3305,6 +3305,41 @@ export const keyListCall = async ( } }; +export const keyAliasesCall = async ( + accessToken: String +): Promise<{ aliases: string[] }> => { + /** + * Get all key aliases from proxy + */ + try { + let url = proxyBaseUrl ? `${proxyBaseUrl}/key/aliases` : `/key/aliases`; + console.log("in keyAliasesCall"); + + const response = await fetch(url, { + method: "GET", + headers: { + [globalLitellmHeaderName]: `Bearer ${accessToken}`, + "Content-Type": "application/json", + }, + }); + + if (!response.ok) { + const errorData = await response.json(); + const errorMessage = deriveErrorMessage(errorData); + handleError(errorMessage); + throw new Error(errorMessage); + } + + const data = await response.json(); + console.log("/key/aliases API Response:", data); + return data; // { aliases: string[] } + } catch (error) { + console.error("Failed to fetch key aliases:", error); + throw error; + } +}; + + export const spendUsersCall = async (accessToken: String, userID: String) => { try { const url = proxyBaseUrl ? `${proxyBaseUrl}/spend/users` : `/spend/users`; From 6ba077593f1601bcadb7f15aa729002d38a49ee9 Mon Sep 17 00:00:00 2001 From: = Date: Sat, 4 Oct 2025 14:36:19 -0700 Subject: [PATCH 02/22] Update test_key_generate_prisma.py --- tests/proxy_unit_tests/test_key_generate_prisma.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/tests/proxy_unit_tests/test_key_generate_prisma.py b/tests/proxy_unit_tests/test_key_generate_prisma.py index a8df57283ca..1968fde8436 100644 --- a/tests/proxy_unit_tests/test_key_generate_prisma.py +++ b/tests/proxy_unit_tests/test_key_generate_prisma.py @@ -60,7 +60,8 @@ from litellm.proxy.management_endpoints.key_management_endpoints import ( info_key_fn, list_keys, regenerate_key_fn, - update_key_fn, key_aliases, + update_key_fn, + key_aliases, ) from litellm.proxy.management_endpoints.team_endpoints import ( new_team, @@ -151,7 +152,6 @@ def prisma_client(): @pytest.mark.flaky(retries=6, delay=1) async def test_new_user_response(prisma_client): try: - print("prisma client=", prisma_client) setattr(litellm.proxy.proxy_server, "prisma_client", prisma_client) @@ -424,7 +424,6 @@ async def test_call_with_valid_model_using_all_models(prisma_client): setattr(litellm.proxy.proxy_server, "prisma_client", prisma_client) setattr(litellm.proxy.proxy_server, "master_key", "sk-1234") try: - await litellm.proxy.proxy_server.prisma_client.connect() team_request = NewTeamRequest( @@ -1786,7 +1785,6 @@ async def test_call_with_key_over_model_budget( litellm.callbacks.append(model_budget_limiter) try: - # set budget for chatgpt-v-3 to 0.000001, expect the next request to fail model_max_budget = { "gpt-4o-mini": { From 1ce21d1f58df807fcb11660f1ce6466545d153b4 Mon Sep 17 00:00:00 2001 From: = Date: Sat, 4 Oct 2025 16:02:15 -0700 Subject: [PATCH 03/22] Update key_management_endpoints.py --- litellm/proxy/management_endpoints/key_management_endpoints.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index 54e409bba79..cf101543011 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -15,7 +15,7 @@ import json import secrets import traceback from datetime import datetime, timedelta, timezone -from typing import List, Literal, Optional, Tuple, cast, Set +from typing import List, Literal, Optional, Tuple, cast import fastapi from fastapi import APIRouter, Depends, Header, HTTPException, Query, Request, status From acf8ec5b3c4708e21a747a8e8d901a741dcf3214 Mon Sep 17 00:00:00 2001 From: Otavio Brito Date: Sun, 5 Oct 2025 20:55:18 -0300 Subject: [PATCH 04/22] add context caching support for vertex ai --- .../context_caching/transformation.py | 18 +++- .../vertex_ai_context_caching.py | 71 ++++++++++++-- .../llms/vertex_ai/gemini/transformation.py | 93 ++++++++++--------- .../vertex_and_google_ai_studio_gemini.py | 21 ++++- 4 files changed, 142 insertions(+), 61 deletions(-) diff --git a/litellm/llms/vertex_ai/context_caching/transformation.py b/litellm/llms/vertex_ai/context_caching/transformation.py index f3ca699546f..bb40b7665c1 100644 --- a/litellm/llms/vertex_ai/context_caching/transformation.py +++ b/litellm/llms/vertex_ai/context_caching/transformation.py @@ -5,7 +5,7 @@ Why separate file? Make it easy to see how transformation works """ import re -from typing import List, Optional, Tuple +from typing import List, Optional, Tuple, Literal from litellm.types.llms.openai import AllMessageValues from litellm.types.llms.vertex_ai import CachedContentRequestBody @@ -155,13 +155,18 @@ def separate_cached_messages( def transform_openai_messages_to_gemini_context_caching( - model: str, messages: List[AllMessageValues], cache_key: str + model: str, + messages: List[AllMessageValues], + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + cache_key: str, + vertex_project: Optional[str], + vertex_location: Optional[str], ) -> CachedContentRequestBody: # Extract TTL from cached messages BEFORE system message transformation ttl = extract_ttl_from_cached_messages(messages) supports_system_message = get_supports_system_message( - model=model, custom_llm_provider="gemini" + model=model, custom_llm_provider=custom_llm_provider ) transformed_system_messages, new_messages = _transform_system_message( @@ -170,9 +175,14 @@ def transform_openai_messages_to_gemini_context_caching( transformed_messages = _gemini_convert_messages_with_history(messages=new_messages) + model_name = "models/{}".format(model) + + if custom_llm_provider == "vertex_ai" or custom_llm_provider == "vertex_ai_beta": + model_name = f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/{model_name}" + data = CachedContentRequestBody( contents=transformed_messages, - model="models/{}".format(model), + model=model_name, displayName=cache_key, ) diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py index 33a480aa6bb..d8e36471ae5 100644 --- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py +++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py @@ -23,6 +23,9 @@ from .transformation import ( transform_openai_messages_to_gemini_context_caching, ) +from litellm.types.llms.vertex_ai import * + + local_cache_obj = Cache( type=LiteLLMCacheType.LOCAL ) # only used for calling 'get_cache_key' function @@ -41,8 +44,11 @@ class ContextCachingEndpoints(VertexBase): def _get_token_and_url_context_caching( self, gemini_api_key: Optional[str], - custom_llm_provider: Literal["gemini"], + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], api_base: Optional[str], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], ) -> Tuple[Optional[str], str]: """ Internal function. Returns the token and url for the call. @@ -58,7 +64,10 @@ class ContextCachingEndpoints(VertexBase): url = "https://generativelanguage.googleapis.com/v1beta/{}?key={}".format( endpoint, gemini_api_key ) - + elif custom_llm_provider == "vertex_ai": + auth_header = vertex_auth_header + endpoint = "cachedContents" + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" else: raise NotImplementedError @@ -80,6 +89,10 @@ class ContextCachingEndpoints(VertexBase): api_key: str, api_base: Optional[str], logging_obj: Logging, + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], ) -> Optional[str]: """ Checks if content already cached. @@ -94,8 +107,11 @@ class ContextCachingEndpoints(VertexBase): _, url = self._get_token_and_url_context_caching( gemini_api_key=api_key, - custom_llm_provider="gemini", + custom_llm_provider=custom_llm_provider, api_base=api_base, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) try: ## LOGGING @@ -145,6 +161,10 @@ class ContextCachingEndpoints(VertexBase): api_key: str, api_base: Optional[str], logging_obj: Logging, + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str] ) -> Optional[str]: """ Checks if content already cached. @@ -159,8 +179,11 @@ class ContextCachingEndpoints(VertexBase): _, url = self._get_token_and_url_context_caching( gemini_api_key=api_key, - custom_llm_provider="gemini", + custom_llm_provider=custom_llm_provider, api_base=api_base, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) try: ## LOGGING @@ -212,6 +235,10 @@ class ContextCachingEndpoints(VertexBase): client: Optional[HTTPHandler], timeout: Optional[Union[float, httpx.Timeout]], logging_obj: Logging, + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], extra_headers: Optional[dict] = None, cached_content: Optional[str] = None, ) -> Tuple[List[AllMessageValues], dict, Optional[str]]: @@ -240,8 +267,11 @@ class ContextCachingEndpoints(VertexBase): ## AUTHORIZATION ## token, url = self._get_token_and_url_context_caching( gemini_api_key=api_key, - custom_llm_provider="gemini", + custom_llm_provider=custom_llm_provider, api_base=api_base, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) headers = { @@ -273,6 +303,10 @@ class ContextCachingEndpoints(VertexBase): api_key=api_key, api_base=api_base, logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) if google_cache_name: return non_cached_messages, optional_params, google_cache_name @@ -280,7 +314,12 @@ class ContextCachingEndpoints(VertexBase): ## TRANSFORM REQUEST cached_content_request_body = ( transform_openai_messages_to_gemini_context_caching( - model=model, messages=cached_messages, cache_key=generated_cache_key + model=model, + messages=cached_messages, + cache_key=generated_cache_key, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, ) ) @@ -328,6 +367,10 @@ class ContextCachingEndpoints(VertexBase): client: Optional[AsyncHTTPHandler], timeout: Optional[Union[float, httpx.Timeout]], logging_obj: Logging, + custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], extra_headers: Optional[dict] = None, cached_content: Optional[str] = None, ) -> Tuple[List[AllMessageValues], dict, Optional[str]]: @@ -356,8 +399,11 @@ class ContextCachingEndpoints(VertexBase): ## AUTHORIZATION ## token, url = self._get_token_and_url_context_caching( gemini_api_key=api_key, - custom_llm_provider="gemini", + custom_llm_provider=custom_llm_provider, api_base=api_base, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) headers = { @@ -386,6 +432,10 @@ class ContextCachingEndpoints(VertexBase): api_key=api_key, api_base=api_base, logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header ) if google_cache_name: @@ -394,7 +444,12 @@ class ContextCachingEndpoints(VertexBase): ## TRANSFORM REQUEST cached_content_request_body = ( transform_openai_messages_to_gemini_context_caching( - model=model, messages=cached_messages, cache_key=generated_cache_key + model=model, + messages=cached_messages, + cache_key=generated_cache_key, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, ) ) diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index ccaf28e5906..3d313456d19 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -514,34 +514,35 @@ def sync_transform_request_body( logging_obj: LiteLLMLoggingObj, custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], litellm_params: dict, + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], ) -> RequestBody: from ..context_caching.vertex_ai_context_caching import ContextCachingEndpoints context_caching_endpoints = ContextCachingEndpoints() - if gemini_api_key is not None: - ( - messages, - optional_params, - cached_content, - ) = context_caching_endpoints.check_and_create_cache( - messages=messages, - optional_params=optional_params, - api_key=gemini_api_key, - api_base=api_base, - model=model, - client=client, - timeout=timeout, - extra_headers=extra_headers, - cached_content=optional_params.pop("cached_content", None), - logging_obj=logging_obj, - ) - else: # [TODO] implement context caching for gemini as well - cached_content = None - if "cached_content" in optional_params: - cached_content = optional_params.pop("cached_content") - elif "cachedContent" in optional_params: - cached_content = optional_params.pop("cachedContent") + ( + messages, + optional_params, + cached_content, + ) = context_caching_endpoints.check_and_create_cache( + messages=messages, + optional_params=optional_params, + api_key=gemini_api_key or "dummy", + api_base=api_base, + model=model, + client=client, + timeout=timeout, + extra_headers=extra_headers, + cached_content=optional_params.pop("cached_content", None), + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header, + ) + return _transform_request_body( messages=messages, @@ -565,34 +566,34 @@ async def async_transform_request_body( logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, # type: ignore custom_llm_provider: Literal["vertex_ai", "vertex_ai_beta", "gemini"], litellm_params: dict, + vertex_project: Optional[str], + vertex_location: Optional[str], + vertex_auth_header: Optional[str], ) -> RequestBody: from ..context_caching.vertex_ai_context_caching import ContextCachingEndpoints context_caching_endpoints = ContextCachingEndpoints() - if gemini_api_key is not None: - ( - messages, - optional_params, - cached_content, - ) = await context_caching_endpoints.async_check_and_create_cache( - messages=messages, - optional_params=optional_params, - api_key=gemini_api_key, - api_base=api_base, - model=model, - client=client, - timeout=timeout, - extra_headers=extra_headers, - cached_content=optional_params.pop("cached_content", None), - logging_obj=logging_obj, - ) - else: # [TODO] implement context caching for gemini as well - cached_content = None - if "cached_content" in optional_params: - cached_content = optional_params.pop("cached_content") - elif "cachedContent" in optional_params: - cached_content = optional_params.pop("cachedContent") + ( + messages, + optional_params, + cached_content, + ) = await context_caching_endpoints.async_check_and_create_cache( + messages=messages, + optional_params=optional_params, + api_key=gemini_api_key or "dummy", + api_base=api_base, + model=model, + client=client, + timeout=timeout, + extra_headers=extra_headers, + cached_content=optional_params.pop("cached_content", None), + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=vertex_auth_header, + ) return _transform_request_body( messages=messages, diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index 9376b28cbec..8e071d7759b 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -1698,7 +1698,6 @@ class VertexLLM(VertexBase): gemini_api_key: Optional[str] = None, extra_headers: Optional[dict] = None, ) -> CustomStreamWrapper: - request_body = await async_transform_request_body(**data) # type: ignore should_use_v1beta1_features = self.is_using_v1beta1_features( optional_params=optional_params @@ -1732,6 +1731,13 @@ class VertexLLM(VertexBase): litellm_params=litellm_params, ) + request_body = await async_transform_request_body( + **data, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=auth_header) # type: ignore + + ## LOGGING logging_obj.pre_call( input=messages, @@ -1819,7 +1825,12 @@ class VertexLLM(VertexBase): litellm_params=litellm_params, ) - request_body = await async_transform_request_body(**data) # type: ignore + request_body = await async_transform_request_body( + **data, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=auth_header) # type: ignore + _async_client_params = {} if timeout: _async_client_params["timeout"] = timeout @@ -1994,7 +2005,11 @@ class VertexLLM(VertexBase): ) ## TRANSFORMATION ## - data = sync_transform_request_body(**transform_request_params) + data = sync_transform_request_body( + **transform_request_params, + vertex_project=vertex_project, + vertex_location=vertex_location, + vertex_auth_header=auth_header) ## LOGGING logging_obj.pre_call( From 80edf7020693ce8d8748462a465df6864f71a737 Mon Sep 17 00:00:00 2001 From: Otavio Brito Date: Sun, 5 Oct 2025 20:59:51 -0300 Subject: [PATCH 05/22] fix import --- .../vertex_ai/context_caching/vertex_ai_context_caching.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py index d8e36471ae5..1747f2f67ab 100644 --- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py +++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py @@ -23,9 +23,6 @@ from .transformation import ( transform_openai_messages_to_gemini_context_caching, ) -from litellm.types.llms.vertex_ai import * - - local_cache_obj = Cache( type=LiteLLMCacheType.LOCAL ) # only used for calling 'get_cache_key' function From 251d56ba5e3db403e7e48d346543558ef59bcb28 Mon Sep 17 00:00:00 2001 From: Otavio Brito Date: Sun, 5 Oct 2025 23:47:24 -0300 Subject: [PATCH 06/22] update docs --- docs/my-website/docs/providers/vertex.md | 156 +++++++++++++++- .../vertex_ai_context_caching.py | 5 +- .../test_vertex_ai_context_caching.py | 166 ++++++++++++++++-- 3 files changed, 309 insertions(+), 18 deletions(-) diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 3f4d1068958..e4a9c1b6b80 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -191,7 +191,7 @@ print(json.loads(completion.choices[0].message.content)) model_list: - model_name: gemini-2.5-pro litellm_params: - model: vertex_ai/gemini-1.5-pro + model: vertex_ai/gemini-2.5-pro vertex_project: "project-id" vertex_location: "us-central1" vertex_credentials: "/path/to/service_account.json" # [OPTIONAL] Do this OR `!gcloud auth application-default login` - run this to add vertex credentials to your env @@ -264,7 +264,7 @@ except JSONSchemaValidationError as e: model_list: - model_name: gemini-2.5-pro litellm_params: - model: vertex_ai/gemini-1.5-pro + model: vertex_ai/gemini-2.5-pro vertex_project: "project-id" vertex_location: "us-central1" vertex_credentials: "/path/to/service_account.json" # [OPTIONAL] Do this OR `!gcloud auth application-default login` - run this to add vertex credentials to your env @@ -811,11 +811,155 @@ curl http://0.0.0.0:4000/v1/chat/completions \ ### **Context Caching** -Use Vertex AI context caching is supported by calling provider api directly. (Unified Endpoint support coming soon.). +#### Unified Endpoint + +Use Vertex AI context caching in the same way as [**Google AI Studio - Context Caching**](../providers/gemini.md#context-caching) + + +##### Example usage + + + + +```python +from litellm import completion + +for _ in range(2): + resp = completion( + model="vertex_ai/gemini-2.5-pro", + messages=[ + # System Message + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" * 4000, + "cache_control": {"type": "ephemeral"}, # 👈 KEY CHANGE + } + ], + }, + # marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache. + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": {"type": "ephemeral"}, + } + ], + }] + ) + + print(resp.usage) # 👈 2nd usage block will be less, since cached tokens used +``` + + + + +```python +from litellm import completion + +# Cache for 2 hours (7200 seconds) +resp = completion( + model="vertex_ai/gemini-2.5-pro", + messages=[ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Here is the full text of a complex legal agreement" * 4000, + "cache_control": { + "type": "ephemeral", + "ttl": "7200s" # 👈 Cache for 2 hours + }, + } + ], + }, + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What are the key terms and conditions in this agreement?", + "cache_control": { + "type": "ephemeral", + "ttl": "3600s" # 👈 This TTL will be ignored (first one is used) + }, + } + ], + } + ] +) + +print(resp.usage) +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: gemini-2.5-pro + litellm_params: + model: vertex_ai/gemini-2.5-pro + vertex_project: "project-id" + vertex_location: "us-central1" + vertex_credentials: "/path/to/service_account.json" +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash + +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gemini-2.5-flash", + "messages": [ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Long cache message (must be >= 1025 tokens)", + "cache_control": { + "type": "ephemeral", + "ttl": "7200s" + } + } + ] + }, + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What is the text about?" + } + ] + } + ] +}' + +``` + +#### Calling provider api directly [**Go straight to provider**](../pass_through/vertex_ai.md#context-caching) -#### 1. Create the Cache +##### 1. Create the Cache First, create the cache by sending a `POST` request to the `cachedContents` endpoint via the LiteLLM proxy. @@ -841,7 +985,7 @@ curl http://0.0.0.0:4000/vertex_ai/v1/projects/{project_id}/locations/{location} -#### 2. Get the Cache Name from the Response +##### 2. Get the Cache Name from the Response Vertex AI will return a response containing the `name` of the cached content. This name is the identifier for your cached data. @@ -860,7 +1004,7 @@ Vertex AI will return a response containing the `name` of the cached content. Th } ``` -#### 3. Use the Cached Content +##### 3. Use the Cached Content Use the `name` from the response as `cachedContent` or `cached_content` in subsequent API calls to reuse the cached information. This is passed in the body of your request to `/chat/completions`. diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py index 1747f2f67ab..70b068b5a4d 100644 --- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py +++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py @@ -66,7 +66,10 @@ class ContextCachingEndpoints(VertexBase): endpoint = "cachedContents" url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" else: - raise NotImplementedError + auth_header = vertex_auth_header + endpoint = "cachedContents" + url = f"https://{vertex_location}-aiplatform.googleapis.com/v1beta1/projects/{vertex_project}/locations/{vertex_location}/{endpoint}" + return self._check_custom_proxy( api_base=api_base, diff --git a/tests/test_litellm/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py b/tests/test_litellm/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py index ef3404353ae..0320092c7a3 100644 --- a/tests/test_litellm/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py +++ b/tests/test_litellm/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py @@ -55,6 +55,9 @@ class TestContextCachingEndpoints: self.sample_optional_params = {"tools": self.sample_tools.copy()} + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) @@ -62,12 +65,14 @@ class TestContextCachingEndpoints: "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.local_cache_obj" ) def test_check_and_create_cache_with_cached_content( - self, mock_cache_obj, mock_separate + self, mock_cache_obj, mock_separate, custom_llm_provider ): """Test check_and_create_cache when cached_content is provided""" # Setup cached_content = "cached_content_123" optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute result = self.context_caching.check_and_create_cache( @@ -80,6 +85,10 @@ class TestContextCachingEndpoints: timeout=30.0, logging_obj=self.mock_logging, cached_content=cached_content, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert @@ -92,14 +101,21 @@ class TestContextCachingEndpoints: mock_separate.assert_not_called() mock_cache_obj.get_cache_key.assert_not_called() + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) - def test_check_and_create_cache_no_cached_messages(self, mock_separate): + def test_check_and_create_cache_no_cached_messages( + self, mock_separate, custom_llm_provider + ): """Test check_and_create_cache when no cached messages are found""" # Setup mock_separate.return_value = ([], self.sample_messages) # No cached messages optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute result = self.context_caching.check_and_create_cache( @@ -111,6 +127,10 @@ class TestContextCachingEndpoints: client=self.mock_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert @@ -119,6 +139,9 @@ class TestContextCachingEndpoints: assert returned_params == optional_params assert returned_cache is None + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) @@ -127,7 +150,7 @@ class TestContextCachingEndpoints: ) @patch.object(ContextCachingEndpoints, "check_cache") def test_check_and_create_cache_existing_cache_found( - self, mock_check_cache, mock_cache_obj, mock_separate + self, mock_check_cache, mock_cache_obj, mock_separate, custom_llm_provider ): """Test check_and_create_cache when existing cache is found""" # Setup @@ -139,6 +162,8 @@ class TestContextCachingEndpoints: mock_check_cache.return_value = "existing_cache_name" optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute result = self.context_caching.check_and_create_cache( @@ -150,6 +175,10 @@ class TestContextCachingEndpoints: client=self.mock_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert @@ -163,6 +192,9 @@ class TestContextCachingEndpoints: messages=cached_messages, tools=self.sample_tools ) + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) @@ -181,6 +213,7 @@ class TestContextCachingEndpoints: mock_transform, mock_cache_obj, mock_separate, + custom_llm_provider, ): """Test check_and_create_cache when creating new cache""" # Setup @@ -203,6 +236,8 @@ class TestContextCachingEndpoints: self.mock_client.post.return_value = mock_response optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute result = self.context_caching.check_and_create_cache( @@ -214,6 +249,10 @@ class TestContextCachingEndpoints: client=self.mock_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert @@ -228,6 +267,9 @@ class TestContextCachingEndpoints: assert "tools" in call_args.kwargs["json"] assert call_args.kwargs["json"]["tools"] == self.sample_tools + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) @@ -237,7 +279,12 @@ class TestContextCachingEndpoints: @patch.object(ContextCachingEndpoints, "check_cache") @patch.object(ContextCachingEndpoints, "_get_token_and_url_context_caching") def test_check_and_create_cache_http_error( - self, mock_get_token_url, mock_check_cache, mock_cache_obj, mock_separate + self, + mock_get_token_url, + mock_check_cache, + mock_cache_obj, + mock_separate, + custom_llm_provider, ): """Test check_and_create_cache handles HTTP errors properly""" # Setup @@ -259,6 +306,8 @@ class TestContextCachingEndpoints: self.mock_client.post.side_effect = http_error optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute and Assert with pytest.raises(VertexAIError) as exc_info: @@ -271,12 +320,19 @@ class TestContextCachingEndpoints: client=self.mock_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) assert exc_info.value.status_code == 400 assert "Bad Request" in str(exc_info.value.message) @pytest.mark.asyncio + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) @@ -284,12 +340,14 @@ class TestContextCachingEndpoints: "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.local_cache_obj" ) async def test_async_check_and_create_cache_with_cached_content( - self, mock_cache_obj, mock_separate + self, mock_cache_obj, mock_separate, custom_llm_provider ): """Test async_check_and_create_cache when cached_content is provided""" # Setup cached_content = "cached_content_123" optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute result = await self.context_caching.async_check_and_create_cache( @@ -302,6 +360,10 @@ class TestContextCachingEndpoints: timeout=30.0, logging_obj=self.mock_logging, cached_content=cached_content, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert @@ -311,14 +373,21 @@ class TestContextCachingEndpoints: assert returned_cache == cached_content @pytest.mark.asyncio + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) - async def test_async_check_and_create_cache_no_cached_messages(self, mock_separate): + async def test_async_check_and_create_cache_no_cached_messages( + self, mock_separate, custom_llm_provider + ): """Test async_check_and_create_cache when no cached messages are found""" # Setup mock_separate.return_value = ([], self.sample_messages) optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute result = await self.context_caching.async_check_and_create_cache( @@ -330,6 +399,10 @@ class TestContextCachingEndpoints: client=self.mock_async_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert @@ -339,6 +412,9 @@ class TestContextCachingEndpoints: assert returned_cache is None @pytest.mark.asyncio + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) @@ -347,7 +423,7 @@ class TestContextCachingEndpoints: ) @patch.object(ContextCachingEndpoints, "async_check_cache") async def test_async_check_and_create_cache_existing_cache_found( - self, mock_async_check_cache, mock_cache_obj, mock_separate + self, mock_async_check_cache, mock_cache_obj, mock_separate, custom_llm_provider ): """Test async_check_and_create_cache when existing cache is found""" # Setup @@ -359,6 +435,8 @@ class TestContextCachingEndpoints: mock_async_check_cache.return_value = "existing_cache_name" optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute result = await self.context_caching.async_check_and_create_cache( @@ -370,6 +448,10 @@ class TestContextCachingEndpoints: client=self.mock_async_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert @@ -384,6 +466,9 @@ class TestContextCachingEndpoints: ) @pytest.mark.asyncio + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) @@ -406,6 +491,7 @@ class TestContextCachingEndpoints: mock_transform, mock_cache_obj, mock_separate, + custom_llm_provider, ): """Test async_check_and_create_cache when creating new cache""" # Setup @@ -428,6 +514,8 @@ class TestContextCachingEndpoints: self.mock_async_client.post = AsyncMock(return_value=mock_response) optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute result = await self.context_caching.async_check_and_create_cache( @@ -439,6 +527,10 @@ class TestContextCachingEndpoints: client=self.mock_async_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert @@ -454,6 +546,9 @@ class TestContextCachingEndpoints: assert call_args.kwargs["json"]["tools"] == self.sample_tools @pytest.mark.asyncio + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) @patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" ) @@ -472,6 +567,7 @@ class TestContextCachingEndpoints: mock_async_check_cache, mock_cache_obj, mock_separate, + custom_llm_provider, ): """Test async_check_and_create_cache handles timeout errors properly""" # Setup @@ -489,6 +585,8 @@ class TestContextCachingEndpoints: ) optional_params = self.sample_optional_params.copy() + test_project = "test_project" + test_location = "test_location" # Execute and Assert with pytest.raises(VertexAIError) as exc_info: @@ -501,12 +599,21 @@ class TestContextCachingEndpoints: client=self.mock_async_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) assert exc_info.value.status_code == 408 assert "Timeout error occurred" in str(exc_info.value.message) - def test_check_and_create_cache_tools_popped_from_optional_params(self): + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) + def test_check_and_create_cache_tools_popped_from_optional_params( + self, custom_llm_provider + ): """Test that tools are properly popped from optional_params when there are cached messages""" with patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" @@ -520,6 +627,8 @@ class TestContextCachingEndpoints: optional_params = self.sample_optional_params.copy() original_tools = optional_params["tools"].copy() + test_project = "test_project" + test_location = "test_location" # Mock the check_cache to return existing cache so we don't make HTTP calls with patch.object( @@ -535,6 +644,10 @@ class TestContextCachingEndpoints: client=self.mock_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert tools were popped from optional_params @@ -543,7 +656,12 @@ class TestContextCachingEndpoints: # But original tools should still be available for comparison assert original_tools == self.sample_tools - def test_check_and_create_cache_tools_not_popped_when_no_cached_messages(self): + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) + def test_check_and_create_cache_tools_not_popped_when_no_cached_messages( + self, custom_llm_provider + ): """Test that tools are NOT popped from optional_params when there are no cached messages""" with patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" @@ -555,6 +673,8 @@ class TestContextCachingEndpoints: optional_params = self.sample_optional_params.copy() original_tools = optional_params["tools"].copy() + test_project = "test_project" + test_location = "test_location" # Execute result = self.context_caching.check_and_create_cache( @@ -566,6 +686,10 @@ class TestContextCachingEndpoints: client=self.mock_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert tools were NOT popped from optional_params (early return) @@ -573,8 +697,11 @@ class TestContextCachingEndpoints: assert optional_params["tools"] == original_tools @pytest.mark.asyncio + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) async def test_async_check_and_create_cache_tools_not_popped_when_no_cached_messages( - self, + self, custom_llm_provider ): """Test that tools are NOT popped from optional_params in async version when there are no cached messages""" with patch( @@ -587,6 +714,8 @@ class TestContextCachingEndpoints: optional_params = self.sample_optional_params.copy() original_tools = optional_params["tools"].copy() + test_project = "test_project" + test_location = "test_location" # Execute result = await self.context_caching.async_check_and_create_cache( @@ -598,6 +727,10 @@ class TestContextCachingEndpoints: client=self.mock_async_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert tools were NOT popped from optional_params (early return) @@ -605,7 +738,12 @@ class TestContextCachingEndpoints: assert optional_params["tools"] == original_tools @pytest.mark.asyncio - async def test_async_check_and_create_cache_tools_popped_from_optional_params(self): + @pytest.mark.parametrize( + "custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"] + ) + async def test_async_check_and_create_cache_tools_popped_from_optional_params( + self, custom_llm_provider + ): """Test that tools are properly popped from optional_params in async version when there are cached messages""" with patch( "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages" @@ -619,6 +757,8 @@ class TestContextCachingEndpoints: optional_params = self.sample_optional_params.copy() original_tools = optional_params["tools"].copy() + test_project = "test_project" + test_location = "test_location" # Mock the async_check_cache to return existing cache so we don't make HTTP calls with patch.object( @@ -634,6 +774,10 @@ class TestContextCachingEndpoints: client=self.mock_async_client, timeout=30.0, logging_obj=self.mock_logging, + custom_llm_provider=custom_llm_provider, + vertex_project=test_project, + vertex_location=test_location, + vertex_auth_header="vertext_test_token", ) # Assert tools were popped from optional_params From b2a734c1f8677934c907ad146d3006ef11059234 Mon Sep 17 00:00:00 2001 From: Otavio Brito Date: Mon, 6 Oct 2025 09:30:23 -0300 Subject: [PATCH 07/22] fix ttl tests --- docs/my-website/docs/providers/vertex.md | 2 +- .../test_context_caching_ttl.py | 72 +++++++++++++++---- 2 files changed, 61 insertions(+), 13 deletions(-) diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index e4a9c1b6b80..3d8e7818ff3 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -933,7 +933,7 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ "content": [ { "type": "text", - "text": "Long cache message (must be >= 1025 tokens)", + "text": "Long cache message (must be >= 1024 tokens)", "cache_control": { "type": "ephemeral", "ttl": "7200s" diff --git a/tests/test_litellm/llms/vertex_ai/context_caching/test_context_caching_ttl.py b/tests/test_litellm/llms/vertex_ai/context_caching/test_context_caching_ttl.py index cce6055ab3f..f230d814ae6 100644 --- a/tests/test_litellm/llms/vertex_ai/context_caching/test_context_caching_ttl.py +++ b/tests/test_litellm/llms/vertex_ai/context_caching/test_context_caching_ttl.py @@ -191,7 +191,8 @@ class TestTTLExtraction: class TestTransformationWithTTL: """Test the complete transformation with TTL support""" - def test_transform_with_valid_ttl(self): + @pytest.mark.parametrize("custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"]) + def test_transform_with_valid_ttl(self, custom_llm_provider): """Test transformation includes TTL when provided""" messages = [ { @@ -205,19 +206,32 @@ class TestTransformationWithTTL: ] } ] + + vertex_location="test_location" + vertex_project="test_project" result = transform_openai_messages_to_gemini_context_caching( model="gemini-1.5-pro", messages=messages, - cache_key="test-cache-key" + cache_key="test-cache-key", + custom_llm_provider=custom_llm_provider, + vertex_location="test_location", + vertex_project="test_project" ) assert "ttl" in result assert result["ttl"] == "3600s" - assert result["model"] == "models/gemini-1.5-pro" + + if custom_llm_provider == "gemini": + assert result["model"] == "models/gemini-1.5-pro" + else: + assert result["model"] == f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/gemini-1.5-pro" + + assert result["displayName"] == "test-cache-key" - def test_transform_without_ttl(self): + @pytest.mark.parametrize("custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"]) + def test_transform_without_ttl(self, custom_llm_provider): """Test transformation without TTL""" messages = [ { @@ -231,18 +245,30 @@ class TestTransformationWithTTL: ] } ] + + vertex_location="test_location" + vertex_project="test_project" result = transform_openai_messages_to_gemini_context_caching( model="gemini-1.5-pro", messages=messages, - cache_key="test-cache-key" + cache_key="test-cache-key", + custom_llm_provider=custom_llm_provider, + vertex_location=vertex_location, + vertex_project=vertex_project ) assert "ttl" not in result - assert result["model"] == "models/gemini-1.5-pro" + + if custom_llm_provider == "gemini": + assert result["model"] == "models/gemini-1.5-pro" + else: + assert result["model"] == f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/gemini-1.5-pro" + assert result["displayName"] == "test-cache-key" - def test_transform_with_invalid_ttl(self): + @pytest.mark.parametrize("custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"]) + def test_transform_with_invalid_ttl(self, custom_llm_provider): """Test transformation with invalid TTL (should be ignored)""" messages = [ { @@ -256,18 +282,29 @@ class TestTransformationWithTTL: ] } ] + vertex_location="test_location" + vertex_project="test_project" result = transform_openai_messages_to_gemini_context_caching( model="gemini-1.5-pro", messages=messages, - cache_key="test-cache-key" + cache_key="test-cache-key", + custom_llm_provider=custom_llm_provider, + vertex_location=vertex_location, + vertex_project=vertex_project ) assert "ttl" not in result - assert result["model"] == "models/gemini-1.5-pro" + + if custom_llm_provider == "gemini": + assert result["model"] == "models/gemini-1.5-pro" + else: + assert result["model"] == f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/gemini-1.5-pro" + assert result["displayName"] == "test-cache-key" - def test_transform_with_system_message_and_ttl(self): + @pytest.mark.parametrize("custom_llm_provider", ["gemini", "vertex_ai", "vertex_ai_beta"]) + def test_transform_with_system_message_and_ttl(self, custom_llm_provider): """Test transformation with system message and TTL""" messages = [ { @@ -290,17 +327,28 @@ class TestTransformationWithTTL: ] } ] + + vertex_location="test_location" + vertex_project="test_project" result = transform_openai_messages_to_gemini_context_caching( model="gemini-1.5-pro", messages=messages, - cache_key="test-cache-key" + cache_key="test-cache-key", + custom_llm_provider=custom_llm_provider, + vertex_location=vertex_location, + vertex_project=vertex_project ) assert "ttl" in result assert result["ttl"] == "7200s" assert "system_instruction" in result - assert result["model"] == "models/gemini-1.5-pro" + + if custom_llm_provider == "gemini": + assert result["model"] == "models/gemini-1.5-pro" + else: + assert result["model"] == f"projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/gemini-1.5-pro" + assert result["displayName"] == "test-cache-key" From cd257823593d0abbf65fce07af3a5e117ed646ba Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 7 Oct 2025 01:29:19 +0530 Subject: [PATCH 08/22] fix passing headers for gemini (#15231) --- litellm/main.py | 4 +- .../test_gemini_header_forwarding.py | 172 ++++++++++++++++++ 2 files changed, 174 insertions(+), 2 deletions(-) create mode 100644 tests/test_litellm/llms/vertex_ai/test_gemini_header_forwarding.py diff --git a/litellm/main.py b/litellm/main.py index cfb0bef0797..46a024a7631 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -2872,7 +2872,7 @@ def completion( # type: ignore # noqa: PLR0915 custom_llm_provider=custom_llm_provider, # type: ignore client=client, api_base=api_base, - extra_headers=extra_headers, + extra_headers=headers, ) elif custom_llm_provider == "vertex_ai": @@ -2941,7 +2941,7 @@ def completion( # type: ignore # noqa: PLR0915 custom_llm_provider=custom_llm_provider, # type: ignore client=client, api_base=api_base, - extra_headers=extra_headers, + extra_headers=headers, ) elif "openai" in model: # Vertex Model Garden - OpenAI compatible models diff --git a/tests/test_litellm/llms/vertex_ai/test_gemini_header_forwarding.py b/tests/test_litellm/llms/vertex_ai/test_gemini_header_forwarding.py new file mode 100644 index 00000000000..16eaaac265a --- /dev/null +++ b/tests/test_litellm/llms/vertex_ai/test_gemini_header_forwarding.py @@ -0,0 +1,172 @@ +""" +Test to verify that custom headers are correctly forwarded to Gemini/Vertex AI API calls. + +This test verifies the fix for the issue where headers configured via +forward_client_headers_to_llm_api were not being passed to Gemini/Vertex AI providers. +""" + +import pytest +from unittest.mock import Mock, patch, MagicMock +import litellm +from litellm import completion + + +class TestGeminiHeaderForwarding: + """Test cases for verifying header forwarding to Gemini/Vertex AI.""" + + @pytest.mark.parametrize( + "custom_llm_provider,model", + [ + ("gemini", "gemini/gemini-1.5-pro"), + ("vertex_ai_beta", "gemini-1.5-pro"), + ("vertex_ai", "gemini-1.5-pro"), + ], + ) + def test_headers_forwarded_to_gemini(self, custom_llm_provider, model): + """ + Test that headers from kwargs are correctly merged and passed to Gemini completion. + + This test verifies that when headers are passed via kwargs (as the proxy does when + forward_client_headers_to_llm_api is configured), they are correctly merged with + extra_headers and passed to the Vertex AI completion handler. + """ + messages = [{"role": "user", "content": "Hello"}] + + # Headers that would be set by the proxy when forwarding client headers + custom_headers = { + "X-Custom-Header": "CustomValue", + "X-BYOK-Token": "secret-token", + } + + # Mock the vertex completion handler + with patch( + "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM.completion" + ) as mock_vertex_completion: + # Configure the mock to return a proper response + mock_response = Mock() + mock_response.choices = [Mock()] + mock_response.choices[0].message.content = "Hello back!" + mock_vertex_completion.return_value = mock_response + + try: + # Call completion with custom headers via kwargs + # This simulates what the proxy does when forward_client_headers_to_llm_api is set + completion( + model=model, + messages=messages, + headers=custom_headers, # This is how proxy passes forwarded headers + custom_llm_provider=custom_llm_provider, + api_key="dummy-key", + ) + + # Verify that the completion handler was called + assert mock_vertex_completion.called, "Vertex completion handler should be called" + + # Get the actual call arguments + call_kwargs = mock_vertex_completion.call_args.kwargs + + # Verify that extra_headers parameter contains our custom headers + assert "extra_headers" in call_kwargs, "extra_headers should be passed to completion" + + passed_headers = call_kwargs["extra_headers"] + assert passed_headers is not None, "extra_headers should not be None" + + # Verify our custom headers are present in the passed headers + for header_key, header_value in custom_headers.items(): + assert ( + header_key in passed_headers + or header_key.lower() in passed_headers + ), f"Header {header_key} should be in extra_headers" + + print(f"✓ Test passed for {custom_llm_provider}/{model}") + print(f" Headers correctly forwarded: {passed_headers}") + + except Exception as e: + pytest.fail( + f"Failed to forward headers to {custom_llm_provider}/{model}: {str(e)}" + ) + + def test_extra_headers_and_headers_merge(self): + """ + Test that both extra_headers and headers parameters are correctly merged. + + This ensures that headers from kwargs (forwarded by proxy) and extra_headers + (passed explicitly) are both included in the final headers sent to the provider. + """ + messages = [{"role": "user", "content": "Hello"}] + + # Headers from proxy (via kwargs["headers"]) + proxy_headers = {"X-Forwarded-Header": "ProxyValue"} + + # Explicit extra_headers + explicit_headers = {"X-Explicit-Header": "ExplicitValue"} + + with patch( + "litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini.VertexLLM.completion" + ) as mock_vertex_completion: + mock_response = Mock() + mock_response.choices = [Mock()] + mock_response.choices[0].message.content = "Response" + mock_vertex_completion.return_value = mock_response + + try: + completion( + model="gemini/gemini-1.5-pro", + messages=messages, + headers=proxy_headers, # From proxy forwarding + extra_headers=explicit_headers, # Explicitly passed + custom_llm_provider="gemini", + api_key="dummy-key", + ) + + call_kwargs = mock_vertex_completion.call_args.kwargs + passed_headers = call_kwargs.get("extra_headers", {}) + + # Both sets of headers should be present + assert ( + "X-Forwarded-Header" in passed_headers + or "x-forwarded-header" in passed_headers + ), "Proxy forwarded header should be present" + + assert ( + "X-Explicit-Header" in passed_headers + or "x-explicit-header" in passed_headers + ), "Explicitly passed header should be present" + + print("✓ Both header sources correctly merged and forwarded") + print(f" Final headers: {passed_headers}") + + except Exception as e: + pytest.fail(f"Failed to merge and forward headers: {str(e)}") + + +if __name__ == "__main__": + # Run the tests + test_instance = TestGeminiHeaderForwarding() + + print("\n" + "="*80) + print("Testing Gemini/Vertex AI Header Forwarding") + print("="*80 + "\n") + + # Test each provider + for provider, model in [ + ("gemini", "gemini/gemini-1.5-pro"), + ("vertex_ai_beta", "gemini-1.5-pro"), + ("vertex_ai", "gemini-1.5-pro"), + ]: + print(f"\nTesting {provider}/{model}...") + try: + test_instance.test_headers_forwarded_to_gemini(provider, model) + except Exception as e: + print(f"✗ Test failed: {e}") + + print("\n\nTesting header merging...") + try: + test_instance.test_extra_headers_and_headers_merge() + except Exception as e: + print(f"✗ Test failed: {e}") + + print("\n" + "="*80) + print("All tests completed!") + print("="*80 + "\n") + From a56069b722261b5bc10954ec89777ff864f0fb32 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Mon, 6 Oct 2025 13:16:52 -0700 Subject: [PATCH 09/22] UI: new build --- ...odel_prices_and_context_window_backup.json | 301 ++++++++++++++++++ .../_buildManifest.js | 0 .../_ssgManifest.js | 0 .../static/chunks/154-f87cf692dcea3018.js | 1 - ...7ff1a4ad9f4.js => 162-ffcd7d9fbb033bdf.js} | 2 +- ...0a98e9413e5.js => 313-0025fb08e386c4b8.js} | 4 +- .../static/chunks/49-b6f167418ea8dbf4.js | 1 + .../static/chunks/851-0a73701a3a0b0187.js | 1 + .../static/chunks/851-bbe6d02cf41bb87a.js | 1 - ...0e07cf314f6.js => 866-9e1803a09e9ae8da.js} | 2 +- ...5eb79b09.js => layout-df4939b4f2ec37d7.js} | 2 +- ...73f13202c4.js => page-d7915f579b770030.js} | 2 +- .../model_hub_table/page-2f23c22a47b20607.js | 1 + .../model_hub_table/page-5d1aa98a47f9e9fd.js | 1 - .../app/onboarding/page-099f7aa4c559d470.js | 1 - .../app/onboarding/page-1fd26064f407ad1d.js | 1 + .../chunks/app/page-5b9ff2d173a47e2c.js | 1 + .../chunks/app/page-f400068ac45ce482.js | 1 - ...45d2c7.js => main-app-bb35cb240fbb9dca.js} | 2 +- .../out/_next/static/css/4103fa525703177b.css | 3 - .../out/_next/static/css/c200be8dd8638678.css | 3 + .../out/assets/logos/snowflake.svg | 9 + litellm/proxy/_experimental/out/index.html | 2 +- litellm/proxy/_experimental/out/index.txt | 4 +- litellm/proxy/_experimental/out/model_hub.txt | 4 +- .../_experimental/out/model_hub_table.html | 1 + .../_experimental/out/model_hub_table.txt | 4 +- .../out/model_hub_table/index.html | 1 - .../proxy/_experimental/out/onboarding.html | 1 + .../proxy/_experimental/out/onboarding.txt | 4 +- 30 files changed, 336 insertions(+), 25 deletions(-) rename litellm/proxy/_experimental/out/_next/static/{WkpkdsewrdPMuTzVGS_5j => _S0i_Y-CCoYQWc9dIuLxF}/_buildManifest.js (100%) rename litellm/proxy/_experimental/out/_next/static/{WkpkdsewrdPMuTzVGS_5j => _S0i_Y-CCoYQWc9dIuLxF}/_ssgManifest.js (100%) delete mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/154-f87cf692dcea3018.js rename litellm/proxy/_experimental/out/_next/static/chunks/{162-dd6427ff1a4ad9f4.js => 162-ffcd7d9fbb033bdf.js} (50%) rename litellm/proxy/_experimental/out/_next/static/chunks/{313-27c820a98e9413e5.js => 313-0025fb08e386c4b8.js} (72%) create mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/49-b6f167418ea8dbf4.js create mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/851-0a73701a3a0b0187.js delete mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/851-bbe6d02cf41bb87a.js rename litellm/proxy/_experimental/out/_next/static/chunks/{866-3523e0e07cf314f6.js => 866-9e1803a09e9ae8da.js} (99%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/{layout-e5e6fe835eb79b09.js => layout-df4939b4f2ec37d7.js} (59%) rename litellm/proxy/_experimental/out/_next/static/chunks/app/model_hub/{page-237d2973f13202c4.js => page-d7915f579b770030.js} (50%) create mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/model_hub_table/page-2f23c22a47b20607.js delete mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/model_hub_table/page-5d1aa98a47f9e9fd.js delete mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/onboarding/page-099f7aa4c559d470.js create mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/onboarding/page-1fd26064f407ad1d.js create mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/page-5b9ff2d173a47e2c.js delete mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/page-f400068ac45ce482.js rename litellm/proxy/_experimental/out/_next/static/chunks/{main-app-0591b0cd3445d2c7.js => main-app-bb35cb240fbb9dca.js} (54%) delete mode 100644 litellm/proxy/_experimental/out/_next/static/css/4103fa525703177b.css create mode 100644 litellm/proxy/_experimental/out/_next/static/css/c200be8dd8638678.css create mode 100644 litellm/proxy/_experimental/out/assets/logos/snowflake.svg create mode 100644 litellm/proxy/_experimental/out/model_hub_table.html delete mode 100644 litellm/proxy/_experimental/out/model_hub_table/index.html create mode 100644 litellm/proxy/_experimental/out/onboarding.html diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 7ed04dc79c9..fd09aca4c1a 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -22173,6 +22173,307 @@ "supports_tool_choice": true, "supports_vision": false }, + "watsonx/bigscience/mt0-xxl-13b": { + "max_tokens": 8192, + "max_input_tokens": 8192, + "max_output_tokens": 8192, + 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or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(j,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(_,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(j,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(j,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(_,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(j,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(y,"\n").concat(t)}},49817:function(e,t,n){var a,s,r,i;n.d(t,{KP:function(){return s},vf:function(){return l}}),(r=a||(a={})).IMAGE_GENERATION="image_generation",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",(i=s||(s={})).IMAGE="image",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages";let o={image_generation:"image",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=o[e];return console.log("endpointType:",t),t}return"chat"}},29488:function(e,t,n){n.d(t,{Hc:function(){return 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a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(j,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(j,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(_,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# 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