From 732063074d282da439cc422727d196289d6b2b07 Mon Sep 17 00:00:00 2001 From: Classic298 <27028174+Classic298@users.noreply.github.com> Date: Mon, 13 Oct 2025 14:20:24 +0200 Subject: [PATCH 01/82] correct claude opus --- model_prices_and_context_window.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 91a23b7f00f..0b0172bee6f 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -21560,8 +21560,8 @@ "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "vertex_ai-anthropic_models", "max_input_tokens": 200000, - "max_output_tokens": 4096, - "max_tokens": 4096, + "max_output_tokens": 32000, + "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 7.5e-05, "output_cost_per_token_batches": 3.75e-05, @@ -21577,8 +21577,8 @@ "input_cost_per_token_batches": 7.5e-06, "litellm_provider": "vertex_ai-anthropic_models", "max_input_tokens": 200000, - "max_output_tokens": 4096, - "max_tokens": 4096, + "max_output_tokens": 32000, + "max_tokens": 32000, "mode": "chat", "output_cost_per_token": 7.5e-05, "output_cost_per_token_batches": 3.75e-05, From 55e44e1b1d037e06b5effeb29e234225cea12aa7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Hana=20Volk=C5=AF?= Date: Tue, 14 Oct 2025 14:51:55 +0200 Subject: [PATCH 02/82] Prompt caching for anthropic models with openrouter --- .../model_prices_and_context_window_backup.json | 17 +++++++++++++++++ model_prices_and_context_window.json | 17 +++++++++++++++++ 2 files changed, 34 insertions(+) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 91a23b7f00f..0ea89b178ee 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -17153,6 +17153,8 @@ }, "openrouter/anthropic/claude-opus-4": { "input_cost_per_image": 0.0048, + "cache_creation_input_token_cost": 1.875e-05, + "cache_read_input_token_cost": 1.5e-06, "input_cost_per_token": 1.5e-05, "litellm_provider": "openrouter", "max_input_tokens": 200000, @@ -17163,6 +17165,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, @@ -17170,6 +17173,9 @@ }, "openrouter/anthropic/claude-opus-4.1": { "input_cost_per_image": 0.0048, + "cache_creation_input_token_cost": 1.875e-05, + "cache_creation_input_token_cost_above_1hr": 3e-05, + "cache_read_input_token_cost": 1.5e-06, "input_cost_per_token": 1.5e-05, "litellm_provider": "openrouter", "max_input_tokens": 200000, @@ -17180,6 +17186,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, @@ -17187,6 +17194,10 @@ }, "openrouter/anthropic/claude-sonnet-4": { "input_cost_per_image": 0.0048, + "cache_creation_input_token_cost": 3.75e-06, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost": 3e-07, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -17199,6 +17210,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, @@ -17206,9 +17218,13 @@ }, "openrouter/anthropic/claude-sonnet-4.5": { "input_cost_per_image": 0.0048, + "cache_creation_input_token_cost": 3.75e-06, + "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "openrouter", "max_input_tokens": 1000000, "max_output_tokens": 1000000, @@ -17218,6 +17234,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 91a23b7f00f..0ea89b178ee 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -17153,6 +17153,8 @@ }, "openrouter/anthropic/claude-opus-4": { "input_cost_per_image": 0.0048, + "cache_creation_input_token_cost": 1.875e-05, + "cache_read_input_token_cost": 1.5e-06, "input_cost_per_token": 1.5e-05, "litellm_provider": "openrouter", "max_input_tokens": 200000, @@ -17163,6 +17165,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, @@ -17170,6 +17173,9 @@ }, "openrouter/anthropic/claude-opus-4.1": { "input_cost_per_image": 0.0048, + "cache_creation_input_token_cost": 1.875e-05, + "cache_creation_input_token_cost_above_1hr": 3e-05, + "cache_read_input_token_cost": 1.5e-06, "input_cost_per_token": 1.5e-05, "litellm_provider": "openrouter", "max_input_tokens": 200000, @@ -17180,6 +17186,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, @@ -17187,6 +17194,10 @@ }, "openrouter/anthropic/claude-sonnet-4": { "input_cost_per_image": 0.0048, + "cache_creation_input_token_cost": 3.75e-06, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost": 3e-07, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, @@ -17199,6 +17210,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, @@ -17206,9 +17218,13 @@ }, "openrouter/anthropic/claude-sonnet-4.5": { "input_cost_per_image": 0.0048, + "cache_creation_input_token_cost": 3.75e-06, + "cache_read_input_token_cost": 3e-07, "input_cost_per_token": 3e-06, "input_cost_per_token_above_200k_tokens": 6e-06, "output_cost_per_token_above_200k_tokens": 2.25e-05, + "cache_creation_input_token_cost_above_200k_tokens": 7.5e-06, + "cache_read_input_token_cost_above_200k_tokens": 6e-07, "litellm_provider": "openrouter", "max_input_tokens": 1000000, "max_output_tokens": 1000000, @@ -17218,6 +17234,7 @@ "supports_assistant_prefill": true, "supports_computer_use": true, "supports_function_calling": true, + "supports_prompt_caching": true, "supports_reasoning": true, "supports_tool_choice": true, "supports_vision": true, From 9a980f36d458b5a2b29e5f6f814bdf680246319f Mon Sep 17 00:00:00 2001 From: huangyafei Date: Mon, 13 Oct 2025 10:46:37 +0800 Subject: [PATCH 03/82] Add anthropic/claude-sonnet-4.5 to OpenRouter cost map --- ...odel_prices_and_context_window_backup.json | 19 +++++++++++++++++++ model_prices_and_context_window.json | 19 +++++++++++++++++++ 2 files changed, 38 insertions(+) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 645ecb9779f..91a23b7f00f 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -17204,6 +17204,25 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, + "openrouter/anthropic/claude-sonnet-4.5": { + "input_cost_per_image": 0.0048, + "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "litellm_provider": "openrouter", + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 159 + }, "openrouter/bytedance/ui-tars-1.5-7b": { "input_cost_per_token": 1e-07, "litellm_provider": "openrouter", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 645ecb9779f..91a23b7f00f 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -17204,6 +17204,25 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, + "openrouter/anthropic/claude-sonnet-4.5": { + "input_cost_per_image": 0.0048, + "input_cost_per_token": 3e-06, + "input_cost_per_token_above_200k_tokens": 6e-06, + "output_cost_per_token_above_200k_tokens": 2.25e-05, + "litellm_provider": "openrouter", + "max_input_tokens": 1000000, + "max_output_tokens": 1000000, + "max_tokens": 1000000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 159 + }, "openrouter/bytedance/ui-tars-1.5-7b": { "input_cost_per_token": 1e-07, "litellm_provider": "openrouter", From 6424480571ae1c5b85119815bacc3f44b7214322 Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Sun, 12 Oct 2025 16:13:56 -0400 Subject: [PATCH 04/82] add ecs to docs --- docs/my-website/docs/proxy/deploy.md | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index d731c0a3c1d..4d51aa34dc3 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -788,6 +788,30 @@ docker run --name litellm-proxy \ ## Platform-specific Guide + ### Kubernetes (AWS EKS) From 4f33bc4e4690b903cd4637f8707ce226f6cc0fc0 Mon Sep 17 00:00:00 2001 From: Kowyo Date: Tue, 30 Sep 2025 04:21:36 +0000 Subject: [PATCH 05/82] fix(ollama/chat): 'think' param handling --- litellm/llms/ollama/chat/transformation.py | 3 +++ litellm/llms/ollama/completion/transformation.py | 3 +++ 2 files changed, 6 insertions(+) diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index 3b755e79330..dfd53a5e2ba 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -281,6 +281,7 @@ class OllamaChatConfig(BaseConfig): stream = optional_params.pop("stream", False) format = optional_params.pop("format", None) keep_alive = optional_params.pop("keep_alive", None) + think = optional_params.pop("think", None) function_name = optional_params.pop("function_name", None) litellm_params["function_name"] = function_name tools = optional_params.pop("tools", None) @@ -344,6 +345,8 @@ class OllamaChatConfig(BaseConfig): data["tools"] = tools if keep_alive is not None: data["keep_alive"] = keep_alive + if think is not None: + data["think"] = think return data diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index 981a987ec91..662affcb278 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -412,6 +412,7 @@ class OllamaConfig(BaseConfig): stream = optional_params.pop("stream", False) format = optional_params.pop("format", None) images = optional_params.pop("images", None) + think = optional_params.pop("think", None) data = { "model": model, "prompt": ollama_prompt, @@ -425,6 +426,8 @@ class OllamaConfig(BaseConfig): data["images"] = [ _convert_image(convert_to_ollama_image(image)) for image in images ] + if think is not None: + data["think"] = think return data From e30b716ba8942f98038bb04b16bbc3b56d06d0a5 Mon Sep 17 00:00:00 2001 From: Kowyo Date: Tue, 30 Sep 2025 12:46:22 +0000 Subject: [PATCH 06/82] fix: add 'think' parameter handling in ollama_chat.py --- litellm/llms/ollama_chat.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/litellm/llms/ollama_chat.py b/litellm/llms/ollama_chat.py index 082312d28f2..e186636de99 100644 --- a/litellm/llms/ollama_chat.py +++ b/litellm/llms/ollama_chat.py @@ -59,6 +59,7 @@ def get_ollama_response( # noqa: PLR0915 stream = optional_params.pop("stream", False) format = optional_params.pop("format", None) keep_alive = optional_params.pop("keep_alive", None) + think = optional_params.pop("think", None) function_name = optional_params.pop("function_name", None) tools = optional_params.pop("tools", None) @@ -98,6 +99,8 @@ def get_ollama_response( # noqa: PLR0915 data["tools"] = tools if keep_alive is not None: data["keep_alive"] = keep_alive + if think is not None: + data["think"] = think ## LOGGING logging_obj.pre_call( input=None, From 9557a37d37b83543c96b4febeed1d678dd236c72 Mon Sep 17 00:00:00 2001 From: Kowyo Date: Sat, 4 Oct 2025 02:50:00 +0000 Subject: [PATCH 07/82] fix: update 'think' parameter assignment to use provided value in transformation.py --- litellm/llms/ollama/chat/transformation.py | 2 +- litellm/llms/ollama/completion/transformation.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index dfd53a5e2ba..c2c5a366ecb 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -186,7 +186,7 @@ class OllamaChatConfig(BaseConfig): optional_params["format"] = value["json_schema"]["schema"] ### FUNCTION CALLING LOGIC ### if param == "reasoning_effort" and value is not None: - optional_params["think"] = True + optional_params["think"] = value if param == "tools": ## CHECK IF MODEL SUPPORTS TOOL CALLING ## try: diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index 662affcb278..dd0280a00bf 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -180,7 +180,7 @@ class OllamaConfig(BaseConfig): elif param == "stop": optional_params["stop"] = value elif param == "reasoning_effort" and value is not None: - optional_params["think"] = True + optional_params["think"] = value elif param == "response_format" and isinstance(value, dict): if value["type"] == "json_object": optional_params["format"] = "json" From 6896c60b5b11798a9ff9e75cce81a2708fdaca11 Mon Sep 17 00:00:00 2001 From: kowyo Date: Sun, 5 Oct 2025 12:16:00 +0800 Subject: [PATCH 08/82] fix: only use think level for gpt-oss model --- litellm/llms/ollama/chat/transformation.py | 5 ++++- litellm/llms/ollama/completion/transformation.py | 5 ++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index c2c5a366ecb..e78e9920bb0 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -186,7 +186,10 @@ class OllamaChatConfig(BaseConfig): optional_params["format"] = value["json_schema"]["schema"] ### FUNCTION CALLING LOGIC ### if param == "reasoning_effort" and value is not None: - optional_params["think"] = value + if model.startswith("gpt-oss"): + optional_params["think"] = value + else: + optional_params["think"] = True if param == "tools": ## CHECK IF MODEL SUPPORTS TOOL CALLING ## try: diff --git a/litellm/llms/ollama/completion/transformation.py b/litellm/llms/ollama/completion/transformation.py index dd0280a00bf..b476e5c8a63 100644 --- a/litellm/llms/ollama/completion/transformation.py +++ b/litellm/llms/ollama/completion/transformation.py @@ -180,7 +180,10 @@ class OllamaConfig(BaseConfig): elif param == "stop": optional_params["stop"] = value elif param == "reasoning_effort" and value is not None: - optional_params["think"] = value + if model.startswith("gpt-oss"): + optional_params["think"] = value + else: + optional_params["think"] = True elif param == "response_format" and isinstance(value, dict): if value["type"] == "json_object": optional_params["format"] = "json" From 92bd238b87316e9f1329f12829271f55dd9d73a7 Mon Sep 17 00:00:00 2001 From: kowyo Date: Sun, 5 Oct 2025 12:17:08 +0800 Subject: [PATCH 09/82] fix comment --- litellm/llms/ollama/chat/transformation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index e78e9920bb0..b740eb122fd 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -184,12 +184,12 @@ class OllamaChatConfig(BaseConfig): ): if value.get("json_schema") and value["json_schema"].get("schema"): optional_params["format"] = value["json_schema"]["schema"] - ### FUNCTION CALLING LOGIC ### if param == "reasoning_effort" and value is not None: if model.startswith("gpt-oss"): optional_params["think"] = value else: optional_params["think"] = True + ### FUNCTION CALLING LOGIC ### if param == "tools": ## CHECK IF MODEL SUPPORTS TOOL CALLING ## try: From 6df051e87b399a24a4a13e623e766028c46aa6ce Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Sat, 11 Oct 2025 18:00:26 -0700 Subject: [PATCH 10/82] docs: update benchmark results with improved infrastructure - Update to 2 instance baseline (1035 RPS @ 200ms median) - Add 4 instance results (1170 RPS @ 100ms median) - Update machine specs to 4 CPU / 8GB RAM - Update Locust settings to 1000 users --- docs/my-website/docs/benchmarks.md | 57 ++++++++++++++---------------- 1 file changed, 26 insertions(+), 31 deletions(-) diff --git a/docs/my-website/docs/benchmarks.md b/docs/my-website/docs/benchmarks.md index 43ab82b8e61..be175a9212f 100644 --- a/docs/my-website/docs/benchmarks.md +++ b/docs/my-website/docs/benchmarks.md @@ -16,19 +16,17 @@ model_list: api_key: "test" ``` -### 1 Instance LiteLLM Proxy +### 2 Instance LiteLLM Proxy In these tests the baseline latency characteristics are measured against a fake-openai-endpoint. #### Performance Metrics -| Metric | Value | -|--------|-------| -| **Requests per Second (RPS)** | 475 | -| **End-to-End Latency P50 (ms)** | 100 | -| **LiteLLM Overhead P50 (ms)** | 3 | -| **LiteLLM Overhead P90 (ms)** | 17 | -| **LiteLLM Overhead P99 (ms)** | 31 | +| **Type** | **Name** | **Median (ms)** | **95%ile (ms)** | **99%ile (ms)** | **Average (ms)** | **Current RPS** | +| --- | --- | --- | --- | --- | --- | --- | +| POST | /chat/completions | 200 | 630 | 1200 | 262.46 | 1035.7 | +| Custom | LiteLLM Overhead Duration (ms) | 12 | 29 | 43 | 14.74 | 1035.7 | +| | Aggregated | 100 | 430 | 930 | 138.6 | 2071.4 | @@ -36,28 +34,32 @@ In these tests the baseline latency characteristics are measured against a fake- --> + +### 4 Instances + +| **Type** | **Name** | **Median (ms)** | **95%ile (ms)** | **99%ile (ms)** | **Average (ms)** | **Current RPS** | +| --- | --- | --- | --- | --- | --- | --- | +| POST | /chat/completions | 100 | 150 | 240 | 111.73 | 1170 | +| Custom | LiteLLM Overhead Duration (ms) | 2 | 8 | 13 | 3.32 | 1170 | +| | Aggregated | 77 | 130 | 180 | 57.53 | 2340 | + #### Key Findings -- Single instance: 475 RPS @ 100ms median latency -- LiteLLM adds 3ms P50 overhead, 17ms P90 overhead, 31ms P99 overhead -- 2 LiteLLM instances: 950 RPS @ 100ms latency -- 4 LiteLLM instances: 1900 RPS @ 100ms latency - -### 2 Instances - -**Adding 1 instance, will double the RPS and maintain the `100ms-110ms` median latency.** - -| Metric | Litellm Proxy (2 Instances) | -|--------|------------------------| -| Median Latency (ms) | 100 | -| RPS | 950 | - +- Doubling from 2 to 4 LiteLLM instances cuts median latency from 200 ms to 100 ms. +- High-percentile latencies drop significantly: P95 from 430 ms to 130 ms, P99 from 930 ms to 180 ms. +- Setting workers equal to CPU count gives optimal performance. ## Machine Spec used for testing Each machine deploying LiteLLM had the following specs: -- 2 CPU -- 4GB RAM +- 4 CPU +- 8GB RAM + + +## Locust Settings + +- 1000 Users +- 500 user Ramp Up ## How to measure LiteLLM Overhead @@ -137,10 +139,3 @@ Using LangSmith has **no impact on latency, RPS compared to Basic Litellm Proxy* |--------|------------------------|---------------------| | RPS | 1133.2 | 1135 | | Median Latency (ms) | 140 | 132 | - - - -## Locust Settings - -- 2500 Users -- 100 user Ramp Up From 2e57d19a55a19b712fb63b287445d28262b176b0 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Sat, 11 Oct 2025 18:06:37 -0700 Subject: [PATCH 11/82] update benchmarks --- docs/my-website/docs/benchmarks.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/my-website/docs/benchmarks.md b/docs/my-website/docs/benchmarks.md index be175a9212f..47697355dbf 100644 --- a/docs/my-website/docs/benchmarks.md +++ b/docs/my-website/docs/benchmarks.md @@ -44,8 +44,8 @@ In these tests the baseline latency characteristics are measured against a fake- | | Aggregated | 77 | 130 | 180 | 57.53 | 2340 | #### Key Findings -- Doubling from 2 to 4 LiteLLM instances cuts median latency from 200 ms to 100 ms. -- High-percentile latencies drop significantly: P95 from 430 ms to 130 ms, P99 from 930 ms to 180 ms. +- Doubling from 2 to 4 LiteLLM instances halves median latency: 200 ms → 100 ms. +- High-percentile latencies drop significantly: P95 630 ms → 150 ms, P99 1,200 ms → 240 ms. - Setting workers equal to CPU count gives optimal performance. ## Machine Spec used for testing From 09662b50819cfe52d285944cfd2409bda84bd4dd Mon Sep 17 00:00:00 2001 From: Dhruv Yadav Date: Sat, 11 Oct 2025 18:03:58 +0530 Subject: [PATCH 12/82] direct cost calculation from openrouter --- .../llms/openrouter/chat/transformation.py | 62 +++++++++++++++++++ 1 file changed, 62 insertions(+) diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py index d5861c79b2d..f1eafe4e294 100644 --- a/litellm/llms/openrouter/chat/transformation.py +++ b/litellm/llms/openrouter/chat/transformation.py @@ -147,8 +147,70 @@ class OpenrouterConfig(OpenAIGPTConfig): model, messages, optional_params, litellm_params, headers ) response.update(extra_body) + + # ALWAYS add usage parameter to get cost data from OpenRouter + # This ensures cost tracking works for all OpenRouter models + if "usage" not in response: + response["usage"] = {"include": True} + return response + def transform_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ModelResponse, + logging_obj: Any, + request_data: dict, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ModelResponse: + """ + Transform the response from OpenRouter API. + + Extracts cost information from response headers if available. + + Returns: + ModelResponse: The transformed response with cost information. + """ + # Call parent transform_response to get the standard ModelResponse + model_response = super().transform_response( + model=model, + raw_response=raw_response, + model_response=model_response, + logging_obj=logging_obj, + request_data=request_data, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + encoding=encoding, + api_key=api_key, + json_mode=json_mode, + ) + + # Extract cost from OpenRouter response body + # OpenRouter returns cost information in the usage object when usage.include=true + try: + response_json = raw_response.json() + if "usage" in response_json and response_json["usage"]: + response_cost = response_json["usage"].get("cost") + if response_cost is not None: + # Store cost in hidden params for the cost calculator to use + if not hasattr(model_response, "_hidden_params"): + model_response._hidden_params = {} + if "additional_headers" not in model_response._hidden_params: + model_response._hidden_params["additional_headers"] = {} + model_response._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"] = float(response_cost) + except Exception: + # If we can't extract cost, continue without it - don't fail the response + pass + + return model_response + def get_error_class( self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] ) -> BaseLLMException: From b57406e53e7de2d40a1f1226dcc195929391a8c6 Mon Sep 17 00:00:00 2001 From: Dhruv Yadav Date: Sun, 12 Oct 2025 12:36:24 +0530 Subject: [PATCH 13/82] add tests for openrouter cost tracking --- .../test_openrouter_chat_transformation.py | 126 +++++++++++++++++- 1 file changed, 125 insertions(+), 1 deletion(-) diff --git a/tests/test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py b/tests/test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py index 660ced53e5e..64ac299fd79 100644 --- a/tests/test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py +++ b/tests/test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py @@ -1,12 +1,14 @@ import os import sys +import httpx import pytest sys.path.insert( 0, os.path.abspath("../../../../..") ) # Adds the parent directory to the system path +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig from litellm.llms.openrouter.chat.transformation import ( OpenRouterChatCompletionStreamingHandler, OpenrouterConfig, @@ -364,4 +366,126 @@ def test_openrouter_transform_request_multiple_cache_controls(): assert system_message["content"][4]["cache_control"] == {"type": "ephemeral"} assert "cache_control" not in system_message - \ No newline at end of file + + +def test_openrouter_cost_tracking_non_streaming(): + """ + Test OpenRouter cost tracking for non-streaming completions. + + Verifies: + 1. Request includes usage.include=true to get cost data + 2. Response extracts cost from usage.cost and stores in _hidden_params + """ + from unittest.mock import Mock, patch + from litellm.types.utils import ModelResponse, Choices, Message, Usage + + config = OpenrouterConfig() + + # Test request adds usage parameter + transformed_request = config.transform_request( + model="openrouter/anthropic/claude-sonnet-4.5", + messages=[{"role": "user", "content": "Hello"}], + optional_params={}, + litellm_params={}, + headers={}, + ) + assert "usage" in transformed_request + assert transformed_request["usage"] == {"include": True} + + # Test response extracts cost + mock_response = Mock(spec=httpx.Response) + mock_response.json.return_value = { + "id": "gen-123", + "model": "openrouter/anthropic/claude-sonnet-4.5", + "choices": [{"message": {"role": "assistant", "content": "Hello!"}, "finish_reason": "stop", "index": 0}], + "usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30, "cost": 0.00015} + } + mock_response.headers = {} + + model_response = ModelResponse( + id="gen-123", + choices=[Choices(finish_reason="stop", index=0, message=Message(content="Hello!", role="assistant"))], + created=1234567890, + model="openrouter/anthropic/claude-sonnet-4.5", + object="chat.completion", + usage=Usage(prompt_tokens=10, completion_tokens=20, total_tokens=30) + ) + + with patch.object(OpenAIGPTConfig, 'transform_response', return_value=model_response): + result = config.transform_response( + model="openrouter/anthropic/claude-sonnet-4.5", + raw_response=mock_response, + model_response=model_response, + logging_obj=Mock(), + request_data={}, + messages=[{"role": "user", "content": "Hello"}], + optional_params={}, + litellm_params={}, + encoding=None, + ) + + assert hasattr(result, "_hidden_params") + assert "llm_provider-x-litellm-response-cost" in result._hidden_params["additional_headers"] + assert result._hidden_params["additional_headers"]["llm_provider-x-litellm-response-cost"] == 0.00015 + + +def test_openrouter_cost_tracking_streaming(): + """ + Test OpenRouter cost tracking for streaming completions. + + Verifies: + 1. Request includes usage.include=true (same as non-streaming) + 2. Streaming chunks preserve usage/cost data in the final chunk + 3. Cost field is accessible in the usage object + """ + config = OpenrouterConfig() + + # Test request adds usage parameter for streaming + transformed_request = config.transform_request( + model="openrouter/anthropic/claude-sonnet-4.5", + messages=[{"role": "user", "content": "Hello"}], + optional_params={}, + litellm_params={}, + headers={}, + ) + assert "usage" in transformed_request + assert transformed_request["usage"] == {"include": True} + + # Test streaming chunks preserve cost data + handler = OpenRouterChatCompletionStreamingHandler( + streaming_response=None, sync_stream=True + ) + + # First chunk - content only + chunk1 = { + "id": "gen-stream-456", + "created": 1234567890, + "model": "openrouter/anthropic/claude-sonnet-4.5", + "choices": [{"delta": {"content": "Hello", "reasoning": None}, "index": 0}], + } + + # Final chunk - usage and cost + chunk2 = { + "id": "gen-stream-456", + "created": 1234567890, + "model": "openrouter/anthropic/claude-sonnet-4.5", + "usage": {"prompt_tokens": 5, "completion_tokens": 10, "total_tokens": 15, "cost": 0.0001}, + "choices": [{"delta": {"content": "", "reasoning": None}, "finish_reason": "stop", "index": 0}], + } + + result1 = handler.chunk_parser(chunk1) + result2 = handler.chunk_parser(chunk2) + + # First chunk has content, no usage + assert result1.choices[0]["delta"]["content"] == "Hello" + assert result1.usage is None + + # Final chunk has usage with cost preserved + assert result2.choices[0]["finish_reason"] == "stop" + assert result2.usage is not None + assert result2.usage.prompt_tokens == 5 + assert result2.usage.completion_tokens == 10 + assert result2.usage.total_tokens == 15 + # Verify cost field is preserved in the Usage object - this is the key data for cost tracking + # The chunk_parser converts the dict to a Usage Pydantic model which includes the cost field + assert result2.usage.cost == 0.0001 From cb29e33cad048348561a502f05b1e9a4f772a859 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Mon, 13 Oct 2025 09:59:07 -0700 Subject: [PATCH 14/82] docs: fix doc --- docs/my-website/docs/proxy/deploy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md index 4d51aa34dc3..7d2389383d1 100644 --- a/docs/my-website/docs/proxy/deploy.md +++ b/docs/my-website/docs/proxy/deploy.md @@ -788,7 +788,7 @@ docker run --name litellm-proxy \ ## Platform-specific Guide - ### Terraform-based ECS Deployment From fcb85f8856b100d8400f10948083f768918630e9 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 13 Oct 2025 11:20:25 -0700 Subject: [PATCH 15/82] perf(router): optimize timing functions in completion hot path Replace time.time() with more appropriate timing functions for better performance and reliability: - Use time.perf_counter() for duration measurements in acompletion(), _acompletion(), and async_get_available_deployment() - Use time.monotonic() for timeout calculations in scheduler methods (schedule_acompletion and _schedule_factory) Benefits: - 30-40% faster timing calls (~300ns savings per call) - time.monotonic() provides reliable timeouts unaffected by system clock changes (NTP adjustments, DST, manual time changes) - time.perf_counter() offers highest resolution for performance metrics - Follows Python best practices for timing operations --- litellm/router.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/litellm/router.py b/litellm/router.py index 5972b06f01a..854e230cec3 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -1057,7 +1057,7 @@ class Router: self._update_kwargs_before_fallbacks(model=model, kwargs=kwargs) request_priority = kwargs.get("priority") or self.default_priority - start_time = time.time() + start_time = time.perf_counter() _is_prompt_management_model = self._is_prompt_management_model(model) if _is_prompt_management_model: @@ -1070,7 +1070,7 @@ class Router: response = await self.schedule_acompletion(**kwargs) else: response = await self.async_function_with_fallbacks(**kwargs) - end_time = time.time() + end_time = time.perf_counter() _duration = end_time - start_time asyncio.create_task( self.service_logger_obj.async_service_success_hook( @@ -1245,7 +1245,7 @@ class Router: input_kwargs_for_streaming_fallback["model"] = model parent_otel_span = _get_parent_otel_span_from_kwargs(kwargs) - start_time = time.time() + start_time = time.perf_counter() deployment = await self.async_get_available_deployment( model=model, messages=messages, @@ -1254,7 +1254,7 @@ class Router: ) _timeout_debug_deployment_dict = deployment - end_time = time.time() + end_time = time.perf_counter() _duration = end_time - start_time asyncio.create_task( self.service_logger_obj.async_service_success_hook( @@ -1834,8 +1834,8 @@ class Router: await self.scheduler.add_request(request=item) ## POLL QUEUE - end_time = time.time() + self.timeout - curr_time = time.time() + end_time = time.monotonic() + self.timeout + curr_time = time.monotonic() poll_interval = self.scheduler.polling_interval # poll every 3ms make_request = False @@ -1852,7 +1852,7 @@ class Router: break else: ## ELSE -> loop till default_timeout await asyncio.sleep(poll_interval) - curr_time = time.time() + curr_time = time.monotonic() if make_request: try: @@ -1896,8 +1896,8 @@ class Router: await self.scheduler.add_request(request=item) ## POLL QUEUE - end_time = time.time() + self.timeout - curr_time = time.time() + end_time = time.monotonic() + self.timeout + curr_time = time.monotonic() poll_interval = self.scheduler.polling_interval # poll every 3ms make_request = False @@ -1914,7 +1914,7 @@ class Router: break else: ## ELSE -> loop till default_timeout await asyncio.sleep(poll_interval) - curr_time = time.time() + curr_time = time.monotonic() if make_request: try: @@ -7070,7 +7070,7 @@ class Router: if isinstance(healthy_deployments, dict): return healthy_deployments - start_time = time.time() + start_time = time.perf_counter() if ( self.routing_strategy == "usage-based-routing-v2" and self.lowesttpm_logger_v2 is not None @@ -7137,7 +7137,7 @@ class Router: f"get_available_deployment for model: {model}, Selected deployment: {self.print_deployment(deployment)} for model: {model}" ) - end_time = time.time() + end_time = time.perf_counter() _duration = end_time - start_time asyncio.create_task( self.service_logger_obj.async_service_success_hook( From b0d963cc0c9a45cd7b0f033181421e879bf26ecf Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Mon, 13 Oct 2025 10:56:14 -0700 Subject: [PATCH 16/82] docs(index.md): bump rc --- docs/my-website/release_notes/v1.78.0-stable/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/my-website/release_notes/v1.78.0-stable/index.md b/docs/my-website/release_notes/v1.78.0-stable/index.md index e9a471f45b5..63d5eaca0b0 100644 --- a/docs/my-website/release_notes/v1.78.0-stable/index.md +++ b/docs/my-website/release_notes/v1.78.0-stable/index.md @@ -40,7 +40,7 @@ import TabItem from '@theme/TabItem'; docker run \ -e STORE_MODEL_IN_DB=True \ -p 4000:4000 \ -ghcr.io/berriai/litellm:v1.78.0.rc.1 +ghcr.io/berriai/litellm:v1.78.0.rc.2 ``` @@ -48,7 +48,7 @@ ghcr.io/berriai/litellm:v1.78.0.rc.1 ``` showLineNumbers title="pip install litellm" -pip install litellm==1.78.0.rc.1 +pip install litellm==1.78.0.rc.2 ``` From 5bffa58ee0626d23e2ae163ae2f4c5223921c65c Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 13 Oct 2025 09:56:32 -0700 Subject: [PATCH 17/82] feat(ssl): add configurable ECDH curve for TLS performance Add `ssl_ecdh_curve` setting to configure TLS key exchange curve. Allows disabling PQC on OpenSSL 3.x for better performance. Configurable via SDK (litellm.ssl_ecdh_curve), YAML (litellm_settings), or env var (SSL_ECDH_CURVE). Common curves: X25519, prime256v1, secp384r1. --- .../docs/guides/security_settings.md | 44 ++++++++++++++++++- litellm/__init__.py | 1 + litellm/llms/custom_httpx/http_handler.py | 23 ++++++++++ 3 files changed, 67 insertions(+), 1 deletion(-) diff --git a/docs/my-website/docs/guides/security_settings.md b/docs/my-website/docs/guides/security_settings.md index 7995f6c3c9c..d6397a7c197 100644 --- a/docs/my-website/docs/guides/security_settings.md +++ b/docs/my-website/docs/guides/security_settings.md @@ -117,10 +117,52 @@ litellm_settings: ```bash export SSL_CERTIFICATE="/path/to/certificate.pem" ``` + -## 5. Use HTTP_PROXY environment variable +## 5. Configure ECDH Curve for SSL/TLS Performance + +The `ssl_ecdh_curve` setting allows you to configure the Elliptic Curve Diffie-Hellman (ECDH) curve used for SSL/TLS key exchange. This is particularly useful for disabling Post-Quantum Cryptography (PQC) to improve performance in environments where PQC is not required. + +**Use Case:** Some OpenSSL 3.x systems enable PQC by default, which can slow down TLS handshakes. Setting the ECDH curve to `X25519` disables PQC and can significantly improve connection performance. + + + + +```python +import litellm +litellm.ssl_ecdh_curve = "X25519" # Disables PQC for better performance +``` + + + + +```yaml +litellm_settings: + ssl_ecdh_curve: "X25519" +``` + + + + +```bash +export SSL_ECDH_CURVE="X25519" +``` + + + + +**Common Valid Curves:** + +- `X25519` - Modern, fast curve (recommended for disabling PQC) +- `prime256v1` - NIST P-256 curve +- `secp384r1` - NIST P-384 curve +- `secp521r1` - NIST P-521 curve + +**Note:** If an invalid curve name is provided or if your Python/OpenSSL version doesn't support this feature, LiteLLM will log a warning and continue with default curves. + +## 6. Use HTTP_PROXY environment variable Both httpx and aiohttp libraries use `urllib.request.getproxies` from environment variables. Before client initialization, you may set proxy (and optional SSL_CERT_FILE) by setting the environment variables: diff --git a/litellm/__init__.py b/litellm/__init__.py index e461c88efd6..95a761cd464 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -263,6 +263,7 @@ use_client: bool = False ssl_verify: Union[str, bool] = True ssl_security_level: Optional[str] = None ssl_certificate: Optional[str] = None +ssl_ecdh_curve: Optional[str] = None # Set to 'X25519' to disable PQC and improve performance disable_streaming_logging: bool = False disable_token_counter: bool = False disable_add_transform_inline_image_block: bool = False diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index a3ad2c67272..accdddbc4dd 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -1,6 +1,7 @@ import asyncio import os import ssl +import sys import time from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional, Union @@ -114,6 +115,28 @@ def get_ssl_configuration( # but falls back to widely compatible ones custom_ssl_context.set_ciphers(DEFAULT_SSL_CIPHERS) + # Configure ECDH curve for key exchange (e.g., to disable PQC and improve performance) + # Set SSL_ECDH_CURVE env var or litellm.ssl_ecdh_curve to 'X25519' to disable PQC + # Common valid curves: X25519, prime256v1, secp384r1, secp521r1 + ssl_ecdh_curve = os.getenv("SSL_ECDH_CURVE", litellm.ssl_ecdh_curve) + if ssl_ecdh_curve and isinstance(ssl_ecdh_curve, str): + try: + custom_ssl_context.set_ecdh_curve(ssl_ecdh_curve) + verbose_logger.debug(f"SSL ECDH curve set to: {ssl_ecdh_curve}") + except AttributeError: + verbose_logger.warning( + f"SSL ECDH curve configuration not supported. " + f"Python version: {sys.version.split()[0]}, OpenSSL version: {ssl.OPENSSL_VERSION}. " + f"Requested curve: {ssl_ecdh_curve}. Continuing with default curves." + ) + except ValueError as e: + # Invalid curve name + verbose_logger.warning( + f"Invalid SSL ECDH curve name: '{ssl_ecdh_curve}'. {e}. " + f"Common valid curves: X25519, prime256v1, secp384r1, secp521r1. " + f"Continuing with default curves (including PQC)." + ) + # Use our custom SSL context instead of the original ssl_verify value return custom_ssl_context From 65163c7ccbf3e7ce2459ae742ec9d8b218052400 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 13 Oct 2025 10:58:47 -0700 Subject: [PATCH 18/82] [Fix] GEMINI - CLI - add google_routes to llm_api_routes (#15500) * fix: add google_routes to llm_api_routes * test: test_virtual_key_llm_api_routes_allows_google_routes --- litellm/proxy/_types.py | 1 + .../proxy/auth/test_route_checks.py | 25 +++++++++++++++++++ 2 files changed, 26 insertions(+) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 41b36d46c75..d205d73da8e 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -377,6 +377,7 @@ class LiteLLMRoutes(enum.Enum): llm_api_routes = ( openai_routes + anthropic_routes + + google_routes + mapped_pass_through_routes + passthrough_routes_wildcard + apply_guardrail_routes diff --git a/tests/test_litellm/proxy/auth/test_route_checks.py b/tests/test_litellm/proxy/auth/test_route_checks.py index 0c72c8bb163..7d00b812a5c 100644 --- a/tests/test_litellm/proxy/auth/test_route_checks.py +++ b/tests/test_litellm/proxy/auth/test_route_checks.py @@ -156,6 +156,31 @@ def test_virtual_key_llm_api_route_includes_passthrough_prefix(route): assert result is True +@pytest.mark.parametrize( + "route", + [ + "/v1beta/models/gemini-2.5-flash:countTokens", + "/v1beta/models/gemini-2.0-flash:generateContent", + "/v1beta/models/gemini-1.5-pro:streamGenerateContent", + "/models/gemini-2.5-flash:countTokens", + "/models/gemini-2.0-flash:generateContent", + "/models/gemini-1.5-pro:streamGenerateContent", + ], +) +def test_virtual_key_llm_api_routes_allows_google_routes(route): + """ + Test that virtual keys with llm_api_routes permission can access Google AI Studio routes. + """ + + valid_token = UserAPIKeyAuth(user_id="test_user", allowed_routes=["llm_api_routes"]) + + result = RouteChecks.is_virtual_key_allowed_to_call_route( + route=route, valid_token=valid_token + ) + + assert result is True + + def test_virtual_key_allowed_routes_with_multiple_litellm_routes_member_names(): """Test that virtual key works with multiple LiteLLMRoutes member names in allowed_routes""" From 900a57f5b1f66a8a43e6b6c7348340717b1d685c Mon Sep 17 00:00:00 2001 From: Eddie Richter Date: Tue, 14 Oct 2025 16:56:36 -0600 Subject: [PATCH 19/82] Adding new models to the lemonade provider --- ...odel_prices_and_context_window_backup.json | 52 ++++++++++++++++++- model_prices_and_context_window.json | 52 ++++++++++++++++++- 2 files changed, 100 insertions(+), 4 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 91a23b7f00f..27812d9bf35 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -13650,8 +13650,56 @@ "lemonade/Qwen3-Coder-30B-A3B-Instruct-GGUF": { "input_cost_per_token": 0, "litellm_provider": "lemonade", - "max_tokens": 32768, - "max_input_tokens": 32768, + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "lemonade/gpt-oss-20b-mxfp4-GGUF": { + "input_cost_per_token": 0, + "litellm_provider": "lemonade", + "max_tokens": 131072, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "lemonade/gpt-oss-120b-mxfp-GGUF": { + "input_cost_per_token": 0, + "litellm_provider": "lemonade", + "max_tokens": 131072, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "lemonade/Gemma-3-4b-it-GGUF": { + "input_cost_per_token": 0, + "litellm_provider": "lemonade", + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 0, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "lemonade/Qwen3-4B-Instruct-2507-GGUF": { + "input_cost_per_token": 0, + "litellm_provider": "lemonade", + "max_tokens": 262144, + "max_input_tokens": 262144, "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 0, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 91a23b7f00f..27812d9bf35 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -13650,8 +13650,56 @@ "lemonade/Qwen3-Coder-30B-A3B-Instruct-GGUF": { "input_cost_per_token": 0, "litellm_provider": "lemonade", - "max_tokens": 32768, - "max_input_tokens": 32768, + "max_tokens": 262144, + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "lemonade/gpt-oss-20b-mxfp4-GGUF": { + "input_cost_per_token": 0, + "litellm_provider": "lemonade", + "max_tokens": 131072, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "lemonade/gpt-oss-120b-mxfp-GGUF": { + "input_cost_per_token": 0, + "litellm_provider": "lemonade", + "max_tokens": 131072, + "max_input_tokens": 131072, + "max_output_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 0, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "lemonade/Gemma-3-4b-it-GGUF": { + "input_cost_per_token": 0, + "litellm_provider": "lemonade", + "max_tokens": 128000, + "max_input_tokens": 128000, + "max_output_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 0, + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "lemonade/Qwen3-4B-Instruct-2507-GGUF": { + "input_cost_per_token": 0, + "litellm_provider": "lemonade", + "max_tokens": 262144, + "max_input_tokens": 262144, "max_output_tokens": 32768, "mode": "chat", "output_cost_per_token": 0, From d094a334d9a07d95a8f2961a233a6e08916f89bc Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 14 Oct 2025 16:38:04 -0700 Subject: [PATCH 20/82] add: unit test --- .../llms/custom_httpx/test_http_handler.py | 38 +++++++++++++++++++ 1 file changed, 38 insertions(+) diff --git a/tests/test_litellm/llms/custom_httpx/test_http_handler.py b/tests/test_litellm/llms/custom_httpx/test_http_handler.py index 0e31699fd83..09fc31d18b7 100644 --- a/tests/test_litellm/llms/custom_httpx/test_http_handler.py +++ b/tests/test_litellm/llms/custom_httpx/test_http_handler.py @@ -399,3 +399,41 @@ async def test_session_validation(): mock_valid_session = MockClientSession() transport3 = AsyncHTTPHandler._create_aiohttp_transport(shared_session=mock_valid_session) # type: ignore assert transport3.client is mock_valid_session # Should reuse session + + +@pytest.mark.parametrize( + "env_curve,litellm_curve,expected_curve,should_call", + [ + # env_curve: SSL_ECDH_CURVE env var | litellm_curve: litellm.ssl_ecdh_curve variable + # expected_curve: curve that should be set | should_call: whether set_ecdh_curve() should be called + + # Valid configurations + ("X25519", None, "X25519", True), # Env var only + ("prime256v1", None, "prime256v1", True), # Different valid curve + (None, "secp384r1", "secp384r1", True), # litellm variable only + ("X25519", "secp521r1", "X25519", True), # Env var takes precedence + # Empty/None configurations - should skip + ("", None, None, False), # Empty string - skip configuration + (None, None, None, False), # None value - skip configuration + ] +) +def test_ssl_ecdh_curve(env_curve, litellm_curve, expected_curve, should_call, monkeypatch): + """Test SSL ECDH curve configuration with valid curves and precedence""" + with patch.dict(os.environ, clear=True): + if env_curve: + monkeypatch.setenv("SSL_ECDH_CURVE", env_curve) + + original_value = litellm.ssl_ecdh_curve + try: + litellm.ssl_ecdh_curve = litellm_curve + + with patch.object(ssl.SSLContext, 'set_ecdh_curve') as mock_set_curve: + ssl_context = get_ssl_configuration() + + if should_call: + mock_set_curve.assert_called_once_with(expected_curve) + else: + mock_set_curve.assert_not_called() + assert isinstance(ssl_context, ssl.SSLContext) + finally: + litellm.ssl_ecdh_curve = original_value From 0526334d9c3b25c37345a24bbd96f81f4eadf65e Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Wed, 15 Oct 2025 08:33:23 -0700 Subject: [PATCH 21/82] docs: fix url --- docs/my-website/docs/tutorials/claude_responses_api.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/my-website/docs/tutorials/claude_responses_api.md b/docs/my-website/docs/tutorials/claude_responses_api.md index 098c311b30f..87d019f11b9 100644 --- a/docs/my-website/docs/tutorials/claude_responses_api.md +++ b/docs/my-website/docs/tutorials/claude_responses_api.md @@ -252,7 +252,7 @@ litellm --config /path/to/config.yaml 3. Use the MCP server in Claude Code ```bash -claude mcp add --transport http litellm_proxy http://0.0.0.0:4000 --header "Authorization: Bearer sk-LITELLM_VIRTUAL_KEY" +claude mcp add --transport http litellm_proxy http://0.0.0.0:4000/github_mcp/mcp --header "Authorization: Bearer sk-LITELLM_VIRTUAL_KEY" ``` 4. Authenticate via Claude Code From 74bfbeb439e0a0730262853ecdb3cfa41a9ec1f7 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Wed, 15 Oct 2025 08:43:29 -0700 Subject: [PATCH 22/82] fix(proxy/utils.py): support configuring db schema for non-iam auth db url --- litellm/model_prices_and_context_window_backup.json | 4 ++-- litellm/proxy/utils.py | 4 ++++ 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index c965fa2092b..91a23b7f00f 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -13197,11 +13197,11 @@ "text" ], "supports_function_calling": true, - "supports_native_streaming": true, + "supports_native_streaming": false, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, - "supports_reasoning": true, + "supports_reasoning": false, "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index 8aa2f407555..803c9c1a4ff 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -3707,6 +3707,7 @@ def construct_database_url_from_env_vars() -> Optional[str]: database_username = os.getenv("DATABASE_USERNAME") database_password = os.getenv("DATABASE_PASSWORD") database_name = os.getenv("DATABASE_NAME") + database_schema = os.getenv("DATABASE_SCHEMA") if database_host and database_username and database_name: # Handle the problem of special character escaping in the database URL @@ -3722,6 +3723,9 @@ def construct_database_url_from_env_vars() -> Optional[str]: else: database_url = f"postgresql://{database_username_enc}@{database_host}/{database_name_enc}" + if database_schema: + database_url += f"?schema={database_schema}" + return database_url return None From a87a3d0f95bc005b3b78cf1a835af072cb8140d1 Mon Sep 17 00:00:00 2001 From: Felipe Gare Date: Wed, 15 Oct 2025 16:06:44 -0300 Subject: [PATCH 23/82] adding support parameters to model_prices json --- model_prices_and_context_window.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 91a23b7f00f..c965fa2092b 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -13197,11 +13197,11 @@ "text" ], "supports_function_calling": true, - "supports_native_streaming": false, + "supports_native_streaming": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, - "supports_reasoning": false, + "supports_reasoning": true, "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, From 36c9066372d6412cf98d372f86a4016d83456e77 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Wed, 15 Oct 2025 15:12:40 -0700 Subject: [PATCH 24/82] perf(router): optimize model lookups with O(1) index maps and standardize timing - Use model_id_to_deployment_index_map and model_name_to_deployment_indices for O(1) lookups in get_model_info, get_deployment_by_model_group_name, and get_model_ids --- litellm/router.py | 54 ++++++++++++++++++++++++++++++++++------------- 1 file changed, 39 insertions(+), 15 deletions(-) diff --git a/litellm/router.py b/litellm/router.py index 5972b06f01a..82c9d28bb9c 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -5519,9 +5519,15 @@ class Router: Returns -> Deployment or None Raise Exception -> if model found in invalid format + + Optimized with O(1) index lookup instead of O(n) linear scan. """ - for model in self.model_list: - if model["model_name"] == model_group_name: + # O(1) lookup in model_name index + if model_group_name in self.model_name_to_deployment_indices: + indices = self.model_name_to_deployment_indices[model_group_name] + if indices: + # Return first deployment for this model_name + model = self.model_list[indices[0]] if isinstance(model, dict): return Deployment(**model) elif isinstance(model, Deployment): @@ -5631,11 +5637,13 @@ class Router: Returns - dict: the model in list with 'model_name', 'litellm_params', Optional['model_info'] - None: could not find deployment in list + + Optimized with O(1) index lookup instead of O(n) linear scan. """ - for model in self.model_list: - if "model_info" in model and "id" in model["model_info"]: - if id == model["model_info"]["id"]: - return model + # O(1) lookup via model_id_to_deployment_index_map + if id in self.model_id_to_deployment_index_map: + idx = self.model_id_to_deployment_index_map[id] + return self.model_list[idx] return None def get_model_group(self, id: str) -> Optional[List]: @@ -6169,17 +6177,33 @@ class Router: if 'model_name' is none, returns all. Returns list of model id's. + + Optimized with O(1) or O(k) index lookup when model_name provided, + instead of O(n) linear scan. """ ids = [] - for model in self.model_list: - if "model_info" in model and "id" in model["model_info"]: - id = model["model_info"]["id"] - if exclude_team_models and model["model_info"].get("team_id"): - continue - if model_name is not None and model["model_name"] == model_name: - ids.append(id) - elif model_name is None: - ids.append(id) + + if model_name is not None: + # O(1) lookup in model_name index, then O(k) iteration where k = deployments for this model_name + if model_name in self.model_name_to_deployment_indices: + indices = self.model_name_to_deployment_indices[model_name] + for idx in indices: + model = self.model_list[idx] + if "model_info" in model and "id" in model["model_info"]: + if exclude_team_models and model["model_info"].get("team_id"): + continue + ids.append(model["model_info"]["id"]) + else: + # When model_name is None, return all model IDs + # Use the index map keys for O(n) where n = total deployments + for model_id in self.model_id_to_deployment_index_map.keys(): + idx = self.model_id_to_deployment_index_map[model_id] + model = self.model_list[idx] + if "model_info" in model and "id" in model["model_info"]: + if exclude_team_models and model["model_info"].get("team_id"): + continue + ids.append(model_id) + return ids def has_model_id(self, candidate_id: str) -> bool: From c98a30a4b64b2198f08710101d0f69d420b2e070 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Wed, 15 Oct 2025 15:44:09 -0700 Subject: [PATCH 25/82] perf(router): optimize string concatenation in hash generation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace string concatenation in loop with list append + join pattern. This improves time complexity from O(n²) to O(n) and avoids creating many temporary string objects during hash ID generation. --- litellm/router.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/litellm/router.py b/litellm/router.py index 5972b06f01a..f8ce4b23605 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -4915,22 +4915,25 @@ class Router: - hash - use hash as id """ - concat_str = model_group + # Optimized: Use list and join instead of string concatenation in loop + # This avoids creating many temporary string objects (O(n) vs O(n²) complexity) + parts = [model_group] for k, v in litellm_params.items(): if isinstance(k, str): - concat_str += k + parts.append(k) elif isinstance(k, dict): - concat_str += json.dumps(k) + parts.append(json.dumps(k)) else: - concat_str += str(k) + parts.append(str(k)) if isinstance(v, str): - concat_str += v + parts.append(v) elif isinstance(v, dict): - concat_str += json.dumps(v) + parts.append(json.dumps(v)) else: - concat_str += str(v) + parts.append(str(v)) + concat_str = "".join(parts) hash_object = hashlib.sha256(concat_str.encode()) return hash_object.hexdigest() From 97ed3d01ec359288cd12ea7923a58ca090dff492 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Wed, 15 Oct 2025 15:58:08 -0700 Subject: [PATCH 26/82] test(router): update error message assertion after string concat optimization Update test_generate_model_id_with_deployment_model_name to accept the new error message format that results from the list+join optimization. The function still correctly rejects None values with a TypeError, but the error message changed from 'unsupported operand type(s) for +=' to 'expected str instance, NoneType found' due to the implementation change from string concatenation to list joining. --- tests/router_unit_tests/test_router_helper_utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/router_unit_tests/test_router_helper_utils.py b/tests/router_unit_tests/test_router_helper_utils.py index a31c4d8210f..c2339d9eec5 100644 --- a/tests/router_unit_tests/test_router_helper_utils.py +++ b/tests/router_unit_tests/test_router_helper_utils.py @@ -1411,7 +1411,8 @@ def test_generate_model_id_with_deployment_model_name(model_list): "Expected TypeError when model_group is None - this confirms our fix is needed" ) except TypeError as e: - assert "unsupported operand type(s) for +=" in str(e) + # After optimization, error message changed but still fails appropriately on None + assert "unsupported operand type(s) for +=" in str(e) or "expected str instance, NoneType found" in str(e) print(f"✓ Correctly failed with None model_group (as expected): {e}") except Exception as e: pytest.fail(f"Unexpected error with None model_group: {e}") From 294fb94b1873dd9bd885228915f34aa32ad7b7be Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Wed, 15 Oct 2025 16:25:37 -0700 Subject: [PATCH 27/82] perf(router): use shallow copy instead of deepcopy for model aliases Replace copy.deepcopy() with dict.copy() in _get_all_deployments when creating model aliases. Safe because: 1. Only modifies top-level 'model_name' field (isolated by shallow copy) 2. Nested dicts (litellm_params, model_info) are never modified after return 3. When model_alias=None, returns original dict with NO copy, proving callers expect nested structures to be read-only 4. Key insight: Code that needs to modify nested structures does deepcopy FIRST. This proves the contract is: \"treat returned deployments as read-only for nested fields.\" (see line 6894: copy.deepcopy before modifying litellm_params) Performance: ~10-100x faster than deepcopy on nested dict structures. Tests: All 114 router unit tests pass. --- litellm/router.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/litellm/router.py b/litellm/router.py index 5972b06f01a..4616d77b2c9 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -6257,7 +6257,9 @@ class Router: model_name=model_name, model=model, team_id=team_id ): if model_alias is not None: - alias_model = copy.deepcopy(model) + # Optimized: Use shallow copy since we only modify top-level model_name + # This is much faster than deepcopy for nested dict structures + alias_model = model.copy() alias_model["model_name"] = model_alias returned_models.append(alias_model) else: @@ -6271,7 +6273,8 @@ class Router: model_name=model_name, model=model, team_id=team_id ): if model_alias is not None: - alias_model = copy.deepcopy(model) + # Optimized: Use shallow copy since we only modify top-level model_name + alias_model = model.copy() alias_model["model_name"] = model_alias returned_models.append(alias_model) else: From 6e4da1da65578de5697a11067d90cbebde5cd879 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Wed, 15 Oct 2025 16:41:40 -0700 Subject: [PATCH 28/82] perf(router): optimize model lookups with O(1) data structures Replace O(n) scans with index map lookups and convert model_names to set. Standardize timing calls. --- litellm/router.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/litellm/router.py b/litellm/router.py index 5972b06f01a..05fff0afaf5 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -189,7 +189,7 @@ class RoutingArgs(enum.Enum): class Router: - model_names: List = [] + model_names: set = set() cache_responses: Optional[bool] = False default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour tenacity = None @@ -5154,7 +5154,7 @@ class Router: verbose_router_logger.debug( f"\nInitialized Model List {self.get_model_names()}" ) - self.model_names = [m["model_name"] for m in model_list] + self.model_names = {m["model_name"] for m in model_list} # Build model_name index for O(1) lookups self._build_model_name_index(self.model_list) @@ -5360,7 +5360,7 @@ class Router: self._add_model_to_list_and_index_map( model=_deployment, model_id=deployment.model_info.id ) - self.model_names.append(deployment.model_name) + self.model_names.add(deployment.model_name) return deployment def _update_deployment_indices_after_removal( From bc26845ec4f000404e0c00b4abe0fe975e59aef2 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 15 Oct 2025 17:20:01 -0700 Subject: [PATCH 29/82] [Feat] Native /ocr endpoint support (#15573) * [Feat] Add native litellm.ocr() functions (#15567) * fix get_supported_ocr_params * add get_provider_ocr_config * init OCR * init ocr functions * add OCRResponse Base Model * add ocr to llm http handlers * add main.py for OCR * fix linting for OCR * TestMistralOCR * update to use DocumentType for Mistral * fix _prepare_ocr_request * fix transform * add main.py for OCR * add spec to init * fix OCR * TestMistralOCR * ruff fix * Potential fix for code scanning alert no. 3521: Clear-text logging of sensitive information Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> --------- Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> * [Feat] Add /ocr route on LiteLLM AI Gateway - Adds support for native mistral ocr calling (#15571) * fix get_supported_ocr_params * add get_provider_ocr_config * init OCR * init ocr functions * add OCRResponse Base Model * add ocr to llm http handlers * add main.py for OCR * fix linting for OCR * TestMistralOCR * update to use DocumentType for Mistral * fix _prepare_ocr_request * fix transform * add main.py for OCR * add spec to init * fix OCR * TestMistralOCR * ruff fix * add router.ocr() methods * add OCR routes * feat add ocr routes * add OCR routes * feat: add OCR routes in proxy server * working /ocr routes * test_router_aocr_with_mistral * docs Mistral OCR * docs OCR * [Feat] Add Azure AI Mistral OCR Integration (#15572) * fix get_supported_ocr_params * add get_provider_ocr_config * init OCR * init ocr functions * add OCRResponse Base Model * add ocr to llm http handlers * add main.py for OCR * fix linting for OCR * TestMistralOCR * update to use DocumentType for Mistral * fix _prepare_ocr_request * fix transform * add main.py for OCR * add spec to init * fix OCR * TestMistralOCR * ruff fix * add router.ocr() methods * add OCR routes * feat add ocr routes * add OCR routes * feat: add OCR routes in proxy server * working /ocr routes * test_router_aocr_with_mistral * docs Mistral OCR * docs OCR * add azure ai to get_provider_ocr_config * add AzureAIOCRConfig * TestAzureAIOCR * TestAzureAIOCR * test fixes for azure ai ocr * fix async OCR transform for Azure * fix transform_ocr_request --------- Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> --- .circleci/config.yml | 53 ++- docs/my-website/docs/ocr.md | 257 +++++++++++++++ docs/my-website/sidebars.js | 1 + litellm/__init__.py | 1 + litellm/llms/azure_ai/ocr/__init__.py | 5 + litellm/llms/azure_ai/ocr/transformation.py | 268 ++++++++++++++++ litellm/llms/base_llm/ocr/__init__.py | 22 ++ litellm/llms/base_llm/ocr/transformation.py | 207 ++++++++++++ litellm/llms/custom_httpx/llm_http_handler.py | 285 +++++++++++++++++ litellm/llms/mistral/ocr/__init__.py | 2 + litellm/llms/mistral/ocr/transformation.py | 223 +++++++++++++ litellm/ocr/__init__.py | 5 + litellm/ocr/main.py | 301 ++++++++++++++++++ litellm/proxy/common_request_processing.py | 2 + litellm/proxy/ocr_endpoints/__init__.py | 2 + litellm/proxy/ocr_endpoints/endpoints.py | 97 ++++++ litellm/proxy/proxy_config.yaml | 14 +- litellm/proxy/proxy_server.py | 10 +- litellm/proxy/route_llm_request.py | 2 + litellm/router.py | 14 + litellm/utils.py | 21 ++ tests/ocr_tests/base_ocr_unit_tests.py | 140 ++++++++ tests/ocr_tests/test_ocr_azure_ai.py | 27 ++ tests/ocr_tests/test_ocr_mistral.py | 88 +++++ 24 files changed, 2032 insertions(+), 15 deletions(-) create mode 100644 docs/my-website/docs/ocr.md create mode 100644 litellm/llms/azure_ai/ocr/__init__.py create mode 100644 litellm/llms/azure_ai/ocr/transformation.py create mode 100644 litellm/llms/base_llm/ocr/__init__.py create mode 100644 litellm/llms/base_llm/ocr/transformation.py create mode 100644 litellm/llms/mistral/ocr/__init__.py create mode 100644 litellm/llms/mistral/ocr/transformation.py create mode 100644 litellm/ocr/__init__.py create mode 100644 litellm/ocr/main.py create mode 100644 litellm/proxy/ocr_endpoints/__init__.py create mode 100644 litellm/proxy/ocr_endpoints/endpoints.py create mode 100644 tests/ocr_tests/base_ocr_unit_tests.py create mode 100644 tests/ocr_tests/test_ocr_azure_ai.py create mode 100644 tests/ocr_tests/test_ocr_mistral.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 0bfbbcb4405..8ae399c5c5f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1041,6 +1041,49 @@ jobs: paths: - llm_responses_api_coverage.xml - llm_responses_api_coverage + ocr_testing: + docker: + - image: cimg/python:3.11 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + + steps: + - checkout + - setup_google_dns + - run: + name: Install Dependencies + command: | + python -m pip install --upgrade pip + python -m pip install -r requirements.txt + pip install "pytest==7.3.1" + pip install "pytest-retry==1.6.3" + pip install "pytest-cov==5.0.0" + pip install "pytest-asyncio==0.21.1" + pip install "respx==0.22.0" + # Run pytest and generate JUnit XML report + - run: + name: Run tests + command: | + pwd + ls + python -m pytest -vv tests/ocr_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 + no_output_timeout: 120m + - run: + name: Rename the coverage files + command: | + mv coverage.xml ocr_coverage.xml + mv .coverage ocr_coverage + + # Store test results + - store_test_results: + path: test-results + - persist_to_workspace: + root: . + paths: + - ocr_coverage.xml + - ocr_coverage litellm_mapped_tests: docker: - image: cimg/python:3.11 @@ -2741,7 +2784,7 @@ jobs: python -m venv venv . venv/bin/activate pip install coverage - coverage combine llm_translation_coverage llm_responses_api_coverage mcp_coverage logging_coverage litellm_router_coverage local_testing_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage + coverage combine llm_translation_coverage llm_responses_api_coverage ocr_coverage mcp_coverage logging_coverage litellm_router_coverage local_testing_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage coverage xml - codecov/upload: file: ./coverage.xml @@ -3289,6 +3332,12 @@ workflows: only: - main - /litellm_.*/ + - ocr_testing: + filters: + branches: + only: + - main + - /litellm_.*/ - litellm_mapped_enterprise_tests: filters: branches: @@ -3338,6 +3387,7 @@ workflows: - google_generate_content_endpoint_testing - guardrails_testing - llm_responses_api_testing + - ocr_testing - litellm_mapped_tests - litellm_mapped_enterprise_tests - batches_testing @@ -3400,6 +3450,7 @@ workflows: - mcp_testing - google_generate_content_endpoint_testing - llm_responses_api_testing + - ocr_testing - litellm_mapped_tests - litellm_mapped_enterprise_tests - batches_testing diff --git a/docs/my-website/docs/ocr.md b/docs/my-website/docs/ocr.md new file mode 100644 index 00000000000..d966097b326 --- /dev/null +++ b/docs/my-website/docs/ocr.md @@ -0,0 +1,257 @@ +# /ocr + +:::tip + +LiteLLM follows the [Mistral API request/response for the OCR API](https://docs.mistral.ai/capabilities/vision/#optical-character-recognition-ocr) + +::: + +## **LiteLLM Python SDK Usage** +### Quick Start + +```python +from litellm import ocr +import os + +os.environ["MISTRAL_API_KEY"] = "sk-.." + +response = ocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "document_url", + "document_url": "https://arxiv.org/pdf/2201.04234" + } +) + +# Access extracted text +for page in response.pages: + print(f"Page {page.index}:") + print(page.markdown) +``` + +### Async Usage + +```python +from litellm import aocr +import os, asyncio + +os.environ["MISTRAL_API_KEY"] = "sk-.." + +async def test_async_ocr(): + response = await aocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "document_url", + "document_url": "https://arxiv.org/pdf/2201.04234" + } + ) + + # Access extracted text + for page in response.pages: + print(f"Page {page.index}:") + print(page.markdown) + +asyncio.run(test_async_ocr()) +``` + +### Using Base64 Encoded Documents + +```python +import base64 +from litellm import ocr + +# Encode PDF to base64 +with open("document.pdf", "rb") as f: + base64_pdf = base64.b64encode(f.read()).decode('utf-8') + +response = ocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "document_url", + "document_url": f"data:application/pdf;base64,{base64_pdf}" + } +) +``` + +### Optional Parameters + +```python +response = ocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "document_url", + "document_url": "https://example.com/doc.pdf" + }, + # Optional Mistral parameters + pages=[0, 1, 2], # Only process specific pages + include_image_base64=True, # Include extracted images + image_limit=10, # Max images to return + image_min_size=100 # Min image size to include +) +``` + +## **LiteLLM Proxy Usage** + +LiteLLM provides a Mistral API compatible `/ocr` endpoint for OCR calls. + +**Setup** + +Add this to your litellm proxy config.yaml + +```yaml +model_list: + - model_name: mistral-ocr + litellm_params: + model: mistral/mistral-ocr-latest + api_key: os.environ/MISTRAL_API_KEY +``` + +Start litellm + +```bash +litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +Test request + +```bash +curl http://0.0.0.0:4000/v1/ocr \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "mistral-ocr", + "document": { + "type": "document_url", + "document_url": "https://arxiv.org/pdf/2201.04234" + } + }' +``` + + +## **Request/Response Format** + +:::info + +LiteLLM follows the **Mistral OCR API specification**. + +See the [official Mistral OCR documentation](https://docs.mistral.ai/capabilities/vision/#optical-character-recognition-ocr) for complete details. + +::: + +### Example Request + +```python +{ + "model": "mistral/mistral-ocr-latest", + "document": { + "type": "document_url", + "document_url": "https://arxiv.org/pdf/2201.04234" + }, + "pages": [0, 1, 2], # Optional: specific pages to process + "include_image_base64": True, # Optional: include extracted images + "image_limit": 10, # Optional: max images to return + "image_min_size": 100 # Optional: min image size in pixels +} +``` + +### Request Parameters + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `model` | string | Yes | The OCR model to use (e.g., `"mistral/mistral-ocr-latest"`) | +| `document` | object | Yes | Document to process. Must contain `type` and URL field | +| `document.type` | string | Yes | Either `"document_url"` for PDFs/docs or `"image_url"` for images | +| `document.document_url` | string | Conditional | URL to the document (required if `type` is `"document_url"`) | +| `document.image_url` | string | Conditional | URL to the image (required if `type` is `"image_url"`) | +| `pages` | array | No | List of specific page indices to process (0-indexed) | +| `include_image_base64` | boolean | No | Whether to include extracted images as base64 strings | +| `image_limit` | integer | No | Maximum number of images to return | +| `image_min_size` | integer | No | Minimum size (in pixels) for images to include | + +#### Document Format Examples + +**For PDFs and documents:** +```json +{ + "type": "document_url", + "document_url": "https://example.com/document.pdf" +} +``` + +**For images:** +```json +{ + "type": "image_url", + "image_url": "https://example.com/image.png" +} +``` + +**For base64-encoded content:** +```json +{ + "type": "document_url", + "document_url": "data:application/pdf;base64,JVBERi0xLjQKJ..." +} +``` + +### Response Format + +The response follows Mistral's OCR format with the following structure: + +```json +{ + "pages": [ + { + "index": 0, + "markdown": "# Document Title\n\nExtracted text content...", + "dimensions": { + "dpi": 200, + "height": 2200, + "width": 1700 + }, + "images": [ + { + "image_base64": "base64string...", + "bbox": { + "x": 100, + "y": 200, + "width": 300, + "height": 400 + } + } + ] + } + ], + "model": "mistral-ocr-2505-completion", + "usage_info": { + "pages_processed": 29, + "doc_size_bytes": 3002783 + }, + "document_annotation": null, + "object": "ocr" +} +``` + +#### Response Fields + +| Field | Type | Description | +|-------|------|-------------| +| `pages` | array | List of processed pages with extracted content | +| `pages[].index` | integer | Page number (0-indexed) | +| `pages[].markdown` | string | Extracted text in Markdown format | +| `pages[].dimensions` | object | Page dimensions (dpi, height, width in pixels) | +| `pages[].images` | array | Extracted images from the page (if `include_image_base64=true`) | +| `model` | string | The model used for OCR processing | +| `usage_info` | object | Processing statistics (pages processed, document size) | +| `document_annotation` | object | Optional document-level annotations | +| `object` | string | Always `"ocr"` for OCR responses | + + +## **Supported Providers** + +| Provider | Link to Usage | +|-------------|--------------------| +| Mistral AI | [Usage](#quick-start) | + diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 53577029e88..73a17f6c551 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -347,6 +347,7 @@ const sidebars = { ] }, "moderation", + "ocr", { type: "category", label: "Pass-through Endpoints (Anthropic SDK, etc.)", diff --git a/litellm/__init__.py b/litellm/__init__.py index e461c88efd6..fc20eecbb05 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1324,6 +1324,7 @@ from .batch_completion.main import * # type: ignore from .rerank_api.main import * from .llms.anthropic.experimental_pass_through.messages.handler import * from .responses.main import * +from .ocr.main import * from .realtime_api.main import _arealtime from .fine_tuning.main import * from .files.main import * diff --git a/litellm/llms/azure_ai/ocr/__init__.py b/litellm/llms/azure_ai/ocr/__init__.py new file mode 100644 index 00000000000..86f7e53d60b --- /dev/null +++ b/litellm/llms/azure_ai/ocr/__init__.py @@ -0,0 +1,5 @@ +"""Azure AI OCR module.""" +from .transformation import AzureAIOCRConfig + +__all__ = ["AzureAIOCRConfig"] + diff --git a/litellm/llms/azure_ai/ocr/transformation.py b/litellm/llms/azure_ai/ocr/transformation.py new file mode 100644 index 00000000000..eade2dd765f --- /dev/null +++ b/litellm/llms/azure_ai/ocr/transformation.py @@ -0,0 +1,268 @@ +""" +Azure AI OCR transformation implementation. +""" +from typing import Dict, Optional + +from litellm._logging import verbose_logger +from litellm.litellm_core_utils.prompt_templates.image_handling import ( + async_convert_url_to_base64, + convert_url_to_base64, +) +from litellm.llms.base_llm.ocr.transformation import DocumentType, OCRRequestData +from litellm.llms.mistral.ocr.transformation import MistralOCRConfig +from litellm.secret_managers.main import get_secret_str + + +class AzureAIOCRConfig(MistralOCRConfig): + """ + Azure AI OCR transformation configuration. + + Azure AI uses Mistral's OCR API but with a different endpoint format. + Inherits transformation logic from MistralOCRConfig since they use the same format. + + Reference: Azure AI Foundry OCR documentation + + Important: Azure AI only supports base64 data URIs (data:image/..., data:application/pdf;base64,...). + Regular URLs are not supported. + """ + + def __init__(self) -> None: + super().__init__() + + def validate_environment( + self, + headers: Dict, + model: str, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + **kwargs, + ) -> Dict: + """ + Validate environment and return headers for Azure AI OCR. + + Azure AI uses Bearer token authentication with AZURE_AI_API_KEY. + """ + # Get API key from environment if not provided + if api_key is None: + api_key = get_secret_str("AZURE_AI_API_KEY") + + if api_key is None: + raise ValueError( + "Missing Azure AI API Key - A call is being made to Azure AI but no key is set either in the environment variables or via params" + ) + + # Validate API base is provided + if api_base is None: + api_base = get_secret_str("AZURE_AI_API_BASE") + + if api_base is None: + raise ValueError( + "Missing Azure AI API Base - Set AZURE_AI_API_BASE environment variable or pass api_base parameter" + ) + + headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + **headers, + } + + return headers + + def get_complete_url( + self, + api_base: Optional[str], + model: str, + optional_params: dict, + **kwargs, + ) -> str: + """ + Get complete URL for Azure AI OCR endpoint. + + Azure AI endpoint format: https:///providers/mistral/azure/ocr + + Args: + api_base: Azure AI API base URL + model: Model name (not used in URL construction) + optional_params: Optional parameters + + Returns: Complete URL for Azure AI OCR endpoint + """ + if api_base is None: + raise ValueError( + "Missing Azure AI API Base - Set AZURE_AI_API_BASE environment variable or pass api_base parameter" + ) + + # Ensure no trailing slash + api_base = api_base.rstrip("/") + + # Azure AI OCR endpoint format + return f"{api_base}/providers/mistral/azure/ocr" + + def _convert_url_to_data_uri_sync(self, url: str) -> str: + """ + Synchronously convert a URL to a base64 data URI. + + Azure AI OCR doesn't have internet access, so we need to fetch URLs + and convert them to base64 data URIs. + + Args: + url: The URL to convert + + Returns: + Base64 data URI string + """ + verbose_logger.debug(f"Azure AI OCR: Converting URL to base64 data URI (sync): {url}") + + # Fetch and convert to base64 data URI + # convert_url_to_base64 already returns a full data URI like "data:image/jpeg;base64,..." + data_uri = convert_url_to_base64(url=url) + + verbose_logger.debug(f"Azure AI OCR: Converted URL to data URI (length: {len(data_uri)})") + + return data_uri + + async def _convert_url_to_data_uri_async(self, url: str) -> str: + """ + Asynchronously convert a URL to a base64 data URI. + + Azure AI OCR doesn't have internet access, so we need to fetch URLs + and convert them to base64 data URIs. + + Args: + url: The URL to convert + + Returns: + Base64 data URI string + """ + verbose_logger.debug(f"Azure AI OCR: Converting URL to base64 data URI (async): {url}") + + # Fetch and convert to base64 data URI asynchronously + # async_convert_url_to_base64 already returns a full data URI like "data:image/jpeg;base64,..." + data_uri = await async_convert_url_to_base64(url=url) + + verbose_logger.debug(f"Azure AI OCR: Converted URL to data URI (length: {len(data_uri)})") + + return data_uri + + def transform_ocr_request( + self, + model: str, + document: DocumentType, + optional_params: dict, + headers: dict, + **kwargs, + ) -> OCRRequestData: + """ + Transform OCR request for Azure AI, converting URLs to base64 data URIs (sync). + + Azure AI OCR doesn't have internet access, so we automatically fetch + any URLs and convert them to base64 data URIs synchronously. + + Args: + model: Model name + document: Document dict from user + optional_params: Already mapped optional parameters + headers: Request headers + **kwargs: Additional arguments + + Returns: + OCRRequestData with JSON data + """ + verbose_logger.debug(f"Azure AI OCR transform_ocr_request (sync) - model: {model}") + + if not isinstance(document, dict): + raise ValueError(f"Expected document dict, got {type(document)}") + + # Check if we need to convert URL to base64 + doc_type = document.get("type") + transformed_document = document.copy() + + if doc_type == "document_url": + document_url = document.get("document_url", "") + # If it's not already a data URI, convert it + if document_url and not document_url.startswith("data:"): + verbose_logger.debug( + "Azure AI OCR: Converting document URL to base64 data URI (sync)" + ) + data_uri = self._convert_url_to_data_uri_sync(url=document_url) + transformed_document["document_url"] = data_uri + elif doc_type == "image_url": + image_url = document.get("image_url", "") + # If it's not already a data URI, convert it + if image_url and not image_url.startswith("data:"): + verbose_logger.debug( + "Azure AI OCR: Converting image URL to base64 data URI (sync)" + ) + data_uri = self._convert_url_to_data_uri_sync(url=image_url) + transformed_document["image_url"] = data_uri + + # Call parent's transform to build the request + return super().transform_ocr_request( + model=model, + document=transformed_document, + optional_params=optional_params, + headers=headers, + **kwargs, + ) + + async def async_transform_ocr_request( + self, + model: str, + document: DocumentType, + optional_params: dict, + headers: dict, + **kwargs, + ) -> OCRRequestData: + """ + Transform OCR request for Azure AI, converting URLs to base64 data URIs (async). + + Azure AI OCR doesn't have internet access, so we automatically fetch + any URLs and convert them to base64 data URIs asynchronously. + + Args: + model: Model name + document: Document dict from user + optional_params: Already mapped optional parameters + headers: Request headers + **kwargs: Additional arguments + + Returns: + OCRRequestData with JSON data + """ + verbose_logger.debug(f"Azure AI OCR async_transform_ocr_request - model: {model}") + + if not isinstance(document, dict): + raise ValueError(f"Expected document dict, got {type(document)}") + + # Check if we need to convert URL to base64 + doc_type = document.get("type") + transformed_document = document.copy() + + if doc_type == "document_url": + document_url = document.get("document_url", "") + # If it's not already a data URI, convert it + if document_url and not document_url.startswith("data:"): + verbose_logger.debug( + "Azure AI OCR: Converting document URL to base64 data URI (async)" + ) + data_uri = await self._convert_url_to_data_uri_async(url=document_url) + transformed_document["document_url"] = data_uri + elif doc_type == "image_url": + image_url = document.get("image_url", "") + # If it's not already a data URI, convert it + if image_url and not image_url.startswith("data:"): + verbose_logger.debug( + "Azure AI OCR: Converting image URL to base64 data URI (async)" + ) + data_uri = await self._convert_url_to_data_uri_async(url=image_url) + transformed_document["image_url"] = data_uri + + # Call parent's transform to build the request + return super().transform_ocr_request( + model=model, + document=transformed_document, + optional_params=optional_params, + headers=headers, + **kwargs, + ) + diff --git a/litellm/llms/base_llm/ocr/__init__.py b/litellm/llms/base_llm/ocr/__init__.py new file mode 100644 index 00000000000..5965af5f2b7 --- /dev/null +++ b/litellm/llms/base_llm/ocr/__init__.py @@ -0,0 +1,22 @@ +"""Base OCR transformation module.""" +from .transformation import ( + BaseOCRConfig, + DocumentType, + OCRPage, + OCRPageDimensions, + OCRPageImage, + OCRRequestData, + OCRResponse, + OCRUsageInfo, +) + +__all__ = [ + "BaseOCRConfig", + "DocumentType", + "OCRResponse", + "OCRPage", + "OCRPageDimensions", + "OCRPageImage", + "OCRUsageInfo", + "OCRRequestData", +] diff --git a/litellm/llms/base_llm/ocr/transformation.py b/litellm/llms/base_llm/ocr/transformation.py new file mode 100644 index 00000000000..41d7d31e6bc --- /dev/null +++ b/litellm/llms/base_llm/ocr/transformation.py @@ -0,0 +1,207 @@ +""" +Base OCR transformation configuration. +""" +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union + +import httpx +from pydantic import BaseModel + +from litellm.llms.base_llm.chat.transformation import BaseLLMException + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +# DocumentType for OCR - Mistral format document dict +DocumentType = Dict[str, str] + + +class OCRPageDimensions(BaseModel): + """Page dimensions from OCR response.""" + dpi: Optional[int] = None + height: Optional[int] = None + width: Optional[int] = None + + +class OCRPageImage(BaseModel): + """Image extracted from OCR page.""" + image_base64: Optional[str] = None + bbox: Optional[Dict[str, Any]] = None + + model_config = {"extra": "allow"} + + +class OCRPage(BaseModel): + """Single page from OCR response.""" + index: int + markdown: str + images: Optional[List[OCRPageImage]] = None + dimensions: Optional[OCRPageDimensions] = None + + model_config = {"extra": "allow"} + + +class OCRUsageInfo(BaseModel): + """Usage information from OCR response.""" + pages_processed: Optional[int] = None + doc_size_bytes: Optional[int] = None + + model_config = {"extra": "allow"} + + +class OCRResponse(BaseModel): + """ + Standard OCR response format. + Standardized to Mistral OCR format - other providers should transform to this format. + """ + pages: List[OCRPage] + model: str + document_annotation: Optional[Any] = None + usage_info: Optional[OCRUsageInfo] = None + object: str = "ocr" + + model_config = {"extra": "allow"} + + +class OCRRequestData(BaseModel): + """OCR request data structure.""" + data: Optional[Union[Dict, bytes]] = None + files: Optional[Dict[str, Any]] = None + + +class BaseOCRConfig: + """ + Base configuration for OCR transformations. + Handles provider-agnostic OCR operations. + """ + + def __init__(self) -> None: + pass + + def get_supported_ocr_params(self, model: str) -> list: + """ + Get supported OCR parameters for this provider. + Override this method in provider-specific implementations. + """ + return [] + + def map_ocr_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + ) -> dict: + """Map OCR parameters to provider-specific parameters.""" + return optional_params + + def validate_environment( + self, + headers: Dict, + model: str, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + **kwargs, + ) -> Dict: + """ + Validate environment and return headers. + Override in provider-specific implementations. + """ + return headers + + def get_complete_url( + self, + api_base: Optional[str], + model: str, + optional_params: dict, + **kwargs, + ) -> str: + """ + Get complete URL for OCR endpoint. + Override in provider-specific implementations. + """ + raise NotImplementedError("get_complete_url must be implemented by provider") + + def transform_ocr_request( + self, + model: str, + document: DocumentType, + optional_params: dict, + headers: dict, + **kwargs, + ) -> OCRRequestData: + """ + Transform OCR request to provider-specific format. + Override in provider-specific implementations. + + Args: + model: Model name + document: Document to process (Mistral format dict, or file path, bytes, etc.) + optional_params: Optional parameters for the request + headers: Request headers + + Returns: + OCRRequestData with data and files fields + """ + raise NotImplementedError("transform_ocr_request must be implemented by provider") + + async def async_transform_ocr_request( + self, + model: str, + document: DocumentType, + optional_params: dict, + headers: dict, + **kwargs, + ) -> OCRRequestData: + """ + Async transform OCR request to provider-specific format. + Optional method - providers can override if they need async transformations + (e.g., Azure AI for URL-to-base64 conversion). + + Default implementation falls back to sync transform_ocr_request. + + Args: + model: Model name + document: Document to process (Mistral format dict, or file path, bytes, etc.) + optional_params: Optional parameters for the request + headers: Request headers + + Returns: + OCRRequestData with data and files fields + """ + # Default implementation: call sync version + return self.transform_ocr_request( + model=model, + document=document, + optional_params=optional_params, + headers=headers, + **kwargs, + ) + + def transform_ocr_response( + self, + model: str, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + **kwargs, + ) -> OCRResponse: + """ + Transform provider-specific OCR response to standard format. + Override in provider-specific implementations. + """ + raise NotImplementedError("transform_ocr_response must be implemented by provider") + + def get_error_class( + self, + error_message: str, + status_code: int, + headers: dict, + ) -> Exception: + """Get appropriate error class for the provider.""" + return BaseLLMException( + status_code=status_code, + message=error_message, + headers=headers, + ) + diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 5037f4d8d44..d7b7987b670 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -39,6 +39,7 @@ from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig from litellm.llms.base_llm.image_generation.transformation import ( BaseImageGenerationConfig, ) +from litellm.llms.base_llm.ocr.transformation import BaseOCRConfig, OCRResponse from litellm.llms.base_llm.realtime.transformation import BaseRealtimeConfig from litellm.llms.base_llm.rerank.transformation import BaseRerankConfig from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig @@ -1256,6 +1257,289 @@ class BaseLLMHTTPHandler: api_key=api_key, ) + def _prepare_ocr_request( + self, + model: str, + document: Dict[str, str], + optional_params: dict, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + api_base: Optional[str], + headers: Optional[Dict[str, Any]], + provider_config: BaseOCRConfig, + litellm_params: dict, + ) -> Tuple[Dict[str, Any], str, Dict[str, Any], None]: + """ + Shared logic for preparing OCR requests. + Returns: (headers, complete_url, data, files) + """ + from litellm.llms.base_llm.ocr.transformation import OCRRequestData + + headers = provider_config.validate_environment( + api_key=api_key, + api_base=api_base, + headers=headers or {}, + model=model, + ) + + complete_url = provider_config.get_complete_url( + api_base=api_base, + model=model, + optional_params=optional_params, + ) + + # Transform the request to get data and files + transformed_result = provider_config.transform_ocr_request( + model=model, + document=document, + optional_params=optional_params, + headers=headers, + ) + + # All providers return OCRRequestData + if not isinstance(transformed_result, OCRRequestData): + raise ValueError( + f"Provider {provider_config.__class__.__name__} must return OCRRequestData" + ) + + # Data is always a dict for Mistral OCR format + if not isinstance(transformed_result.data, dict): + raise ValueError(f"Expected dict data for OCR request, got {type(transformed_result.data)}") + + data = transformed_result.data + + ## LOGGING + logging_obj.pre_call( + input="OCR document processing", + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "api_base": complete_url, + "headers": headers, + }, + ) + + return headers, complete_url, data, None + + async def _async_prepare_ocr_request( + self, + model: str, + document: Dict[str, str], + optional_params: dict, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + api_base: Optional[str], + headers: Optional[Dict[str, Any]], + provider_config: BaseOCRConfig, + litellm_params: dict, + ) -> Tuple[Dict[str, Any], str, Dict[str, Any], None]: + """ + Async version of _prepare_ocr_request for providers that need async transforms. + Returns: (headers, complete_url, data, files) + """ + from litellm.llms.base_llm.ocr.transformation import OCRRequestData + + headers = provider_config.validate_environment( + api_key=api_key, + api_base=api_base, + headers=headers or {}, + model=model, + ) + + complete_url = provider_config.get_complete_url( + api_base=api_base, + model=model, + optional_params=optional_params, + ) + + # Use async transform (providers can override this method if they need async operations) + transformed_result = await provider_config.async_transform_ocr_request( + model=model, + document=document, + optional_params=optional_params, + headers=headers, + ) + + # All providers return OCRRequestData + if not isinstance(transformed_result, OCRRequestData): + raise ValueError( + f"Provider {provider_config.__class__.__name__} must return OCRRequestData" + ) + + # Data is always a dict for Mistral OCR format + if not isinstance(transformed_result.data, dict): + raise ValueError(f"Expected dict data for OCR request, got {type(transformed_result.data)}") + + data = transformed_result.data + + ## LOGGING + logging_obj.pre_call( + input="OCR document processing", + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "api_base": complete_url, + "headers": headers, + }, + ) + + return headers, complete_url, data, None + + def _transform_ocr_response( + self, + provider_config: BaseOCRConfig, + model: str, + response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> OCRResponse: + """Shared logic for transforming OCR responses.""" + return provider_config.transform_ocr_response( + model=model, + raw_response=response, + logging_obj=logging_obj, + ) + + def ocr( + self, + model: str, + document: Dict[str, str], + optional_params: dict, + timeout: Union[float, httpx.Timeout], + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + api_base: Optional[str], + custom_llm_provider: str, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + aocr: bool = False, + headers: Optional[Dict[str, Any]] = None, + provider_config: Optional[BaseOCRConfig] = None, + litellm_params: Optional[dict] = None, + ) -> Union[OCRResponse, Coroutine[Any, Any, OCRResponse]]: + """ + Sync OCR handler. + """ + if provider_config is None: + raise ValueError( + f"No provider config found for model: {model} and provider: {custom_llm_provider}" + ) + + if litellm_params is None: + litellm_params = {} + + if aocr is True: + return self.async_ocr( + model=model, + document=document, + optional_params=optional_params, + timeout=timeout, + logging_obj=logging_obj, + api_key=api_key, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + client=client, + headers=headers, + provider_config=provider_config, + litellm_params=litellm_params, + ) + + # Prepare the request + headers, complete_url, data, files = self._prepare_ocr_request( + model=model, + document=document, + optional_params=optional_params, + logging_obj=logging_obj, + api_key=api_key, + api_base=api_base, + headers=headers, + provider_config=provider_config, + litellm_params=litellm_params, + ) + + if client is None or not isinstance(client, HTTPHandler): + client = _get_httpx_client() + + try: + # Make the POST request with JSON data (Mistral format) + response = client.post( + url=complete_url, + headers=headers, + json=data, + timeout=timeout, + ) + except Exception as e: + raise self._handle_error(e=e, provider_config=provider_config) + + return self._transform_ocr_response( + provider_config=provider_config, + model=model, + response=response, + logging_obj=logging_obj, + ) + + async def async_ocr( + self, + model: str, + document: Dict[str, str], + optional_params: dict, + timeout: Union[float, httpx.Timeout], + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + api_base: Optional[str], + custom_llm_provider: str, + client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None, + headers: Optional[Dict[str, Any]] = None, + provider_config: Optional[BaseOCRConfig] = None, + litellm_params: Optional[dict] = None, + ) -> OCRResponse: + """ + Async OCR handler. + """ + if provider_config is None: + raise ValueError( + f"No provider config found for model: {model} and provider: {custom_llm_provider}" + ) + + if litellm_params is None: + litellm_params = {} + + # Prepare the request using async prepare method + headers, complete_url, data, files = await self._async_prepare_ocr_request( + model=model, + document=document, + optional_params=optional_params, + logging_obj=logging_obj, + api_key=api_key, + api_base=api_base, + headers=headers, + provider_config=provider_config, + litellm_params=litellm_params, + ) + + if client is None or not isinstance(client, AsyncHTTPHandler): + async_httpx_client = get_async_httpx_client( + llm_provider=litellm.LlmProviders(custom_llm_provider), + ) + else: + async_httpx_client = client + + try: + # Make the async POST request with JSON data (Mistral format) + response = await async_httpx_client.post( + url=complete_url, + headers=headers, + json=data, + timeout=timeout, + ) + except Exception as e: + raise self._handle_error(e=e, provider_config=provider_config) + + return self._transform_ocr_response( + provider_config=provider_config, + model=model, + response=response, + logging_obj=logging_obj, + ) + async def async_anthropic_messages_handler( self, model: str, @@ -2995,6 +3279,7 @@ class BaseLLMHTTPHandler: BaseGoogleGenAIGenerateContentConfig, BaseAnthropicMessagesConfig, BaseBatchesConfig, + BaseOCRConfig, "BasePassthroughConfig", ], ): diff --git a/litellm/llms/mistral/ocr/__init__.py b/litellm/llms/mistral/ocr/__init__.py new file mode 100644 index 00000000000..40cc62696be --- /dev/null +++ b/litellm/llms/mistral/ocr/__init__.py @@ -0,0 +1,2 @@ +"""Mistral OCR transformation module.""" + diff --git a/litellm/llms/mistral/ocr/transformation.py b/litellm/llms/mistral/ocr/transformation.py new file mode 100644 index 00000000000..f17c872f536 --- /dev/null +++ b/litellm/llms/mistral/ocr/transformation.py @@ -0,0 +1,223 @@ +""" +Mistral OCR transformation implementation. +""" +from typing import Any, Dict, Optional + +import httpx + +from litellm._logging import verbose_logger +from litellm.llms.base_llm.ocr.transformation import ( + BaseOCRConfig, + DocumentType, + OCRRequestData, + OCRResponse, +) +from litellm.secret_managers.main import get_secret_str + + +class MistralOCRConfig(BaseOCRConfig): + """ + Mistral OCR transformation configuration. + + Reference: https://docs.mistral.ai/api/#tag/ocr + """ + + def __init__(self) -> None: + super().__init__() + + def get_supported_ocr_params(self, model: str) -> list: + """ + Get supported OCR parameters for Mistral OCR. + + Mistral OCR supports: + - pages: List of page numbers to process + - include_image_base64: Whether to include base64 encoded images + - image_limit: Maximum number of images to return + - image_min_size: Minimum size of images to include + - bbox_annotation_format: Format for bounding box annotations + - document_annotation_format: Format for document annotations + """ + return [ + "pages", + "include_image_base64", + "image_limit", + "image_min_size", + "bbox_annotation_format", + "document_annotation_format", + ] + + def map_ocr_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + ) -> dict: + """ + Map OCR parameters to Mistral-specific format. + + Mistral accepts these parameters directly, so no transformation needed. + Just filter out unsupported params. + """ + supported_params = self.get_supported_ocr_params(model=model) + + # Only include params that are in the supported list + mapped_params = {} + for param, value in non_default_params.items(): + if param in supported_params: + mapped_params[param] = value + + return mapped_params + + def validate_environment( + self, + headers: Dict, + model: str, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + **kwargs, + ) -> Dict: + """ + Validate environment and return headers for Mistral OCR. + """ + # Get API key from environment if not provided + if api_key is None: + api_key = ( + get_secret_str("MISTRAL_API_KEY") + ) + + if api_key is None: + raise ValueError( + "Missing Mistral API Key - A call is being made to Mistral but no key is set either in the environment variables or via params" + ) + + headers = { + "Authorization": f"Bearer {api_key}", + **headers, + } + + # Don't set Content-Type for multipart/form-data - httpx will handle it + + return headers + + def get_complete_url( + self, + api_base: Optional[str], + model: str, + optional_params: dict, + **kwargs, + ) -> str: + """ + Get complete URL for Mistral OCR endpoint. + + Returns: https://api.mistral.ai/v1/ocr + """ + if api_base is None: + api_base = "https://api.mistral.ai/v1" + + # Ensure no trailing slash + api_base = api_base.rstrip("/") + + # Remove /v1 if it's already in the base to avoid duplication + if api_base.endswith("/v1"): + return f"{api_base}/ocr" + + return f"{api_base}/v1/ocr" + + + def transform_ocr_request( + self, + model: str, + document: DocumentType, + optional_params: dict, + headers: dict, + **kwargs, + ) -> OCRRequestData: + """ + Transform OCR request to Mistral-specific format. + + Mistral OCR API accepts: + { + "model": "mistral-ocr-latest", + "document": { + "type": "document_url", + "document_url": "" + }, + "pages": [0], # optional + "include_image_base64": false, # optional + ... + } + + Args: + model: Model name (e.g., "mistral-ocr-latest") + document: Document dict from user (Mistral format) - already validated in main.py + optional_params: Already mapped optional parameters + headers: Request headers + + Returns: + OCRRequestData with JSON data + """ + verbose_logger.debug(f"Mistral OCR transform_ocr_request - model: {model}") + + # Document parameter is the Mistral-format dict from the user + # Just pass it through as-is to the Mistral API + if not isinstance(document, dict): + raise ValueError(f"Expected document dict, got {type(document)}") + + # Build request data - use document dict directly + data = { + "model": model, + "document": document, # Pass through the Mistral-format document dict + } + + # Add all optional parameters from the already-mapped optional_params + data.update(optional_params) + + # No multipart files - using JSON + return OCRRequestData(data=data, files=None) + + def transform_ocr_response( + self, + model: str, + raw_response: httpx.Response, + logging_obj: Any, + **kwargs, + ) -> OCRResponse: + """ + Return Mistral OCR response in native format. + + Mistral OCR is the standard format for LiteLLM OCR responses. + No transformation needed - return native response. + + Mistral OCR returns: + { + "pages": [ + { + "index": 0, + "markdown": "extracted text content", + "images": [...], + "dimensions": {...} + }, + ... + ], + "model": "mistral-ocr-2505-completion", + "document_annotation": null, + "usage_info": {...} + } + """ + try: + response_json = raw_response.json() + + verbose_logger.debug(f"Mistral OCR response keys: {response_json.keys()}") + + # Return native Mistral format - no transformation + return OCRResponse( + pages=response_json.get("pages", []), + model=response_json.get("model", model), + document_annotation=response_json.get("document_annotation"), + usage_info=response_json.get("usage_info"), + object="ocr", + ) + except Exception as e: + verbose_logger.error(f"Error parsing Mistral OCR response: {e}") + raise e + diff --git a/litellm/ocr/__init__.py b/litellm/ocr/__init__.py new file mode 100644 index 00000000000..53f455619d7 --- /dev/null +++ b/litellm/ocr/__init__.py @@ -0,0 +1,5 @@ +"""OCR module for LiteLLM.""" +from .main import aocr, ocr + +__all__ = ["ocr", "aocr"] + diff --git a/litellm/ocr/main.py b/litellm/ocr/main.py new file mode 100644 index 00000000000..62172b0fbae --- /dev/null +++ b/litellm/ocr/main.py @@ -0,0 +1,301 @@ +""" +Main OCR function for LiteLLM. +""" +import asyncio +import contextvars +from functools import partial +from typing import Any, Coroutine, Dict, Optional, Union + +import httpx + +import litellm +from litellm._logging import verbose_logger +from litellm.constants import request_timeout +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.ocr.transformation import BaseOCRConfig, OCRResponse +from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler +from litellm.utils import ProviderConfigManager, client + +####### ENVIRONMENT VARIABLES ################### +base_llm_http_handler = BaseLLMHTTPHandler() +################################################# + + +@client +async def aocr( + model: str, + document: Dict[str, str], + api_key: Optional[str] = None, + api_base: Optional[str] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + **kwargs, +) -> OCRResponse: + """ + Async OCR function. + + Args: + model: Model name (e.g., "mistral/mistral-ocr-latest") + document: Document to process in Mistral format: + {"type": "document_url", "document_url": "https://..."} for PDFs/docs or + {"type": "image_url", "image_url": "https://..."} for images + api_key: Optional API key + api_base: Optional API base URL + timeout: Optional timeout + custom_llm_provider: Optional custom LLM provider + extra_headers: Optional extra headers + **kwargs: Additional parameters (e.g., include_image_base64, pages, image_limit) + + Returns: + OCRResponse in Mistral OCR format with pages, model, usage_info, etc. + + Example: + ```python + import litellm + + # OCR with PDF + response = await litellm.aocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "document_url", + "document_url": "https://arxiv.org/pdf/2201.04234" + }, + include_image_base64=True + ) + + # OCR with image + response = await litellm.aocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "image_url", + "image_url": "https://example.com/image.png" + } + ) + + # OCR with base64 encoded PDF + response = await litellm.aocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "document_url", + "document_url": f"data:application/pdf;base64,{base64_pdf}" + } + ) + ``` + """ + local_vars = locals() + try: + loop = asyncio.get_event_loop() + kwargs["aocr"] = True + + # Get custom llm provider + if custom_llm_provider is None: + _, custom_llm_provider, _, _ = litellm.get_llm_provider( + model=model, api_base=api_base + ) + + func = partial( + ocr, + model=model, + document=document, + api_key=api_key, + api_base=api_base, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + extra_headers=extra_headers, + **kwargs, + ) + + ctx = contextvars.copy_context() + func_with_context = partial(ctx.run, func) + init_response = await loop.run_in_executor(None, func_with_context) + + if asyncio.iscoroutine(init_response): + response = await init_response + else: + response = init_response + + if response is None: + raise ValueError( + f"Got an unexpected None response from the OCR API: {response}" + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + + +@client +def ocr( + model: str, + document: Dict[str, str], + api_key: Optional[str] = None, + api_base: Optional[str] = None, + timeout: Optional[Union[float, httpx.Timeout]] = None, + custom_llm_provider: Optional[str] = None, + extra_headers: Optional[Dict[str, Any]] = None, + **kwargs, +) -> Union[OCRResponse, Coroutine[Any, Any, OCRResponse]]: + """ + Synchronous OCR function. + + Args: + model: Model name (e.g., "mistral/mistral-ocr-latest") + document: Document to process in Mistral format: + {"type": "document_url", "document_url": "https://..."} for PDFs/docs or + {"type": "image_url", "image_url": "https://..."} for images + api_key: Optional API key + api_base: Optional API base URL + timeout: Optional timeout + custom_llm_provider: Optional custom LLM provider + extra_headers: Optional extra headers + **kwargs: Additional parameters (e.g., include_image_base64, pages, image_limit) + + Returns: + OCRResponse in Mistral OCR format with pages, model, usage_info, etc. + + Example: + ```python + import litellm + + # OCR with PDF + response = litellm.ocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "document_url", + "document_url": "https://arxiv.org/pdf/2201.04234" + }, + include_image_base64=True + ) + + # OCR with image + response = litellm.ocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "image_url", + "image_url": "https://example.com/image.png" + } + ) + + # OCR with base64 encoded PDF + response = litellm.ocr( + model="mistral/mistral-ocr-latest", + document={ + "type": "document_url", + "document_url": f"data:application/pdf;base64,{base64_pdf}" + } + ) + + # Access pages + for page in response.pages: + print(f"Page {page.index}: {page.markdown}") + ``` + """ + local_vars = locals() + try: + litellm_logging_obj: LiteLLMLoggingObj = kwargs.pop("litellm_logging_obj") # type: ignore + litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) + _is_async = kwargs.pop("aocr", False) is True + + # Validate document parameter format (Mistral spec) + if not isinstance(document, dict): + raise ValueError(f"document must be a dict with 'type' and URL field, got {type(document)}") + + doc_type = document.get("type") + if doc_type not in ["document_url", "image_url"]: + raise ValueError(f"Invalid document type: {doc_type}. Must be 'document_url' or 'image_url'") + + model, custom_llm_provider, dynamic_api_key, dynamic_api_base = ( + litellm.get_llm_provider( + model=model, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, + ) + ) + + # Update with dynamic values if available + if dynamic_api_key: + api_key = dynamic_api_key + if dynamic_api_base: + api_base = dynamic_api_base + + # Get provider config + ocr_provider_config: Optional[BaseOCRConfig] = ( + ProviderConfigManager.get_provider_ocr_config( + model=model, + provider=litellm.LlmProviders(custom_llm_provider), + ) + ) + + if ocr_provider_config is None: + raise ValueError( + f"OCR is not supported for provider: {custom_llm_provider}" + ) + + verbose_logger.debug( + f"OCR call - model: {model}, provider: {custom_llm_provider}" + ) + + # Extract OCR-specific parameters from kwargs + supported_params = ocr_provider_config.get_supported_ocr_params(model=model) + non_default_params = {} + for param in supported_params: + if param in kwargs: + non_default_params[param] = kwargs.pop(param) + + # Map parameters to provider-specific format + optional_params = ocr_provider_config.map_ocr_params( + non_default_params=non_default_params, + optional_params={}, + model=model, + ) + + verbose_logger.debug(f"OCR optional_params after mapping: {optional_params}") + + # Pre Call logging + litellm_logging_obj.update_environment_variables( + model=model, + optional_params=optional_params, + litellm_params={ + "litellm_call_id": litellm_call_id, + "api_base": api_base, + }, + custom_llm_provider=custom_llm_provider, + ) + + # Call the handler - pass document dict directly + response = base_llm_http_handler.ocr( + model=model, + document=document, # Pass the entire document dict + optional_params=optional_params, + timeout=timeout or request_timeout, + logging_obj=litellm_logging_obj, + api_key=api_key, + api_base=api_base, + custom_llm_provider=custom_llm_provider, + aocr=_is_async, + headers=extra_headers, + provider_config=ocr_provider_config, + litellm_params={ + "api_base": api_base, + "api_key": api_key, + }, + ) + + return response + except Exception as e: + raise litellm.exception_type( + model=model, + custom_llm_provider=custom_llm_provider, + original_exception=e, + completion_kwargs=local_vars, + extra_kwargs=kwargs, + ) + diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index 9263142dc90..9b19abd9c37 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -277,6 +277,7 @@ class ProxyBaseLLMRequestProcessing: "allm_passthrough_route", "avector_store_search", "avector_store_create", + "aocr", ], version: Optional[str] = None, user_model: Optional[str] = None, @@ -367,6 +368,7 @@ class ProxyBaseLLMRequestProcessing: "allm_passthrough_route", "avector_store_search", "avector_store_create", + "aocr", ], proxy_logging_obj: ProxyLogging, general_settings: dict, diff --git a/litellm/proxy/ocr_endpoints/__init__.py b/litellm/proxy/ocr_endpoints/__init__.py new file mode 100644 index 00000000000..3488912f661 --- /dev/null +++ b/litellm/proxy/ocr_endpoints/__init__.py @@ -0,0 +1,2 @@ +# OCR Endpoints + diff --git a/litellm/proxy/ocr_endpoints/endpoints.py b/litellm/proxy/ocr_endpoints/endpoints.py new file mode 100644 index 00000000000..c1092a06b48 --- /dev/null +++ b/litellm/proxy/ocr_endpoints/endpoints.py @@ -0,0 +1,97 @@ +#### OCR Endpoints ##### + +import orjson +from fastapi import APIRouter, Depends, Request, Response +from fastapi.responses import ORJSONResponse + +from litellm.proxy._types import * +from litellm.proxy.auth.user_api_key_auth import UserAPIKeyAuth, user_api_key_auth +from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing + +router = APIRouter() + + +@router.post( + "/v1/ocr", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, + tags=["ocr"], +) +@router.post( + "/ocr", + dependencies=[Depends(user_api_key_auth)], + response_class=ORJSONResponse, + tags=["ocr"], +) +async def ocr( + request: Request, + fastapi_response: Response, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + OCR endpoint for extracting text from documents and images. + + Follows the Mistral OCR API spec: + https://docs.mistral.ai/capabilities/vision/#optical-character-recognition-ocr + + Example: + ```bash + curl -X POST "http://localhost:4000/v1/ocr" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "mistral/mistral-ocr-latest", + "document": { + "type": "document_url", + "document_url": "https://arxiv.org/pdf/2201.04234" + } + }' + ``` + """ + from litellm.proxy.proxy_server import ( + general_settings, + llm_router, + proxy_config, + proxy_logging_obj, + select_data_generator, + user_api_base, + user_max_tokens, + user_model, + user_request_timeout, + user_temperature, + version, + ) + + # Read request body + body = await request.body() + data = orjson.loads(body) + + # Process request using ProxyBaseLLMRequestProcessing + processor = ProxyBaseLLMRequestProcessing(data=data) + try: + return await processor.base_process_llm_request( + request=request, + fastapi_response=fastapi_response, + user_api_key_dict=user_api_key_dict, + route_type="aocr", + proxy_logging_obj=proxy_logging_obj, + llm_router=llm_router, + general_settings=general_settings, + proxy_config=proxy_config, + select_data_generator=select_data_generator, + model=None, + user_model=user_model, + user_temperature=user_temperature, + user_request_timeout=user_request_timeout, + user_max_tokens=user_max_tokens, + user_api_base=user_api_base, + version=version, + ) + except Exception as e: + raise await processor._handle_llm_api_exception( + e=e, + user_api_key_dict=user_api_key_dict, + proxy_logging_obj=proxy_logging_obj, + version=version, + ) + diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 338fa98118c..0cf9556d909 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -1,16 +1,6 @@ model_list: - - model_name: db-openai-endpoint + - model_name: mistral/* litellm_params: - model: openai/gm - api_key: hi - api_base: https://exampleopenaiendpoint-production.up.railway.app/ + model: mistral/* -litellm_settings: - callbacks: ["dynamic_rate_limiter_v3"] - priority_reservation: - "prod": 0.9 # 90% reserved for production (9 RPM) - "dev": 0.1 # 10% reserved for development (1 RPM) - priority_reservation_settings: - default_priority: 0.2 # Weight (0%) assigned to keys without explicit priority metadata - saturation_threshold: 0.50 # A model is saturated if it has hit 50% of its RPM limit \ No newline at end of file diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 8f5a046be03..540d6d78444 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -259,7 +259,9 @@ from litellm.proxy.management_endpoints.customer_endpoints import ( from litellm.proxy.management_endpoints.internal_user_endpoints import ( router as internal_user_router, ) -from litellm.proxy.management_endpoints.internal_user_endpoints import user_update +from litellm.proxy.management_endpoints.internal_user_endpoints import ( + user_update, +) from litellm.proxy.management_endpoints.key_management_endpoints import ( delete_verification_tokens, duration_in_seconds, @@ -303,10 +305,13 @@ from litellm.proxy.management_endpoints.user_agent_analytics_endpoints import ( ) from litellm.proxy.management_helpers.audit_logs import create_audit_log_for_update from litellm.proxy.middleware.prometheus_auth_middleware import PrometheusAuthMiddleware +from litellm.proxy.ocr_endpoints.endpoints import router as ocr_router from litellm.proxy.openai_files_endpoints.files_endpoints import ( router as openai_files_router, ) -from litellm.proxy.openai_files_endpoints.files_endpoints import set_files_config +from litellm.proxy.openai_files_endpoints.files_endpoints import ( + set_files_config, +) from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( passthrough_endpoint_router, ) @@ -9767,6 +9772,7 @@ app.include_router(response_router) app.include_router(batches_router) app.include_router(public_endpoints_router) app.include_router(rerank_router) +app.include_router(ocr_router) app.include_router(image_router) app.include_router(fine_tuning_router) app.include_router(vector_store_router) diff --git a/litellm/proxy/route_llm_request.py b/litellm/proxy/route_llm_request.py index 1ae87637be0..f7b4cf0cbe1 100644 --- a/litellm/proxy/route_llm_request.py +++ b/litellm/proxy/route_llm_request.py @@ -25,6 +25,7 @@ ROUTE_ENDPOINT_MAPPING = { "alist_input_items": "/responses/{response_id}/input_items", "aimage_edit": "/images/edits", "acancel_responses": "/responses/{response_id}/cancel", + "aocr": "/ocr", } @@ -98,6 +99,7 @@ async def route_request( "allm_passthrough_route", "avector_store_search", "avector_store_create", + "aocr", ], ): """ diff --git a/litellm/router.py b/litellm/router.py index 5972b06f01a..b136dcbae83 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -872,6 +872,14 @@ class Router: generate_content_stream, call_type="generate_content_stream" ) + ######################################################### + # OCR routes + ######################################################### + from litellm.ocr import aocr, ocr + + self.aocr = self.factory_function(aocr, call_type="aocr") + self.ocr = self.factory_function(ocr, call_type="ocr") + def validate_fallbacks(self, fallback_param: Optional[List]): """ Validate the fallbacks parameter. @@ -3537,6 +3545,9 @@ class Router: "avector_store_create", "vector_store_search", "vector_store_create", + "aocr", + "ocr", + "aadapter_generate_content" ] = "assistants", ): """ @@ -3553,6 +3564,7 @@ class Router: "generate_content_stream", "vector_store_search", "vector_store_create", + "ocr", ): def sync_wrapper( @@ -3595,6 +3607,8 @@ class Router: "aimage_edit", "agenerate_content", "agenerate_content_stream", + "aocr", + "ocr", ): return await self._ageneric_api_call_with_fallbacks( original_function=original_function, diff --git a/litellm/utils.py b/litellm/utils.py index 5861d703a34..80ba37307df 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -143,8 +143,10 @@ from litellm.litellm_core_utils.token_counter import get_modified_max_tokens from litellm.llms.base_llm.google_genai.transformation import ( BaseGoogleGenAIGenerateContentConfig, ) +from litellm.llms.base_llm.ocr.transformation import BaseOCRConfig from litellm.llms.bedrock.common_utils import BedrockModelInfo from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler +from litellm.llms.mistral.ocr.transformation import MistralOCRConfig from litellm.router_utils.get_retry_from_policy import ( get_num_retries_from_retry_policy, reset_retry_policy, @@ -7578,6 +7580,25 @@ class ProviderConfigManager: return LiteLLMProxyImageEditConfig() return None + @staticmethod + def get_provider_ocr_config( + model: str, + provider: LlmProviders, + ) -> Optional["BaseOCRConfig"]: + """ + Get OCR configuration for a given provider. + """ + from litellm.llms.azure_ai.ocr.transformation import AzureAIOCRConfig + + PROVIDER_TO_CONFIG_MAP = { + litellm.LlmProviders.MISTRAL: MistralOCRConfig, + litellm.LlmProviders.AZURE_AI: AzureAIOCRConfig, + } + config_class = PROVIDER_TO_CONFIG_MAP.get(provider, None) + if config_class is None: + return None + return config_class() + @staticmethod def get_provider_google_genai_generate_content_config( model: str, diff --git a/tests/ocr_tests/base_ocr_unit_tests.py b/tests/ocr_tests/base_ocr_unit_tests.py new file mode 100644 index 00000000000..12264844792 --- /dev/null +++ b/tests/ocr_tests/base_ocr_unit_tests.py @@ -0,0 +1,140 @@ +""" +Base test class for OCR functionality across different providers. + +This follows the same pattern as BaseLLMChatTest in tests/llm_translation/base_llm_unit_tests.py +""" +import pytest +import litellm +from abc import ABC, abstractmethod + + +# Test resources +TEST_IMAGE_PATH = "test_image_edit.png" +TEST_PDF_URL = "https://arxiv.org/pdf/2201.04234" + + +class BaseOCRTest(ABC): + """ + Abstract base test class that enforces common OCR tests across all providers. + + Each provider-specific test class should inherit from this and implement + get_base_ocr_call_args() to return provider-specific configuration. + """ + + @abstractmethod + def get_base_ocr_call_args(self) -> dict: + """Must return the base OCR call args for the specific provider""" + pass + + @pytest.fixture(autouse=True) + def _handle_rate_limits(self): + """Fixture to handle rate limit errors for all test methods""" + try: + yield + except litellm.RateLimitError: + pytest.skip("Rate limit exceeded") + except litellm.InternalServerError: + pytest.skip("Model is overloaded") + + @pytest.mark.parametrize("sync_mode", [True, False]) + @pytest.mark.asyncio + async def test_basic_ocr_with_url(self, sync_mode): + """ + Test basic OCR with a public URL. + """ + litellm._turn_on_debug() + base_ocr_call_args = self.get_base_ocr_call_args() + print("BASE OCR Call args=", base_ocr_call_args) + + try: + if sync_mode: + response = litellm.ocr( + document={ + "type": "document_url", + "document_url": TEST_PDF_URL + }, + **base_ocr_call_args, + ) + else: + response = await litellm.aocr( + document={ + "type": "document_url", + "document_url": TEST_PDF_URL + }, + **base_ocr_call_args, + ) + + print(f"\n{'='*80}") + print(f"Sync Mode: {sync_mode}") + print(f"Response type: {type(response)}") + print(f"Response object: {response.object if hasattr(response, 'object') else 'N/A'}") + + # Check if response has expected OCR format + assert hasattr(response, "pages"), "Response should have 'pages' attribute" + assert hasattr(response, "model"), "Response should have 'model' attribute" + assert hasattr(response, "object"), "Response should have 'object' attribute" + assert response.object == "ocr", f"Expected object='ocr', got '{response.object}'" + + # Validate pages structure + assert isinstance(response.pages, list), "pages should be a list" + assert len(response.pages) > 0, "Should have at least one page" + + # Check first page structure + first_page = response.pages[0] + assert hasattr(first_page, "index"), "Page should have 'index' attribute" + assert hasattr(first_page, "markdown"), "Page should have 'markdown' attribute" + + # Extract text from all pages for validation + total_text = "\n\n".join(page.markdown for page in response.pages if page.markdown) + print(f"Total pages: {len(response.pages)}") + print(f"Total extracted text length: {len(total_text)} characters") + print(f"First 200 chars: {total_text[:200]}") + print(f"Model: {response.model}") + if response.usage_info: + print(f"Pages processed: {response.usage_info.pages_processed}") + print(f"{'='*80}\n") + + assert len(total_text) > 0, "Should extract some text from the document" + + except Exception as e: + pytest.fail(f"OCR call failed: {str(e)}") + + def test_ocr_response_structure(self): + """ + Test that the OCR response has the correct structure. + """ + litellm.set_verbose = True + base_ocr_call_args = self.get_base_ocr_call_args() + + response = litellm.ocr( + document={ + "type": "document_url", + "document_url": TEST_PDF_URL + }, + **base_ocr_call_args, + ) + + # Validate response structure + assert hasattr(response, "pages"), "Response should have 'pages' attribute" + assert hasattr(response, "model"), "Response should have 'model' attribute" + assert hasattr(response, "object"), "Response should have 'object' attribute" + assert hasattr(response, "usage_info"), "Response should have 'usage_info' attribute" + + assert isinstance(response.pages, list), "pages should be a list" + assert len(response.pages) > 0, "Should have at least one page" + assert response.object == "ocr", "object should be 'ocr'" + + # Validate first page structure + first_page = response.pages[0] + assert hasattr(first_page, "index"), "Page should have 'index' attribute" + assert hasattr(first_page, "markdown"), "Page should have 'markdown' attribute" + assert isinstance(first_page.markdown, str), "markdown should be a string" + + print(f"\nResponse structure validated:") + print(f" - object: {response.object}") + print(f" - model: {response.model}") + print(f" - pages: {len(response.pages)}") + if response.usage_info: + print(f" - pages_processed: {response.usage_info.pages_processed}") + print(f" - doc_size_bytes: {response.usage_info.doc_size_bytes}") + diff --git a/tests/ocr_tests/test_ocr_azure_ai.py b/tests/ocr_tests/test_ocr_azure_ai.py new file mode 100644 index 00000000000..34fd9b37ba2 --- /dev/null +++ b/tests/ocr_tests/test_ocr_azure_ai.py @@ -0,0 +1,27 @@ +""" +Test OCR functionality with Azure AI API. + +Note: Azure AI OCR automatically converts URLs to base64 data URIs since +the Azure AI endpoint doesn't have internet access. +""" +import os +from base_ocr_unit_tests import BaseOCRTest + +class TestAzureAIOCR(BaseOCRTest): + """ + Test class for Azure AI OCR functionality. + Inherits from BaseOCRTest and provides Azure AI-specific configuration. + + Note: For Azure AI, LiteLLM will automatically convert URLs to base64 data URIs before + sending to the API, since Azure AI OCR endpoint doesn't have internet access. + """ + + def get_base_ocr_call_args(self) -> dict: + """ + Return the base OCR call args for Azure AI. + """ + return { + "model": "azure_ai/mistral-document-ai-2505", + "api_key": os.getenv("AZURE_AI_API_KEY_MISTRAL"), + "api_base": os.getenv("AZURE_AI_API_BASE_MISTRAL"), + } diff --git a/tests/ocr_tests/test_ocr_mistral.py b/tests/ocr_tests/test_ocr_mistral.py new file mode 100644 index 00000000000..ea647459271 --- /dev/null +++ b/tests/ocr_tests/test_ocr_mistral.py @@ -0,0 +1,88 @@ +""" +Test OCR functionality with Mistral API. +""" +import os +import sys +import pytest +import litellm +from litellm import Router +from base_ocr_unit_tests import BaseOCRTest, TEST_PDF_URL + + +class TestMistralOCR(BaseOCRTest): + """ + Test class for Mistral OCR functionality. + """ + + def get_base_ocr_call_args(self) -> dict: + """Return the base OCR call args for Mistral""" + return { + "model": "mistral/mistral-ocr-latest", + "api_key": os.getenv("MISTRAL_API_KEY"), + } + +@pytest.mark.asyncio +async def test_router_aocr_with_mistral(): + """ + Test OCR with Router using Mistral OCR deployment. + """ + litellm.set_verbose = True + + # Create router with Mistral OCR deployment + router = Router( + model_list=[ + { + "model_name": "mistral-ocr", + "litellm_params": { + "model": "mistral/mistral-ocr-latest", + "api_key": os.getenv("MISTRAL_API_KEY"), + }, + } + ] + ) + + try: + # Call OCR through router + response = await router.aocr( + model="mistral-ocr", + document={ + "type": "document_url", + "document_url": TEST_PDF_URL + }, + ) + + print(f"\n{'='*80}") + print("Router OCR Test") + print(f"Response type: {type(response)}") + print(f"Response object: {response.object if hasattr(response, 'object') else 'N/A'}") + + # Check if response has expected Mistral OCR format + assert hasattr(response, "pages"), "Response should have 'pages' attribute" + assert hasattr(response, "model"), "Response should have 'model' attribute" + assert hasattr(response, "object"), "Response should have 'object' attribute" + assert response.object == "ocr", f"Expected object='ocr', got '{response.object}'" + + # Validate pages structure + assert isinstance(response.pages, list), "pages should be a list" + assert len(response.pages) > 0, "Should have at least one page" + + # Check first page structure + first_page = response.pages[0] + assert hasattr(first_page, "index"), "Page should have 'index' attribute" + assert hasattr(first_page, "markdown"), "Page should have 'markdown' attribute" + + # Extract text from all pages for validation + total_text = "\n\n".join(page.markdown for page in response.pages if page.markdown) + print(f"Total pages: {len(response.pages)}") + print(f"Total extracted text length: {len(total_text)} characters") + print(f"First 200 chars: {total_text[:200]}") + print(f"Model: {response.model}") + if response.usage_info: + print(f"Pages processed: {response.usage_info.pages_processed}") + print(f"{'='*80}\n") + + assert len(total_text) > 0, "Should extract some text from the document" + + except Exception as e: + pytest.fail(f"Router OCR call failed: {str(e)}") + From ad7f4411a35b917fa3d332ee1f33222ab07beed2 Mon Sep 17 00:00:00 2001 From: Mubashir Osmani Date: Wed, 15 Oct 2025 20:28:49 -0400 Subject: [PATCH 30/82] added claude-haiku-4.5 (#15579) --- model_prices_and_context_window.json | 42 ++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 91a23b7f00f..d9c6cbde3da 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -4612,6 +4612,48 @@ "supports_web_search": true, "tool_use_system_prompt_tokens": 264 }, + "claude-haiku-4-5-20251001": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_creation_input_token_cost_above_1hr": 2e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 5e-06, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_computer_use": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "claude-haiku-4-5": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_creation_input_token_cost_above_1hr": 2e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 5e-06, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_computer_use": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "claude-3-5-sonnet-20240620": { "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, From 495e6c0a2b4e62cb9a2724ab076dd5e0005fefe9 Mon Sep 17 00:00:00 2001 From: Aniket Maurya Date: Thu, 16 Oct 2025 01:36:32 +0100 Subject: [PATCH 31/82] Add type hint to function_to_dict and fix typo Updated type hint for function_to_dict and fixed typo in docstring. --- litellm/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/utils.py b/litellm/utils.py index 80ba37307df..52c6c067ef4 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -5121,9 +5121,9 @@ def json_schema_type(python_type_name: str): return python_to_json_schema_types.get(python_type_name, "string") -def function_to_dict(input_function): # noqa: C901 +def function_to_dict(input_function) -> dict: # noqa: C901 """Using type hints and numpy-styled docstring, - produce a dictionnary usable for OpenAI function calling + produce a dictionary usable for OpenAI function calling Parameters ---------- From 354e62bde473c5e7c9de0660d2b8b20d2f7d71b9 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 15 Oct 2025 17:41:09 -0700 Subject: [PATCH 32/82] [Feat] Add anthropic.claude-haiku-4-5-20251001-v1:0 on Bedrock, VertexAI (#15581) * add claude 4-5 * add anthropic.claude-haiku-4-5-20251001-v1:0 --- litellm/constants.py | 1 + ...odel_prices_and_context_window_backup.json | 156 ++++++++++++++++++ model_prices_and_context_window.json | 114 +++++++++++++ 3 files changed, 271 insertions(+) diff --git a/litellm/constants.py b/litellm/constants.py index 54ac3e6d6b8..4579e59f4f3 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -849,6 +849,7 @@ BEDROCK_CONVERSE_MODELS = [ "deepseek.v3-v1:0", "openai.gpt-oss-20b-1:0", "openai.gpt-oss-120b-1:0", + "anthropic.claude-haiku-4-5-20251001-v1:0", "anthropic.claude-sonnet-4-5-20250929-v1:0", "anthropic.claude-opus-4-1-20250805-v1:0", "anthropic.claude-opus-4-20250514-v1:0", diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 91a23b7f00f..6025c3da270 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -400,6 +400,44 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "anthropic.claude-haiku-4-5@20251001": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, "litellm_provider": "bedrock", @@ -810,6 +848,25 @@ "supports_tool_choice": true, "supports_vision": true }, + "apac.anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "apac.anthropic.claude-3-sonnet-20240229-v1:0": { "input_cost_per_token": 3e-06, "litellm_provider": "bedrock", @@ -4612,6 +4669,48 @@ "supports_web_search": true, "tool_use_system_prompt_tokens": 264 }, + "claude-haiku-4-5-20251001": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_creation_input_token_cost_above_1hr": 2e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 5e-06, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_computer_use": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "claude-haiku-4-5": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_creation_input_token_cost_above_1hr": 2e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 5e-06, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_computer_use": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, "claude-3-5-sonnet-20240620": { "cache_creation_input_token_cost": 3.75e-06, "cache_creation_input_token_cost_above_1hr": 6e-06, @@ -7741,6 +7840,25 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "eu.anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, "litellm_provider": "bedrock", @@ -20097,6 +20215,25 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "us.anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "us.anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, "litellm_provider": "bedrock", @@ -21368,6 +21505,25 @@ "supports_pdf_input": true, "supports_tool_choice": true }, + "vertex_ai/claude-haiku-4-5@20251001": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude/haiku-4-5", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "vertex_ai/claude-3-5-sonnet": { "input_cost_per_token": 3e-06, "litellm_provider": "vertex_ai-anthropic_models", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index d9c6cbde3da..6025c3da270 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -400,6 +400,44 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, + "anthropic.claude-haiku-4-5@20251001": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, "litellm_provider": "bedrock", @@ -810,6 +848,25 @@ "supports_tool_choice": true, "supports_vision": true }, + "apac.anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "apac.anthropic.claude-3-sonnet-20240229-v1:0": { "input_cost_per_token": 3e-06, "litellm_provider": "bedrock", @@ -7783,6 +7840,25 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "eu.anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, "litellm_provider": "bedrock", @@ -20139,6 +20215,25 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "us.anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "us.anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, "litellm_provider": "bedrock", @@ -21410,6 +21505,25 @@ "supports_pdf_input": true, "supports_tool_choice": true }, + "vertex_ai/claude-haiku-4-5@20251001": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude/haiku-4-5", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "vertex_ai/claude-3-5-sonnet": { "input_cost_per_token": 3e-06, "litellm_provider": "vertex_ai-anthropic_models", From 8ab720409cfa743dd2d770ea8581e305d55e7938 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 15 Oct 2025 17:43:43 -0700 Subject: [PATCH 33/82] bump v --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 0a379ec752b..457b6741b4c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm" -version = "1.78.1" +version = "1.78.2" description = "Library to easily interface with LLM API providers" authors = ["BerriAI"] license = "MIT" @@ -157,7 +157,7 @@ requires = ["poetry-core", "wheel"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "1.78.1" +version = "1.78.2" version_files = [ "pyproject.toml:^version" ] From fd83b292a6322d1758d70351cab0376ec2bf7777 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 15 Oct 2025 17:46:44 -0700 Subject: [PATCH 34/82] test_bedrock_anthropic_prompt_caching --- .../llm_cost_calc/test_llm_cost_calc_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py index 1541eea4939..664ecb896b6 100644 --- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py +++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py @@ -649,5 +649,5 @@ def test_bedrock_anthropic_prompt_caching(): assert prompt_cost >= 0 assert completion_cost >= 0 - assert round(prompt_cost, 3) == 0.845 + assert round(prompt_cost, 3) == 0.111 assert round(completion_cost, 5) == 0.00820 From f1fbae8409afdc37cc93b79a065f5db16866b126 Mon Sep 17 00:00:00 2001 From: Andreas <21068727+ndrsfel@users.noreply.github.com> Date: Thu, 16 Oct 2025 02:50:15 +0200 Subject: [PATCH 35/82] feat(pricing): Add Gemini 2.5 Flash Image (Nano Banana) in GA (#15557) * feat(pricing): Add Gemini 2.5 Flash Image (Nano Banana) in GA * change mode to image_generation --------- Co-authored-by: Andreas Feldl --- model_prices_and_context_window.json | 96 ++++++++++++++++++++++++++++ 1 file changed, 96 insertions(+) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 6025c3da270..bfb7352b5f1 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -9604,6 +9604,54 @@ "supports_vision": true, "supports_web_search": true }, + "gemini-2.5-flash-image": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "max_pdf_size_mb": 30, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "image_generation", + "output_cost_per_image": 0.039, + "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 2.5e-06, + "rpm": 100000, + "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 8000000 + }, "gemini-2.5-flash-image-preview": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_audio_token": 1e-06, @@ -11057,6 +11105,54 @@ "supports_web_search": true, "tpm": 8000000 }, + "gemini/gemini-2.5-flash-image": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "max_pdf_size_mb": 30, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "image_generation", + "output_cost_per_image": 0.039, + "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 2.5e-06, + "rpm": 100000, + "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 8000000 + }, "gemini/gemini-2.5-flash-image-preview": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_audio_token": 1e-06, From 56838e2388d92073f95ddcec1287df2ae7d76819 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Wed, 15 Oct 2025 17:52:44 -0700 Subject: [PATCH 36/82] test: ensure model_names is a O(1) datastructure --- tests/router_unit_tests/test_router_index_management.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/tests/router_unit_tests/test_router_index_management.py b/tests/router_unit_tests/test_router_index_management.py index 04ea9214991..63f9d118349 100644 --- a/tests/router_unit_tests/test_router_index_management.py +++ b/tests/router_unit_tests/test_router_index_management.py @@ -177,3 +177,11 @@ class TestRouterIndexManagement: # Verify: New entry is added assert "claude-3" in router.model_name_to_deployment_indices assert router.model_name_to_deployment_indices["claude-3"] == [0] + + def test_model_names_is_set(self): + """Verify that model_names uses a set for O(1) lookups, not a list (O(n))""" + router = Router(model_list=[]) + + assert isinstance(router.model_names, set), ( + f"model_names should be a set for O(1) lookups, but got {type(router.model_names)}" + ) From 6b166e046dc4700c315b7d65f6c7dec8bc574876 Mon Sep 17 00:00:00 2001 From: berri-teddy Date: Wed, 15 Oct 2025 17:17:55 -0700 Subject: [PATCH 37/82] fix: correct EntraID app roles JWT claim extraction - Fix get_app_roles_from_id_token to use 'app_roles' claim instead of 'roles' - Add comprehensive unit tests for EntraID app roles functionality - Prevent regressions in Microsoft EntraID SSO authentication Resolves issue where EntraID app roles were not being extracted correctly from JWT tokens, causing authentication failures for users with assigned app roles in Microsoft EntraID. --- litellm/proxy/management_endpoints/ui_sso.py | 30 +-- .../test_entraid_app_roles.py | 215 ++++++++++++++++++ 2 files changed, 230 insertions(+), 15 deletions(-) create mode 100644 tests/test_litellm/proxy/management_endpoints/test_entraid_app_roles.py diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index d8f426e1ae6..54909b9712b 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -875,9 +875,9 @@ async def insert_sso_user( if user_defined_values.get("max_budget") is None: user_defined_values["max_budget"] = litellm.max_internal_user_budget if user_defined_values.get("budget_duration") is None: - user_defined_values["budget_duration"] = ( - litellm.internal_user_budget_duration - ) + user_defined_values[ + "budget_duration" + ] = litellm.internal_user_budget_duration if user_defined_values["user_role"] is None: user_defined_values["user_role"] = LitellmUserRoles.INTERNAL_USER_VIEW_ONLY @@ -1118,9 +1118,9 @@ class SSOAuthenticationHandler: generic_authorization_endpoint and "okta" in generic_authorization_endpoint ): - redirect_params["state"] = ( - uuid.uuid4().hex - ) # set state param for okta - required + redirect_params[ + "state" + ] = uuid.uuid4().hex # set state param for okta - required return redirect_params @@ -1699,9 +1699,9 @@ class MicrosoftSSOHandler: # if user is trying to get the raw sso response for debugging, return the raw sso response if return_raw_sso_response: - original_msft_result[MicrosoftSSOHandler.GRAPH_API_RESPONSE_KEY] = ( - user_team_ids - ) + original_msft_result[ + MicrosoftSSOHandler.GRAPH_API_RESPONSE_KEY + ] = user_team_ids original_msft_result["app_roles"] = app_roles return original_msft_result or {} @@ -1739,7 +1739,7 @@ class MicrosoftSSOHandler: Extract app roles from the Microsoft Entra ID (Azure AD) id_token JWT. App roles are assigned in the Azure AD Enterprise Application and appear - in the 'roles' claim of the id_token. + in the 'app_roles' claim of the id_token. Args: id_token (Optional[str]): The JWT id_token from Microsoft SSO @@ -1758,8 +1758,8 @@ class MicrosoftSSOHandler: # (signature is already verified by fastapi_sso) decoded_token = jwt.decode(id_token, options={"verify_signature": False}) - # Extract roles claim from the token - roles = decoded_token.get("roles", []) + # Extract app_roles claim from the token + roles = decoded_token.get("app_roles", []) if roles and isinstance(roles, list): verbose_proxy_logger.debug( @@ -1817,9 +1817,9 @@ class MicrosoftSSOHandler: # Fetch user membership from Microsoft Graph API all_group_ids = [] - next_link: Optional[str] = ( - MicrosoftSSOHandler.graph_api_user_groups_endpoint - ) + next_link: Optional[ + str + ] = MicrosoftSSOHandler.graph_api_user_groups_endpoint auth_headers = {"Authorization": f"Bearer {access_token}"} page_count = 0 diff --git a/tests/test_litellm/proxy/management_endpoints/test_entraid_app_roles.py b/tests/test_litellm/proxy/management_endpoints/test_entraid_app_roles.py new file mode 100644 index 00000000000..de984496824 --- /dev/null +++ b/tests/test_litellm/proxy/management_endpoints/test_entraid_app_roles.py @@ -0,0 +1,215 @@ +""" +Unit tests for EntraID app roles JWT claim extraction. + +This module tests the get_app_roles_from_id_token method to ensure it correctly +extracts app roles from Microsoft EntraID JWT tokens and prevents regressions. +""" + +import pytest +from unittest.mock import patch +import jwt + +from litellm.proxy.management_endpoints.ui_sso import MicrosoftSSOHandler + + +class TestEntraIDAppRoles: + """Test EntraID app roles extraction from JWT tokens""" + + @pytest.fixture + def sample_jwt_token(self): + """Create a sample JWT token with app_roles claim""" + payload = { + "sub": "user123", + "email": "user@company.com", + "app_roles": ["proxy_admin"], + "aud": "litellm-app", + "iss": "https://login.microsoftonline.com/tenant-id/v2.0", + "exp": 9999999999, + } + return jwt.encode(payload, "secret", algorithm="HS256") + + @pytest.fixture + def sample_jwt_token_single_role(self): + """Create a sample JWT token with single app role""" + payload = { + "sub": "user456", + "email": "admin@company.com", + "app_roles": ["proxy_admin_viewer"], + "aud": "litellm-app", + "iss": "https://login.microsoftonline.com/tenant-id/v2.0", + "exp": 9999999999, + } + return jwt.encode(payload, "secret", algorithm="HS256") + + @pytest.fixture + def sample_jwt_token_no_roles(self): + """Create a sample JWT token without app_roles claim""" + payload = { + "sub": "user789", + "email": "user@company.com", + "aud": "litellm-app", + "iss": "https://login.microsoftonline.com/tenant-id/v2.0", + "exp": 9999999999, + } + return jwt.encode(payload, "secret", algorithm="HS256") + + @pytest.fixture + def sample_jwt_token_empty_roles(self): + """Create a sample JWT token with empty app_roles array""" + payload = { + "sub": "user000", + "email": "user@company.com", + "app_roles": [], + "aud": "litellm-app", + "iss": "https://login.microsoftonline.com/tenant-id/v2.0", + "exp": 9999999999, + } + return jwt.encode(payload, "secret", algorithm="HS256") + + def test_get_app_roles_from_id_token_single_role( + self, sample_jwt_token_single_role + ): + """Test extracting single app role from JWT token""" + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token( + sample_jwt_token_single_role + ) + + # Assert + assert result == ["proxy_admin_viewer"] + assert len(result) == 1 + + def test_get_app_roles_from_id_token_no_roles_claim( + self, sample_jwt_token_no_roles + ): + """Test handling JWT token without app_roles claim""" + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token( + sample_jwt_token_no_roles + ) + + # Assert + assert result == [] + assert len(result) == 0 + + def test_get_app_roles_from_id_token_empty_roles( + self, sample_jwt_token_empty_roles + ): + """Test handling JWT token with empty app_roles array""" + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token( + sample_jwt_token_empty_roles + ) + + # Assert + assert result == [] + assert len(result) == 0 + + def test_get_app_roles_from_id_token_none_input(self): + """Test handling None input""" + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token(None) + + # Assert + assert result == [] + assert len(result) == 0 + + def test_get_app_roles_from_id_token_empty_string(self): + """Test handling empty string input""" + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token("") + + # Assert + assert result == [] + assert len(result) == 0 + + def test_get_app_roles_from_id_token_invalid_jwt(self): + """Test handling invalid JWT token""" + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token("invalid.jwt.token") + + # Assert + assert result == [] + assert len(result) == 0 + + def test_get_app_roles_from_id_token_malformed_roles(self): + """Test handling JWT with malformed app_roles (not a list)""" + # Arrange + payload = { + "sub": "user123", + "app_roles": "not_a_list", # Should be a list + "exp": 9999999999, + } + malformed_token = jwt.encode(payload, "secret", algorithm="HS256") + + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token(malformed_token) + + # Assert + assert result == [] + assert len(result) == 0 + + def test_get_app_roles_from_id_token_jwt_decode_exception(self): + """Test handling JWT decode exceptions gracefully""" + # Arrange + invalid_token = "completely.invalid.token" + + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token(invalid_token) + + # Assert + assert result == [] + assert len(result) == 0 + + def test_get_app_roles_from_id_token_import_error(self): + """Test handling import error for jwt library""" + # Arrange + with patch( + "builtins.__import__", side_effect=ImportError("No module named 'jwt'") + ): + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token("any.token") + + # Assert + assert result == [] + assert len(result) == 0 + + def test_get_app_roles_from_id_token_uses_correct_claim_name( + self, sample_jwt_token + ): + """Test that the method uses 'app_roles' claim, not 'roles' claim""" + # This test ensures we don't regress to the old bug where 'roles' was used + + # Arrange - Create a token with both claims to verify correct one is used + payload = { + "sub": "user123", + "roles": ["old_roles_claim"], # This should be ignored + "app_roles": ["proxy_admin"], # This should be used + "exp": 9999999999, + } + token_with_both_claims = jwt.encode(payload, "secret", algorithm="HS256") + + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token(token_with_both_claims) + + # Assert + assert result == ["proxy_admin"] # Should use app_roles, not roles + assert "old_roles_claim" not in result + + def test_get_app_roles_from_id_token_case_sensitivity(self): + """Test that app roles are extracted as-is (case sensitive)""" + # Arrange + payload = { + "sub": "user123", + "app_roles": ["PROXY_ADMIN", "Internal_User"], # Mixed case + "exp": 9999999999, + } + mixed_case_token = jwt.encode(payload, "secret", algorithm="HS256") + + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token(mixed_case_token) + + # Assert + assert result == ["PROXY_ADMIN", "Internal_User"] + assert "PROXY_ADMIN" in result + assert "Internal_User" in result From 1b55803c76fc74771c89b168a3f9e6699c6c2d00 Mon Sep 17 00:00:00 2001 From: berri-teddy Date: Wed, 15 Oct 2025 18:15:04 -0700 Subject: [PATCH 38/82] test: reduce EntraID app roles tests to essential scenarios - Keep only 2 focused tests: single role extraction and no roles claim - Remove complex fixtures and edge case tests - Maintain clean, maintainable test coverage --- .../test_entraid_app_roles.py | 214 +++--------------- 1 file changed, 27 insertions(+), 187 deletions(-) diff --git a/tests/test_litellm/proxy/management_endpoints/test_entraid_app_roles.py b/tests/test_litellm/proxy/management_endpoints/test_entraid_app_roles.py index de984496824..f6248d36628 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_entraid_app_roles.py +++ b/tests/test_litellm/proxy/management_endpoints/test_entraid_app_roles.py @@ -6,7 +6,6 @@ extracts app roles from Microsoft EntraID JWT tokens and prevents regressions. """ import pytest -from unittest.mock import patch import jwt from litellm.proxy.management_endpoints.ui_sso import MicrosoftSSOHandler @@ -15,201 +14,42 @@ from litellm.proxy.management_endpoints.ui_sso import MicrosoftSSOHandler class TestEntraIDAppRoles: """Test EntraID app roles extraction from JWT tokens""" - @pytest.fixture - def sample_jwt_token(self): - """Create a sample JWT token with app_roles claim""" + def test_get_app_roles_from_id_token_works_without_roles(self): + """Test that JWT token works fine without app_roles claim""" + # Arrange - Token without app_roles (normal user) payload = { "sub": "user123", "email": "user@company.com", + "aud": "litellm-app", + "iss": "https://login.microsoftonline.com/tenant-id/v2.0", + "exp": 9999999999, + } + no_roles_token = jwt.encode(payload, "secret", algorithm="HS256") + + # Act + result = MicrosoftSSOHandler.get_app_roles_from_id_token(no_roles_token) + + # Assert - Should return empty list, not error + assert result == [] + assert len(result) == 0 + + def test_get_app_roles_from_id_token_assigns_roles_when_present(self): + """Test that valid app roles are properly assigned when present""" + # Arrange - Token with valid roles + payload = { + "sub": "user123", + "email": "admin@company.com", "app_roles": ["proxy_admin"], "aud": "litellm-app", "iss": "https://login.microsoftonline.com/tenant-id/v2.0", "exp": 9999999999, } - return jwt.encode(payload, "secret", algorithm="HS256") + valid_roles_token = jwt.encode(payload, "secret", algorithm="HS256") - @pytest.fixture - def sample_jwt_token_single_role(self): - """Create a sample JWT token with single app role""" - payload = { - "sub": "user456", - "email": "admin@company.com", - "app_roles": ["proxy_admin_viewer"], - "aud": "litellm-app", - "iss": "https://login.microsoftonline.com/tenant-id/v2.0", - "exp": 9999999999, - } - return jwt.encode(payload, "secret", algorithm="HS256") - - @pytest.fixture - def sample_jwt_token_no_roles(self): - """Create a sample JWT token without app_roles claim""" - payload = { - "sub": "user789", - "email": "user@company.com", - "aud": "litellm-app", - "iss": "https://login.microsoftonline.com/tenant-id/v2.0", - "exp": 9999999999, - } - return jwt.encode(payload, "secret", algorithm="HS256") - - @pytest.fixture - def sample_jwt_token_empty_roles(self): - """Create a sample JWT token with empty app_roles array""" - payload = { - "sub": "user000", - "email": "user@company.com", - "app_roles": [], - "aud": "litellm-app", - "iss": "https://login.microsoftonline.com/tenant-id/v2.0", - "exp": 9999999999, - } - return jwt.encode(payload, "secret", algorithm="HS256") - - def test_get_app_roles_from_id_token_single_role( - self, sample_jwt_token_single_role - ): - """Test extracting single app role from JWT token""" # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token( - sample_jwt_token_single_role - ) + result = MicrosoftSSOHandler.get_app_roles_from_id_token(valid_roles_token) - # Assert - assert result == ["proxy_admin_viewer"] + # Assert - Should extract the role + assert result == ["proxy_admin"] assert len(result) == 1 - - def test_get_app_roles_from_id_token_no_roles_claim( - self, sample_jwt_token_no_roles - ): - """Test handling JWT token without app_roles claim""" - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token( - sample_jwt_token_no_roles - ) - - # Assert - assert result == [] - assert len(result) == 0 - - def test_get_app_roles_from_id_token_empty_roles( - self, sample_jwt_token_empty_roles - ): - """Test handling JWT token with empty app_roles array""" - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token( - sample_jwt_token_empty_roles - ) - - # Assert - assert result == [] - assert len(result) == 0 - - def test_get_app_roles_from_id_token_none_input(self): - """Test handling None input""" - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token(None) - - # Assert - assert result == [] - assert len(result) == 0 - - def test_get_app_roles_from_id_token_empty_string(self): - """Test handling empty string input""" - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token("") - - # Assert - assert result == [] - assert len(result) == 0 - - def test_get_app_roles_from_id_token_invalid_jwt(self): - """Test handling invalid JWT token""" - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token("invalid.jwt.token") - - # Assert - assert result == [] - assert len(result) == 0 - - def test_get_app_roles_from_id_token_malformed_roles(self): - """Test handling JWT with malformed app_roles (not a list)""" - # Arrange - payload = { - "sub": "user123", - "app_roles": "not_a_list", # Should be a list - "exp": 9999999999, - } - malformed_token = jwt.encode(payload, "secret", algorithm="HS256") - - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token(malformed_token) - - # Assert - assert result == [] - assert len(result) == 0 - - def test_get_app_roles_from_id_token_jwt_decode_exception(self): - """Test handling JWT decode exceptions gracefully""" - # Arrange - invalid_token = "completely.invalid.token" - - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token(invalid_token) - - # Assert - assert result == [] - assert len(result) == 0 - - def test_get_app_roles_from_id_token_import_error(self): - """Test handling import error for jwt library""" - # Arrange - with patch( - "builtins.__import__", side_effect=ImportError("No module named 'jwt'") - ): - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token("any.token") - - # Assert - assert result == [] - assert len(result) == 0 - - def test_get_app_roles_from_id_token_uses_correct_claim_name( - self, sample_jwt_token - ): - """Test that the method uses 'app_roles' claim, not 'roles' claim""" - # This test ensures we don't regress to the old bug where 'roles' was used - - # Arrange - Create a token with both claims to verify correct one is used - payload = { - "sub": "user123", - "roles": ["old_roles_claim"], # This should be ignored - "app_roles": ["proxy_admin"], # This should be used - "exp": 9999999999, - } - token_with_both_claims = jwt.encode(payload, "secret", algorithm="HS256") - - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token(token_with_both_claims) - - # Assert - assert result == ["proxy_admin"] # Should use app_roles, not roles - assert "old_roles_claim" not in result - - def test_get_app_roles_from_id_token_case_sensitivity(self): - """Test that app roles are extracted as-is (case sensitive)""" - # Arrange - payload = { - "sub": "user123", - "app_roles": ["PROXY_ADMIN", "Internal_User"], # Mixed case - "exp": 9999999999, - } - mixed_case_token = jwt.encode(payload, "secret", algorithm="HS256") - - # Act - result = MicrosoftSSOHandler.get_app_roles_from_id_token(mixed_case_token) - - # Assert - assert result == ["PROXY_ADMIN", "Internal_User"] - assert "PROXY_ADMIN" in result - assert "Internal_User" in result + assert "proxy_admin" in result From 55a6dd3a8b399de8215cf58f2cb8e99f8c061572 Mon Sep 17 00:00:00 2001 From: TensorNull Date: Thu, 16 Oct 2025 13:08:14 +0800 Subject: [PATCH 39/82] feat(cometapi): Add CometAPI provider support (embeddings, image generation, docs) - Add CometAPI embedding and image generation transformations and configs - Add image cost calculator and export/init files - Register provider in constants, utils, main (embedding path) and sidebars - Add CometAPI docs page and cookbook notebook (Colab) for usage examples --- cookbook/LiteLLM_CometAPI.ipynb | 474 ++++++++++++++++++ docs/my-website/docs/providers/cometapi.md | 4 + docs/my-website/sidebars.js | 1 + litellm/__init__.py | 1 + litellm/constants.py | 1 + .../litellm_core_utils/llm_cost_calc/utils.py | 9 + litellm/llms/cometapi/embed/__init__.py | 3 + litellm/llms/cometapi/embed/transformation.py | 157 ++++++ .../cometapi/image_generation/__init__.py | 13 + .../image_generation/cost_calculator.py | 25 + .../image_generation/transformation.py | 170 +++++++ litellm/main.py | 27 + litellm/utils.py | 8 + 13 files changed, 893 insertions(+) create mode 100644 cookbook/LiteLLM_CometAPI.ipynb create mode 100644 litellm/llms/cometapi/embed/__init__.py create mode 100644 litellm/llms/cometapi/embed/transformation.py create mode 100644 litellm/llms/cometapi/image_generation/__init__.py create mode 100644 litellm/llms/cometapi/image_generation/cost_calculator.py create mode 100644 litellm/llms/cometapi/image_generation/transformation.py diff --git a/cookbook/LiteLLM_CometAPI.ipynb b/cookbook/LiteLLM_CometAPI.ipynb new file mode 100644 index 00000000000..09177aaadb0 --- /dev/null +++ b/cookbook/LiteLLM_CometAPI.ipynb @@ -0,0 +1,474 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "iFEmsVJI_2BR" + }, + "source": [ + "# LiteLLM CometAPI Cookbook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "cBlUhCEP_xj4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: litellm in /Users/xmx/.miniforge3/lib/python3.12/site-packages (1.78.2)\n", + "Requirement already satisfied: aiohttp>=3.10 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from litellm) (3.11.18)\n", + "Requirement already satisfied: click in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from litellm) (8.3.0)\n", + "Requirement already satisfied: fastuuid>=0.13.0 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from litellm) (0.13.3)\n", + "Requirement already satisfied: httpx>=0.23.0 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from litellm) (0.28.1)\n", + "Requirement already satisfied: importlib-metadata>=6.8.0 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from litellm) (8.6.1)\n", + "Requirement already satisfied: jinja2<4.0.0,>=3.1.2 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from litellm) 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+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/xmx/.miniforge3/lib/python3.12/site-packages (from requests>=2.26.0->tiktoken>=0.7.0->litellm) (1.26.20)\n" + ] + } + ], + "source": [ + "!pip install litellm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Completion" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "p-MQqWOT_1a7" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ['COMETAPI_KEY'] = \"sk-ag946XQFwFsGPVGi84JHpQBStXM2yEiQYhMxcMicI7xuVswp\"\n", + "api_key = os.getenv('COMETAPI_KEY')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ze8JqMqWAARO", + "outputId": "64f3e836-69fa-4f8e-fb35-088a913bbe98" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ModelResponse(id='msg_017L3DDDit8AkEgHRe2DQBc9', created=1760589916, model='claude-sonnet-4-5-20250929', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='I\\'ll create a simple Python script that says hi.\\n\\n\\nhello.py\\n#!/usr/bin/env python3\\n\"\"\"\\nA simple script that says hi!\\n\"\"\"\\n\\ndef say_hi(name=None):\\n \"\"\"Say hi to someone, or just say hi generally.\"\"\"\\n if name:\\n print(f\"Hi, {name}!\")\\n else:\\n print(\"Hi!\")\\n\\nif __name__ == \"__main__\":\\n # Say hi generally\\n say_hi()\\n \\n # Say hi to someone specific\\n say_hi(\"World\")\\n\\n\\n\\nI\\'ve created a simple Python script called `hello.py` that:\\n\\n1. Defines a `say_hi()` function that can optionally take a name parameter\\n2. Prints \"Hi!\" if no name is provided\\n3. Prints \"Hi, [name]!\" if a name is provided\\n4. Demonstrates both usages when run\\n\\nYou can run it with:\\n```bash\\npython hello.py\\n```\\n\\nThis will output:\\n```\\nHi!\\nHi, World!\\n```\\n\\nWould you like me to create versions in other programming languages, or modify this in any way?', role='assistant', tool_calls=None, function_call=None, provider_specific_fields=None), provider_specific_fields={})], usage=Usage(completion_tokens=290, prompt_tokens=26, total_tokens=316, completion_tokens_details=None, prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=None, text_tokens=None, image_tokens=None, cached_tokens_details={})))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from litellm import completion\n", + "response = completion(\n", + " model=\"cometapi/claude-sonnet-4-5-20250929\",\n", + " messages=[{\"role\": \"user\", \"content\": \"write code for saying hi\"}]\n", + ")\n", + "response" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-LnhELrnAM_J", + "outputId": "d51c7ab7-d761-4bd1-f849-1534d9df4cd0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ModelResponse(id='chatcmpl-CRA9Uo6nsQ9C7kMJv1J4kyNDFJym7', created=1760589916, model='gpt-5-chat-latest', object='chat.completion', system_fingerprint='fp_2da73a467a', choices=[Choices(finish_reason='stop', index=0, message=Message(content='Sure! I can help you write a simple code that prints out \"Hi\" in different programming languages. \\n\\nHere’s an example in **Python**:\\n\\n```python\\n# Simple Python program to say \"Hi\"\\nprint(\"Hi\")\\n```\\n\\nExample in **JavaScript**:\\n\\n```javascript\\n// Simple JavaScript program to say \"Hi\"\\nconsole.log(\"Hi\");\\n```\\n\\nExample in **C**:\\n\\n```c\\n#include \\n\\nint main() {\\n printf(\"Hi\\\\n\");\\n return 0;\\n}\\n```\\n\\nExample in **Java**:\\n\\n```java\\npublic class SayHi {\\n public static void main(String[] args) {\\n System.out.println(\"Hi\");\\n }\\n}\\n```\\n\\nWhich language would you like me to focus on, or do you want me to make it interactive so the program greets the user by name?', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={})], usage=Usage(completion_tokens=174, prompt_tokens=12, total_tokens=186, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0, text_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=0, cached_tokens=0, text_tokens=None, image_tokens=None)))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response = completion(\n", + " model=\"cometapi/gpt-5-chat-latest\",\n", + " messages=[{\"role\": \"user\", \"content\": \"write code for saying hi\"}]\n", + ")\n", + "response" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dJBOUYdwCEn1", + "outputId": "ffa18679-ec15-4dad-fe2b-68665cdf36b0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ModelResponse(id='02176058998406949c23b3bf3d52941de23f13565a086747738f0', created=1760589991, model='deepseek-v3.2-exp', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content='Here are several ways to say \"hi\" in different programming languages:\\n\\n## Python\\n```python\\nprint(\"Hi!\")\\n```\\n\\n## JavaScript (Browser)\\n```javascript\\nconsole.log(\"Hi!\");\\n// or\\nalert(\"Hi!\");\\n```\\n\\n## JavaScript (Node.js)\\n```javascript\\nconsole.log(\"Hi!\");\\n```\\n\\n## Java\\n```java\\npublic class Hello {\\n public static void main(String[] args) {\\n System.out.println(\"Hi!\");\\n }\\n}\\n```\\n\\n## C\\n```c\\n#include \\n\\nint main() {\\n printf(\"Hi!\\\\n\");\\n return 0;\\n}\\n```\\n\\n## C++\\n```cpp\\n#include \\n\\nint main() {\\n std::cout << \"Hi!\" << std::endl;\\n return 0;\\n}\\n```\\n\\n## C#\\n```csharp\\nusing System;\\n\\nclass Program {\\n static void Main() {\\n Console.WriteLine(\"Hi!\");\\n }\\n}\\n```\\n\\n## PHP\\n```php\\n\\n```\\n\\n## Ruby\\n```ruby\\nputs \"Hi!\"\\n```\\n\\n## Go\\n```go\\npackage main\\n\\nimport \"fmt\"\\n\\nfunc main() {\\n fmt.Println(\"Hi!\")\\n}\\n```\\n\\n## Rust\\n```rust\\nfn main() {\\n println!(\"Hi!\");\\n}\\n```\\n\\n## Swift\\n```swift\\nprint(\"Hi!\")\\n```\\n\\n## Kotlin\\n```kotlin\\nfun main() {\\n println(\"Hi!\")\\n}\\n```\\n\\n## HTML (webpage)\\n```html\\n\\n\\n\\n Hi Page\\n\\n\\n

Hi!

\\n\\n\\n```\\n\\nThe Python version is probably the simplest if you\\'re just getting started!', role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}), provider_specific_fields={})], usage=Usage(completion_tokens=347, prompt_tokens=10, total_tokens=357, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=0, rejected_prediction_tokens=None, text_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=None, cached_tokens=0, text_tokens=None, image_tokens=None)), service_tier='default')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response = completion(\n", + " model=\"cometapi/deepseek-v3.2-exp\",\n", + " messages=[{\"role\": \"user\", \"content\": \"write code for saying hi\"}]\n", + ")\n", + "response" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Streaming" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Streaming Responses" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'm\n", + " doing\n", + " well\n", + " —\n", + " thanks\n", + " for\n", + " asking\n", + "!\n", + " How\n", + " can\n", + " I\n", + " help\n", + " you\n", + " today\n", + "?\n", + "\n" + ] + } + ], + "source": [ + "messages = [{\"role\": \"user\", \"content\": \"Hey, how's it going?\"}]\n", + "response = completion(model=\"cometapi/gpt-5-mini\", messages=messages, stream=True)\n", + "for part in response:\n", + " print(part.choices[0].delta.content or \"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Async Completion" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ModelResponse(id='chatcmpl-CRAAkmfczlmCEnCM55D9CKexbRenn', created=1760589994, model='gpt-5-mini-2025-08-07', object='chat.completion', system_fingerprint=None, choices=[Choices(finish_reason='stop', index=0, message=Message(content=\"I'm doing well, thanks — how are you? How can I help today?\", role='assistant', tool_calls=None, function_call=None, provider_specific_fields={'refusal': None}, annotations=[]), provider_specific_fields={'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'protected_material_code': {'filtered': False, 'detected': False}, 'protected_material_text': {'filtered': False, 'detected': False}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}})], usage=Usage(completion_tokens=26, prompt_tokens=12, total_tokens=38, completion_tokens_details=CompletionTokensDetailsWrapper(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0, text_tokens=None), prompt_tokens_details=PromptTokensDetailsWrapper(audio_tokens=0, cached_tokens=0, text_tokens=None, image_tokens=None)), prompt_filter_results=[{'prompt_index': 0, 'content_filter_results': {'hate': {'filtered': False, 'severity': 'safe'}, 'jailbreak': {'filtered': False, 'detected': False}, 'self_harm': {'filtered': False, 'severity': 'safe'}, 'sexual': {'filtered': False, 'severity': 'safe'}, 'violence': {'filtered': False, 'severity': 'safe'}}}])\n" + ] + } + ], + "source": [ + "from litellm import acompletion\n", + "import asyncio\n", + "\n", + "async def test_get_response():\n", + " user_message = \"Hello, how are you?\"\n", + " messages = [{\"content\": user_message, \"role\": \"user\"}]\n", + " response = await acompletion(model=\"cometapi/gpt-5-mini\", messages=messages)\n", + " return response\n", + "\n", + "response = await test_get_response()\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Async Streaming" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test acompletion + streaming\n", + "response: \n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='Hi', role='assistant', function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' —', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' I', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='’m', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' doing', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' well', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=',', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' thanks', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='!', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' How', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' are', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' you', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='?', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' What', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' can', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' I', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' help', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' you', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' with', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content=' today', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason=None, index=0, delta=Delta(provider_specific_fields=None, content='?', role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None, citations=None)\n", + "ModelResponseStream(id='chatcmpl-CRAAl9VMDBB5skZt638Qx86K9h1Hb', created=1760589996, model='gpt-5-mini', object='chat.completion.chunk', system_fingerprint=None, choices=[StreamingChoices(finish_reason='stop', index=0, delta=Delta(provider_specific_fields=None, content=None, role=None, function_call=None, tool_calls=None, audio=None), logprobs=None)], provider_specific_fields=None)\n" + ] + } + ], + "source": [ + "from litellm import acompletion\n", + "import asyncio, os, traceback\n", + "\n", + "async def completion_call():\n", + " try:\n", + " print(\"test acompletion + streaming\")\n", + " response = await acompletion(\n", + " model=\"cometapi/gpt-5-mini\", \n", + " messages=[{\"content\": \"Hello, how are you?\", \"role\": \"user\"}], \n", + " stream=True\n", + " )\n", + " print(f\"response: {response}\")\n", + " async for chunk in response:\n", + " print(chunk)\n", + " except:\n", + " print(f\"error occurred: {traceback.format_exc()}\")\n", + " pass\n", + "\n", + "await completion_call()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Embedding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EmbeddingResponse(model='text-embedding-3-small', data=[{'object': 'embedding', 'index': 0, 'embedding': [-0.018048199, 0.0047550877, -0.013976435, -0.021936804, -0.038773336, -0.03708264, 0.03854791, -0.0007172257, 0.026473511, -0.0027438616, -0.019823432, -0.011947598, -0.013426959, -0.0059914105, 0.020485623, 0.04269012, -0.028276922, -0.015216281, -0.03325039, 0.045057096, 0.0037477135, 0.015793936, -0.005188329, -0.0071713766, 0.008446445, 0.0070938864, 0.0027632343, 0.025656339, 0.022091785, -0.026797561, -0.029840818, -0.0542714, 0.017907308, -0.03454659, -0.014582269, 0.0429719, 0.03575826, 0.007939235, -0.010123054, -0.029587213, 0.018921727, 0.022556728, 0.019598005, 0.008819807, 0.01655475, 0.043310042, -0.034321167, 0.004441604, 0.032686826, 0.047226828, -0.0043253684, 0.006681779, 0.008995921, 0.06593721, 0.0066078105, -0.023190739, -0.00777721, 0.049875587, -0.028023317, -0.019386668, -0.0013252605, 0.009214303, -0.0055828253, -0.007432026, -0.01199691, 0.0054384116, -0.024247425, 0.047255006, -0.013743965, 0.012799991, 0.009883538, -0.005579303, -0.012616833, -0.014709071, -0.004473305, -0.022204498, 0.010454148, -0.02202134, 0.034631126, 0.0097567355, 0.041478455, -0.010750021, -0.0005340668, -0.041450277, -0.04516981, 0.014709071, -0.06142869, 0.021640932, -0.0008752884, -0.0012671428, -0.045254346, -0.004216178, -0.028220564, -0.011074071, 0.010693664, -0.0029481545, -0.039590508, -0.030178957, -0.019062618, -0.03539194, 0.019978413, 0.019161243, -0.025924034, 0.0058329077, -0.029305428, 0.028840488, 0.02731886, -0.0008048426, -0.056582022, 0.003043256, -0.08459125, -0.012560476, 0.00081100664, 0.0015771041, 0.015188103, -0.0049206354, 0.046888687, -0.027107522, 0.019245777, -0.011412211, 0.039336905, -0.018611765, 0.0429719, -0.04302826, -0.018132735, -0.0074883825, -0.02035882, -0.073038146, -0.042380158, 0.04485985, 0.03361671, -0.033842135, -0.0017259207, -0.0017091898, -0.049199305, -0.024148801, -0.044803493, 0.040943068, -0.03093977, 0.045057096, -0.0186963, -0.014053926, -0.009848315, -0.0070480965, -0.0060054995, -0.02813603, -0.022105874, 0.010235767, -0.004476827, 0.027220234, -0.026290352, 0.0216832, -0.05559578, 0.042464696, 0.0253182, -0.0031594916, 0.02266944, -0.030798879, -0.02896729, -0.0005891025, 0.004061197, -0.042295624, -0.008383043, 0.017146494, -0.025797231, -0.046522368, 0.044127215, 0.021105545, -0.04302826, 0.024430584, -0.014934498, -0.01556851, -0.075743265, -0.022415835, -0.028586883, 0.032743182, 0.03539194, -0.0034483192, -0.04669144, 0.051932603, 0.021626843, 0.03127791, 0.015610777, -0.016470214, -0.0056744046, -0.012743635, 0.060132485, 0.017428277, -0.0039942735, -0.017118316, 0.025106862, 0.008974788, 0.018513141, 0.0016035212, -0.0049593803, 0.0017514573, 0.031221554, -0.034856554, -0.011461522, 0.04621241, 0.044239927, 0.034715664, -0.0121941585, -0.012053267, -0.08526753, -0.011813751, -0.025769053, 0.0125182085, -0.0046670306, 0.038266126, 0.1187997, 0.005787118, -0.030038064, -0.054553185, -0.041506633, -0.024078354, 0.0071854657, -0.013391736, 0.03192601, -0.059625275, 0.0023458432, 0.027924692, 0.09163582, 0.030967949, 0.017639615, 0.01489223, 0.029559033, 0.042943723, 0.0003306547, 0.0047198646, 0.029897174, 0.00012603184, 0.0046811197, -0.0427183, 0.01789322, 0.018175002, -0.0081857955, 0.02581132, 0.009890582, 0.03840702, -0.094453655, -0.01076411, 0.06858598, 0.041647524, 0.033532172, 0.007467249, -0.008235107, -0.030967949, 0.0151317455, 0.027361127, 0.011834885, 0.008707094, 0.008178751, -0.022458103, 0.02844599, 0.003605061, -0.02399382, 0.05212985, 0.06041427, -0.023317542, -0.013335379, -0.044099037, -0.040802173, -0.0047656544, -0.023909286, 0.017315563, 0.017428277, 0.00736158, -0.0016070436, -0.055454887, -0.038012523, -0.020626513, 0.018273626, 0.03260229, -0.016991513, 0.038463376, -0.022458103, -0.0109613575, -0.021810003, 0.04846667, -0.042521052, 0.008601425, -0.019259866, -0.0040048403, -0.03308132, -0.02499415, 0.026783472, 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-0.023303451, -0.025797231, -0.009524264]}], object='list', usage=Usage(completion_tokens=0, prompt_tokens=3, total_tokens=3, completion_tokens_details=None, prompt_tokens_details=None))\n" + ] + } + ], + "source": [ + "import litellm\n", + "\n", + "\n", + "async def main():\n", + " response = await litellm.aembedding(\n", + " model=\"cometapi/text-embedding-3-small\", # The model name must include prefix \"openai\" + the model name from ai/ml api\n", + " api_key=api_key, # your aiml api-key\n", + " api_base=\"https://api.cometapi.com/v1\", # 👈 the URL has changed from v2 to v1\n", + " input=\"Your text string\",\n", + " )\n", + " print(response)\n", + "\n", + "await main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EmbeddingResponse(model='text-embedding-3-small', data=[{'object': 'embedding', 'index': 0, 'embedding': [-0.018048199, 0.0047550877, -0.013976435, 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prompt_tokens_details=None))\n" + ] + } + ], + "source": [ + "import litellm\n", + "\n", + "\n", + "async def main():\n", + " response = await litellm.aembedding(\n", + " model=\"cometapi/text-embedding-3-small\", # The model name must include prefix \"cometapi/\" + the model name from CometAPI\n", + " api_key=api_key, # your CometAPI api-key\n", + " api_base=\"https://api.cometapi.com/v1\",\n", + " input=\"Your text string\",\n", + " )\n", + " print(response)\n", + "\n", + "\n", + "await main()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Async Image Generation" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ImageResponse(created=1760591151, background=None, data=[ImageObject(b64_json=None, revised_prompt=\"Generate an image of an adorable baby sea otter. It should be floating on its back in a calm, clear ocean, playfully grasping a colorful shell in its small paws. The sun is setting in the background, casting a peaceful orange and purple hue across the sky and reflecting upon the ocean waves. The otter's fur is a deep, rich brown and appears silky and wet, with glints of sunlight catching on it. Its eyes are bright, expressing joy and curiosity as it examines its newfound treasure.\", url='https://oaidalleapiprodscus.blob.core.windows.net/private/org-OKnsK88id12jfvnKByup1O0l/user-3GxuMyEg9YMU8LFCPHi31prf/img-7PUEF8Wb6thGDAuZWLJjSnfP.png?st=2025-10-16T04%3A05%3A51Z&se=2025-10-16T06%3A05%3A51Z&sp=r&sv=2024-08-04&sr=b&rscd=inline&rsct=image/png&skoid=38e27a3b-6174-4d3e-90ac-d7d9ad49543f&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2025-10-16T02%3A51%3A01Z&ske=2025-10-17T02%3A51%3A01Z&sks=b&skv=2024-08-04&sig=IZKG2VE%2B6VdOe5Tq0Zk/5bVyGK/oK/yO8g%2BDX4krpug%3D')], output_format=None, quality=None, size=None, usage=Usage(completion_tokens=0, prompt_tokens=0, total_tokens=0, completion_tokens_details=None, prompt_tokens_details=None, input_tokens=0, input_tokens_details={'image_tokens': 0, 'text_tokens': 0}, output_tokens=0))\n" + ] + } + ], + "source": [ + "import asyncio\n", + "\n", + "import litellm\n", + "\n", + "\n", + "async def main():\n", + " response = await litellm.aimage_generation(\n", + " model=\"cometapi/dall-e-3\", # The model name must include prefix \"cometapi/\" + the model name from CometAPI\n", + " api_key=api_key, # your cometapi api-key\n", + " api_base=\"https://api.cometapi.com/v1\",\n", + " prompt=\"A cute baby sea otter\",\n", + " )\n", + " print(response)\n", + "\n", + "\n", + "await main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/my-website/docs/providers/cometapi.md b/docs/my-website/docs/providers/cometapi.md index 1245bacfad4..a7f6e65519d 100644 --- a/docs/my-website/docs/providers/cometapi.md +++ b/docs/my-website/docs/providers/cometapi.md @@ -1,6 +1,10 @@ # CometAPI LiteLLM supports all AI models from [CometAPI](https://www.cometapi.com/). CometAPI provides access to 500+ AI models through a unified API interface, including cutting-edge models like GPT-5, Claude Opus 4.1, and various other state-of-the-art language models. + + Open In Colab + + ## Authentication To use CometAPI models, you need to obtain an API key from [CometAPI Token Console](https://api.cometapi.com/console/token). CometAPI offers free tokens for new users - you can get your free API key instantly by registering. diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 73a17f6c551..71f730c4542 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -537,6 +537,7 @@ const sidebars = { "providers/datarobot", "providers/ovhcloud", "providers/wandb_inference", + "providers/cometapi", ], }, { diff --git a/litellm/__init__.py b/litellm/__init__.py index fc20eecbb05..162f07c56ec 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1287,6 +1287,7 @@ from .llms.hyperbolic.chat.transformation import HyperbolicChatConfig from .llms.vercel_ai_gateway.chat.transformation import VercelAIGatewayConfig from .llms.ovhcloud.chat.transformation import OVHCloudChatConfig from .llms.ovhcloud.embedding.transformation import OVHCloudEmbeddingConfig +from .llms.cometapi.embed.transformation import CometAPIEmbeddingConfig from .llms.lemonade.chat.transformation import LemonadeChatConfig from .main import * # type: ignore from .integrations import * diff --git a/litellm/constants.py b/litellm/constants.py index 4579e59f4f3..d1858bdfd84 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -525,6 +525,7 @@ openai_compatible_providers: List = [ "vercel_ai_gateway", "aiml", "wandb", + "cometapi", ] openai_text_completion_compatible_providers: List = ( [ # providers that support `/v1/completions` diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py index 626a3f3625f..2fd0b44962e 100644 --- a/litellm/litellm_core_utils/llm_cost_calc/utils.py +++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py @@ -693,6 +693,15 @@ class CostCalculatorUtils: model=model, image_response=completion_response, ) + elif custom_llm_provider == litellm.LlmProviders.COMETAPI.value: + from litellm.llms.cometapi.image_generation.cost_calculator import ( + cost_calculator as cometapi_image_cost_calculator, + ) + + return cometapi_image_cost_calculator( + model=model, + image_response=completion_response, + ) elif custom_llm_provider == litellm.LlmProviders.GEMINI.value: from litellm.llms.gemini.image_generation.cost_calculator import ( cost_calculator as gemini_image_cost_calculator, diff --git a/litellm/llms/cometapi/embed/__init__.py b/litellm/llms/cometapi/embed/__init__.py new file mode 100644 index 00000000000..a36647f46c6 --- /dev/null +++ b/litellm/llms/cometapi/embed/__init__.py @@ -0,0 +1,3 @@ +from .transformation import CometAPIEmbeddingConfig + +__all__ = ["CometAPIEmbeddingConfig"] diff --git a/litellm/llms/cometapi/embed/transformation.py b/litellm/llms/cometapi/embed/transformation.py new file mode 100644 index 00000000000..5cfd1253149 --- /dev/null +++ b/litellm/llms/cometapi/embed/transformation.py @@ -0,0 +1,157 @@ +""" +CometAPI Embedding API support - OpenAI compatible +""" + +from typing import List, Optional, Union + +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.base_llm.embedding.transformation import BaseEmbeddingConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues +from litellm.types.utils import EmbeddingResponse, Usage + +from ..common_utils import CometAPIException + + +class CometAPIEmbeddingConfig(BaseEmbeddingConfig): + """ + Configuration class for CometAPI Embedding API. + + Since CometAPI is OpenAI-compatible, this class provides OpenAI-standard + embedding functionality with CometAPI-specific authentication and endpoints. + """ + + def __init__(self) -> None: + pass + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete URL for the CometAPI embedding endpoint. + """ + api_base = ( + "https://api.cometapi.com/v1" if api_base is None else api_base.rstrip("/") + ) + complete_url = f"{api_base}/embeddings" + return complete_url + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate and set up authentication headers for CometAPI. + """ + if api_key is None: + api_key = get_secret_str("COMETAPI_KEY") + + default_headers = { + "Authorization": f"Bearer {api_key}", + "accept": "application/json", + "Content-Type": "application/json", + } + + if "Authorization" in headers: + default_headers["Authorization"] = headers["Authorization"] + + return {**default_headers, **headers} + + def get_supported_openai_params(self, model: str) -> List[str]: + """ + Get the supported OpenAI parameters for embedding requests. + CometAPI supports standard OpenAI embedding parameters. + """ + return [ + "dimensions", + "encoding_format", + "user", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to CometAPI format. + """ + supported_openai_params = self.get_supported_openai_params(model) + for param, value in non_default_params.items(): + if param in supported_openai_params: + optional_params[param] = value + return optional_params + + def transform_embedding_request( + self, + model: str, + input: AllEmbeddingInputValues, + optional_params: dict, + headers: dict, + ) -> dict: + """ + Transform the embedding request into CometAPI format. + """ + return {"input": input, "model": model, **optional_params} + + def transform_embedding_response( + self, + model: str, + raw_response: httpx.Response, + model_response: EmbeddingResponse, + logging_obj: LiteLLMLoggingObj, + api_key: Optional[str], + request_data: dict, + optional_params: dict, + litellm_params: dict, + ) -> EmbeddingResponse: + """ + Transform CometAPI response into standard EmbeddingResponse format. + """ + try: + raw_response_json = raw_response.json() + except Exception: + raise CometAPIException( + message=raw_response.text, + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + model_response.model = raw_response_json.get("model") + model_response.data = raw_response_json.get("data") + model_response.object = raw_response_json.get("object") + + usage = Usage( + prompt_tokens=raw_response_json.get("usage", {}).get("prompt_tokens", 0), + total_tokens=raw_response_json.get("usage", {}).get("total_tokens", 0), + ) + + model_response.usage = usage + return model_response + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + """ + Get the appropriate error class for CometAPI exceptions. + """ + return CometAPIException( + message=error_message, status_code=status_code, headers=headers + ) diff --git a/litellm/llms/cometapi/image_generation/__init__.py b/litellm/llms/cometapi/image_generation/__init__.py new file mode 100644 index 00000000000..8d7630f2b30 --- /dev/null +++ b/litellm/llms/cometapi/image_generation/__init__.py @@ -0,0 +1,13 @@ +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) + +from .transformation import CometAPIImageGenerationConfig + +__all__ = [ + "CometAPIImageGenerationConfig", +] + + +def get_cometapi_image_generation_config(model: str) -> BaseImageGenerationConfig: + return CometAPIImageGenerationConfig() diff --git a/litellm/llms/cometapi/image_generation/cost_calculator.py b/litellm/llms/cometapi/image_generation/cost_calculator.py new file mode 100644 index 00000000000..b10c9d09087 --- /dev/null +++ b/litellm/llms/cometapi/image_generation/cost_calculator.py @@ -0,0 +1,25 @@ +from typing import Any + +import litellm +from litellm.types.utils import ImageResponse + + +def cost_calculator( + model: str, + image_response: Any, +) -> float: + """ + CometAPI image generation cost calculator + """ + _model_info = litellm.get_model_info( + model=model, + custom_llm_provider=litellm.LlmProviders.COMETAPI.value, + ) + output_cost_per_image: float = _model_info.get("output_cost_per_image") or 0.0 + num_images: int = 0 + if isinstance(image_response, ImageResponse): + if image_response.data: + num_images = len(image_response.data) + return output_cost_per_image * num_images + else: + raise ValueError(f"image_response must be of type ImageResponse got type={type(image_response)}") diff --git a/litellm/llms/cometapi/image_generation/transformation.py b/litellm/llms/cometapi/image_generation/transformation.py new file mode 100644 index 00000000000..bf1ca9ddde6 --- /dev/null +++ b/litellm/llms/cometapi/image_generation/transformation.py @@ -0,0 +1,170 @@ +from typing import TYPE_CHECKING, Any, List, Optional + +import httpx + +from litellm.llms.base_llm.image_generation.transformation import ( + BaseImageGenerationConfig, +) +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIImageGenerationOptionalParams, +) +from litellm.types.utils import ImageObject, ImageResponse + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class CometAPIImageGenerationConfig(BaseImageGenerationConfig): + DEFAULT_BASE_URL: str = "https://api.cometapi.com" + IMAGE_GENERATION_ENDPOINT: str = "v1/images/generations" + + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIImageGenerationOptionalParams]: + """ + https://api.cometapi.com/v1/images/generations + """ + return [ + "n", + "quality", + "response_format", + "size", + "style", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + supported_params = self.get_supported_openai_params(model) + + for k in non_default_params.keys(): + if k not in optional_params.keys(): + if k in supported_params: + # CometAPI uses OpenAI-compatible parameters, so we can pass them directly + optional_params[k] = non_default_params[k] + elif drop_params: + pass + else: + raise ValueError( + f"Parameter {k} is not supported for model {model}. Supported parameters are {supported_params}. Set drop_params=True to drop unsupported parameters." + ) + + return optional_params + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete url for the request + """ + complete_url: str = ( + api_base + or get_secret_str("COMETAPI_BASE_URL") + or get_secret_str("COMETAPI_API_BASE") + or self.DEFAULT_BASE_URL + ) + + complete_url = complete_url.rstrip("/") + complete_url = f"{complete_url}/{self.IMAGE_GENERATION_ENDPOINT}" + return complete_url + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + final_api_key: Optional[str] = ( + api_key or + get_secret_str("COMETAPI_KEY") or + get_secret_str("COMETAPI_API_KEY") + ) + if not final_api_key: + raise ValueError("COMETAPI_KEY or COMETAPI_API_KEY is not set") + + headers["Authorization"] = f"Bearer {final_api_key}" + headers["Content-Type"] = "application/json" + return headers + + def transform_image_generation_request( + self, + model: str, + prompt: str, + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the image generation request to the CometAPI image generation request body + + https://api.cometapi.com/v1/images/generations + """ + # CometAPI uses OpenAI-compatible format + request_body = { + "prompt": prompt, + "model": model, + **optional_params, + } + return request_body + + def transform_image_generation_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ImageResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ImageResponse: + """ + Transform the image generation response to the litellm image response + + https://api.cometapi.com/v1/images/generations + """ + try: + response_data = raw_response.json() + except Exception as e: + raise self.get_error_class( + error_message=f"Error transforming image generation response: {e}", + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + if not model_response.data: + model_response.data = [] + + # CometAPI returns OpenAI-compatible format + # Expected format: {"created": timestamp, "data": [{"url": "...", "b64_json": "..."}]} + if "data" in response_data: + for image_data in response_data["data"]: + image_obj = ImageObject( + b64_json=image_data.get("b64_json"), + url=image_data.get("url"), + ) + model_response.data.append(image_obj) + + return model_response diff --git a/litellm/main.py b/litellm/main.py index 3955a0f32f8..5c50f096460 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -4754,6 +4754,33 @@ def embedding( # noqa: PLR0915 aembedding=aembedding, litellm_params={}, ) + elif custom_llm_provider == "cometapi": + api_key = ( + api_key + or litellm.cometapi_key + or get_secret_str("COMETAPI_KEY") + or litellm.api_key + ) + api_base = ( + api_base + or litellm.api_base + or get_secret_str("COMETAPI_API_BASE") + or "https://api.cometapi.com/v1" + ) + response = base_llm_http_handler.embedding( + model=model, + input=input, + custom_llm_provider=custom_llm_provider, + api_base=api_base, + api_key=api_key, + logging_obj=logging, + timeout=timeout, + model_response=EmbeddingResponse(), + optional_params=optional_params, + client=client, + aembedding=aembedding, + litellm_params={}, + ) elif custom_llm_provider in litellm._custom_providers: custom_handler: Optional[CustomLLM] = None for item in litellm.custom_provider_map: diff --git a/litellm/utils.py b/litellm/utils.py index 80ba37307df..1a4efb6e2a7 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -7213,6 +7213,8 @@ class ProviderConfigManager: return VolcEngineEmbeddingConfig() elif litellm.LlmProviders.OVHCLOUD == provider: return litellm.OVHCloudEmbeddingConfig() + elif litellm.LlmProviders.COMETAPI == provider: + return litellm.CometAPIEmbeddingConfig() return None @staticmethod @@ -7520,6 +7522,12 @@ class ProviderConfigManager: ) return get_aiml_image_generation_config(model) + elif LlmProviders.COMETAPI == provider: + from litellm.llms.cometapi.image_generation import ( + get_cometapi_image_generation_config, + ) + + return get_cometapi_image_generation_config(model) elif LlmProviders.GEMINI == provider: from litellm.llms.gemini.image_generation import ( get_gemini_image_generation_config, From e3566faf159830b417b199ffc831792c9c960570 Mon Sep 17 00:00:00 2001 From: TensorNull Date: Thu, 16 Oct 2025 14:14:58 +0800 Subject: [PATCH 40/82] fix(cookbook): Remove the CometAPI key used for testing. --- cookbook/LiteLLM_CometAPI.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cookbook/LiteLLM_CometAPI.ipynb b/cookbook/LiteLLM_CometAPI.ipynb index 09177aaadb0..bdd916c5bfe 100644 --- a/cookbook/LiteLLM_CometAPI.ipynb +++ b/cookbook/LiteLLM_CometAPI.ipynb @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "id": "p-MQqWOT_1a7" }, @@ -91,7 +91,7 @@ "source": [ "import os\n", "\n", - "os.environ['COMETAPI_KEY'] = \"sk-ag946XQFwFsGPVGi84JHpQBStXM2yEiQYhMxcMicI7xuVswp\"\n", + "os.environ['COMETAPI_KEY'] = \"Your_CometAPI_Key_Here\"\n", "api_key = os.getenv('COMETAPI_KEY')" ] }, From 074b9954a025916be1617fa6a3ebaff0cef995f9 Mon Sep 17 00:00:00 2001 From: TensorNull Date: Thu, 16 Oct 2025 14:18:55 +0800 Subject: [PATCH 41/82] docs(readme): Add CometAPI to supported providers table --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index c785ee82ffa..812b20e6986 100644 --- a/README.md +++ b/README.md @@ -347,6 +347,7 @@ curl 'http://0.0.0.0:4000/key/generate' \ | [Nebius AI Studio](https://docs.litellm.ai/docs/providers/nebius) | ✅ | ✅ | ✅ | ✅ | ✅ | | | [Heroku](https://docs.litellm.ai/docs/providers/heroku) | ✅ | ✅ | | | | | | [OVHCloud AI Endpoints](https://docs.litellm.ai/docs/providers/ovhcloud) | ✅ | ✅ | | | | | +| [CometAPI](https://docs.litellm.ai/docs/providers/cometapi) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [**Read the Docs**](https://docs.litellm.ai/docs/) From 359a0953193b84d60add0284cd3d39872a4684ea Mon Sep 17 00:00:00 2001 From: shinharaguchi Date: Thu, 16 Oct 2025 21:52:44 +0900 Subject: [PATCH 42/82] Add jp.anthropic.claude-haiku-4-5-20251001 price --- ...odel_prices_and_context_window_backup.json | 19 +++++++++++++++++++ model_prices_and_context_window.json | 19 +++++++++++++++++++ 2 files changed, 38 insertions(+) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 6025c3da270..6129754e887 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -14584,6 +14584,25 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 346 }, + "jp.anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "lambda_ai/deepseek-llama3.3-70b": { "input_cost_per_token": 2e-07, "litellm_provider": "lambda_ai", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index bfb7352b5f1..45757335457 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -14680,6 +14680,25 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 346 }, + "jp.anthropic.claude-haiku-4-5-20251001-v1:0": { + "cache_creation_input_token_cost": 1.25e-06, + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 200000, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 5e-06, + "source": "https://aws.amazon.com/about-aws/whats-new/2025/10/claude-4-5-haiku-anthropic-amazon-bedrock", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "lambda_ai/deepseek-llama3.3-70b": { "input_cost_per_token": 2e-07, "litellm_provider": "lambda_ai", From b075cf4a6c9e69572427707e3a462d887c28dc73 Mon Sep 17 00:00:00 2001 From: Ariel Fogel Date: Wed, 15 Oct 2025 14:52:13 +0300 Subject: [PATCH 43/82] PLR-2400: support no persistence in litellm proxy --- .../docs/proxy/guardrails/pillar_security.md | 154 +++++++++++++++++- .../guardrail_hooks/pillar/__init__.py | 24 +++ .../guardrail_hooks/pillar/pillar.py | 97 ++++++++++- litellm/types/guardrails.py | 16 ++ .../guardrails/guardrail_hooks/pillar.py | 16 ++ .../test_pillar_guardrails.py | 62 +++++++ 6 files changed, 357 insertions(+), 12 deletions(-) diff --git a/docs/my-website/docs/proxy/guardrails/pillar_security.md b/docs/my-website/docs/proxy/guardrails/pillar_security.md index c730da5b416..a5a416839f6 100644 --- a/docs/my-website/docs/proxy/guardrails/pillar_security.md +++ b/docs/my-website/docs/proxy/guardrails/pillar_security.md @@ -38,13 +38,17 @@ model_list: api_key: os.environ/OPENAI_API_KEY guardrails: - - guardrail_name: "pillar-minitor-everything" # you can change my name + - guardrail_name: "pillar-monitor-everything" # you can change my name litellm_params: guardrail: pillar mode: [pre_call, post_call] # Monitor both input and output api_key: os.environ/PILLAR_API_KEY # Your Pillar API key api_base: os.environ/PILLAR_API_BASE # Pillar API endpoint on_flagged_action: "monitor" # Log threats but allow requests + persist_session: true # Keep conversations visible in Pillar dashboard + async_mode: false # Request synchronous verdicts + include_scanners: true # Return scanner category breakdown + include_evidence: true # Include detailed findings for triage default_on: true # Enable for all requests general_settings: @@ -104,10 +108,14 @@ guardrails: api_key: os.environ/PILLAR_API_KEY # Your Pillar API key api_base: os.environ/PILLAR_API_BASE # Pillar API endpoint on_flagged_action: "block" # Block malicious requests + persist_session: true # Keep records for investigation + async_mode: false # Require an immediate verdict + include_scanners: true # Understand which rule triggered + include_evidence: true # Capture concrete evidence default_on: true # Enable for all requests general_settings: - master_key: "your-master-key-here" + master_key: "YOUR_LITELLM_PROXY_MASTER_KEY" litellm_settings: set_verbose: true @@ -136,10 +144,14 @@ guardrails: api_key: os.environ/PILLAR_API_KEY # Your Pillar API key api_base: os.environ/PILLAR_API_BASE # Pillar API endpoint on_flagged_action: "monitor" # Log threats but allow requests + persist_session: false # Skip dashboard storage for low latency + async_mode: false # Still receive results inline + include_scanners: false # Minimal payload for performance + include_evidence: false # Omit details to keep responses light default_on: true # Enable for all requests general_settings: - master_key: "your-secure-master-key-here" + master_key: "YOUR_LITELLM_PROXY_MASTER_KEY" litellm_settings: set_verbose: true # Enable detailed logging @@ -169,10 +181,14 @@ guardrails: api_key: os.environ/PILLAR_API_KEY # Your Pillar API key api_base: os.environ/PILLAR_API_BASE # Pillar API endpoint on_flagged_action: "block" # Block threats on input and output + persist_session: true # Preserve conversations in Pillar dashboard + async_mode: false # Require synchronous approval + include_scanners: true # Inspect which scanners fired + include_evidence: true # Include detailed evidence for auditing default_on: true # Enable for all requests general_settings: - master_key: "your-secure-master-key-here" + master_key: "YOUR_LITELLM_PROXY_MASTER_KEY" litellm_settings: set_verbose: true # Enable detailed logging @@ -229,19 +245,139 @@ Logs the violation but allows the request to proceed: on_flagged_action: "monitor" ``` +## Advanced Configuration + +**Quick takeaways** +- Every request still runs *all* Pillar scanners; these options only change what comes back. +- Choose richer responses when you need audit trails, lighter responses when latency or cost matters. +- Blocking is controlled by LiteLLM’s `on_flagged_action` configuration—Pillar headers do not change block/monitor behaviour. + +Pillar Security executes the full scanner suite on each call. The settings below tune the Protect response headers LiteLLM sends, letting you balance fidelity, retention, and latency. + +### Response Control + +#### Data Retention (`persist_session`) +```yaml +persist_session: false # Default: true +``` +- **Why**: Controls whether Pillar stores session data for dashboard visibility. +- **Set false for**: Ephemeral testing, privacy-sensitive interactions. +- **Set true for**: Production monitoring, compliance, historical review (default behaviour). +- **Impact**: `false` means the conversation will *not* appear in the Pillar dashboard. + +#### Response Detail Level +The following toggles grow the payload size without changing detection behaviour. + +```yaml +include_scanners: true # → plr_scanners (default true in LiteLLM) +include_evidence: true # → plr_evidence (default true in LiteLLM) +``` + +- **Minimal response** (`include_scanners=false`, `include_evidence=false`) + ```json + { + "session_id": "abc-123", + "flagged": true + } + ``` + Use when you only care about whether Pillar detected a threat. + + > **📝 Note:** `flagged: true` means Pillar’s scanners recommend blocking. Pillar only reports this verdict—LiteLLM enforces your policy via the `on_flagged_action` configuration (no Pillar header controls it): + > - `on_flagged_action: "block"` → LiteLLM raises a 400 guardrail error + > - `on_flagged_action: "monitor"` → LiteLLM logs the threat but still returns the LLM response + +- **Scanner breakdown** (`include_scanners=true`) + ```json + { + "session_id": "abc-123", + "flagged": true, + "scanners": { + "jailbreak": true, + "prompt_injection": false, + "pii": false, + "secret": false, + "toxic_language": false + /* ... more categories ... */ + } + } + ``` + Use when you need to know which categories triggered. + +- **Full context** (both toggles true) + ```json + { + "session_id": "abc-123", + "flagged": true, + "scanners": { /* ... */ }, + "evidence": [ + { + "category": "jailbreak", + "type": "prompt_injection", + "evidence": "Ignore previous instructions", + "metadata": { "start_idx": 0, "end_idx": 28 } + } + ] + } + ``` + Ideal for debugging, audit logs, or compliance exports. + +### Processing Mode (`async_mode`) +```yaml +async_mode: true # Default: false +``` +- **Why**: Queue the request for background processing instead of waiting for a synchronous verdict. +- **Response shape**: + ```json + { + "status": "queued", + "session_id": "abc-123", + "position": 1 + } + ``` +- **Set true for**: Large batch jobs, latency-tolerant pipelines. +- **Set false for**: Real-time user flows (default). +- ⚠️ **Note**: Async mode returns only a 202 queue acknowledgment (no flagged verdict). LiteLLM treats that as “no block,” so the pre-call hook always allows the request. Use async mode only for post-call or monitor-only workflows where delayed review is acceptable. + +### Complete Examples + +```yaml +guardrails: + # Production: full fidelity & dashboard visibility + - guardrail_name: "pillar-production" + litellm_params: + guardrail: pillar + mode: [pre_call, post_call] + persist_session: true + include_scanners: true + include_evidence: true + on_flagged_action: "block" + + # Testing: lightweight, no persistence + - guardrail_name: "pillar-testing" + litellm_params: + guardrail: pillar + mode: pre_call + persist_session: false + include_scanners: false + include_evidence: false + on_flagged_action: "monitor" +``` + +Keep in mind that LiteLLM forwards these values as the documented `plr_*` headers, so any direct HTTP integrations outside the proxy can reuse the same guidance. + ## Examples -**Safe requset** +**Safe request** ```bash # Test with safe content curl -X POST "http://localhost:4000/v1/chat/completions" \ -H "Content-Type: application/json" \ - -H "Authorization: Bearer your-master-key-here" \ + -H "Authorization: Bearer YOUR_LITELLM_PROXY_MASTER_KEY" \ -d '{ "model": "gpt-4.1-mini", "messages": [{"role": "user", "content": "Hello! Can you tell me a joke?"}], @@ -300,7 +436,7 @@ curl -X POST "http://localhost:4000/v1/chat/completions" \ ```bash curl -X POST "http://localhost:4000/v1/chat/completions" \ -H "Content-Type: application/json" \ - -H "Authorization: Bearer your-master-key-here" \ + -H "Authorization: Bearer YOUR_LITELLM_PROXY_MASTER_KEY" \ -d '{ "model": "gpt-4.1-mini", "messages": [ @@ -350,7 +486,7 @@ curl -X POST "http://localhost:4000/v1/chat/completions" \ ```bash curl -X POST "http://localhost:4000/v1/chat/completions" \ -H "Content-Type: application/json" \ - -H "Authorization: Bearer your-master-key-here" \ + -H "Authorization: Bearer YOUR_LITELLM_PROXY_MASTER_KEY" \ -d '{ "model": "gpt-4.1-mini", "messages": [ @@ -405,4 +541,4 @@ Feel free to contact us at support@pillar.security - [Pillar Security API Docs](https://docs.pillar.security/docs/api/introduction) - [Pillar Security Dashboard](https://app.pillar.security) - [Pillar Security Website](https://pillar.security) -- [LiteLLM Docs](https://docs.litellm.ai) \ No newline at end of file +- [LiteLLM Docs](https://docs.litellm.ai) diff --git a/litellm/proxy/guardrails/guardrail_hooks/pillar/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/pillar/__init__.py index 556c22b9495..29ede085ed6 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/pillar/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/pillar/__init__.py @@ -23,6 +23,8 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail" if not guardrail_name: raise ValueError("Pillar guardrail name is required") + optional_params = getattr(litellm_params, "optional_params", None) + _pillar_callback = PillarGuardrail( guardrail_name=guardrail_name, api_key=litellm_params.api_key, @@ -30,12 +32,34 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail" on_flagged_action=getattr(litellm_params, "on_flagged_action", "monitor"), event_hook=litellm_params.mode, default_on=litellm_params.default_on, + async_mode=_get_config_value( + litellm_params, optional_params, "async_mode" + ), + persist_session=_get_config_value( + litellm_params, optional_params, "persist_session" + ), + include_scanners=_get_config_value( + litellm_params, optional_params, "include_scanners" + ), + include_evidence=_get_config_value( + litellm_params, optional_params, "include_evidence" + ), ) litellm.logging_callback_manager.add_litellm_callback(_pillar_callback) return _pillar_callback +def _get_config_value(litellm_params, optional_params, attribute_name): + """Return guardrail configuration value prioritising optional params when present.""" + + if optional_params is not None: + value = getattr(optional_params, attribute_name, None) + if value is not None: + return value + return getattr(litellm_params, attribute_name, None) + + guardrail_initializer_registry = { SupportedGuardrailIntegrations.PILLAR.value: initialize_guardrail, } diff --git a/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py b/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py index f4741aa8e00..e19125ed031 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py +++ b/litellm/proxy/guardrails/guardrail_hooks/pillar/pillar.py @@ -69,6 +69,10 @@ class PillarGuardrail(CustomGuardrail): api_key: Optional[str] = None, api_base: Optional[str] = None, on_flagged_action: Optional[str] = None, + async_mode: Optional[bool] = None, + persist_session: Optional[bool] = None, + include_scanners: Optional[bool] = None, + include_evidence: Optional[bool] = None, **kwargs, ) -> None: """ @@ -110,6 +114,31 @@ class PillarGuardrail(CustomGuardrail): f"Pillar Guardrail: Initialized with on_flagged_action: {self.on_flagged_action}" ) + self.async_mode = self._resolve_bool_config( + provided_value=async_mode, + env_var="PILLAR_ASYNC", + default=None, + setting_name="async_mode", + ) + self.persist_session = self._resolve_bool_config( + provided_value=persist_session, + env_var="PILLAR_PERSIST", + default=None, + setting_name="persist_session", + ) + self.include_scanners = self._resolve_bool_config( + provided_value=include_scanners, + env_var="PILLAR_INCLUDE_SCANNERS", + default=True, + setting_name="include_scanners", + ) + self.include_evidence = self._resolve_bool_config( + provided_value=include_evidence, + env_var="PILLAR_INCLUDE_EVIDENCE", + default=True, + setting_name="include_evidence", + ) + # Define supported event hooks supported_event_hooks = [ GuardrailEventHooks.pre_call, @@ -347,12 +376,74 @@ class PillarGuardrail(CustomGuardrail): "Content-Type": "application/json", } - # Add Pillar-specific headers for enhanced response data - headers["plr_evidence"] = "true" - headers["plr_scanners"] = "true" + # Add Pillar-specific headers based on configuration + self._set_bool_header(headers, "plr_scanners", self.include_scanners) + self._set_bool_header(headers, "plr_evidence", self.include_evidence) + self._set_bool_header(headers, "plr_async", self.async_mode) + self._set_bool_header(headers, "plr_persist", self.persist_session) return headers + def _set_bool_header( + self, headers: Dict[str, str], header_name: str, value: Optional[bool] + ) -> None: + """Apply a boolean value as a lowercase string HTTP header when provided.""" + + if value is None: + return + headers[header_name] = "true" if value else "false" + + def _resolve_bool_config( + self, + provided_value: Optional[Union[bool, str, int]], + env_var: Optional[str], + default: Optional[bool], + setting_name: str, + ) -> Optional[bool]: + """Resolve configuration precedence: explicit value -> environment -> default.""" + + if provided_value is not None: + try: + return self._parse_bool_value(provided_value) + except ValueError: + verbose_proxy_logger.warning( + "Pillar Guardrail: Invalid boolean value '%s' for %s, falling back to default.", + provided_value, + setting_name, + ) + return default + + if env_var: + env_value = os.getenv(env_var) + if env_value is not None: + try: + return self._parse_bool_value(env_value) + except ValueError: + verbose_proxy_logger.warning( + "Pillar Guardrail: Invalid boolean env value '%s' for %s, falling back to default.", + env_value, + env_var, + ) + return default + + return default + + @staticmethod + def _parse_bool_value(value: Union[bool, str, int]) -> bool: + """Normalise various truthy/falsey inputs to a strict boolean.""" + + if isinstance(value, bool): + return value + if isinstance(value, int): + return bool(value) + + value_str = str(value).strip().lower() + if value_str in {"true", "1", "yes", "y", "on"}: + return True + if value_str in {"false", "0", "no", "n", "off"}: + return False + raise ValueError(f"Unrecognised boolean value: {value}") + def _extract_model_and_provider(self, data: dict) -> Tuple[str, str]: """ Extract the model and provider from the request data. diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py index e75f969892e..115cce9fb94 100644 --- a/litellm/types/guardrails.py +++ b/litellm/types/guardrails.py @@ -360,6 +360,22 @@ class PillarGuardrailConfigModel(BaseModel): default="monitor", description="Action to take when content is flagged: 'block' (raise exception) or 'monitor' (log only)", ) + async_mode: Optional[bool] = Field( + default=None, + description="Set to True to request asynchronous analysis (sets `plr_async` header). Defaults to provider behaviour when omitted.", + ) + persist_session: Optional[bool] = Field( + default=None, + description="Controls Pillar session persistence (sets `plr_persist` header). Set to False to disable persistence.", + ) + include_scanners: Optional[bool] = Field( + default=True, + description="Include scanner category summaries in responses (sets `plr_scanners` header).", + ) + include_evidence: Optional[bool] = Field( + default=True, + description="Include detailed evidence payloads in responses (sets `plr_evidence` header).", + ) class NomaGuardrailConfigModel(BaseModel): diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/pillar.py b/litellm/types/proxy/guardrails/guardrail_hooks/pillar.py index e18f8dfb20e..4d0c9ed1cc5 100644 --- a/litellm/types/proxy/guardrails/guardrail_hooks/pillar.py +++ b/litellm/types/proxy/guardrails/guardrail_hooks/pillar.py @@ -15,6 +15,22 @@ class PillarGuardrailConfigModelOptionalParams(BaseModel): default="monitor", description="Action to take when content is flagged: 'block' (raise exception) or 'monitor' (log only). If not provided, the `PILLAR_ON_FLAGGED_ACTION` environment variable is checked, defaults to 'monitor'.", ) + async_mode: Optional[bool] = Field( + default=None, + description="Set to True to request asynchronous analysis (sets `plr_async` header).", + ) + persist_session: Optional[bool] = Field( + default=None, + description="Set to False to disable session persistence (sets `plr_persist` header).", + ) + include_scanners: Optional[bool] = Field( + default=True, + description="Include scanner summaries in response payloads (sets `plr_scanners` header).", + ) + include_evidence: Optional[bool] = Field( + default=True, + description="Include detailed evidence objects in response payloads (sets `plr_evidence` header).", + ) class PillarGuardrailConfigModel( diff --git a/tests/guardrails_tests/test_pillar_guardrails.py b/tests/guardrails_tests/test_pillar_guardrails.py index aeb2227f9b9..31f257c94f9 100644 --- a/tests/guardrails_tests/test_pillar_guardrails.py +++ b/tests/guardrails_tests/test_pillar_guardrails.py @@ -8,6 +8,7 @@ and following LiteLLM testing patterns and best practices. # Standard library imports import os import sys +from typing import Dict from unittest.mock import Mock, patch # Third-party imports @@ -221,6 +222,18 @@ def mock_llm_response(): return mock_response +@pytest.fixture +def pillar_async_response(): + """Fixture providing an asynchronous Pillar API queue response.""" + return Response( + json={"status": "queued", "session_id": "async-session", "position": 1}, + status_code=202, + request=Request( + method="POST", url="https://api.pillar.security/api/v1/protect" + ), + ) + + @pytest.fixture def mock_llm_response_with_tools(): """Fixture providing a mock LLM response with tool calls.""" @@ -440,6 +453,55 @@ async def test_post_call_hook_with_tool_calls( assert result == mock_llm_response_with_tools +# ========================================================================= +# HEADER CONFIGURATION TESTS +# ========================================================================= + + +@pytest.mark.asyncio +async def test_pre_call_hook_custom_header_overrides( + sample_request_data, + user_api_key_dict, + dual_cache, + pillar_async_response, +): + """Ensure configuration values translate into correct Protect headers.""" + + guardrail = PillarGuardrail( + guardrail_name="pillar-header-test", + api_key="test-pillar-key", + api_base="https://api.pillar.security", + on_flagged_action="monitor", + persist_session=False, + async_mode=True, + include_scanners=False, + include_evidence=False, + ) + + captured_headers: Dict[str, str] = {} + + async def _mock_post(*args, **kwargs): + captured_headers.update(kwargs.get("headers", {})) + return pillar_async_response + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + new=_mock_post, + ): + result = await guardrail.async_pre_call_hook( + data=sample_request_data, + cache=dual_cache, + user_api_key_dict=user_api_key_dict, + call_type="completion", + ) + + assert result == sample_request_data + assert captured_headers.get("plr_persist") == "false" + assert captured_headers.get("plr_async") == "true" + assert captured_headers.get("plr_scanners") == "false" + assert captured_headers.get("plr_evidence") == "false" + + # ============================================================================ # EDGE CASE TESTS # ============================================================================ From 5e929dad2d62e84c1767090fa1f63ebca4784076 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Thu, 16 Oct 2025 09:04:57 -0700 Subject: [PATCH 44/82] test: add static analysis to prevent O(n) linear scans in router Add AST-based test to detect 'for ... in self.model_list' anti-pattern. Enforces use of index maps (model_id_to_deployment_index_map and model_name_to_deployment_indices) for O(1) lookups instead of O(n) iteration. --- .../test_router_index_management.py | 88 +++++++++++++++++++ 1 file changed, 88 insertions(+) diff --git a/tests/router_unit_tests/test_router_index_management.py b/tests/router_unit_tests/test_router_index_management.py index 04ea9214991..28a48604a01 100644 --- a/tests/router_unit_tests/test_router_index_management.py +++ b/tests/router_unit_tests/test_router_index_management.py @@ -1,6 +1,7 @@ import sys import os import pytest +import ast sys.path.insert( 0, os.path.abspath("../..") @@ -177,3 +178,90 @@ class TestRouterIndexManagement: # Verify: New entry is added assert "claude-3" in router.model_name_to_deployment_indices assert router.model_name_to_deployment_indices["claude-3"] == [0] + + def test_no_linear_scans_in_router(self): + """ + Static analysis test to ensure Router doesn't use O(n) linear scans. + + Scans router.py for 'in self.model_list' pattern which indicates + inefficient O(n) iteration instead of using index-based O(1) lookups. + + Methods should use: + - model_id_to_deployment_index_map for O(1) model_id lookups + - model_name_to_deployment_indices for O(1) + O(k) model_name lookups + """ + # Methods that are allowed to iterate through self.model_list + ALLOWED_METHODS = [ + "_get_deployment_by_litellm_model", # Edge case: lookup by litellm_params.model (not indexed) + ] + + # Get path to router.py + router_file = os.path.join( + os.path.dirname(os.path.dirname(os.path.dirname(__file__))), + "litellm", + "router.py" + ) + + # Read the file + with open(router_file, 'r') as f: + content = f.read() + + # Parse with AST + tree = ast.parse(content) + + # Find violations + violations = [] + ignore_methods = set(ALLOWED_METHODS) + + for node in ast.walk(tree): + if isinstance(node, ast.FunctionDef): + method_name = node.name + + # Skip ignored methods + if method_name in ignore_methods: + continue + + # Get source for this method + try: + method_source = ast.get_source_segment(content, node) + if not method_source: + continue + + # Check for the anti-pattern: "in self.model_list" + # This catches: for x in self.model_list, if x in self.model_list, etc. + if "in self.model_list" in method_source: + # Extract the specific line for better error reporting + lines = method_source.split('\n') + pattern_line = None + for line in lines: + if "in self.model_list" in line: + pattern_line = line.strip() + break + + violations.append({ + "method": method_name, + "line": node.lineno, + "pattern": pattern_line or "in self.model_list" + }) + except Exception: + # Skip if we can't get source segment + pass + + # Assert no violations + if violations: + error_msg = "\n".join([ + f" - {v['method']}() at line {v['line']}: {v['pattern']}" + for v in violations + ]) + + pytest.fail( + f"\n{'='*70}\n" + f"Found O(n) linear scan pattern in router.py:\n\n" + f"{error_msg}\n\n" + f"These methods should use index maps instead:\n" + f" - model_id_to_deployment_index_map (for model_id lookups)\n" + f" - model_name_to_deployment_indices (for model_name lookups)\n\n" + f"If a method legitimately needs O(n) iteration, add it to\n" + f"ALLOWED_METHODS in this test method.\n" + f"{'='*70}\n" + ) From 59c3aa02c353ddeb1e50b794ff71becc939463de Mon Sep 17 00:00:00 2001 From: Ariel Fogel Date: Thu, 16 Oct 2025 20:38:49 +0300 Subject: [PATCH 45/82] respond to review comments --- .../proxy/guardrails}/test_pillar_guardrails.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) rename tests/{guardrails_tests => test_litellm/proxy/guardrails}/test_pillar_guardrails.py (99%) diff --git a/tests/guardrails_tests/test_pillar_guardrails.py b/tests/test_litellm/proxy/guardrails/test_pillar_guardrails.py similarity index 99% rename from tests/guardrails_tests/test_pillar_guardrails.py rename to tests/test_litellm/proxy/guardrails/test_pillar_guardrails.py index 31f257c94f9..67030a8161f 100644 --- a/tests/guardrails_tests/test_pillar_guardrails.py +++ b/tests/test_litellm/proxy/guardrails/test_pillar_guardrails.py @@ -11,6 +11,9 @@ import sys from typing import Dict from unittest.mock import Mock, patch +# Add parent directory to path for imports +sys.path.insert(0, os.path.abspath("../../..")) + # Third-party imports import pytest from fastapi.exceptions import HTTPException @@ -27,9 +30,6 @@ from litellm.proxy.guardrails.guardrail_hooks.pillar import ( ) from litellm.proxy.guardrails.init_guardrails import init_guardrails_v2 -# Add parent directory to path for imports -sys.path.insert(0, os.path.abspath("../..")) - # ============================================================================ # FIXTURES From 18c5ec8ff735813b702cfbdfeebeaaa66ce2df97 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Thu, 16 Oct 2025 11:12:13 -0700 Subject: [PATCH 46/82] Add missing env key --- docs/my-website/docs/proxy/config_settings.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 4e440857261..627b1bb2374 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -752,6 +752,7 @@ router_settings: | SPEND_LOGS_URL | URL for retrieving spend logs | SPEND_LOG_CLEANUP_BATCH_SIZE | Number of logs deleted per batch during cleanup. Default is 1000 | SSL_CERTIFICATE | Path to the SSL certificate file +| SSL_ECDH_CURVE | ECDH curve for SSL/TLS key exchange (e.g., 'X25519' to disable PQC). | SSL_SECURITY_LEVEL | [BETA] Security level for SSL/TLS connections. E.g. `DEFAULT@SECLEVEL=1` | SSL_VERIFY | Flag to enable or disable SSL certificate verification | SSL_CERT_FILE | Path to the SSL certificate file for custom CA bundle From 8a325530fc7d49154d7e4961a02d4559f5403847 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Thu, 16 Oct 2025 11:25:03 -0700 Subject: [PATCH 47/82] fix(proxy): change check_file_size_under_limit to accept Collection[str] Fixes mypy type error where router_model_names (a set) was incompatible with List[str] parameter type. Changed check_file_size_under_limit to accept Collection[str] instead of List[str] since the parameter is only used for membership checks, making it more flexible and avoiding unnecessary type conversions. Resolves: proxy/proxy_server.py:5107 arg-type error --- litellm/proxy/common_utils/http_parsing_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/common_utils/http_parsing_utils.py b/litellm/proxy/common_utils/http_parsing_utils.py index 33d432e8695..807b895bd03 100644 --- a/litellm/proxy/common_utils/http_parsing_utils.py +++ b/litellm/proxy/common_utils/http_parsing_utils.py @@ -1,6 +1,6 @@ import json import re -from typing import Any, Dict, List, Optional +from typing import Any, Collection, Dict, List, Optional import orjson from fastapi import Request, UploadFile, status @@ -149,7 +149,7 @@ def _safe_get_request_headers(request: Optional[Request]) -> dict: def check_file_size_under_limit( request_data: dict, file: UploadFile, - router_model_names: List[str], + router_model_names: Collection[str], ) -> bool: """ Check if any files passed in request are under max_file_size_mb From da8f88bebaaa7f992c3ab8e2bd70faa1f2b86ee8 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Thu, 16 Oct 2025 12:13:54 -0700 Subject: [PATCH 48/82] init changes to add PKCE for OKTA --- ...odel_prices_and_context_window_backup.json | 100 +++++++++++- litellm/proxy/management_endpoints/ui_sso.py | 148 +++++++++++++++++- 2 files changed, 240 insertions(+), 8 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 209faf999d8..caca89d23f8 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -9604,6 +9604,54 @@ "supports_vision": true, "supports_web_search": true }, + "gemini-2.5-flash-image": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "max_pdf_size_mb": 30, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "image_generation", + "output_cost_per_image": 0.039, + "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 2.5e-06, + "rpm": 100000, + "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 8000000 + }, "gemini-2.5-flash-image-preview": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_audio_token": 1e-06, @@ -11057,6 +11105,54 @@ "supports_web_search": true, "tpm": 8000000 }, + "gemini/gemini-2.5-flash-image": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "max_pdf_size_mb": 30, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "image_generation", + "output_cost_per_image": 0.039, + "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 2.5e-06, + "rpm": 100000, + "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 8000000 + }, "gemini/gemini-2.5-flash-image-preview": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_audio_token": 1e-06, @@ -13315,11 +13411,11 @@ "text" ], "supports_function_calling": true, - "supports_native_streaming": false, + "supports_native_streaming": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, - "supports_reasoning": false, + "supports_reasoning": true, "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index 54909b9712b..a8dc88dd1f7 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -9,7 +9,10 @@ Has all /sso/* routes """ import asyncio +import base64 +import hashlib import os +import secrets from copy import deepcopy from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast @@ -381,7 +384,10 @@ async def get_generic_sso_response( try: result = await generic_sso.verify_and_process( request, - params={"include_client_id": generic_include_client_id}, + params=SSOAuthenticationHandler.prepare_token_exchange_parameters( + request=request, + generic_include_client_id=generic_include_client_id, + ), headers=additional_generic_sso_headers_dict, ) @@ -1072,14 +1078,66 @@ class SSOAuthenticationHandler: # or a cryptographicly signed state that we can verify stateless # For simplification we are using a static state, this is not perfect but some # SSO providers do not allow stateless verification - redirect_params = ( + redirect_params, code_verifier = ( SSOAuthenticationHandler._get_generic_sso_redirect_params( state=state, generic_authorization_endpoint=generic_authorization_endpoint, ) ) - return await generic_sso.get_login_redirect(**redirect_params) # type: ignore + # Separate PKCE params from state params (fastapi-sso doesn't accept code_challenge) + pkce_params = {} + state_only_params = {} + for key, value in redirect_params.items(): + if key in ("code_challenge", "code_challenge_method"): + pkce_params[key] = value + else: + state_only_params[key] = value + + # Get the redirect response from fastapi-sso with only state param + redirect_response = await generic_sso.get_login_redirect(**state_only_params) # type: ignore + + # If PKCE is enabled, add PKCE parameters to the redirect URL + if code_verifier and "state" in redirect_params: + from urllib.parse import parse_qs, urlencode, urlparse, urlunparse + + from litellm.proxy.proxy_server import user_api_key_cache + + # Store code_verifier in cache (10 min TTL) + cache_key = f"pkce_verifier:{redirect_params['state']}" + user_api_key_cache.set_cache( + key=cache_key, + value=code_verifier, + ttl=600, + ) + + # Add PKCE parameters to the authorization URL + if pkce_params: + parsed_url = urlparse(str(redirect_response.headers["location"])) + query_params = parse_qs(parsed_url.query) + + # Add PKCE parameters + for key, value in pkce_params.items(): + query_params[key] = [value] + + # Reconstruct the URL with PKCE parameters + new_query = urlencode(query_params, doseq=True) + new_url = urlunparse(( + parsed_url.scheme, + parsed_url.netloc, + parsed_url.path, + parsed_url.params, + new_query, + parsed_url.fragment + )) + + # Update the redirect response + redirect_response.headers["location"] = new_url + verbose_proxy_logger.debug( + "PKCE parameters added to authorization URL" + ) + + return redirect_response raise ValueError( "Unknown SSO provider. Please setup SSO with client IDs https://docs.litellm.ai/docs/proxy/admin_ui_sso" ) @@ -1088,9 +1146,10 @@ class SSOAuthenticationHandler: def _get_generic_sso_redirect_params( state: Optional[str] = None, generic_authorization_endpoint: Optional[str] = None, - ) -> dict: + ) -> Tuple[dict, Optional[str]]: """ Get redirect parameters for Generic SSO with proper state priority handling. + Optionally generates PKCE parameters if GENERIC_CLIENT_USE_PKCE is enabled. Priority order: 1. CLI state (if provided) @@ -1102,9 +1161,12 @@ class SSOAuthenticationHandler: generic_authorization_endpoint: Authorization endpoint URL Returns: - dict: Redirect parameters for SSO login + Tuple[dict, Optional[str]]: + - Redirect parameters for SSO login (may include PKCE params) + - code_verifier (if PKCE is enabled, None otherwise) """ redirect_params = {} + code_verifier: Optional[str] = None if state: # CLI state takes priority @@ -1122,7 +1184,18 @@ class SSOAuthenticationHandler: "state" ] = uuid.uuid4().hex # set state param for okta - required - return redirect_params + # Handle PKCE (Proof Key for Code Exchange) if enabled + # Set GENERIC_CLIENT_USE_PKCE=true to enable PKCE for enhanced OAuth security + use_pkce = os.getenv("GENERIC_CLIENT_USE_PKCE", "false").lower() == "true" + if use_pkce: + code_verifier, code_challenge = SSOAuthenticationHandler.generate_pkce_params() + redirect_params["code_challenge"] = code_challenge + redirect_params["code_challenge_method"] = "S256" + verbose_proxy_logger.debug( + "PKCE enabled - code_challenge added to authorization request" + ) + + return redirect_params, code_verifier @staticmethod def should_use_sso_handler( @@ -1606,6 +1679,69 @@ class SSOAuthenticationHandler: redirect_response = RedirectResponse(url=litellm_dashboard_ui, status_code=303) redirect_response.set_cookie(key="token", value=jwt_token) return redirect_response + + + @staticmethod + def prepare_token_exchange_parameters( + request: Request, + generic_include_client_id: bool, + ) -> dict: + """ + Prepare token exchange parameters for Generic SSO. + + Args: + request: Request object + generic_include_client_id: Generic OAuth Client ID + + Returns: + dict: Token exchange parameters + """ + # Prepare token exchange parameters + token_params = {"include_client_id": generic_include_client_id} + + # Retrieve PKCE code_verifier if PKCE was used in authorization + query_params = dict(request.query_params) + state = query_params.get("state") + if state: + from litellm.proxy.proxy_server import user_api_key_cache + + cache_key = f"pkce_verifier:{state}" + code_verifier = user_api_key_cache.get_cache(key=cache_key) + + if code_verifier: + # Add code_verifier to token exchange parameters + token_params["code_verifier"] = code_verifier + verbose_proxy_logger.debug( + "PKCE code_verifier retrieved and will be included in token exchange" + ) + + # Clean up the cache entry (single-use verifier) + user_api_key_cache.delete_cache(key=cache_key) + return token_params + + + @staticmethod + def generate_pkce_params() -> Tuple[str, str]: + """ + Generate PKCE (Proof Key for Code Exchange) parameters for OAuth 2.0. + + Returns: + Tuple[str, str]: (code_verifier, code_challenge) + - code_verifier: Random 43-128 character string (we use 43 for efficiency) + - code_challenge: Base64-URL-encoded SHA256 hash of the code_verifier + + Reference: https://datatracker.ietf.org/doc/html/rfc7636 + """ + # Generate a cryptographically random code_verifier (43 characters) + # Using 32 random bytes which becomes 43 characters when base64-url-encoded + code_verifier = base64.urlsafe_b64encode(secrets.token_bytes(32)).decode('utf-8').rstrip('=') + + # Generate code_challenge using S256 method (SHA256) + code_challenge_bytes = hashlib.sha256(code_verifier.encode('utf-8')).digest() + code_challenge = base64.urlsafe_b64encode(code_challenge_bytes).decode('utf-8').rstrip('=') + + return code_verifier, code_challenge + class MicrosoftSSOHandler: From 1e8d9b3159807f82d07f1fddd48d0d548c13d83e Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Thu, 16 Oct 2025 12:21:39 -0700 Subject: [PATCH 49/82] fix ui sso --- litellm/proxy/management_endpoints/ui_sso.py | 144 ++++++++++--------- 1 file changed, 79 insertions(+), 65 deletions(-) diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index a8dc88dd1f7..f9f52b2596c 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -1073,74 +1073,88 @@ class SSOAuthenticationHandler: allow_insecure_http=True, scope=generic_scope, ) - with generic_sso: - # TODO: state should be a random string and added to the user session with cookie - # or a cryptographicly signed state that we can verify stateless - # For simplification we are using a static state, this is not perfect but some - # SSO providers do not allow stateless verification - redirect_params, code_verifier = ( - SSOAuthenticationHandler._get_generic_sso_redirect_params( - state=state, - generic_authorization_endpoint=generic_authorization_endpoint, - ) - ) - - # Separate PKCE params from state params (fastapi-sso doesn't accept code_challenge) - pkce_params = {} - state_only_params = {} - for key, value in redirect_params.items(): - if key in ("code_challenge", "code_challenge_method"): - pkce_params[key] = value - else: - state_only_params[key] = value - - # Get the redirect response from fastapi-sso with only state param - redirect_response = await generic_sso.get_login_redirect(**state_only_params) # type: ignore - - # If PKCE is enabled, add PKCE parameters to the redirect URL - if code_verifier and "state" in redirect_params: - from urllib.parse import parse_qs, urlencode, urlparse, urlunparse - - from litellm.proxy.proxy_server import user_api_key_cache - - # Store code_verifier in cache (10 min TTL) - cache_key = f"pkce_verifier:{redirect_params['state']}" - user_api_key_cache.set_cache( - key=cache_key, - value=code_verifier, - ttl=600, - ) - - # Add PKCE parameters to the authorization URL - if pkce_params: - parsed_url = urlparse(str(redirect_response.headers["location"])) - query_params = parse_qs(parsed_url.query) - - # Add PKCE parameters - for key, value in pkce_params.items(): - query_params[key] = [value] - - # Reconstruct the URL with PKCE parameters - new_query = urlencode(query_params, doseq=True) - new_url = urlunparse(( - parsed_url.scheme, - parsed_url.netloc, - parsed_url.path, - parsed_url.params, - new_query, - parsed_url.fragment - )) - - # Update the redirect response - redirect_response.headers["location"] = new_url - verbose_proxy_logger.debug( - "PKCE parameters added to authorization URL" - ) - - return redirect_response + return SSOAuthenticationHandler.get_generic_sso_redirect_response( + generic_sso=generic_sso, + state=state, + generic_authorization_endpoint=generic_authorization_endpoint, + ) raise ValueError( "Unknown SSO provider. Please setup SSO with client IDs https://docs.litellm.ai/docs/proxy/admin_ui_sso" ) + + @staticmethod + def get_generic_sso_redirect_response( + generic_sso: Any, + state: Optional[str] = None, + generic_authorization_endpoint: Optional[str] = None, + ) -> Optional[RedirectResponse]: + """ + Get the redirect response for Generic SSO + """ + from urllib.parse import parse_qs, urlencode, urlparse, urlunparse + + from litellm.proxy.proxy_server import user_api_key_cache + with generic_sso: + # TODO: state should be a random string and added to the user session with cookie + # or a cryptographicly signed state that we can verify stateless + # For simplification we are using a static state, this is not perfect but some + # SSO providers do not allow stateless verification + redirect_params, code_verifier = ( + SSOAuthenticationHandler._get_generic_sso_redirect_params( + state=state, + generic_authorization_endpoint=generic_authorization_endpoint, + ) + ) + + # Separate PKCE params from state params (fastapi-sso doesn't accept code_challenge) + pkce_params = {} + state_only_params = {} + for key, value in redirect_params.items(): + if key in ("code_challenge", "code_challenge_method"): + pkce_params[key] = value + else: + state_only_params[key] = value + + # Get the redirect response from fastapi-sso with only state param + redirect_response = await generic_sso.get_login_redirect(**state_only_params) # type: ignore + + # If PKCE is enabled, add PKCE parameters to the redirect URL + if code_verifier and "state" in redirect_params: + + # Store code_verifier in cache (10 min TTL) + cache_key = f"pkce_verifier:{redirect_params['state']}" + user_api_key_cache.set_cache( + key=cache_key, + value=code_verifier, + ttl=600, + ) + + # Add PKCE parameters to the authorization URL + if pkce_params: + parsed_url = urlparse(str(redirect_response.headers["location"])) + query_params = parse_qs(parsed_url.query) + + # Add PKCE parameters + for key, value in pkce_params.items(): + query_params[key] = [value] + + # Reconstruct the URL with PKCE parameters + new_query = urlencode(query_params, doseq=True) + new_url = urlunparse(( + parsed_url.scheme, + parsed_url.netloc, + parsed_url.path, + parsed_url.params, + new_query, + parsed_url.fragment + )) + + # Update the redirect response + redirect_response.headers["location"] = new_url + verbose_proxy_logger.debug( + "PKCE parameters added to authorization URL" + ) + return redirect_response @staticmethod def _get_generic_sso_redirect_params( From 086904f3db0e6a1e99a4fe3d2c66c561ce4587f5 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Thu, 16 Oct 2025 13:18:31 -0700 Subject: [PATCH 50/82] TestPKCEFunctionality --- .../proxy/management_endpoints/test_ui_sso.py | 104 ++++++++++++++++++ 1 file changed, 104 insertions(+) diff --git a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py index 941517db120..d8a41553565 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py +++ b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py @@ -1772,3 +1772,107 @@ class TestProcessSSOJWTAccessToken: # Even empty team IDs should be set assert result.team_ids == [] + +class TestPKCEFunctionality: + """Test PKCE (Proof Key for Code Exchange) functionality""" + + def test_generate_pkce_params(self): + """ + Test that generate_pkce_params generates valid PKCE parameters + """ + import base64 + import hashlib + + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Act + code_verifier, code_challenge = SSOAuthenticationHandler.generate_pkce_params() + + # Assert + assert len(code_verifier) == 43 + assert isinstance(code_verifier, str) + + # Verify code_challenge is correctly generated from code_verifier + expected_challenge_bytes = hashlib.sha256(code_verifier.encode('utf-8')).digest() + expected_challenge = base64.urlsafe_b64encode(expected_challenge_bytes).decode('utf-8').rstrip('=') + assert code_challenge == expected_challenge + + # Verify both are base64url encoded (no padding) + assert '=' not in code_verifier + assert '=' not in code_challenge + + @pytest.mark.asyncio + async def test_prepare_token_exchange_parameters_with_pkce(self): + """ + Test prepare_token_exchange_parameters retrieves PKCE code_verifier from cache + """ + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Mock request with state parameter + mock_request = MagicMock(spec=Request) + test_state = "test_oauth_state_123" + mock_request.query_params = {"state": test_state} + + # Mock cache + mock_cache = MagicMock() + test_code_verifier = "test_code_verifier_abc123xyz" + mock_cache.get_cache.return_value = test_code_verifier + + with patch("litellm.proxy.proxy_server.user_api_key_cache", mock_cache): + # Act + token_params = SSOAuthenticationHandler.prepare_token_exchange_parameters( + request=mock_request, + generic_include_client_id=False + ) + + # Assert + assert token_params["include_client_id"] is False + assert token_params["code_verifier"] == test_code_verifier + + # Verify cache was accessed and deleted + mock_cache.get_cache.assert_called_once_with(key=f"pkce_verifier:{test_state}") + mock_cache.delete_cache.assert_called_once_with(key=f"pkce_verifier:{test_state}") + + @pytest.mark.asyncio + async def test_get_generic_sso_redirect_response_with_pkce(self): + """ + Test get_generic_sso_redirect_response with PKCE enabled stores verifier and adds challenge to URL + """ + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Mock SSO provider + mock_sso = MagicMock() + mock_redirect_response = MagicMock() + original_location = "https://auth.example.com/authorize?state=test456&client_id=abc" + mock_redirect_response.headers = {"location": original_location} + mock_sso.get_login_redirect = AsyncMock(return_value=mock_redirect_response) + mock_sso.__enter__ = MagicMock(return_value=mock_sso) + mock_sso.__exit__ = MagicMock(return_value=False) + + test_state = "test456" + mock_cache = MagicMock() + + with patch.dict(os.environ, {"GENERIC_CLIENT_USE_PKCE": "true"}): + with patch("litellm.proxy.proxy_server.user_api_key_cache", mock_cache): + # Act + result = await SSOAuthenticationHandler.get_generic_sso_redirect_response( + generic_sso=mock_sso, + state=test_state, + generic_authorization_endpoint="https://auth.example.com/authorize" + ) + + # Assert + # Verify cache was called to store code_verifier + mock_cache.set_cache.assert_called_once() + cache_call = mock_cache.set_cache.call_args + assert cache_call.kwargs["key"] == f"pkce_verifier:{test_state}" + assert cache_call.kwargs["ttl"] == 600 + assert len(cache_call.kwargs["value"]) == 43 + + # Verify PKCE parameters were added to the redirect URL + assert result is not None + updated_location = str(result.headers["location"]) + assert "code_challenge=" in updated_location + assert "code_challenge_method=S256" in updated_location + assert f"state={test_state}" in updated_location + From 8b029aff5ed2f8a4538367ac3ee57035cde9a32a Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Thu, 16 Oct 2025 13:18:46 -0700 Subject: [PATCH 51/82] get_generic_sso_redirect_response --- litellm/proxy/management_endpoints/ui_sso.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index f9f52b2596c..b9110afa222 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -1073,7 +1073,7 @@ class SSOAuthenticationHandler: allow_insecure_http=True, scope=generic_scope, ) - return SSOAuthenticationHandler.get_generic_sso_redirect_response( + return await SSOAuthenticationHandler.get_generic_sso_redirect_response( generic_sso=generic_sso, state=state, generic_authorization_endpoint=generic_authorization_endpoint, @@ -1083,7 +1083,7 @@ class SSOAuthenticationHandler: ) @staticmethod - def get_generic_sso_redirect_response( + async def get_generic_sso_redirect_response( generic_sso: Any, state: Optional[str] = None, generic_authorization_endpoint: Optional[str] = None, From 6f69b009c84710376247d0638529e066c982f76a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 16 Oct 2025 13:24:24 -0700 Subject: [PATCH 52/82] [Fix] Dall-e-2 for Image Edits API (#15604) * feat: add DallE2ImageEditConfig * fix: get_openai_image_edit_config * fix get_provider_image_edit_config * fix _add_image_to_files * fix DallE2ImageEditConfig * fix TestOpenAIImageEditDallE2 --- litellm/llms/openai/image_edit/__init__.py | 26 ++++ .../image_edit/dalle2_transformation.py | 101 ++++++++++++++ .../llms/openai/image_edit/transformation.py | 39 ++++-- ...odel_prices_and_context_window_backup.json | 100 +++++++++++++- litellm/utils.py | 6 +- test_image_edit.png | Bin 1620683 -> 2035893 bytes tests/image_gen_tests/test_image_edits.py | 123 +++++++++++++----- 7 files changed, 342 insertions(+), 53 deletions(-) create mode 100644 litellm/llms/openai/image_edit/__init__.py create mode 100644 litellm/llms/openai/image_edit/dalle2_transformation.py diff --git a/litellm/llms/openai/image_edit/__init__.py b/litellm/llms/openai/image_edit/__init__.py new file mode 100644 index 00000000000..c1898326b72 --- /dev/null +++ b/litellm/llms/openai/image_edit/__init__.py @@ -0,0 +1,26 @@ +from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig + +from .dalle2_transformation import DallE2ImageEditConfig +from .transformation import OpenAIImageEditConfig + +__all__ = ["OpenAIImageEditConfig", "DallE2ImageEditConfig", "get_openai_image_edit_config"] + + +def get_openai_image_edit_config(model: str) -> BaseImageEditConfig: + """ + Get the appropriate OpenAI image edit config based on the model. + + Args: + model: The model name (e.g., "dall-e-2", "gpt-image-1") + + Returns: + The appropriate config instance for the model + """ + model_normalized = model.lower().replace("-", "").replace("_", "") + + if model_normalized == "dalle2": + return DallE2ImageEditConfig() + else: + # Default to standard OpenAI config for gpt-image-1 and other models + return OpenAIImageEditConfig() + diff --git a/litellm/llms/openai/image_edit/dalle2_transformation.py b/litellm/llms/openai/image_edit/dalle2_transformation.py new file mode 100644 index 00000000000..37e92be17a8 --- /dev/null +++ b/litellm/llms/openai/image_edit/dalle2_transformation.py @@ -0,0 +1,101 @@ +from io import BufferedReader +from typing import TYPE_CHECKING, Any, Dict, List, Tuple, cast + +from httpx._types import RequestFiles + +import litellm +from litellm.images.utils import ImageEditRequestUtils +from litellm.types.images.main import ImageEditRequestParams +from litellm.types.llms.openai import FileTypes +from litellm.types.router import GenericLiteLLMParams + +from .transformation import OpenAIImageEditConfig + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class DallE2ImageEditConfig(OpenAIImageEditConfig): + """ + DALL-E-2 specific configuration for image edit API. + + DALL-E-2 only supports editing a single image (not an array). + Uses "image" field name instead of "image[]". + """ + + def transform_image_edit_request( + self, + model: str, + prompt: str, + image: FileTypes, + image_edit_optional_request_params: Dict, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Tuple[Dict, RequestFiles]: + """ + Transform image edit request for DALL-E-2. + + DALL-E-2 only accepts a single image with field name "image" (not "image[]"). + """ + request = ImageEditRequestParams( + model=model, + image=image, + prompt=prompt, + **image_edit_optional_request_params, + ) + request_dict = cast(Dict, request) + + ######################################################### + # Separate images and masks as `files` and send other parameters as `data` + ######################################################### + _image_list = request_dict.get("image") + _mask = request_dict.get("mask") + data_without_files = { + k: v for k, v in request_dict.items() if k not in ["image", "mask"] + } + files_list: List[Tuple[str, Any]] = [] + + # Handle image parameter - DALL-E-2 only supports single image + if _image_list is not None: + image_list = ( + [_image_list] if not isinstance(_image_list, list) else _image_list + ) + + # Validate only one image is provided + if len(image_list) > 1: + raise litellm.BadRequestError( + message="DALL-E-2 only supports editing a single image. Please provide one image.", + model=model, + llm_provider="openai", + ) + + # Use "image" field name (singular) for DALL-E-2 + for _image in image_list: + if _image is not None: + self._add_image_to_files( + files_list=files_list, + image=_image, + field_name="image", + ) + + # Handle mask parameter if provided + if _mask is not None: + # Handle case where mask can be a list (extract first mask) + if isinstance(_mask, list): + _mask = _mask[0] if _mask else None + + if _mask is not None: + mask_content_type: str = ImageEditRequestUtils.get_image_content_type( + _mask + ) + if isinstance(_mask, BufferedReader): + files_list.append(("mask", (_mask.name, _mask, mask_content_type))) + else: + files_list.append(("mask", ("mask.png", _mask, mask_content_type))) + + return data_without_files, files_list + diff --git a/litellm/llms/openai/image_edit/transformation.py b/litellm/llms/openai/image_edit/transformation.py index be960641154..1b90d96fa92 100644 --- a/litellm/llms/openai/image_edit/transformation.py +++ b/litellm/llms/openai/image_edit/transformation.py @@ -27,6 +27,11 @@ else: class OpenAIImageEditConfig(BaseImageEditConfig): + """ + Base configuration for OpenAI image edit API. + Used for models like gpt-image-1 that support multiple images. + """ + def get_supported_openai_params(self, model: str) -> list: """ All OpenAI Image Edits params are supported @@ -57,6 +62,20 @@ class OpenAIImageEditConfig(BaseImageEditConfig): """No mapping applied since inputs are in OpenAI spec already""" return dict(image_edit_optional_params) + def _add_image_to_files( + self, + files_list: List[Tuple[str, Any]], + image: Any, + field_name: str, + ) -> None: + """Add an image to the files list with appropriate content type""" + image_content_type = ImageEditRequestUtils.get_image_content_type(image) + + if isinstance(image, BufferedReader): + files_list.append((field_name, (image.name, image, image_content_type))) + else: + files_list.append((field_name, ("image.png", image, image_content_type))) + def transform_image_edit_request( self, model: str, @@ -67,9 +86,10 @@ class OpenAIImageEditConfig(BaseImageEditConfig): headers: dict, ) -> Tuple[Dict, RequestFiles]: """ - No transform applied since inputs are in OpenAI spec already + Transform image edit request to OpenAI API format. - This handles buffered readers as images to be sent as multipart/form-data for OpenAI + Handles multipart/form-data for images. Uses "image[]" field name + to support multiple images (e.g., for gpt-image-1). """ request = ImageEditRequestParams( model=model, @@ -94,19 +114,14 @@ class OpenAIImageEditConfig(BaseImageEditConfig): image_list = ( [_image_list] if not isinstance(_image_list, list) else _image_list ) + for _image in image_list: if _image is not None: - image_content_type: str = ( - ImageEditRequestUtils.get_image_content_type(_image) + self._add_image_to_files( + files_list=files_list, + image=_image, + field_name="image[]", ) - if isinstance(_image, BufferedReader): - files_list.append( - ("image[]", (_image.name, _image, image_content_type)) - ) - else: - files_list.append( - ("image[]", ("image.png", _image, image_content_type)) - ) # Handle mask parameter if provided if _mask is not None: # Handle case where mask can be a list (extract first mask) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 209faf999d8..caca89d23f8 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -9604,6 +9604,54 @@ "supports_vision": true, "supports_web_search": true }, + "gemini-2.5-flash-image": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "max_pdf_size_mb": 30, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "image_generation", + "output_cost_per_image": 0.039, + "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 2.5e-06, + "rpm": 100000, + "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 8000000 + }, "gemini-2.5-flash-image-preview": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_audio_token": 1e-06, @@ -11057,6 +11105,54 @@ "supports_web_search": true, "tpm": 8000000 }, + "gemini/gemini-2.5-flash-image": { + "cache_read_input_token_cost": 3e-08, + "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "vertex_ai-language-models", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 32768, + "max_output_tokens": 32768, + "max_tokens": 32768, + "max_pdf_size_mb": 30, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "image_generation", + "output_cost_per_image": 0.039, + "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 2.5e-06, + "rpm": 100000, + "source": "https://ai.google.dev/gemini-api/docs/pricing#gemini-2.5-flash-image", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 8000000 + }, "gemini/gemini-2.5-flash-image-preview": { "cache_read_input_token_cost": 7.5e-08, "input_cost_per_audio_token": 1e-06, @@ -13315,11 +13411,11 @@ "text" ], "supports_function_calling": true, - "supports_native_streaming": false, + "supports_native_streaming": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, "supports_prompt_caching": true, - "supports_reasoning": false, + "supports_reasoning": true, "supports_response_schema": true, "supports_system_messages": false, "supports_tool_choice": true, diff --git a/litellm/utils.py b/litellm/utils.py index 45cc901a1a0..f017543ceaa 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -7559,11 +7559,9 @@ class ProviderConfigManager: provider: LlmProviders, ) -> Optional[BaseImageEditConfig]: if LlmProviders.OPENAI == provider: - from litellm.llms.openai.image_edit.transformation import ( - OpenAIImageEditConfig, - ) + from litellm.llms.openai.image_edit import get_openai_image_edit_config - return OpenAIImageEditConfig() + return get_openai_image_edit_config(model=model) elif LlmProviders.AZURE == provider: from litellm.llms.azure.image_edit.transformation import ( AzureImageEditConfig, diff --git a/test_image_edit.png b/test_image_edit.png index 0380114f241870d3ad48ffa87d1cdbef608cfca3..0386d2af106e1e5b70db118d3dc6576313e25cc6 100644 GIT binary patch literal 2035893 zcmeFZbyOWqvoAawcMt9o95(JAT!OoAY~$_(Nq|7`00Dvq3l@UAgy8NFBm{R7Jow$o zllML6+X2>T2?sXryQW0AMOAJkbOI1lT14fC2|Q z{RnQI20(8IAQsa4zs~kvPL{CSl(4fEgxl2uzy)wZ0H72QeCr1P`vw5;U;d$z|A)%y zzfyU*I}87!6ov>`@`A0bdD$U+f`aV4f`Yv47M2hjc5Z$lD?tG+E=wURD_CEkPk-1k z|HID0Uaw+1AsJM1c9XR?fdT+fX}MI74k_eWI5-=cCu9RF8)KpZWgPXAZ~)bdNxB73MaOgv|Zwc-TKikkG&vL z>WV%vOK?D3oIPPywD9t@9l+;@L0{f|>}L;jmk z{Wl&k=inasW$MWn2FRb@fgTgngc-mKfWzQ>`aA^=w6A_Ws8!RsKvW8mspTgv?MB`ug0q0Y84Qe2o| z^j~%87zirX&K_JiV60z7Gz?^IcQ0#KD1-|gjQZ;i5$Z8 zB?q;PiXt`CnfebE*Z_==gbY(^R16M;g!20W9)t`p4IsdOwRPr9pTEEMlun^4?c*H6 zfj1272-hz~HR_o=#2-?V7Mx`;CoFtDy*xNw 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