Litellm Minor Fixes & Improvements (10/12/2024) (#6179)

* build(model_prices_and_context_window.json): add bedrock llama3.2 pricing

* build(model_prices_and_context_window.json): add bedrock cross region inference pricing

* Revert "(perf) move s3 logging to Batch logging + async [94% faster perf under 100 RPS on 1 litellm instance] (#6165)"

This reverts commit 2a5624af47.

* add azure/gpt-4o-2024-05-13 (#6174)

* LiteLLM Minor Fixes & Improvements (10/10/2024)  (#6158)

* refactor(vertex_ai_partner_models/anthropic): refactor anthropic to use partner model logic

* fix(vertex_ai/): support passing custom api base to partner models

Fixes https://github.com/BerriAI/litellm/issues/4317

* fix(proxy_server.py): Fix prometheus premium user check logic

* docs(prometheus.md): update quick start docs

* fix(custom_llm.py): support passing dynamic api key + api base

* fix(realtime_api/main.py): Add request/response logging for realtime api endpoints

Closes https://github.com/BerriAI/litellm/issues/6081

* feat(openai/realtime): add openai realtime api logging

Closes https://github.com/BerriAI/litellm/issues/6081

* fix(realtime_streaming.py): fix linting errors

* fix(realtime_streaming.py): fix linting errors

* fix: fix linting errors

* fix pattern match router

* Add literalai in the sidebar observability category (#6163)

* fix: add literalai in the sidebar

* fix: typo

* update (#6160)

* Feat: Add Langtrace integration (#5341)

* Feat: Add Langtrace integration

* add langtrace service name

* fix timestamps for traces

* add tests

* Discard Callback + use existing otel logger

* cleanup

* remove print statments

* remove callback

* add docs

* docs

* add logging docs

* format logging

* remove emoji and add litellm proxy example

* format logging

* format `logging.md`

* add langtrace docs to logging.md

* sync conflict

* docs fix

* (perf) move s3 logging to Batch logging + async [94% faster perf under 100 RPS on 1 litellm instance] (#6165)

* fix move s3 to use customLogger

* add basic s3 logging test

* add s3 to custom logger compatible

* use batch logger for s3

* s3 set flush interval and batch size

* fix s3 logging

* add notes on s3 logging

* fix s3 logging

* add basic s3 logging test

* fix s3 type errors

* add test for sync logging on s3

* fix: fix to debug log

---------

Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Willy Douhard <willy.douhard@gmail.com>
Co-authored-by: yujonglee <yujonglee.dev@gmail.com>
Co-authored-by: Ali Waleed <ali@scale3labs.com>

* docs(custom_llm_server.md): update doc on passing custom params

* fix(pass_through_endpoints.py): don't require headers

Fixes https://github.com/BerriAI/litellm/issues/6128

* feat(utils.py): add support for caching rerank endpoints

Closes https://github.com/BerriAI/litellm/issues/6144

* feat(litellm_logging.py'): add response headers for failed requests

Closes https://github.com/BerriAI/litellm/issues/6159

---------

Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Willy Douhard <willy.douhard@gmail.com>
Co-authored-by: yujonglee <yujonglee.dev@gmail.com>
Co-authored-by: Ali Waleed <ali@scale3labs.com>

docs/my-website/docs/providers/custom_llm_server.md

@@ -251,6 +251,105 @@ Expected Response
 }
 ```
 
+## Additional Parameters
+
+Additional parameters are passed inside `optional_params` key in the `completion` or `image_generation` function.
+
+Here's how to set this: 
+
+<Tabs>
+<TabItem value="sdk" label="SDK">
+
+```python
+import litellm
+from litellm import CustomLLM, completion, get_llm_provider
+
+
+class MyCustomLLM(CustomLLM):
+    def completion(self, *args, **kwargs) -> litellm.ModelResponse:
+        assert kwargs["optional_params"] == {"my_custom_param": "my-custom-param"} # 👈 CHECK HERE
+        return litellm.completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hello world"}],
+            mock_response="Hi!",
+        )  # type: ignore
+
+my_custom_llm = MyCustomLLM()
+
+litellm.custom_provider_map = [ # 👈 KEY STEP - REGISTER HANDLER
+        {"provider": "my-custom-llm", "custom_handler": my_custom_llm}
+    ]
+
+resp = completion(model="my-custom-llm/my-model", my_custom_param="my-custom-param")
+```
+
+</TabItem>
+<TabItem value="proxy" label="Proxy">
+
+
+1. Setup your `custom_handler.py` file 
+```python
+import litellm
+from litellm import CustomLLM
+from litellm.types.utils import ImageResponse, ImageObject
+
+
+class MyCustomLLM(CustomLLM):
+    async def aimage_generation(self, model: str, prompt: str, model_response: ImageResponse, optional_params: dict, logging_obj: Any, timeout: Optional[Union[float, httpx.Timeout]] = None, client: Optional[AsyncHTTPHandler] = None,) -> ImageResponse:
+        assert optional_params == {"my_custom_param": "my-custom-param"} # 👈 CHECK HERE
+        return ImageResponse(
+            created=int(time.time()),
+            data=[ImageObject(url="https://example.com/image.png")],
+        )
+
+my_custom_llm = MyCustomLLM()
+```
+
+
+2. Add to `config.yaml` 
+
+In the config below, we pass
+
+python_filename: `custom_handler.py`
+custom_handler_instance_name: `my_custom_llm`. This is defined in Step 1
+
+custom_handler: `custom_handler.my_custom_llm`
+
+```yaml
+model_list:
+  - model_name: "test-model"             
+    litellm_params:
+      model: "openai/text-embedding-ada-002"
+  - model_name: "my-custom-model"
+    litellm_params:
+      model: "my-custom-llm/my-model"
+      my_custom_param: "my-custom-param" # 👈 CUSTOM PARAM
+
+litellm_settings:
+  custom_provider_map:
+  - {"provider": "my-custom-llm", "custom_handler": custom_handler.my_custom_llm}
+```
+
+```bash
+litellm --config /path/to/config.yaml
+```
+
+3. Test it! 
+
+```bash
+curl -X POST 'http://0.0.0.0:4000/v1/images/generations' \
+-H 'Content-Type: application/json' \
+-H 'Authorization: Bearer sk-1234' \
+-d '{
+    "model": "my-custom-model",
+    "prompt": "A cute baby sea otter",
+}'
+```
+
+</TabItem>
+</Tabs>
+
+
 
 ## Custom Handler Spec