Merge branch 'main' into litellm_dev_11_21_2024

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Krish Dholakia 2024-11-22 01:53:44 +05:30 committed by GitHub
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@ -1039,6 +1039,48 @@ jobs:
ls
python -m pytest -vv tests/otel_tests -x --junitxml=test-results/junit.xml --durations=5
no_output_timeout: 120m
# Clean up first container
- run:
name: Stop and remove first container
command: |
docker stop my-app
docker rm my-app
# Second Docker Container Run with Different Config
# NOTE: We intentionally pass a "bad" license here. We need to ensure proxy starts and serves request even with bad license
- run:
name: Run Second Docker container
command: |
docker run -d \
-p 4000:4000 \
-e DATABASE_URL=$PROXY_DATABASE_URL \
-e REDIS_HOST=$REDIS_HOST \
-e REDIS_PASSWORD=$REDIS_PASSWORD \
-e REDIS_PORT=$REDIS_PORT \
-e LITELLM_MASTER_KEY="sk-1234" \
-e OPENAI_API_KEY=$OPENAI_API_KEY \
-e LITELLM_LICENSE="bad-license" \
--name my-app-3 \
-v $(pwd)/litellm/proxy/example_config_yaml/enterprise_config.yaml:/app/config.yaml \
my-app:latest \
--config /app/config.yaml \
--port 4000 \
--detailed_debug
- run:
name: Start outputting logs for second container
command: docker logs -f my-app-2
background: true
- run:
name: Wait for second app to be ready
command: dockerize -wait http://localhost:4000 -timeout 5m
- run:
name: Run second round of tests
command: |
python -m pytest -vv tests/basic_proxy_startup_tests -x --junitxml=test-results/junit-2.xml --durations=5
no_output_timeout: 120m
# Store test results
- store_test_results:
@ -1093,6 +1135,7 @@ jobs:
pip install "asyncio==3.4.3"
pip install "PyGithub==1.59.1"
pip install "google-cloud-aiplatform==1.59.0"
pip install anthropic
- run:
name: Build Docker image
command: docker build -t my-app:latest -f ./docker/Dockerfile.database .
@ -1104,6 +1147,7 @@ jobs:
-e DATABASE_URL=$PROXY_DATABASE_URL \
-e LITELLM_MASTER_KEY="sk-1234" \
-e OPENAI_API_KEY=$OPENAI_API_KEY \
-e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
-e LITELLM_LICENSE=$LITELLM_LICENSE \
--name my-app \
-v $(pwd)/litellm/proxy/example_config_yaml/pass_through_config.yaml:/app/config.yaml \

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@ -86,3 +86,20 @@ Be aware that if you are continuing an existing trace, and you set `update_trace
## Support
For any question or issue with the integration you can reach out to the OpenLLMetry maintainers on [Slack](https://traceloop.com/slack) or via [email](mailto:dev@traceloop.com).
## Troubleshooting
### Trace LiteLLM Proxy user/key/org/team information on failed requests
LiteLLM emits the user_api_key_metadata
- key hash
- key_alias
- org_id
- user_id
- team_id
for successful + failed requests
click under `litellm_request` in the trace
<Image img={require('../../img/otel_debug_trace.png')} />

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@ -45,9 +45,7 @@ from litellm.types.llms.openai import (
ChatCompletionUsageBlock,
)
from litellm.types.utils import GenericStreamingChunk
from litellm.types.utils import Message as LitellmMessage
from litellm.types.utils import PromptTokensDetailsWrapper
from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
from litellm.utils import CustomStreamWrapper, ModelResponse
from ...base import BaseLLM
from ..common_utils import AnthropicError, process_anthropic_headers
@ -201,163 +199,6 @@ class AnthropicChatCompletion(BaseLLM):
def __init__(self) -> None:
super().__init__()
def _process_response(
self,
model: str,
response: Union[requests.Response, httpx.Response],
model_response: ModelResponse,
stream: bool,
logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, # type: ignore
optional_params: dict,
api_key: str,
data: Union[dict, str],
messages: List,
print_verbose,
encoding,
json_mode: bool,
) -> ModelResponse:
_hidden_params: Dict = {}
_hidden_params["additional_headers"] = process_anthropic_headers(
dict(response.headers)
)
## LOGGING
logging_obj.post_call(
input=messages,
api_key=api_key,
original_response=response.text,
additional_args={"complete_input_dict": data},
)
print_verbose(f"raw model_response: {response.text}")
## RESPONSE OBJECT
try:
completion_response = response.json()
except Exception as e:
response_headers = getattr(response, "headers", None)
raise AnthropicError(
message="Unable to get json response - {}, Original Response: {}".format(
str(e), response.text
),
status_code=response.status_code,
headers=response_headers,
)
if "error" in completion_response:
response_headers = getattr(response, "headers", None)
raise AnthropicError(
message=str(completion_response["error"]),
status_code=response.status_code,
headers=response_headers,
)
else:
text_content = ""
tool_calls: List[ChatCompletionToolCallChunk] = []
for idx, content in enumerate(completion_response["content"]):
if content["type"] == "text":
text_content += content["text"]
## TOOL CALLING
elif content["type"] == "tool_use":
tool_calls.append(
ChatCompletionToolCallChunk(
id=content["id"],
type="function",
function=ChatCompletionToolCallFunctionChunk(
name=content["name"],
arguments=json.dumps(content["input"]),
),
index=idx,
)
)
_message = litellm.Message(
tool_calls=tool_calls,
content=text_content or None,
)
## HANDLE JSON MODE - anthropic returns single function call
if json_mode and len(tool_calls) == 1:
json_mode_content_str: Optional[str] = tool_calls[0]["function"].get(
"arguments"
)
if json_mode_content_str is not None:
_converted_message = self._convert_tool_response_to_message(
tool_calls=tool_calls,
)
if _converted_message is not None:
completion_response["stop_reason"] = "stop"
_message = _converted_message
model_response.choices[0].message = _message # type: ignore
model_response._hidden_params["original_response"] = completion_response[
"content"
] # allow user to access raw anthropic tool calling response
model_response.choices[0].finish_reason = map_finish_reason(
completion_response["stop_reason"]
)
## CALCULATING USAGE
prompt_tokens = completion_response["usage"]["input_tokens"]
completion_tokens = completion_response["usage"]["output_tokens"]
_usage = completion_response["usage"]
cache_creation_input_tokens: int = 0
cache_read_input_tokens: int = 0
model_response.created = int(time.time())
model_response.model = model
if "cache_creation_input_tokens" in _usage:
cache_creation_input_tokens = _usage["cache_creation_input_tokens"]
prompt_tokens += cache_creation_input_tokens
if "cache_read_input_tokens" in _usage:
cache_read_input_tokens = _usage["cache_read_input_tokens"]
prompt_tokens += cache_read_input_tokens
prompt_tokens_details = PromptTokensDetailsWrapper(
cached_tokens=cache_read_input_tokens
)
total_tokens = prompt_tokens + completion_tokens
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
prompt_tokens_details=prompt_tokens_details,
cache_creation_input_tokens=cache_creation_input_tokens,
cache_read_input_tokens=cache_read_input_tokens,
)
setattr(model_response, "usage", usage) # type: ignore
model_response._hidden_params = _hidden_params
return model_response
@staticmethod
def _convert_tool_response_to_message(
tool_calls: List[ChatCompletionToolCallChunk],
) -> Optional[LitellmMessage]:
"""
In JSON mode, Anthropic API returns JSON schema as a tool call, we need to convert it to a message to follow the OpenAI format
"""
## HANDLE JSON MODE - anthropic returns single function call
json_mode_content_str: Optional[str] = tool_calls[0]["function"].get(
"arguments"
)
try:
if json_mode_content_str is not None:
args = json.loads(json_mode_content_str)
if (
isinstance(args, dict)
and (values := args.get("values")) is not None
):
_message = litellm.Message(content=json.dumps(values))
return _message
else:
# a lot of the times the `values` key is not present in the tool response
# relevant issue: https://github.com/BerriAI/litellm/issues/6741
_message = litellm.Message(content=json.dumps(args))
return _message
except json.JSONDecodeError:
# json decode error does occur, return the original tool response str
return litellm.Message(content=json_mode_content_str)
return None
async def acompletion_stream_function(
self,
model: str,
@ -454,7 +295,7 @@ class AnthropicChatCompletion(BaseLLM):
headers=error_headers,
)
return self._process_response(
return AnthropicConfig._process_response(
model=model,
response=response,
model_response=model_response,
@ -630,7 +471,7 @@ class AnthropicChatCompletion(BaseLLM):
headers=error_headers,
)
return self._process_response(
return AnthropicConfig._process_response(
model=model,
response=response,
model_response=model_response,
@ -855,7 +696,7 @@ class ModelResponseIterator:
tool_use: The ChatCompletionToolCallChunk to use in the chunk response
"""
if self.json_mode is True and tool_use is not None:
message = AnthropicChatCompletion._convert_tool_response_to_message(
message = AnthropicConfig._convert_tool_response_to_message(
tool_calls=[tool_use]
)
if message is not None:

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@ -1,7 +1,14 @@
import json
import time
import types
from typing import List, Literal, Optional, Tuple, Union
from re import A
from typing import Dict, List, Literal, Optional, Tuple, Union
import httpx
import requests
import litellm
from litellm.litellm_core_utils.core_helpers import map_finish_reason
from litellm.llms.prompt_templates.factory import anthropic_messages_pt
from litellm.types.llms.anthropic import (
AllAnthropicToolsValues,
@ -18,12 +25,23 @@ from litellm.types.llms.openai import (
AllMessageValues,
ChatCompletionCachedContent,
ChatCompletionSystemMessage,
ChatCompletionToolCallChunk,
ChatCompletionToolCallFunctionChunk,
ChatCompletionToolParam,
ChatCompletionToolParamFunctionChunk,
ChatCompletionUsageBlock,
)
from litellm.types.utils import Message as LitellmMessage
from litellm.types.utils import PromptTokensDetailsWrapper
from litellm.utils import (
CustomStreamWrapper,
ModelResponse,
Usage,
add_dummy_tool,
has_tool_call_blocks,
)
from litellm.utils import add_dummy_tool, has_tool_call_blocks
from ..common_utils import AnthropicError
from ..common_utils import AnthropicError, process_anthropic_headers
class AnthropicConfig:
@ -534,3 +552,162 @@ class AnthropicConfig:
if not is_vertex_request:
data["model"] = model
return data
@staticmethod
def _process_response(
model: str,
response: Union[requests.Response, httpx.Response],
model_response: ModelResponse,
stream: bool,
logging_obj: litellm.litellm_core_utils.litellm_logging.Logging, # type: ignore
optional_params: dict,
api_key: str,
data: Union[dict, str],
messages: List,
print_verbose,
encoding,
json_mode: bool,
) -> ModelResponse:
_hidden_params: Dict = {}
_hidden_params["additional_headers"] = process_anthropic_headers(
dict(response.headers)
)
## LOGGING
logging_obj.post_call(
input=messages,
api_key=api_key,
original_response=response.text,
additional_args={"complete_input_dict": data},
)
print_verbose(f"raw model_response: {response.text}")
## RESPONSE OBJECT
try:
completion_response = response.json()
except Exception as e:
response_headers = getattr(response, "headers", None)
raise AnthropicError(
message="Unable to get json response - {}, Original Response: {}".format(
str(e), response.text
),
status_code=response.status_code,
headers=response_headers,
)
if "error" in completion_response:
response_headers = getattr(response, "headers", None)
raise AnthropicError(
message=str(completion_response["error"]),
status_code=response.status_code,
headers=response_headers,
)
else:
text_content = ""
tool_calls: List[ChatCompletionToolCallChunk] = []
for idx, content in enumerate(completion_response["content"]):
if content["type"] == "text":
text_content += content["text"]
## TOOL CALLING
elif content["type"] == "tool_use":
tool_calls.append(
ChatCompletionToolCallChunk(
id=content["id"],
type="function",
function=ChatCompletionToolCallFunctionChunk(
name=content["name"],
arguments=json.dumps(content["input"]),
),
index=idx,
)
)
_message = litellm.Message(
tool_calls=tool_calls,
content=text_content or None,
)
## HANDLE JSON MODE - anthropic returns single function call
if json_mode and len(tool_calls) == 1:
json_mode_content_str: Optional[str] = tool_calls[0]["function"].get(
"arguments"
)
if json_mode_content_str is not None:
_converted_message = (
AnthropicConfig._convert_tool_response_to_message(
tool_calls=tool_calls,
)
)
if _converted_message is not None:
completion_response["stop_reason"] = "stop"
_message = _converted_message
model_response.choices[0].message = _message # type: ignore
model_response._hidden_params["original_response"] = completion_response[
"content"
] # allow user to access raw anthropic tool calling response
model_response.choices[0].finish_reason = map_finish_reason(
completion_response["stop_reason"]
)
## CALCULATING USAGE
prompt_tokens = completion_response["usage"]["input_tokens"]
completion_tokens = completion_response["usage"]["output_tokens"]
_usage = completion_response["usage"]
cache_creation_input_tokens: int = 0
cache_read_input_tokens: int = 0
model_response.created = int(time.time())
model_response.model = model
if "cache_creation_input_tokens" in _usage:
cache_creation_input_tokens = _usage["cache_creation_input_tokens"]
prompt_tokens += cache_creation_input_tokens
if "cache_read_input_tokens" in _usage:
cache_read_input_tokens = _usage["cache_read_input_tokens"]
prompt_tokens += cache_read_input_tokens
prompt_tokens_details = PromptTokensDetailsWrapper(
cached_tokens=cache_read_input_tokens
)
total_tokens = prompt_tokens + completion_tokens
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
prompt_tokens_details=prompt_tokens_details,
cache_creation_input_tokens=cache_creation_input_tokens,
cache_read_input_tokens=cache_read_input_tokens,
)
setattr(model_response, "usage", usage) # type: ignore
model_response._hidden_params = _hidden_params
return model_response
@staticmethod
def _convert_tool_response_to_message(
tool_calls: List[ChatCompletionToolCallChunk],
) -> Optional[LitellmMessage]:
"""
In JSON mode, Anthropic API returns JSON schema as a tool call, we need to convert it to a message to follow the OpenAI format
"""
## HANDLE JSON MODE - anthropic returns single function call
json_mode_content_str: Optional[str] = tool_calls[0]["function"].get(
"arguments"
)
try:
if json_mode_content_str is not None:
args = json.loads(json_mode_content_str)
if (
isinstance(args, dict)
and (values := args.get("values")) is not None
):
_message = litellm.Message(content=json.dumps(values))
return _message
else:
# a lot of the times the `values` key is not present in the tool response
# relevant issue: https://github.com/BerriAI/litellm/issues/6741
_message = litellm.Message(content=json.dumps(args))
return _message
except json.JSONDecodeError:
# json decode error does occur, return the original tool response str
return litellm.Message(content=json_mode_content_str)
return None

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@ -2904,6 +2904,18 @@
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
},
"vertex_ai/claude-3-sonnet": {
"max_tokens": 4096,
"max_input_tokens": 200000,
"max_output_tokens": 4096,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000015,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-sonnet@20240229": {
"max_tokens": 4096,
"max_input_tokens": 200000,
@ -2916,6 +2928,18 @@
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-sonnet": {
"max_tokens": 8192,
"max_input_tokens": 200000,
"max_output_tokens": 8192,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000015,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-sonnet@20240620": {
"max_tokens": 8192,
"max_input_tokens": 200000,
@ -2928,6 +2952,18 @@
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-sonnet-v2": {
"max_tokens": 8192,
"max_input_tokens": 200000,
"max_output_tokens": 8192,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000015,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-sonnet-v2@20241022": {
"max_tokens": 8192,
"max_input_tokens": 200000,
@ -2940,10 +2976,10 @@
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-haiku@20241022": {
"max_tokens": 8192,
"vertex_ai/claude-3-haiku": {
"max_tokens": 4096,
"max_input_tokens": 200000,
"max_output_tokens": 8192,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000025,
"output_cost_per_token": 0.00000125,
"litellm_provider": "vertex_ai-anthropic_models",
@ -2964,6 +3000,17 @@
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-haiku": {
"max_tokens": 8192,
"max_input_tokens": 200000,
"max_output_tokens": 8192,
"input_cost_per_token": 0.000001,
"output_cost_per_token": 0.000005,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-haiku@20241022": {
"max_tokens": 8192,
"max_input_tokens": 200000,
@ -2975,6 +3022,18 @@
"supports_function_calling": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-opus": {
"max_tokens": 4096,
"max_input_tokens": 200000,
"max_output_tokens": 4096,
"input_cost_per_token": 0.000015,
"output_cost_per_token": 0.000075,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-opus@20240229": {
"max_tokens": 4096,
"max_input_tokens": 200000,

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1:null

View file

@ -6,10 +6,11 @@ model_list:
- model_name: rerank-model
litellm_params:
model: jina_ai/jina-reranker-v2-base-multilingual
- model_name: jina_ai/jina-embeddings-v3
- model_name: anthropic-vertex
litellm_params:
model: jina_ai/jina-embeddings-v3
max_tokens: 8192
model: vertex_ai/claude-3-5-sonnet-v2
vertex_ai_project: "adroit-crow-413218"
vertex_ai_location: "us-east5"
router_settings:
model_group_alias:

View file

@ -192,6 +192,10 @@ class RouteChecks:
return True
if "/langfuse/" in route:
return True
if "/anthropic/" in route:
return True
if "/azure/" in route:
return True
return False
@staticmethod

View file

@ -242,7 +242,9 @@ def initialize_callbacks_on_proxy( # noqa: PLR0915
if "prometheus" in value:
if premium_user is not True:
raise Exception(CommonProxyErrors.not_premium_user.value)
verbose_proxy_logger.warning(
f"Prometheus metrics are only available for premium users. {CommonProxyErrors.not_premium_user.value}"
)
from litellm.proxy.proxy_server import app
verbose_proxy_logger.debug("Starting Prometheus Metrics on /metrics")

View file

@ -0,0 +1,17 @@
model_list:
- model_name: gpt-4
litellm_params:
model: openai/fake
api_key: fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.app/
tags: ["teamA"]
model_info:
id: "team-a-model"
litellm_settings:
cache: true
callbacks: ["prometheus"]
router_settings:
enable_tag_filtering: True # 👈 Key Change

View file

@ -2,10 +2,8 @@
What is this?
Provider-specific Pass-Through Endpoints
"""
"""
1. Create pass-through endpoints for any LITELLM_BASE_URL/gemini/<endpoint> map to https://generativelanguage.googleapis.com/<endpoint>
Use litellm with Anthropic SDK, Vertex AI SDK, Cohere SDK, etc.
"""
import ast

View file

@ -45,11 +45,11 @@ router = APIRouter()
pass_through_endpoint_logging = PassThroughEndpointLogging()
def get_response_body(response: httpx.Response):
def get_response_body(response: httpx.Response) -> Optional[dict]:
try:
return response.json()
except Exception:
return response.text
return None
async def set_env_variables_in_header(custom_headers: Optional[dict]) -> Optional[dict]:
@ -303,9 +303,29 @@ def get_response_headers(headers: httpx.Headers) -> dict:
def get_endpoint_type(url: str) -> EndpointType:
if ("generateContent") in url or ("streamGenerateContent") in url:
return EndpointType.VERTEX_AI
elif ("api.anthropic.com") in url:
return EndpointType.ANTHROPIC
return EndpointType.GENERIC
async def stream_response(
response: httpx.Response,
logging_obj: LiteLLMLoggingObj,
endpoint_type: EndpointType,
start_time: datetime,
url: str,
) -> AsyncIterable[bytes]:
async for chunk in chunk_processor(
response.aiter_bytes(),
litellm_logging_obj=logging_obj,
endpoint_type=endpoint_type,
start_time=start_time,
passthrough_success_handler_obj=pass_through_endpoint_logging,
url_route=str(url),
):
yield chunk
async def pass_through_request( # noqa: PLR0915
request: Request,
target: str,
@ -445,19 +465,14 @@ async def pass_through_request( # noqa: PLR0915
status_code=e.response.status_code, detail=await e.response.aread()
)
async def stream_response() -> AsyncIterable[bytes]:
async for chunk in chunk_processor(
response.aiter_bytes(),
litellm_logging_obj=logging_obj,
return StreamingResponse(
stream_response(
response=response,
logging_obj=logging_obj,
endpoint_type=endpoint_type,
start_time=start_time,
passthrough_success_handler_obj=pass_through_endpoint_logging,
url_route=str(url),
):
yield chunk
return StreamingResponse(
stream_response(),
url=str(url),
),
headers=get_response_headers(response.headers),
status_code=response.status_code,
)
@ -478,10 +493,9 @@ async def pass_through_request( # noqa: PLR0915
json=_parsed_body,
)
if (
response.headers.get("content-type") is not None
and response.headers["content-type"] == "text/event-stream"
):
verbose_proxy_logger.debug("response.headers= %s", response.headers)
if _is_streaming_response(response) is True:
try:
response.raise_for_status()
except httpx.HTTPStatusError as e:
@ -489,19 +503,14 @@ async def pass_through_request( # noqa: PLR0915
status_code=e.response.status_code, detail=await e.response.aread()
)
async def stream_response() -> AsyncIterable[bytes]:
async for chunk in chunk_processor(
response.aiter_bytes(),
litellm_logging_obj=logging_obj,
return StreamingResponse(
stream_response(
response=response,
logging_obj=logging_obj,
endpoint_type=endpoint_type,
start_time=start_time,
passthrough_success_handler_obj=pass_through_endpoint_logging,
url_route=str(url),
):
yield chunk
return StreamingResponse(
stream_response(),
url=str(url),
),
headers=get_response_headers(response.headers),
status_code=response.status_code,
)
@ -519,10 +528,12 @@ async def pass_through_request( # noqa: PLR0915
content = await response.aread()
## LOG SUCCESS
passthrough_logging_payload["response_body"] = get_response_body(response)
response_body: Optional[dict] = get_response_body(response)
passthrough_logging_payload["response_body"] = response_body
end_time = datetime.now()
await pass_through_endpoint_logging.pass_through_async_success_handler(
httpx_response=response,
response_body=response_body,
url_route=str(url),
result="",
start_time=start_time,
@ -619,6 +630,13 @@ def create_pass_through_route(
return endpoint_func
def _is_streaming_response(response: httpx.Response) -> bool:
_content_type = response.headers.get("content-type")
if _content_type is not None and "text/event-stream" in _content_type:
return True
return False
async def initialize_pass_through_endpoints(pass_through_endpoints: list):
verbose_proxy_logger.debug("initializing pass through endpoints")

View file

@ -2,12 +2,17 @@ import json
import re
import threading
from datetime import datetime
from typing import Union
from typing import Optional, Union
import httpx
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.litellm_logging import (
get_standard_logging_object_payload,
)
from litellm.llms.anthropic.chat.transformation import AnthropicConfig
from litellm.llms.vertex_ai_and_google_ai_studio.gemini.vertex_and_google_ai_studio_gemini import (
VertexLLM,
)
@ -23,9 +28,13 @@ class PassThroughEndpointLogging:
"predict",
]
# Anthropic
self.TRACKED_ANTHROPIC_ROUTES = ["/messages"]
async def pass_through_async_success_handler(
self,
httpx_response: httpx.Response,
response_body: Optional[dict],
logging_obj: LiteLLMLoggingObj,
url_route: str,
result: str,
@ -45,6 +54,18 @@ class PassThroughEndpointLogging:
cache_hit=cache_hit,
**kwargs,
)
elif self.is_anthropic_route(url_route):
await self.anthropic_passthrough_handler(
httpx_response=httpx_response,
response_body=response_body or {},
logging_obj=logging_obj,
url_route=url_route,
result=result,
start_time=start_time,
end_time=end_time,
cache_hit=cache_hit,
**kwargs,
)
else:
standard_logging_response_object = StandardPassThroughResponseObject(
response=httpx_response.text
@ -76,6 +97,12 @@ class PassThroughEndpointLogging:
return True
return False
def is_anthropic_route(self, url_route: str):
for route in self.TRACKED_ANTHROPIC_ROUTES:
if route in url_route:
return True
return False
def extract_model_from_url(self, url: str) -> str:
pattern = r"/models/([^:]+)"
match = re.search(pattern, url)
@ -83,6 +110,72 @@ class PassThroughEndpointLogging:
return match.group(1)
return "unknown"
async def anthropic_passthrough_handler(
self,
httpx_response: httpx.Response,
response_body: dict,
logging_obj: LiteLLMLoggingObj,
url_route: str,
result: str,
start_time: datetime,
end_time: datetime,
cache_hit: bool,
**kwargs,
):
"""
Transforms Anthropic response to OpenAI response, generates a standard logging object so downstream logging can be handled
"""
model = response_body.get("model", "")
litellm_model_response: litellm.ModelResponse = (
AnthropicConfig._process_response(
response=httpx_response,
model_response=litellm.ModelResponse(),
model=model,
stream=False,
messages=[],
logging_obj=logging_obj,
optional_params={},
api_key="",
data={},
print_verbose=litellm.print_verbose,
encoding=None,
json_mode=False,
)
)
response_cost = litellm.completion_cost(
completion_response=litellm_model_response,
model=model,
)
kwargs["response_cost"] = response_cost
kwargs["model"] = model
# Make standard logging object for Vertex AI
standard_logging_object = get_standard_logging_object_payload(
kwargs=kwargs,
init_response_obj=litellm_model_response,
start_time=start_time,
end_time=end_time,
logging_obj=logging_obj,
status="success",
)
# pretty print standard logging object
verbose_proxy_logger.debug(
"standard_logging_object= %s", json.dumps(standard_logging_object, indent=4)
)
kwargs["standard_logging_object"] = standard_logging_object
await logging_obj.async_success_handler(
result=litellm_model_response,
start_time=start_time,
end_time=end_time,
cache_hit=cache_hit,
**kwargs,
)
pass
async def vertex_passthrough_handler(
self,
httpx_response: httpx.Response,

View file

@ -4,6 +4,7 @@ from typing import Optional, TypedDict
class EndpointType(str, Enum):
VERTEX_AI = "vertex-ai"
ANTHROPIC = "anthropic"
GENERIC = "generic"

View file

@ -203,6 +203,9 @@ 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.pass_through_endpoints.llm_passthrough_endpoints import (
router as llm_passthrough_router,
)
from litellm.proxy.pass_through_endpoints.pass_through_endpoints import (
initialize_pass_through_endpoints,
)
@ -233,9 +236,6 @@ from litellm.proxy.utils import (
reset_budget,
update_spend,
)
from litellm.proxy.vertex_ai_endpoints.google_ai_studio_endpoints import (
router as gemini_router,
)
from litellm.proxy.vertex_ai_endpoints.langfuse_endpoints import (
router as langfuse_router,
)
@ -5654,6 +5654,7 @@ async def moderations(
tags=["[beta] Anthropic `/v1/messages`"],
dependencies=[Depends(user_api_key_auth)],
response_model=AnthropicResponse,
include_in_schema=False,
)
async def anthropic_response( # noqa: PLR0915
anthropic_data: AnthropicMessagesRequest,
@ -9127,7 +9128,7 @@ app.include_router(router)
app.include_router(rerank_router)
app.include_router(fine_tuning_router)
app.include_router(vertex_router)
app.include_router(gemini_router)
app.include_router(llm_passthrough_router)
app.include_router(langfuse_router)
app.include_router(pass_through_router)
app.include_router(health_router)

View file

@ -2904,6 +2904,18 @@
"mode": "chat",
"source": "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models#foundation_models"
},
"vertex_ai/claude-3-sonnet": {
"max_tokens": 4096,
"max_input_tokens": 200000,
"max_output_tokens": 4096,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000015,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-sonnet@20240229": {
"max_tokens": 4096,
"max_input_tokens": 200000,
@ -2916,6 +2928,18 @@
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-sonnet": {
"max_tokens": 8192,
"max_input_tokens": 200000,
"max_output_tokens": 8192,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000015,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-sonnet@20240620": {
"max_tokens": 8192,
"max_input_tokens": 200000,
@ -2928,6 +2952,18 @@
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-sonnet-v2": {
"max_tokens": 8192,
"max_input_tokens": 200000,
"max_output_tokens": 8192,
"input_cost_per_token": 0.000003,
"output_cost_per_token": 0.000015,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-sonnet-v2@20241022": {
"max_tokens": 8192,
"max_input_tokens": 200000,
@ -2940,10 +2976,10 @@
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-haiku@20241022": {
"max_tokens": 8192,
"vertex_ai/claude-3-haiku": {
"max_tokens": 4096,
"max_input_tokens": 200000,
"max_output_tokens": 8192,
"max_output_tokens": 4096,
"input_cost_per_token": 0.00000025,
"output_cost_per_token": 0.00000125,
"litellm_provider": "vertex_ai-anthropic_models",
@ -2964,6 +3000,17 @@
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-haiku": {
"max_tokens": 8192,
"max_input_tokens": 200000,
"max_output_tokens": 8192,
"input_cost_per_token": 0.000001,
"output_cost_per_token": 0.000005,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-5-haiku@20241022": {
"max_tokens": 8192,
"max_input_tokens": 200000,
@ -2975,6 +3022,18 @@
"supports_function_calling": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-opus": {
"max_tokens": 4096,
"max_input_tokens": 200000,
"max_output_tokens": 4096,
"input_cost_per_token": 0.000015,
"output_cost_per_token": 0.000075,
"litellm_provider": "vertex_ai-anthropic_models",
"mode": "chat",
"supports_function_calling": true,
"supports_vision": true,
"supports_assistant_prefill": true
},
"vertex_ai/claude-3-opus@20240229": {
"max_tokens": 4096,
"max_input_tokens": 200000,

View file

@ -0,0 +1,38 @@
"""
This test ensures that the proxy can passthrough anthropic requests
"""
import pytest
import anthropic
client = anthropic.Anthropic(
base_url="http://0.0.0.0:4000/anthropic", api_key="sk-1234"
)
def test_anthropic_basic_completion():
print("making basic completion request to anthropic passthrough")
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[{"role": "user", "content": "Say 'hello test' and nothing else"}],
)
print(response)
def test_anthropic_streaming():
print("making streaming request to anthropic passthrough")
collected_output = []
with client.messages.stream(
max_tokens=10,
messages=[
{"role": "user", "content": "Say 'hello stream test' and nothing else"}
],
model="claude-3-5-sonnet-20241022",
) as stream:
for text in stream.text_stream:
collected_output.append(text)
full_response = "".join(collected_output)
print(full_response)

View file

@ -0,0 +1,51 @@
"""
This test ensures that the proxy starts and serves requests even with a bad license.
in ci/cd config.yml, we set the license to "bad-license"
"""
import pytest
import aiohttp
from typing import Optional
@pytest.mark.asyncio
async def test_health_and_chat_completion():
"""
Test health endpoints and chat completion:
1. Check /health/readiness
2. Check /health/liveness
3. Make a chat completion call
"""
async with aiohttp.ClientSession() as session:
# Test readiness endpoint
async with session.get("http://0.0.0.0:4000/health/readiness") as response:
assert response.status == 200
readiness_response = await response.json()
assert readiness_response["status"] == "connected"
# Test liveness endpoint
async with session.get("http://0.0.0.0:4000/health/liveness") as response:
assert response.status == 200
liveness_response = await response.json()
print("liveness_response", liveness_response)
# Make a chat completion call
url = "http://0.0.0.0:4000/chat/completions"
headers = {
"Authorization": "Bearer sk-1234",
"Content-Type": "application/json",
}
data = {
"model": "gpt-4",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"},
],
}
async with session.post(url, headers=headers, json=data) as response:
assert response.status == 200
completion_response = await response.json()
assert "choices" in completion_response

View file

@ -721,9 +721,7 @@ def test_convert_tool_response_to_message_with_values():
)
]
message = AnthropicChatCompletion._convert_tool_response_to_message(
tool_calls=tool_calls
)
message = AnthropicConfig._convert_tool_response_to_message(tool_calls=tool_calls)
assert message is not None
assert message.content == '{"name": "John", "age": 30}'
@ -748,9 +746,7 @@ def test_convert_tool_response_to_message_without_values():
)
]
message = AnthropicChatCompletion._convert_tool_response_to_message(
tool_calls=tool_calls
)
message = AnthropicConfig._convert_tool_response_to_message(tool_calls=tool_calls)
assert message is not None
assert message.content == '{"name": "John", "age": 30}'
@ -769,9 +765,7 @@ def test_convert_tool_response_to_message_invalid_json():
)
]
message = AnthropicChatCompletion._convert_tool_response_to_message(
tool_calls=tool_calls
)
message = AnthropicConfig._convert_tool_response_to_message(tool_calls=tool_calls)
assert message is not None
assert message.content == "invalid json"
@ -788,8 +782,6 @@ def test_convert_tool_response_to_message_no_arguments():
)
]
message = AnthropicChatCompletion._convert_tool_response_to_message(
tool_calls=tool_calls
)
message = AnthropicConfig._convert_tool_response_to_message(tool_calls=tool_calls)
assert message is None

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@ -770,7 +770,7 @@ def test_vertex_ai_claude_completion_cost():
)
],
created=1700775391,
model="vertex_ai/claude-3-sonnet@20240229",
model="claude-3-sonnet",
object="chat.completion",
system_fingerprint=None,
usage=Usage(
@ -780,7 +780,7 @@ def test_vertex_ai_claude_completion_cost():
),
)
cost = litellm.completion_cost(
model="vertex_ai/claude-3-sonnet@20240229",
model="vertex_ai/claude-3-sonnet",
completion_response=response,
messages=[{"role": "user", "content": "Hey, how's it going?"}],
)

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@ -683,7 +683,7 @@ def test_completion_ollama_hosted_stream():
[
# "claude-3-5-haiku-20241022",
# "claude-2",
# "mistral/mistral-medium",
# "mistral/mistral-small-latest",
"openrouter/openai/gpt-4o-mini",
],
)

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@ -0,0 +1,38 @@
"""
This test ensures that the proxy can passthrough anthropic requests
"""
import pytest
import anthropic
client = anthropic.Anthropic(
base_url="http://0.0.0.0:4000/anthropic", api_key="sk-1234"
)
def test_anthropic_basic_completion():
print("making basic completion request to anthropic passthrough")
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
messages=[{"role": "user", "content": "Say 'hello test' and nothing else"}],
)
print(response)
def test_anthropic_streaming():
print("making streaming request to anthropic passthrough")
collected_output = []
with client.messages.stream(
max_tokens=10,
messages=[
{"role": "user", "content": "Say 'hello stream test' and nothing else"}
],
model="claude-3-5-sonnet-20241022",
) as stream:
for text in stream.text_stream:
collected_output.append(text)
full_response = "".join(collected_output)
print(full_response)

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@ -27,6 +27,9 @@ from fastapi import HTTPException, Request
import pytest
from litellm.proxy.auth.route_checks import RouteChecks
from litellm.proxy._types import LiteLLM_UserTable, LitellmUserRoles, UserAPIKeyAuth
from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
router as llm_passthrough_router,
)
# Replace the actual hash_token function with our mock
import litellm.proxy.auth.route_checks
@ -56,12 +59,21 @@ def test_is_llm_api_route():
assert RouteChecks.is_llm_api_route("/vertex-ai/text") is True
assert RouteChecks.is_llm_api_route("/gemini/generate") is True
assert RouteChecks.is_llm_api_route("/cohere/generate") is True
assert RouteChecks.is_llm_api_route("/anthropic/messages") is True
assert RouteChecks.is_llm_api_route("/anthropic/v1/messages") is True
assert RouteChecks.is_llm_api_route("/azure/endpoint") is True
# check non-matching routes
assert RouteChecks.is_llm_api_route("/some/random/route") is False
assert RouteChecks.is_llm_api_route("/key/regenerate/82akk800000000jjsk") is False
assert RouteChecks.is_llm_api_route("/key/82akk800000000jjsk/delete") is False
# check all routes in llm_passthrough_router, ensure they are considered llm api routes
for route in llm_passthrough_router.routes:
route_path = str(route.path)
print("route_path", route_path)
assert RouteChecks.is_llm_api_route(route_path) is True
# Test _route_matches_pattern
def test_route_matches_pattern():

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@ -1794,7 +1794,7 @@ async def test_add_callback_via_key_litellm_pre_call_utils_langsmith(
async def test_gemini_pass_through_endpoint():
from starlette.datastructures import URL
from litellm.proxy.vertex_ai_endpoints.google_ai_studio_endpoints import (
from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
Request,
Response,
gemini_proxy_route,

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