forked from phoenix/litellm-mirror
Merge branch 'main' into litellm_dev_11_21_2024
This commit is contained in:
commit
9f02d47364
90 changed files with 740 additions and 443 deletions
|
@ -1039,6 +1039,48 @@ jobs:
|
|||
ls
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||||
python -m pytest -vv tests/otel_tests -x --junitxml=test-results/junit.xml --durations=5
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no_output_timeout: 120m
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||||
# Clean up first container
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||||
- run:
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||||
name: Stop and remove first container
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command: |
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||||
docker stop my-app
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docker rm my-app
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||||
|
||||
# Second Docker Container Run with Different Config
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||||
# NOTE: We intentionally pass a "bad" license here. We need to ensure proxy starts and serves request even with bad license
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- run:
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name: Run Second Docker container
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||||
command: |
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||||
docker run -d \
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-p 4000:4000 \
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-e DATABASE_URL=$PROXY_DATABASE_URL \
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-e REDIS_HOST=$REDIS_HOST \
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-e REDIS_PASSWORD=$REDIS_PASSWORD \
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-e REDIS_PORT=$REDIS_PORT \
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||||
-e LITELLM_MASTER_KEY="sk-1234" \
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-e OPENAI_API_KEY=$OPENAI_API_KEY \
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-e LITELLM_LICENSE="bad-license" \
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--name my-app-3 \
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-v $(pwd)/litellm/proxy/example_config_yaml/enterprise_config.yaml:/app/config.yaml \
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my-app:latest \
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--config /app/config.yaml \
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--port 4000 \
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--detailed_debug
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- run:
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name: Start outputting logs for second container
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command: docker logs -f my-app-2
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background: true
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- run:
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name: Wait for second app to be ready
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command: dockerize -wait http://localhost:4000 -timeout 5m
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- run:
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name: Run second round of tests
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command: |
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python -m pytest -vv tests/basic_proxy_startup_tests -x --junitxml=test-results/junit-2.xml --durations=5
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no_output_timeout: 120m
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# Store test results
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- store_test_results:
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|
@ -1093,6 +1135,7 @@ jobs:
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pip install "asyncio==3.4.3"
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pip install "PyGithub==1.59.1"
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pip install "google-cloud-aiplatform==1.59.0"
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pip install anthropic
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- run:
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name: Build Docker image
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command: docker build -t my-app:latest -f ./docker/Dockerfile.database .
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|
@ -1104,6 +1147,7 @@ jobs:
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-e DATABASE_URL=$PROXY_DATABASE_URL \
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-e LITELLM_MASTER_KEY="sk-1234" \
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-e OPENAI_API_KEY=$OPENAI_API_KEY \
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||||
-e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
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-e LITELLM_LICENSE=$LITELLM_LICENSE \
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--name my-app \
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-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).
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||||
|
||||
## Troubleshooting
|
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|
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### Trace LiteLLM Proxy user/key/org/team information on failed requests
|
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|
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LiteLLM emits the user_api_key_metadata
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- key hash
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- key_alias
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- org_id
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- user_id
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- team_id
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|
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for successful + failed requests
|
||||
|
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click under `litellm_request` in the trace
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|
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<Image img={require('../../img/otel_debug_trace.png')} />
|
BIN
docs/my-website/img/otel_debug_trace.png
Normal file
BIN
docs/my-website/img/otel_debug_trace.png
Normal file
Binary file not shown.
After Width: | Height: | Size: 437 KiB |
|
@ -45,9 +45,7 @@ from litellm.types.llms.openai import (
|
|||
ChatCompletionUsageBlock,
|
||||
)
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||||
from litellm.types.utils import GenericStreamingChunk
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from litellm.types.utils import Message as LitellmMessage
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from litellm.types.utils import PromptTokensDetailsWrapper
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from litellm.utils import CustomStreamWrapper, ModelResponse, Usage
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from litellm.utils import CustomStreamWrapper, ModelResponse
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|
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from ...base import BaseLLM
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from ..common_utils import AnthropicError, process_anthropic_headers
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||||
|
@ -201,163 +199,6 @@ class AnthropicChatCompletion(BaseLLM):
|
|||
def __init__(self) -> None:
|
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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
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||||
optional_params: dict,
|
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api_key: str,
|
||||
data: Union[dict, str],
|
||||
messages: List,
|
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print_verbose,
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encoding,
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||||
json_mode: bool,
|
||||
) -> ModelResponse:
|
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_hidden_params: Dict = {}
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_hidden_params["additional_headers"] = process_anthropic_headers(
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dict(response.headers)
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||||
)
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||||
## LOGGING
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logging_obj.post_call(
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input=messages,
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api_key=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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||||
)
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print_verbose(f"raw model_response: {response.text}")
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## RESPONSE OBJECT
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||||
try:
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completion_response = response.json()
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except Exception as e:
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response_headers = getattr(response, "headers", None)
|
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raise AnthropicError(
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message="Unable to get json response - {}, Original Response: {}".format(
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str(e), response.text
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),
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status_code=response.status_code,
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headers=response_headers,
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||||
)
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if "error" in completion_response:
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response_headers = getattr(response, "headers", None)
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raise AnthropicError(
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message=str(completion_response["error"]),
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status_code=response.status_code,
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headers=response_headers,
|
||||
)
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||||
else:
|
||||
text_content = ""
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||||
tool_calls: List[ChatCompletionToolCallChunk] = []
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for idx, content in enumerate(completion_response["content"]):
|
||||
if content["type"] == "text":
|
||||
text_content += content["text"]
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## TOOL CALLING
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elif content["type"] == "tool_use":
|
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tool_calls.append(
|
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ChatCompletionToolCallChunk(
|
||||
id=content["id"],
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||||
type="function",
|
||||
function=ChatCompletionToolCallFunctionChunk(
|
||||
name=content["name"],
|
||||
arguments=json.dumps(content["input"]),
|
||||
),
|
||||
index=idx,
|
||||
)
|
||||
)
|
||||
|
||||
_message = litellm.Message(
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||||
tool_calls=tool_calls,
|
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content=text_content or None,
|
||||
)
|
||||
|
||||
## HANDLE JSON MODE - anthropic returns single function call
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||||
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:
|
||||
|
|
|
@ -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
|
||||
|
|
|
@ -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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|
||||
6:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"LiteLLM Dashboard"}],["$","meta","3",{"name":"description","content":"LiteLLM Proxy Admin UI"}],["$","link","4",{"rel":"icon","href":"/ui/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","5",{"name":"next-size-adjust"}]]
|
||||
1:null
|
||||
|
|
|
@ -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:
|
||||
|
|
|
@ -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
|
||||
|
|
|
@ -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")
|
||||
|
|
17
litellm/proxy/example_config_yaml/enterprise_config.yaml
Normal file
17
litellm/proxy/example_config_yaml/enterprise_config.yaml
Normal 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
|
||||
|
|
@ -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
|
|
@ -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")
|
||||
|
|
|
@ -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,
|
||||
|
|
|
@ -4,6 +4,7 @@ from typing import Optional, TypedDict
|
|||
|
||||
class EndpointType(str, Enum):
|
||||
VERTEX_AI = "vertex-ai"
|
||||
ANTHROPIC = "anthropic"
|
||||
GENERIC = "generic"
|
||||
|
||||
|
||||
|
|
|
@ -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)
|
||||
|
|
|
@ -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,
|
||||
|
|
38
tests/anthropic_passthrough/test_anthropic_passthrough.py
Normal file
38
tests/anthropic_passthrough/test_anthropic_passthrough.py
Normal 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)
|
51
tests/basic_proxy_startup_tests/test_basic_proxy_startup.py
Normal file
51
tests/basic_proxy_startup_tests/test_basic_proxy_startup.py
Normal 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
|
|
@ -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
|
||||
|
|
|
@ -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?"}],
|
||||
)
|
||||
|
|
|
@ -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",
|
||||
],
|
||||
)
|
||||
|
|
38
tests/pass_through_tests/test_anthropic_passthrough.py
Normal file
38
tests/pass_through_tests/test_anthropic_passthrough.py
Normal 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)
|
|
@ -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():
|
||||
|
|
|
@ -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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|||
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|
|
@ -1 +0,0 @@
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|||
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|
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|
@ -1 +0,0 @@
|
|||
self.__BUILD_MANIFEST={__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},"/_error":["static/chunks/pages/_error-d6107f1aac0c574c.js"],sortedPages:["/_app","/_error"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB();
|
|
@ -1 +0,0 @@
|
|||
self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()
|
|
@ -1 +0,0 @@
|
|||
self.__BUILD_MANIFEST = {__rewrites:{afterFiles:[],beforeFiles:[],fallback:[]},sortedPages:["\u002F_app"]};self.__BUILD_MANIFEST_CB && self.__BUILD_MANIFEST_CB()
|
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|
@ -1,22 +0,0 @@
|
|||
"use strict";
|
||||
/*
|
||||
* ATTENTION: An "eval-source-map" devtool has been used.
|
||||
* This devtool is neither made for production nor for readable output files.
|
||||
* It uses "eval()" calls to create a separate source file with attached SourceMaps in the browser devtools.
|
||||
* If you are trying to read the output file, select a different devtool (https://webpack.js.org/configuration/devtool/)
|
||||
* or disable the default devtool with "devtool: false".
|
||||
* If you are looking for production-ready output files, see mode: "production" (https://webpack.js.org/configuration/mode/).
|
||||
*/
|
||||
self["webpackHotUpdate_N_E"]("app/layout",{
|
||||
|
||||
/***/ "(app-pages-browser)/./src/app/globals.css":
|
||||
/*!*****************************!*\
|
||||
!*** ./src/app/globals.css ***!
|
||||
\*****************************/
|
||||
/***/ (function(module, __webpack_exports__, __webpack_require__) {
|
||||
|
||||
eval(__webpack_require__.ts("__webpack_require__.r(__webpack_exports__);\n/* harmony default export */ __webpack_exports__[\"default\"] = (\"70cc4ebb5bef\");\nif (true) { module.hot.accept() }\n//# sourceURL=[module]\n//# sourceMappingURL=data:application/json;charset=utf-8;base64,eyJ2ZXJzaW9uIjozLCJmaWxlIjoiKGFwcC1wYWdlcy1icm93c2VyKS8uL3NyYy9hcHAvZ2xvYmFscy5jc3MiLCJtYXBwaW5ncyI6IjtBQUFBLCtEQUFlLGNBQWM7QUFDN0IsSUFBSSxJQUFVLElBQUksaUJBQWlCIiwic291cmNlcyI6WyJ3ZWJwYWNrOi8vX05fRS8uL3NyYy9hcHAvZ2xvYmFscy5jc3M/MzU3NSJdLCJzb3VyY2VzQ29udGVudCI6WyJleHBvcnQgZGVmYXVsdCBcIjcwY2M0ZWJiNWJlZlwiXG5pZiAobW9kdWxlLmhvdCkgeyBtb2R1bGUuaG90LmFjY2VwdCgpIH1cbiJdLCJuYW1lcyI6W10sInNvdXJjZVJvb3QiOiIifQ==\n//# sourceURL=webpack-internal:///(app-pages-browser)/./src/app/globals.css\n"));
|
||||
|
||||
/***/ })
|
||||
|
||||
});
|
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|
@ -1 +1 @@
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5:I[31778,[],""]
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1:null
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|
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Loading…
Add table
Add a link
Reference in a new issue