build(deps-dev): bump black to 26.3.1 and apply formatting (#28525)
* build(deps-dev): bump black 24.10.0 -> 26.3.1 * style: apply black 26.3.1 formatting * chore: authorize black 26.3.1 license in liccheck.ini
This commit is contained in:
yuneng-jiang
GitHub
parent
0715ed3359
commit
2a5dfcd5bc
@ -6,7 +6,6 @@ Always uses fastuuid for performance.
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import fastuuid as _uuid # type: ignore
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# Expose a module-like alias so callers can use: uuid.uuid4()
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uuid = _uuid
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@ -9,7 +9,6 @@ from typing import Dict, Optional
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from .exceptions import AnthropicErrorResponse, AnthropicErrorType
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# HTTP status code -> Anthropic error type
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# Source: https://docs.anthropic.com/en/api/errors
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ANTHROPIC_ERROR_TYPE_MAP: Dict[int, AnthropicErrorType] = {
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@ -2,7 +2,6 @@
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from typing_extensions import Literal, Required, TypedDict
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# Known Anthropic error types
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# Source: https://docs.anthropic.com/en/api/errors
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AnthropicErrorType = Literal[
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@ -5,7 +5,6 @@ Auto-detect content type per message: code, JSON, or text.
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import json
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import re
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_CODE_KEYWORDS = re.compile(
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r"\b(?:def |function |class |import |from |require\(|#include|fn |func |const |let |var |public |private |static )\b"
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)
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@ -1,6 +1,5 @@
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from typing import AsyncIterator, Dict, Iterator, Literal, NamedTuple, Union
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FileContentProvider = Literal[
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"openai", "azure", "vertex_ai", "bedrock", "hosted_vllm", "anthropic", "manus"
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]
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@ -1,10 +1,10 @@
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"""
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Google GenAI Adapters for LiteLLM
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This module provides adapters for transforming Google GenAI generate_content requests
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This module provides adapters for transforming Google GenAI generate_content requests
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to/from LiteLLM completion format with full support for:
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- Text content transformation
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- Tool calling (function declarations, function calls, function responses)
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- Tool calling (function declarations, function calls, function responses)
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- Streaming (both regular and tool calling)
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- Mixed content (text + tool calls)
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"""
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@ -1,9 +1,9 @@
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"""
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Handles Batching + sending Httpx Post requests to slack
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Handles Batching + sending Httpx Post requests to slack
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Slack alerts are sent every 10s or when events are greater than X events
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Slack alerts are sent every 10s or when events are greater than X events
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see custom_batch_logger.py for more details / defaults
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see custom_batch_logger.py for more details / defaults
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"""
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from typing import TYPE_CHECKING, Any
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@ -18,7 +18,7 @@ else:
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def process_slack_alerting_variables(
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alert_to_webhook_url: Optional[Dict[AlertType, Union[List[str], str]]]
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alert_to_webhook_url: Optional[Dict[AlertType, Union[List[str], str]]],
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) -> Optional[Dict[AlertType, Union[List[str], str]]]:
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"""
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process alert_to_webhook_url
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@ -1,5 +1,5 @@
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"""
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Base class for Additional Logging Utils for CustomLoggers
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Base class for Additional Logging Utils for CustomLoggers
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- Health Check for the logging util
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- Get Request / Response Payload for the logging util
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@ -1,5 +1,5 @@
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"""
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Custom Logger that handles batching logic
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Custom Logger that handles batching logic
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Use this if you want your logs to be stored in memory and flushed periodically.
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"""
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@ -9,7 +9,6 @@ import polars as pl
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from .schema import FOCUS_NORMALIZED_SCHEMA
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_TAG_KEYS = (
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"team_id",
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"team_alias",
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@ -105,7 +105,7 @@ def _remove_nulls(x: Dict[str, Any]) -> Dict[str, Any]:
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def get_traces_and_spans_from_payload(
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payload: List[Dict[str, Any]]
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payload: List[Dict[str, Any]],
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) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
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"""
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Separate traces and spans from payload.
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@ -1,8 +1,8 @@
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"""
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s3 Bucket Logging Integration
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async_log_success_event: Processes the event, stores it in memory for DEFAULT_S3_FLUSH_INTERVAL_SECONDS seconds or until DEFAULT_S3_BATCH_SIZE and then flushes to s3
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async_log_failure_event: Processes the event, stores it in memory for DEFAULT_S3_FLUSH_INTERVAL_SECONDS seconds or until DEFAULT_S3_BATCH_SIZE and then flushes to s3
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async_log_success_event: Processes the event, stores it in memory for DEFAULT_S3_FLUSH_INTERVAL_SECONDS seconds or until DEFAULT_S3_BATCH_SIZE and then flushes to s3
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async_log_failure_event: Processes the event, stores it in memory for DEFAULT_S3_FLUSH_INTERVAL_SECONDS seconds or until DEFAULT_S3_BATCH_SIZE and then flushes to s3
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NOTE 1: S3 does not provide a BATCH PUT API endpoint, so we create tasks to upload each element individually
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"""
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@ -49,7 +49,6 @@ from litellm.types.interactions import InteractionEnvironment
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from litellm.types.router import GenericLiteLLMParams
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from litellm.utils import client
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# ------------------------------------------------------------------ #
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# Shared helpers #
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# ------------------------------------------------------------------ #
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@ -8,25 +8,25 @@ Per OpenAPI spec (https://ai.google.dev/static/api/interactions.openapi.json):
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Usage:
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import litellm
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# Create an interaction with a model
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response = litellm.interactions.create(
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model="gemini-2.5-flash",
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input="Hello, how are you?"
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)
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# Create an interaction with an agent
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response = litellm.interactions.create(
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agent="deep-research-pro-preview-12-2025",
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input="Research the current state of cancer research"
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)
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# Async version
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response = await litellm.interactions.acreate(...)
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# Get an interaction
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response = litellm.interactions.get(interaction_id="...")
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# Delete an interaction
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result = litellm.interactions.delete(interaction_id="...")
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"""
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@ -994,10 +994,8 @@ class Logging(LiteLLMLoggingBaseClass):
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try:
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# [Non-blocking Extra Debug Information in metadata]
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if turn_off_message_logging is True:
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_metadata["raw_request"] = (
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"redacted by litellm. \
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_metadata["raw_request"] = "redacted by litellm. \
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'litellm.turn_off_message_logging=True'"
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)
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else:
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curl_command = self._get_request_curl_command(
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api_base=additional_args.get("api_base", ""),
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@ -1031,12 +1029,8 @@ class Logging(LiteLLMLoggingBaseClass):
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error=str(e),
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)
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)
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_metadata["raw_request"] = (
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"Unable to Log \
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raw request: {}".format(
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str(e)
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)
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)
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_metadata["raw_request"] = "Unable to Log \
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raw request: {}".format(str(e))
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if getattr(self, "logger_fn", None) and callable(self.logger_fn):
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try:
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self.logger_fn(
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@ -5590,9 +5590,7 @@ def default_response_schema_prompt(response_schema: dict) -> str:
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prompt_str = """Use this JSON schema:
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```json
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{}
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```""".format(
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response_schema
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)
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```""".format(response_schema)
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return prompt_str
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@ -1,9 +1,9 @@
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"""
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This is a cache for LangfuseLoggers.
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Langfuse Python SDK initializes a thread for each client.
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Langfuse Python SDK initializes a thread for each client.
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This ensures we do
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This ensures we do
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1. Proper cleanup of Langfuse initialized clients.
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2. Re-use created langfuse clients.
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"""
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@ -13,7 +13,6 @@ from typing import Any, AsyncIterator, Dict, List, Optional, cast
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from litellm._logging import verbose_logger
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# ---------------------------------------------------------------------------
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# SSE parsing helpers (module-level to keep the class lean)
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# ---------------------------------------------------------------------------
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@ -4,10 +4,10 @@ Support for o1 and o3 model families
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https://platform.openai.com/docs/guides/reasoning
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Translations handled by LiteLLM:
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- modalities: image => drop param (if user opts in to dropping param)
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- role: system ==> translate to role 'user'
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- streaming => faked by LiteLLM
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- Tools, response_format => drop param (if user opts in to dropping param)
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- modalities: image => drop param (if user opts in to dropping param)
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- role: system ==> translate to role 'user'
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- streaming => faked by LiteLLM
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- Tools, response_format => drop param (if user opts in to dropping param)
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- Logprobs => drop param (if user opts in to dropping param)
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- Temperature => drop param (if user opts in to dropping param)
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"""
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@ -1,5 +1,5 @@
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"""
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Transformation logic from OpenAI /v1/embeddings format to Azure AI Cohere's /v1/embed.
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Transformation logic from OpenAI /v1/embeddings format to Azure AI Cohere's /v1/embed.
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Why separate file? Make it easy to see how transformation works
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@ -1,5 +1,5 @@
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"""
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Translate between Cohere's `/rerank` format and Azure AI's `/rerank` format.
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Translate between Cohere's `/rerank` format and Azure AI's `/rerank` format.
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"""
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from typing import Optional
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@ -4,7 +4,6 @@ import litellm
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from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
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from litellm.secret_managers.main import get_secret_str
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CLAUDE_PLATFORM_SERVICE_NAME: Literal["aws-external-anthropic"] = (
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"aws-external-anthropic"
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)
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@ -1,5 +1,5 @@
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"""
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Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan G1 /invoke format.
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Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Titan G1 /invoke format.
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Why separate file? Make it easy to see how transformation works
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@ -1,5 +1,5 @@
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"""
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Transformation logic from OpenAI /v1/embeddings format to Bedrock Cohere /invoke format.
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Transformation logic from OpenAI /v1/embeddings format to Bedrock Cohere /invoke format.
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Why separate file? Make it easy to see how transformation works
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"""
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@ -16,7 +16,6 @@ from litellm.secret_managers.main import get_secret_str
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from ...openai_like.chat.transformation import OpenAILikeChatConfig
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BEDROCK_MANTLE_DEFAULT_REGION = "us-east-1"
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@ -1,5 +1,5 @@
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"""
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Legacy /v1/embedding handler for Bedrock Cohere.
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Legacy /v1/embedding handler for Bedrock Cohere.
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"""
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import json
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@ -13,7 +13,6 @@ from typing import Tuple
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import httpx
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# ---------------------------------------------------------------------------
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# Pre-built response templates
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# ---------------------------------------------------------------------------
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@ -1,5 +1,5 @@
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"""
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Cost calculator for Dashscope Chat models.
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Cost calculator for Dashscope Chat models.
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Handles tiered pricing and prompt caching scenarios.
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"""
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@ -1,5 +1,5 @@
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"""
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Support for OpenAI's `/v1/chat/completions` endpoint.
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Support for OpenAI's `/v1/chat/completions` endpoint.
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Calls done in OpenAI/openai.py as DataRobot is openai-compatible.
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"""
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@ -1,5 +1,5 @@
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"""
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Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format.
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Translate between Cohere's `/rerank` format and Deepinfra's `/rerank` format.
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"""
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from typing import Any, Dict, List, Optional, Union
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@ -1,5 +1,5 @@
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"""
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Cost calculator for DeepSeek Chat models.
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Cost calculator for DeepSeek Chat models.
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Handles prompt caching scenario.
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"""
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@ -22,7 +22,6 @@ from litellm.types.utils import all_litellm_params
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from ..common_utils import ElevenLabsException
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.types.llms.openai import HttpxBinaryResponseContent
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@ -23,7 +23,6 @@ from litellm.types.agents import (
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AgentVersionsResponse,
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)
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# Keys inside litellm_params that should be forwarded to the Gemini
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# create-agent body verbatim.
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_GEMINI_AGENT_BODY_KEYS = ("base_agent", "instructions", "base_environment")
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@ -55,7 +55,7 @@ def _convert_image_to_gemini_format(image_file) -> Dict[str, str]:
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def _usage_video_resolution_from_parameters(
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parameters: Dict[str, Any]
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parameters: Dict[str, Any],
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) -> Optional[str]:
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"""Normalize Veo ``parameters.resolution`` for usage and cost tracking."""
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res = parameters.get("resolution")
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@ -1,5 +1,5 @@
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"""
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Transformation logic from Cohere's /v1/rerank format to Infinity's `/v1/rerank` format.
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Transformation logic from Cohere's /v1/rerank format to Infinity's `/v1/rerank` format.
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Why separate file? Make it easy to see how transformation works
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"""
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@ -1,5 +1,5 @@
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"""
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Transformation logic from Cohere's /v1/rerank format to Jina AI's `/v1/rerank` format.
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Transformation logic from Cohere's /v1/rerank format to Jina AI's `/v1/rerank` format.
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Why separate file? Make it easy to see how transformation works
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@ -1,5 +1,5 @@
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"""
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Transformation logic from OpenAI /v1/embeddings format to LM Studio's `/v1/embeddings` format.
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Transformation logic from OpenAI /v1/embeddings format to LM Studio's `/v1/embeddings` format.
|
||||
|
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Why separate file? Make it easy to see how transformation works
|
||||
|
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|
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@ -1,5 +1,5 @@
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"""
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Support for OpenAI's `/v1/chat/completions` endpoint.
|
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Support for OpenAI's `/v1/chat/completions` endpoint.
|
||||
|
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Calls done in OpenAI/openai.py as Novita AI is openai-compatible.
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|
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|
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@ -1,7 +1,7 @@
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"""
|
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Nvidia NIM endpoint: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer
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Nvidia NIM endpoint: https://docs.api.nvidia.com/nim/reference/databricks-dbrx-instruct-infer
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|
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This is OpenAI compatible
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This is OpenAI compatible
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||||
|
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This file only contains param mapping logic
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||||
|
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|
||||
@ -1,7 +1,7 @@
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"""
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Nvidia NIM embeddings endpoint: https://docs.api.nvidia.com/nim/reference/nvidia-nv-embedqa-e5-v5-infer
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||||
|
||||
This is OpenAI compatible
|
||||
This is OpenAI compatible
|
||||
|
||||
This file only contains param mapping logic
|
||||
|
||||
|
||||
@ -1,14 +1,14 @@
|
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"""
|
||||
Support for o1/o3 model family
|
||||
Support for o1/o3 model family
|
||||
|
||||
https://platform.openai.com/docs/guides/reasoning
|
||||
|
||||
Translations handled by LiteLLM:
|
||||
- modalities: image => drop param (if user opts in to dropping param)
|
||||
- role: system ==> translate to role 'user'
|
||||
- streaming => faked by LiteLLM
|
||||
- Tools, response_format => drop param (if user opts in to dropping param)
|
||||
- Logprobs => drop param (if user opts in to dropping param)
|
||||
- modalities: image => drop param (if user opts in to dropping param)
|
||||
- role: system ==> translate to role 'user'
|
||||
- streaming => faked by LiteLLM
|
||||
- Tools, response_format => drop param (if user opts in to dropping param)
|
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- Logprobs => drop param (if user opts in to dropping param)
|
||||
"""
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|
||||
from typing import Any, Coroutine, List, Literal, Optional, Union, cast, overload
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@ -201,7 +201,7 @@ class BaseOpenAILLM:
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|
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@staticmethod
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def get_openai_client_initialization_param_fields(
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client_type: Literal["openai", "azure"]
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client_type: Literal["openai", "azure"],
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) -> Tuple[str, ...]:
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"""Returns a tuple of fields that are used to initialize the OpenAI client"""
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if client_type == "openai":
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@ -49,7 +49,6 @@ from litellm.types.utils import (
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)
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from litellm.llms.openrouter.common_utils import OpenRouterException
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|
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|
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
||||
else:
|
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|
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@ -1,7 +1,7 @@
|
||||
"""
|
||||
Translate from OpenAI's `/v1/chat/completions` to Sagemaker's `/invoke`
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||||
|
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In the Huggingface TGI format.
|
||||
In the Huggingface TGI format.
|
||||
"""
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||||
|
||||
import json
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||||
|
||||
@ -1,7 +1,7 @@
|
||||
"""
|
||||
Translate from OpenAI's `/v1/embeddings` to Sagemaker's `/invoke`
|
||||
|
||||
In the Huggingface TGI format.
|
||||
In the Huggingface TGI format.
|
||||
"""
|
||||
|
||||
from typing import TYPE_CHECKING, Any, List, Optional, Union
|
||||
|
||||
@ -207,7 +207,7 @@ def resolve_resource_group(sources: List[Source]) -> Optional[str]:
|
||||
|
||||
|
||||
def _parse_service_key_once(
|
||||
service_key: Optional[Union[str, dict]]
|
||||
service_key: Optional[Union[str, dict]],
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
Pre-parse service_key if it's a string to avoid repeated JSON parsing.
|
||||
|
||||
@ -14,7 +14,6 @@ from ...openai_like.chat.transformation import OpenAIGPTConfig
|
||||
|
||||
from ..utils import SnowflakeBaseConfig
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
Support for OpenAI's `/v1/chat/completions` endpoint.
|
||||
Support for OpenAI's `/v1/chat/completions` endpoint.
|
||||
|
||||
Calls done in OpenAI/openai.py as TogetherAI is openai-compatible.
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
Support for OpenAI's `/v1/embeddings` endpoint.
|
||||
Support for OpenAI's `/v1/embeddings` endpoint.
|
||||
|
||||
Calls done in OpenAI/openai.py as TogetherAI is openai-compatible.
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
Transformation logic from Cohere's /v1/rerank format to Together AI's `/v1/rerank` format.
|
||||
Transformation logic from Cohere's /v1/rerank format to Together AI's `/v1/rerank` format.
|
||||
|
||||
Why separate file? Make it easy to see how transformation works
|
||||
"""
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
Transformation logic for context caching.
|
||||
Transformation logic for context caching.
|
||||
|
||||
Why separate file? Make it easy to see how transformation works
|
||||
"""
|
||||
@ -19,7 +19,7 @@ from ..gemini.transformation import (
|
||||
|
||||
|
||||
def get_first_continuous_block_idx(
|
||||
filtered_messages: List[Tuple[int, AllMessageValues]] # (idx, message)
|
||||
filtered_messages: List[Tuple[int, AllMessageValues]], # (idx, message)
|
||||
) -> int:
|
||||
"""
|
||||
Find the array index that ends the first continuous sequence of message blocks.
|
||||
|
||||
@ -1073,16 +1073,14 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915
|
||||
contents.append(ContentType(role="user", parts=tool_call_responses))
|
||||
|
||||
if len(contents) == 0:
|
||||
verbose_logger.warning(
|
||||
"""
|
||||
verbose_logger.warning("""
|
||||
No contents in messages. Contents are required. See
|
||||
https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.publishers.models/generateContent#request-body.
|
||||
If the original request did not comply to OpenAI API requirements it should have failed by now,
|
||||
but LiteLLM does not check for missing messages.
|
||||
Setting an empty content to prevent an 400 error.
|
||||
Relevant Issue - https://github.com/BerriAI/litellm/issues/9733
|
||||
"""
|
||||
)
|
||||
""")
|
||||
contents.append(ContentType(role="user", parts=[PartType(text=" ")]))
|
||||
return contents
|
||||
except Exception as e:
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
Transformation logic from OpenAI /v1/embeddings format to Google AI Studio /batchEmbedContents format.
|
||||
Transformation logic from OpenAI /v1/embeddings format to Google AI Studio /batchEmbedContents format.
|
||||
|
||||
Why separate file? Make it easy to see how transformation works
|
||||
"""
|
||||
|
||||
@ -139,7 +139,7 @@ class VertexTextToSpeechAPI(VertexLLM):
|
||||
########## End of logging ############
|
||||
####### Send the request ###################
|
||||
if _is_async is True:
|
||||
return self.async_audio_speech( # type:ignore
|
||||
return self.async_audio_speech( # type: ignore
|
||||
logging_obj=logging_obj, url=url, headers=headers, request=request
|
||||
)
|
||||
sync_handler = _get_httpx_client()
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
Translates from OpenAI's `/v1/chat/completions` to the VLLM sdk `llm.generate`.
|
||||
Translates from OpenAI's `/v1/chat/completions` to the VLLM sdk `llm.generate`.
|
||||
|
||||
NOT RECOMMENDED FOR PRODUCTION USE. Use `hosted_vllm/` instead.
|
||||
"""
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
"""
|
||||
This module is used to transform the request and response for the Voyage contextualized embeddings API.
|
||||
This would be used for all the contextualized embeddings models in Voyage.
|
||||
This module is used to transform the request and response for the Voyage contextualized embeddings API.
|
||||
This would be used for all the contextualized embeddings models in Voyage.
|
||||
"""
|
||||
|
||||
from typing import List, Optional, Union
|
||||
|
||||
@ -305,7 +305,7 @@ def build_input_schema(operation: Dict[str, Any]) -> Dict[str, Any]:
|
||||
|
||||
|
||||
def _merge_openapi_tool_request_headers(
|
||||
static_headers: Dict[str, str]
|
||||
static_headers: Dict[str, str],
|
||||
) -> Dict[str, str]:
|
||||
"""Merge static closure headers with per-request ContextVar overrides.
|
||||
|
||||
|
||||
@ -11,7 +11,6 @@ from litellm.router_utils.fallback_event_handlers import get_fallback_model_grou
|
||||
from litellm.types.router import CredentialLiteLLMParams, LiteLLM_Params
|
||||
from litellm.utils import get_valid_models
|
||||
|
||||
|
||||
_CREDENTIAL_LITELLM_PARAM_FIELDS = set(CredentialLiteLLMParams.model_fields)
|
||||
|
||||
|
||||
|
||||
@ -324,7 +324,7 @@ class CustomOpenAPISpec:
|
||||
|
||||
@staticmethod
|
||||
def add_chat_completion_request_schema(
|
||||
openapi_schema: Dict[str, Any]
|
||||
openapi_schema: Dict[str, Any],
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Add ProxyChatCompletionRequest schema to chat completion endpoints for documentation.
|
||||
@ -380,7 +380,7 @@ class CustomOpenAPISpec:
|
||||
|
||||
@staticmethod
|
||||
def add_responses_api_request_schema(
|
||||
openapi_schema: Dict[str, Any]
|
||||
openapi_schema: Dict[str, Any],
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Add ResponsesAPIRequestParams schema to responses API endpoints for documentation.
|
||||
@ -410,7 +410,7 @@ class CustomOpenAPISpec:
|
||||
|
||||
@staticmethod
|
||||
def add_llm_api_request_schema_body(
|
||||
openapi_schema: Dict[str, Any]
|
||||
openapi_schema: Dict[str, Any],
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Add LLM API request schema bodies to OpenAPI specification for documentation.
|
||||
|
||||
@ -12,7 +12,6 @@ from litellm.proxy.common_utils.callback_utils import (
|
||||
)
|
||||
from litellm.types.router import Deployment
|
||||
|
||||
|
||||
_FORM_CONTENT_TYPES: frozenset[str] = frozenset(
|
||||
{"application/x-www-form-urlencoded", "multipart/form-data"}
|
||||
)
|
||||
@ -301,7 +300,7 @@ async def get_form_data(request: Request) -> Dict[str, Any]:
|
||||
|
||||
|
||||
async def convert_upload_files_to_file_data(
|
||||
form_data: Dict[str, Any]
|
||||
form_data: Dict[str, Any],
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Convert FastAPI UploadFile objects to file data tuples for litellm.
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
Contains utils used by OpenAI compatible endpoints
|
||||
Contains utils used by OpenAI compatible endpoints
|
||||
"""
|
||||
|
||||
from typing import Optional, Set
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
What is this?
|
||||
What is this?
|
||||
|
||||
CRUD endpoints for managing pass-through endpoints
|
||||
"""
|
||||
|
||||
@ -34,8 +34,7 @@ async def create_missing_views(db: _db): # noqa: PLR0915
|
||||
if not any(marker in error_msg for marker in _VIEW_NOT_FOUND_MARKERS):
|
||||
raise
|
||||
# If an error occurs, the view does not exist, so create it
|
||||
await db.execute_raw(
|
||||
"""
|
||||
await db.execute_raw("""
|
||||
CREATE VIEW "LiteLLM_VerificationTokenView" AS
|
||||
SELECT
|
||||
v.*,
|
||||
@ -47,8 +46,7 @@ async def create_missing_views(db: _db): # noqa: PLR0915
|
||||
FROM "LiteLLM_VerificationToken" v
|
||||
LEFT JOIN "LiteLLM_TeamTable" t ON v.team_id = t.team_id
|
||||
LEFT JOIN "LiteLLM_ProjectTable" p ON v.project_id = p.project_id;
|
||||
"""
|
||||
)
|
||||
""")
|
||||
|
||||
verbose_logger.debug("LiteLLM_VerificationTokenView Created!")
|
||||
|
||||
|
||||
@ -10,7 +10,6 @@ every text fragment.
|
||||
|
||||
from typing import Any, Callable, Dict, FrozenSet, Iterator, List
|
||||
|
||||
|
||||
# Call types whose body carries free-form chat / prompt text that
|
||||
# text-content guardrails (banned keywords, content moderation, secret
|
||||
# detection, …) should inspect. The proxy ingress passes ``route_type``
|
||||
|
||||
@ -4,7 +4,6 @@ from litellm.types.guardrails import SupportedGuardrailIntegrations
|
||||
|
||||
from .akto import AktoGuardrail
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from litellm.types.guardrails import Guardrail, LitellmParams
|
||||
|
||||
|
||||
@ -6,7 +6,7 @@ The actual skill logic is in litellm/llms/litellm_proxy/skills/.
|
||||
|
||||
Usage:
|
||||
from litellm.proxy.hooks.litellm_skills import SkillsInjectionHook
|
||||
|
||||
|
||||
# Register hook in proxy
|
||||
litellm.callbacks.append(SkillsInjectionHook())
|
||||
"""
|
||||
|
||||
@ -1,9 +1,9 @@
|
||||
"""
|
||||
BUDGET MANAGEMENT
|
||||
|
||||
All /budget management endpoints
|
||||
All /budget management endpoints
|
||||
|
||||
/budget/new
|
||||
/budget/new
|
||||
/budget/info
|
||||
/budget/update
|
||||
/budget/delete
|
||||
|
||||
@ -1,9 +1,9 @@
|
||||
"""
|
||||
CUSTOMER MANAGEMENT
|
||||
|
||||
All /customer management endpoints
|
||||
All /customer management endpoints
|
||||
|
||||
/customer/new
|
||||
/customer/new
|
||||
/customer/info
|
||||
/customer/update
|
||||
/customer/delete
|
||||
|
||||
@ -546,7 +546,7 @@ async def _update_existing_team_model_assignment(
|
||||
"""
|
||||
|
||||
def _get_team_public_model_name(
|
||||
model_info: Optional[Union[dict, str]]
|
||||
model_info: Optional[Union[dict, str]],
|
||||
) -> Optional[str]:
|
||||
if isinstance(model_info, dict):
|
||||
value = model_info.get("team_public_model_name")
|
||||
|
||||
@ -7,7 +7,7 @@ variables.
|
||||
|
||||
Environment Variables:
|
||||
- MICROSOFT_AUTHORIZATION_ENDPOINT: Custom authorization endpoint URL
|
||||
- MICROSOFT_TOKEN_ENDPOINT: Custom token endpoint URL
|
||||
- MICROSOFT_TOKEN_ENDPOINT: Custom token endpoint URL
|
||||
- MICROSOFT_USERINFO_ENDPOINT: Custom userinfo endpoint URL
|
||||
|
||||
If these are not set, the default Microsoft endpoints are used.
|
||||
|
||||
@ -4381,9 +4381,7 @@ async def list_team(
|
||||
except Exception as e:
|
||||
team_exception = """Invalid team object for team_id: {}. team_object={}.
|
||||
Error: {}
|
||||
""".format(
|
||||
team.team_id, team.model_dump(), str(e)
|
||||
)
|
||||
""".format(team.team_id, team.model_dump(), str(e))
|
||||
verbose_proxy_logger.exception(team_exception)
|
||||
continue
|
||||
# Sort the responses by team_alias
|
||||
|
||||
@ -3,7 +3,7 @@ User Agent Analytics Endpoints
|
||||
|
||||
This module provides optimized endpoints for tracking user agent activity metrics including:
|
||||
- Daily Active Users (DAU) by tags for configurable number of days
|
||||
- Weekly Active Users (WAU) by tags for configurable number of weeks
|
||||
- Weekly Active Users (WAU) by tags for configurable number of weeks
|
||||
- Monthly Active Users (MAU) by tags for configurable number of months
|
||||
- Summary analytics by tags
|
||||
|
||||
|
||||
@ -18,7 +18,6 @@ from litellm.litellm_core_utils.litellm_logging import (
|
||||
from litellm.proxy._types import PassThroughEndpointLoggingTypedDict
|
||||
from litellm.types.utils import StandardPassThroughResponseObject
|
||||
|
||||
|
||||
CURSOR_AGENT_ENDPOINTS: Dict[str, str] = {
|
||||
"POST /v0/agents": "cursor:agent:create",
|
||||
"GET /v0/agents": "cursor:agent:list",
|
||||
|
||||
@ -321,9 +321,7 @@ class ProxyInitializationHelpers:
|
||||
_endpoint_str = (
|
||||
f"curl --location 'http://0.0.0.0:{port}/chat/completions' \\"
|
||||
)
|
||||
curl_command = (
|
||||
_endpoint_str
|
||||
+ """
|
||||
curl_command = _endpoint_str + """
|
||||
--header 'Content-Type: application/json' \\
|
||||
--data ' {
|
||||
"model": "gpt-3.5-turbo",
|
||||
@ -336,7 +334,6 @@ class ProxyInitializationHelpers:
|
||||
}'
|
||||
\n
|
||||
"""
|
||||
)
|
||||
print() # noqa
|
||||
print( # noqa
|
||||
'\033[1;34mLiteLLM: Test your local proxy with: "litellm --test" This runs an openai.ChatCompletion request to your proxy [In a new terminal tab]\033[0m\n'
|
||||
@ -412,11 +409,9 @@ class ProxyInitializationHelpers:
|
||||
with open(os.devnull, "w") as devnull:
|
||||
subprocess.Popen(command, stdout=devnull, stderr=devnull)
|
||||
except Exception as e:
|
||||
print( # noqa
|
||||
f"""
|
||||
print(f"""
|
||||
LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve`
|
||||
"""
|
||||
) # noqa
|
||||
""") # noqa # noqa
|
||||
|
||||
@staticmethod
|
||||
def _is_port_in_use(port):
|
||||
|
||||
@ -2710,11 +2710,9 @@ def run_ollama_serve():
|
||||
with open(os.devnull, "w") as devnull:
|
||||
subprocess.Popen(command, stdout=devnull, stderr=devnull)
|
||||
except Exception as e:
|
||||
verbose_proxy_logger.debug(
|
||||
f"""
|
||||
verbose_proxy_logger.debug(f"""
|
||||
LiteLLM Warning: proxy started with `ollama` model\n`ollama serve` failed with Exception{e}. \nEnsure you run `ollama serve`
|
||||
"""
|
||||
)
|
||||
""")
|
||||
|
||||
|
||||
def _get_process_rss_mb() -> Optional[float]:
|
||||
|
||||
@ -3184,16 +3184,14 @@ async def provider_budgets() -> ProviderBudgetResponse:
|
||||
async def get_spend_by_tags(
|
||||
prisma_client: PrismaClient, start_date=None, end_date=None
|
||||
):
|
||||
response = await prisma_client.db.query_raw(
|
||||
"""
|
||||
response = await prisma_client.db.query_raw("""
|
||||
SELECT
|
||||
jsonb_array_elements_text(request_tags) AS individual_request_tag,
|
||||
COUNT(*) AS log_count,
|
||||
SUM(spend) AS total_spend
|
||||
FROM "LiteLLM_SpendLogs"
|
||||
GROUP BY individual_request_tag;
|
||||
"""
|
||||
)
|
||||
""")
|
||||
|
||||
return response
|
||||
|
||||
|
||||
@ -2979,8 +2979,7 @@ class PrismaClient:
|
||||
required_view = "LiteLLM_VerificationTokenView"
|
||||
expected_views_str = ", ".join(f"'{view}'" for view in expected_views)
|
||||
pg_schema = os.getenv("DATABASE_SCHEMA", "public")
|
||||
ret = await self.db.query_raw(
|
||||
f"""
|
||||
ret = await self.db.query_raw(f"""
|
||||
WITH existing_views AS (
|
||||
SELECT viewname
|
||||
FROM pg_views
|
||||
@ -2992,8 +2991,7 @@ class PrismaClient:
|
||||
(SELECT COUNT(*) FROM existing_views) AS view_count,
|
||||
ARRAY_AGG(viewname) AS view_names
|
||||
FROM existing_views
|
||||
"""
|
||||
)
|
||||
""")
|
||||
expected_total_views = len(expected_views)
|
||||
if ret[0]["view_count"] == expected_total_views:
|
||||
verbose_proxy_logger.info("All necessary views exist!")
|
||||
@ -3002,8 +3000,7 @@ class PrismaClient:
|
||||
## check if required view exists ##
|
||||
if ret[0]["view_names"] and required_view not in ret[0]["view_names"]:
|
||||
await self.health_check() # make sure we can connect to db
|
||||
await self.db.execute_raw(
|
||||
"""
|
||||
await self.db.execute_raw("""
|
||||
CREATE VIEW "LiteLLM_VerificationTokenView" AS
|
||||
SELECT
|
||||
v.*,
|
||||
@ -3013,8 +3010,7 @@ class PrismaClient:
|
||||
t.rpm_limit AS team_rpm_limit
|
||||
FROM "LiteLLM_VerificationToken" v
|
||||
LEFT JOIN "LiteLLM_TeamTable" t ON v.team_id = t.team_id;
|
||||
"""
|
||||
)
|
||||
""")
|
||||
|
||||
verbose_proxy_logger.info(
|
||||
"LiteLLM_VerificationTokenView Created in DB!"
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
What is this?
|
||||
What is this?
|
||||
|
||||
Logging Pass-Through Endpoints
|
||||
"""
|
||||
|
||||
@ -848,7 +848,7 @@ class Router:
|
||||
|
||||
@staticmethod
|
||||
def _normalize_strategy(
|
||||
strategy: Union[RoutingStrategy, str, None]
|
||||
strategy: Union[RoutingStrategy, str, None],
|
||||
) -> Optional[str]:
|
||||
if strategy is None:
|
||||
return None
|
||||
|
||||
@ -103,7 +103,7 @@ def _last_user_content(messages: Optional[List[Dict[str, Any]]]) -> Optional[str
|
||||
|
||||
|
||||
def _recent_tool_results(
|
||||
messages: Optional[List[Dict[str, Any]]]
|
||||
messages: Optional[List[Dict[str, Any]]],
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Extract the current turn's tool result payloads from the request messages.
|
||||
|
||||
|
||||
@ -24,7 +24,6 @@ from litellm.router_strategy.adaptive_router.config import (
|
||||
TOOL_CALL_HISTORY_MAX,
|
||||
)
|
||||
|
||||
|
||||
# ---- Public types ---------------------------------------------------------
|
||||
|
||||
|
||||
|
||||
@ -10,11 +10,11 @@ This means you can use this with weighted-pick, lowest-latency, simple-shuffle,
|
||||
Example:
|
||||
```
|
||||
openai:
|
||||
budget_limit: 0.000000000001
|
||||
time_period: 1d
|
||||
budget_limit: 0.000000000001
|
||||
time_period: 1d
|
||||
anthropic:
|
||||
budget_limit: 100
|
||||
time_period: 7d
|
||||
budget_limit: 100
|
||||
time_period: 7d
|
||||
```
|
||||
"""
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
Get num retries for an exception.
|
||||
Get num retries for an exception.
|
||||
|
||||
- Account for retry policy by exception type.
|
||||
"""
|
||||
|
||||
@ -34,7 +34,7 @@ class PatternUtils:
|
||||
|
||||
@staticmethod
|
||||
def sorted_patterns(
|
||||
patterns: Dict[str, List[Dict]]
|
||||
patterns: Dict[str, List[Dict]],
|
||||
) -> List[Tuple[str, List[Dict]]]:
|
||||
"""
|
||||
Cached property for patterns sorted by specificity.
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
Helper functions to get/set num success and num failures per deployment
|
||||
Helper functions to get/set num success and num failures per deployment
|
||||
|
||||
|
||||
set_deployment_failures_for_current_minute
|
||||
|
||||
@ -4,7 +4,7 @@ This is a file for the AWS Secret Manager Integration
|
||||
Relevant issue: https://github.com/BerriAI/litellm/issues/1883
|
||||
|
||||
Requires:
|
||||
* `os.environ["AWS_REGION_NAME"],
|
||||
* `os.environ["AWS_REGION_NAME"],
|
||||
* `pip install boto3>=1.28.57`
|
||||
"""
|
||||
|
||||
|
||||
@ -10,7 +10,7 @@ Handles Async Operations for:
|
||||
Relevant issue: https://github.com/BerriAI/litellm/issues/1883
|
||||
|
||||
Requires:
|
||||
* `os.environ["AWS_REGION_NAME"],
|
||||
* `os.environ["AWS_REGION_NAME"],
|
||||
* `pip install boto3>=1.28.57`
|
||||
"""
|
||||
|
||||
|
||||
@ -21,7 +21,7 @@ class VectorStoreFileRequestUtils:
|
||||
|
||||
@staticmethod
|
||||
def get_create_request_params(
|
||||
params: Dict[str, Any]
|
||||
params: Dict[str, Any],
|
||||
) -> VectorStoreFileCreateRequest:
|
||||
filtered = VectorStoreFileRequestUtils._filter_params(
|
||||
params=params, model=VectorStoreFileCreateRequest
|
||||
@ -37,7 +37,7 @@ class VectorStoreFileRequestUtils:
|
||||
|
||||
@staticmethod
|
||||
def get_update_request_params(
|
||||
params: Dict[str, Any]
|
||||
params: Dict[str, Any],
|
||||
) -> VectorStoreFileUpdateRequest:
|
||||
filtered = VectorStoreFileRequestUtils._filter_params(
|
||||
params=params, model=VectorStoreFileUpdateRequest
|
||||
|
||||
@ -132,7 +132,7 @@ litellm-proxy = "litellm.proxy.client.cli:cli"
|
||||
dev = [
|
||||
"diff-cover==9.7.2",
|
||||
"flake8==7.3.0",
|
||||
"black==24.10.0",
|
||||
"black==26.3.1",
|
||||
"mypy==1.19.0",
|
||||
"pytest==9.0.3",
|
||||
"pytest-mock==3.15.1",
|
||||
|
||||
@ -156,6 +156,7 @@ pytest: >=9.0.3 # MIT license
|
||||
pytest-postgresql: >=7.0.2 # LGPLv3+ license
|
||||
pytest-xdist: >=3.8.0 # MIT License
|
||||
ruff: >=0.15.3 # MIT License
|
||||
black: >=26.3.1 # MIT License manually verified (uses PEP 639 License-Expression: MIT, not the legacy License field, so liccheck reports it as unknown)
|
||||
types-requests: >=2.32.4.20260107 # Apache 2.0 license (typeshed)
|
||||
types-pyyaml: >=6.0.12.20250915 # Apache 2.0 license (typeshed)
|
||||
fakeredis: >=2.34.1 # BSD license
|
||||
|
||||
76
uv.lock
generated
76
uv.lock
generated
@ -9,7 +9,7 @@ resolution-markers = [
|
||||
]
|
||||
|
||||
[options]
|
||||
exclude-newer = "0001-01-01T00:00:00Z" # This has no effect and is included for backwards compatibility when using relative exclude-newer values.
|
||||
exclude-newer = "2026-05-19T00:08:46.706629Z"
|
||||
exclude-newer-span = "P3D"
|
||||
|
||||
[manifest]
|
||||
@ -539,7 +539,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "black"
|
||||
version = "24.10.0"
|
||||
version = "26.3.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "click" },
|
||||
@ -547,28 +547,33 @@ dependencies = [
|
||||
{ name = "packaging" },
|
||||
{ name = "pathspec" },
|
||||
{ name = "platformdirs" },
|
||||
{ name = "pytokens" },
|
||||
{ name = "tomli", marker = "python_full_version < '3.11'" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.11'" },
|
||||
]
|
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sdist = { url = "https://files.pythonhosted.org/packages/d8/0d/cc2fb42b8c50d80143221515dd7e4766995bd07c56c9a3ed30baf080b6dc/black-24.10.0.tar.gz", hash = "sha256:846ea64c97afe3bc677b761787993be4991810ecc7a4a937816dd6bddedc4875", size = 645813, upload-time = "2024-10-07T19:20:50.361Z" }
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sdist = { url = "https://files.pythonhosted.org/packages/e1/c5/61175d618685d42b005847464b8fb4743a67b1b8fdb75e50e5a96c31a27a/black-26.3.1.tar.gz", hash = "sha256:2c50f5063a9641c7eed7795014ba37b0f5fa227f3d408b968936e24bc0566b07", size = 666155, upload-time = "2026-03-12T03:36:03.593Z" }
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wheels = [
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[[package]]
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Loading…
Reference in New Issue
Block a user