90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
import io
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import json
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from os import PathLike
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from typing import Optional
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from litellm.types.llms.openai import FileTypes, OpenAIFilesPurpose
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class InMemoryFile(io.BytesIO):
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def __init__(self, content: bytes, name: str):
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super().__init__(content)
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self.name = name
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def should_replace_model_in_jsonl(
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purpose: OpenAIFilesPurpose,
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) -> bool:
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"""
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Check if the model name should be replaced in the JSONL file for the deployment model name.
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Azure raises an error on create batch if the model name for deployment is not in the .jsonl.
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"""
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if purpose == "batch":
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return True
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return False
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def replace_model_in_jsonl(file_content: FileTypes, new_model_name: str) -> FileTypes:
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try:
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## if pathlike, return the original file content
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if isinstance(file_content, PathLike):
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return file_content
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# Decode the bytes to a string and split into lines
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# If file_content is a file-like object, read the bytes
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if hasattr(file_content, "read"):
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file_content_bytes = file_content.read() # type: ignore
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elif isinstance(file_content, tuple):
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file_content_bytes = file_content[1]
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else:
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file_content_bytes = file_content
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# Decode the bytes to a string and split into lines
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if isinstance(file_content_bytes, bytes):
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file_content_str = file_content_bytes.decode("utf-8")
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elif isinstance(file_content_bytes, str):
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file_content_str = file_content_bytes
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else:
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return file_content
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lines = file_content_str.splitlines()
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modified_lines = []
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for line in lines:
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# Parse each line as a JSON object
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json_object = json.loads(line.strip())
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# Replace the model name if it exists
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if "body" in json_object:
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json_object["body"]["model"] = new_model_name
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# Convert the modified JSON object back to a string
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modified_lines.append(json.dumps(json_object))
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# Reassemble the modified lines and return as bytes
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modified_file_content = "\n".join(modified_lines).encode("utf-8")
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return InMemoryFile(modified_file_content, name="modified_file.jsonl") # type: ignore
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except (json.JSONDecodeError, UnicodeDecodeError, TypeError):
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# return the original file content if there is an error replacing the model name
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return file_content
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def _get_router_metadata_variable_name(function_name: Optional[str]) -> str:
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"""
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Helper to return what the "metadata" field should be called in the request data
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For all /thread or /assistant endpoints we need to call this "litellm_metadata"
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For ALL other endpoints we call this "metadata
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"""
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ROUTER_METHODS_USING_LITELLM_METADATA = set(
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["batch", "generic_api_call", "_acreate_batch", "file", "_ageneric_api_call_with_fallbacks"]
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)
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if function_name and any(
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method in function_name for method in ROUTER_METHODS_USING_LITELLM_METADATA
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):
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return "litellm_metadata"
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else:
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return "metadata"
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