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"""
Transformation logic for context caching.
Why separate file? Make it easy to see how transformation works
"""
from typing import List, Tuple
from litellm.types.llms.openai import AllMessageValues
from litellm.types.llms.vertex_ai import CachedContentRequestBody
from litellm.utils import is_cached_message
from ..common_utils import get_supports_system_message
from ..gemini.transformation import (
_gemini_convert_messages_with_history,
_transform_system_message,
)
def get_first_continuous_block_idx(
filtered_messages: List[Tuple[int, AllMessageValues]] # (idx, message)
) -> int:
"""
Find the array index that ends the first continuous sequence of message blocks.
Args:
filtered_messages: List of tuples containing (index, message) pairs
Returns:
int: The array index where the first continuous sequence ends
"""
if not filtered_messages:
return -1
if len(filtered_messages) == 1:
return 0
current_value = filtered_messages[0][0]
# Search forward through the array indices
for i in range(1, len(filtered_messages)):
if filtered_messages[i][0] != current_value + 1:
return i - 1
current_value = filtered_messages[i][0]
# If we made it through the whole list, return the last index
return len(filtered_messages) - 1
def separate_cached_messages(
messages: List[AllMessageValues],
) -> Tuple[List[AllMessageValues], List[AllMessageValues]]:
"""
Returns separated cached and non-cached messages.
Args:
messages: List of messages to be separated.
Returns:
Tuple containing:
- cached_messages: List of cached messages.
- non_cached_messages: List of non-cached messages.
"""
cached_messages: List[AllMessageValues] = []
non_cached_messages: List[AllMessageValues] = []
# Extract cached messages and their indices
filtered_messages: List[Tuple[int, AllMessageValues]] = []
for idx, message in enumerate(messages):
if is_cached_message(message=message):
filtered_messages.append((idx, message))
# Validate only one block of continuous cached messages
last_continuous_block_idx = get_first_continuous_block_idx(filtered_messages)
# Separate messages based on the block of cached messages
if filtered_messages and last_continuous_block_idx is not None:
first_cached_idx = filtered_messages[0][0]
last_cached_idx = filtered_messages[last_continuous_block_idx][0]
cached_messages = messages[first_cached_idx : last_cached_idx + 1]
non_cached_messages = (
messages[:first_cached_idx] + messages[last_cached_idx + 1 :]
)
else:
non_cached_messages = messages
return cached_messages, non_cached_messages
def transform_openai_messages_to_gemini_context_caching(
model: str, messages: List[AllMessageValues], cache_key: str
) -> CachedContentRequestBody:
supports_system_message = get_supports_system_message(
model=model, custom_llm_provider="gemini"
)
transformed_system_messages, new_messages = _transform_system_message(
supports_system_message=supports_system_message, messages=messages
)
transformed_messages = _gemini_convert_messages_with_history(messages=new_messages)
data = CachedContentRequestBody(
contents=transformed_messages,
model="models/{}".format(model),
displayName=cache_key,
)
if transformed_system_messages is not None:
data["system_instruction"] = transformed_system_messages
return data
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