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feat(vertex_ai_context_caching.py): support making context caching calls to vertex ai in a normal chat completion call (anthropic caching format)

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Closes https://github.com/BerriAI/litellm/issues/5213
This commit is contained in:
Krrish Dholakia 2024-08-26 18:47:45 -07:00
parent 6ff17f1acd
commit 074e30fa10
16 changed files with 594 additions and 90 deletions
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18
litellm/main.py
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@ -95,8 +95,6 @@ from .llms import (
replicate,
together_ai,
triton,
vertex_ai,
vertex_ai_anthropic,
vllm,
watsonx,
)
@ -124,8 +122,16 @@ from .llms.sagemaker.sagemaker import SagemakerLLM
from .llms.text_completion_codestral import CodestralTextCompletion
from .llms.text_to_speech.vertex_ai import VertexTextToSpeechAPI
from .llms.triton import TritonChatCompletion
from .llms.vertex_ai_partner import VertexAIPartnerModels
from .llms.vertex_httpx import VertexLLM
from .llms.vertex_ai_and_google_ai_studio import (
vertex_ai_anthropic,
vertex_ai_non_gemini,
)
from .llms.vertex_ai_and_google_ai_studio.vertex_ai_partner_models import (
VertexAIPartnerModels,
)
from .llms.vertex_ai_and_google_ai_studio.vertex_and_google_ai_studio_gemini import (
VertexLLM,
)
from .llms.watsonx import IBMWatsonXAI
from .types.llms.openai import HttpxBinaryResponseContent
from .types.utils import (
@ -2112,7 +2118,7 @@ def completion(
extra_headers=extra_headers,
)
else:
model_response = vertex_ai.completion(
model_response = vertex_ai_non_gemini.completion(
model=model,
messages=messages,
model_response=model_response,
@ -3558,7 +3564,7 @@ def embedding(
print_verbose=print_verbose,
)
else:
response = vertex_ai.embedding(
response = vertex_ai_non_gemini.embedding(
model=model,
input=input,
encoding=encoding,