phoenix/litellm-mirror
0
0
Fork 1
mirror of https://github.com/BerriAI/litellm.git synced 2025-04-25 18:54:30 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

(fix - watsonx) Fixed issues with watsonx embedding/async endpoints

Browse source
This commit is contained in:
Simon Sanchez Viloria 2024-07-07 17:48:25 +02:00
parent c7338f9798
commit 06e6f52358
2 changed files with 186 additions and 119 deletions
Download patch file Download diff file Expand all files Collapse all files

10
litellm/main.py
View file

@ -108,6 +108,7 @@ from .llms.databricks import DatabricksChatCompletion
from .llms.huggingface_restapi import Huggingface
from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion
from .llms.predibase import PredibaseChatCompletion
from .llms.watsonx import IBMWatsonXAI
from .llms.prompt_templates.factory import (
custom_prompt,
function_call_prompt,
@ -152,6 +153,7 @@ triton_chat_completions = TritonChatCompletion()
bedrock_chat_completion = BedrockLLM()
bedrock_converse_chat_completion = BedrockConverseLLM()
vertex_chat_completion = VertexLLM()
watsonxai = IBMWatsonXAI()
####### COMPLETION ENDPOINTS ################
@ -369,6 +371,7 @@ async def acompletion(
or custom_llm_provider == "bedrock"
or custom_llm_provider == "databricks"
or custom_llm_provider == "clarifai"
or custom_llm_provider == "watsonx"
or custom_llm_provider in litellm.openai_compatible_providers
): # currently implemented aiohttp calls for just azure, openai, hf, ollama, vertex ai soon all.
init_response = await loop.run_in_executor(None, func_with_context)
@ -2352,7 +2355,7 @@ def completion(
response = response
elif custom_llm_provider == "watsonx":
custom_prompt_dict = custom_prompt_dict or litellm.custom_prompt_dict
response = watsonx.IBMWatsonXAI().completion(
response = watsonxai.completion(
model=model,
messages=messages,
custom_prompt_dict=custom_prompt_dict,
@ -2364,6 +2367,7 @@ def completion(
encoding=encoding,
logging_obj=logging,
timeout=timeout, # type: ignore
acompletion=acompletion,
)
if (
"stream" in optional_params
@ -3030,6 +3034,7 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse:
or custom_llm_provider == "ollama"
or custom_llm_provider == "vertex_ai"
or custom_llm_provider == "databricks"
or custom_llm_provider == "watsonx"
): # currently implemented aiohttp calls for just azure and openai, soon all.
# Await normally
init_response = await loop.run_in_executor(None, func_with_context)
@ -3537,13 +3542,14 @@ def embedding(
aembedding=aembedding,
)
elif custom_llm_provider == "watsonx":
response = watsonx.IBMWatsonXAI().embedding(
response = watsonxai.embedding(
model=model,
input=input,
encoding=encoding,
logging_obj=logging,
optional_params=optional_params,
model_response=EmbeddingResponse(),
aembedding=aembedding,
)
else:
args = locals()