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feat(main.py): support multiple deployments in 1 completion call

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Krrish Dholakia 2023-10-20 13:01:53 -07:00
parent 62d9ce6e66
commit 1f1cf7a11c
3 changed files with 79 additions and 3 deletions
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30
litellm/main.py
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@ -178,6 +178,7 @@ def completion(
api_base: Optional[str] = None,
api_version: Optional[str] = None,
api_key: Optional[str] = None,
model_list: Optional[list] = None, # pass in a list of api_base,keys, etc.
# Optional liteLLM function params
**kwargs,
@ -205,6 +206,7 @@ def completion(
api_base (str, optional): Base URL for the API (default is None).
api_version (str, optional): API version (default is None).
api_key (str, optional): API key (default is None).
model_list (list, optional): List of api base, version, keys
LITELLM Specific Params
mock_response (str, optional): If provided, return a mock completion response for testing or debugging purposes (default is None).
@ -233,7 +235,7 @@ def completion(
headers = kwargs.get("headers", None)
######## end of unpacking kwargs ###########
openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key"]
litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers"]
litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list"]
default_params = openai_params + litellm_params
non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
if mock_response:
@ -246,6 +248,9 @@ def completion(
)
if fallbacks is not None:
return completion_with_fallbacks(**args)
if model_list is not None:
deployments = [m["litellm_params"] for m in model_list if m["model_name"] == model]
return batch_completion_models(deployments=deployments, **args)
if litellm.model_alias_map and model in litellm.model_alias_map:
args["model_alias_map"] = litellm.model_alias_map
model = litellm.model_alias_map[
@ -1375,6 +1380,29 @@ def batch_completion_models(*args, **kwargs):
for model, future in sorted(futures.items(), key=lambda x: models.index(x[0])):
if future.result() is not None:
return future.result()
elif "deployments" in kwargs:
deployments = kwargs["deployments"]
kwargs.pop("deployments")
kwargs.pop("model_list")
nested_kwargs = kwargs.pop("kwargs", {})
futures = {}
with concurrent.futures.ThreadPoolExecutor(max_workers=len(deployments)) as executor:
for deployment in deployments:
for key in kwargs.keys():
if key not in deployment: # don't override deployment values e.g. model name, api base, etc.
deployment[key] = kwargs[key]
kwargs = {**deployment, **nested_kwargs}
futures[deployment["model"]] = executor.submit(completion, **kwargs)
print(f"futures: {futures}")
# done, not_done = concurrent.futures.wait(futures.values(), return_when=concurrent.futures.FIRST_COMPLETED)
# done is a set of futures that completed
for _, future in futures.items():
if future.result() is not None:
return future.result()
# for future in done:
# return future.result()
return None # If no response is received from any model