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

litellm/main.py

@@ -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
 

litellm/tests/test_multiple_deployments.py

@@ -0,0 +1,48 @@
+#### What this tests ####
+#    This tests error handling + logging (esp. for sentry breadcrumbs)
+
+import sys, os
+import traceback
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import pytest
+from litellm import completion
+
+messages=[{"role": "user", "content": "Hey, how's it going?"}]
+
+## All your mistral deployments ##
+model_list = [{
+	"model_name": "mistral-7b-instruct",
+	"litellm_params": { # params for litellm completion/embedding call 
+        "model": "replicate/mistralai/mistral-7b-instruct-v0.1:83b6a56e7c828e667f21fd596c338fd4f0039b46bcfa18d973e8e70e455fda70", 
+        "api_key": os.getenv("REPLICATE_API_KEY"),
+    }
+}, {
+	"model_name": "mistral-7b-instruct",
+	"litellm_params": { # params for litellm completion/embedding call 
+        "model": "together_ai/mistralai/Mistral-7B-Instruct-v0.1", 
+        "api_key": os.getenv("TOGETHERAI_API_KEY"),
+    }
+}, {
+	"model_name": "mistral-7b-instruct",
+	"litellm_params": { # params for litellm completion/embedding call 
+        "model": "mistral-7b-instruct", 
+        "api_base": "https://api.perplexity.ai",
+        "api_key": os.getenv("PERPLEXITYAI_API_KEY")
+    }
+}, {
+	"model_name": "mistral-7b-instruct",
+	"litellm_params": {
+		"model": "deepinfra/mistralai/Mistral-7B-Instruct-v0.1",
+		"api_key": os.getenv("DEEPINFRA_API_KEY")
+	}
+}]
+
+def test_multiple_deployments():
+    try: 
+        ## LiteLLM completion call ## returns first response 
+        response = completion(model="mistral-7b-instruct", messages=messages, model_list=model_list)
+    except Exception as e:
+        pytest.fail(f"An exception occurred: {e}")

litellm/utils.py

@@ -2798,8 +2798,8 @@ def exception_type(
                         )
                 exception_mapping_worked = True
                 raise APIError(
-                    status_code=original_exception.status_code, 
-                    message=f"ReplicateException - {original_exception.message}",
+                    status_code=500, 
+                    message=f"ReplicateException - {str(original_exception)}",
                     llm_provider="replicate",
                     model=model
                 )