diff --git a/ragulate/cli_commands/query.py b/ragulate/cli_commands/query.py
index 6853199..88f7759 100644
--- a/ragulate/cli_commands/query.py
+++ b/ragulate/cli_commands/query.py
@@ -17,14 +17,14 @@ def setup_query(subparsers):
     )
     query_parser.add_argument(
         "-s",
-        "--script_path",
+        "--script",
         type=str,
         help="The path to the python script that contains the query method",
         required=True,
     )
     query_parser.add_argument(
         "-m",
-        "--method-name",
+        "--method",
         type=str,
         help="The name of the method in the script to run query",
         required=True,
@@ -90,12 +90,34 @@ def setup_query(subparsers):
         ),
         action="store_true",
     )
+    query_parser.add_argument(
+        "--provider",
+        type=str,
+        help=("The name of the LLM Provider to use for Evaluation."),
+        choices=[
+            "OpenAI",
+            "AzureOpenAI",
+            "Bedrock",
+            "LiteLLM",
+            "Langchain",
+            "Huggingface",
+        ],
+        default="OpenAI",
+    )
+    query_parser.add_argument(
+        "--model",
+        type=str,
+        help=(
+            "The name or id of the LLM model or deployment to use for Evaluation.",
+            "Generally used in combination with the --provider param.",
+        ),
+    )
     query_parser.set_defaults(func=lambda args: call_query(**vars(args)))
 
     def call_query(
         name: str,
-        script_path: str,
-        method_name: str,
+        script: str,
+        method: str,
         var_name: List[str],
         var_value: List[str],
         dataset: List[str],
@@ -103,6 +125,8 @@ def call_query(
         sample: float,
         seed: int,
         restart: bool,
+        provider: str,
+        model: str,
         **kwargs,
     ):
         if sample <= 0.0 or sample > 1.0:
@@ -124,12 +148,14 @@ def call_query(
 
         query_pipeline = QueryPipeline(
             recipe_name=name,
-            script_path=script_path,
-            method_name=method_name,
+            script_path=script,
+            method_name=method,
             ingredients=ingredients,
             datasets=datasets,
             sample_percent=sample,
             random_seed=seed,
             restart_pipeline=restart,
+            llm_provider=provider,
+            model_name=model,
         )
         query_pipeline.query()
diff --git a/ragulate/pipelines/query_pipeline.py b/ragulate/pipelines/query_pipeline.py
index 432b6d7..9124839 100644
--- a/ragulate/pipelines/query_pipeline.py
+++ b/ragulate/pipelines/query_pipeline.py
@@ -5,7 +5,15 @@
 
 from tqdm import tqdm
 from trulens_eval import Tru, TruChain
-from trulens_eval.feedback.provider import OpenAI
+from trulens_eval.feedback.provider import (
+    AzureOpenAI,
+    Bedrock,
+    Huggingface,
+    Langchain,
+    LiteLLM,
+    OpenAI,
+)
+from trulens_eval.feedback.provider.base import LLMProvider
 from trulens_eval.schema.feedback import FeedbackMode, FeedbackResultStatus
 
 from ragulate.datasets import BaseDataset
@@ -47,7 +55,9 @@ def __init__(
         datasets: List[BaseDataset],
         sample_percent: float = 1.0,
         random_seed: Optional[int] = None,
-        restart_pipeline: bool = False,
+        restart_pipeline: Optional[bool] = False,
+        llm_provider: Optional[str] = "OpenAI",
+        model_name: Optional[str] = None,
         **kwargs,
     ):
         super().__init__(
@@ -61,6 +71,8 @@ def __init__(
         self.sample_percent = sample_percent
         self.random_seed = random_seed
         self.restart_pipeline = restart_pipeline
+        self.llm_provider = llm_provider
+        self.model_name = model_name
 
         # Set up the signal handler for SIGINT (Ctrl-C)
         signal.signal(signal.SIGINT, self.signal_handler)
@@ -136,11 +148,30 @@ def update_progress(self, query_change: int = 0):
 
         self._finished_feedbacks = done
 
+    def get_provider(self) -> LLMProvider:
+        provider_name = self.provider_name.lower()
+        model_name = self.model_name
+
+        if provider_name == "openai":
+            return OpenAI(model_engine=model_name)
+        elif provider_name == "azureopenai":
+            return AzureOpenAI(deployment_name=model_name)
+        elif provider_name == "bedrock":
+            return Bedrock(model_id=model_name)
+        elif provider_name == "litellm":
+            return LiteLLM(model_engine=model_name)
+        elif provider_name == "Langchain":
+            return Langchain(model_engine=model_name)
+        elif provider_name == "huggingface":
+            return Huggingface(name=model_name)
+        else:
+            raise ValueError(f"Unsupported provider: {provider_name}")
+
     def query(self):
         query_method = self.get_method()
 
         pipeline = query_method(**self.ingredients)
-        llm_provider = OpenAI(model_engine="gpt-3.5-turbo")
+        llm_provider = self.get_provider()
 
         feedbacks = Feedbacks(llm_provider=llm_provider, pipeline=pipeline)