support optional params for bedrock amazon

litellm/llms/bedrock.py

@@ -94,14 +94,8 @@ def completion(
     else: # amazon titan
         data = json.dumps({
             "inputText": prompt, 
-            "textGenerationConfig":{
-                "maxTokenCount":4096,
-                "stopSequences":[],
-                "temperature":0,
-                "topP":0.9
-                }
+            "textGenerationConfig": optional_params,
             }) 
-
     ## LOGGING
     logging_obj.pre_call(
             input=prompt,

litellm/tests/test_completion.py

@@ -650,14 +650,15 @@ def test_completion_bedrock_titan():
             model="bedrock/amazon.titan-tg1-large", 
             messages=messages,
             temperature=0.2,
-            max_tokens=20,
+            max_tokens=200,
+            top_p=0.8,
             logger_fn=logger_fn
         )
         # Add any assertions here to check the response
         print(response)
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
-# test_completion_bedrock_titan()
+test_completion_bedrock_titan()
 
 
 def test_completion_bedrock_ai21():

litellm/utils.py

@@ -931,6 +931,21 @@ def get_optional_params(  # use the openai defaults
                 optional_params["temperature"] = temperature
             if top_p != 1:
                 optional_params["top_p"] = top_p
+    elif custom_llm_provider == "bedrock":
+        if "ai21" in model or "anthropic" in model:
+            pass
+
+        elif "amazon" in model: # amazon titan llms
+            # see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large
+            if max_tokens != float("inf"):
+                optional_params["maxTokenCount"] = max_tokens
+            if temperature != 1:
+                optional_params["temperature"] = temperature
+            if stop != None:
+                optional_params["stopSequences"] = stop
+            if top_p != 1:
+                optional_params["topP"] = top_p
+
     elif model in litellm.aleph_alpha_models:
         if max_tokens != float("inf"):
             optional_params["maximum_tokens"] = max_tokens