Fix AWS bedrock issues with newer model invocation (#572)

- Fixed models so that global.* models work
- Fixed Claude 4.5 & 4.7 invocation by removing top_p top_k params

trustgraph-bedrock/trustgraph/model/text_completion/bedrock/llm.py

@@ -21,8 +21,6 @@ default_ident = "text-completion"
 default_model = 'mistral.mistral-large-2407-v1:0'
 default_temperature = 0.0
 default_max_output = 2048
-default_top_p = 0.99
-default_top_k = 40
 
 # Actually, these could all just be None, no need to get environment
 # variables, as Boto3 would pick all these up if not passed in as args
@@ -38,16 +36,10 @@ class ModelHandler:
     def __init__(self):
         self.temperature = default_temperature
         self.max_output = default_max_output
-        self.top_p = default_top_p
-        self.top_k = default_top_k
     def set_temperature(self, temperature):
         self.temperature = temperature
     def set_max_output(self, max_output):
         self.max_output = max_output
-    def set_top_p(self, top_p):
-        self.top_p = top_p
-    def set_top_k(self, top_k):
-        self.top_k = top_k
     def encode_request(self, system, prompt):
         raise RuntimeError("format_request not implemented")
     def decode_response(self, response):
@@ -57,15 +49,12 @@ class ModelHandler:
 
 class Mistral(ModelHandler):
     def __init__(self):
-        self.top_p = 0.99
-        self.top_k = 40
+        pass
     def encode_request(self, system, prompt):
         return json.dumps({
             "prompt": f"{system}\n\n{prompt}",
             "max_tokens": self.max_output,
             "temperature": self.temperature,
-            "top_p": self.top_p,
-            "top_k": self.top_k,
         })
     def decode_response(self, response):
         response_body = json.loads(response.get("body").read())
@@ -79,13 +68,12 @@ class Mistral(ModelHandler):
 # Llama 3
 class Meta(ModelHandler):
     def __init__(self):
-        self.top_p = 0.95
+        pass
     def encode_request(self, system, prompt):
         return json.dumps({
             "prompt": f"{system}\n\n{prompt}",
             "max_gen_len": self.max_output,
             "temperature": self.temperature,
-            "top_p": self.top_p,
         })
     def decode_response(self, response):
         model_response = json.loads(response["body"].read())
@@ -96,13 +84,12 @@ class Meta(ModelHandler):
 
 class Anthropic(ModelHandler):
     def __init__(self):
-        self.top_p = 0.999
+        pass
     def encode_request(self, system, prompt):
         return json.dumps({
             "anthropic_version": "bedrock-2023-05-31",
             "max_tokens": self.max_output,
             "temperature": self.temperature,
-            "top_p": self.top_p,
             "messages": [
                 {
                     "role": "user",
@@ -127,12 +114,11 @@ class Anthropic(ModelHandler):
 
 class Ai21(ModelHandler):
     def __init__(self):
-        self.top_p = 0.9
+        pass
     def encode_request(self, system, prompt):
         return json.dumps({
             "max_tokens": self.max_output,
             "temperature": self.temperature,
-            "top_p": self.top_p,
             "messages": [
                 {
                     "role": "user",
@@ -230,30 +216,17 @@ class Processor(LlmService):
 
     def determine_variant(self, model):
 
-        # FIXME: Missing, Amazon models, Deepseek
-
-        # This set of conditions deals with normal bedrock on-demand usage
-        if model.startswith("mistral"):
+        if ".anthropic." in model or model.startswith("anthropic"):
+            return Anthropic
+        elif ".meta." in model or model.startswith("meta"):
+            return Meta
+        elif ".mistral." in model or model.startswith("mistral"):
             return Mistral
-        elif model.startswith("meta"):
-            return Meta
-        elif model.startswith("anthropic"):
-            return Anthropic
-        elif model.startswith("ai21"):
+        elif ".ai21." in model or model.startswith("ai21"):
             return Ai21
-        elif model.startswith("cohere"):
+        elif ".cohere." in model or model.startswith("cohere"):
             return Cohere
 
-        # The inference profiles
-        if model.startswith("us.meta"):
-            return Meta
-        elif model.startswith("us.anthropic"):
-            return Anthropic
-        elif model.startswith("eu.meta"):
-            return Meta
-        elif model.startswith("eu.anthropic"):
-            return Anthropic
-
         return Default
                         
     def _get_or_create_variant(self, model_name, temperature=None):