aws[patch]: support ChatBedrock(max_tokens, temperature)

libs/aws/langchain_aws/chat_models/bedrock.py

@@ -431,9 +431,9 @@ def _get_ls_params(
             ls_model_name=self.model_id,
             ls_model_type="chat",
         )
-        if ls_temperature := params.get("temperature"):
+        if ls_temperature := params.get("temperature", self.temperature):
             ls_params["ls_temperature"] = ls_temperature
-        if ls_max_tokens := params.get("max_tokens"):
+        if ls_max_tokens := params.get("max_tokens", self.max_tokens):
             ls_params["ls_max_tokens"] = ls_max_tokens
         if ls_stop := stop or params.get("stop", None):
             ls_params["ls_stop"] = ls_stop
@@ -818,6 +818,10 @@ def _as_converse(self) -> ChatBedrockConverse:
             for k, v in (self.model_kwargs or {}).items()
             if k in ("stop", "stop_sequences", "max_tokens", "temperature", "top_p")
         }
+        if self.max_tokens:
+            kwargs["max_tokens"] = self.max_tokens
+        if self.temperature is not None:
+            kwargs["temperature"] = self.temperature
         return ChatBedrockConverse(
             model=self.model_id,
             region_name=self.region_name,

libs/aws/langchain_aws/llms/bedrock.py

@@ -263,6 +263,9 @@ def prepare_input(
         system: Optional[str] = None,
         messages: Optional[List[Dict]] = None,
         tools: Optional[List[AnthropicTool]] = None,
+        *,
+        max_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
     ) -> Dict[str, Any]:
         input_body = {**model_kwargs}
         if provider == "anthropic":
@@ -273,18 +276,44 @@ def prepare_input(
                 input_body["messages"] = messages
                 if system:
                     input_body["system"] = system
-                if "max_tokens" not in input_body:
+                if max_tokens:
+                    input_body["max_tokens"] = max_tokens
+                elif "max_tokens" not in input_body:
                     input_body["max_tokens"] = 1024
+
             if prompt:
                 input_body["prompt"] = _human_assistant_format(prompt)
-                if "max_tokens_to_sample" not in input_body:
+                if max_tokens:
+                    input_body["max_tokens_to_sample"] = max_tokens
+                elif "max_tokens_to_sample" not in input_body:
                     input_body["max_tokens_to_sample"] = 1024
+
+            if temperature is not None:
+                input_body["temperature"] = temperature
+
         elif provider in ("ai21", "cohere", "meta", "mistral"):
             input_body["prompt"] = prompt
+            if max_tokens:
+                if provider == "cohere":
+                    input_body["max_tokens"] = max_tokens
+                elif provider == "meta":
+                    input_body["max_gen_len"] = max_tokens
+                elif provider == "mistral":
+                    input_body["max_tokens"] = max_tokens
+                else:
+                    # TODO: Add AI21 support, param depends on specific model.
+                    pass
+            if temperature is not None:
+                input_body["temperature"] = temperature
+
         elif provider == "amazon":
             input_body = dict()
             input_body["inputText"] = prompt
             input_body["textGenerationConfig"] = {**model_kwargs}
+            if max_tokens:
+                input_body["textGenerationConfig"]["maxTokenCount"] = max_tokens
+            if temperature is not None:
+                input_body["textGenerationConfig"]["temperature"] = temperature
         else:
             input_body["inputText"] = prompt
 
@@ -453,7 +482,7 @@ class BedrockBase(BaseLanguageModel, ABC):
 
     client: Any = Field(default=None, exclude=True)  #: :meta private:
 
-    region_name: Optional[str] = None
+    region_name: Optional[str] = Field(default=None, alias="region")
     """The aws region e.g., `us-west-2`. Fallsback to AWS_DEFAULT_REGION env variable
     or region specified in ~/.aws/config in case it is not provided here.
     """
@@ -591,6 +620,9 @@ async def on_llm_error(
                 ...Logic to handle guardrail intervention...
     """  # noqa: E501
 
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+
     @property
     def lc_secrets(self) -> Dict[str, str]:
         return {
@@ -603,6 +635,17 @@ def lc_secrets(self) -> Dict[str, str]:
     def validate_environment(self) -> Self:
         """Validate that AWS credentials to and python package exists in environment."""
 
+        if self.model_kwargs:
+            if "temperature" in self.model_kwargs:
+                if self.temperature is None:
+                    self.temperature = self.model_kwargs["temperature"]
+                self.model_kwargs.pop("temperature")
+
+            if "max_tokens" in self.model_kwargs:
+                if not self.max_tokens:
+                    self.max_tokens = self.model_kwargs["max_tokens"]
+                self.model_kwargs.pop("max_tokens")
+
         # Skip creating new client if passed in constructor
         if self.client is not None:
             return self
@@ -739,23 +782,27 @@ def _prepare_input_and_invoke(
 
         provider = self._get_provider()
         params = {**_model_kwargs, **kwargs}
-        input_body = LLMInputOutputAdapter.prepare_input(
-            provider=provider,
-            model_kwargs=params,
-            prompt=prompt,
-            system=system,
-            messages=messages,
-        )
-        if "claude-3" in self._get_model():
-            if _tools_in_params(params):
-                input_body = LLMInputOutputAdapter.prepare_input(
-                    provider=provider,
-                    model_kwargs=params,
-                    prompt=prompt,
-                    system=system,
-                    messages=messages,
-                    tools=params["tools"],
-                )
+        if "claude-3" in self._get_model() and _tools_in_params(params):
+            input_body = LLMInputOutputAdapter.prepare_input(
+                provider=provider,
+                model_kwargs=params,
+                prompt=prompt,
+                system=system,
+                messages=messages,
+                tools=params["tools"],
+                max_tokens=self.max_tokens,
+                temperature=self.temperature,
+            )
+        else:
+            input_body = LLMInputOutputAdapter.prepare_input(
+                provider=provider,
+                model_kwargs=params,
+                prompt=prompt,
+                system=system,
+                messages=messages,
+                max_tokens=self.max_tokens,
+                temperature=self.temperature,
+            )
         body = json.dumps(input_body)
         accept = "application/json"
         contentType = "application/json"
@@ -876,6 +923,8 @@ def _prepare_input_and_invoke_stream(
             system=system,
             messages=messages,
             model_kwargs=params,
+            max_tokens=self.max_tokens,
+            temperature=self.temperature,
         )
         coerce_content_to_string = True
         if "claude-3" in self._get_model():
@@ -888,6 +937,8 @@ def _prepare_input_and_invoke_stream(
                     system=system,
                     messages=messages,
                     tools=params["tools"],
+                    max_tokens=self.max_tokens,
+                    temperature=self.temperature,
                 )
         body = json.dumps(input_body)
 
@@ -961,6 +1012,8 @@ async def _aprepare_input_and_invoke_stream(
                 system=system,
                 messages=messages,
                 tools=params["tools"],
+                max_tokens=self.max_tokens,
+                temperature=self.temperature,
             )
         else:
             input_body = LLMInputOutputAdapter.prepare_input(
@@ -969,6 +1022,8 @@ async def _aprepare_input_and_invoke_stream(
                 system=system,
                 messages=messages,
                 model_kwargs=params,
+                max_tokens=self.max_tokens,
+                temperature=self.temperature,
             )
         body = json.dumps(input_body)
 

libs/aws/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-aws"
-version = "0.2.1"
+version = "0.2.2"
 description = "An integration package connecting AWS and LangChain"
 authors = []
 readme = "README.md"

libs/aws/tests/integration_tests/chat_models/test_standard.py

@@ -20,7 +20,7 @@ def chat_model_params(self) -> dict:
 
     @property
     def standard_chat_model_params(self) -> dict:
-        return {}
+        return {"temperature": 0, "max_tokens": 100}
 
     @pytest.mark.xfail(reason="Not implemented.")
     def test_stop_sequence(self, model: BaseChatModel) -> None:
@@ -57,11 +57,11 @@ def chat_model_params(self) -> dict:
     @property
     def standard_chat_model_params(self) -> dict:
         return {
+            "temperature": 0,
+            "max_tokens": 100,
             "model_kwargs": {
-                "temperature": 0,
-                "max_tokens": 100,
                 "stop": [],
-            }
+            },
         }
 
     @property

libs/aws/tests/unit_tests/__snapshots__/test_standard.ambr

@@ -14,12 +14,11 @@
         'guardrailVersion': None,
         'trace': None,
       }),
+      'max_tokens': 100,
       'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0',
       'model_kwargs': dict({
-        'max_tokens': 100,
         'stop': list([
         ]),
-        'temperature': 0,
       }),
       'provider_stop_reason_key_map': dict({
         'ai21': 'finishReason',
@@ -36,6 +35,7 @@
         'mistral': 'stop_sequences',
       }),
       'region_name': 'us-east-1',
+      'temperature': 0,
     }),
     'lc': 1,
     'name': 'ChatBedrock',