fix(bedrock): omit topP for Anthropic Converse; use AWSBedrockConfig in LlmFactory (#4469)

mem0/configs/llms/aws_bedrock.py

@@ -16,7 +16,7 @@ class AWSBedrockConfig(BaseLlmConfig):
         model: Optional[str] = None,
         temperature: float = 0.1,
         max_tokens: int = 2000,
-        top_p: float = 0.9,
+        top_p: Optional[float] = None,
         top_k: int = 1,
         aws_access_key_id: Optional[str] = None,
         aws_secret_access_key: Optional[str] = None,
@@ -33,7 +33,8 @@ class AWSBedrockConfig(BaseLlmConfig):
             model: Bedrock model identifier (e.g., "amazon.nova-3-mini-20241119-v1:0")
             temperature: Controls randomness (0.0 to 2.0)
             max_tokens: Maximum tokens to generate
-            top_p: Nucleus sampling parameter (0.0 to 1.0)
+            top_p: Nucleus sampling (0.0–1.0). Default None omits topP on Converse
+                (required for Anthropic, which rejects temperature and topP together).
             top_k: Top-k sampling parameter (1 to 40)
             aws_access_key_id: AWS access key (optional, uses env vars if not provided)
             aws_secret_access_key: AWS secret key (optional, uses env vars if not provided)
@@ -75,13 +76,16 @@ class AWSBedrockConfig(BaseLlmConfig):
 
     def get_model_config(self) -> Dict[str, Any]:
         """Get model-specific configuration parameters."""
-        base_config = {
+        base_config: Dict[str, Any] = {
             "temperature": self.temperature,
             "max_tokens": self.max_tokens,
-            "top_p": self.top_p,
             "top_k": self.top_k,
         }
 
+        # Only include top_p when explicitly set by the user.
+        if self.top_p is not None:
+            base_config["top_p"] = self.top_p
+
         # Add custom model kwargs
         base_config.update(self.model_kwargs)
 

mem0/llms/aws_bedrock.py

@@ -228,6 +228,12 @@ class AWSBedrockLLM(LLMBase):
 
         return "\n\nHuman: " + "".join(formatted_messages) + "\n\nAssistant:"
 
+    def _merge_optional_top_p(self, target: Dict[str, Any], *, key: str = "top_p") -> None:
+        """Add nucleus sampling to ``target`` only when ``model_config`` has ``top_p`` set."""
+        top_p = self.model_config.get("top_p")
+        if top_p is not None:
+            target[key] = top_p
+
     def _prepare_input(self, prompt: str) -> Dict[str, Any]:
         """
         Prepare input for the current provider's model.
@@ -268,44 +274,38 @@ class AWSBedrockLLM(LLMBase):
                     "messages": [{"role": "user", "content": prompt}],
                     "max_tokens": self.model_config.get("max_tokens", 5000),
                     "temperature": self.model_config.get("temperature", 0.1),
-                    "top_p": self.model_config.get("top_p", 0.9),
                 }
+                self._merge_optional_top_p(input_body)
             else:
                 # Legacy Amazon models
-                input_body = {
-                    "inputText": prompt,
-                    "textGenerationConfig": {
-                        "maxTokenCount": self.model_config.get("max_tokens", 5000),
-                        "topP": self.model_config.get("top_p", 0.9),
-                        "temperature": self.model_config.get("temperature", 0.1),
-                    },
-                }
-                # Remove None values
-                input_body["textGenerationConfig"] = {
-                    k: v for k, v in input_body["textGenerationConfig"].items() if v is not None
+                text_gen_config: Dict[str, Any] = {
+                    "maxTokenCount": self.model_config.get("max_tokens", 5000),
+                    "temperature": self.model_config.get("temperature", 0.1),
                 }
+                self._merge_optional_top_p(text_gen_config, key="topP")
+                input_body = {"inputText": prompt, "textGenerationConfig": text_gen_config}
         elif self.provider == "anthropic":
             input_body = {
                 "messages": [{"role": "user", "content": [{"type": "text", "text": prompt}]}],
                 "max_tokens": self.model_config.get("max_tokens", 2000),
                 "temperature": self.model_config.get("temperature", 0.1),
-                "top_p": self.model_config.get("top_p", 0.9),
                 "anthropic_version": "bedrock-2023-05-31",
             }
+            self._merge_optional_top_p(input_body)
         elif self.provider == "meta":
             input_body = {
                 "prompt": prompt,
                 "max_gen_len": self.model_config.get("max_tokens", 5000),
                 "temperature": self.model_config.get("temperature", 0.1),
-                "top_p": self.model_config.get("top_p", 0.9),
             }
+            self._merge_optional_top_p(input_body)
         elif self.provider == "mistral":
             input_body = {
                 "prompt": prompt,
                 "max_tokens": self.model_config.get("max_tokens", 5000),
                 "temperature": self.model_config.get("temperature", 0.1),
-                "top_p": self.model_config.get("top_p", 0.9),
             }
+            self._merge_optional_top_p(input_body)
         else:
             # Generic case - add all model config parameters
             input_body.update(self.model_config)
@@ -479,6 +479,29 @@ class AWSBedrockLLM(LLMBase):
 
         return converse_tools
 
+    def _default_max_tokens_for_converse(self) -> int:
+        """Default maxTokens if ``max_tokens`` is missing (Nova: 5000, else 2000)."""
+        model_id = (self.config.model or "").lower()
+        if self.provider == "amazon" and "nova" in model_id:
+            return 5000
+        return 2000
+
+    def _build_inference_config(self) -> Dict[str, Any]:
+        """Build Converse ``inferenceConfig``. Anthropic allows only one of temperature or topP; we keep temperature and omit topP."""
+        inference_config: Dict[str, Any] = {
+            "maxTokens": self.model_config.get("max_tokens", self._default_max_tokens_for_converse()),
+            "temperature": self.model_config.get("temperature", 0.1),
+        }
+
+        top_p = self.model_config.get("top_p")
+        if top_p is not None:
+            if self.provider == "anthropic":
+                logger.debug("Omitting topP for Anthropic Converse (using temperature); top_p=%s", top_p)
+            else:
+                inference_config["topP"] = top_p
+
+        return inference_config
+
     def _generate_with_tools(self, messages: List[Dict[str, str]], tools: List[Dict], stream: bool = False) -> Dict[str, Any]:
         """Generate response with tool calling support using correct message format."""
         # Format messages for tool-enabled models
@@ -501,11 +524,7 @@ class AWSBedrockLLM(LLMBase):
         converse_params = {
             "modelId": self.config.model,
             "messages": formatted_messages,
-            "inferenceConfig": {
-                "maxTokens": self.model_config.get("max_tokens", 2000),
-                "temperature": self.model_config.get("temperature", 0.1),
-                "topP": self.model_config.get("top_p", 0.9),
-            }
+            "inferenceConfig": self._build_inference_config(),
         }
 
         # Add system message if present (for Anthropic)
@@ -531,11 +550,7 @@ class AWSBedrockLLM(LLMBase):
             converse_params = {
                 "modelId": self.config.model,
                 "messages": formatted_messages,
-                "inferenceConfig": {
-                    "maxTokens": self.model_config.get("max_tokens", 2000),
-                    "temperature": self.model_config.get("temperature", 0.1),
-                    "topP": self.model_config.get("top_p", 0.9),
-                }
+                "inferenceConfig": self._build_inference_config(),
             }
 
             # Add system message if present
@@ -554,26 +569,13 @@ class AWSBedrockLLM(LLMBase):
                 return str(response)
 
         elif self.provider == "amazon" and "nova" in self.config.model.lower():
-            # Nova models use converse API even without tools
+            # Nova models use the Converse API even without tools
             formatted_messages = self._format_messages_amazon(messages)
-            input_body = {
-                "messages": formatted_messages,
-                "max_tokens": self.model_config.get("max_tokens", 5000),
-                "temperature": self.model_config.get("temperature", 0.1),
-                "top_p": self.model_config.get("top_p", 0.9),
-            }
-            
-            # Use converse API for Nova models
             response = self.client.converse(
                 modelId=self.config.model,
-                messages=input_body["messages"],
-                inferenceConfig={
-                    "maxTokens": input_body["max_tokens"],
-                    "temperature": input_body["temperature"],
-                    "topP": input_body["top_p"],
-                }
+                messages=formatted_messages,
+                inferenceConfig=self._build_inference_config(),
             )
-            
             return self._parse_response(response)
         else:
             # For other providers and legacy Amazon models (like Titan)

mem0/utils/factory.py

@@ -3,6 +3,7 @@ from typing import Dict, Optional, Union
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
 from mem0.configs.llms.anthropic import AnthropicConfig
+from mem0.configs.llms.aws_bedrock import AWSBedrockConfig
 from mem0.configs.llms.azure import AzureOpenAIConfig
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.configs.llms.deepseek import DeepSeekConfig
@@ -38,7 +39,7 @@ class LlmFactory:
         "openai": ("mem0.llms.openai.OpenAILLM", OpenAIConfig),
         "groq": ("mem0.llms.groq.GroqLLM", BaseLlmConfig),
         "together": ("mem0.llms.together.TogetherLLM", BaseLlmConfig),
-        "aws_bedrock": ("mem0.llms.aws_bedrock.AWSBedrockLLM", BaseLlmConfig),
+        "aws_bedrock": ("mem0.llms.aws_bedrock.AWSBedrockLLM", AWSBedrockConfig),
         "litellm": ("mem0.llms.litellm.LiteLLM", BaseLlmConfig),
         "azure_openai": ("mem0.llms.azure_openai.AzureOpenAILLM", AzureOpenAIConfig),
         "openai_structured": ("mem0.llms.openai_structured.OpenAIStructuredLLM", OpenAIConfig),

tests/llms/test_aws_bedrock.py

@@ -0,0 +1,330 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from mem0.configs.llms.aws_bedrock import AWSBedrockConfig
+from mem0.llms.aws_bedrock import AWSBedrockLLM, extract_provider
+from mem0.utils.factory import LlmFactory
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+@pytest.fixture
+def mock_boto3():
+    """Patch boto3 so no real AWS calls are made during unit tests."""
+    with patch("mem0.llms.aws_bedrock.boto3") as mock_b3:
+        runtime_client = MagicMock()
+        bedrock_client = MagicMock()
+        bedrock_client.list_foundation_models.return_value = {"modelSummaries": []}
+
+        def _client(service, **kwargs):
+            if service == "bedrock-runtime":
+                return runtime_client
+            return bedrock_client
+
+        mock_b3.client.side_effect = _client
+        yield runtime_client
+
+
+def _make_llm(model: str, mock_boto3, **kwargs) -> AWSBedrockLLM:
+    """Instantiate AWSBedrockLLM with a given model, all AWS calls mocked."""
+    config = AWSBedrockConfig(model=model, **kwargs)
+    return AWSBedrockLLM(config)
+
+
+def _converse_response(text: str = "ok") -> dict:
+    """Minimal Converse API response dict."""
+    return {"output": {"message": {"content": [{"text": text}]}}}
+
+
+# ---------------------------------------------------------------------------
+# extract_provider
+# ---------------------------------------------------------------------------
+
+class TestExtractProvider:
+    def test_standard_anthropic_model(self):
+        assert extract_provider("anthropic.claude-3-5-sonnet-20240620-v1:0") == "anthropic"
+
+    def test_inference_profile_us_prefix(self):
+        # Cross-region inference profile IDs look like us.anthropic.<model>
+        assert extract_provider("us.anthropic.claude-haiku-4-5-20251001-v1:0") == "anthropic"
+
+    def test_inference_profile_eu_prefix(self):
+        assert extract_provider("eu.anthropic.claude-sonnet-4-5-20250929-v1:0") == "anthropic"
+
+    def test_inference_profile_ap_prefix(self):
+        assert extract_provider("ap.anthropic.claude-3-opus-20240229-v1:0") == "anthropic"
+
+    def test_amazon_model(self):
+        assert extract_provider("amazon.nova-3-mini-20241119-v1:0") == "amazon"
+
+    def test_meta_model(self):
+        assert extract_provider("meta.llama3-8b-instruct-v1:0") == "meta"
+
+    def test_mistral_model(self):
+        assert extract_provider("mistral.mistral-7b-instruct-v0:2") == "mistral"
+
+    def test_unknown_model_raises(self):
+        with pytest.raises(ValueError, match="Unknown provider"):
+            extract_provider("unknown-vendor.some-model-v1:0")
+
+
+# ---------------------------------------------------------------------------
+# AWSBedrockConfig
+# ---------------------------------------------------------------------------
+
+class TestAWSBedrockConfig:
+    def test_top_p_defaults_to_none(self):
+        config = AWSBedrockConfig(model="anthropic.claude-3-5-sonnet-20240620-v1:0")
+        assert config.top_p is None
+
+    def test_top_p_explicit_value_stored(self):
+        config = AWSBedrockConfig(model="anthropic.claude-3-5-sonnet-20240620-v1:0", top_p=0.8)
+        assert config.top_p == 0.8
+
+    def test_get_model_config_excludes_top_p_by_default(self):
+        config = AWSBedrockConfig(model="anthropic.claude-3-5-sonnet-20240620-v1:0", temperature=0.5)
+        model_cfg = config.get_model_config()
+        assert "top_p" not in model_cfg
+
+    def test_get_model_config_includes_top_p_when_set(self):
+        config = AWSBedrockConfig(model="anthropic.claude-3-5-sonnet-20240620-v1:0", top_p=0.7)
+        model_cfg = config.get_model_config()
+        assert model_cfg["top_p"] == 0.7
+
+    def test_get_model_config_top_p_via_model_kwargs(self):
+        """model_kwargs can supply top_p after merge; same semantics as explicit top_p."""
+        config = AWSBedrockConfig(
+            model="anthropic.claude-3-5-sonnet-20240620-v1:0",
+            model_kwargs={"top_p": 0.88},
+        )
+        assert config.get_model_config()["top_p"] == 0.88
+
+    def test_get_model_config_always_includes_temperature(self):
+        config = AWSBedrockConfig(model="anthropic.claude-3-5-sonnet-20240620-v1:0", temperature=0.3)
+        model_cfg = config.get_model_config()
+        assert model_cfg["temperature"] == 0.3
+
+    def test_aws_region_stored(self):
+        config = AWSBedrockConfig(
+            model="anthropic.claude-3-5-sonnet-20240620-v1:0",
+            aws_region="us-east-2",
+        )
+        assert config.aws_region == "us-east-2"
+
+
+# ---------------------------------------------------------------------------
+# LlmFactory
+# ---------------------------------------------------------------------------
+
+class TestLlmFactory:
+    def test_aws_bedrock_uses_aws_bedrock_config(self):
+        _, config_class = LlmFactory.provider_to_class["aws_bedrock"]
+        assert config_class is AWSBedrockConfig
+
+    def test_factory_create_accepts_aws_region(self, mock_boto3):
+        """LlmFactory.create must not crash when aws_region is in the config dict.
+
+        Before the fix, the factory mapped aws_bedrock to BaseLlmConfig which has
+        no aws_region parameter, causing: TypeError: __init__() got an unexpected
+        keyword argument 'aws_region'.
+        """
+        user_dict = {
+            "model": "anthropic.claude-3-5-sonnet-20240620-v1:0",
+            "aws_region": "us-east-2",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        }
+        mock_boto3.converse.return_value = _converse_response()
+
+        # This must not raise TypeError
+        llm = LlmFactory.create("aws_bedrock", user_dict)
+
+        assert isinstance(llm.config, AWSBedrockConfig)
+        assert llm.config.aws_region == "us-east-2"
+        assert llm.config.top_p is None
+
+
+# ---------------------------------------------------------------------------
+# _build_inference_config
+# ---------------------------------------------------------------------------
+
+class TestBuildInferenceConfig:
+    """
+    Unit tests for the _build_inference_config helper.
+    Validates the exact keys present in the returned dict.
+    """
+
+    def test_anthropic_only_temperature_by_default(self, mock_boto3):
+        llm = _make_llm("anthropic.claude-3-5-sonnet-20240620-v1:0", mock_boto3, temperature=0.5)
+        cfg = llm._build_inference_config()
+        assert "temperature" in cfg
+        assert cfg["temperature"] == 0.5
+        assert "topP" not in cfg, "topP must be absent when top_p not configured"
+
+    def test_anthropic_top_p_explicitly_set_still_omits_top_p(self, mock_boto3):
+        # Anthropic rejects both; topP must still be omitted even when set
+        llm = _make_llm(
+            "anthropic.claude-3-5-sonnet-20240620-v1:0",
+            mock_boto3,
+            temperature=0.5,
+            top_p=0.9,
+        )
+        cfg = llm._build_inference_config()
+        assert "temperature" in cfg
+        assert "topP" not in cfg
+
+    def test_anthropic_inference_profile_omits_top_p(self, mock_boto3):
+        # Cross-region inference profiles (us.anthropic.*) follow the same rule
+        llm = _make_llm("us.anthropic.claude-haiku-4-5-20251001-v1:0", mock_boto3, temperature=0.1)
+        cfg = llm._build_inference_config()
+        assert "topP" not in cfg
+
+    def test_amazon_includes_top_p_when_set(self, mock_boto3):
+        llm = _make_llm(
+            "amazon.nova-3-mini-20241119-v1:0",
+            mock_boto3,
+            temperature=0.5,
+            top_p=0.85,
+        )
+        cfg = llm._build_inference_config()
+        assert cfg["temperature"] == 0.5
+        assert cfg["topP"] == 0.85
+
+    def test_amazon_omits_top_p_when_not_set(self, mock_boto3):
+        llm = _make_llm("amazon.nova-3-mini-20241119-v1:0", mock_boto3, temperature=0.5)
+        cfg = llm._build_inference_config()
+        assert "topP" not in cfg
+
+    def test_max_tokens_present(self, mock_boto3):
+        llm = _make_llm(
+            "anthropic.claude-3-5-sonnet-20240620-v1:0",
+            mock_boto3,
+            max_tokens=1024,
+        )
+        cfg = llm._build_inference_config()
+        assert cfg["maxTokens"] == 1024
+
+    def test_nova_fallback_max_tokens_when_absent(self, mock_boto3):
+        """Legacy Nova Converse used 5000 when max_tokens was missing from the dict."""
+        llm = _make_llm("amazon.nova-3-mini-20241119-v1:0", mock_boto3)
+        llm.model_config.pop("max_tokens", None)
+        cfg = llm._build_inference_config()
+        assert cfg["maxTokens"] == 5000
+
+    def test_anthropic_fallback_max_tokens_when_absent(self, mock_boto3):
+        llm = _make_llm("anthropic.claude-3-5-sonnet-20240620-v1:0", mock_boto3)
+        llm.model_config.pop("max_tokens", None)
+        cfg = llm._build_inference_config()
+        assert cfg["maxTokens"] == 2000
+
+
+# ---------------------------------------------------------------------------
+# generate_response — Converse API call assertions
+# ---------------------------------------------------------------------------
+
+MESSAGES = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Hello"},
+]
+
+TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "add_memory",
+            "description": "Store a memory",
+            "parameters": {
+                "type": "object",
+                "properties": {"data": {"type": "string"}},
+                "required": ["data"],
+            },
+        },
+    }
+]
+
+
+class TestGenerateResponseConverse:
+    def test_standard_anthropic_no_top_p_in_converse_call(self, mock_boto3):
+        mock_boto3.converse.return_value = _converse_response()
+        llm = _make_llm("anthropic.claude-3-5-sonnet-20240620-v1:0", mock_boto3, temperature=0.2)
+
+        llm.generate_response(MESSAGES)
+
+        _, kwargs = mock_boto3.converse.call_args
+        inference_cfg = kwargs["inferenceConfig"]
+        assert "topP" not in inference_cfg
+        assert inference_cfg["temperature"] == 0.2
+
+    def test_standard_anthropic_inference_profile_no_top_p(self, mock_boto3):
+        mock_boto3.converse.return_value = _converse_response()
+        llm = _make_llm("us.anthropic.claude-haiku-4-5-20251001-v1:0", mock_boto3, temperature=0.1)
+
+        llm.generate_response(MESSAGES)
+
+        _, kwargs = mock_boto3.converse.call_args
+        assert "topP" not in kwargs["inferenceConfig"]
+
+    def test_with_tools_anthropic_no_top_p(self, mock_boto3):
+        mock_boto3.converse.return_value = _converse_response()
+        llm = _make_llm("anthropic.claude-3-5-sonnet-20240620-v1:0", mock_boto3, temperature=0.2)
+
+        llm.generate_response(MESSAGES, tools=TOOLS)
+
+        _, kwargs = mock_boto3.converse.call_args
+        assert "topP" not in kwargs["inferenceConfig"]
+
+    def test_with_tools_anthropic_top_p_set_still_omitted(self, mock_boto3):
+        # Even if user explicitly sets top_p, Anthropic must not receive topP
+        mock_boto3.converse.return_value = _converse_response()
+        llm = _make_llm(
+            "anthropic.claude-3-5-sonnet-20240620-v1:0",
+            mock_boto3,
+            temperature=0.2,
+            top_p=0.9,
+        )
+
+        llm.generate_response(MESSAGES, tools=TOOLS)
+
+        _, kwargs = mock_boto3.converse.call_args
+        assert "topP" not in kwargs["inferenceConfig"]
+
+    def test_nova_includes_top_p_when_explicitly_set(self, mock_boto3):
+        mock_boto3.converse.return_value = _converse_response()
+        llm = _make_llm(
+            "amazon.nova-3-mini-20241119-v1:0",
+            mock_boto3,
+            temperature=0.5,
+            top_p=0.85,
+        )
+
+        llm.generate_response(MESSAGES)
+
+        _, kwargs = mock_boto3.converse.call_args
+        assert kwargs["inferenceConfig"]["topP"] == 0.85
+
+    def test_nova_omits_top_p_when_not_set(self, mock_boto3):
+        mock_boto3.converse.return_value = _converse_response()
+        llm = _make_llm("amazon.nova-3-mini-20241119-v1:0", mock_boto3, temperature=0.5)
+
+        llm.generate_response(MESSAGES)
+
+        _, kwargs = mock_boto3.converse.call_args
+        assert "topP" not in kwargs["inferenceConfig"]
+
+    def test_anthropic_model_kwargs_top_p_still_omits_top_p_in_converse(self, mock_boto3):
+        """top_p injected via model_kwargs must not add topP for Anthropic Converse."""
+        mock_boto3.converse.return_value = _converse_response()
+        llm = _make_llm(
+            "anthropic.claude-3-5-sonnet-20240620-v1:0",
+            mock_boto3,
+            temperature=0.2,
+            model_kwargs={"top_p": 0.88},
+        )
+
+        llm.generate_response(MESSAGES)
+
+        _, kwargs = mock_boto3.converse.call_args
+        assert "topP" not in kwargs["inferenceConfig"]