fix bedrock models not running properly in langchain

Author: jsonbailey

packages/ai-providers/server-ai-langchain/src/ldai_langchain/langchain_provider.py

@@ -134,18 +134,18 @@ def map_provider(ld_provider_name: str) -> str:
         """
         Map LaunchDarkly provider names to LangChain provider names.
 
-        This method enables seamless integration between LaunchDarkly's standardized
-        provider naming and LangChain's naming conventions.
-
         :param ld_provider_name: LaunchDarkly provider name
         :return: LangChain-compatible provider name
         """
         lowercased_name = ld_provider_name.lower()
+        # Bedrock is the only provider that uses "provider:model_family" (e.g. Bedrock:Anthropic).
+        if lowercased_name.startswith('bedrock:'):
+            return 'bedrock_converse'
 
         mapping: Dict[str, str] = {
             'gemini': 'google-genai',
+            'bedrock': 'bedrock_converse',
         }
-
         return mapping.get(lowercased_name, lowercased_name)
 
     @staticmethod
@@ -232,10 +232,15 @@ def create_langchain_model(ai_config: AIConfigKind) -> BaseChatModel:
 
         model_name = model_dict.get('name', '')
         provider = provider_dict.get('name', '')
-        parameters = model_dict.get('parameters') or {}
+        parameters = dict(model_dict.get('parameters') or {})
+        mapped_provider = LangChainProvider.map_provider(provider)
 
+        # Bedrock requires the foundation provider (e.g. Bedrock:Anthropic) passed in
+        # parameters separately from model_provider, which is used for LangChain routing.
+        if mapped_provider == 'bedrock_converse' and 'provider' not in parameters:
+            parameters['provider'] = provider
         return init_chat_model(
             model_name,
-            model_provider=LangChainProvider.map_provider(provider),
+            model_provider=mapped_provider,
             **parameters,
         )

packages/ai-providers/server-ai-langchain/tests/test_langchain_provider.py

@@ -130,6 +130,14 @@ def test_maps_gemini_to_google_genai(self):
         assert LangChainProvider.map_provider('Gemini') == 'google-genai'
         assert LangChainProvider.map_provider('GEMINI') == 'google-genai'
 
+    def test_maps_bedrock_and_model_families_to_bedrock_converse(self):
+        """Should map bedrock and bedrock:model_family to bedrock_converse."""
+        assert LangChainProvider.map_provider('bedrock') == 'bedrock_converse'
+        assert LangChainProvider.map_provider('Bedrock:Anthropic') == 'bedrock_converse'
+        assert LangChainProvider.map_provider('bedrock:anthropic') == 'bedrock_converse'
+        assert LangChainProvider.map_provider('bedrock:amazon') == 'bedrock_converse'
+        assert LangChainProvider.map_provider('bedrock:cohere') == 'bedrock_converse'
+
     def test_returns_provider_name_unchanged_for_unmapped_providers(self):
         """Should return provider name unchanged for unmapped providers."""
         assert LangChainProvider.map_provider('openai') == 'openai'
@@ -197,7 +205,8 @@ def mock_llm(self):
     @pytest.mark.asyncio
     async def test_returns_success_true_for_successful_invocation(self, mock_llm):
         """Should return success=True for successful invocation."""
-        mock_response = {'result': 'structured data'}
+        parsed_data = {'result': 'structured data'}
+        mock_response = {'parsed': parsed_data, 'raw': None}
         mock_structured_llm = MagicMock()
         mock_structured_llm.ainvoke = AsyncMock(return_value=mock_response)
         mock_llm.with_structured_output = MagicMock(return_value=mock_structured_llm)
@@ -208,7 +217,7 @@ async def test_returns_success_true_for_successful_invocation(self, mock_llm):
         result = await provider.invoke_structured_model(messages, response_structure)
 
         assert result.metrics.success is True
-        assert result.data == mock_response
+        assert result.data == parsed_data
 
     @pytest.mark.asyncio
     async def test_returns_success_false_when_structured_model_invocation_throws_error(self, mock_llm):