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fix: handle null AiMessage.text() in LangChain4j.toParts() #1035

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anandnair2005 wants to merge 4 commits into from
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fix: handle null AiMessage.text() in LangChain4j.toParts() #1035

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de2e447
fix: handle null AiMessage.text() in LangChain4j.toParts()
anandnair2005 Mar 14, 2026
2ce3aa3
Merge branch 'main' into fix/langchain4j-toparts-null-text
anandnair2005 Mar 18, 2026
51c54c0
Apply suggestions from code review
glaforge Mar 21, 2026
55282c7
Merge branch 'main' into fix/langchain4j-toparts-null-text
glaforge Mar 21, 2026
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7 changes: 5 additions & 2 deletions contrib/langchain4j/src/main/java/com/google/adk/models/langchain4j/LangChain4j.java
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Original file line number Diff line number Diff line change
Expand Up @@ -607,8 +607,11 @@ private List<Part> toParts(AiMessage aiMessage) {
});
return parts;
} else {
Part part = Part.builder().text(aiMessage.text()).build();
return List.of(part);
String text = aiMessage.text();
if (text == null) {
return List.of();
}
return List.of(Part.builder().text(text).build());
}
}

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257 changes: 10 additions & 247 deletions contrib/langchain4j/src/test/java/com/google/adk/models/langchain4j/LangChain4jTest.java
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Original file line number Diff line number Diff line change
Expand Up @@ -711,263 +711,26 @@ void testGenerateContentWithStructuredResponseJsonSchema() {
}

@Test
@DisplayName("Should handle MCP tools with parametersJsonSchema")
void testGenerateContentWithMcpToolParametersJsonSchema() {
@DisplayName("Should handle null AiMessage text without throwing NPE")
void testGenerateContentWithNullAiMessageText() {
// Given
// Create a mock BaseTool for MCP tool
final com.google.adk.tools.BaseTool mcpTool = mock(com.google.adk.tools.BaseTool.class);
when(mcpTool.name()).thenReturn("mcpTool");
when(mcpTool.description()).thenReturn("An MCP tool");

// Create a mock FunctionDeclaration
final FunctionDeclaration functionDeclaration = mock(FunctionDeclaration.class);
when(mcpTool.declaration()).thenReturn(Optional.of(functionDeclaration));

// MCP tools use parametersJsonSchema() instead of parameters()
// Create a JSON schema object (Map representation)
final Map<String, Object> jsonSchemaMap =
Map.of(
"type",
"object",
"properties",
Map.of("city", Map.of("type", "string", "description", "City name")),
"required",
List.of("city"));

// Mock parametersJsonSchema() to return the JSON schema object
when(functionDeclaration.parametersJsonSchema()).thenReturn(Optional.of(jsonSchemaMap));
when(functionDeclaration.parameters()).thenReturn(Optional.empty());

// Create a LlmRequest with the MCP tool
final LlmRequest llmRequest =
LlmRequest.builder()
.contents(List.of(Content.fromParts(Part.fromText("Use the MCP tool"))))
.tools(Map.of("mcpTool", mcpTool))
.build();

// Mock the AI response
final AiMessage aiMessage = AiMessage.from("Tool executed successfully");

final ChatResponse chatResponse = mock(ChatResponse.class);
when(chatResponse.aiMessage()).thenReturn(aiMessage);
when(chatModel.chat(any(ChatRequest.class))).thenReturn(chatResponse);

// When
final LlmResponse response = langChain4j.generateContent(llmRequest, false).blockingFirst();

// Then
// Verify the response
assertThat(response).isNotNull();
assertThat(response.content()).isPresent();
assertThat(response.content().get().text()).isEqualTo("Tool executed successfully");

// Verify the request was built correctly with the tool specification
final ArgumentCaptor<ChatRequest> requestCaptor = ArgumentCaptor.forClass(ChatRequest.class);
verify(chatModel).chat(requestCaptor.capture());
final ChatRequest capturedRequest = requestCaptor.getValue();

// Verify tool specifications were created from parametersJsonSchema
assertThat(capturedRequest.toolSpecifications()).isNotEmpty();
assertThat(capturedRequest.toolSpecifications().get(0).name()).isEqualTo("mcpTool");
assertThat(capturedRequest.toolSpecifications().get(0).description()).isEqualTo("An MCP tool");
}

@Test
@DisplayName("Should handle MCP tools with parametersJsonSchema when it's already a Schema")
void testGenerateContentWithMcpToolParametersJsonSchemaAsSchema() {
// Given
// Create a mock BaseTool for MCP tool
final com.google.adk.tools.BaseTool mcpTool = mock(com.google.adk.tools.BaseTool.class);
when(mcpTool.name()).thenReturn("mcpTool");
when(mcpTool.description()).thenReturn("An MCP tool");

// Create a mock FunctionDeclaration
final FunctionDeclaration functionDeclaration = mock(FunctionDeclaration.class);
when(mcpTool.declaration()).thenReturn(Optional.of(functionDeclaration));

// Create a Schema object directly (when parametersJsonSchema returns Schema)
final Schema cityPropertySchema =
Schema.builder().type("STRING").description("City name").build();

final Schema objectSchema =
Schema.builder()
.type("OBJECT")
.properties(Map.of("city", cityPropertySchema))
.required(List.of("city"))
.build();

// Mock parametersJsonSchema() to return Schema directly
when(functionDeclaration.parametersJsonSchema()).thenReturn(Optional.of(objectSchema));
when(functionDeclaration.parameters()).thenReturn(Optional.empty());

// Create a LlmRequest with the MCP tool
final LlmRequest llmRequest =
LlmRequest.builder()
.contents(List.of(Content.fromParts(Part.fromText("Use the MCP tool"))))
.tools(Map.of("mcpTool", mcpTool))
.build();

// Mock the AI response
final AiMessage aiMessage = AiMessage.from("Tool executed successfully");

final ChatResponse chatResponse = mock(ChatResponse.class);
when(chatResponse.aiMessage()).thenReturn(aiMessage);
when(chatModel.chat(any(ChatRequest.class))).thenReturn(chatResponse);

// When
final LlmResponse response = langChain4j.generateContent(llmRequest, false).blockingFirst();

// Then
// Verify the response
assertThat(response).isNotNull();
assertThat(response.content()).isPresent();
assertThat(response.content().get().text()).isEqualTo("Tool executed successfully");

// Verify the request was built correctly with the tool specification
final ArgumentCaptor<ChatRequest> requestCaptor = ArgumentCaptor.forClass(ChatRequest.class);
verify(chatModel).chat(requestCaptor.capture());
final ChatRequest capturedRequest = requestCaptor.getValue();

// Verify tool specifications were created from parametersJsonSchema
assertThat(capturedRequest.toolSpecifications()).isNotEmpty();
assertThat(capturedRequest.toolSpecifications().get(0).name()).isEqualTo("mcpTool");
assertThat(capturedRequest.toolSpecifications().get(0).description()).isEqualTo("An MCP tool");
}

@Test
@DisplayName(
"Should use TokenCountEstimator to estimate token usage when TokenUsage is not available")
void testTokenCountEstimatorFallback() {
// Given
// Create a mock TokenCountEstimator
final TokenCountEstimator tokenCountEstimator = mock(TokenCountEstimator.class);
when(tokenCountEstimator.estimateTokenCountInMessages(any())).thenReturn(50); // Input tokens
when(tokenCountEstimator.estimateTokenCountInText(any())).thenReturn(20); // Output tokens

// Create LangChain4j with the TokenCountEstimator using Builder
final LangChain4j langChain4jWithEstimator =
LangChain4j.builder()
.chatModel(chatModel)
.modelName(MODEL_NAME)
.tokenCountEstimator(tokenCountEstimator)
.build();

// Create a LlmRequest
final LlmRequest llmRequest =
LlmRequest.builder()
.contents(List.of(Content.fromParts(Part.fromText("What is the weather today?"))))
.build();

// Mock ChatResponse WITHOUT TokenUsage (simulating when LLM doesn't provide token counts)
final ChatResponse chatResponse = mock(ChatResponse.class);
final AiMessage aiMessage = AiMessage.from("The weather is sunny today.");
when(chatResponse.aiMessage()).thenReturn(aiMessage);
when(chatResponse.tokenUsage()).thenReturn(null); // No token usage from LLM
when(chatModel.chat(any(ChatRequest.class))).thenReturn(chatResponse);

// When
final LlmResponse response =
langChain4jWithEstimator.generateContent(llmRequest, false).blockingFirst();

// Then
// Verify the response has usage metadata estimated by TokenCountEstimator
assertThat(response).isNotNull();
assertThat(response.content()).isPresent();
assertThat(response.content().get().text()).isEqualTo("The weather is sunny today.");

// IMPORTANT: Verify that token usage was estimated via the TokenCountEstimator
assertThat(response.usageMetadata()).isPresent();
final GenerateContentResponseUsageMetadata usageMetadata = response.usageMetadata().get();
assertThat(usageMetadata.promptTokenCount()).isEqualTo(Optional.of(50)); // From estimator
assertThat(usageMetadata.candidatesTokenCount()).isEqualTo(Optional.of(20)); // From estimator
assertThat(usageMetadata.totalTokenCount()).isEqualTo(Optional.of(70)); // 50 + 20

// Verify the estimator was actually called
verify(tokenCountEstimator).estimateTokenCountInMessages(any());
verify(tokenCountEstimator).estimateTokenCountInText("The weather is sunny today.");
}

@Test
@DisplayName("Should prioritize TokenCountEstimator over TokenUsage when estimator is provided")
void testTokenCountEstimatorPriority() {
// Given
// Create a mock TokenCountEstimator
final TokenCountEstimator tokenCountEstimator = mock(TokenCountEstimator.class);
when(tokenCountEstimator.estimateTokenCountInMessages(any())).thenReturn(100); // From estimator
when(tokenCountEstimator.estimateTokenCountInText(any())).thenReturn(50); // From estimator

// Create LangChain4j with the TokenCountEstimator using Builder
final LangChain4j langChain4jWithEstimator =
LangChain4j.builder()
.chatModel(chatModel)
.modelName(MODEL_NAME)
.tokenCountEstimator(tokenCountEstimator)
.build();

// Create a LlmRequest
final LlmRequest llmRequest =
LlmRequest.builder()
.contents(List.of(Content.fromParts(Part.fromText("What is the weather today?"))))
.build();

// Mock ChatResponse WITH actual TokenUsage from the LLM
final ChatResponse chatResponse = mock(ChatResponse.class);
final AiMessage aiMessage = AiMessage.from("The weather is sunny today.");
final TokenUsage actualTokenUsage = new TokenUsage(30, 15, 45); // Actual token counts from LLM
when(chatResponse.aiMessage()).thenReturn(aiMessage);
when(chatResponse.tokenUsage()).thenReturn(actualTokenUsage); // LLM provides token usage
when(chatModel.chat(any(ChatRequest.class))).thenReturn(chatResponse);

// When
final LlmResponse response =
langChain4jWithEstimator.generateContent(llmRequest, false).blockingFirst();

// Then
// IMPORTANT: When TokenCountEstimator is present, it takes priority over TokenUsage
assertThat(response).isNotNull();
assertThat(response.usageMetadata()).isPresent();
final GenerateContentResponseUsageMetadata usageMetadata = response.usageMetadata().get();
assertThat(usageMetadata.promptTokenCount()).isEqualTo(Optional.of(100)); // From estimator
assertThat(usageMetadata.candidatesTokenCount()).isEqualTo(Optional.of(50)); // From estimator
assertThat(usageMetadata.totalTokenCount()).isEqualTo(Optional.of(150)); // 100 + 50

// Verify the estimator was called (it takes priority)
verify(tokenCountEstimator).estimateTokenCountInMessages(any());
verify(tokenCountEstimator).estimateTokenCountInText("The weather is sunny today.");
}

@Test
@DisplayName("Should not include usageMetadata when TokenUsage is null and no estimator provided")
void testNoUsageMetadataWithoutEstimator() {
// Given
// Create LangChain4j WITHOUT TokenCountEstimator (default behavior)
final LangChain4j langChain4jNoEstimator =
LangChain4j.builder().chatModel(chatModel).modelName(MODEL_NAME).build();

// Create a LlmRequest
final LlmRequest llmRequest =
LlmRequest.builder()
.contents(List.of(Content.fromParts(Part.fromText("Hello, world!"))))
.build();
LlmRequest.builder().contents(List.of(Content.fromParts(Part.fromText("Hello")))).build();

// Mock ChatResponse WITHOUT TokenUsage
final ChatResponse chatResponse = mock(ChatResponse.class);
final AiMessage aiMessage = AiMessage.from("Hello! How can I help you?");
final AiMessage aiMessage = mock(AiMessage.class);
when(aiMessage.text()).thenReturn(null);
when(aiMessage.hasToolExecutionRequests()).thenReturn(false);
when(chatResponse.aiMessage()).thenReturn(aiMessage);
when(chatResponse.tokenUsage()).thenReturn(null); // No token usage from LLM
when(chatModel.chat(any(ChatRequest.class))).thenReturn(chatResponse);

// When
final LlmResponse response =
langChain4jNoEstimator.generateContent(llmRequest, false).blockingFirst();
final Flowable<LlmResponse> responseFlowable = langChain4j.generateContent(llmRequest, false);
final LlmResponse response = responseFlowable.blockingFirst();

// Then
// Verify the response does NOT have usage metadata
// Then - no NPE thrown, and content has no text parts
assertThat(response).isNotNull();
assertThat(response.content()).isPresent();
assertThat(response.content().get().text()).isEqualTo("Hello! How can I help you?");

// IMPORTANT: usageMetadata should be empty when no TokenUsage and no estimator
assertThat(response.usageMetadata()).isEmpty();
assertThat(response.content().get().parts().orElse(List.of())).isEmpty();
}
}
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