feat(plugin): Add API endpoint for invoking LLM with structured output (#21624)

api/controllers/inner_api/plugin/plugin.py

@@ -17,6 +17,7 @@ from core.plugin.entities.request import (
     RequestInvokeApp,
     RequestInvokeEncrypt,
     RequestInvokeLLM,
+    RequestInvokeLLMWithStructuredOutput,
     RequestInvokeModeration,
     RequestInvokeParameterExtractorNode,
     RequestInvokeQuestionClassifierNode,
@@ -47,6 +48,21 @@ class PluginInvokeLLMApi(Resource):
         return length_prefixed_response(0xF, generator())
 
 
+class PluginInvokeLLMWithStructuredOutputApi(Resource):
+    @setup_required
+    @plugin_inner_api_only
+    @get_user_tenant
+    @plugin_data(payload_type=RequestInvokeLLMWithStructuredOutput)
+    def post(self, user_model: Account | EndUser, tenant_model: Tenant, payload: RequestInvokeLLMWithStructuredOutput):
+        def generator():
+            response = PluginModelBackwardsInvocation.invoke_llm_with_structured_output(
+                user_model.id, tenant_model, payload
+            )
+            return PluginModelBackwardsInvocation.convert_to_event_stream(response)
+
+        return length_prefixed_response(0xF, generator())
+
+
 class PluginInvokeTextEmbeddingApi(Resource):
     @setup_required
     @plugin_inner_api_only
@@ -291,6 +307,7 @@ class PluginFetchAppInfoApi(Resource):
 
 
 api.add_resource(PluginInvokeLLMApi, "/invoke/llm")
+api.add_resource(PluginInvokeLLMWithStructuredOutputApi, "/invoke/llm/structured-output")
 api.add_resource(PluginInvokeTextEmbeddingApi, "/invoke/text-embedding")
 api.add_resource(PluginInvokeRerankApi, "/invoke/rerank")
 api.add_resource(PluginInvokeTTSApi, "/invoke/tts")

api/core/plugin/backwards_invocation/model.py

@@ -2,11 +2,14 @@ import tempfile
 from binascii import hexlify, unhexlify
 from collections.abc import Generator
 
+from core.llm_generator.output_parser.structured_output import invoke_llm_with_structured_output
 from core.model_manager import ModelManager
 from core.model_runtime.entities.llm_entities import (
     LLMResult,
     LLMResultChunk,
     LLMResultChunkDelta,
+    LLMResultChunkWithStructuredOutput,
+    LLMResultWithStructuredOutput,
 )
 from core.model_runtime.entities.message_entities import (
     PromptMessage,
@@ -16,6 +19,7 @@ from core.model_runtime.entities.message_entities import (
 from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
 from core.plugin.entities.request import (
     RequestInvokeLLM,
+    RequestInvokeLLMWithStructuredOutput,
     RequestInvokeModeration,
     RequestInvokeRerank,
     RequestInvokeSpeech2Text,
@@ -85,6 +89,72 @@ class PluginModelBackwardsInvocation(BaseBackwardsInvocation):
 
             return handle_non_streaming(response)
 
+    @classmethod
+    def invoke_llm_with_structured_output(
+        cls, user_id: str, tenant: Tenant, payload: RequestInvokeLLMWithStructuredOutput
+    ):
+        """
+        invoke llm with structured output
+        """
+        model_instance = ModelManager().get_model_instance(
+            tenant_id=tenant.id,
+            provider=payload.provider,
+            model_type=payload.model_type,
+            model=payload.model,
+        )
+
+        model_schema = model_instance.model_type_instance.get_model_schema(payload.model, model_instance.credentials)
+
+        if not model_schema:
+            raise ValueError(f"Model schema not found for {payload.model}")
+
+        response = invoke_llm_with_structured_output(
+            provider=payload.provider,
+            model_schema=model_schema,
+            model_instance=model_instance,
+            prompt_messages=payload.prompt_messages,
+            json_schema=payload.structured_output_schema,
+            tools=payload.tools,
+            stop=payload.stop,
+            stream=True if payload.stream is None else payload.stream,
+            user=user_id,
+            model_parameters=payload.completion_params,
+        )
+
+        if isinstance(response, Generator):
+
+            def handle() -> Generator[LLMResultChunkWithStructuredOutput, None, None]:
+                for chunk in response:
+                    if chunk.delta.usage:
+                        llm_utils.deduct_llm_quota(
+                            tenant_id=tenant.id, model_instance=model_instance, usage=chunk.delta.usage
+                        )
+                    chunk.prompt_messages = []
+                    yield chunk
+
+            return handle()
+        else:
+            if response.usage:
+                llm_utils.deduct_llm_quota(tenant_id=tenant.id, model_instance=model_instance, usage=response.usage)
+
+            def handle_non_streaming(
+                response: LLMResultWithStructuredOutput,
+            ) -> Generator[LLMResultChunkWithStructuredOutput, None, None]:
+                yield LLMResultChunkWithStructuredOutput(
+                    model=response.model,
+                    prompt_messages=[],
+                    system_fingerprint=response.system_fingerprint,
+                    structured_output=response.structured_output,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=response.message,
+                        usage=response.usage,
+                        finish_reason="",
+                    ),
+                )
+
+            return handle_non_streaming(response)
+
     @classmethod
     def invoke_text_embedding(cls, user_id: str, tenant: Tenant, payload: RequestInvokeTextEmbedding):
         """