Feat/json mode (#2563)

api/core/model_runtime/entities/defaults.py

@@ -81,5 +81,18 @@ PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
         'min': 1,
         'max': 2048,
         'precision': 0,
+    },
+    DefaultParameterName.RESPONSE_FORMAT: {
+        'label': {
+            'en_US': 'Response Format',
+            'zh_Hans': '回复格式',
+        },
+        'type': 'string',
+        'help': {
+            'en_US': 'Set a response format, ensure the output from llm is a valid code block as possible, such as JSON, XML, etc.',
+            'zh_Hans': '设置一个返回格式，确保llm的输出尽可能是有效的代码块，如JSON、XML等',
+        },
+        'required': False,
+        'options': ['JSON', 'XML'],
     }
 }

api/core/model_runtime/entities/model_entities.py

@@ -91,6 +91,7 @@ class DefaultParameterName(Enum):
     PRESENCE_PENALTY = "presence_penalty"
     FREQUENCY_PENALTY = "frequency_penalty"
     MAX_TOKENS = "max_tokens"
+    RESPONSE_FORMAT = "response_format"
 
     @classmethod
     def value_of(cls, value: Any) -> 'DefaultParameterName':

api/core/model_runtime/model_providers/__base/ai_model.py

@@ -262,23 +262,23 @@ class AIModel(ABC):
                 try:
                     default_parameter_name = DefaultParameterName.value_of(parameter_rule.use_template)
                     default_parameter_rule = self._get_default_parameter_rule_variable_map(default_parameter_name)
-                    if not parameter_rule.max:
+                    if not parameter_rule.max and 'max' in default_parameter_rule:
                         parameter_rule.max = default_parameter_rule['max']
-                    if not parameter_rule.min:
+                    if not parameter_rule.min and 'min' in default_parameter_rule:
                         parameter_rule.min = default_parameter_rule['min']
-                    if not parameter_rule.precision:
+                    if not parameter_rule.default and 'default' in default_parameter_rule:
                         parameter_rule.default = default_parameter_rule['default']
-                    if not parameter_rule.precision:
+                    if not parameter_rule.precision and 'precision' in default_parameter_rule:
                         parameter_rule.precision = default_parameter_rule['precision']
-                    if not parameter_rule.required:
+                    if not parameter_rule.required and 'required' in default_parameter_rule:
                         parameter_rule.required = default_parameter_rule['required']
-                    if not parameter_rule.help:
+                    if not parameter_rule.help and 'help' in default_parameter_rule:
                         parameter_rule.help = I18nObject(
                             en_US=default_parameter_rule['help']['en_US'],
                         )
-                    if not parameter_rule.help.en_US:
+                    if not parameter_rule.help.en_US and ('help' in default_parameter_rule and 'en_US' in default_parameter_rule['help']):
                         parameter_rule.help.en_US = default_parameter_rule['help']['en_US']
-                    if not parameter_rule.help.zh_Hans:
+                    if not parameter_rule.help.zh_Hans and ('help' in default_parameter_rule and 'zh_Hans' in default_parameter_rule['help']):
                         parameter_rule.help.zh_Hans = default_parameter_rule['help'].get('zh_Hans', default_parameter_rule['help']['en_US'])
                 except ValueError:
                     pass

api/core/model_runtime/model_providers/__base/large_language_model.py

@@ -9,7 +9,13 @@ from typing import Optional, Union
 from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.callbacks.logging_callback import LoggingCallback
 from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
-from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, PromptMessageTool
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    PromptMessageTool,
+    SystemPromptMessage,
+    UserPromptMessage,
+)
 from core.model_runtime.entities.model_entities import (
     ModelPropertyKey,
     ModelType,
@@ -74,7 +80,20 @@ class LargeLanguageModel(AIModel):
         )
 
         try:
-            result = self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+            if "response_format" in model_parameters:
+                result = self._code_block_mode_wrapper(
+                    model=model,
+                    credentials=credentials,
+                    prompt_messages=prompt_messages,
+                    model_parameters=model_parameters,
+                    tools=tools,
+                    stop=stop,
+                    stream=stream,
+                    user=user,
+                    callbacks=callbacks
+                )
+            else:
+                result = self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
         except Exception as e:
             self._trigger_invoke_error_callbacks(
                 model=model,
@@ -120,6 +139,239 @@ class LargeLanguageModel(AIModel):
 
         return result
 
+    def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+                           model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
+                           stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
+                           callbacks: list[Callback] = None) -> Union[LLMResult, Generator]:
+        """
+        Code block mode wrapper, ensure the response is a code block with output markdown quote
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param tools: tools for tool calling
+        :param stop: stop words
+        :param stream: is stream response
+        :param user: unique user id
+        :param callbacks: callbacks
+        :return: full response or stream response chunk generator result
+        """
+
+        block_prompts = """You should always follow the instructions and output a valid {{block}} object.
+The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
+if you are not sure about the structure.
+
+<instructions>
+{{instructions}}
+</instructions>
+"""
+
+        code_block = model_parameters.get("response_format", "")
+        if not code_block:
+            return self._invoke(
+                model=model,
+                credentials=credentials,
+                prompt_messages=prompt_messages,
+                model_parameters=model_parameters,
+                tools=tools,
+                stop=stop,
+                stream=stream,
+                user=user
+            )
+        
+        model_parameters.pop("response_format")
+        stop = stop or []
+        stop.extend(["\n```", "```\n"])
+        block_prompts = block_prompts.replace("{{block}}", code_block)
+
+        # check if there is a system message
+        if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
+            # override the system message
+            prompt_messages[0] = SystemPromptMessage(
+                content=block_prompts
+                    .replace("{{instructions}}", prompt_messages[0].content)
+            )
+        else:
+            # insert the system message
+            prompt_messages.insert(0, SystemPromptMessage(
+                content=block_prompts
+                    .replace("{{instructions}}", f"Please output a valid {code_block} object.")
+            ))
+
+        if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage):
+            # add ```JSON\n to the last message
+            prompt_messages[-1].content += f"\n```{code_block}\n"
+        else:
+            # append a user message
+            prompt_messages.append(UserPromptMessage(
+                content=f"```{code_block}\n"
+            ))
+
+        response = self._invoke(
+            model=model,
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
+            tools=tools,
+            stop=stop,
+            stream=stream,
+            user=user
+        )
+
+        if isinstance(response, Generator):
+            first_chunk = next(response)
+            def new_generator():
+                yield first_chunk
+                yield from response
+
+            if first_chunk.delta.message.content and first_chunk.delta.message.content.startswith("`"):
+                return self._code_block_mode_stream_processor_with_backtick(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    input_generator=new_generator()
+                )
+            else:
+                return self._code_block_mode_stream_processor(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    input_generator=new_generator()
+                )
+            
+        return response
+
+    def _code_block_mode_stream_processor(self, model: str, prompt_messages: list[PromptMessage], 
+                                          input_generator: Generator[LLMResultChunk, None, None]
+                                        ) -> Generator[LLMResultChunk, None, None]:
+        """
+        Code block mode stream processor, ensure the response is a code block with output markdown quote
+
+        :param model: model name
+        :param prompt_messages: prompt messages
+        :param input_generator: input generator
+        :return: output generator
+        """
+        state = "normal"
+        backtick_count = 0
+        for piece in input_generator:
+            if piece.delta.message.content:
+                content = piece.delta.message.content
+                piece.delta.message.content = ""
+                yield piece
+                piece = content
+            else:
+                yield piece
+                continue
+            new_piece = ""
+            for char in piece:
+                if state == "normal":
+                    if char == "`":
+                        state = "in_backticks"
+                        backtick_count = 1
+                    else:
+                        new_piece += char
+                elif state == "in_backticks":
+                    if char == "`":
+                        backtick_count += 1
+                        if backtick_count == 3:
+                            state = "skip_content"
+                            backtick_count = 0
+                    else:
+                        new_piece += "`" * backtick_count + char
+                        state = "normal"
+                        backtick_count = 0
+                elif state == "skip_content":
+                    if char.isspace():
+                        state = "normal"
+
+            if new_piece:
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=AssistantPromptMessage(
+                            content=new_piece,
+                            tool_calls=[]
+                        ),
+                    )
+                )
+
+    def _code_block_mode_stream_processor_with_backtick(self, model: str, prompt_messages: list, 
+                                        input_generator:  Generator[LLMResultChunk, None, None]) \
+                                    ->  Generator[LLMResultChunk, None, None]:
+        """
+        Code block mode stream processor, ensure the response is a code block with output markdown quote.
+        This version skips the language identifier that follows the opening triple backticks.
+
+        :param model: model name
+        :param prompt_messages: prompt messages
+        :param input_generator: input generator
+        :return: output generator
+        """
+        state = "search_start"
+        backtick_count = 0
+
+        for piece in input_generator:
+            if piece.delta.message.content:
+                content = piece.delta.message.content
+                # Reset content to ensure we're only processing and yielding the relevant parts
+                piece.delta.message.content = ""
+                # Yield a piece with cleared content before processing it to maintain the generator structure
+                yield piece
+                piece = content
+            else:
+                # Yield pieces without content directly
+                yield piece
+                continue
+
+            if state == "done":
+                continue
+
+            new_piece = ""
+            for char in piece:
+                if state == "search_start":
+                    if char == "`":
+                        backtick_count += 1
+                        if backtick_count == 3:
+                            state = "skip_language"
+                            backtick_count = 0
+                    else:
+                        backtick_count = 0
+                elif state == "skip_language":
+                    # Skip everything until the first newline, marking the end of the language identifier
+                    if char == "\n":
+                        state = "in_code_block"
+                elif state == "in_code_block":
+                    if char == "`":
+                        backtick_count += 1
+                        if backtick_count == 3:
+                            state = "done"
+                            break
+                    else:
+                        if backtick_count > 0:
+                            # If backticks were counted but we're still collecting content, it was a false start
+                            new_piece += "`" * backtick_count
+                            backtick_count = 0
+                        new_piece += char
+
+                elif state == "done":
+                    break
+
+            if new_piece:
+                # Only yield content collected within the code block
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=AssistantPromptMessage(
+                            content=new_piece,
+                            tool_calls=[]
+                        ),
+                    )
+                )
+
     def _invoke_result_generator(self, model: str, result: Generator, credentials: dict,
                                  prompt_messages: list[PromptMessage], model_parameters: dict,
                                  tools: Optional[list[PromptMessageTool]] = None,
@@ -204,7 +456,7 @@ class LargeLanguageModel(AIModel):
         :return: full response or stream response chunk generator result
         """
         raise NotImplementedError
-
+    
     @abstractmethod
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: Optional[list[PromptMessageTool]] = None) -> int:

api/core/model_runtime/model_providers/anthropic/llm/claude-2.1.yaml

@@ -27,6 +27,8 @@ parameter_rules:
     default: 4096
     min: 1
     max: 4096
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '8.00'
   output: '24.00'

api/core/model_runtime/model_providers/anthropic/llm/claude-2.yaml

@@ -27,6 +27,8 @@ parameter_rules:
     default: 4096
     min: 1
     max: 4096
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '8.00'
   output: '24.00'

api/core/model_runtime/model_providers/anthropic/llm/claude-instant-1.yaml

@@ -26,6 +26,8 @@ parameter_rules:
     default: 4096
     min: 1
     max: 4096
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '1.63'
   output: '5.51'

api/core/model_runtime/model_providers/anthropic/llm/llm.py

@@ -6,6 +6,7 @@ from anthropic import Anthropic, Stream
 from anthropic.types import Completion, completion_create_params
 from httpx import Timeout
 
+from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -25,9 +26,16 @@ from core.model_runtime.errors.invoke import (
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 
+ANTHROPIC_BLOCK_MODE_PROMPT = """You should always follow the instructions and output a valid {{block}} object.
+The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
+if you are not sure about the structure.
 
-class AnthropicLargeLanguageModel(LargeLanguageModel):
+<instructions>
+{{instructions}}
+</instructions>
+"""
 
+class AnthropicLargeLanguageModel(LargeLanguageModel):
     def _invoke(self, model: str, credentials: dict,
                 prompt_messages: list[PromptMessage], model_parameters: dict,
                 tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
@@ -48,6 +56,53 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         """
         # invoke model
         return self._generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
+    
+    def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+                           model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
+                           stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
+                           callbacks: list[Callback] = None) -> Union[LLMResult, Generator]:
+        """
+        Code block mode wrapper for invoking large language model
+        """
+        if 'response_format' in model_parameters and model_parameters['response_format']:
+            stop = stop or []
+            self._transform_json_prompts(
+                model, credentials, prompt_messages, model_parameters, tools, stop, stream, user, model_parameters['response_format']
+            )
+            model_parameters.pop('response_format')
+
+        return self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+
+    def _transform_json_prompts(self, model: str, credentials: dict, 
+                               prompt_messages: list[PromptMessage], model_parameters: dict, 
+                               tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
+                               stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
+                            -> None:
+        """
+        Transform json prompts
+        """
+        if "```\n" not in stop:
+            stop.append("```\n")
+
+        # check if there is a system message
+        if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
+            # override the system message
+            prompt_messages[0] = SystemPromptMessage(
+                content=ANTHROPIC_BLOCK_MODE_PROMPT
+                    .replace("{{instructions}}", prompt_messages[0].content)
+                    .replace("{{block}}", response_format)
+            )
+        else:
+            # insert the system message
+            prompt_messages.insert(0, SystemPromptMessage(
+                content=ANTHROPIC_BLOCK_MODE_PROMPT
+                    .replace("{{instructions}}", f"Please output a valid {response_format} object.")
+                    .replace("{{block}}", response_format)
+            ))
+
+        prompt_messages.append(AssistantPromptMessage(
+            content=f"```{response_format}\n"
+        ))
 
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: Optional[list[PromptMessageTool]] = None) -> int:

api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml

@@ -27,6 +27,8 @@ parameter_rules:
     default: 2048
     min: 1
     max: 2048
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/llm.py

@@ -31,6 +31,16 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 
 logger = logging.getLogger(__name__)
 
+GEMINI_BLOCK_MODE_PROMPT = """You should always follow the instructions and output a valid {{block}} object.
+The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
+if you are not sure about the structure.
+
+<instructions>
+{{instructions}}
+</instructions>
+"""
+
+
 class GoogleLargeLanguageModel(LargeLanguageModel):
 
     def _invoke(self, model: str, credentials: dict,
@@ -53,7 +63,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         """
         # invoke model
         return self._generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
-
+    
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: Optional[list[PromptMessageTool]] = None) -> int:
         """

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-0125.yaml

@@ -24,6 +24,18 @@ parameter_rules:
     default: 512
     min: 1
     max: 4096
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
 pricing:
   input: '0.0005'
   output: '0.0015'

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-0613.yaml

@@ -24,6 +24,8 @@ parameter_rules:
     default: 512
     min: 1
     max: 4096
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.0015'
   output: '0.002'

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-1106.yaml

@@ -24,6 +24,18 @@ parameter_rules:
     default: 512
     min: 1
     max: 4096
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
 pricing:
   input: '0.001'
   output: '0.002'

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-16k-0613.yaml

@@ -24,6 +24,8 @@ parameter_rules:
     default: 512
     min: 1
     max: 16385
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.003'
   output: '0.004'

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-16k.yaml

@@ -24,6 +24,8 @@ parameter_rules:
     default: 512
     min: 1
     max: 16385
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.003'
   output: '0.004'

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-instruct.yaml

@@ -21,6 +21,8 @@ parameter_rules:
     default: 512
     min: 1
     max: 4096
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.0015'
   output: '0.002'

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo.yaml

@@ -24,6 +24,18 @@ parameter_rules:
     default: 512
     min: 1
     max: 4096
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
 pricing:
   input: '0.001'
   output: '0.002'

api/core/model_runtime/model_providers/openai/llm/llm.py

@@ -9,6 +9,7 @@ from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletio
 from openai.types.chat.chat_completion_chunk import ChoiceDeltaFunctionCall, ChoiceDeltaToolCall
 from openai.types.chat.chat_completion_message import FunctionCall
 
+from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -28,6 +29,14 @@ from core.model_runtime.model_providers.openai._common import _CommonOpenAI
 
 logger = logging.getLogger(__name__)
 
+OPENAI_BLOCK_MODE_PROMPT = """You should always follow the instructions and output a valid {{block}} object.
+The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
+if you are not sure about the structure.
+
+<instructions>
+{{instructions}}
+</instructions>
+"""
 
 class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
     """
@@ -84,6 +93,131 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 user=user
             )
 
+    def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+                           model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
+                           stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
+                           callbacks: list[Callback] = None) -> Union[LLMResult, Generator]:
+        """
+        Code block mode wrapper for invoking large language model
+        """
+        # handle fine tune remote models
+        base_model = model
+        if model.startswith('ft:'):
+            base_model = model.split(':')[1]
+
+        # get model mode
+        model_mode = self.get_model_mode(base_model, credentials)
+
+        # transform response format
+        if 'response_format' in model_parameters and model_parameters['response_format'] in ['JSON', 'XML']:
+            stop = stop or []
+            if model_mode == LLMMode.CHAT:
+                # chat model
+                self._transform_chat_json_prompts(
+                    model=base_model,
+                    credentials=credentials,
+                    prompt_messages=prompt_messages,
+                    model_parameters=model_parameters,
+                    tools=tools,
+                    stop=stop,
+                    stream=stream,
+                    user=user,
+                    response_format=model_parameters['response_format']
+                )
+            else:
+                self._transform_completion_json_prompts(
+                    model=base_model,
+                    credentials=credentials,
+                    prompt_messages=prompt_messages,
+                    model_parameters=model_parameters,
+                    tools=tools,
+                    stop=stop,
+                    stream=stream,
+                    user=user,
+                    response_format=model_parameters['response_format']
+                )
+            model_parameters.pop('response_format')
+
+        return self._invoke(
+            model=model,
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
+            tools=tools,
+            stop=stop,
+            stream=stream,
+            user=user
+        )
+
+    def _transform_chat_json_prompts(self, model: str, credentials: dict, 
+                               prompt_messages: list[PromptMessage], model_parameters: dict, 
+                               tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
+                               stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
+                            -> None:
+        """
+        Transform json prompts
+        """
+        if "```\n" not in stop:
+            stop.append("```\n")
+        if "\n```" not in stop:
+            stop.append("\n```")
+
+        # check if there is a system message
+        if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
+            # override the system message
+            prompt_messages[0] = SystemPromptMessage(
+                content=OPENAI_BLOCK_MODE_PROMPT
+                    .replace("{{instructions}}", prompt_messages[0].content)
+                    .replace("{{block}}", response_format)
+            )
+            prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}\n"))
+        else:
+            # insert the system message
+            prompt_messages.insert(0, SystemPromptMessage(
+                content=OPENAI_BLOCK_MODE_PROMPT
+                    .replace("{{instructions}}", f"Please output a valid {response_format} object.")
+                    .replace("{{block}}", response_format)
+            ))
+            prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
+    
+    def _transform_completion_json_prompts(self, model: str, credentials: dict,
+                                            prompt_messages: list[PromptMessage], model_parameters: dict,
+                                            tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
+                                            stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
+            -> None:
+        """
+        Transform json prompts
+        """
+        if "```\n" not in stop:
+            stop.append("```\n")
+        if "\n```" not in stop:
+            stop.append("\n```")
+
+        # override the last user message
+        user_message = None
+        for i in range(len(prompt_messages) - 1, -1, -1):
+            if isinstance(prompt_messages[i], UserPromptMessage):
+                user_message = prompt_messages[i]
+                break
+
+        if user_message:
+            if prompt_messages[i].content[-11:] == 'Assistant: ':
+                # now we are in the chat app, remove the last assistant message
+                prompt_messages[i].content = prompt_messages[i].content[:-11]
+                prompt_messages[i] = UserPromptMessage(
+                    content=OPENAI_BLOCK_MODE_PROMPT
+                        .replace("{{instructions}}", user_message.content)
+                        .replace("{{block}}", response_format)
+                )
+                prompt_messages[i].content += f"Assistant:\n```{response_format}\n"
+            else:
+                prompt_messages[i] = UserPromptMessage(
+                    content=OPENAI_BLOCK_MODE_PROMPT
+                        .replace("{{instructions}}", user_message.content)
+                        .replace("{{block}}", response_format)
+                )
+                prompt_messages[i].content += f"\n```{response_format}\n"
+
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: Optional[list[PromptMessageTool]] = None) -> int:
         """

api/core/model_runtime/model_providers/tongyi/llm/llm.py

@@ -13,6 +13,7 @@ from dashscope.common.error import (
 )
 from langchain.llms.tongyi import generate_with_retry, stream_generate_with_retry
 
+from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -57,6 +58,88 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         """
         # invoke model
         return self._generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
+    
+    def _code_block_mode_wrapper(self, model: str, credentials: dict, 
+                                 prompt_messages: list[PromptMessage], model_parameters: dict, 
+                                 tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
+                                 stream: bool = True, user: str | None = None, callbacks: list[Callback] = None) \
+                            -> LLMResult | Generator:
+        """
+        Wrapper for code block mode
+        """
+        block_prompts = """You should always follow the instructions and output a valid {{block}} object.
+The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
+if you are not sure about the structure.
+
+<instructions>
+{{instructions}}
+</instructions>
+"""
+
+        code_block = model_parameters.get("response_format", "")
+        if not code_block:
+            return self._invoke(
+                model=model,
+                credentials=credentials,
+                prompt_messages=prompt_messages,
+                model_parameters=model_parameters,
+                tools=tools,
+                stop=stop,
+                stream=stream,
+                user=user
+            )
+        
+        model_parameters.pop("response_format")
+        stop = stop or []
+        stop.extend(["\n```", "```\n"])
+        block_prompts = block_prompts.replace("{{block}}", code_block)
+
+        # check if there is a system message
+        if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
+            # override the system message
+            prompt_messages[0] = SystemPromptMessage(
+                content=block_prompts
+                    .replace("{{instructions}}", prompt_messages[0].content)
+            )
+        else:
+            # insert the system message
+            prompt_messages.insert(0, SystemPromptMessage(
+                content=block_prompts
+                    .replace("{{instructions}}", f"Please output a valid {code_block} object.")
+            ))
+
+        mode = self.get_model_mode(model, credentials)
+        if mode == LLMMode.CHAT:
+            if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage):
+                # add ```JSON\n to the last message
+                prompt_messages[-1].content += f"\n```{code_block}\n"
+            else:
+                # append a user message
+                prompt_messages.append(UserPromptMessage(
+                    content=f"```{code_block}\n"
+                ))
+        else:
+            prompt_messages.append(AssistantPromptMessage(content=f"```{code_block}\n"))
+
+        response = self._invoke(
+            model=model,
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
+            tools=tools,
+            stop=stop,
+            stream=stream,
+            user=user
+        )
+
+        if isinstance(response, Generator):
+            return self._code_block_mode_stream_processor_with_backtick(
+                model=model,
+                prompt_messages=prompt_messages,
+                input_generator=response
+            )
+        
+        return response
 
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: Optional[list[PromptMessageTool]] = None) -> int:
@@ -117,7 +200,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         """
         extra_model_kwargs = {}
         if stop:
-            extra_model_kwargs['stop_sequences'] = stop
+            extra_model_kwargs['stop'] = stop
 
         # transform credentials to kwargs for model instance
         credentials_kwargs = self._to_credential_kwargs(credentials)
@@ -131,7 +214,8 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         params = {
             'model': model,
             **model_parameters,
-            **credentials_kwargs
+            **credentials_kwargs,
+            **extra_model_kwargs,
         }
 
         mode = self.get_model_mode(model, credentials)

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-1201.yaml

@@ -57,3 +57,5 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
     required: false
+  - name: response_format
+    use_template: response_format

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-longcontext.yaml

@@ -57,3 +57,5 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
     required: false
+  - name: response_format
+    use_template: response_format

api/core/model_runtime/model_providers/tongyi/llm/qwen-max.yaml

@@ -57,3 +57,5 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
     required: false
+  - name: response_format
+    use_template: response_format

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus.yaml

@@ -56,6 +56,8 @@ parameter_rules:
     help:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.02'
   output: '0.02'

api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo.yaml

@@ -57,6 +57,8 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
     required: false
+  - name: response_format
+    use_template: response_format
 pricing:
   input: '0.008'
   output: '0.008'

api/core/model_runtime/model_providers/wenxin/llm/ernie-bot-4.yaml

@@ -25,6 +25,8 @@ parameter_rules:
     use_template: presence_penalty
   - name: frequency_penalty
     use_template: frequency_penalty
+  - name: response_format
+    use_template: response_format
   - name: disable_search
     label:
       zh_Hans: 禁用搜索

api/core/model_runtime/model_providers/wenxin/llm/ernie-bot-8k.yaml

@@ -25,6 +25,8 @@ parameter_rules:
     use_template: presence_penalty
   - name: frequency_penalty
     use_template: frequency_penalty
+  - name: response_format
+    use_template: response_format
   - name: disable_search
     label:
       zh_Hans: 禁用搜索

api/core/model_runtime/model_providers/wenxin/llm/ernie-bot-turbo.yaml

@@ -25,3 +25,5 @@ parameter_rules:
     use_template: presence_penalty
   - name: frequency_penalty
     use_template: frequency_penalty
+  - name: response_format
+    use_template: response_format

api/core/model_runtime/model_providers/wenxin/llm/ernie-bot.yaml

@@ -34,3 +34,5 @@ parameter_rules:
       zh_Hans: 禁用模型自行进行外部搜索。
       en_US: Disable the model to perform external search.
     required: false
+  - name: response_format
+    use_template: response_format

api/core/model_runtime/model_providers/wenxin/llm/llm.py

@@ -1,6 +1,7 @@
 from collections.abc import Generator
-from typing import cast
+from typing import Optional, Union, cast
 
+from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -29,8 +30,18 @@ from core.model_runtime.model_providers.wenxin.llm.ernie_bot_errors import (
     RateLimitReachedError,
 )
 
+ERNIE_BOT_BLOCK_MODE_PROMPT = """You should always follow the instructions and output a valid {{block}} object.
+The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
+if you are not sure about the structure.
 
-class ErnieBotLarguageModel(LargeLanguageModel):
+<instructions>
+{{instructions}}
+</instructions>
+
+You should also complete the text started with ``` but not tell ``` directly.
+"""
+
+class ErnieBotLargeLanguageModel(LargeLanguageModel):
     def _invoke(self, model: str, credentials: dict, 
                 prompt_messages: list[PromptMessage], model_parameters: dict, 
                 tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
@@ -39,6 +50,62 @@ class ErnieBotLarguageModel(LargeLanguageModel):
         return self._generate(model=model, credentials=credentials, prompt_messages=prompt_messages,
                                 model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
 
+    def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+                           model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
+                           stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
+                           callbacks: list[Callback] = None) -> Union[LLMResult, Generator]:
+        """
+        Code block mode wrapper for invoking large language model
+        """
+        if 'response_format' in model_parameters and model_parameters['response_format'] in ['JSON', 'XML']:
+            response_format = model_parameters['response_format']
+            stop = stop or []
+            self._transform_json_prompts(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user, response_format)
+            model_parameters.pop('response_format')
+            if stream:
+                return self._code_block_mode_stream_processor(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    input_generator=self._invoke(model=model, credentials=credentials, prompt_messages=prompt_messages,
+                                                    model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
+                )
+            
+        return self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+
+    def _transform_json_prompts(self, model: str, credentials: dict, 
+                                prompt_messages: list[PromptMessage], model_parameters: dict, 
+                                tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
+                                stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
+                            -> None:
+        """
+        Transform json prompts to model prompts
+        """
+
+        # check if there is a system message
+        if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
+            # override the system message
+            prompt_messages[0] = SystemPromptMessage(
+                content=ERNIE_BOT_BLOCK_MODE_PROMPT
+                    .replace("{{instructions}}", prompt_messages[0].content)
+                    .replace("{{block}}", response_format)
+            )
+        else:
+            # insert the system message
+            prompt_messages.insert(0, SystemPromptMessage(
+                content=ERNIE_BOT_BLOCK_MODE_PROMPT
+                    .replace("{{instructions}}", f"Please output a valid {response_format} object.")
+                    .replace("{{block}}", response_format)
+            ))
+
+        if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage):
+            # add ```JSON\n to the last message
+            prompt_messages[-1].content += "\n```JSON\n{\n"
+        else:
+            # append a user message
+            prompt_messages.append(UserPromptMessage(
+                content="```JSON\n{\n"
+            ))
+
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: list[PromptMessageTool] | None = None) -> int:
         # tools is not supported yet

api/core/model_runtime/model_providers/zhipuai/llm/llm.py

@@ -19,6 +19,17 @@ from core.model_runtime.model_providers.zhipuai.zhipuai_sdk.types.chat.chat_comp
 from core.model_runtime.model_providers.zhipuai.zhipuai_sdk.types.chat.chat_completion_chunk import ChatCompletionChunk
 from core.model_runtime.utils import helper
 
+GLM_JSON_MODE_PROMPT = """You should always follow the instructions and output a valid JSON object.
+The structure of the JSON object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
+if you are not sure about the structure.
+
+And you should always end the block with a "```" to indicate the end of the JSON object.
+
+<instructions>
+{{instructions}}
+</instructions>
+
+```JSON"""
 
 class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
 
@@ -44,8 +55,42 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         credentials_kwargs = self._to_credential_kwargs(credentials)
 
         # invoke model
+        # stop = stop or []
+        # self._transform_json_prompts(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
         return self._generate(model, credentials_kwargs, prompt_messages, model_parameters, tools, stop, stream, user)
 
+    # def _transform_json_prompts(self, model: str, credentials: dict, 
+    #                             prompt_messages: list[PromptMessage], model_parameters: dict, 
+    #                             tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
+    #                             stream: bool = True, user: str | None = None) \
+    #                         -> None:
+    #     """
+    #     Transform json prompts to model prompts
+    #     """
+    #     if "}\n\n" not in stop:
+    #         stop.append("}\n\n")
+
+    #     # check if there is a system message
+    #     if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
+    #         # override the system message
+    #         prompt_messages[0] = SystemPromptMessage(
+    #             content=GLM_JSON_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content)
+    #         )
+    #     else:
+    #         # insert the system message
+    #         prompt_messages.insert(0, SystemPromptMessage(
+    #             content=GLM_JSON_MODE_PROMPT.replace("{{instructions}}", "Please output a valid JSON object.")
+    #         ))
+    #     # check if the last message is a user message
+    #     if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage):
+    #         # add ```JSON\n to the last message
+    #         prompt_messages[-1].content += "\n```JSON\n"
+    #     else:
+    #         # append a user message
+    #         prompt_messages.append(UserPromptMessage(
+    #             content="```JSON\n"
+    #         ))
+
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: Optional[list[PromptMessageTool]] = None) -> int:
         """
@@ -106,7 +151,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         """
         extra_model_kwargs = {}
         if stop:
-            extra_model_kwargs['stop_sequences'] = stop
+            extra_model_kwargs['stop'] = stop
 
         client = ZhipuAI(
             api_key=credentials_kwargs['api_key']
@@ -256,10 +301,10 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
             ]
 
         if stream:
-            response = client.chat.completions.create(stream=stream, **params)
+            response = client.chat.completions.create(stream=stream, **params, **extra_model_kwargs)
             return self._handle_generate_stream_response(model, credentials_kwargs, tools, response, prompt_messages)
 
-        response = client.chat.completions.create(**params)
+        response = client.chat.completions.create(**params, **extra_model_kwargs)
         return self._handle_generate_response(model, credentials_kwargs, tools, response, prompt_messages)
         
     def _handle_generate_response(self, model: str, 

api/tests/integration_tests/model_runtime/wenxin/test_llm.py

@@ -7,18 +7,18 @@ from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk,
 from core.model_runtime.entities.message_entities import AssistantPromptMessage, SystemPromptMessage, UserPromptMessage
 from core.model_runtime.entities.model_entities import AIModelEntity
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.wenxin.llm.llm import ErnieBotLarguageModel
+from core.model_runtime.model_providers.wenxin.llm.llm import ErnieBotLargeLanguageModel
 
 
 def test_predefined_models():
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
     model_schemas = model.predefined_models()
     assert len(model_schemas) >= 1
     assert isinstance(model_schemas[0], AIModelEntity)
 
 def test_validate_credentials_for_chat_model():
     sleep(3)
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
 
     with pytest.raises(CredentialsValidateFailedError):
         model.validate_credentials(
@@ -39,7 +39,7 @@ def test_validate_credentials_for_chat_model():
 
 def test_invoke_model_ernie_bot():
     sleep(3)
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
 
     response = model.invoke(
         model='ernie-bot',
@@ -67,7 +67,7 @@ def test_invoke_model_ernie_bot():
 
 def test_invoke_model_ernie_bot_turbo():
     sleep(3)
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
 
     response = model.invoke(
         model='ernie-bot-turbo',
@@ -95,7 +95,7 @@ def test_invoke_model_ernie_bot_turbo():
 
 def test_invoke_model_ernie_8k():
     sleep(3)
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
 
     response = model.invoke(
         model='ernie-bot-8k',
@@ -123,7 +123,7 @@ def test_invoke_model_ernie_8k():
 
 def test_invoke_model_ernie_bot_4():
     sleep(3)
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
 
     response = model.invoke(
         model='ernie-bot-4',
@@ -151,7 +151,7 @@ def test_invoke_model_ernie_bot_4():
 
 def test_invoke_stream_model():
     sleep(3)
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
 
     response = model.invoke(
         model='ernie-bot',
@@ -182,7 +182,7 @@ def test_invoke_stream_model():
 
 def test_invoke_model_with_system():
     sleep(3)
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
 
     response = model.invoke(
         model='ernie-bot',
@@ -212,7 +212,7 @@ def test_invoke_model_with_system():
 
 def test_invoke_with_search():
     sleep(3)
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
 
     response = model.invoke(
         model='ernie-bot',
@@ -250,7 +250,7 @@ def test_invoke_with_search():
 
 def test_get_num_tokens():
     sleep(3)
-    model = ErnieBotLarguageModel()
+    model = ErnieBotLargeLanguageModel()
 
     response = model.get_num_tokens(
         model='ernie-bot',