feat: remove langchain from output parsers (#3473)

api/core/entities/message_entities.py

 import enum
-from typing import Any, cast
+from typing import Any
 
-from langchain.schema import AIMessage, BaseMessage, FunctionMessage, HumanMessage, SystemMessage
 from pydantic import BaseModel
 
-from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
-    ImagePromptMessageContent,
-    PromptMessage,
-    SystemPromptMessage,
-    TextPromptMessageContent,
-    ToolPromptMessage,
-    UserPromptMessage,
-)
-
 
 class PromptMessageFileType(enum.Enum):
     IMAGE = 'image'
 
     type: PromptMessageFileType = PromptMessageFileType.IMAGE
     detail: DETAIL = DETAIL.LOW
-
-
-class LCHumanMessageWithFiles(HumanMessage):
-    # content: Union[str, list[Union[str, Dict]]]
-    content: str
-    files: list[PromptMessageFile]
-
-
-def lc_messages_to_prompt_messages(messages: list[BaseMessage]) -> list[PromptMessage]:
-    prompt_messages = []
-    for message in messages:
-        if isinstance(message, HumanMessage):
-            if isinstance(message, LCHumanMessageWithFiles):
-                file_prompt_message_contents = []
-                for file in message.files:
-                    if file.type == PromptMessageFileType.IMAGE:
-                        file = cast(ImagePromptMessageFile, file)
-                        file_prompt_message_contents.append(ImagePromptMessageContent(
-                            data=file.data,
-                            detail=ImagePromptMessageContent.DETAIL.HIGH
-                            if file.detail.value == "high" else ImagePromptMessageContent.DETAIL.LOW
-                        ))
-
-                prompt_message_contents = [TextPromptMessageContent(data=message.content)]
-                prompt_message_contents.extend(file_prompt_message_contents)
-
-                prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
-            else:
-                prompt_messages.append(UserPromptMessage(content=message.content))
-        elif isinstance(message, AIMessage):
-            message_kwargs = {
-                'content': message.content
-            }
-
-            if 'function_call' in message.additional_kwargs:
-                message_kwargs['tool_calls'] = [
-                    AssistantPromptMessage.ToolCall(
-                        id=message.additional_kwargs['function_call']['id'],
-                        type='function',
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                            name=message.additional_kwargs['function_call']['name'],
-                            arguments=message.additional_kwargs['function_call']['arguments']
-                        )
-                    )
-                ]
-
-            prompt_messages.append(AssistantPromptMessage(**message_kwargs))
-        elif isinstance(message, SystemMessage):
-            prompt_messages.append(SystemPromptMessage(content=message.content))
-        elif isinstance(message, FunctionMessage):
-            prompt_messages.append(ToolPromptMessage(content=message.content, tool_call_id=message.name))
-
-    return prompt_messages
-
-
-def prompt_messages_to_lc_messages(prompt_messages: list[PromptMessage]) -> list[BaseMessage]:
-    messages = []
-    for prompt_message in prompt_messages:
-        if isinstance(prompt_message, UserPromptMessage):
-            if isinstance(prompt_message.content, str):
-                messages.append(HumanMessage(content=prompt_message.content))
-            else:
-                message_contents = []
-                for content in prompt_message.content:
-                    if isinstance(content, TextPromptMessageContent):
-                        message_contents.append(content.data)
-                    elif isinstance(content, ImagePromptMessageContent):
-                        message_contents.append({
-                            'type': 'image',
-                            'data': content.data,
-                            'detail': content.detail.value
-                        })
-
-                messages.append(HumanMessage(content=message_contents))
-        elif isinstance(prompt_message, AssistantPromptMessage):
-            message_kwargs = {
-                'content': prompt_message.content
-            }
-
-            if prompt_message.tool_calls:
-                message_kwargs['additional_kwargs'] = {
-                    'function_call': {
-                        'id': prompt_message.tool_calls[0].id,
-                        'name': prompt_message.tool_calls[0].function.name,
-                        'arguments': prompt_message.tool_calls[0].function.arguments
-                    }
-                }
-
-            messages.append(AIMessage(**message_kwargs))
-        elif isinstance(prompt_message, SystemPromptMessage):
-            messages.append(SystemMessage(content=prompt_message.content))
-        elif isinstance(prompt_message, ToolPromptMessage):
-            messages.append(FunctionMessage(name=prompt_message.tool_call_id, content=prompt_message.content))
-
-    return messages

api/core/llm_generator/llm_generator.py

 import json
 import logging
 
-from langchain.schema import OutputParserException
-
+from core.llm_generator.output_parser.errors import OutputParserException
 from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
 from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
 from core.llm_generator.prompts import CONVERSATION_TITLE_PROMPT, GENERATOR_QA_PROMPT

api/core/llm_generator/output_parser/errors.py

+class OutputParserException(Exception):
+    pass

api/core/llm_generator/output_parser/rule_config_generator.py

 from typing import Any
 
-from langchain.schema import BaseOutputParser, OutputParserException
-
+from core.llm_generator.output_parser.errors import OutputParserException
 from core.llm_generator.prompts import RULE_CONFIG_GENERATE_TEMPLATE
 from libs.json_in_md_parser import parse_and_check_json_markdown
 
 
-class RuleConfigGeneratorOutputParser(BaseOutputParser):
+class RuleConfigGeneratorOutputParser:
 
     def get_format_instructions(self) -> str:
         return RULE_CONFIG_GENERATE_TEMPLATE

api/core/llm_generator/output_parser/suggested_questions_after_answer.py

 import re
 from typing import Any
 
-from langchain.schema import BaseOutputParser
-
 from core.llm_generator.prompts import SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT
 
 
-class SuggestedQuestionsAfterAnswerOutputParser(BaseOutputParser):
+class SuggestedQuestionsAfterAnswerOutputParser:
 
     def get_format_instructions(self) -> str:
         return SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT

api/core/tools/provider/builtin/twilio/tools/send_message.py

     and the environment variables ``TWILIO_ACCOUNT_SID``, ``TWILIO_AUTH_TOKEN``, and
     ``TWILIO_FROM_NUMBER``, or pass `account_sid`, `auth_token`, and `from_number` as
     named parameters to the constructor.
-
-    Example:
-        .. code-block:: python
-
-            from langchain.utilities.twilio import TwilioAPIWrapper
-            twilio = TwilioAPIWrapper(
-                account_sid="ACxxx",
-                auth_token="xxx",
-                from_number="+10123456789"
-            )
-            twilio.run('test', '+12484345508')
     """
 
     client: Any  #: :meta private:

api/libs/json_in_md_parser.py

 import json
 
-from langchain.schema import OutputParserException
+from core.llm_generator.output_parser.errors import OutputParserException
 
 
 def parse_json_markdown(json_string: str) -> dict: