Source code for langchain_community.callbacks.labelstudio_callback

import os
import warnings
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Union
from uuid import UUID

from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage, ChatMessage
from langchain_core.outputs import Generation, LLMResult

[docs]class LabelStudioMode(Enum): """Label Studio mode enumerator.""" PROMPT = "prompt" CHAT = "chat"
[docs]def get_default_label_configs( mode: Union[str, LabelStudioMode], ) -> Tuple[str, LabelStudioMode]: """Get default Label Studio configs for the given mode. Parameters: mode: Label Studio mode ("prompt" or "chat") Returns: Tuple of Label Studio config and mode """ _default_label_configs = { LabelStudioMode.PROMPT.value: """ <View> <Style> .prompt-box { background-color: white; border-radius: 10px; box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1); padding: 20px; } </Style> <View className="root"> <View className="prompt-box"> <Text name="prompt" value="$prompt"/> </View> <TextArea name="response" toName="prompt" maxSubmissions="1" editable="true" required="true"/> </View> <Header value="Rate the response:"/> <Rating name="rating" toName="prompt"/> </View>""", LabelStudioMode.CHAT.value: """ <View> <View className="root"> <Paragraphs name="dialogue" value="$prompt" layout="dialogue" textKey="content" nameKey="role" granularity="sentence"/> <Header value="Final response:"/> <TextArea name="response" toName="dialogue" maxSubmissions="1" editable="true" required="true"/> </View> <Header value="Rate the response:"/> <Rating name="rating" toName="dialogue"/> </View>""", } if isinstance(mode, str): mode = LabelStudioMode(mode) return _default_label_configs[mode.value], mode
[docs]class LabelStudioCallbackHandler(BaseCallbackHandler): """Label Studio callback handler. Provides the ability to send predictions to Label Studio for human evaluation, feedback and annotation. Parameters: api_key: Label Studio API key url: Label Studio URL project_id: Label Studio project ID project_name: Label Studio project name project_config: Label Studio project config (XML) mode: Label Studio mode ("prompt" or "chat") Examples: >>> from langchain_community.llms import OpenAI >>> from langchain_community.callbacks import LabelStudioCallbackHandler >>> handler = LabelStudioCallbackHandler( ... api_key='<your_key_here>', ... url='http://localhost:8080', ... project_name='LangChain-%Y-%m-%d', ... mode='prompt' ... ) >>> llm = OpenAI(callbacks=[handler]) >>> llm.invoke('Tell me a story about a dog.') """ DEFAULT_PROJECT_NAME: str = "LangChain-%Y-%m-%d"
[docs] def __init__( self, api_key: Optional[str] = None, url: Optional[str] = None, project_id: Optional[int] = None, project_name: str = DEFAULT_PROJECT_NAME, project_config: Optional[str] = None, mode: Union[str, LabelStudioMode] = LabelStudioMode.PROMPT, ): super().__init__() # Import LabelStudio SDK try: import label_studio_sdk as ls except ImportError: raise ImportError( f"You're using {self.__class__.__name__} in your code," f" but you don't have the LabelStudio SDK " f"Python package installed or upgraded to the latest version. " f"Please run `pip install -U label-studio-sdk`" f" before using this callback." ) # Check if Label Studio API key is provided if not api_key: if os.getenv("LABEL_STUDIO_API_KEY"): api_key = str(os.getenv("LABEL_STUDIO_API_KEY")) else: raise ValueError( f"You're using {self.__class__.__name__} in your code," f" Label Studio API key is not provided. " f"Please provide Label Studio API key: " f"go to the Label Studio instance, navigate to " f"Account & Settings -> Access Token and copy the key. " f"Use the key as a parameter for the callback: " f"{self.__class__.__name__}" f"(label_studio_api_key='<your_key_here>', ...) or " f"set the environment variable LABEL_STUDIO_API_KEY=<your_key_here>" ) self.api_key = api_key if not url: if os.getenv("LABEL_STUDIO_URL"): url = os.getenv("LABEL_STUDIO_URL") else: warnings.warn( f"Label Studio URL is not provided, " f"using default URL: {ls.LABEL_STUDIO_DEFAULT_URL}" f"If you want to provide your own URL, use the parameter: " f"{self.__class__.__name__}" f"(label_studio_url='<your_url_here>', ...) " f"or set the environment variable LABEL_STUDIO_URL=<your_url_here>" ) url = ls.LABEL_STUDIO_DEFAULT_URL self.url = url # Maps run_id to prompts self.payload: Dict[str, Dict] = {} self.ls_client = ls.Client(url=self.url, api_key=self.api_key) self.project_name = project_name if project_config: self.project_config = project_config self.mode = None else: self.project_config, self.mode = get_default_label_configs(mode) self.project_id = project_id or os.getenv("LABEL_STUDIO_PROJECT_ID") if self.project_id is not None: self.ls_project = self.ls_client.get_project(int(self.project_id)) else: project_title = existing_projects = self.ls_client.get_projects(title=project_title) if existing_projects: self.ls_project = existing_projects[0] self.project_id = else: self.ls_project = self.ls_client.create_project( title=project_title, label_config=self.project_config ) self.project_id = self.parsed_label_config = self.ls_project.parsed_label_config # Find the first TextArea tag # "from_name", "to_name", "value" will be used to create predictions self.from_name, self.to_name, self.value, self.input_type = ( None, None, None, None, ) for tag_name, tag_info in self.parsed_label_config.items(): if tag_info["type"] == "TextArea": self.from_name = tag_name self.to_name = tag_info["to_name"][0] self.value = tag_info["inputs"][0]["value"] self.input_type = tag_info["inputs"][0]["type"] break if not self.from_name: error_message = ( f'Label Studio project "{self.project_name}" ' f"does not have a TextArea tag. " f"Please add a TextArea tag to the project." ) if self.mode == LabelStudioMode.PROMPT: error_message += ( "\nHINT: go to project Settings -> " "Labeling Interface -> Browse Templates" ' and select "Generative AI -> ' 'Supervised Language Model Fine-tuning" template.' ) else: error_message += ( "\nHINT: go to project Settings -> " "Labeling Interface -> Browse Templates" " and check available templates under " '"Generative AI" section.' ) raise ValueError(error_message)
[docs] def add_prompts_generations( self, run_id: str, generations: List[List[Generation]] ) -> None: # Create tasks in Label Studio tasks = [] prompts = self.payload[run_id]["prompts"] model_version = ( self.payload[run_id]["kwargs"] .get("invocation_params", {}) .get("model_name") ) for prompt, generation in zip(prompts, generations): tasks.append( { "data": { self.value: prompt, "run_id": run_id, }, "predictions": [ { "result": [ { "from_name": self.from_name, "to_name": self.to_name, "type": "textarea", "value": {"text": [g.text for g in generation]}, } ], "model_version": model_version, } ], } ) self.ls_project.import_tasks(tasks)
[docs] def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any, ) -> None: """Save the prompts in memory when an LLM starts.""" if self.input_type != "Text": raise ValueError( f'\nLabel Studio project "{self.project_name}" ' f"has an input type <{self.input_type}>. " f'To make it work with the mode="chat", ' f"the input type should be <Text>.\n" f"Read more here" ) run_id = str(kwargs["run_id"]) self.payload[run_id] = {"prompts": prompts, "kwargs": kwargs}
def _get_message_role(self, message: BaseMessage) -> str: """Get the role of the message.""" if isinstance(message, ChatMessage): return message.role else: return message.__class__.__name__
[docs] def on_chat_model_start( self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> Any: """Save the prompts in memory when an LLM starts.""" if self.input_type != "Paragraphs": raise ValueError( f'\nLabel Studio project "{self.project_name}" ' f"has an input type <{self.input_type}>. " f'To make it work with the mode="chat", ' f"the input type should be <Paragraphs>.\n" f"Read more here" ) prompts = [] for message_list in messages: dialog = [] for message in message_list: dialog.append( { "role": self._get_message_role(message), "content": message.content, } ) prompts.append(dialog) self.payload[str(run_id)] = { "prompts": prompts, "tags": tags, "metadata": metadata, "run_id": run_id, "parent_run_id": parent_run_id, "kwargs": kwargs, }
[docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None: """Do nothing when a new token is generated.""" pass
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Create a new Label Studio task for each prompt and generation.""" run_id = str(kwargs["run_id"]) # Submit results to Label Studio self.add_prompts_generations(run_id, response.generations) # Pop current run from `self.runs` self.payload.pop(run_id)
[docs] def on_llm_error(self, error: BaseException, **kwargs: Any) -> None: """Do nothing when LLM outputs an error.""" pass
[docs] def on_chain_start( self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any ) -> None: pass
[docs] def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: pass
[docs] def on_chain_error(self, error: BaseException, **kwargs: Any) -> None: """Do nothing when LLM chain outputs an error.""" pass
[docs] def on_tool_start( self, serialized: Dict[str, Any], input_str: str, **kwargs: Any, ) -> None: """Do nothing when tool starts.""" pass
[docs] def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: """Do nothing when agent takes a specific action.""" pass
[docs] def on_tool_end( self, output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any, ) -> None: """Do nothing when tool ends.""" pass
[docs] def on_tool_error(self, error: BaseException, **kwargs: Any) -> None: """Do nothing when tool outputs an error.""" pass
[docs] def on_text(self, text: str, **kwargs: Any) -> None: """Do nothing""" pass
[docs] def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None: """Do nothing""" pass