mirror of
https://github.com/hwchase17/langchain.git
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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
This commit is contained in:
Bagatur
GitHub
@@ -0,0 +1,390 @@
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import os
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import warnings
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from datetime import datetime
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from enum import Enum
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from typing import Any, Dict, List, Optional, Tuple, Union
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from uuid import UUID
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from langchain_core.agents import AgentAction, AgentFinish
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from langchain_core.callbacks import BaseCallbackHandler
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from langchain_core.messages import BaseMessage, ChatMessage
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from langchain_core.outputs import Generation, LLMResult
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class LabelStudioMode(Enum):
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"""Label Studio mode enumerator."""
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PROMPT = "prompt"
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CHAT = "chat"
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def get_default_label_configs(
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mode: Union[str, LabelStudioMode],
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) -> Tuple[str, LabelStudioMode]:
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"""Get default Label Studio configs for the given mode.
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Parameters:
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mode: Label Studio mode ("prompt" or "chat")
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Returns: Tuple of Label Studio config and mode
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"""
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_default_label_configs = {
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LabelStudioMode.PROMPT.value: """
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<View>
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<Style>
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.prompt-box {
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background-color: white;
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border-radius: 10px;
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box-shadow: 0px 4px 6px rgba(0, 0, 0, 0.1);
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padding: 20px;
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}
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</Style>
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<View className="root">
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<View className="prompt-box">
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<Text name="prompt" value="$prompt"/>
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</View>
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<TextArea name="response" toName="prompt"
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maxSubmissions="1" editable="true"
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required="true"/>
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</View>
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<Header value="Rate the response:"/>
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<Rating name="rating" toName="prompt"/>
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</View>""",
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LabelStudioMode.CHAT.value: """
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<View>
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<View className="root">
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<Paragraphs name="dialogue"
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value="$prompt"
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layout="dialogue"
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textKey="content"
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nameKey="role"
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granularity="sentence"/>
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<Header value="Final response:"/>
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<TextArea name="response" toName="dialogue"
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maxSubmissions="1" editable="true"
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required="true"/>
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</View>
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<Header value="Rate the response:"/>
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<Rating name="rating" toName="dialogue"/>
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</View>""",
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}
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if isinstance(mode, str):
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mode = LabelStudioMode(mode)
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return _default_label_configs[mode.value], mode
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class LabelStudioCallbackHandler(BaseCallbackHandler):
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"""Label Studio callback handler.
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Provides the ability to send predictions to Label Studio
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for human evaluation, feedback and annotation.
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Parameters:
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api_key: Label Studio API key
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url: Label Studio URL
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project_id: Label Studio project ID
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project_name: Label Studio project name
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project_config: Label Studio project config (XML)
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mode: Label Studio mode ("prompt" or "chat")
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Examples:
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>>> from langchain_community.llms import OpenAI
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>>> from langchain_community.callbacks import LabelStudioCallbackHandler
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>>> handler = LabelStudioCallbackHandler(
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... api_key='<your_key_here>',
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... url='http://localhost:8080',
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... project_name='LangChain-%Y-%m-%d',
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... mode='prompt'
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... )
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>>> llm = OpenAI(callbacks=[handler])
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>>> llm.predict('Tell me a story about a dog.')
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"""
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DEFAULT_PROJECT_NAME: str = "LangChain-%Y-%m-%d"
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def __init__(
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self,
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api_key: Optional[str] = None,
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url: Optional[str] = None,
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project_id: Optional[int] = None,
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project_name: str = DEFAULT_PROJECT_NAME,
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project_config: Optional[str] = None,
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mode: Union[str, LabelStudioMode] = LabelStudioMode.PROMPT,
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):
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super().__init__()
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# Import LabelStudio SDK
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try:
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import label_studio_sdk as ls
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except ImportError:
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raise ImportError(
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f"You're using {self.__class__.__name__} in your code,"
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f" but you don't have the LabelStudio SDK "
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f"Python package installed or upgraded to the latest version. "
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f"Please run `pip install -U label-studio-sdk`"
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f" before using this callback."
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)
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# Check if Label Studio API key is provided
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if not api_key:
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if os.getenv("LABEL_STUDIO_API_KEY"):
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api_key = str(os.getenv("LABEL_STUDIO_API_KEY"))
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else:
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raise ValueError(
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f"You're using {self.__class__.__name__} in your code,"
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f" Label Studio API key is not provided. "
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f"Please provide Label Studio API key: "
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f"go to the Label Studio instance, navigate to "
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f"Account & Settings -> Access Token and copy the key. "
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f"Use the key as a parameter for the callback: "
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f"{self.__class__.__name__}"
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f"(label_studio_api_key='<your_key_here>', ...) or "
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f"set the environment variable LABEL_STUDIO_API_KEY=<your_key_here>"
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)
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self.api_key = api_key
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if not url:
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if os.getenv("LABEL_STUDIO_URL"):
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url = os.getenv("LABEL_STUDIO_URL")
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else:
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warnings.warn(
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f"Label Studio URL is not provided, "
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f"using default URL: {ls.LABEL_STUDIO_DEFAULT_URL}"
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f"If you want to provide your own URL, use the parameter: "
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f"{self.__class__.__name__}"
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f"(label_studio_url='<your_url_here>', ...) "
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f"or set the environment variable LABEL_STUDIO_URL=<your_url_here>"
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)
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url = ls.LABEL_STUDIO_DEFAULT_URL
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self.url = url
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# Maps run_id to prompts
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self.payload: Dict[str, Dict] = {}
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self.ls_client = ls.Client(url=self.url, api_key=self.api_key)
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self.project_name = project_name
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if project_config:
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self.project_config = project_config
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self.mode = None
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else:
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self.project_config, self.mode = get_default_label_configs(mode)
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self.project_id = project_id or os.getenv("LABEL_STUDIO_PROJECT_ID")
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if self.project_id is not None:
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self.ls_project = self.ls_client.get_project(int(self.project_id))
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else:
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project_title = datetime.today().strftime(self.project_name)
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existing_projects = self.ls_client.get_projects(title=project_title)
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if existing_projects:
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self.ls_project = existing_projects[0]
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self.project_id = self.ls_project.id
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else:
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self.ls_project = self.ls_client.create_project(
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title=project_title, label_config=self.project_config
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)
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self.project_id = self.ls_project.id
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self.parsed_label_config = self.ls_project.parsed_label_config
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# Find the first TextArea tag
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# "from_name", "to_name", "value" will be used to create predictions
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self.from_name, self.to_name, self.value, self.input_type = (
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None,
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None,
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None,
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None,
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)
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for tag_name, tag_info in self.parsed_label_config.items():
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if tag_info["type"] == "TextArea":
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self.from_name = tag_name
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self.to_name = tag_info["to_name"][0]
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self.value = tag_info["inputs"][0]["value"]
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self.input_type = tag_info["inputs"][0]["type"]
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break
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if not self.from_name:
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error_message = (
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f'Label Studio project "{self.project_name}" '
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f"does not have a TextArea tag. "
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f"Please add a TextArea tag to the project."
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)
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if self.mode == LabelStudioMode.PROMPT:
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error_message += (
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"\nHINT: go to project Settings -> "
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"Labeling Interface -> Browse Templates"
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' and select "Generative AI -> '
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'Supervised Language Model Fine-tuning" template.'
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)
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else:
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error_message += (
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"\nHINT: go to project Settings -> "
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"Labeling Interface -> Browse Templates"
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" and check available templates under "
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'"Generative AI" section.'
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)
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raise ValueError(error_message)
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def add_prompts_generations(
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self, run_id: str, generations: List[List[Generation]]
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) -> None:
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# Create tasks in Label Studio
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tasks = []
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prompts = self.payload[run_id]["prompts"]
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model_version = (
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self.payload[run_id]["kwargs"]
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.get("invocation_params", {})
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.get("model_name")
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)
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for prompt, generation in zip(prompts, generations):
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tasks.append(
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{
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"data": {
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self.value: prompt,
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"run_id": run_id,
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},
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"predictions": [
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{
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"result": [
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{
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"from_name": self.from_name,
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"to_name": self.to_name,
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"type": "textarea",
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"value": {"text": [g.text for g in generation]},
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}
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],
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"model_version": model_version,
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}
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],
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}
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)
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self.ls_project.import_tasks(tasks)
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def on_llm_start(
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self,
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serialized: Dict[str, Any],
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prompts: List[str],
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**kwargs: Any,
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) -> None:
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"""Save the prompts in memory when an LLM starts."""
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if self.input_type != "Text":
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raise ValueError(
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f'\nLabel Studio project "{self.project_name}" '
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f"has an input type <{self.input_type}>. "
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f'To make it work with the mode="chat", '
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f"the input type should be <Text>.\n"
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f"Read more here https://labelstud.io/tags/text"
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)
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run_id = str(kwargs["run_id"])
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self.payload[run_id] = {"prompts": prompts, "kwargs": kwargs}
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def _get_message_role(self, message: BaseMessage) -> str:
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"""Get the role of the message."""
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if isinstance(message, ChatMessage):
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return message.role
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else:
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return message.__class__.__name__
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def on_chat_model_start(
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self,
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serialized: Dict[str, Any],
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messages: List[List[BaseMessage]],
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*,
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run_id: UUID,
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parent_run_id: Optional[UUID] = None,
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tags: Optional[List[str]] = None,
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metadata: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> Any:
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"""Save the prompts in memory when an LLM starts."""
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if self.input_type != "Paragraphs":
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raise ValueError(
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f'\nLabel Studio project "{self.project_name}" '
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f"has an input type <{self.input_type}>. "
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f'To make it work with the mode="chat", '
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f"the input type should be <Paragraphs>.\n"
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f"Read more here https://labelstud.io/tags/paragraphs"
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)
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prompts = []
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for message_list in messages:
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dialog = []
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for message in message_list:
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dialog.append(
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{
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"role": self._get_message_role(message),
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"content": message.content,
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}
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)
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prompts.append(dialog)
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self.payload[str(run_id)] = {
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"prompts": prompts,
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"tags": tags,
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"metadata": metadata,
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"run_id": run_id,
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"parent_run_id": parent_run_id,
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"kwargs": kwargs,
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}
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def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
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"""Do nothing when a new token is generated."""
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pass
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def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
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"""Create a new Label Studio task for each prompt and generation."""
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run_id = str(kwargs["run_id"])
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# Submit results to Label Studio
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self.add_prompts_generations(run_id, response.generations)
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# Pop current run from `self.runs`
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self.payload.pop(run_id)
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def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
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"""Do nothing when LLM outputs an error."""
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pass
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def on_chain_start(
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self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
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) -> None:
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pass
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def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
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pass
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def on_chain_error(self, error: BaseException, **kwargs: Any) -> None:
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"""Do nothing when LLM chain outputs an error."""
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pass
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def on_tool_start(
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self,
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serialized: Dict[str, Any],
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input_str: str,
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**kwargs: Any,
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) -> None:
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"""Do nothing when tool starts."""
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pass
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def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
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"""Do nothing when agent takes a specific action."""
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pass
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def on_tool_end(
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self,
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output: str,
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observation_prefix: Optional[str] = None,
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llm_prefix: Optional[str] = None,
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**kwargs: Any,
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) -> None:
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"""Do nothing when tool ends."""
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pass
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def on_tool_error(self, error: BaseException, **kwargs: Any) -> None:
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"""Do nothing when tool outputs an error."""
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pass
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def on_text(self, text: str, **kwargs: Any) -> None:
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"""Do nothing"""
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pass
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def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
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"""Do nothing"""
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pass
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