URL, API key and OpenAI API key:import osos.environ['LABEL_STUDIO_URL'] = '<YOUR-LABEL-STUDIO-URL>'  # e.g. http://localhost:8080os.environ['LABEL_STUDIO_API_KEY'] = '<YOUR-LABEL-STUDIO-API-KEY>'os.environ['OPENAI_API_KEY'] = '<YOUR-OPENAI-API-KEY>'Collecting LLMs prompts and responses‚ÄãThe data used for labeling is stored in projects within Label Studio. Every project is identified by an XML configuration that details the specifications for input and output data. Create a project that takes human input in text format and outputs an editable LLM response in a text area:<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>To create a project in Label Studio, click on the "Create" button. Enter a name for your project in the "Project Name" field, such as My Project.Navigate to Labeling Setup > Custom Template and paste the XML configuration provided above.You can collect input LLM prompts and output responses in a LabelStudio project, connecting it via LabelStudioCallbackHandler:from langchain.llms import OpenAIfrom langchain.callbacks import LabelStudioCallbackHandlerllm = OpenAI(    temperature=0,    callbacks=[        LabelStudioCallbackHandler(            project_name="My Project"    )])print(llm("Tell me a joke"))In the Label Studio, open My Project. You will see the prompts, responses, and metadata like the model name. Collecting Chat model Dialogues‚ÄãYou can also track and display full chat dialogues in LabelStudio, with the ability to rate and modify the last response:Open Label Studio and click on the "Create"
Label Studio is an open-source data labeling platform that provides LangChain with flexibility when it comes to labeling data for fine-tuning large language models (LLMs). It also enables the preparation of custom training data and the collection and evaluation of responses through human feedback.
Label Studio is an open-source data labeling platform that provides LangChain with flexibility when it comes to labeling data for fine-tuning large language models (LLMs). It also enables the preparation of custom training data and the collection and evaluation of responses through human feedback. ->: URL, API key and OpenAI API key:import osos.environ['LABEL_STUDIO_URL'] = '<YOUR-LABEL-STUDIO-URL>'  # e.g. http://localhost:8080os.environ['LABEL_STUDIO_API_KEY'] = '<YOUR-LABEL-STUDIO-API-KEY>'os.environ['OPENAI_API_KEY'] = '<YOUR-OPENAI-API-KEY>'Collecting LLMs prompts and responses‚ÄãThe data used for labeling is stored in projects within Label Studio. Every project is identified by an XML configuration that details the specifications for input and output data. Create a project that takes human input in text format and outputs an editable LLM response in a text area:<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>To create a project in Label Studio, click on the "Create" button. Enter a name for your project in the "Project Name" field, such as My Project.Navigate to Labeling Setup > Custom Template and paste the XML configuration provided above.You can collect input LLM prompts and output responses in a LabelStudio project, connecting it via LabelStudioCallbackHandler:from langchain.llms import OpenAIfrom langchain.callbacks import LabelStudioCallbackHandlerllm = OpenAI(    temperature=0,    callbacks=[        LabelStudioCallbackHandler(            project_name="My Project"    )])print(llm("Tell me a joke"))In the Label Studio, open My Project. You will see the prompts, responses, and metadata like the model name. Collecting Chat model Dialogues‚ÄãYou can also track and display full chat dialogues in LabelStudio, with the ability to rate and modify the last response:Open Label Studio and click on the "Create"