langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler¶
class langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler(logger: Logger, handler: Any)[source]¶
Callback Handler for logging to WhyLabs. This callback handler utilizes
langkit to extract features from the prompts & responses when interacting with
an LLM. These features can be used to guardrail, evaluate, and observe interactions
over time to detect issues relating to hallucinations, prompt engineering,
or output validation. LangKit is an LLM monitoring toolkit developed by WhyLabs.
Here are some examples of what can be monitored with LangKit:
* Text Quality
readability score
complexity and grade scores
Text Relevance
- Similarity scores between prompt/responses
- Similarity scores against user-defined themes
- Topic classification
Security and Privacy
- patterns - count of strings matching a user-defined regex pattern group
- jailbreaks - similarity scores with respect to known jailbreak attempts
- prompt injection - similarity scores with respect to known prompt attacks
- refusals - similarity scores with respect to known LLM refusal responses
Sentiment and Toxicity
- sentiment analysis
- toxicity analysis
For more information, see https://docs.whylabs.ai/docs/language-model-monitoring
or check out the LangKit repo here: https://github.com/whylabs/langkit
—
:param api_key: WhyLabs API key. Optional because the preferred
way to specify the API key is with environment variable
WHYLABS_API_KEY.
Parameters
org_id (Optional[str]) – WhyLabs organization id to write profiles to.
Optional because the preferred way to specify the organization id is
with environment variable WHYLABS_DEFAULT_ORG_ID.
dataset_id (Optional[str]) – WhyLabs dataset id to write profiles to.
Optional because the preferred way to specify the dataset id is