langchain-halu
A lightweight open-source Python library released under the Apache-2.0 license that integrates LLM hallucination detection into LangChain LCEL pipelines via a Runnable interface. It supports three operational strategies: annotation, interception, and automatic retry.
Timeline (last 90 days)
langchain-halu is an open-source project licensed under Apache-2.0 that integrates LLM hallucination detection into LangChain call chains.
langchain-halu provides small Runnables that can be inserted in-place within an LCEL chain to perform hallucination checks without refactoring existing call logic.
langchain-halu offers three downstream processing strategies: annotate (annotating hallucination probability scores), gate (blocking content determined to be hallucinations), and auto-retry (automatically retrying generation with feedback information).
In high-throughput scenarios, annotate should be prioritized to avoid latency caused by retries, while gate should be mandatorily enabled in compliance scenarios.
The detector requires a free API key to use, and its detection capability relies on a remote service, while the integration layer code is fully open-source and transparent.
langchain-halu provides a probability signal rather than a fact-checker and cannot verify the truth or falsity of content.
langchain-halu currently supports only English and works best on complete responses, with limited support for streaming or fragmented output.
Both the detector and the integration layer of langchain-halu were developed by the same individual developer.
The reliability of LLM applications is moving from the model level down to the engineering level, where hallucination outputs are guarded against through detection, interception, and retry mechanisms within the call chain.
All Facts (9)
langchain-halu is an open-source project licensed under Apache-2.0 that integrates LLM hallucination detection into LangChain call chains.
50%Unverifiedlangchain-halu provides small Runnables that can be inserted in-place within an LCEL chain to perform hallucination checks without refactoring existing call logic.
50%Unverifiedlangchain-halu offers three downstream processing strategies: annotate (annotating hallucination probability scores), gate (blocking content determined to be hallucinations), and auto-retry (automatically retrying generation with feedback information).
50%UnverifiedIn high-throughput scenarios, annotate should be prioritized to avoid latency caused by retries, while gate should be mandatorily enabled in compliance scenarios.
50%UnverifiedThe detector requires a free API key to use, and its detection capability relies on a remote service, while the integration layer code is fully open-source and transparent.
50%Unverifiedlangchain-halu provides a probability signal rather than a fact-checker and cannot verify the truth or falsity of content.
50%Unverifiedlangchain-halu currently supports only English and works best on complete responses, with limited support for streaming or fragmented output.
50%UnverifiedBoth the detector and the integration layer of langchain-halu were developed by the same individual developer.
50%UnverifiedThe reliability of LLM applications is moving from the model level down to the engineering level, where hallucination outputs are guarded against through detection, interception, and retry mechanisms within the call chain.
50%