trulens.apps.langchain.guardrails

trulens.apps.langchain.guardrails

Classes

WithFeedbackFilterDocuments

Bases: VectorStoreRetriever

Attributes

threshold instance-attribute

threshold: float

A VectorStoreRetriever that filters documents using a minimum threshold on a feedback function before returning them.

PARAMETER	DESCRIPTION
feedback	use this feedback function to score each document. TYPE: Feedback
threshold	and keep documents only if their feedback value is at least this threshold. TYPE: float

Example: "Using TruLens guardrail context filters with Langchain"

```python
from trulens.apps.langchain import WithFeedbackFilterDocuments

# note: feedback function used for guardrail must only return a score, not also reasons
feedback = Feedback(provider.context_relevance).on_input().on(context)

filtered_retriever = WithFeedbackFilterDocuments.of_retriever(
    retriever=retriever,
    feedback=feedback,
    threshold=0.5
)

rag_chain = {"context": filtered_retriever | format_docs, "question": RunnablePassthrough()} | prompt | llm | StrOutputParser()

tru_recorder = TruChain(rag_chain,
    app_name='ChatApplication',
    app_version='filtered_retriever',
)

with tru_recorder as recording:
    llm_response = rag_chain.invoke("What is Task Decomposition?")
```

Functions

of_retriever staticmethod

of_retriever(
    retriever: VectorStoreRetriever, **kwargs: Any
)

Create a new instance of WithFeedbackFilterDocuments based on an existing retriever.

The new instance will:

1. Get relevant documents (like the existing retriever its based on).
2. Evaluate documents with a specified feedback function.
3. Filter out documents that do not meet the minimum threshold.

PARAMETER	DESCRIPTION
retriever	VectorStoreRetriever - the base retriever to use. TYPE: VectorStoreRetriever
**kwargs	additional keyword arguments. TYPE: Any DEFAULT: {}

Returns: - WithFeedbackFilterDocuments: a new instance of WithFeedbackFilterDocuments.

Functions