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.
|
threshold |
and keep documents only if their feedback value is at least this threshold.
|
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:
- Get relevant documents (like the existing retriever its based on).
- Evaluate documents with a specified feedback function.
- Filter out documents that do not meet the minimum threshold.
PARAMETER | DESCRIPTION |
---|---|
retriever |
VectorStoreRetriever - the base retriever to use.
TYPE:
|
**kwargs |
additional keyword arguments.
TYPE:
|
Returns: - WithFeedbackFilterDocuments: a new instance of WithFeedbackFilterDocuments.