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Intrinsic functions related to retrieval-augmented generation.

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Functions

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FUNC check_answerability 

check_answerability(question: str, documents: collections.abc.Iterable[Document], context: ChatContext, backend: AdapterMixin) -> float
Test a user’s question for answerability. Intrinsic function that checks whether the question in the last user turn of a chat can be answered by a provided set of RAG documents. Args:
  • question: Question that the user has posed in response to the last turn in context.
  • documents: Document snippets retrieved that may or may not answer the indicated question.
  • context: Chat context containing the conversation thus far.
  • backend: Backend instance that supports adding the LoRA or aLoRA adapters for answerability checks.
Returns:
  • Answerability score as a floating-point value from 0 to 1.

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FUNC rewrite_question 

rewrite_question(question: str, context: ChatContext, backend: AdapterMixin) -> float
Rewrite a user’s question for retrieval. Intrinsic function that rewrites the question in the next user turn into a self-contained query that can be passed to the retriever. Args:
  • question: Question that the user has posed in response to the last turn in context.
  • context: Chat context containing the conversation thus far.
  • backend: Backend instance that supports adding the LoRA or aLoRA adapters.
Returns:
  • Rewritten version of question.

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FUNC clarify_query 

clarify_query(question: str, documents: collections.abc.Iterable[Document], context: ChatContext, backend: AdapterMixin) -> str
Generate clarification for an ambiguous query. Intrinsic function that determines if a user’s question requires clarification based on the retrieved documents and conversation context, and generates an appropriate clarification question if needed. Args:
  • question: Question that the user has posed.
  • documents: Document snippets retrieved for the question.
  • context: Chat context containing the conversation thus far.
  • backend: Backend instance that supports the adapters that implement this intrinsic.
Returns:
  • Clarification question string (e.g., “Do you mean A or B?”), or
  • the string “CLEAR” if no clarification is needed.

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FUNC find_citations 

find_citations(response: str, documents: collections.abc.Iterable[Document], context: ChatContext, backend: AdapterMixin) -> list[dict]
Find information in documents that supports an assistant response. Intrinsic function that finds sentences in RAG documents that support sentences in a potential assistant response to a user question. Args:
  • response: Potential assistant response.
  • documents: Documents that were used to generate response. These documents should set the doc_id field; otherwise the intrinsic will be unable to specify which document was the source of a given citation.
  • context: Context of the dialog between user and assistant at the point where the user has just asked a question that will be answered with RAG documents.
  • backend: Backend that supports one of the adapters that implements this intrinsic.
Returns:
  • List of records with the following fields: response_begin,
  • response_end, response_text, citation_doc_id, citation_begin,
  • citation_end, citation_text. Begin and end offsets are character
  • offsets into their respective UTF-8 strings.

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FUNC check_context_relevance 

check_context_relevance(question: str, document: Document, context: ChatContext, backend: AdapterMixin) -> float
Test whether a document is relevant to a user’s question. Intrinsic function that checks whether a single document contains part or all of the answer to a user’s question. Does not consider the context in which the question was asked. Args:
  • question: Question that the user has posed.
  • document: A retrieved document snippet.
  • context: The chat up to the point where the user asked a question.
  • backend: Backend instance that supports the adapters that implement this intrinsic.
Returns:
  • Context relevance score as a floating-point value from 0 to 1.

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FUNC flag_hallucinated_content 

flag_hallucinated_content(response: str, documents: collections.abc.Iterable[Document], context: ChatContext, backend: AdapterMixin) -> float
Flag potentially-hallucinated sentences in an agent’s response. Intrinsic function that checks whether the sentences in an agent’s response to a user question are faithful to the retrieved document snippets. Sentences that do not align with the retrieved snippets are flagged as potential hallucinations. Args:
  • response: The assistant’s response to the user’s question in the last turn of context.
  • documents: Document snippets that were used to generate response.
  • context: A chat log that ends with a user asking a question.
  • backend: Backend instance that supports the adapters that implement this intrinsic.
Returns:
  • List of records with the following fields: response_begin,
  • response_end, response_text, faithfulness_likelihood,
  • explanation.

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FUNC rewrite_answer_for_relevance 

rewrite_answer_for_relevance(rewrite_threshold: float = 0.5) -> str
Rewrite an assistant answer to improve relevance to the user’s question. Args:
  • response: The assistant’s response to the user’s question in the last turn of context.
  • documents: Document snippets that were used to generate response.
  • context: A chat log that ends with a user asking a question.
  • backend: Backend instance that supports the adapters that implement this intrinsic.
  • rewrite_threshold: Number between 0.0 and 1.0 that determines how eagerly to skip rewriting the assistant’s answer for relevance. 0.0 means never rewrite and 1.0 means always rewrite.
Returns:
  • Either the original response, or a rewritten version of the original response.