Join the Gemini API Developer Competition! Learn more

google.ai.generativelanguage.GenerateAnswerResponse

Response from the model for a grounded answer.

Main aliases

google.ai.generativelanguage_v1beta.GenerateAnswerResponse

Attributes
`answer`	`google.ai.generativelanguage.Candidate` Candidate answer from the model. Note: The model always attempts to provide a grounded answer, even when the answer is unlikely to be answerable from the given passages. In that case, a low-quality or ungrounded answer may be provided, along with a low `answerable_probability`.
`answerable_probability`	`float` Output only. The model's estimate of the probability that its answer is correct and grounded in the input passages. A low answerable_probability indicates that the answer might not be grounded in the sources. When `answerable_probability` is low, some clients may wish to: Display a message to the effect of "We couldn’t answer that question" to the user. Fall back to a general-purpose LLM that answers the question from world knowledge. The threshold and nature of such fallbacks will depend on individual clients’ use cases. 0.5 is a good starting threshold.
`input_feedback`	`google.ai.generativelanguage.GenerateAnswerResponse.InputFeedback` Output only. Feedback related to the input data used to answer the question, as opposed to model-generated response to the question. "Input data" can be one or more of the following: Question specified by the last entry in `GenerateAnswerRequest.content` Conversation history specified by the other entries in `GenerateAnswerRequest.content` Grounding sources (`GenerateAnswerRequest.semantic_retriever` or `GenerateAnswerRequest.inline_passages`)

Attributes

answer

Candidate answer from the model.

answerable_probability

float

Output only. The model's estimate of the probability that its answer is correct and grounded in the input passages.

A low answerable_probability indicates that the answer might not be grounded in the sources.

When answerable_probability is low, some clients may wish to:

Display a message to the effect of "We couldn’t answer that question" to the user.
Fall back to a general-purpose LLM that answers the question from world knowledge. The threshold and nature of such fallbacks will depend on individual clients’ use cases. 0.5 is a good starting threshold.

input_feedback

Output only. Feedback related to the input data used to answer the question, as opposed to model-generated response to the question.

"Input data" can be one or more of the following:

Question specified by the last entry in GenerateAnswerRequest.content
Conversation history specified by the other entries in GenerateAnswerRequest.content
Grounding sources (GenerateAnswerRequest.semantic_retriever or GenerateAnswerRequest.inline_passages)