Prompt and Response (LLM)
How to import prompt and response from Large Large Model (LLM)
For the Retrieval-Augmented Generation (RAG) use case, see the Retrieval section.
Dataframe
Below shows a relevant subsection of the dataframe. The embedding
of the prompt is also shown.
prompt
embedding
response
who was the first person that walked on the moon
[-0.0126, 0.0039, 0.0217, ...
Neil Alden Armstrong
who was the 15th prime minister of australia
[0.0351, 0.0632, -0.0609, ...
Francis Michael Forde
Schema
See Retrieval for the Retrieval-Augmented Generation (RAG) use case where relevant documents are retrieved for the question before constructing the context for the LLM.
primary_schema = Schema(
prediction_id_column_name="id",
prompt_column_names=EmbeddingColumnNames(
vector_column_name="embedding",
raw_data_column_name="prompt",
)
response_column_names="response",
)
Inferences
Define the inferences by pairing the dataframe with the schema.
primary_inferences = px.Inferences(primary_dataframe, primary_schema)
Application
session = px.launch_app(primary_inferences)
Last updated
Was this helpful?