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In Retrieval-Augmented Generation (RAG), the retrieval step returns from a (proprietary) knowledge base (a.k.a. corpus) a list of documents relevant to the user query, then the generation step adds the retrieved documents to the prompt context to improve response accuracy of the Large Language Model (LLM). The IDs of the retrieval documents along with the relevance scores, if present, can be imported into Phoenix as follows.

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Dataframe

Below shows only the relevant subsection of the dataframe. The retrieved_document_ids should matched the ids in the corpus data. Note that for each row, the list under the relevance_scores column have a matching length as the one under the retrievals column. But it’s not necessary for all retrieval lists to have the same length.
queryembeddingretrieved_document_idsrelevance_scores
who was the first person that walked on the moon[-0.0126, 0.0039, 0.0217, …[7395, 567965, 323794, …[11.30, 7.67, 5.85, …
who was the 15th prime minister of australia[0.0351, 0.0632, -0.0609, …[38906, 38909, 38912, …[11.28, 9.10, 8.39, …
why is amino group in aniline an ortho para di…[-0.0431, -0.0407, -0.0597, …[779579, 563725, 309367, …[-10.89, -10.90, -10.94, …

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Schema

Both the retrievals and scores are grouped under prompt_column_names along with the embedding of the query. 
primary_schema = Schema(
    prediction_id_column_name="id",
    prompt_column_names=RetrievalEmbeddingColumnNames(
        vector_column_name="embedding",
        raw_data_column_name="query",
        context_retrieval_ids_column_name="retrieved_document_ids",
        context_retrieval_scores_column_name="relevance_scores",
    )
)

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Inferences

Define the inferences by pairing the dataframe with the schema. 
primary_inferences = px.Inferences(primary_dataframe, primary_schema)

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Application

session = px.launch_app(primary_inferences)