GenAI query classes
The FileNet P8 Content Engine Java™ and .NET APIs or the FileNet P8 Content Services GraphQL API supports the following Content Engine classes:
GenaiBaseQuery
The
GenaiBaseQuery
class enables an API caller to submit a question and generate a response from the watsonx LLM. The base class can be used directly but it does not allow the question to be augmented with document context. For more information, seeGenaiBaseQuery
class in the FNCM documentation.GenaiVectorQuery
The
GenaiVectorQuery
class inherits from theGenaiBaseQuery
class. It enables an API caller to perform a vector query across the vector index for an entire object store. Before any of the document chunks are used for LLM inferencing, or returned to the caller, the Content Platform Engine server verifies whether the caller hasVIEW_CONTENT
permissions for the underlying documents. The set of documents that closely match the input prompt are then returned. For more information, seeGenaiVectorQuery
class in the FNCM documentation.GenaiDocumentQuery
The class inherits from the
GenaiVectorQuery
class. It enables an API caller to perform a vector query that is filtered to include chunks from a single document. The Content Platform Engine server verifies whether the call hasVIEW_CONTENT
access for the underlying document. The set of document chunks that closely match the input prompt are then returned. For more information, seeGenaiDocumentQuery
class in the FNCM documentation.GenaiMultiDocumentQuery
The class inherits from the
GenaiVectorQuery
class. It enables an API caller to perform a vector query that is filtered to include chunks from a specified document list. The Content Platform Engine server verifies whether the call hasVIEW_CONTENT
access for the specified documents. The set of document chunks that closely match the input prompt are then returned. For more information, seeGenaiMultiDocumentQuery
class in the FNCM documentation.GenaiAdhocSummary
The
GenaiAdhocSummary
inherits from theGenaiBaseQuery
class. The class takes a list of documents as input and generates the watsonx summary for the documents by passing a subset of the document content to the LLM. The amount of text taken from each input document depends on the number of input documents and the LLM context window.