Melhores práticas

Esta seção inclui informações sobre como usar a Embasamento de Contexto de forma eficaz.

Introdução

To start using Context Grounding, follow these steps:

1. Certifique-se de usar o Studio Web ou o Studio Desktop versão 2024.4 ou mais recente.
2. In Automation Cloud^TM, in Integration Service, establish a connection to the UiPath GenAI Activities connector.

   This allows you to use the GenAI Activities in Studio.

3. To create and manage indexes, access the Context Grounding tab in Automation Cloud™ > Admin > AI Trust Layer. You can then query indexes from Autopilot for everyone or from the GenAI Activities.

Context Grounding in UiPath GenAI Activities

Para usar a Embasamento de contexto, você precisa de um conjunto de dados (por exemplo, um grupo de documentos) prontamente disponível. Em seguida, o embasamento de contexto pode interagir com seus dados em três fases:

1. Estabeleça sua origem de dados para o Embasamento de contexto.
   • Context Grounding follows shared folder permissions. Use a folder with appropriate access to manage and query from data.
   • Create a connection to the supported Integration Service data sources or add data to a shared Orchestrator bucket location.
2. Ingerir dados de sua origem de dados para o Embasamento de Contexto.
   • Use the Context Grounding tab in the AI Trust Layer admin to create, sync, and delete indexes.
   • Use the Update Context Grounding Index activity to sync an index you have created. This allows non-admins to help manage the data freshness of an index based on new data.
3. Consulte e confirme com seus dados.
   • Use the Content Generation activity or Autopilot for everyone to query over documents and use information to augment or ground prompts.

Gerenciamento do pipeline de ingestão

You can manage the ingestion pipelines in two ways:

• Through the Context Grounding tab in AI Trust Layer. See Governing contextual data for GenAI features.
• Through the Update Context Grounding Index activity, part of the UiPath GenAI activities package.

Querying data with Context Grounding

The Content Generation activity features two options for working with documents: File resource and Existing index.

Recurso de arquivo

The File resource option allows you to use file variables from previous activities (like a document downloaded from Google Drive) and have Context Grounding perform a just-in-time (JIT) style of RAG. This means it ingests the document into an index, searches across it, augments the prompt, and then deletes the index, so the data is not persistent.

Observação:

• Mantenha esses documentos menores (menos de 50 páginas). As atividades do Integration Service têm uma janela de tempo limite na qual todo o processamento acima deve ocorrer.
• Se você tiver um PDF com imagens digitalizadas, recomendamos usar a opção Document Understanding OCR na atividade Extract Data após ter baixado um arquivo para extrair o texto dessas imagens (já que o Contexto embasamento ainda não suporta imagens). Passe essa saída extracted_data no prompt com esse Recurso de arquivo apontando para seu arquivo baixado.

Índice Existente

The Existing index option allows you to use an index you have previously created through AI Trust Layer. You query across a persistent index into which you've ingested documents from your data source. You can re-use this index as many times as you like until you delete it.

Use the Log Message activity after Content Generation in the workflow sequence to gather detailed information:

• Input the Top Generated Text or Citations string output variables from Content Generation to see the LLM generation response after the workflow execution.
• Input the Citations string variable to see the semantic search results used to influence the generation output. This works only for PDF and JSON data types.