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Promptly

The Promptly provider includes processors that are high level abstractions built on top of processors/models from other providers. For example, it includes processors like Text-Chat built on top of OpenAI's chat completions API and includes data retrieval from vector store.

Text-Chat​

The Text-Chat processor is a high level abstraction built on top of OpenAI's chat completions API. It allows you to chat with an AI model using a simple prompt-response interface. It also includes data retrieval from vector store.

Input​

  • question: The question to ask the AI model.
  • search_filters: The search filters to use to retrieve data from the vector store as a string. It is of the format key1 == value1 || key2 == value2 or key1 == value1 && key2 == value2.

Configuration​

datasources: List of datasource UUIDs to use to retrieve data from the vector store for the asked question. If not provided, it will not provide any context to the LLM model.

  • model: LLM model to use for the chat.
  • system_messsage_prefix: Prefix to use for system message to the LLM.
  • instructions: Instructions to pass in the messages to the LLM.
  • documents_count: Maximum number of chunks of data to retrieve from the vector store for the asked question.
  • chat_history_limit: Maximum number of chat history messages to save in a session and pass to the LLM in the next prompt.
  • temperature: Temperature to use for the LLM.
  • use_azure_if_available: Use Azure's OpenAI models if a key is configured in the settings or in the organization that the user is part of.
  • chat_history_in_doc_search: Include chat history in the search query to the vector store.
  • show_citations: Cites the sources used to generate the answer.
  • citation_instructions: Instructions to pass in the messages to the LLM for citations. This can be used to control how the citations are generated and presented.

Output​

  • answer: The answer from the AI model.
  • citations: The list citations for the answer.

The Datasource search processor allows you to search for data in the vector store using a simple prompt-response interface and optional metadata filtering.

Input​

  • query: The query to search in the vector store.

Configuration​

  • datasources: List of datasource UUIDs to use to retrieve data from the vector store for the asked query.
  • document_limit: Maximum number of documents to retrieve from the vector store for the asked query.
  • search_filters: The search filters to use to retrieve data from the vector store as a string. It is of the format key1 == value1 || key2 == value2 or key1 == value1 && key2 == value2.

Output​

  • answers: Array of documents matching the query.
  • answers_text: Array of documents matching the query as a single blob of text.

URL Extractor​

Extracts text from a URL.

Input​

  • url: The URL to extract text from.
  • query: An optional query to semantic search in the extracted text.

Configuration​

  • document_limit: Maximum number of documents to retrieve from the vector store for the asked query.
  • text_chunk_size: Chunk size of document to use for semantic search.

Output​

  • text: The extracted text from the URL.

File Extractor​

Extracts text from a file.

Input​

  • file: The file to extract text from.
  • file_data: Alternative to file input. The base64 encoded file data to extract text from. This is useful when you want to extract text from a file that is uploaded via API or when wiring processors together.
  • query: An optional query to semantic search in the extracted text.

Configuration​

  • document_limit: Maximum number of documents to retrieve from the vector store for the asked query.
  • text_chunk_size: Chunk size of document to use for semantic search.

Output​

  • text: The extracted text from the file.