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Integrating PDF Content into Your Chat Agent

Adding PDF content to your chat agent enriches its conversational capabilities by drawing on the vast amount of information stored in PDF documents. This functionality is critical for creating a chat agent that can provide detailed, accurate answers derived from a wide range of documents. The pdf_chat method simplifies the process of making PDF content searchable and accessible to your chat agent.

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Function Overview

The pdf_chat function is specifically designed to ingest PDF content into your chat agent, leveraging various parameters to optimize how this content is processed and utilized during conversations.

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Parameters

  • input_dir (Optional[str]): The directory containing PDF files to be added. If specified, the function scans this directory for PDF files.
  • input_files (Optional[List]): A list of specific PDF file paths to be added. If provided, input_dir is ignored.
  • exclude_hidden (bool): When set to True, hidden files or files starting with a dot (.) in input_dir are excluded.
  • filename_as_id (bool): Uses the filename as the unique identifier for each PDF document if set to True.
  • recursive (bool): If True, includes files from subdirectories within input_dir.
  • required_exts (Optional[List[str]]): Specifies file extensions to include, with the default targeting PDF files.
  • system_prompt (str): An optional prompt to guide the system in processing PDF content.
  • query_wrapper_prompt (str): An optional prompt to enhance query relevance by wrapping user queries in a specific context.
  • embed_model (Union[str, EmbedType]): The embedding model used for text extraction and embedding from PDF documents. Defaults to a predefined model.
  • llm_params (dict): Parameters for integrating Large Language Models to augment content understanding and query processing.
  • vector_store_params (dict): Configuration for vector storage, specifying how and where the extracted content embeddings are stored.
  • service_context_params (dict): Additional parameters for customizing the service context for PDF content.
  • chat_engine_params (dict): Customization parameters for the chat engine, influencing how the chat agent utilizes the PDF content during conversations.
  • retriever_params (dict): Configuration for the document retriever component, determining how PDF content is indexed and retrieved based on user queries.

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Example Usage

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Adding PDF Files from a Directory

chat_agent.pdf_chat(
    input_dir="/path/to/pdf/documents",
    recursive=True
)

This example adds PDF documents from the specified directory (and its subdirectories, if recursive is True) to the chat agent’s database.

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Adding Specific PDF Files

chat_agent.pdf_chat(
    input_files=["/path/to/specific/document1.pdf", "/path/to/specific/document2.pdf"],
)

Here, specific PDF files are directly added to the chat agent, allowing it to draw upon their content in conversations.

Overview📄 Docx file