Document Splitting with Open Parse Python Library for RAG and LLMs Improving Accuracy
OpenParse is an open-source Python package that simplifies parsing documents. It's designed to index essential aspects like document pages and sections, providing information relevant to search purposes. This indexing can help human users or large language models (LLMs) use texts more effectively.
Most of you must have heard about retrieval augmented generation (RAG) systems. These are systems that assist in information retrieval as LLMs process data. Businesses with large volumes of internal data can find these systems especially useful.
Such internal data should be parsed, indexed, and segmented for later use. Traditionally, businesses would divide documents into small chunks of text, convert these chunks into vector embeddings, and then store these embeddings in a database. Once stored, they could use a vector search algorithm to find document segments related to a specific inquiry.
This approach allows the LLM to use the retrieved information to generate accurate responses to the asked questions. OpenParse, however, stands out because it already takes segmented documents and parses them out visually, considering the heavy lifting done by human users who format the documents into paragraphs, headers, etc. OpenParse can effectively parse out data from a visual perspective and save it in a database.
The information can then be extracted as required for input into an LLM. This makes OpenParse a more effective tool in comparison to other libraries like LayoutParser. The main problem with splitting text documents or image documents is that the context and meaning of the content can be lost during naive splitting. To index data for large language models or internal search systems, we need to segment the document to create search indexes.
OpenParse handles this by capturing formatting, meanings, headings, sections, bullet points, and tabular data, which provides a far more accurate and meaningful extraction of text within the document. Even table extraction is possible with OpenParse, which is advantageous when the source is in PDF format. Unlike typical text splitting methods, OpenParse allows for a more accurate and detailed identification of text segments.
As a result, if you are building a RAG system or just looking to index data within documents, OpenParse is a tool that is worth considering. When you use OpenParse to split your documents, you will get much better performance over traditional methods of text splitting. OpenParse offers the ability to segment information that has been parsed and processed by humans already.
You just need to import OpenParse, identify your document, and parse it. This will result in a much more refined and semantically meaningful way to generate token for a search index, making it an excellent choice for businesses that need to index their documents and generate search indexes.
