LlamaIndex supports custom document formats through flexible data connectors and preprocessing tools. The framework allows developers to create custom data loaders that parse any file type into structured text and metadata. This is achieved by extending base classes like SimpleDirectoryReader or implementing the BaseReader interface, which lets you handle files that aren’t natively supported. For example, if you have log files in a proprietary format, you could write a loader that extracts timestamps and error messages into text chunks, then pairs them with metadata like severity levels. LlamaIndex doesn’t restrict you to common formats like PDF or DOCX—you define how raw data becomes usable content.
Once loaded, documents are processed using customizable pipelines. LlamaIndex provides tools to split text into chunks (e.g., SentenceSplitter), but you can override these for domain-specific needs. For instance, a developer working with code documentation might create a splitter that keeps function definitions and comments together, avoiding arbitrary breaks. You can also apply transformations like filtering low-value content or adding context (e.g., appending “This section discusses API endpoints” to every chunk from an API guide). These steps ensure the data aligns with your retrieval and generation goals, regardless of the original format.
Finally, LlamaIndex enables metadata-driven querying for custom formats. When parsing documents, you can extract structured information (e.g., headers in Markdown, sections in legal contracts) and attach it to text chunks. During searches, this metadata filters or boosts results—like prioritizing chunks tagged as “critical” in a technical manual. Developers can also integrate external data, such as linking CSV rows to related diagrams in a separate image loader. By decoupling parsing logic from retrieval, LlamaIndex lets teams adapt to niche formats without reworking their entire search pipeline, making it practical to handle domain-specific data at scale.