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Parser component

As explained in Grokking AI Applications

A workflow component that formats parsed document content and associated metadata into text for downstream processing.

A Parser component formats a document chunk into text that downstream RAG steps can consume. It can pass only the chunk text or combine the text with useful metadata. section 4.2 section 4.3

Use the component as a small formatting stage in a retrieval workflow:

  • Receive chunks retrieved from a document or vector database. section 4.2
  • Set a template that determines what the language model receives. section 4.3
  • Include metadata when fields such as file path or year help interpret the content. section 4.3

Passing a source with each chunk

A basic template uses {text}. To preserve provenance, use a template such as Source={file_path};Text={text}, which places the file path alongside the chunk before it reaches the LLM. section 4.3

The Parser does not retrieve or filter chunks itself. Advanced retrieval methods such as self-query can use metadata at search time, while the Parser controls the formatted text passed onward. section 4.3

How Grokking AI Applications covers it

How deep it goes
Explains template substitution with text and metadata, but defers advanced metadata filtering to self-query techniques.
Examples
Demonstrates {text} and Source={file_path};Text={text} templates for passing chunk content and provenance.
What it assumes
Assumes familiarity with document chunks and metadata, while showing how to combine both into downstream text.
What you can do after
Apply it via Langflow to format retrieved chunks and include selected metadata in prompts sent to an LLM.
How it connects
Connects RAG with document chunks, metadata, vector databases, retrievers, and language models.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Grokking AI Applications. The section links point to the parts of the book it draws on.