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Document

As explained in Build Python Web Apps with Streamlit

A Document is a LangChain data structure containing source text and associated metadata.

A Document is a LangChain data structure that keeps source text together with associated metadata. Loaders create Document objects from files and other data sources, giving later retrieval steps a consistent input. section 10.5.2

Use Documents as the working unit in a retrieval pipeline:

  • Load text files with DirectoryLoader and TextLoader. section 10.5.2
  • Split large Documents into smaller Documents with RecursiveCharacterTextSplitter, using chunk sizes and overlap to preserve useful context. section 10.5.2
  • Store the resulting Documents in a vector store, then retrieve relevant Documents through similarity search. section 10.5.2
Proper care ensures your InkStream and RoyalQuill fountain pens write smoothly for years.

- Cleaning: Flush the nib with warm water every few weeks.
- Refilling: Use high-quality ink to prevent clogging.
- Storage: Store pens upright to avoid leaks and ensure ink flow.

Treat the supplied care instructions as source text: a loader can turn this material into a Document before it is chunked and indexed. section 10.5.2

How Build Python Web Apps with Streamlit covers it

How deep it goes
Explains the structure and pipeline role, then demonstrates loading, splitting, storing, and retrieving without detailing metadata fields.
Examples
Uses text files, support articles, chunked excerpts, and fountain-pen care instructions as ingestible source material.
What you can do after
Apply it via LangChain loaders and splitters to prepare source material for vector-store retrieval.
How it connects
Connects Documents with document loaders, text splitters, vector stores, embeddings, similarity search, and retrieval-augmented generation.

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This explainer was generated by Manning's knowledge graph from the text of Build Python Web Apps with Streamlit. The section links point to the parts of the book it draws on.