Principle:Neuml Txtai Document Collection
| Knowledge Sources | |
|---|---|
| Domains | NLP, Information_Retrieval, RAG |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Document collection is the first stage of any Retrieval-Augmented Generation (RAG) pipeline, where source materials are gathered from local files, remote URLs, or in-memory text before being fed into downstream extraction and indexing stages.
Description
Every RAG system begins with a corpus of documents that contain the knowledge the system will draw upon when answering questions. Document collection is the process of assembling that corpus into a uniform representation, typically a list of references (file paths, URLs, or raw text strings), that subsequent pipeline stages can consume.
This stage is inherently user-defined. Unlike text extraction or embedding, document collection does not depend on any particular library API. The user writes plain application code -- scanning directories, querying databases, calling web services, or simply hard-coding a list of strings -- to produce the input set. The only contract is that the output must be an iterable of strings, where each string is either a local file path, a remote URL, or a block of raw text content.
Careful document collection directly affects the quality of a RAG system. Including irrelevant material dilutes search precision, while omitting important sources creates coverage gaps. A well-designed collection step filters by file type, date, size, or metadata so that only high-quality, relevant content enters the pipeline.
Usage
Use document collection at the start of every RAG pipeline build or refresh cycle. This principle applies whenever you need to:
- Assemble a set of PDF, DOCX, HTML, or plain-text files from disk for indexing.
- Scrape or crawl URLs to gather web-based content.
- Pull text records from a database or API and package them for downstream processing.
- Combine multiple heterogeneous sources (local files, URLs, and in-memory strings) into a single input list.
Theoretical Basis
Document collection can be modeled as a function C that maps a set of source references S to an ordered list of document identifiers:
C(S) -> [d_1, d_2, ..., d_n]
where each d_i is a string reference (path, URL, or literal text). The function C may apply filtering predicates F to exclude unwanted items:
C(S, F) -> [d_i for d_i in S if F(d_i) = True]
Common filtering predicates include:
- Type filter: accept only files with specific extensions (e.g., .pdf, .docx, .html).
- Size filter: reject files below or above a size threshold.
- Date filter: include only documents modified within a given time window.
- Deduplication: remove duplicate paths or content hashes to prevent redundant indexing.
The output of C is consumed by the extraction stage, which transforms each reference into machine-readable text. The ordering of the output list may or may not matter depending on the downstream pipeline, but preserving a deterministic order aids reproducibility.
In pseudocode, a typical collection step looks like:
FUNCTION collect_documents(root_directory, extensions):
results = []
FOR each file IN walk(root_directory):
IF file.extension IN extensions:
results.APPEND(file.path)
RETURN results