Retrieval-augmented generation becomes difficult when documents arrive in many formats and useful context must be found quickly. LangChain provides a practical ingestion pipeline that loads source data, structures it for retrieval, and fetches relevant chunks for language-model responses. section 6.4.3
The pipeline combines three roles:
- Document loaders import files and other sources as documents containing text and metadata. section 6.4.3
- Document transformers process and structure content, including splitting large documents into representative subdocuments. section 6.4.3
- Retrievers fetch relevant document chunks at query time; similarity, semantic, federated, keyword, and hybrid search are possible approaches. section 6.4.3
Loading source data
A text file or CSV can be loaded with a dedicated loader, while directories, PDFs, HTML, JSON, Markdown, web pages, transcripts, and corporate documents have corresponding ingestion options. section 6.4.3
from langchain_community.document_loaders import TextLoader
# Load text data from a file using TextLoader
loader = TextLoader("./your_data/YourText.txt")
document = loader.load()
A small Python listing shows the basic loader pattern: configure a TextLoader with a file path and call load to obtain documents. section 6.4.3
