RAG Pipeline Explorer

Step through the six phases of RAG — from document loading to generation. Tune variables like chunk size, top-k retrieval, and temperature to observe the trade-offs on latency, cost, and hallucination risk.

Switch tool:Text Chunk Visualizer RAG Pipeline Explorer Token Counter & Visualizer Embedding Similarity Playground

Tunable RAG Architecture Variables

Adjust retrieval parameters to observe their impact across all 6 phases

Simulated User QueryEdit or replace with any question

Sample queries:

Chunk Size500 tokens

200 (Granular)1200 (Broad)

Top-K Retrievedk = 4

k = 1k = 10

Temperature0.0

0.0 (Strict)1.0 (Creative)

MMR Re-Ranking

Enabled

Penalizes duplicate redundant chunks

Context Window Load~2,018 tokens

Estimated Latency~390 ms

Hallucination RiskLow (Grounded)

Retrieval StrategyVector + MMR

Step 1 of 6: Document Loading17%

Phase 1 of 6

Document Loading

Bring your data in

The first step is ingesting documents from various sources — PDFs, web pages, databases, APIs, or local files. Each document becomes a structured object with content and metadata (source, page number, date). The quality of your RAG pipeline starts here: garbage in, garbage out.

Key Architecture Concepts

Document loaders handle format-specific parsing
Metadata is preserved for source attribution
Supports batch loading from directories
Common formats: PDF, HTML, Markdown, CSV, JSON

Common Engineering Pitfalls

!Losing document structure (headers, tables) during parsing
!Not preserving metadata — you need it for citations
!Loading stale data without a refresh strategy

Python Implementation Example

pythonLive Tuned Parameters Active

from langchain_community.document_loaders import PyPDFLoader

loader = PyPDFLoader("report.pdf")
documents = loader.load()

# Each document has .page_content and .metadata
print(documents[0].metadata)
# {'source': 'report.pdf', 'page': 0}

Phase 1 of 6

Phase 1/6

Document Loading

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