When working with data collected from multiple scraping tools—BeautifulSoup, Parsel, and Playwright—teams often face inconsistent formats, missing fields, and difficulty turning raw rows into usable assets for downstream tasks like retrieval‑augmented generation or product analytics. A practical approach is to consolidate the rows early, normalize the text, and create deterministic chunks that can be indexed reliably. First, combine all rows into a single list, then extract the most relevant textual field (preview, rendered text, or description) and apply a lightweight normalization step (lowercasing, stripping extra whitespace). Splitting the cleaned text into sentences and packing them into chunks of a fixed character limit ensures each piece stays within model token bounds while preserving context. Assign each chunk a stable ID by hashing the URL plus the chunk text; this prevents duplicates when the same content appears across different crawls.
After chunking, convert the raw rows into structured DataFrames. Coerce numeric columns such as price, stock, and rating to proper types, compute derived metrics like inventory value, and flag missing values for later inspection. Build a directed link graph from the BeautifulSoup rows to visualize site architecture; writing the graph to GraphML enables external analysis tools to inspect connectivity, identify hubs, and detect isolated components.
To make results actionable, export the combined crawl as JSON, the normalized product catalog as CSV, and the RAG‑ready chunks as JSONL. Generate a quick price bar chart using the product DataFrame, labeling each bar with SKU and source for immediate visual feedback. Finally, assemble a summary that counts rows per parser, total products, chunk count, and graph statistics, and write it to a markdown‑free text file for easy consumption by CI pipelines or team notifications. By keeping each step modular—extraction, normalization, chunking, analysis, and output—you reduce coupling, simplify debugging, and scale the pipeline as new data sources are added.
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