07.09 — Recap: From Crawling to Indexing

Overview

We've completed the hardest part of the pipeline — getting the data. In production RAG applications, data acquisition is typically the most error-prone and time-consuming phase. Now we transition to preparing the data for the vector store.

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What We've Done

flowchart LR
    subgraph Done["✅ Completed"]
        SRC["🌐 LangChain Docs"]
        CRAWL["🕷️ Tavily\n(Map + Extract)"]
        DOCS["📄 LangChain Documents\n(500+ pages)"]

        SRC --> CRAWL --> DOCS
    end

    subgraph Next["📋 Next Steps"]
        SPLIT["✂️ Chunk into pieces"]
        EMBED["🔢 Embed into vectors"]
        INDEX["🗄️ Index in vector store"]

        SPLIT --> EMBED --> INDEX
    end

    DOCS -.-> SPLIT

    style Done fill:#10b981,color:#fff
    style Next fill:#4a9eff,color:#fff

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What's Coming Next

Step	What Happens	Lesson
Chunking	Split large documents into smaller, searchable pieces (4000 chars, 200 overlap)	10
Batch indexing	Embed chunks and store vectors in Pinecone — concurrently, with rate limit handling	11

The API-heavy crawling phase is behind us. The remaining ingestion steps are computationally simpler but involve important design decisions (chunk size, overlap, batch sizing) that directly impact retrieval quality.