06.06 — Recap: From Ingestion to Retrieval

Overview

This is a transition point between the two halves of the RAG pipeline. The ingestion pipeline (lessons 03–05) is complete — our document is chunked, embedded, and stored in Pinecone. Now we move to the retrieval pipeline — taking user queries, finding relevant chunks, and generating grounded answers.

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What We've Built So Far

flowchart LR
    subgraph Done["✅ Completed — Ingestion"]
        DOC["📄 mediumblog.txt"]
        SPLIT["✂️ 20 chunks"]
        EMBED["🔢 20 vectors"]
        STORE["🗄️ Pinecone Index\n(populated)"]
        DOC --> SPLIT --> EMBED --> STORE
    end

    subgraph Next["📤 Next — Retrieval"]
        Q["❓ User Query"]
        S["🔍 Similarity Search"]
        A["📝 Augmented Prompt"]
        L["🤖 LLM → Answer"]
        Q --> S --> A --> L
    end

    STORE -.->|"vectors available\nfor search"| S

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

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What's Coming Next

The retrieval pipeline converts a user's question into a grounded answer:

Step	What Happens	Tool
1. Embed query	The user's question is converted to a vector	OpenAIEmbeddings
2. Similarity search	The query vector is compared against all stored vectors; top K nearest chunks are returned	PineconeVectorStore.as_retriever()
3. Augment prompt	The retrieved chunks are injected as context alongside the original question	ChatPromptTemplate
4. Generate answer	The LLM produces an answer grounded in the retrieved context	ChatOpenAI

We'll implement this in two ways: 1. Naive approach (Lesson 07) — manual step-by-step, no LCEL 2. LCEL-based chain (Lesson 08) — composable, traceable, production-ready