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RAG Ingestion Vector Store

This solution answers one question: once you have decided to do RAG, what should the first standard engineering path be.

1. When it fits​

  • Enterprise knowledge base Q&A
  • Ingesting documents such as PDF / Word / web pages
  • Wanting to wire up ingestion, embedding, vector store, and the retrieval chain end to end first

This is the standard baseline for RAG, not bound to any particular vector store brand.

2. Core module combination​

The main chain is typically:

  • Document loading / cleaning / chunking
  • IngestionPipeline
  • VectorStore
  • RagService
  • Optional Reranker / citations / trace

The point is to establish an evolvable standard RAG pipeline, not just to reproduce a single vendor demo.

3. Strengths of this solution​

  • Freedom to choose the vector store
  • Easier to upgrade rerank, citation, and trace later
  • Clean separation between the ingestion chain and the retrieval chain
  • Better suited as a team's RAG baseline

4. When it is not enough​

The following cases need further refinement:

  • Pinecone is already mandated
  • Public web search augmentation is required
  • A domain-specific solution with high evidence requirements is required

In those cases, see also:

5. Mainline pages to read first​

  1. Core SDK / Search & RAG
  2. Core SDK / Package Map
  3. Spring Boot / Bean Extension

6. Diving into implementation details​

If you want to look at:

  • The complete ingestion pipeline
  • Document processors
  • Vector store samples
  • Retrieval validation and rerank upgrades

See the deeper pages:

7. Key objects​

If you want to move from the solution into implementation, look at these objects first:

  • rag/ingestion/IngestionPipeline.java
  • vector/store/VectorStore.java
  • rag/Reranker.java
  • rag/RagService

These four objects correspond to the main lines of ingestion, storage, ranking, and retrieval service respectively.

8. The most important boundaries in this solution​

Three boundaries must be established first for this solution:

  • ingestion is responsible for how knowledge enters the system, not for answer generation
  • vector store is responsible for candidate storage and retrieval, not for the final answering strategy
  • rerank and citations are incremental enhancements on top of the baseline, not hard-bound from the start

Once the boundaries are clear, the cost of later swapping the vector store, adding rerank, or adding evidence chains is much lower.

9. What to validate first when shipping​

It is recommended to validate the following first:

  1. Whether documents are chunked stably and vectorized successfully
  2. Whether dataset / metadata can support downstream filtering and citation
  3. Whether retrieval results can be traced back to the original documents consistently

These three things matter more than polishing the answer to sound more natural first.