RAG Indexing, Demystified: How We Turn Raw Content Into Searchable Knowledge

Retrieval-Augmented Generation (RAG) is quickly becoming the backbone of practical AI apps. But the magic doesn't start at "chat." It starts with indexing—the pipeline that transforms messy documents into fast, accurate, semantic search.

This post explains the indexing workflow your app needs before a single question hits the model. We'll keep it pragmatic, with examples and best practices you can ship today.

Why Indexing Matters

LLMs are great at reasoning, but they don't "know" your private content. RAG fixes that by retrieving relevant snippets from your knowledge base and feeding them into the model as context.

If retrieval is slow, noisy, or off-target, your answers will be too. A solid index is the difference between "pretty good" and "production-ready."

The Indexing Pipeline (at a glance)

[ Load ] → [ Split ] → [ Embed ] → [ Store ]
                 (AI starts here ↑)

• Load: Bring documents into your pipeline (PDFs, HTML, docs, wikis).
• Split: Break large docs into smaller chunks for precise search.
• Embed: Use an embedding model to convert each chunk into a vector (AI step).
• Store: Save vectors + metadata in a vector database for fast similarity search.

Later, at query time:

[ User Query ] → [ Embed Query ] → [ Similarity Search ] → [ Top Chunks ] → [ LLM Answer ]

1) Load: Get Your Content In

What happens: You ingest data from wherever it lives: file systems, Google Drive, Notion, websites, databases.

How to do it:

• Use "document loaders" (frameworks like LangChain/LlamaIndex have dozens).
• Normalize to a single internal schema: text + metadata (source URL, author, created_at, permissions, etc.).

Tips:

• Strip boilerplate (nav bars, footers).
• Preserve structure (titles, headings) in metadata. It helps ranking and UX.

2) Split: Make the Data Searchable (No AI Yet)

What happens: You cut long text into smaller, overlapping chunks.

Why:

• Better retrieval: Matching a 200–500 token paragraph beats matching an entire 20-page PDF.
• Fits the model: Chunks easily fit inside the LLM's context window.

Common strategies:

• By tokens (e.g., 400–800 tokens) with overlap (e.g., 10–20%) to keep context.
• By structure: split at headings/sections, then sub-split by tokens.

3) Embed: The AI Step That Turns Text Into Meaning

What happens: Each chunk is fed to an embedding model that returns a vector—an array of numbers that captures the chunk's meaning.

• Example output: [0.12, -0.98, 0.33, ...] (e.g., 768 or 1536 dimensions)
• Close vectors = semantically similar text.

Models: Popular options include OpenAI embedding models and open-source encoders. Choose based on quality, cost, and dimension.

Tip: Keep the raw text alongside the embedding. You'll need it for display and grounding.

4) Store: Index For Fast Semantic Search

What happens: You write vectors + metadata into a vector database that supports nearest neighbor search.

Popular stores: Weaviate, Pinecone, Milvus, pgvector (Postgres), Chroma.

What to store (per chunk):

• id
• text (the chunk)
• embedding (vector)
• metadata (source, title, url, page, section, created_at, access controls)

Index type: HNSW or IVF are common for high-performance approximate search.

Query-Time Retrieval (How It Works Once Indexed)

1. Embed the user query with the same embedding model.
2. Similarity search in the vector DB for top-k nearest chunks.
3. (Optional) Re-rank with a cross-encoder for extra precision.
4. Assemble a context window from the best chunks.
5. Prompt the LLM with the user question + retrieved context.
6. Answer with citations (link back to your sources).

Minimal Example (Python-ish Pseudocode)

# 1) Load
docs = load_documents(["/docs/policies.pdf", "/docs/handbook.md"])  # returns [{text, metadata}, ...]

# 2) Split
chunks = []
for doc in docs:
  chunks += split_into_chunks(doc.text, size=700, overlap=100, respect_headings=True, metadata=doc.metadata)

# 3) Embed (AI step)
for ch in chunks:
  ch.embedding = embed(ch.text)  # returns a vector

# 4) Store
for ch in chunks:
  vector_store.upsert(
      id=ch.id,
      vector=ch.embedding,
      metadata={**ch.metadata, "text": ch.text}
  )

# --- Later, at query time ---
query_vec = embed("What is the refund policy?")
neighbors = vector_store.search(query_vec, top_k=5, filter={"doc_type": "policy"})
context = "\n\n".join([n.metadata["text"] for n in neighbors])

answer = llm("""
Answer the user's question using only the context below. Cite sources.

Question: What is the refund policy?
Context:
""" + context)

Best Practices We Recommend

Chunking

• Start 500–800 tokens with 10–20% overlap.
• Align to headings where possible.

Metadata

• Include: source, title, url, page/section, created_at, audience, permissions.
• Use metadata filters at query time (e.g., team = sales, doc_type = policy) before similarity search.

Hybrid Search

• Combine keyword AND vector search for robustness (e.g., lexical pre-filter + semantic ranking).

Freshness & Drift

• Schedule re-indexing for updated docs.
• Version your embeddings if you swap models.

Quality Control

• Add a re-ranker (cross-encoder) over the top 50 hits to pick the best 5–10.
• Deduplicate near-identical chunks before sending to the LLM.

Security

• Enforce access control in retrieval: filter by user/role in metadata before ranking.

Common Pitfalls (and how to avoid them)

• Chunks too big → poor recall; relevant info gets buried.
• No overlap → answers lack surrounding context.
• Missing metadata → can't filter effectively; noisy results.
• Mixing models (different embedders for indexing vs. query) → bad matches.
• No re-ranking → top-k contains "close but wrong" chunks.
• Indexing entire PDFs as one vector → impossible to retrieve the right paragraph.

Tooling: What We Use Most

• Vector DB: Weaviate (great schema, hybrid search, filters), Pinecone, or pgvector for teams already on Postgres.
• Frameworks:
  • LangChain: rich loaders, text splitters, vector store integrations.
  • LlamaIndex: strong connectors, simple high-level indexes.
• Embeddings: high-quality cloud models or a strong open-source encoder if you need on-prem.

Implementation Checklist

TL;DR

• Indexing is the foundation of RAG.
• Split is simple preprocessing.
• Embed is where AI begins.
• Store vectors + metadata for fast semantic search.
• Do it well, and your LLM answers become accurate, explainable, and fast.