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What does indexing do in a RAG system?
Indexing converts a document collection into records that a retrieval system can find later. A record generally contains a passage of extracted text, an embedding vector representing that passage, and metadata that identifies where the passage came from. The query path uses the same embedding model or a compatible one to represent a user’s question, searches for relevant records, and supplies retrieved text to a generation model.
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Microsoft Learn’s Retrieval-Augmented Generation (RAG) with Azure Files describes this indexing-and-query sequence, including top-K retrieval and storing text, vectors, and source metadata. Its example illustrates the workflow; it does not mean Azure OpenAI or a cloud service is required for RAG.
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Local can describe the files alone, or every component that touches them. Decide which parts must remain on the machine or within a controlled network before choosing tools. A locally run embedding model does not by itself make the whole system local: queries or retrieved passages could still be sent to a hosted answer model, and logs or backups may have their own destinations.
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| Component | Question to answer |
|---|---|
| Source files and parser | Are files read and text extracted on the device or server you control, or uploaded to a service? |
| Embeddings | Does the embedding model run locally, or does extracted text go to a hosted embedding API? |
| Vector and text storage | Are chunks and vectors stored in a local/self-managed database or a remote service? |
| Question and answer generation | Does the user’s question, retrieved text, or both leave your environment for the language model? |
| Logs, caches, backups | Where are prompts, extracted content, vectors, and backups retained, and who can access them? |
MongoDB’s local RAG tutorial demonstrates a local embedding model and local Atlas deployment, but describes local deployments as intended for testing and directs production use to a cluster. That example is not evidence that every part of an application is private or production-ready; map each data flow in your own setup.
How do you build the index?
Keep one stable identity for each source document and a way to relate every passage back to it. The pipeline below is deliberately independent of any one parser or vector database; file-format support, index definitions, and update APIs vary by implementation.
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Inventory the source collection
Choose the folders and file types to include. Exclude irrelevant or transient material such as build output, temporary files, and duplicate exports. Decide whether this is a one-time snapshot or a collection that will change, and establish how the system will notice additions, edits, moves, and deletions.
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Extract text and preserve provenance
Parse supported files into text while retaining useful structure such as headings, page numbers, and section boundaries when the parser provides them. Save identifying metadata—at minimum, a stable document ID and source location—so a result can be traced to the right file. Filename, file size, and MIME type can also help with administration or filtering. Microsoft’s workflow includes source metadata; OpenRAG’s documented ingestion flow includes filename, size, and MIME type.
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Normalize without flattening meaning
Convert extracted content into a consistent representation, but do not discard information that helps retrieval. Tables, code blocks, page boundaries, and headings may need format-specific handling. OpenRAG documents an approach that exports processed DoclingDocument data to Markdown, including image placeholders, before splitting; this is one implementation, not a requirement for every corpus. If a PDF contains important information only in images, verify that your extraction path actually captures it instead of assuming ordinary text parsing will.
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Split content into retrieval-sized chunks
Choose boundaries based on the structure of the material and the questions people will ask. Keep headings or section labels with their passages when useful. The trade-offs between common approaches are summarized below; MongoDB’s RAG guidance discusses these strategies but does not prescribe a universally correct chunk size or overlap.
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Embed each chunk
Pass each chunk through the selected embedding model and record the model and relevant version or configuration used. The vector dimensions must match the vector index definition. At query time, use a compatible embedding model: vectors produced by different models or incompatible configurations should not be assumed to share a meaningful space.
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Store searchable records and configure indexes
Store each chunk with its vector and source metadata. Create the vector index for the embedding field, and configure metadata fields for filtering if the chosen store requires it. MongoDB documents that its vector indexes are separate from other database indexes and that metadata prefilters need to be supported by the index definition. Check the current documentation for the database version and deployment you actually use.
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Retrieve passages and ground the answer
For each question, create a compatible query embedding, retrieve relevant passages, and send those passages together with the question to the generation model. Set retrieval limits and filters based on the application, then have the answer cite or link to sources when users need to verify claims. A retrieved passage is evidence to consider, not a guarantee that the generated answer is correct.
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How should you choose a chunking strategy?
Chunk boundaries affect what the retriever can find and how much context reaches the answer model. Start with the document’s natural organization, then test a suitable approach against representative questions rather than treating a particular chunk length as a rule.
| Strategy | When it can fit | Trade-off to check |
|---|---|---|
| Fixed-token chunks | Relatively uniform material where simple, consistent splitting is useful. | A boundary can cut across a sentence, section, or idea; inspect whether passages remain understandable. |
| Fixed-token chunks with overlap | General prose where context may span a boundary. | Overlap can preserve nearby context but also create repeated material in results and increase stored and embedded text. |
| Recursive splitting | Prose where preserving paragraphs and sentences is preferable to splitting at arbitrary positions. | Check whether the resulting passages still fit the task and retain enough context. |
| Language-aware recursive splitting | Code or technical documentation with language-specific structure. | Use boundaries appropriate to the relevant language or format; prose-oriented splitting may break useful units. |
| Semantic splitting | Content with few clear structural boundaries, where topic changes can help determine splits. | Evaluate the resulting boundaries on actual queries; semantic splitting is an option, not an automatic quality improvement. |
Compare strategies using the same representative questions. Look for whether the right passage is retrieved, whether it includes enough surrounding context, whether multiple results repeat the same material, and how storage and embedding work change. No reviewed vendor guidance establishes a single benchmarked chunk size or universally best method.
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Should retrieval use vectors, keywords, or both?
Vector search is useful for finding passages that are conceptually related even when the wording differs. Lexical or full-text search is useful when a question depends on exact words, names, codes, or identifiers. Hybrid retrieval combines these signals and is worth evaluating when both kinds of queries matter. MongoDB documents semantic, hybrid, and generative search; Milvus documents BM25 hybrid retrieval.
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Metadata filters can narrow results to a document, category, date, or other supported field. Keep those values consistently formatted and confirm which types and filter operators your selected store supports; capabilities are product- and index-specific. MongoDB documents filters for fields including boolean, date, object ID, numeric, string, and UUID types within its implementation.
How do you keep the index in sync with a changing folder?
The source folder and the derived index are separate states. If files change and the index does not, retrieval can return stale passages or miss new information. Define a synchronization policy before relying on the index as current.
- Identify documents consistently: Assign or derive a stable document ID so edits can be related to existing indexed passages.
- Detect changes: Use a chosen mechanism to determine which inputs were added or altered. The reviewed sources do not establish a universal file-watcher or change-detection design.
- Replace affected records: Re-parse and re-chunk changed documents, then upsert the replacement chunks and vectors. Ensure obsolete chunks for that document are removed or superseded according to the store’s deletion semantics.
- Handle moves and deletions: Decide whether a move changes the document identity or only its source path, and remove passages for files that no longer belong in the corpus.
- Make failures recoverable: Track work that fails during parsing, embedding, or storage so the index does not silently appear current when an update was only partly applied.
- Plan for model changes: Record the embedding model and configuration. If they change, determine which existing documents need re-embedding and check that the vector index dimensions and query embeddings remain compatible.
Milvus documents document updates through upsert, and MongoDB describes automated embedding synchronization as data changes. Those are product-specific mechanisms, not a cross-vendor synchronization recipe; verify how the selected system treats replacement, deletion, retries, and partial failures.
How should you evaluate a local RAG index?
Use a small but representative set of questions from the people who will use the system. Include questions about exact identifiers as well as questions expressed in different words from the source, and check results against the original documents. Do not infer quality from a successful indexing run alone: parsing can succeed while retrieval still returns incomplete, irrelevant, or poorly attributed passages.
- Retrieval relevance: Does the returned set contain the passage that supports the answer?
- Context completeness: Does the passage include enough of the relevant section, table, or code to make its meaning clear?
- Source traceability: Can a user open or identify the correct file and location from a result?
- Exact-match behavior: Are names, codes, and quoted phrases found reliably, and would lexical or hybrid retrieval help?
- Filtering: Do category, date, or document filters narrow results as intended?
- Freshness and resilience: After an edit or deletion, does the index update correctly, and can failed ingestion work be identified?
- Operational fit: Compare data locality, file-format support, filters, hybrid search, update behavior, backup and export, resource needs, latency, and measured retrieval quality for your workload.
These checks support a local implementation whether you use a self-managed vector store or a service with a local development mode. Microsoft’s Azure Files overview is a cloud-service example; MongoDB’s local tutorial is a testing-oriented example. Neither establishes that one deployment model or product is best for every local-document RAG system.
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