Principle:Neuml Txtai RAG Index Building
| Knowledge Sources | |
|---|---|
| Domains | NLP, Information_Retrieval, RAG |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
RAG index building is the process of creating a content-enabled embeddings index that stores both dense vector representations and the original document text, enabling retrieval of full context passages for generation.
Description
In a standard semantic search scenario, an embeddings index maps document identifiers to dense vectors and returns ranked IDs in response to queries. A RAG pipeline, however, requires more: the retrieved results must include the actual text content so that it can be injected into a language model prompt as context. This means the index must function as both a vector store and a document store simultaneously.
Building a RAG-ready index involves three key operations. First, each text chunk is transformed into a dense vector using a sentence-transformer or similar embedding model. Second, these vectors are loaded into an approximate nearest neighbor (ANN) data structure (such as HNSW or IVF) for efficient similarity search. Third, the original text of each chunk is persisted in a content database (typically SQLite) alongside its vector ID, so that search results can return full text rather than just identifiers.
The critical distinction between a pure-search index and a RAG index is the content storage requirement. Without stored content, a search can return IDs and scores, but the RAG pipeline has no text to pass to the language model. Enabling content storage is therefore a mandatory configuration step for any RAG application.
Usage
Use RAG index building when you need to:
- Create a searchable knowledge base that returns full document text, not just IDs.
- Prepare an index for use with a RAG pipeline that will feed context into an LLM.
- Build a hybrid index combining dense vector search with a relational document database.
- Support operations like upsert, delete, and content-based filtering alongside vector search.
Theoretical Basis
A RAG-ready index I consists of two coupled components:
I = (ANN, DB)
where:
- ANN is an approximate nearest neighbor index that maps vectors to identifiers. Given a query vector q, ANN returns the top-k nearest neighbors: ANN(q, k) -> [(id_1, score_1), ..., (id_k, score_k)].
- DB is a content database that maps identifiers to document text: DB(id_i) -> text_i.
The index building procedure operates in three phases:
FUNCTION build_rag_index(chunks, embedding_model):
vectors = []
FOR i, chunk IN enumerate(chunks):
v = embedding_model.encode(chunk)
vectors.APPEND((i, v))
DB.INSERT(i, chunk)
ANN.BUILD(vectors)
RETURN (ANN, DB)
At query time, the two components are composed:
FUNCTION retrieve_context(query, k):
q = embedding_model.encode(query)
results = ANN.SEARCH(q, k)
contexts = [DB.LOOKUP(id) FOR (id, score) IN results]
RETURN contexts
The quality of the index depends on several factors:
- Embedding model choice: determines how well semantic similarity is captured in vector space.
- ANN algorithm and parameters: control the trade-off between search speed and recall.
- Content granularity: the size of stored chunks affects both retrieval precision and the amount of context available for generation.
The content database also enables metadata filtering, where queries can be constrained by document attributes (date, source, category) before or after vector search, improving result relevance in domain-specific applications.