> For the complete documentation index, see [llms.txt](https://docs.hexabot.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.hexabot.ai/retrieval-augmented-generation/choose-a-rag-helper.md).

# Choose a RAG helper

A RAG helper decides how Hexabot indexes, searches, filters, and ranks CMS content. Hexabot can discover several installed helpers, but `global_settings.default_rag_helper` selects the one used by `retrieve_rag_content` and the RAG search API.

### Helper comparison

| Helper or npm package                                                                    | Database                  | Retrieval                                               | Embedding provider | Indexing model                                                     | Best for                                                                             |
| ---------------------------------------------------------------------------------------- | ------------------------- | ------------------------------------------------------- | ------------------ | ------------------------------------------------------------------ | ------------------------------------------------------------------------------------ |
| `fulltext-search` (built in)                                                             | SQLite or PostgreSQL      | Lexical/full-text                                       | No                 | Database-native; Content remains the source of truth               | Exact terms, names, codes, policies, FAQs, low-operational-overhead deployments      |
| [`hexabot-helper-sqlite-vector`](https://hexabot.ai/extensions/6a6daf6d147efb889b884892) | SQLite                    | Semantic vector similarity                              | Yes                | Vector data stored in SQLite; content changes are indexed directly | Local or small deployments that need semantic retrieval without PostgreSQL           |
| [`hexabot-helper-pgvector`](https://hexabot.ai/extensions/6a6dafd8147efb889b884994)      | PostgreSQL                | Semantic vector similarity                              | Yes                | Durable, trigger-backed indexing queue in PostgreSQL               | Production PostgreSQL deployments and larger semantic corpora                        |
| Custom `hexabot-helper-*` package                                                        | Depends on implementation | Lexical, semantic, graph, hybrid, reranked, or external | Optional           | Defined by the helper                                              | Pinecone, Weaviate, Elasticsearch, GraphRAG, custom ranking, regulated architectures |

Use the npm package name when installing or referring to an extension. Use the shorter runtime name only when configuring **Default RAG helper** or reading the `source` field of a result.

### Decision guide

#### Choose `fulltext-search` when

* your users and content share important keywords;
* queries include product names, error codes, plan names, policy terms, or identifiers;
* you do not want content sent to an embedding provider;
* you want the lowest setup and maintenance overhead;
* you need a reliable baseline before adding semantic complexity.

Read Full-text search.

#### Choose `hexabot-helper-sqlite-vector` when

* the Hexabot database is SQLite;
* users often paraphrase the source content;
* semantic similarity is more important than exact keyword matching;
* the corpus and deployment size are appropriate for an in-process SQLite setup.

Read `hexabot-helper-sqlite-vector`: SQLite vector search, or view the package in the [Hexabot Extension Library](https://hexabot.ai/extensions/6a6daf6d147efb889b884892).

#### Choose `hexabot-helper-pgvector` when

* the Hexabot database is PostgreSQL;
* you need semantic retrieval with durable asynchronous indexing;
* your PostgreSQL environment can provide the `vector` extension;
* you want vector storage and content records to remain in the same database platform.

Read `hexabot-helper-pgvector`: PostgreSQL vector search, or view the package in the [Hexabot Extension Library](https://hexabot.ai/extensions/6a6dafd8147efb889b884994).

#### Build a custom helper when

* vectors must live in Pinecone, Weaviate, Qdrant, Milvus, or another external store;
* you need GraphRAG, document-level access control, custom metadata filters, reranking, or multilingual retrieval logic;
* you want hybrid ranking that combines lexical and semantic signals;
* your organization already has a search or knowledge platform that should remain authoritative.

Read Develop a custom RAG helper.

### Lexical, semantic, and hybrid retrieval

**Lexical retrieval** matches words and language-specific text-search forms. It is predictable, inexpensive, and strong when terminology is shared between the query and source.

**Semantic retrieval** embeds the query and content chunks into vectors. It can match meaning across different wording, but adds provider cost, indexing time, privacy considerations, and operational dependencies.

### Only one helper answers each retrieval call

Installing multiple helpers gives administrators a choice; it does not make every query search all helpers. The configured default helper handles a retrieval call.

Database-specific helpers advertise their availability. `hexabot-helper-pgvector` is not offered on SQLite, and `hexabot-helper-sqlite-vector` is not offered on PostgreSQL.

Before enabling vector retrieval, confirm:

* which content fields will be embedded;
* whether the embedding endpoint is external or self-hosted;
* where vectors and metadata are stored;
* whether inactive content may be transmitted;
* the provider's retention and regional-processing terms;
* whether your access-control model requires per-user filtering that a custom helper must enforce.

The official vector helpers default to indexing only active content. Retrieved content should still be treated as untrusted data when it is supplied to an LLM.


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