> For the complete documentation index, see [llms.txt](https://docs.openmind.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.openmind.com/build/concepts/knowledge_base.md).

# Knowledge Base (RAG)

Give your agent retrieval-augmented context from a local vector index.

### Overview

OM1 supports **retrieval-augmented generation (RAG)**: before each LLM call, the runtime can retrieve the most relevant documents for the current context and include them in the prompt.

The knowledge base has two parts:

* A **local vector index** — an [HNSW](https://github.com/coder/hnsw) graph plus a metadata JSON file, loaded from a knowledge-base directory on disk (`internal/knowledgebase`).
* An **embedding service** — an external HTTP endpoint that turns a query string into an embedding vector so it can be matched against the index.

### Configuration

Add a `knowledge_base` block to your config:

```json5
knowledge_base: {
  knowledge_base_name: "om",                        // required — the KB directory/file name
  base_url: "${KB_BASE_URL:-http://localhost:8100}", // embedding service endpoint
  min_score: 0.6,                                    // drop results below this similarity
  top_k: 3,                                          // number of documents to retrieve
},
```

| Field                 | Type   | Required | Description                                                                                              |
| --------------------- | ------ | -------- | -------------------------------------------------------------------------------------------------------- |
| `knowledge_base_name` | string | Yes      | Name of the knowledge base. The runtime loads `<name>.graph` and `<name>.json` from the KB directory.    |
| `base_url`            | string | No       | Embedding service URL. Defaults to `http://localhost:8100`. Any trailing slash is automatically removed. |
| `knowledge_base_root` | string | No       | Explicit root directory for knowledge bases. If omitted, OM1 auto-resolves it (see below).               |
| `top_k`               | int    | No       | Number of documents to return per query. Default `3`.                                                    |
| `min_score`           | float  | No       | Minimum similarity score; lower-scoring matches are discarded.                                           |

### Where the index is loaded from

If `knowledge_base_root` is not set, OM1 looks for a `knowledge_base/<name>/` directory in these locations, in order:

1. `<cwd>/knowledge_base` — when running from the repo root
2. `<exe dir>/../../knowledge_base` — for the built `./build/om1` binary
3. `<exe dir>/knowledge_base`

The repository ships an example KB at [`knowledge_base/om/`](https://github.com/OpenMind/OM1/tree/main/knowledge_base/om/README.md) containing `om.graph` (the HNSW index) and `om.json` (the document metadata: id, vector, text, source).

### The embedding service

A query is embedded by POSTing it to the service at `base_url`. Run your embedding service (or point `KB_BASE_URL` at a hosted one) before starting an agent that uses a knowledge base. If the embedding step fails, the query is skipped and the failure is reflected in the `om1_kb_queries_total` metric.

### Observability

Knowledge base activity is exported to Prometheus (see [Metrics](/build/observability/metrics.md)):

* `om1_kb_query_latency_seconds` — full query latency (embedding + search)
* `om1_kb_embed_latency_seconds` — embedding-step latency
* `om1_kb_queries_total` — total queries, labeled by outcome


---

# Agent Instructions
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## Querying This Documentation
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Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
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```

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