# Search\_Mode\_And\_Query\_Guide

## Overview

* Keep mode and query style aligned:
  * `keyword`: exact tokens, identifiers, and literal overlap.
  * `semantic`: plain-language intent and conceptual similarity.

### Keyword — “fuzzy keyword search with optional exact matching”

#### What it does

* Runs an Elasticsearch `simple_query_string`-style search with:
  * Default operator: AND across all tokens.
  * Fuzzy matching enabled (e.g., `fuzziness`, `fuzzy_max_expansions`).
  * Prioritizes direct term/phrase overlap. Good recall on misspellings and near-matches; precision improves when you add quotes around phrases.

#### What the AI may do to the user’s query

* Keep the query verbatim, or:
  * Fix obvious spelling errors.
  * Add double quotes around words/phrases the user clearly intends as exact strings (names, IDs, titles, multi-word entities).
  * Build grouped boolean logic using `+`, `|`, `-`, quotes, and parentheses.
  * Build OR lists using the `|` operator. Example: `"enterprise cyber range" | "enterprise cyber training"`.
  * Exclude terms using `-term` or `-"exact phrase"`.
  * Use `-` only with at least one positive term or phrase. Do not send a query that is only exclusions.
  * **Do NOT** use field scoping or more advanced SQS operators (`~`, `*`, `^`, `field:` etc.).
  * **Do NOT** use Lucene boolean operators (`OR`, `AND`).

#### Supported advanced syntax

| Operator | Function                                                 |
| -------- | -------------------------------------------------------- |
| `+`      | Explicit `AND` between terms or groups.                  |
| `\|`     | `OR` between terms or groups.                            |
| `-`      | Exclude the immediately following term or quoted phrase. |
| `"..."`  | Exact phrase match.                                      |
| `(...)`  | Group terms and control precedence.                      |

### Semantic — “Dense vector semantic search (meaning over words)”

#### What it does

* Embeds the user query into a dense vector and retrieves semantically similar content, independent of exact lexical overlap.

#### What the AI may do to the user’s query

* Send verbatim, or apply light reformulation:
  1. Synonym/paraphrase expansion
  2. Query relaxation: drop/soften narrow constraints
* Keep reformulation in plain natural language.

## Examples

* If the chosen tool does not support semantic search, keep the same keyword-style `query` construction and omit `search_mode`.

Direct lookup or exact phrase: Tool: `Search_<Entity>`

```json
{
  "search_mode": "keyword",
  "query": "\"<IDENTIFIER_OR_EXACT_PHRASE>\""
}
```

* Use quoted exact strings for names, titles, and identifiers.
* Add `|` only when the user explicitly gives alternatives.
* Add `-term` only when the user explicitly wants an exclusion.

Advanced keyword syntax with exclusion: Tool: `Search_<Entity>`

```json
{
  "search_mode": "keyword",
  "query": "\"cyber security operations\" -training"
}
```

* This returns records containing the exact phrase `"cyber security operations"` while excluding records that also contain `training`.

Advanced keyword syntax with grouping: Tool: `Search_<Entity>`

```json
{
  "search_mode": "keyword",
  "query": "\"cyber security\" + (systems | training)"
}
```

* This returns records containing the exact phrase `"cyber security"` and also either `systems` or `training`.

Conceptual discovery: Tool: `Search_<Entity>`

```json
{
  "search_mode": "semantic",
  "query": "<plain-language intent>; <domain synonym 1>; <domain synonym 2>; <domain paraphrase>"
}
```

* Keep the query in natural language.
* Add only the 2-4 highest-value synonyms or paraphrases.
* Relax narrow constraints only if recall is likely to be too sparse.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.govtribe.com/user-guide/mcp/guides/search_mode_and_query_guide.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.