Adds ADR for semantic query processing flow

docs/architecture-decisions/0007-semantic-query-processing.md

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+# 7. Semantic Query Processing
+
+Date: 2026-02-27
+
+## Status
+
+Accepted
+
+## Context
+
+We will be using doc-only embedding rather than Bi-encoding (query and document). This distinction is summarized by the [OpenSearch blog](https://opensearch.org/blog/improving-document-retrieval-with-sparse-semantic-encoders/) as follows:
+
+> In bi-encoder mode, both documents and search queries are passed through deep encoders.
+> In document-only mode, documents are still passed through deep encoders, but search queries are instead tokenized.
+
+Our OpenSearch may run in AWS OpenSearch Serverless architecture, which prevents us from installing our own models directly in OpenSearch. Therefore, we need to create our tokens to construct a query outside of OpenSearch.
+
+The specific details on how the will be implemented is not a concerns of this TIMDEX API codebase, but we do care about the information flow which is described below. At a high level, we will be calling an external tool to transform a text string (a user query) into a query structure that we can use in our SemanticQueryBuilder and HybridQueryBuilder.
+
+### Example semantic query structure
+
+We anticipate that for an input of "hello world", we should expect a response similar to:
+
+```json
+{
+  "query": {
+    "bool": {
+      "should": [
+        {
+          "rank_feature": {
+            "field": "embedding_full_record.[CLS]",
+            "boost": 1.0
+          }
+        },
+        {
+          "rank_feature": {
+            "field": "embedding_full_record.[SEP]",
+            "boost": 1.0
+          }
+        },
+        {
+          "rank_feature": {
+            "field": "embedding_full_record.world",
+            "boost": 3.4208686351776123
+          }
+        },
+        {
+          "rank_feature": {
+            "field": "embedding_full_record.hello",
+            "boost": 6.937756538391113
+          }
+        }
+      ]
+    }
+  }
+}
+```
+
+### Semantic and Lexical query flows
+
+```mermaid
+flowchart LR
+  sq(semantic)
+  kq(keyword)
+  tsb(timdex-semantic-builder external service)
+
+  subgraph s [Semantic Query]
+    SemanticQueryBuilder <--> tsb
+  end
+
+  subgraph l [Lexical Query]
+    LexicalQueryBuilder
+  end
+
+  kq --> l --> OpenSearch
+  sq --> s --> OpenSearch
+```
+
+Keyword, or lexical, queries will be handled entirely in this repository codebase.
+
+Semantic queries will be coordinated in this repository codebase, but constructed by a separate external service. The libraries necessary to create the query structure are python libraries and thus can't be done entirely in this ruby codebase.
+
+Hybrid queries are not in the diagram, but generally consist of sending a single combined query that consists of both lexical and semantic parts. Hybrid can thus be exepected to call both LexicalQueryBuilder and SemanticQueryBuilder to construct the single OpenSearch query.
+
+## Decision
+
+We will develop a separate tool (`timdex-semantic-builder` in diagram) outside of TIMDEX API that will accept a user query and return a semantic query structure ready to be used in our Semantic and Hybrid query builders.
+
+## Consequences
+
+By using an external tool, rather than running the model directly in OpenSearch we are able to consider moving to OpenSearch Serverless which may be easier to manage longterm.
+
+We also get to control the query construction more directly, which might end up being useful.
+
+This does mean we will need to mantain an additional tool to generate semantic queries and that we will need to make an additional external call (likely to a lambda, but not decided yet) during semantic query construction which will introduce some amount of latency.