feat: add dedicated Cohere Embed 4 QA vector retrieval

docs/design/723-sgmp-qa-vector-retrieval/README.md

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+# 723. SGMP QA Vector Retrieval
+
+## Goal
+
+Store curated question, answer SQL, answer text, and their combined retrieval
+document in `SGMP_POC`.  Retrieve the top-K closest examples for a new question
+and pass the returned context to the Text2SQL prompt in a later application
+integration.
+
+## Security boundary
+
+- IAM identity: `sgmp-qa-vector-api`
+- IAM group: `sgmp-vector-embed-group`
+- IAM policy: only `use generative-ai-text-embedding in tenancy`
+- Database credential: `SGMP_POC_QA_VECTOR_CRED`, created from that dedicated
+  API signing key only. It does not reuse `SGMP_POC_OCI_DEFAULT_CRED`.
+- Network: HTTPS only to OCI GenAI Chicago EmbedText endpoint on port 443. The
+  `SGMP_POC` ACE is provisioned once by an ADB `ADMIN` connection because an
+  application schema cannot administer network ACLs.
+- The private key is read from `VECTOR_OCI_API_KEY_FILE`; it is never committed,
+  displayed, or persisted outside the encrypted database credential.
+
+## Embedding contract
+
+- Model: `cohere.embed-v4.0`
+- Dimension: `1536` FLOAT32
+- The current ADB `DBMS_VECTOR` OCI adapter does not forward Cohere Embed 4's
+  `input_type` field; both paths therefore use the provider's compatible
+  default request shape. The model and 1536-dimension vector contract remain
+  fixed. Once the adapter exposes Embed 4 `input_type`, switch stored examples
+  to `search_document` and incoming questions to `search_query`.
+- `p_top_k` default: `3` (accepted range `1..20`)
+
+Oracle recommends distinct document/query input types for Cohere Embed 4 RAG
+flows and its default output size is 1536. See [Cohere Embed 4](https://docs.oracle.com/en-us/iaas/Content/generative-ai/cohere-embed-4.htm).
+
+## Database API
+
+```sql
+-- Stores question + answer SQL + optional answer and returns EXAMPLE_ID.
+SELECT sg_qa_vector_store(:question, :answer_sql, :answer_text) FROM dual;
+
+-- Returns EXAMPLE_ID, QUESTION, ANSWER_SQL, ANSWER_TEXT, MODEL and distance.
+DECLARE
+  results SYS_REFCURSOR;
+BEGIN
+  results := sg_qa_vector_search(:question); -- default top 3
+END;
+/
+
+-- Ready-to-insert textual context for a prompt.
+SELECT sg_qa_vector_context(:question, 3) FROM dual;
+```
+
+## Apply
+
+```bash
+export SGMP_POC_DB_PASSWORD='...'
+export SGMP_POC_WALLET_DIR='/path/to/Wallet_SGMPAIPOC'
+export VECTOR_OCI_USER_OCID='...'
+export VECTOR_OCI_TENANCY_OCID='...'
+export VECTOR_OCI_COMPARTMENT_OCID='...'
+export VECTOR_OCI_API_KEY_FILE='/secure/path/sgmp_qa_vector_api_key.pem'
+export VECTOR_OCI_API_KEY_FINGERPRINT='...'
+./scripts/setup-sgmp-qa-vector.sh
+```
+
+For the initial small QA corpus, exact cosine search is deliberate: it makes
+results immediately verifiable. Add a vector index only after the corpus size
+and recall/latency target are measured.