Add embedding testing plan documentation

Document how to test RAG ingestion with embeddings using external
OpenAI-compatible services. Users only need to set 4 environment
variables (OPENAI_API_BASE, OPENAI_API_KEY, OPENAI_MODEL,
OPENAI_EMBEDDING_DIM) and run the test script.

Related to issue #5320 (Batch embedding generation)

RAG_POC/EMBEDDING_TEST_PLAN.md

@@ -0,0 +1,130 @@
+# Embedding Testing Plan
+
+## Prerequisites
+
+1. MySQL server running with test database
+2. OpenAI-compatible embedding service accessible
+
+## Quick Start
+
+```bash
+cd /home/rene/pr5312/proxysql/RAG_POC
+
+# Step 1: Set your embedding service credentials
+export OPENAI_API_BASE="https://your-embedding-service.com/v1"
+export OPENAI_API_KEY="your-api-key-here"
+export OPENAI_MODEL="your-model-name"
+export OPENAI_EMBEDDING_DIM=1536  # Adjust based on your model
+
+# Step 2: Run the test
+./test_rag_ingest.sh
+```
+
+---
+
+## Configuration Options
+
+### OpenAI API
+```bash
+export OPENAI_API_BASE="https://api.openai.com/v1"
+export OPENAI_API_KEY="sk-your-openai-key"
+export OPENAI_MODEL="text-embedding-3-small"
+export OPENAI_EMBEDDING_DIM=1536
+```
+
+### Azure OpenAI
+```bash
+export OPENAI_API_BASE="https://your-resource.openai.azure.com/openai/deployments/your-deployment"
+export OPENAI_API_KEY="your-azure-key"
+export OPENAI_MODEL="text-embedding-ada-002"  # Your deployment name
+export OPENAI_EMBEDDING_DIM=1536
+```
+
+### Other OpenAI-compatible services
+```bash
+# Any service with OpenAI-compatible API
+export OPENAI_API_BASE="https://your-service.com/v1"
+export OPENAI_API_KEY="your-key"
+export OPENAI_MODEL="model-name"
+export OPENAI_EMBEDDING_DIM=dim  # e.g., 768, 1536, 3072
+```
+
+---
+
+## What the Test Does
+
+**Phase 4** (runs automatically with OPENAI_ variables set):
+1. Creates RAG database with schema
+2. Configures embedding with your credentials
+3. Ingests 10 documents from MySQL
+4. Generates embeddings via your service
+5. Verifies:
+   - 10 documents created
+   - 10 chunks created
+   - **10 embeddings created**
+   - Vector self-match works (search finds itself)
+
+---
+
+## Expected Output
+
+```
+==> embedding_json: {"enabled":true,"provider":"openai","api_base":"https://...","api_key":"***","model":"...","dim":1536,"input":{"concat":[{"col":"Title"},{"lit":"\n"},{"chunk_body":true}]}}
+Ingesting source_id=1 name=test_source backend=mysql table=posts
+Done source test_source ingested_docs=10 skipped_docs=0
+OK: rag_documents (embeddings enabled) = 10
+OK: rag_chunks (embeddings enabled) = 10
+OK: rag_vec_chunks (embeddings enabled) = 10
+OK: vec self-match (posts:1#0) = posts:1#0
+```
+
+---
+
+## Verification Queries
+
+After the test, manually verify:
+
+```bash
+sqlite3 rag_ingest_test_openai.db <<SQL
+.load ../deps/sqlite3/sqlite3/vec0.so
+
+-- All chunks have embeddings?
+SELECT 'Missing embeddings: ' || COUNT(*) FROM rag_chunks c
+LEFT JOIN rag_vec_chunks v ON c.chunk_id = v.chunk_id
+WHERE v.chunk_id IS NULL;
+-- Expected: 0
+
+-- Sample embeddings
+SELECT chunk_id, substr(hex(substr(embedding,1,4)),1,8) AS vec_prefix
+FROM rag_vec_chunks LIMIT 5;
+```
+
+---
+
+## Troubleshooting
+
+### Error: "Failed to generate embeddings"
+- Check `OPENAI_API_BASE` is correct
+- Check `OPENAI_API_KEY` is valid
+- Check `OPENAI_MODEL` exists in your service
+
+### Error: "Dimension mismatch"
+- Set `OPENAI_EMBEDDING_DIM` to match your model
+- Common dimensions: 768, 1536, 3072
+
+### Timeout errors
+- The test uses 20-second timeout (configurable in embedding_json)
+- Check network connectivity to embedding service
+
+---
+
+## Testing Different Batch Sizes
+
+To test the batching implementation, you can modify the test temporarily:
+
+```bash
+# Edit test_rag_ingest.sh, line ~339, add batch_size:
+# {"enabled":true,"provider":"openai",...,"batch_size":32}
+```
+
+Then observe the number of API calls in your embedding service dashboard.