spring-ai-rag-media-pgvector

name: spring-ai-rag-media-pgvector description: Build RAG pipelines for media-asset knowledge bases using Spring AI and PostgreSQL pgvector. Use when Codex needs to design database schema, ingestion/chunking/embedding workflow, and retrieval logic that prioritizes internal media knowledge before falling back to general model knowledge.

Spring AI RAG Media PgVector

Workflow

1. Define knowledge scope:

• Enumerate media asset types (movie metadata, synopsis, production notes, tags, reviews, scripts).
• Define source-of-truth systems and ingestion frequency.

2. Provision database and extension:

• Apply references/pgvector-schema.sql via Flyway.
• Confirm vector(1536) dimension matches chosen embedding model.

3. Build ingestion pipeline:

• Normalize source records into JSONL chunks with stable IDs.
• Validate chunk payload using scripts/validate_chunks.py.
• Persist raw document metadata and vectorized chunks.

4. Embed and index:

• Use Spring AI embedding model to generate vectors.
• Upsert chunk vectors in batches.
• Rebuild IVFFlat index only after bulk ingestion.

5. Implement retrieval priority:

• Execute vector similarity search against internal chunks first.
• If top score is below threshold or result count is low, fallback to model-only answer path.
• Keep both retrieved evidence and fallback reason in response metadata.

6. Enforce grounded generation:

• Use retrieval context and source citations in prompt.
• If no reliable internal evidence is found, explicitly state uncertainty.

Guardrails

• Keep chunk size stable (recommended 300-800 Chinese chars; overlap 50-120).
• Version embedding model IDs; avoid mixing vectors from different dimensions in one table.
• Use metadata filters (asset_type, year, language) before distance ranking when possible.
• Log retrieval scores for online quality monitoring.

Resources

• references/pgvector-schema.sql: DB and index template for media RAG.
• references/rag-priority-strategy.md: retrieval-first orchestration strategy.
• references/application-yml-template.md: Spring AI and datasource baseline config.
• scripts/validate_chunks.py: chunk payload validator before embedding ingestion.