Fix prow pipeline: in-cluster image build, RAG config, port-forward fix#3
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- Build llama-stack image in OpenShift internal registry via oc new-build/start-build - Add image-puller role for default SA to pull from internal registry - Add FAISS_VECTOR_STORE_ID and KV_RAG_PATH env vars to lightspeed-stack pod - Add inference, byok_rag, and rag sections to prow lightspeed-stack configs - Use envsubst with specific variable scoping in pipeline-services.sh - Fix free_local_tcp_port to only kill LISTEN sockets (was killing behave process) - Add MCP token secrets and empty OpenAI secret to pipeline.sh - Add rlsapi_v1_infer action to prow RBAC config - Simplify llama-stack.yaml to use pre-built image Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Add the spike doc (decisions up front, background below, 4 proposed
JIRAs) and the spec doc (R1..R6 requirements, architecture, key files
and insertion points, known limitations) under docs/design/byok-pdf/.
The spike is lightweight by design: HTML support shipped under
LCORE-1035 (commit 7f688b0, 2026-01-15), so the architectural pattern,
docling dependency, BaseReader plumbing, CLI shape, and test layout
are all already established. PDF support is a scaffold-and-mirror job
plus a one-line addition to document_processor.py's doc_type branches.
Decisions captured for confirmation (each with options table and
recommendation in the spike doc):
D1: Library -- docling (already a dependency)
D2: OCR for scanned PDFs -- out of scope; track as follow-up
D3: Repo placement -- rag-content (impl) + lightspeed-stack
(BYOK guide update only)
D4: Pipeline knobs -- hard-coded sensible defaults; no CLI
flags in v1 (mirrors HTMLReader)
D5: Chunking strategy -- reuse MarkdownNodeParser; add "pdf" to
document_processor.py:75 and :87
D6: Code organization -- new pdf/ package mirroring html/
D7: Test coverage -- unit/integration in JIRA #2, e2e in #3
Four sub-JIRAs proposed under LCORE-1471 (parseable by
dev-tools/file-jiras.sh):
1. rag-content: Implement PDF support
2. rag-content: Unit and integration tests
3. rag-content: End-to-end test (PDF -> vector store -> stack query)
4. lightspeed-stack: Update BYOK guide for native PDF support
PoC evidence under poc-results/:
01-poc-report.txt Methodology, findings, implications
02-conversion-log.txt Exact commands and timings
03-sample-jira-1311.md Clean conversion (Atlassian Cloud PDF)
04-sample-jira-836.md Body clean, headings degraded
(Confluence PDF, letter-spaced display font)
Honest PoC findings worth surfacing:
- No new dependencies are needed (docling is already in pyproject.toml).
- Body text and tables convert cleanly to Markdown.
- MarkdownNodeParser handles the output -- no parallel chunking pipeline.
- Letter-spaced display fonts (typical of Confluence "Export to PDF")
produce noisy heading text; documented as a v1 known limitation.
- Cold model load is ~5 minutes on CPU; warm conversions ~30-90 s for
small/medium PDFs. Acceptable for offline indexing.
Per howto-run-a-spike.md step 10, poc/ and poc-results/ will be
removed before merge; spike doc and spec doc remain in the repo.
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Summary
FAISS_VECTOR_STORE_IDandKV_RAG_PATHenv vars to lightspeed-stack podinference,byok_rag, andragsections to prow lightspeed-stack configs (vllm provider)free_local_tcp_portto only kill LISTEN sockets — was killing behave process (Error 137)envsubst '${LLAMA_STACK_IMAGE}'in pipeline-services.shTest plan
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