Using the Markdown → JSONL Converter, processed 1,422 of the 1,550 official‑document JSONL items.
Continued execution of the Qwen Validation Debugger from item 46 to 50, and reviewed the generated code, Godot 3/4 engine checks, and JSONL validation results.
Completed common code and JSONL validation for the item 50 debug log; item 48’s dynamic scene‑resource loading created a fixed resource path for a non‑existent model, which the engine kept rejecting.
When retrying, passed the previous engine diagnosis and improved the debugger to use a different stable seed for each generation round without manually registering syntax or answers per item.
Loaded the current JSONL of the 8501 collector into outputs/godot-rag-sqlite/godot-rag.sqlite3 and cross‑checked the 1,570 original URLs, domains, SHA‑256 hashes, and record sources from pages.zip.
Configured SQLite with docs_chunks, api_mapping, label_prototypes, original provenance, and an FTS5 search index, and enabled snapshot creation with the same builder even before the full collection finished.
Compared SQLite and vector‑DB dumps of the collected JSONL for a Git‑ready format, preserved original records and provenance, and chose SQLite for reproducible sharing without a separate server.
Initially added SQLite to Git only as a baseline artifact while keeping the Source Flow Debugger’s F‑strategy pointing at PostgreSQL, which caused consistency issues because the same JSONL had to be reflected separately in SQLite and PostgreSQL.
Consolidated the F‑strategy’s baseline data and execution repository into a single committed SQLite, removing PostgreSQL connection settings and migration paths.
After reading BM25 candidates from SQLite FTS5, calculated Okapi BM25 in Node; stored embeddings together with model, dimension, record‑content SHA‑256, and Float32 vectors in the same SQLite record_embeddings table, re‑indexing only changed records.
Modified the Source Flow Debugger to directly read the committed SQLite’s revision, record count, and embedding count without a separate DB URL, and linked stored vectors to Node for cosine‑similarity search.
Because vector‑DB dumps depend on the embedding model, dimension, and index implementation, they are not kept as separate baseline data; when needed, they are regenerated as derived indexes from the same SQLite.