Civil Judgment Taiwan Vectorstore

Ingest Taiwan civil court judgments (HTML or PDF) — exclusively covering Taiwan civil cases — into Qdrant with Ollama embeddings, preserving traceability, deduplication, and incremental updates.

Taiwan Civil Judgment → Vector DB (Qdrant) Ingestion

Scope: Taiwan civil court judgments only (民事判決). This skill ingests Taiwan civil cases (HTML or PDF files) into Qdrant. All parsing, chunking, and embedding logic lives in scripts/ingest.py — your job is to run the script, not to reimplement the pipeline.

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Quick Start (follow these steps in order)

Step 1 — Activate venv

source {baseDir}/.venv/bin/activate

Step 2 — Identify the run folder

The user will provide an absolute path to a run folder.

Example: /path/to/output/judicialyuan/20260305_142030

Verify it exists and has HTML or PDF files:

ls <RUN_FOLDER>/archive/ | grep -E '\.(html|pdf)$' | head -5

If no archive/*.html or archive/*.pdf files → stop and tell the user the folder has no ingestible data.

Step 3 — Run ingestion

Use absolute paths throughout — no cd needed:

python3 {baseDir}/scripts/ingest.py \
  --run-folder <RUN_FOLDER>

The script handles everything: pre-flight checks, collection auto-creation (creates civil_case_doc / civil_case_chunk if they don't exist), canonicalization, chunking, embedding, Qdrant upsert, manifest + report writing.

Re-running the same command on the same folder is always safe — deterministic IDs mean upsert = overwrite. No special --resume flag needed; just run the same command again.

Step 4 — Check the result

Successful output looks like:

OK files=42 processed=42 skipped=0 errored=0 doc_points=42 chunk_points=187
manifest=<RUN_FOLDER>/ingest_manifest.jsonl
report=<RUN_FOLDER>/ingest_report.md

Read the report (human-readable stats summary):

cat <RUN_FOLDER>/ingest_report.md

If there are errors, check the manifest (machine-readable, one JSON line per file) for per-file diagnosis:

grep -E '"status":"(skipped|error|partial)"' <RUN_FOLDER>/ingest_manifest.jsonl

Step 5 — Report to user

Tell the user:

• How many docs were ingested (doc_points)
• How many chunks were created (chunk_points)
• Whether any were skipped or errored
• Where the report file is

Done. Do not proceed to additional steps unless the user asks.

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DO NOT rules (critical)

• DO NOT write your own HTML parsing, chunking, or embedding code. ingest.py handles all of this.
• DO NOT modify parsing/chunking logic casually. Only change heading detection or chunk fallback when the user explicitly asks to improve PDF/OCR robustness, and validate on a small sample before re-running a large batch.
• DO NOT call Qdrant or Ollama APIs directly. The script does this.
• DO NOT use verify=False or skip SSL verification for any HTTP request.
• DO NOT modify or delete files under archive/. Raw HTML is immutable source of truth.
• DO NOT change chunking defaults (--max-chars, --overlap-chars) unless the user explicitly asks.

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Hard constraints

• Raw HTML/PDF is source of truth; never overwrite it.
• Deterministic: same input → same canonical text → same SHA-256 → same Qdrant point IDs. Safe to re-run.
• Traceability: every Qdrant point carries doc_url + local_path.
• Batched upserts (≤ 64 points/batch) to avoid Qdrant 32MB payload limit.
• parser_version in every point's metadata. Current: v3.5-sentence-boundary.

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Troubleshooting

PREFLIGHT_FAILED: Qdrant not reachable

Qdrant is down or unreachable at the default/configured URL.

# Check if Qdrant is running
curl -s http://localhost:6333/collections | head -1

# If not running, start it (or ask the user)

PREFLIGHT_FAILED: Ollama not reachable

# Check Ollama
curl -s http://localhost:11434/api/tags | head -5

PREFLIGHT_FAILED: Ollama model missing: bge-m3:latest

ollama pull bge-m3:latest

Then re-run Step 3.

PREFLIGHT_FAILED: No archive/*.html or archive/*.pdf found

The run folder exists but has no archived detail pages. Check:

• Is this the correct run folder?

Output shows skipped > 0 or errored > 0

Check ingest_manifest.jsonl for per-file details:

grep -E '"status":"(skipped|error|partial)"' "<RUN_FOLDER>/ingest_manifest.jsonl"

Manifest status	Meaning	Action
ok	Doc + all chunks ingested	None
partial	Doc upserted, but some section chunks failed embedding	Check Ollama stability; can re-run safely
skipped	Doc-level embedding failed — nothing upserted for this doc	Check Ollama; re-run safely
error	HTML read/parse failed	Check if the HTML file is corrupted

Re-running is always safe — use the exact same command. No special flags needed; deterministic IDs → upsert/overwrite.

Override service endpoints

# Via environment variables
OLLAMA_URL=http://localhost:11434 QDRANT_URL=http://localhost:6333 \
  python3 scripts/ingest.py --run-folder "..."

# Via CLI flags (take precedence over env vars)
python3 scripts/ingest.py --run-folder "..." \
  --ollama http://localhost:11434 --qdrant http://localhost:6333

Default endpoints:

Service	Default	Env override
Ollama	http://localhost:11434	$OLLAMA_URL
Qdrant	http://localhost:6333	$QDRANT_URL

Test with a small batch first

python3 scripts/ingest.py --run-folder "..." --limit 5

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Input folder structure (expected)

<run_folder>/
  archive/
    fjud_detail_001.html               ← HTML input
    fjud_detail_002.html
    fjud_detail_003.pdf                ← PDF input (also supported)
    fint_detail_001.html               (if system=both)
  results_fjud.jsonl                   (optional)
  results_fint.jsonl                   (optional)

The script discovers all archive/*.html and archive/*.pdf files automatically (sorted by filename). HTML and PDF files can coexist in the same run folder.

v1 limitation: The system metadata field is currently hardcoded to FJUD. If a run folder contains both FJUD and FINT files, FINT files will be ingested but mislabeled as FJUD. This does not affect chunking or embeddings — only the system metadata field on the resulting Qdrant points.

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CLI reference

python3 scripts/ingest.py --run-folder <PATH> [options]

Flag	Default	Description
--run-folder	(required)	Path to an input folder
--ollama	$OLLAMA_URL or http://localhost:11434	Ollama endpoint
--qdrant	$QDRANT_URL or http://localhost:6333	Qdrant endpoint
--embed-model	bge-m3:latest	Ollama embedding model
--vector-size	1024	Vector dimension
--max-chars	900	Max chars per chunk (500–1000)
--overlap-chars	150	Overlap between chunks (10–20% of max-chars)
--limit	0 (no limit)	Process only first N files sorted by filename (lexicographic order); for testing

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Outputs

• Qdrant collections: civil_case_doc (1 point/doc), civil_case_chunk (many points/doc). Auto-created if they don't exist.
• ingest_report.md: human-readable summary (doc/chunk counts, error counts). Read this first after ingestion.
• ingest_manifest.jsonl: machine-readable, one JSON line per doc with status (ok / partial / skipped / error). Read this to diagnose specific file failures (grep for non-ok statuses). Both files overlap on aggregate counts; the manifest adds per-file detail.

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Roadmap

• v1 (current): doc + section-aware chunks
• v2: candidate issue extraction (爭點抽取)
• v3: issue-level index (civil_case_issue collection)

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Internal details

For metadata schema, canonicalization rules, section-splitting patterns, and chunking implementation, see references/internals.md.

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Lessons learned / operational gotchas

• Qdrant rejects non-UUID/non-integer point IDs (400 Bad Request). The script uses deterministic UUIDs — do not change the ID generation logic.
• Qdrant rejects payloads > 32MB. The script batches at 64 points — do not increase batch size.
• Re-running on the same folder is safe: deterministic IDs mean upsert = overwrite.
• 台灣判決書 section headings 格式不統一（e.g.「理　由」with fullwidth space、兼容字如「⽂」）。目前 parser 已先做 heading normalization；若仍切不出 section，會 fallback 對 full 做 chunking，避免只留下 doc-level points。