Add PDF text extraction workflow

Request:
- Provide a way to install or develop a PDF extraction tool for archived HK IPO documents.

Changes:
- Add requirements.txt with pypdf as the lightweight PDF text extraction dependency.
- Add scripts/extract_pdf_text.py to extract text from PDF source_refs into repo-relative data/extracted_text files.
- Add extracted text outputs and an extracted_text_manifest snapshot for the six archived HKEXnews PDFs.
- Document the extraction workflow in README.md.
- Ignore .venv and keep generated SQLite/Python transient files out of git.
- Use extracted text to verify the 06106 full prospectus, update source_refs, remove the related data gap, and fill 06106 offering terms.

Verification:
- Installed python3.14-venv system support, created a local .venv, and installed requirements.txt.
- Re-ran scripts/bootstrap_historical_data.py and scripts/extract_pdf_text.py.
- Verified extracted text paths and hashes against data/snapshots/extracted_text_manifest.csv.
- Verified SQLite integrity and snapshot row counts.
- Ran git diff --cached --check and searched durable files for machine-specific absolute paths.

scripts/bootstrap_historical_data.py

@@ -13,6 +13,9 @@ ARCHIVE_AS_OF = "2026-06-15T06:15:00Z"
 DB_PATH = Path("data/hk_ipo.sqlite")
 SCHEMA_PATH = Path("schema/hk_ipo.schema.sql")
 SNAPSHOT_DIR = Path("data/snapshots")
+STALE_GAP_IDS = [
+    "06106_full_prospectus_classification_2026_06_15",
+]
 
 
 IPO_MASTER = [
@@ -102,7 +105,7 @@ OFFERING_TERMS = [
     },
     {
         "ticker": "06106",
-        "source_id": "06106_prospectus_notice_2026_06_15",
+        "source_id": "06106_prospectus_candidate_2026_06_15",
         "prospectus_date": "2026-06-15",
         "offer_price_hkd": 101.60,
         "board_lot": 50,
@@ -113,10 +116,10 @@ OFFERING_TERMS = [
         "public_offer_pct_initial": 0.05,
         "over_allotment_offer_shares": 1574550,
         "offer_size_adjustment_offer_shares": 1574550,
-        "market_cap_hkd_m": None,
-        "gross_proceeds_hkd_m": None,
-        "net_proceeds_hkd_m": None,
-        "issued_shares_upon_listing": None,
+        "market_cap_hkd_m": 11226.52568,
+        "gross_proceeds_hkd_m": 1066.52568,
+        "net_proceeds_hkd_m": 995.4,
+        "issued_shares_upon_listing": 110497300,
         "data_as_of": ARCHIVE_AS_OF,
     },
 ]
@@ -195,12 +198,12 @@ SOURCES = [
     {
         "source_id": "06106_prospectus_candidate_2026_06_15",
         "ticker": "06106",
-        "source_type": "prospectus_candidate_pending_verification",
-        "title": "Shanghai Seer Intelligent Technology Co., Ltd. Prospectus Candidate",
+        "source_type": "prospectus",
+        "title": "Shanghai Seer Intelligent Technology Co., Ltd. Prospectus",
         "local_path": "data/raw/06106/prospectus_candidate_2026-06-15.pdf",
         "url": "https://www1.hkexnews.hk/listedco/listconews/sehk/2026/0615/2026061500013.pdf",
         "source_date": "2026-06-15",
-        "notes": "Downloaded from HKEXnews; document role should be verified before using for detailed fact extraction.",
+        "notes": "HKEXnews prospectus; verified by text extraction as a 424-page GLOBAL OFFERING document.",
     },
 ]
 
@@ -226,16 +229,6 @@ DATA_GAPS = [
         "created_at": ARCHIVE_AS_OF,
         "notes": "Update after the HKEXnews allotment results announcement is published.",
     },
-    {
-        "gap_id": "06106_full_prospectus_classification_2026_06_15",
-        "ticker": "06106",
-        "stage": "T0_prospectus",
-        "field_name": "full_prospectus_local_path",
-        "reason": "The archived 2026061500011 PDF is an offering announcement/notice; the separately archived 2026061500013 PDF needs document-role verification before detailed extraction.",
-        "expected_resolution_date": None,
-        "created_at": ARCHIVE_AS_OF,
-        "notes": "Keep both official files in raw archive until classification is confirmed.",
-    },
 ]
 
 
@@ -278,7 +271,7 @@ def export_snapshot(conn: sqlite3.Connection, table: str) -> None:
     cursor = conn.execute(f"SELECT * FROM {table} ORDER BY 1")
     columns = [description[0] for description in cursor.description]
     with (SNAPSHOT_DIR / f"{table}.csv").open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.writer(handle)
+        writer = csv.writer(handle, lineterminator="\n")
         writer.writerow(columns)
         writer.writerows(cursor.fetchall())
 
@@ -287,6 +280,7 @@ def main() -> None:
     DB_PATH.parent.mkdir(parents=True, exist_ok=True)
     with sqlite3.connect(DB_PATH) as conn:
         conn.executescript(SCHEMA_PATH.read_text(encoding="utf-8"))
+        conn.executemany("DELETE FROM data_gaps WHERE gap_id = ?", [(gap_id,) for gap_id in STALE_GAP_IDS])
         upsert_rows(conn, "ipo_master", IPO_MASTER)
         upsert_rows(conn, "offering_terms", OFFERING_TERMS)
         upsert_rows(conn, "ipo_demand", IPO_DEMAND)