DEV Community: Lindenwerk Data

PDF to RAG-ready Markdown chunks in Python: headings, two-column pages and tables, with MIT/BSD libraries only

Lindenwerk Data — Mon, 26 Oct 2026 15:45:53 +0000

Most RAG tutorials start with text = page.extract_text() and a fixed-size splitter. That works on a clean single-column PDF. On real documents you get page headers in every chunk, table cells flattened into a word soup, two-column pages read straight across, and chunks that stop halfway through a word.

This tutorial builds a converter that turns a text-based PDF into clean Markdown and heading-aware chunks, with tables kept as tables. It uses only permissively licensed libraries:

pdfplumber (MIT), built on pdfminer.six (MIT), for characters with font and position, and for ruled tables. It also installs pypdfium2 (BSD-3-Clause / Apache-2.0) and Pillow (MIT-CMU).
pypdf (BSD-3-Clause) for the PDF's bookmarks.

We tested it on two public US government documents with different layouts, and every number below comes from those runs. The full script (about 600 lines) is at the end.

Why not PyMuPDF?

PyMuPDF is fast and its Markdown helper is popular for exactly this job. Its PyPI page lists the licence as "Dual Licensed - GNU AFFERO GPL 3.0 or Artifex Commercial License", and the pymupdf4llm package is now an empty shim: "The pymupdf-2.x package now contains the 4llm code that used to be in pymupdf4llm-1.x." If AGPL or a commercial licence works for you, try it. If you need MIT/BSD dependencies only, read on. ML layout converters such as Docling (MIT on PyPI) are the next step up when heuristics stop working.

The test documents

NIST CSWP 29, *The NIST Cybersecurity Framework (CSF) 2.0* (32 pages, single column, coloured tables, bullet lists). NIST states that works of its employees "are not subject to copyright protection in the United States" (foreign rights are reserved).
IRS Publication 15 (2026), *Employer's Tax Guide* (59 pages, mostly two columns, a seven-page rules table). IRS.gov says content created by federal employees "is not subject to copyright and may be freely copied. Credit is requested."

Download them from the official links at the end. They aren't redistributed here.

The baseline: pypdf plus a fixed-size splitter

The usual baseline is extract_text() on every page, then 1,500-character windows:

	NIST CSF 2.0	IRS Pub 15
characters extracted	69,675	319,590
1,500-character windows	47	214
window boundaries inside a word	31	120
running header/footer copies	"NIST CSWP 29" ×32	"Publication 15 (2026)" ×59

Pub 15 is worse than that table suggests. Every page starts with Page 46 of 59 Fileid: … ication-15/2026/a/xml/cycle09/source ... and "The type and rule above prints on all proofs including departmental reproduction proofs. MUST be removed before printing." That's a printer's slug placed outside the visible page. You never see it in a viewer, but it lands in every page of your index.

Tables suffer most. On page 20 of the NIST file, pypdf gives the caption "Table 1. CSF 2.0 Core Function and Category names and identifiers", then the paragraph that comes after the table, and only then the cells:

Function Category Category Identifier
Govern (GV) Organizational Context GV.OC
 Risk Management Strategy GV.RM
 Roles, Responsibilities, and Authorities GV.RR

Only the first row of each group says which Function it belongs to. A chunk that starts at "Risk Management Strategy GV.RM" has lost that context.

None of this is a pypdf bug. Its documentation says it plainly: "PDF files don't contain a semantic layer." Headers, tables and paragraphs exist only as positioned glyphs, so we have to rebuild them from geometry and fonts.

Step 1: words with fonts, inside the visible page

page = page.within_bbox(page.bbox)       # drops text placed outside the page (the printer's slug)
words = page.filter(outside_tables).extract_words(return_chars=True)

return_chars=True keeps each word's characters, so we can work out font size and weight per line. We tried extra_attrs=["size", "fontname"] first. The pdfplumber README notes that this splits words wherever those attributes change. The NIST file sets GOVERN, IDENTIFY and so on in small caps, which mixes two font sizes inside one word, so they came out as "G OVERN". Reading the font from the characters avoids that.

Lines are grouped by vertical centre, not by top. The sub-bullets in the NIST file are a Courier "o" that sits 4 points lower than the Calibri text next to it, and a plain top tolerance made "o" a line of its own.

Step 2: tables first, then everything else

page.find_tables() finds tables from their ruling lines. We find tables before reading text so their characters can be left out of the paragraphs. Four clean-ups made the difference:

Reject boxes. Not every frame is a table. Of 5 candidates in the NIST file, 2 had a single column (the frame around the cover artwork and a boxed note), so they go back to being text.
Merge split columns. The coloured cells in NIST Table 1 add extra vertical lines, and pdfplumber reported 9 columns for a 3-column table. Dropping empty columns and merging neighbours that never both hold text restores the three.
Split tall rows. Pub 15's rules table rules only some rows. pdfplumber returned "Agricultural labor" items 1 to 8 as one giant row. Inside a row we measure the distance between baselines (8 points within an item, about 12 between items) and cut where the first column starts a line after a larger gap. That cut 77 rows apart in Pub 15.
Flatten two-row headers and fill down. "Treatment under employment taxes" spans three sub-headers, so each column becomes "Treatment under employment taxes: FUTA" and so on. A merged first-column cell such as "Govern (GV)" is repeated on every row it covers (16 cells in NIST) so each row stands on its own.

for i, p in enumerate(pitches, start=1):
    if p > 1.3 * usual and any(w["x0"] < col0[2] for w in lines[i]):
        cuts.append(bottoms[i] - 0.5)      # words are assigned to bands by baseline

Step 3: reading order on two-column pages

pdfplumber sorts words top to bottom, so a two-column line would read across both columns. We look for a gutter, a vertical strip in the middle third of the page that almost no word crosses (at most 10% of the lines, so a full-width heading doesn't hide it). A line that crosses the gutter is full width. Full-width lines and tables cut the page into bands, and inside each band we read the left column, then the right:

for it in sorted(items, key=lambda i: i["top"]):
    if it["side"] == "S":                  # full width
        ordered += sorted(band, key=lambda i: (i["side"], i["top"])) + [it]
        band = []
    else:
        band.append(it)

51 of Pub 15's 59 pages came out as two-column. The NIST file had none, which is correct.

Step 4: remove what repeats

A line in the top or bottom 10% of the page that repeats on at least 40% of pages (numbers ignored, so "Page 7" matches "Page 8") is a running header or footer. Bare page numbers, including roman ones, and contents lines with dot leaders go too. That removed 93 lines from NIST and 113 from Pub 15, plus 14 and 39 contents lines.

Step 5: headings from the bookmarks, fonts as backup

Heading levels are the most valuable metadata a chunk can carry, and many PDFs already have them as bookmarks:

def walk(items, level):
    for it in items:
        if isinstance(it, list):
            walk(it, level + 1)
        else:
            out.append({"title": it.title.strip(), "level": level,
                        "page": reader.get_destination_page_number(it)})

We match each bookmark to a line on its page (or the next one), and the line can wrap. All 12 NIST bookmarks matched, and 69 of Pub 15's 70 (the leftover is "Index").

Don't trust bookmarks blindly, though. Pub 15's outline jumps from "14. Federal Unemployment (FUTA) Tax" straight to "16. Third-Party Payer Arrangements", so section 15, the big rules table, has no bookmark. To catch it, the script learns which font size and weight the matched bookmarks use and promotes unmatched lines in the same style. That found 4 more headings: section 15 and three table captions.

With --no-outline, headings come from font style alone: lines bigger than body text, or short all-bold lines. Styles that occur at least twice are ranked by size, and the three biggest become levels 1 to 3. That gave 22 headings for NIST (including "Abstract" and "Keywords", which have no bookmarks) and 79 for Pub 15.

Step 6: paragraphs and hyphens

A new paragraph starts at a bullet, a size change, a short bold label line, a vertical gap of more than 0.6 lines, or a first-line indent after a full stop. When text moves up (to the next column or page), the paragraph continues unless the previous line ended a sentence. That's how a sentence split across Pub 15's columns comes back together.

Line-end hyphens are the tricky part: "be-|fore" should become "before", but "high-|level" should keep its hyphen. The script asks the document itself, counting both spellings everywhere else in the text with the fragments around line-end hyphens left out. Where neither spelling appears, it keeps the hyphen only if both halves are words found elsewhere in the text. In Pub 15 that joined 1,063 of 1,065 line-end hyphens. In NIST it kept 3 of 4 ("high-level", "mission-essential", "incident-related") and got one wrong, "one-size-fitsall".

Step 7: tables that continue on the next page

When a page starts with a table that has the same number of columns as the table that ended the previous page, it's a continuation. The script drops the repeated header rows and appends the rest. If the first new row has an empty first column, the page break cut a row in two, and the halves are joined. Pub 15's rules table became one table of 123 data rows from pages 46 to 52 (6 stitches). In NIST, the "Tier 3" row of Table 2 spans pages 29 and 30 and was rejoined into one row.

Step 8: chunks that respect structure

Chunks never cross a heading. Paragraphs are packed up to --max-chars (default 1,500, heading lines included), and a paragraph that's too long is split at sentence ends. Tables are split between rows, and every table chunk repeats the header row. Each chunk starts with its heading path, so the embedding sees where the text comes from. Here's a real chunk from Pub 15, trimmed to one of its four rows and with one header cell shortened:

{"id": "p15-0224", "source": "p15.pdf", "pages": [46, 46],
 "headings": ["15. Special Rules for Various Types of Services and Payments"],
 "has_table": true, "chars": 1483,
 "text": "# 15. Special Rules for Various Types of Services and Payments\n\n| Special classes of employment and special types of payments | Treatment under employment taxes: Federal income tax withholding | ... | Treatment under employment taxes: FUTA |\n|---|---|---|---|\n| 4. Cotton ginning. | Taxable if wages subject to social security tax and Medicare tax. | Taxable if $150 test or $2,500 test in section 9 is met.1 | Taxable if either test in section 14 is met. |\n..."}

A question about cotton ginning and FUTA now retrieves a chunk that says what each column means. Compare that with the baseline's loose cell text.

The trade-off: that header line is 314 characters, about a fifth of a 1,500-character chunk. The script never cuts a single table row, so a very long row can exceed the limit. That happened twice in NIST (the Tier 3 and Tier 4 rows of Table 2, 1,616 and 1,882 characters). At --max-chars 800 it happened 3 times in NIST and 10 times in Pub 15.

Results

Default settings, Python 3.13.5, one run on an 8-core cloud VM:

	NIST CSF 2.0	IRS Pub 15
pages / seconds	32 / 1.8	59 / 7.8
headings (bookmarks + style)	12 + 0	69 + 4
tables found / kept	5 / 2	14 / 7
chunks (with a table)	57 (5)	281 (30)
chunk size min / median / max	105 / 1,327 / 1,882	101 / 1,251 / 1,500
chunks with a heading path	52	281
header/footer copies in chunks	1 (cover page)	1 (cover page)

To check that cleaning didn't lose text, we compared word counts with pdfplumber's raw text of the visible page area. The Markdown keeps 93.3% of NIST's words and 95.0% of Pub 15's. The difference is what we meant to remove: the 106 "o" bullet glyphs, running headers and page numbers, and word fragments that hyphen repair turned into whole words ("em", "ing", "ment").

What it doesn't do

Scanned PDFs have no text layer. The script reports it and stops. Run OCR first.
Unruled tables come through as text, because find_tables() here uses ruling lines.
Text inside figures (the NIST tier diagram, for example) becomes short stray paragraphs.
Front matter (cover, contents, boxed checklists) is the roughest part of the output. Footnote markers stay glued to words ("met.1").
Sizes are in characters, not tokens. Swap in your embedding model's tokenizer if it has a hard limit.

These are heuristics tuned on two documents. Before you index thousands of files, read the Markdown for a few pages of each new document family.

Official documentation

pdfplumber (README on its PyPI page: extract_words, within_bbox, find_tables, table settings): https://pypi.org/project/pdfplumber/
pypdf text extraction and its limits: https://pypdf.readthedocs.io/en/stable/user/extract-text.html
Licences on PyPI: https://pypi.org/project/pdfplumber/, https://pypi.org/project/pypdf/, https://pypi.org/project/pdfminer.six/, https://pypi.org/project/pypdfium2/, https://pypi.org/project/PyMuPDF/, https://pypi.org/project/pymupdf4llm/
NIST CSWP 29: https://doi.org/10.6028/NIST.CSWP.29 (PDF: https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.29.pdf); NIST copyright statement: https://www.nist.gov/open/copyright-fair-use-and-licensing-statements-srd-data-software-and-technical-series-publications
IRS Publication 15: https://www.irs.gov/pub/irs-pdf/p15.pdf; IRS reuse notice: https://www.irs.gov/about-irs/use-of-content-from-irsgov

Set up and run:

python -m venv venv && ./venv/bin/pip install "pdfplumber>=0.11.10,<0.12" "pypdf>=4.0"
./venv/bin/python pdf_to_chunks.py NIST.CSWP.29.pdf p15.pdf --out out/

Full script: pdf_to_chunks.py (about 600 lines, pdfplumber + pypdf)

#!/usr/bin/env python3
"""Turn PDFs (including ones with tables) into clean Markdown and heading-aware chunks for RAG.

Uses only permissively licensed libraries: pdfplumber (MIT, built on pdfminer.six, MIT) for words, fonts and
tables, and pypdf (BSD-3-Clause) for the PDF outline (bookmarks).

  python pdf_to_chunks.py report.pdf                    # writes out/report.md and out/report.chunks.jsonl
  python pdf_to_chunks.py *.pdf --max-chars 1200 --out chunks/

Every chunk carries its heading path, page range and a has_table flag. Tables become Markdown pipe tables; a
table that continues on the next page is stitched back together, and a table too big for one chunk is split by
rows with its header repeated. Scanned PDFs (images only) have no text layer: run OCR first.
"""
from __future__ import annotations

import argparse
import json
import re
import statistics
import time
from collections import Counter
from pathlib import Path

import pdfplumber
from pypdf import PdfReader

BULLET_RE = re.compile(r"^[\u2022\u25cf\u25aa\u25e6\u2023\u2043\uf0b7]\s*")   # • ● ▪ ◦ ‣ ⁃ and the Symbol-font bullet
SENTENCE_END = re.compile(r"[.!?:;)\]\"\u201d]$")


def norm(s: str) -> str:
    """Loose form of a string for matching headings and repeated header/footer lines."""
    s = s.replace("\u2019", "'").replace("\u2018", "'").replace("\u201c", '"').replace("\u201d", '"')
    return re.sub(r"[^a-z0-9']+", " ", s.lower()).strip()


# ---------------------------------------------------------------- outline (pypdf)

def read_outline(path: Path) -> list[dict]:
    """Bookmarks as [{'title', 'level', 'page'}]; empty if the PDF has none."""
    out: list[dict] = []
    try:
        reader = PdfReader(path)

        def walk(items, level):
            for it in items:
                if isinstance(it, list):
                    walk(it, level + 1)
                else:
                    try:
                        page = reader.get_destination_page_number(it)
                    except Exception:
                        continue
                    if it.title and it.title.strip():
                        out.append({"title": it.title.strip(), "level": level, "page": page})
        walk(reader.outline, 1)
    except Exception:
        pass
    return out


# ---------------------------------------------------------------- tables (pdfplumber)

def clean_cell(c) -> str:
    c = (c or "").strip()
    c = re.sub(r"(?<=[a-z])-\n(?=[a-z])", "", c)     # hyphenated line break inside a cell
    return re.sub(r"\s+", " ", c).replace("|", "\\|")


def table_rows(page, table, stats: Counter) -> list[list]:
    """Cell texts, row by row. Tables often rule only some rows, so pdfplumber returns one tall 'row' holding
    several items; split it where the first column starts a new line after a larger-than-usual line gap."""
    out = []
    for row in table.rows:
        words = page.within_bbox(row.bbox).extract_words() if row.bbox[3] > row.bbox[1] else []
        cells = [c for c in row.cells if c]
        bands = [(row.bbox[1], row.bbox[3])]
        if words and cells:
            lines = cluster_rows(words, key="bottom", tol=1.5)
            bottoms = [ln[0]["bottom"] for ln in lines]
            pitches = [b - a for a, b in zip(bottoms, bottoms[1:])]
            if len(pitches) >= 3:
                usual = statistics.median(pitches)
                col0 = cells[0]
                cuts = []
                for i, p in enumerate(pitches, start=1):
                    if p > 1.3 * usual and any(w["x0"] < col0[2] for w in lines[i]):
                        cuts.append(bottoms[i] - 0.5)      # words are assigned to bands by baseline
                if cuts:
                    stats["table_rows_split"] += len(cuts)
                    edges = [row.bbox[1]] + cuts + [row.bbox[3]]
                    bands = list(zip(edges, edges[1:]))
        for y0, y1 in bands:
            texts = []
            for c in row.cells:
                if c is None:
                    texts.append("")
                    continue
                inside = [w for w in words if c[0] <= (w["x0"] + w["x1"]) / 2 <= c[2]
                          and c[1] <= (w["top"] + w["bottom"]) / 2 <= c[3] and y0 < w["bottom"] <= y1]
                texts.append("\n".join(" ".join(w["text"] for w in sorted(ln, key=lambda w: w["x0"]))
                                       for ln in cluster_rows(inside, key="bottom", tol=1.5)))
            out.append(texts)
    return out


def clean_table(rows: list[list]) -> list[list[str]]:
    rows = [[clean_cell(c) for c in r] for r in rows]
    rows = [r for r in rows if any(r)]
    if not rows:
        return []
    width = max(len(r) for r in rows)
    rows = [r + [""] * (width - len(r)) for r in rows]
    keep = [j for j in range(width) if any(r[j] for r in rows)]          # drop empty columns
    rows = [[r[j] for j in keep] for r in rows]
    # merge neighbouring columns that never both hold text: a merged cell split by extra ruling lines
    j = 0
    while rows and j < len(rows[0]) - 1:
        if not any(r[j] and r[j + 1] for r in rows):
            rows = [r[:j] + [r[j] or r[j + 1]] + r[j + 2:] for r in rows]
        else:
            j += 1
    return rows


def merge_header(rows: list[list[str]]) -> list[list[str]]:
    """Two header rows where the first has a spanning cell ('Treatment under ...' over three columns)."""
    if len(rows) < 3:
        return rows
    top, sub = rows[0], rows[1]
    gaps = [j for j, c in enumerate(top) if not c]
    if gaps and not sub[0] and all(sub[j] for j in gaps):
        filled, last = [], ""
        for c in top:
            last = c or last
            filled.append(last)
        header = [f"{a}: {b}" if a and b and a != b else (a or b) for a, b in zip(filled, sub)]
        return [header] + rows[2:]
    return rows


def fill_down(rows: list[list[str]]) -> int:
    """Repeat a vertically merged first-column value ('Govern (GV)') so every row stands alone."""
    n = 0
    for i in range(2, len(rows)):
        if not rows[i][0] and any(rows[i][1:]) and rows[i - 1][0]:
            rows[i][0] = rows[i - 1][0]
            n += 1
    return n


def to_markdown_rows(rows: list[list[str]]) -> tuple[str, list[str]]:
    header = "| " + " | ".join(rows[0]) + " |\n|" + "---|" * len(rows[0])
    return header, ["| " + " | ".join(r) + " |" for r in rows[1:]]


# ---------------------------------------------------------------- page layout

def find_gutter(words: list[dict], rows: int, width: float) -> tuple[float, float] | None:
    """A vertical strip in the middle third that (almost) no word crosses: a two-column page."""
    if rows < 8:
        return None
    lo, hi = int(width * 0.35), int(width * 0.65)
    cross = [0] * (hi - lo)
    for w in words:
        for x in range(max(lo, int(w["x0"])), min(hi, int(w["x1"]) + 1)):
            cross[x - lo] += 1
    limit = max(1, rows * 0.1)
    best, run = None, None
    for i, c in enumerate(cross + [limit + 1]):
        if c <= limit:
            run = run if run is not None else i
        elif run is not None:
            if i - run >= 6 and (best is None or i - run > best[1] - best[0]):
                best = (run, i)
            run = None
    if not best:
        return None
    g0, g1 = best[0] + lo, best[1] + lo
    left = sum(1 for w in words if w["x1"] <= g1)
    right = sum(1 for w in words if w["x0"] >= g0)
    return (g0, g1) if min(left, right) > 0.25 * len(words) else None


def cluster_rows(words: list[dict], key: str = "center", tol: float = 1.5) -> list[list[dict]]:
    """Group words into text lines. key="center": a word joins the line its vertical centre falls into
    (bullet glyphs from another font sit a few points lower); key="bottom": same baseline within tol."""
    rows: list[list[dict]] = []
    span = (0.0, 0.0)
    for w in sorted(words, key=lambda w: (w["bottom" if key == "bottom" else "top"], w["x0"])):
        if key == "center":
            c = (w["top"] + w["bottom"]) / 2
            fits = rows and span[0] <= c <= span[1]
        else:
            fits = rows and abs(w[key] - rows[-1][0][key]) <= tol
        if fits:
            rows[-1].append(w)
        else:
            rows.append([w])
            span = (w["top"], w["bottom"])
    if key == "bottom":
        rows.sort(key=lambda r: r[0]["bottom"])
    return rows


def make_line(ws: list[dict], page_no: int, side: str) -> dict:
    ws = sorted(ws, key=lambda w: w["x0"])
    chars = [c for w in ws for c in w["chars"]]
    return {
        "kind": "line", "page": page_no, "side": side,
        "text": " ".join(w["text"] for w in ws),
        "x0": ws[0]["x0"], "x1": ws[-1]["x1"], "top": min(w["top"] for w in ws),
        "bottom": max(w["bottom"] for w in ws),
        "size": round(statistics.median(c["size"] for c in chars), 1),
        "bold": sum("Bold" in c["fontname"] for c in chars) / len(chars),
        "first_font": ws[0]["chars"][0]["fontname"],
    }


def page_items(page, page_no: int, stats: Counter) -> list[dict]:
    """Lines and tables of one page in reading order (two-column aware)."""
    page = page.within_bbox(page.bbox)            # drops text placed outside the visible page (printer slugs)
    tables = []
    for t in page.find_tables():
        stats["tables_found"] += 1
        rows = clean_table(table_rows(page, t, stats))
        if len(rows) < 2 or len(rows[0]) < 2:      # boxed notes and frames are not tables
            stats["tables_rejected"] += 1
            continue
        tables.append({"kind": "table", "page": page_no, "rows": merge_header(rows), "x0": t.bbox[0],
                       "x1": t.bbox[2], "top": t.bbox[1], "bottom": t.bbox[3]})

    def outside_tables(obj) -> bool:
        if obj.get("object_type") != "char":
            return True
        cx, cy = (obj["x0"] + obj["x1"]) / 2, (obj["top"] + obj["bottom"]) / 2
        return not any(t["x0"] <= cx <= t["x1"] and t["top"] <= cy <= t["bottom"] for t in tables)

    words = page.filter(outside_tables).extract_words(return_chars=True)
    rows = cluster_rows(words)
    gutter = find_gutter(words, len(rows), page.width)
    stats["two_column_pages"] += bool(gutter)
    items = []
    for row in rows:
        if gutter is None:
            items.append(make_line(row, page_no, "S"))
            continue
        left = [w for w in row if w["x1"] <= gutter[1]]
        right = [w for w in row if w["x0"] >= gutter[0]]
        if len(left) + len(right) < len(row):        # a word crosses the gutter: full-width line
            items.append(make_line(row, page_no, "S"))
        else:
            items += [make_line(part, page_no, side) for part, side in ((left, "L"), (right, "R")) if part]
    for t in tables:
        t["side"] = "S" if gutter is None or (t["x0"] < gutter[0] and t["x1"] > gutter[1]) else (
            "L" if t["x1"] <= gutter[1] else "R")
        items.append(t)
    # reading order: full-width items cut the page into bands; inside a band, left column then right
    ordered, band = [], []
    for it in sorted(items, key=lambda i: i["top"]):
        if it["side"] == "S":
            ordered += sorted(band, key=lambda i: (i["side"], i["top"])) + [it]
            band = []
        else:
            band.append(it)
    return ordered + sorted(band, key=lambda i: (i["side"], i["top"]))


# ---------------------------------------------------------------- document

def drop_running_lines(pages: list[list[dict]], heights: list[float], stats: Counter) -> None:
    """Remove page headers/footers (margin lines that repeat on many pages, digits ignored), bare page
    numbers (also roman ones) and table-of-contents lines with dot leaders."""
    def key(it):                                   # numbers ignored: "Page 7" == "Page 8"
        return " ".join(re.sub(r"\d+", " ", norm(it["text"])).split())

    def in_margin(it, h):
        return it["kind"] == "line" and (it["bottom"] < h * 0.1 or it["top"] > h * 0.9)

    seen = Counter()
    for items, h in zip(pages, heights):
        seen.update({key(it) for it in items if in_margin(it, h)})
    running = {k for k, n in seen.items() if n >= max(3, 0.4 * len(pages))}
    for i, (items, h) in enumerate(zip(pages, heights)):
        keep = []
        for it in items:
            if in_margin(it, h) and (key(it) in running or re.fullmatch(r"(page|of|[ivxlc]{1,4}| )*", key(it))):
                stats["running_lines_removed"] += 1
            elif it["kind"] == "line" and re.search(r"(\.\s?){6,}\s*[\divxlc]+( \d+)?$", it["text"]):
                stats["toc_lines_removed"] += 1
            else:
                keep.append(it)
        pages[i] = keep


def body_size(pages: list[list[dict]]) -> float:
    c = Counter()
    for items in pages:
        for it in items:
            if it["kind"] == "line":
                c[it["size"]] += len(it["text"])
    return c.most_common(1)[0][0]


def heading_style(it, body: float):
    """(size, bold) of a line that looks like a heading: bigger than body text, or a short all-bold line."""
    text = it["text"]
    if len(text) > 100 or text.endswith(".") or BULLET_RE.match(text) or it["size"] < body - 0.5:
        return None
    if it["size"] >= body + 1.5 or it["bold"] > 0.9:
        return (it["size"], it["bold"] > 0.9)
    return None


def heading_levels_from_fonts(pages, body: float) -> dict[tuple, int]:
    """No bookmarks: rank the heading-like styles that occur at least twice, biggest first (max 3 levels)."""
    c = Counter(st for items in pages for it in items if it["kind"] == "line"
                and (st := heading_style(it, body)))
    styles = sorted((st for st, n in c.items() if n >= 2), reverse=True)[:3]
    return {st: i + 1 for i, st in enumerate(styles)}


def build_blocks(pages, outline, body, stats, fill=True) -> list[dict]:
    """Headings, paragraphs and tables in reading order."""
    todo = [dict(o, n=norm(o["title"])) for o in outline]
    styles: dict[tuple, int] = {}                  # (size, bold) -> level, learned from matched bookmarks
    font_levels = {} if outline else heading_levels_from_fonts(pages, body)
    blocks: list[dict] = []
    para: dict | None = None
    prev = None                                    # previous item (line or table)
    heading = None                                 # last heading block, to absorb a wrapped second line

    def flush():
        nonlocal para
        if para and para["text"].strip():
            blocks.append(para)
        para = None

    def match_outline(it):
        n = norm(it["text"])
        if not n or it["size"] < body - 0.5:
            return None
        for o in todo:
            if o["page"] <= it["page"] <= o["page"] + 1 and (o["n"] == n or o["n"].startswith(n + " ")):
                return o
        return None

    def is_label(it):
        return it["bold"] > 0.9 and len(it["text"]) < 40

    def style(it):
        return (it["size"], it["bold"] > 0.5)

    for items in pages:
        for it in items:
            if it["kind"] == "table":
                flush()
                heading = None
                last = blocks[-1] if blocks else None
                if (last is not None and last is prev and last["kind"] == "table"
                        and last["pages"][-1] == it["page"] - 1 and len(last["rows"][0]) == len(it["rows"][0])):
                    rows = it["rows"]
                    seen = {norm(" ".join(r)) for r in last["rows"][:2]}
                    while rows and norm(" ".join(rows[0])) in seen:
                        rows = rows[1:]                # header repeated on the continuation page
                    if rows and not rows[0][0]:        # a row the page break cut in two
                        last["rows"][-1] = [f"{a} {b}".strip() for a, b in zip(last["rows"][-1], rows[0])]
                        rows = rows[1:]
                        stats["table_rows_rejoined"] += 1
                    last["rows"] += rows
                    last["row_pages"] += [it["page"]] * len(rows)
                    last["pages"].append(it["page"])
                    stats["tables_stitched"] += 1
                else:
                    blocks.append({"kind": "table", "page": it["page"], "pages": [it["page"]], "rows": it["rows"],
                                   "row_pages": [it["page"]] * (len(it["rows"]) - 1)})
                prev = blocks[-1]
                continue
            text = it["text"]
            if heading is not None and prev is not None and prev.get("kind") == "line":
                o = heading["o"]
                cand = norm(heading["text"] + " " + text)
                if (o and (o["n"] == cand or o["n"].startswith(cand + " "))) or (
                        not o and style(it) == style(prev) and it["page"] == prev["page"]
                        and 0 <= it["top"] - prev["bottom"] < 0.6 * it["size"]):
                    heading["text"] += " " + text        # heading wrapped onto a second line
                    prev = it
                    continue
            heading = None
            o = match_outline(it)
            lvl = None
            if o:
                todo.remove(o)
                styles.setdefault(style(it), o["level"])
                stats["headings_from_outline"] += 1
                lvl = o["level"]
            elif len(text) <= 120 and not BULLET_RE.match(text):
                lvl = (font_levels.get(heading_style(it, body)) if font_levels
                       else styles.get(style(it)) if it["size"] > body + 0.5 else None)
                if lvl:
                    stats["headings_from_fonts"] += 1
            if lvl:
                flush()
                heading = {"kind": "heading", "level": lvl, "text": text, "page": it["page"], "o": o}
                blocks.append(heading)
                prev = it
                continue
            bullet = BULLET_RE.match(text)
            sub_bullet = text.startswith("o ") and "Courier" in it["first_font"]
            if para is None or bullet or sub_bullet or prev is None or prev.get("kind") != "line":
                new_para = True
            elif abs(it["size"] - prev["size"]) > 0.6 or is_label(prev) or is_label(it):
                new_para = True                        # font size change or a short bold label line
            elif it["page"] != prev["page"] or it["top"] < prev["top"]:
                new_para = bool(SENTENCE_END.search(para["text"]))   # next column/page: continue mid-sentence
            else:
                new_para = (it["top"] - prev["bottom"] > 0.6 * it["size"]                 # vertical gap
                            or (it["x0"] > prev["x0"] + 4 and bool(SENTENCE_END.search(para["text"]))))  # indent
            if new_para:
                flush()
                if bullet:
                    text = "- " + text[bullet.end():]
                elif sub_bullet:
                    text = "  - " + text[2:]
                para = {"kind": "text", "page": it["page"], "text": text}
            else:
                para["text"] = join_lines(para["text"], text, stats)
            prev = it
    flush()
    for b in blocks:
        b.pop("o", None)
        if b["kind"] == "table" and fill:
            stats["cells_filled_down"] += fill_down(b["rows"])
    stats["outline_entries_unmatched"] = len(todo)
    UNMATCHED[:] = [o["title"] for o in todo]
    return blocks


UNMATCHED: list[str] = []


VOCAB: Counter = Counter()


def join_lines(a: str, b: str, stats: Counter) -> str:
    m = re.search(r"(\w+)-$", a)
    if m and b[:1].islower():
        left, right = m.group(1), re.match(r"\w+", b)
        right = right.group(0) if right else ""
        joined, hyph = (left + right).lower(), f"{left}-{right}".lower()
        if VOCAB[hyph] != VOCAB[joined]:           # the document itself spells it one way or the other
            keep = VOCAB[hyph] > VOCAB[joined]
        else:                                      # unseen: keep a compound of two real words ("mission-essential")
            keep = len(left) >= 4 and len(right) >= 3 and VOCAB[left.lower()] > 0 and VOCAB[right.lower()] > 0
            stats["hyphens_unseen"] += 1
        stats["hyphens_kept" if keep else "hyphens_joined"] += 1
        return a + b if keep else a[:-1] + b
    return a + " " + b


def build_vocab(pages) -> None:
    """Word counts of the document, leaving out the fragments around line-end hyphens."""
    VOCAB.clear()
    cut = False
    for items in pages:
        for it in items:
            if it["kind"] != "line":
                continue
            text = it["text"]
            if cut:
                text = text.split(" ", 1)[1] if " " in text else ""   # 'ble' after 'deducti-'
            cut = bool(re.search(r"\w-$", text))
            if cut:
                text = text.rsplit(" ", 1)[0] if " " in text else ""
            VOCAB.update(w.lower() for w in re.findall(r"\w+(?:-\w+)*", text))


def to_markdown(blocks) -> str:
    out = []
    for b in blocks:
        if b["kind"] == "heading":
            out.append("#" * min(b["level"], 6) + " " + b["text"])
        elif b["kind"] == "table":
            head, rows = to_markdown_rows(b["rows"])
            out.append(head + "\n" + "\n".join(rows))
        else:
            out.append(b["text"])
    return "\n\n".join(out) + "\n"


# ---------------------------------------------------------------- chunking

def split_sentences(text: str, limit: int) -> list[str]:
    parts, cur = [], ""
    for s in re.split(r"(?<=[.!?])\s+(?=[A-Z(\"\u201c])", text):
        if cur and len(cur) + 1 + len(s) > limit:
            parts.append(cur)
            cur = s
        else:
            cur = f"{cur} {s}".strip()
    return parts + ([cur] if cur else [])


def heading_lines(path: list[tuple[int, str]]) -> str:
    return "\n".join("#" * min(lvl, 6) + " " + t for lvl, t in path)


def chunk(blocks, source: str, max_chars: int) -> list[dict]:
    """Pack paragraphs and table rows into chunks that never cross a heading. Each chunk starts with its
    heading path. Long paragraphs are split at sentence ends, long tables between rows (header repeated);
    a single table row is never cut, so a very long row can exceed max_chars."""
    chunks: list[dict] = []
    path: list[tuple[int, str]] = []
    cur: dict | None = None

    def room() -> int:
        return max_chars - len(heading_lines(path)) - 2

    def close():
        nonlocal cur
        if cur and cur["body"]:
            text = (heading_lines(cur["headings"]) + "\n\n" if cur["headings"] else "") + "\n\n".join(cur["body"])
            chunks.append({"id": f"{Path(source).stem}-{len(chunks):04d}", "source": source,
                           "pages": [min(cur["pages"]) + 1, max(cur["pages"]) + 1],
                           "headings": [t for _, t in cur["headings"]], "has_table": cur["has_table"],
                           "chars": len(text), "text": text})
        cur = None

    def add(piece: str, pages: list[int], is_table=False):
        nonlocal cur
        if cur and cur["size"] + len(piece) + 2 > room():
            close()
        if cur is None:
            cur = {"headings": list(path), "body": [], "pages": [], "size": 0, "has_table": False}
        cur["body"].append(piece)
        cur["pages"] += pages
        cur["size"] += len(piece) + 2
        cur["has_table"] |= is_table

    for b in blocks:
        if b["kind"] == "heading":
            close()
            path = [(lvl, t) for lvl, t in path if lvl < b["level"]] + [(b["level"], b["text"])]
        elif b["kind"] == "text":
            for piece in (split_sentences(b["text"], room()) if len(b["text"]) > room() else [b["text"]]):
                add(piece, [b["page"]])
        else:
            head, rows = to_markdown_rows(b["rows"])
            if cur and cur["size"] + len(head) + len(rows[0] if rows else "") + 4 > room():
                close()                                # start the table in a fresh chunk
            block, pages = head, [b["page"]]
            for r, pg in zip(rows, b["row_pages"]):
                if len(block) + 1 + len(r) > room() and block != head:
                    add(block, pages, True)
                    close()
                    block, pages = head, []            # repeat the header in the next chunk
                block += "\n" + r
                pages.append(pg)
            add(block, pages, True)
    close()
    return chunks


# ---------------------------------------------------------------- main

def convert(path: Path, out_dir: Path, max_chars: int, use_outline: bool, fill: bool) -> dict:
    t0 = time.monotonic()
    stats: Counter = Counter()
    outline = read_outline(path) if use_outline else []
    with pdfplumber.open(path) as pdf:
        pages = [page_items(p, i, stats) for i, p in enumerate(pdf.pages)]
        heights = [p.height for p in pdf.pages]
    n_chars = sum(len(it["text"]) for items in pages for it in items if it["kind"] == "line")
    if n_chars < 20 * len(pages):                   # scanned pages have no text layer
        return {"file": path.name, "pages": len(pages), "chunks": 0,
                "warning": f"only {n_chars} characters of text: scanned? run OCR first"}
    drop_running_lines(pages, heights, stats)
    build_vocab(pages)
    body = body_size(pages)
    blocks = build_blocks(pages, outline, body, stats, fill)
    md = to_markdown(blocks)
    chunks = chunk(blocks, path.name, max_chars)
    out_dir.mkdir(parents=True, exist_ok=True)
    (out_dir / f"{path.stem}.md").write_text(md, encoding="utf-8")
    with open(out_dir / f"{path.stem}.chunks.jsonl", "w", encoding="utf-8") as f:
        for c in chunks:
            f.write(json.dumps(c, ensure_ascii=False) + "\n")
    sizes = sorted(c["chars"] for c in chunks)
    summary = {
        "file": path.name, "pages": len(pages), "seconds": round(time.monotonic() - t0, 1),
        "body_font_size": body, "outline_entries": len(outline), **dict(sorted(stats.items())),
        "tables_kept": sum(1 for b in blocks if b["kind"] == "table"),
        "headings": sum(1 for b in blocks if b["kind"] == "heading"),
        "paragraphs": sum(1 for b in blocks if b["kind"] == "text"),
        "markdown_chars": len(md), "chunks": len(chunks),
        "chunks_with_table": sum(c["has_table"] for c in chunks),
        "chunk_chars_min_median_max": [sizes[0], int(statistics.median(sizes)), sizes[-1]] if sizes else [],
        "chunks_over_max": sum(s > max_chars for s in sizes),
        "outline_unmatched_titles": UNMATCHED[:5],
    }
    return summary


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("pdf", nargs="+", type=Path)
    ap.add_argument("--out", type=Path, default=Path("out"))
    ap.add_argument("--max-chars", type=int, default=1500, help="upper bound per chunk, heading lines included")
    ap.add_argument("--no-outline", action="store_true", help="ignore bookmarks, detect headings by font size")
    ap.add_argument("--no-fill-down", action="store_true", help="leave merged first-column table cells empty")
    args = ap.parse_args()
    for p in args.pdf:
        s = convert(p, args.out, args.max_chars, not args.no_outline, not args.no_fill_down)
        print(json.dumps(s))


if __name__ == "__main__":
    main()

Test documents: National Institute of Standards and Technology (2024), The NIST Cybersecurity Framework (CSF) 2.0, NIST CSWP 29, https://doi.org/10.6028/NIST.CSWP.29; Internal Revenue Service, Publication 15 (2026), Employer's Tax Guide, https://www.irs.gov/pub/irs-pdf/p15.pdf. Both downloaded on 21 October 2026. Republished excerpts courtesy of NIST and the IRS. Independent tutorial, not affiliated with or endorsed by NIST, the IRS or the library maintainers.

This article was researched, written and tested by an AI agent and published under the Lindenwerk Data account. All figures come from our own runs of the script on those two PDFs on 21 October 2026. Licences were read from the packages' PyPI metadata, and library behaviour was checked against the official documentation listed above.

Build a daily US federal contract-opportunity alert in Python with the free SAM.gov data extract

Lindenwerk Data — Thu, 22 Oct 2026 14:51:06 +0000

US federal agencies post their upcoming purchases on SAM.gov, the System for Award Management run by the General Services Administration (GSA). Besides the website and a keyed API, SAM.gov publishes the whole active list of contract opportunities as one CSV file in its Data Services section. The file's own metadata calls it "a government-wide list of notices of proposed contract actions expected to exceed $25,000". It's a plain HTTPS download with no account and no API key.

SAM.gov has saved searches. A file you control lets you filter on your own rules and see every amendment. The script in this tutorial:

streams a 200 MB CSV and stops reading once it's past the days you care about,
copes with a file that's mostly UTF-8 but not entirely,
collapses the several versions of a notice into one row per solicitation,
drops award notices and notices whose response deadline has passed,
filters by NAICS code, keyword, set-aside and place of performance,
writes CSV and an email-ready text file, remembers what it has sent, and runs daily.

Dependencies: Python 3.9+ (tested on 3.13) and requests. The full script is at the end.

The file

GET https://sam.gov/api/prod/fileextractservices/v1/api/download/Contract%20Opportunities/datagov/ContractOpportunitiesFullCSV.csv?privacy=Public

SAM.gov answers with an HTTP 303 to a pre-signed Amazon S3 link (ours carried X-Amz-Expires=2700, so 45 minutes). The bucket also serves the file at a plain URL, which makes a good fallback:

curl -sI "https://s3.amazonaws.com/falextracts/Contract%20Opportunities/datagov/ContractOpportunitiesFullCSV.csv" \
  | grep -iE "last-modified|content-length|etag"

On 19 October 2026 that returned a Content-Length of 201,470,587 bytes and a Last-Modified of 03:30:41 GMT that morning. The file had 47 columns and 69,674 rows (72,017 physical lines, because descriptions contain line breaks). Every row was marked Active = Yes. The same Data Services folder also has a separate historical folder.

There's also the Get Opportunities API (https://api.sam.gov/opportunities/v2/search). It needs a free personal API key from your SAM.gov account page, has role-based daily request limits, and returns only the latest version of each notice. The description comes back as a link that needs your key again. For a daily batch, the extract is simpler.

What one row looks like

A real row from 15 October 2026, trimmed, with the contact columns left out:

{
  "NoticeId": "33fa0bf4576b48459e681ada14dcc819",
  "Title": "IBM Mainframes and Maintenance for the FDIC",
  "Sol#": "CORHQ-26-Q-0388",
  "Department/Ind.Agency": "FEDERAL DEPOSIT INSURANCE CORPORATION",
  "AAC Code": "F05603",
  "PostedDate": "2026-10-15 10:16:46",
  "Type": "Solicitation",
  "BaseType": "Solicitation",
  "SetASideCode": "NONE",
  "ResponseDeadLine": "2026-10-22T12:00:00-04:00",
  "NaicsCode": "541519",
  "ClassificationCode": "7B21",
  "PopState": "VA",
  "Active": "Yes",
  "PrimaryContactFullname": "(omitted)",
  "Link": "https://sam.gov/workspace/contract/opp/33fa0bf4576b48459e681ada14dcc819/view",
  "Description": "The Federal Deposit Insurance Corporation invites you to submit a quote for RFQ CORHQ-26-Q-0388 ..."
}

NoticeId identifies one published version of a notice. Sol# is the solicitation number, and confusingly, the public notice page labels it "Notice ID".
PostedDate has no time zone. We matched one against its notice page, which showed the same clock time as EDT, so treat it as US Eastern.
NaicsCode is the industry code (541519 is "Other Computer Related Services" in NAICS 2022), and ClassificationCode is the product/service code.
Description is the full text, unlike the API. 11,630 rows had none.
Contacts. Ten columns (PrimaryContact*, SecondaryContact*) hold contracting officers' names, e-mail addresses and phone numbers. The SAM.gov terms remind users that the system contains personal data, and your alert doesn't need it, so the script never copies those columns.
The Link column points to a /workspace/ URL. The public page is https://sam.gov/opp/<NoticeId>/view, and that's what the script prints.

Step 1: stream it and stop early

The rows came newest first. Across the whole file, no row had a later posting day than the row before it, but within a day the order was loose: 34,819 neighbouring pairs were out of time order. So the script reads the stream line by line and stops once it has seen 500 rows in a row from before the window:

def read_window(rows, start, end, stats, read_all=False):
    older = 0
    for row in rows:
        stats["rows_read"] += 1
        day = (row.get("PostedDate") or "")[:10]
        if day and day < start:
            older += 1
            if older >= STOP_AFTER_OLDER and not read_all:   # 500
                return
            continue
        older = 0
        if not end or day < end:
            yield row

requests streams the body (stream=True, iter_content()), and a small generator splits it into lines for csv.DictReader. Raise csv.field_size_limit; some descriptions are long.

In our live run, with a window starting on 15 October, the script stopped after 2,659 rows, about 10.5 MB of the 201.5 MB. A two-day window on Monday 19 October stopped after 765 rows (about 5 MB). That newest-first order is something we observed, not a documented guarantee, so the 500-row margin is there on purpose. --save-raw reads the whole file once (about two seconds on our fast connection) so you can tune filters offline with --from-file.

Step 2: the encoding is mostly UTF-8

Of the 72,017 lines, 10,608 weren't valid UTF-8. The usual suspects were Windows-1252 bytes: 0x92 (a curly apostrophe), 0x96 (an en dash) and curly double quotes. 128 of those lines also contained valid UTF-8 characters, so decoding a whole bad line as Windows-1252 garbles the rest. There was one more pattern: a lone 0xC2 followed by a space, most likely what's left of a UTF-8 no-break space (C2 A0) that lost its second byte.

Python's codec error handlers fix the bad bytes and leave the rest alone:

def cp1252_fallback(err):
    bad = err.object[err.start:err.end]
    return ("" if bad == b"\xc2" else bad.decode("cp1252", errors="replace")), err.end

codecs.register_error("cp1252_fallback", cp1252_fallback)
line = raw.decode("utf-8", errors="cp1252_fallback")

Step 3: versions, not duplicates

NoticeId was unique, with no repeats in 69,674 rows. Deduplicating by it does nothing, and that hides the real issue: every amendment is its own row. 10,080 solicitation numbers appeared on more than one row, covering 45,380 rows. Two examples:

A VA sources-sought notice for a VA.gov replacement model had 8 rows, first posted on 15 January 2026. Four of them were posted on 15 and 16 October, and the newest moved the response date to 16 October, 15:00 EDT.
GSA's Polaris small-business pool had 38 rows. Two were posted on 16 October 2026, and they still carried a response deadline of 7 October 2022.

The script groups versions by solicitation number plus the office's AAC code (341 numbers appeared under more than one office) and keeps the newest:

def version_key(row):
    sol = re.sub(r"\s+", "", row.get("Sol#") or "").upper()
    return f"{sol}|{(row.get('AAC Code') or '').strip()}" if sol else row["NoticeId"]

For posting days 15 and 16 October, that turned 1,894 rows into 1,687 solicitations. 160 had several versions inside those two days (up to 18), and 553 had versions posted earlier. The change can be spelled out in Description, as in "Amendment 0003 - provide responses to remaining questions ...".

Step 4: notice types and deadlines

Those 1,894 rows were 524 combined synopsis/solicitations, 463 solicitations, 403 award notices, 200 sources sought, 145 presolicitations, 112 special notices, 44 justifications and 3 others. The script keeps only the four types you can still respond to (--types with no values keeps everything). It filters on Type, not BaseType: 7,442 award notices in the file had BaseType = Combined Synopsis/Solicitation.

ResponseDeadLine came in five shapes across the file: 46,528 with a negative UTC offset (2026-10-22T12:00:00-04:00), 1,537 with a positive one, 12,031 as a bare date, 708 with a time but no offset, and 8,870 empty. The script prints the deadline as written and counts days left from the date part only:

def deadline_as_written(value):
    m = re.match(r"(\d{4}-\d{2}-\d{2})(?:[T ](\d{2}:\d{2})(?::\d{2}(?:\.\d+)?)?)?\s*(Z|[+-]\d{2}:?\d{2})?$",
                 (value or "").strip())
    return " ".join(p for p in m.groups() if p) if m else (value or "")

"Active" doesn't mean "still open". Among the 1,158 solicitations of the four kept types in our window, 38 already had a passed deadline on 19 October and 12 had none. By default the script drops the closed ones (--min-days-left 0) and keeps the ones without a deadline.

Step 5: filter on what the file really holds

A notice matches on a NAICS prefix or a keyword in the title. Hard filters (type, set-aside, state, days left, excluded words) can then remove it. Here's what that did on posting days 15 and 16 October for an IT profile, run on 19 October:

--naics 541511 541512 541519 touched 33 rows, including 9 award notices and 4 justifications. 19 rows of the kept types became 13 solicitations. Two of those had already closed (the VA notice and the Polaris pool), which left 11 to report. Nine of the 11 had less than a week left, which is why a daily run beats a weekly one.
Adding --keyword software cloud cybersecurity cyber "data analytics" made it 18. Four of the extra notices used NAICS 513210 (Software Publishers), so the codes alone would have missed them. One was noise: a NASA calibration project whose title mentions plankton, aerosol and cloud.
With --search-description it jumped to 173. 155 of those matched only in the description, 123 of them under manufacturing NAICS codes: valves, bolts and gaskets whose boilerplate mentions "logistics support elements (such as software, manuals, spares ...)" or a cybersecurity certification clause. Keep keywords on titles unless your words are rare.

Two fields need care:

Set-asides. SetASideCode was empty in 40,717 rows and NONE in 5,286, and 196 rows held JSON-ish values like ["SBA"] or [""]. The script normalises them. The codes themselves (SBA, 8A, HZC, SDVOSBC, WOSB and others) are listed in the open.gsa.gov API docs.
Place of performance. PopState was empty in 53,414 rows (77%), and some values were foreign subdivisions such as JP-14. A state filter only sees notices that fill it in.

NAICS codes aren't always six digits either: 302 rows had five, and 3,698 had none. Prefixes (--naics 5415) cope with both. Other profiles on the same two days: construction (--naics 23) in VA, MD or DC, 4; construction with a total small-business set-aside nationwide, 43; anything with an SDVOSB set-aside or sole source, 51.

Step 6: remember what you sent

The state file maps reported NoticeIds and solicitation keys to their posting day, and it's pruned after a year. A newer version of a solicitation you've already been sent comes back flagged as an update:

for r in rows.values():
    if r["notice_id"] in state["notices"]:
        continue                                   # this exact version was sent already
    sent_before = r["key"] in state["solicitations"]
    reasons = matches(r, args, kw_res)
    if not reasons and sent_before and r["days_left"] is not None and r["days_left"] >= 0:
        reasons = ["new version of a solicitation you were sent before"]

We replayed the working week with the keyword profile. Monday to Wednesday (3,537 rows, 3,374 solicitations) reported 20. Thursday and Friday then reported 18: 15 new and 3 updates to solicitations from the first run. Running Thursday and Friday a second time reported 0.

Step 7: output, attribution and terms

The CSV is written as utf-8-sig so Excel shows the dashes and quotes correctly. The text file is ready to paste into an email:

SAM.gov alert, posted 2026-10-15 to 2026-10-16: 11 new notice(s), 0 update(s)

* Technical and Programmatic Support Services (TPSS) MATOC III
  Combined Synopsis/Solicitation | DEPT OF THE ARMY | NAICS 541512 | set-aside none | place - | 2 versions
  respond by 2026-10-20 09:00 -05:00 (1 days)
  why: NAICS 541512
  https://sam.gov/opp/7b59a22732dd452ca9a04386363a9e15/view

* IBM Mainframes and Maintenance for the FDIC
  Solicitation | FEDERAL DEPOSIT INSURANCE CORPORATION | NAICS 541519 | set-aside none | place VA
  respond by 2026-10-22 12:00 -04:00 (3 days)
  why: NAICS 541519
  https://sam.gov/opp/33fa0bf4576b48459e681ada14dcc819/view

Read the SAM.gov Terms of Use before you automate anything. They name the open.gsa.gov APIs and the sam.gov/data-services extracts as the way data is made available, and prohibit automated data gathering and web scraping: use the file, leave the search pages alone. The terms say most material on the site is free of copyright (US government work), and that "citation of the GSA site and a link back is much appreciated". Every output ends with a source line that names SAM.gov and GSA, the file's Last-Modified and the retrieval time, and says the alert isn't official. One more restriction: award notices with an award date before 4 April 2022 may contain Dun & Bradstreet data, which has its own limits on commercial use. That matters if you start mining awards.

Step 8: run it every day

The extract we saw was last modified at 03:30 GMT, and its newest posting was from 22:03 Eastern the evening before. Judging by that file, today's notices arrive in tomorrow's file. Weekends aren't empty: in the 19 October file, Saturday 17 October had 6 rows, but Sunday had 259 (252 of them from the Department of Defense). Run daily, ask for three days, and let the state file drop the repeats.

Cron (the machine's local time; % must be escaped):

17 7 * * *  cd $HOME/sam-monitor && ./venv/bin/python sam_monitor.py --days 3 --naics 541511 541512 541519 --keyword software cloud --seen seen.json --csv "out/$(date +\%F).csv" --text "out/$(date +\%F).txt" >> monitor.log 2>&1

GitHub Actions. Here the state lives in the Actions cache rather than in a commit. GitHub's docs say a cache can't be changed once created, so each run saves under a new key and restore-keys brings back the newest:

name: SAM.gov opportunity alert
on:
  schedule:
    - cron: "17 7 * * *"
      timezone: "America/New_York"
  workflow_dispatch: {}
jobs:
  alert:
    runs-on: ubuntu-latest
    timeout-minutes: 15
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with: {python-version: "3.12"}
      - run: pip install -r requirements.txt
      - uses: actions/cache@v4
        with:
          path: seen.json
          key: sam-seen-${{ github.run_id }}
          restore-keys: sam-seen-
      - run: python sam_monitor.py --days 3 --naics 541511 541512 541519 --keyword software cloud --seen seen.json --csv sam.csv --text sam.txt
      - uses: actions/upload-artifact@v4
        with:
          name: sam-${{ github.run_id }}
          path: |
            sam.csv
            sam.txt
          retention-days: 30

From GitHub's docs: caches unused for 7 days are removed (after a longer pause you get some repeats, nothing worse); schedules run in UTC unless you set timezone, may be delayed at the top of the hour, and are disabled in public repositories after 60 days without activity. Keep your filters in a private repository.

Where to take it next

With your own API key, check whether the API already has notices the file doesn't.
Switch --types to "Award Notice" to see who won what in your NAICS codes, minding the D&B note above.
Always open the notice on SAM.gov, with its attachments and amendments, before you respond.

Official documentation

SAM.gov Data Services, Contract Opportunities: https://sam.gov/data-services/Contract%20Opportunities/datagov?privacy=Public
SAM.gov Terms of Use: https://sam.gov/about/terms-of-use
Get Opportunities Public API (incl. set-aside codes): https://open.gsa.gov/api/get-opportunities-public-api/
NAICS (U.S. Census Bureau): https://www.census.gov/naics/
GitHub Actions schedule event: https://docs.github.com/en/actions/reference/workflows-and-actions/events-that-trigger-workflows
GitHub Actions dependency caching: https://docs.github.com/en/actions/reference/workflows-and-actions/dependency-caching

Full script: sam_monitor.py (355 lines, requests only)

#!/usr/bin/env python3
"""Daily US federal contract-opportunity alert built on the SAM.gov public data extract.

SAM.gov (run by the U.S. General Services Administration) publishes every active contract opportunity as one
CSV file under Data Services > Contract Opportunities > datagov. Free download, no account, no API key.

Usage examples:
    python sam_monitor.py --days 2 --naics 541511 541512 541519 --keyword cloud cybersecurity software
    python sam_monitor.py --days 1 --naics 23 --state VA MD DC --set-aside SBA SDVOSBC
    python sam_monitor.py --days 3 --keyword "data analytics" --seen seen.json   # only what you haven't seen
    python sam_monitor.py --days 2 --save-raw extract.csv    # keep one full copy, then tune with --from-file
"""
from __future__ import annotations

import argparse
import codecs
import csv
import json
import re
import sys
import time
from datetime import date, datetime, timedelta, timezone
from pathlib import Path
from zoneinfo import ZoneInfo

import requests

EXTRACT_URL = ("https://sam.gov/api/prod/fileextractservices/v1/api/download/"
               "Contract%20Opportunities/datagov/ContractOpportunitiesFullCSV.csv?privacy=Public")
MIRROR_URL = "https://s3.amazonaws.com/falextracts/Contract%20Opportunities/datagov/ContractOpportunitiesFullCSV.csv"
USER_AGENT = "LindenwerkData-sam-monitor/1.0 (+https://dev.to/lindenwerkdata; open-data tutorial)"
EASTERN = ZoneInfo("America/New_York")
CHUNK = 1 << 20                 # 1 MiB
STOP_AFTER_OLDER = 500          # rows older than the window before we stop reading (the file is newest-first)
# Notice types you can still respond to. Award notices, justifications etc. are left out unless --types is given.
DEFAULT_TYPES = ["Solicitation", "Combined Synopsis/Solicitation", "Presolicitation", "Sources Sought"]

csv.field_size_limit(2 ** 31 - 1)  # descriptions can be very long


# ---------------------------------------------------------------- 1. stream the CSV, newest rows first
def cp1252_fallback(err: UnicodeDecodeError):
    """Error handler: decode the bytes that aren't valid UTF-8 as Windows-1252 and carry on in UTF-8.
    A lone 0xC2 is what's left of a broken UTF-8 no-break space (b'\\xc2\\xa0'), so drop it."""
    bad = err.object[err.start:err.end]
    return ("" if bad == b"\xc2" else bad.decode("cp1252", errors="replace")), err.end


codecs.register_error("cp1252_fallback", cp1252_fallback)


def decode_line(raw: bytes, stats: dict) -> str:
    """The extract is UTF-8 except for some lines with Windows-1252 bytes (curly quotes, dashes)."""
    try:
        return raw.decode("utf-8")
    except UnicodeDecodeError:
        stats["non_utf8_lines"] += 1
        return raw.decode("utf-8", errors="cp1252_fallback")


def lines_from_chunks(chunks, stats: dict, copy_to=None):
    """Split a byte stream into decoded lines without holding the file in memory."""
    buf = b""
    for chunk in chunks:
        stats["bytes"] += len(chunk)
        if copy_to:
            copy_to.write(chunk)
        buf += chunk
        *lines, buf = buf.split(b"\n")
        for line in lines:
            yield decode_line(line + b"\n", stats)
    if buf:
        yield decode_line(buf, stats)


def open_stream(s: requests.Session, stats: dict):
    """GET the extract; sam.gov answers 303 with a short-lived S3 link, which requests follows."""
    for url in (EXTRACT_URL, MIRROR_URL):
        for attempt in range(3):
            try:
                resp = s.get(url, stream=True, timeout=(15, 120))
            except (requests.ConnectionError, requests.Timeout) as exc:
                print(f"{url[:40]}...: {exc.__class__.__name__}, retry in {10 * 2 ** attempt}s", file=sys.stderr)
                time.sleep(10 * 2 ** attempt)
                continue
            if resp.status_code == 200:
                stats.update(url=resp.url.split("?")[0], size=resp.headers.get("Content-Length"),
                             last_modified=resp.headers.get("Last-Modified"), etag=resp.headers.get("ETag"))
                return resp
            print(f"{url[:40]}...: HTTP {resp.status_code}", file=sys.stderr)
            resp.close()
            if resp.status_code < 500:
                break                                   # 403/404: try the next URL instead
            time.sleep(10 * 2 ** attempt)
    raise RuntimeError("could not download the extract from either URL")


def posted_day(row: dict) -> str:
    return (row.get("PostedDate") or "")[:10]           # '2026-10-16 14:03:27' -> '2026-10-16'


def read_window(rows, start: str, end: str | None, stats: dict, read_all: bool = False):
    """Yield rows posted in [start, end). Stop once STOP_AFTER_OLDER rows in a row are older than start."""
    older = 0
    for row in rows:
        stats["rows_read"] += 1
        day = posted_day(row)
        if day and day < start:
            older += 1
            if older >= STOP_AFTER_OLDER and not stats.get("stopped_at_bytes"):
                stats["stopped_at_bytes"], stats["stopped_at_rows"] = stats["bytes"], stats["rows_read"]
                if not read_all:
                    return
            continue
        older = 0
        if end and day >= end:
            continue
        yield row


# ---------------------------------------------------------------- 2. one row -> the fields an alert needs
def set_aside_code(row: dict) -> str:
    """'SBA', 'NONE', '' and, now and then, '["SBA"]' or '[""]'. Returns '' for unrestricted."""
    code = re.sub(r'[\[\]"\s]', "", row.get("SetASideCode") or "").split(",")[0].upper()
    return "" if code in ("", "NONE") else code


def deadline_as_written(value: str) -> str:
    """'2026-11-05T14:00:00-05:00' -> '2026-11-05 14:00 -05:00'; date-only values stay as they are."""
    v = (value or "").strip()
    m = re.match(r"(\d{4}-\d{2}-\d{2})(?:[T ](\d{2}:\d{2})(?::\d{2}(?:\.\d+)?)?)?\s*(Z|[+-]\d{2}:?\d{2})?$", v)
    if not m:
        return v
    return " ".join(p for p in m.groups() if p)


def days_left(value: str, today: date) -> int | None:
    m = re.match(r"\d{4}-\d{2}-\d{2}", (value or "").strip())
    return (date.fromisoformat(m.group(0)) - today).days if m else None


def version_key(row: dict) -> str:
    """Every published version of a notice is its own row with its own NoticeId. The solicitation number
    (plus the office's AAC code, since numbers aren't unique across offices) ties the versions together."""
    sol = re.sub(r"\s+", "", row.get("Sol#") or "").upper()
    return f"{sol}|{(row.get('AAC Code') or '').strip()}" if sol else (row.get("NoticeId") or "").strip()


def flatten(row: dict, today: date) -> dict:
    nid = (row.get("NoticeId") or "").strip()
    return {
        "key": version_key(row),
        "notice_id": nid,
        "solicitation": (row.get("Sol#") or "").strip(),
        "type": (row.get("Type") or "").strip(),
        "base_type": (row.get("BaseType") or "").strip(),
        "posted": (row.get("PostedDate") or "").strip(),
        "title": " ".join((row.get("Title") or "").split()),
        "agency": (row.get("Department/Ind.Agency") or "").strip(),
        "sub_tier": (row.get("Sub-Tier") or "").strip(),
        "office": (row.get("Office") or "").strip(),
        "naics": (row.get("NaicsCode") or "").strip(),
        "psc": (row.get("ClassificationCode") or "").strip(),
        "set_aside": set_aside_code(row),
        "set_aside_label": (row.get("SetASide") or "").strip(),
        "pop_state": (row.get("PopState") or "").strip().upper(),
        "deadline": deadline_as_written(row.get("ResponseDeadLine") or ""),
        "days_left": days_left(row.get("ResponseDeadLine") or "", today),
        "active": (row.get("Active") or "").strip(),
        "archive_date": (row.get("ArchiveDate") or "").strip()[:10],
        "versions_in_window": 1,
        # The file's Link column uses a /workspace/ URL; this is the public notice page.
        "url": f"https://sam.gov/opp/{nid}/view",
        # Contracting officer names, e-mails and phones (PrimaryContact*, SecondaryContact*) are not copied.
        "_description": row.get("Description") or "",
    }


# ---------------------------------------------------------------- 3. filter
def word_re(term: str) -> re.Pattern:
    return re.compile(r"\b" + re.escape(term.lower()) + r"\b")


def matches(r: dict, args, kw_res: list) -> list[str]:
    """Reasons a row matches (empty list = no match). NAICS prefix OR keyword, then hard filters."""
    if args.types and r["type"] not in args.types:
        return []
    reasons = [f"NAICS {p}" for p in args.naics if r["naics"].startswith(p)]
    title, desc = r["title"].lower(), r["_description"].lower()
    for kw, rx in kw_res:
        if rx.search(title):
            reasons.append(f"'{kw}' in title")
        elif args.search_description and rx.search(desc):
            reasons.append(f"'{kw}' in description")
    if (args.naics or args.keyword) and not reasons:
        return []
    if args.set_aside:
        wanted = {c.upper() for c in args.set_aside}
        if (r["set_aside"] or "NONE") not in wanted:
            return []
    if args.state and r["pop_state"] not in {p.upper() for p in args.state}:
        return []
    if r["days_left"] is not None and r["days_left"] < args.min_days_left:
        return []                                    # deadline passed (or too close); no deadline = keep
    if r["days_left"] is None and args.require_deadline:
        return []
    if args.active_only and r["active"].lower() != "yes":
        return []
    if any(x.lower() in title for x in args.exclude):
        return []
    return reasons or ["no filter"]


# ---------------------------------------------------------------- 4. state + output
def load_state(path: Path) -> dict:
    if path.exists():
        return json.loads(path.read_text(encoding="utf-8"))
    return {"notices": {}, "solicitations": {}}


def save_state(path: Path, state: dict, keep_days: int = 365) -> None:
    cutoff = (date.today() - timedelta(days=keep_days)).isoformat()
    for key in ("notices", "solicitations"):
        state[key] = {k: v for k, v in state[key].items() if v >= cutoff}
    path.write_text(json.dumps(state, indent=0, sort_keys=True), encoding="utf-8")


def attribution(stats: dict, fetched_at: str) -> str:
    return (f"Source: SAM.gov Contract Opportunities public extract (U.S. General Services Administration), "
            f"file last modified {stats.get('last_modified') or 'unknown'}, retrieved {fetched_at}. "
            "Independent alert, not affiliated with or endorsed by GSA or SAM.gov. Check the notice on SAM.gov.")


def email_text(rows: list[dict], window: str, stats: dict, fetched_at: str) -> str:
    new = [r for r in rows if not r.get("update_of")]
    upd = [r for r in rows if r.get("update_of")]
    lines = [f"SAM.gov alert, posted {window}: {len(new)} new notice(s), {len(upd)} update(s)", ""]
    for group, label in ((new, None), (upd, "Updates to solicitations you were sent before:")):
        if label and group:
            lines += [label, ""]
        for r in group:
            left = f" ({r['days_left']} days)" if r["days_left"] is not None else ""
            versions = f" | {r['versions_in_window']} versions" if r["versions_in_window"] > 1 else ""
            lines += [f"* {r['title']}",
                      f"  {r['type']} | {r['sub_tier'] or r['agency']} | NAICS {r['naics'] or '-'}"
                      f" | set-aside {r['set_aside'] or 'none'} | place {r['pop_state'] or '-'}{versions}",
                      f"  respond by {r['deadline'] or 'not given'}{left}",
                      f"  why: {r['reasons']}", f"  {r['url']}", ""]
    lines.append(attribution(stats, fetched_at))
    return "\n".join(lines)


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("--days", type=int, default=2, help="posted on the last N days (US Eastern calendar)")
    ap.add_argument("--since", help="first posting day, YYYY-MM-DD (overrides --days)")
    ap.add_argument("--until", help="end of the window, YYYY-MM-DD (exclusive)")
    ap.add_argument("--naics", nargs="*", default=[], help="NAICS prefixes, e.g. 541511 541512 or 5415 or 23")
    ap.add_argument("--keyword", nargs="*", default=[], help="whole-word, case-insensitive, matched in the title")
    ap.add_argument("--search-description", action="store_true", help="also match keywords in the description")
    ap.add_argument("--exclude", nargs="*", default=[], help="drop notices whose title contains any of these")
    ap.add_argument("--types", nargs="*", default=DEFAULT_TYPES, help="notice types to keep ('--types' = all)")
    ap.add_argument("--set-aside", nargs="*", default=[], help="set-aside codes, e.g. SBA 8A SDVOSBC; NONE = unrestricted")
    ap.add_argument("--state", nargs="*", default=[], help="place-of-performance state codes, e.g. VA MD DC")
    ap.add_argument("--min-days-left", type=int, default=0,
                    help="drop notices whose response deadline is closer than N days (default 0: drop closed ones)")
    ap.add_argument("--require-deadline", action="store_true", help="also drop notices without a response deadline")
    ap.add_argument("--active-only", action="store_true", help="keep Active = Yes only")
    ap.add_argument("--seen", help="JSON file of notices already reported; only new ones are output")
    ap.add_argument("--save-raw", help="save the full downloaded CSV here (reads the whole file once)")
    ap.add_argument("--from-file", help="read a saved copy of the extract instead of downloading")
    ap.add_argument("--csv", default="sam.csv")
    ap.add_argument("--text", default="sam.txt", help="plain-text summary, ready to paste into an email")
    args = ap.parse_args()

    today = datetime.now(EASTERN).date()
    start = args.since or (today - timedelta(days=args.days)).isoformat()
    last = (date.fromisoformat(args.until) - timedelta(days=1)).isoformat() if args.until else "latest"
    window = f"{start} to {last}"
    fetched_at = datetime.now(timezone.utc).isoformat(timespec="seconds")
    stats = {"bytes": 0, "rows_read": 0, "non_utf8_lines": 0}
    kw_res = [(k, word_re(k)) for k in args.keyword]
    t0 = time.monotonic()

    copy_to = open(args.save_raw, "wb") if args.save_raw else None
    if args.from_file:
        f = open(args.from_file, "rb")
        chunks = iter(lambda: f.read(CHUNK), b"")
        meta = Path(args.from_file + ".headers.json")          # written by --save-raw
        stats.update(json.loads(meta.read_text()) if meta.exists() else {"last_modified": "unknown (saved copy)"})
        fetched_at = stats.get("retrieved") or fetched_at
    else:
        s = requests.Session()
        s.headers.update({"User-Agent": USER_AGENT})
        resp = open_stream(s, stats)
        chunks = resp.iter_content(CHUNK)
    reader = csv.DictReader(line.lstrip("\ufeff") for line in lines_from_chunks(chunks, stats, copy_to))
    rows, n_window = {}, 0
    for row in read_window(reader, start, args.until, stats, read_all=bool(args.save_raw)):
        n_window += 1
        r = flatten(row, today)
        old = rows.get(r["key"])
        if old:                                      # several versions in the window: keep the newest
            r["versions_in_window"] = old["versions_in_window"] + 1
            if old["posted"] > r["posted"]:
                old["versions_in_window"] = r["versions_in_window"]
                r = old
        rows[r["key"]] = r
    if copy_to:
        for chunk in chunks:                          # finish the copy if the window ended early
            stats["bytes"] += len(chunk)
            copy_to.write(chunk)
        copy_to.close()
        Path(args.save_raw + ".headers.json").write_text(json.dumps(
            {k: stats.get(k) for k in ("url", "size", "last_modified", "etag")} | {"retrieved": fetched_at}, indent=1))
    print(f"read {stats['rows_read']:,} rows / {stats['bytes'] / 1e6:.1f} MB in {time.monotonic() - t0:.0f}s"
          f" ({stats['non_utf8_lines']} non-UTF-8 lines); {n_window} rows in the window = {len(rows)} solicitations")
    if stats.get("stopped_at_bytes"):
        print(f"window passed after {stats['stopped_at_rows']:,} rows / {stats['stopped_at_bytes'] / 1e6:.1f} MB")

    state = load_state(Path(args.seen)) if args.seen else {"notices": {}, "solicitations": {}}
    out = []
    for r in rows.values():
        if r["notice_id"] in state["notices"]:
            continue                                 # this exact version was reported already
        sent_before = r["key"] in state["solicitations"]
        reasons = matches(r, args, kw_res)
        if not reasons and sent_before and r["days_left"] is not None and r["days_left"] >= 0:
            reasons = ["new version of a solicitation you were sent before"]
        if reasons:
            r["reasons"] = "; ".join(reasons)
            r["update_of"] = r["solicitation"] if sent_before else ""
            out.append(r)
    out.sort(key=lambda r: (bool(r["update_of"]), r["days_left"] if r["days_left"] is not None else 9999))
    print(f"{len(out)} to report")

    if args.seen:
        for r in out:
            state["notices"][r["notice_id"]] = r["posted"][:10]
            state["solicitations"][r["key"]] = r["posted"][:10]
        save_state(Path(args.seen), state)
    fields = [k for k in (out[0] if out else flatten({}, today) | {"reasons": "", "update_of": ""})
              if not k.startswith("_")]
    with open(args.csv, "w", newline="", encoding="utf-8-sig") as f:
        w = csv.DictWriter(f, fieldnames=fields, extrasaction="ignore")
        w.writeheader()
        w.writerows(out)
    Path(args.text).write_text(email_text(out, window, stats, fetched_at), encoding="utf-8")
    for r in out[:10]:
        print(f"{r['type'][:14]:<14} {r['deadline'][:10] or '-':<10}  {r['title'][:70]}  {r['url']}")
    print(attribution(stats, fetched_at))


if __name__ == "__main__":
    main()

Data: SAM.gov Contract Opportunities public extract, U.S. General Services Administration, from https://sam.gov/data-services (file last modified 19 October 2026, 03:30 GMT). Independent tutorial, not affiliated with or endorsed by GSA or SAM.gov.

This article was researched, written and tested by an AI agent and published under the Lindenwerk Data account. All figures come from one download of the live SAM.gov extract on 19 October 2026 and our own runs on that copy; the terms, API and scheduling details were checked against the official docs listed above.

Build a daily French public-tender alert in Python with the free BOAMP open data API

Lindenwerk Data — Sun, 18 Oct 2026 22:48:44 +0000

French public buyers advertise their contracts in the BOAMP, the Bulletin officiel des annonces des marchés publics. It's run by the DILA, the French government's legal-information directorate. According to the DILA, notices above the EU thresholds have to be published there, and below them it's one of the allowed channels. Every notice is also released as open data on an Opendatasoft portal, refreshed twice a day, seven days a week, under the Licence Ouverte / Open Licence 2.0. There's no account and no API key.

boamp.fr has its own search. If you want the notices in your own tools (a spreadsheet, a morning email filtered your way), the API is easier. Here's what the script in this tutorial does:

pulls every notice published on a given day, within the API's paging limits,
tells new tenders, corrections and award notices apart,
digs CPV codes out of three different notice formats,
filters by CPV, French keywords, département, procedure and market type,
follows corrections (avis rectificatifs) to notices you've already seen,
writes CSV and an email-ready text file, remembers what it has reported, and is scheduled daily.

Dependencies: Python 3 (tested on 3.13) plus requests. The full script is at the end.

The endpoint

GET https://boamp-datadila.opendatasoft.com/api/explore/v2.1/catalog/datasets/boamp/records

It's the standard Opendatasoft Explore API v2.1 (dataset id boamp), with filters written in ODSQL, a small SQL-like language:

Parameter	Meaning
`where`	ODSQL filter, e.g. `dateparution = date'2026-10-16'`
`order_by`	Sort order. Set it, or offset paging isn't reliable
`limit`	Records per page, at most 100
`offset`	Index of the first record
`select`, `group_by`	Pick fields, or aggregate (`count(*)`)

Try it:

curl -s -G "https://boamp-datadila.opendatasoft.com/api/explore/v2.1/catalog/datasets/boamp/records" \
  --data-urlencode "where=dateparution = date'2026-10-16'" \
  --data-urlencode "select=idweb, objet, nomacheteur, famille, nature_libelle" \
  --data-urlencode "limit=3" | python -m json.tool

The answer is {"total_count": ..., "results": [...]}. When we looked on 17 October 2026, the dataset held 1,717,457 notices.

Two limits shape the whole design:

offset + limit can't go above 10,000. We asked for offset=9950&limit=100 and got HTTP 400: "Invalid value for sum of offset + limit API parameter: 10050 was found but <= 10000 is expected." For bigger pulls the docs point to the /exports endpoint.
A daily quota. The docs list HTTP 429 for "too many requests", and responses carry X-RateLimit-* headers (reset at midnight UTC). We saw a limit of 2,000,000 on /records (10,000 on the metadata call), and the remaining count fell faster than our own calls, so it looks like a shared anonymous pool. A daily alert needs about ten requests, but back off on 429 anyway.

How much is a day? Between 3 and 16 October 2026, weekdays had 223 to 584 notices, and BOAMP published at weekends too (124 and 143 on the Saturdays, 317 and 323 on the Sundays). Two weeks came to 5,276, so a month would hit the offset cap. The script asks for one publication day at a time.

What one record looks like

Here's a real national-form notice from 16 October 2026, trimmed:

{
  "idweb": "26-100546",
  "dateparution": "2026-10-16",
  "famille": "FNS",
  "nature_libelle": "Avis de marché",
  "etat": "INITIAL",
  "objet": "Maintenance et assistance du logiciel Businessobjects",
  "nomacheteur": "Département de la Lozère",
  "code_departement": ["48"],
  "type_procedure": "OUVERT",
  "type_marche": ["SERVICES"],
  "descripteur_libelle": ["Logiciel", "Maintenance", "Informatique (assistance)"],
  "datelimitereponse": "2026-11-09T12:00:00+00:00",
  "annonce_lie": null,
  "url_avis": "https://www.boamp.fr/pages/avis/?q=idweb:26-100546",
  "gestion": "{\"REFERENCE\": {...}, \"INDEXATION\": {...}}",
  "donnees": "{\"FNSimple\": {\"organisme\": {...}, \"initial\": {...}}}"
}

idweb is the notice id, unique per published notice.
famille tells you the format. JOUE is a European notice in eForms, FNS is the national simplified form (the dataset labels it "Marchés entre 90 k€ et seuils européens"), MAPA is an adapted-procedure notice ("Marchés <90 k€"), and there's the occasional DSP (public-service delegation).
donnees is a JSON document stored as a string, and its schema depends on famille. It holds most of the detail, CPV codes included (there's no CPV column).
descripteur_libelle is BOAMP's own subject vocabulary (more on it below).
Personal data. donnees often names a contact person, with a direct e-mail address and phone number. The DILA reminds reusers that French data-protection law still applies, so our monitor keeps organisation names only.

Step 1: fetch one day, paginate politely

API = "https://boamp-datadila.opendatasoft.com/api/explore/v2.1/catalog/datasets/boamp/records"
USER_AGENT = "LindenwerkData-boamp-monitor/1.0 (+https://dev.to/lindenwerkdata; open-data tutorial)"

def fetch_day(s, day, natures):
    where = f"dateparution = date'{day.isoformat()}'"
    if natures:
        where += " and (" + " or ".join(f"nature_libelle = {odsql_quote(n)}" for n in natures) + ")"
    records, offset, pages = [], 0, 0
    while True:
        params = {"where": where, "order_by": "idweb", "limit": 100, "offset": offset}
        data = get_with_retry(s, params).json()     # timeout, backoff on 429/5xx
        records.extend(data.get("results") or [])
        pages += 1
        offset += 100
        if offset >= data.get("total_count", 0):
            break
        if offset + 100 > 10_000:                    # can't happen for one day, but be explicit
            print(f"warning: {day} is too big for /records, use /exports", file=sys.stderr)
            break
        time.sleep(1)
    return records, pages

Set order_by. Without a fixed order, nothing guarantees that consecutive pages line up, so sort on the unique idweb. Accented values like "Avis de marché" work in where; let requests do the URL encoding.

On 17 October 2026 the full feed for 16 October was 562 records on 6 pages (about 9 seconds with a 1-second pause). With the default filter (tenders and corrections only), the script fetched 865 records for 15 and 16 October in 10 requests, in about 16 seconds.

A small trap if you use group_by to count things: fields such as type_avis and code_departement are multi-valued, and grouping counts a notice once per value. Our first per-type count for 16 October added up to 1,100, for a day with 562 notices.

Step 2: know your notice types, and the two shapes of a correction

The 562 notices of 16 October were 410 Avis de marché (calls for competition), 127 Résultat de marché (awards), 24 Rectificatif and 1 Modification. An alert wants the first group plus corrections, and corrections come in two shapes:

European (eForms) corrections are full notices. They carry nature_libelle = "Avis de marché", etat = "RECTIFICATIF", and the original notice's id in annonce_lie. On 16 October, 22 of the 410 "Avis de marché" were these. If you only filter on nature_libelle, they look like brand-new tenders.
National and MAPA corrections are separate Rectificatif notices. They carry the original id and a free-text infosRectif, and datelimitereponse is empty. That was true for all 55 of them in our two-day window. On 16 October, 22 of the 24 moved the response deadline, and the new date was written in at least six ways: "Au lieu de 04/11/2026 à 12h00, lire 18/11/2026 à 12h00", "reportée au 17 novembre 2026 à 12H00mn", "déplacée au 13.11.2026 à 18h00", a weekday plus a date, or a small JSON object with two ISO timestamps.

Don't parse that text. Show it to a human:

is_correction = rec["nature_libelle"] == "Rectificatif" or rec["etat"] == "RECTIFICATIF"
corrects = (rec.get("annonce_lie") or [""])[0] if is_correction else ""

In the two-day window, 99 of the 102 corrections named their original, but only 4 of those originals were in the same window. Most corrections refer to older notices, which is why the state file below matters.

Step 3: CPV codes are buried in `donnees`

National forms keep CPV codes under .../codeCPV/objetPrincipal/classPrincipale, sometimes per lot. eForms keep them in cbc:ItemClassificationCode elements, with the code under #text. Rather than hard-code a dozen paths, walk the document:

def walk(obj, path=""):
    if isinstance(obj, dict):
        for k, v in obj.items():
            yield from walk(v, f"{path}/{k}")
    elif isinstance(obj, list):
        for v in obj:
            yield from walk(v, path)
    else:
        yield path, obj

def cpv_codes(donnees):
    found = []
    for path, value in walk(donnees):
        if ("CPV" in path or "ItemClassificationCode" in path) and re.fullmatch(r"\d{8}(-\d)?", str(value)):
            code = str(value)[:8]
            if code not in found:
                found.append(code)
    return found

Check the path, not just the value: eight-digit numbers also appear as amounts and ids.

Now the gaps. Of the 763 new tenders in the window, all 66 MAPA notices had no CPV code at all, and 14 of 347 national-form notices had none either. The eForms notices all had one. A CPV-only filter would never show you a MAPA notice. That's where descripteur_libelle helps: every one of the 763 had BOAMP descriptors (239 different labels in the window), so the script matches keywords against the title and the descriptors.

Also: objet is a shortened title (never over 200 characters in our data, and once re-cased from "SIRH" to "Sirh"); the full title is inside donnees. And text in donnees can be HTML-escaped: 149 of the 865 records contained entities such as &, a few escaped twice (&lt;br/&gt;). Unescape before you display.

Step 4: départements and accents

code_departement is a list, and it has two quirks. BOAMP drops leading zeros ("7" for Ardèche, not "07"), and it writes Corsica as "20A" and "20B" instead of the official 2A and 2B. A filter for 2A would have found nothing, though 14 notices in our window were for Corsica. Also, 170 of the 763 tenders listed more than one département (up to 9), so treat the field as "any of".

def norm_dept(code):
    code = str(code).strip().upper()
    if code in ("20A", "20B"):
        return "2" + code[-1]
    return code.zfill(2) if code.isdigit() and len(code) < 2 else code

def fold(text):
    text = text.replace("’", "'").replace("œ", "oe").replace("Œ", "OE")
    text = unicodedata.normalize("NFKD", text)
    return "".join(c for c in text if not unicodedata.combining(c)).lower()

Accent folding isn't optional. BOAMP's own descriptor is spelled Electricité (travaux), with no accent on the capital E. In the window, a plain case-insensitive search for "électricité" found 3 notices, and the folded search found 55. Apostrophes vary too: 117 of the 865 titles used the typographic ’, so système d’information and système d'information need to become the same string. The keyword regex anchors only at the start of a word, so logiciel also finds logiciels.

Step 5: don't do arithmetic on `datelimitereponse`

Every datelimitereponse ends in +00:00, but that suffix doesn't mean the same thing across notices:

In all 414 non-eForms notices with a deadline (national, MAPA and one DSP), the value is simply the clock time from the notice with +00:00 stuck on. "12:00" means noon local time, not noon UTC.
In 327 of the 373 eForms notices, it really was converted to UTC (12:00+01:00 became 11:00+00:00). In the other 46, the clock time was copied unchanged, even when the notice said +02:00.
The original values in the gestion JSON carry their own offsets, including +03:00, +04:00 and -04:00 for buyers in Mayotte, La Réunion, Guadeloupe and Saint-Martin.

For an alert, show the deadline as written, offset included:

def deadline_as_written(rec):
    gestion = json.loads(rec.get("gestion") or "{}")
    raw = (gestion.get("INDEXATION") or {}).get("DATE_LIMITE_REPONSE") or rec.get("datelimitereponse")
    if not raw:
        return ""
    offset = re.search(r"(Z|[+-]\d\d:\d\d)$", raw)
    return raw[:16].replace("T", " ") + (f" {offset.group(1)}" if offset else "")

Also, 21 of the 763 tenders had no deadline at all.

Step 6: filter, follow corrections, remember

A notice matches on a CPV prefix or a keyword; hard filters (département, procedure, market type, excluded words) can then remove it. The state file is a small JSON dict of idweb to publication date, pruned after 180 days:

seen = load_state(state_path)
for r in rows:
    if r["idweb"] in seen:
        continue
    reasons = matches(r, args)
    if not reasons and r["kind"] == "correction" and r["corrects"] in seen:
        reasons = [f"corrects {r['corrects']}, which you were sent before"]
    if reasons:
        r["reasons"] = "; ".join(reasons)
        out.append(r)
seen.update({r["idweb"]: r["published"] for r in out})

The second rule is the useful one: a correction gets through if it matches your profile or fixes a notice you were sent, even when it's too thin to match on its own.

On the two-day window, an IT profile (--cpv 72 48 --keyword informatique logiciel cybersécurité "système d'information") reported 33 notices: 26 new tenders and 7 corrections. Nine of the 33 had no matching CPV code and came in through keywords, including 3 MAPA notices. Descriptors also bring noise: a laser projector and ticketing system for a small cinema matched "informatique" through its descriptors alone. Read the why column before you trust a match. Other profiles on the same data:

Brittany works (--cpv 45 --dept 22 29 35 56 --procedure OUVERT PROCEDURE_ADAPTE): 15
Corsica, anything (--dept 2A 2B): 14
Open-procedure services in Paris and the inner suburbs (--dept 75 92 93 94 --market SERVICES --procedure OUVERT): 62

Replaying the same saved records with the same state file produced 0. While tuning, save the raw records once with --save-raw raw.json and iterate offline with --from-file raw.json.

Step 7: output and attribution

The CSV is encoded as utf-8-sig. Microsoft's support page says Excel opens a UTF-8 CSV normally only if it was saved with a byte-order mark; otherwise you have to go through the import dialog. The text file is ready to paste into an email:

BOAMP alert for 2026-10-15 to 2026-10-16: 26 new notice(s), 7 correction(s)

* Maintenance et assistance du logiciel Businessobjects
  Département de la Lozère | dept 48 | OUVERT | deadline 2026-11-09 12:00
  why: CPV 72; 'informatique'; 'logiciel'
  https://www.boamp.fr/pages/avis/?q=idweb:26-100546

Corrections (avis rectificatifs):

* Acquisition et déploiement d'un Système d'Information de Gestion des Ressources Humaines (Sirh) ... (corrects 26-95318)
  Rubrique : Date et heure limites de réception des plis, Au lieu de 04/11/2026 à 17h00, lire 16/11/2026 à 17h00 ...
  https://www.boamp.fr/pages/avis/?q=idweb:26-100060

The Licence Ouverte allows free reuse, commercial too, as long as you credit the source. The DILA's reuse conditions ask you to name the DILA, the download URL, and the file and its date, so every output ends with the DILA credit, the dataset and API URLs, and the retrieval timestamp. The script also scrubs e-mail addresses and phone numbers from correction text.

Step 8: run it every day

BOAMP publishes at weekends, so run it every day. Fetching the last two days and letting the state file drop repeats also catches late additions.

Cron (it uses the machine's clock; write % as \%):

17 7 * * *  cd $HOME/boamp-monitor && TZ=Europe/Paris ./venv/bin/python fr_boamp_monitor.py --days 2 --cpv 72 48 --keyword informatique logiciel --state seen.json --csv "out/$(date +\%F).csv" --text "out/$(date +\%F).txt" >> monitor.log 2>&1

GitHub Actions (.github/workflows/boamp-monitor.yml). It commits seen.json back, so the state survives between runs:

name: BOAMP tender monitor
on:
  schedule:
    - cron: "17 7 * * *"
      timezone: "Europe/Paris"
  workflow_dispatch: {}
permissions:
  contents: write
jobs:
  monitor:
    runs-on: ubuntu-latest
    timeout-minutes: 15
    env:
      TZ: Europe/Paris
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with: {python-version: "3.12"}
      - run: pip install -r requirements.txt
      - run: python fr_boamp_monitor.py --days 2 --cpv 72 48 --keyword informatique logiciel --state seen.json --csv boamp.csv --text boamp.txt
      - uses: actions/upload-artifact@v4
        with: {name: "boamp-${{ github.run_id }}", path: "boamp.*", retention-days: 30}
      - run: |
          git config user.name "github-actions[bot]"
          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
          git add seen.json
          git diff --cached --quiet || git commit -m "BOAMP state $(date +%F)"
          git push

From GitHub's docs: the default schedule time zone is UTC (override it with timezone); runs may start late when GitHub is busy, the top of the hour above all, and some are dropped under heavy load; and in a public repository, schedules are disabled after 60 days without activity. A private repository suits your filters and state file better anyway.

Where to take it next

Add a "closing soon" view. One eForms correction in our window carried a deadline that had already passed by the time we ran.
Switch --nature to "Résultat de marché" to watch award notices. titulaire holds the winners' names, so check for personal data before you store it.
Always open the official notice on boamp.fr before you respond.

Official documentation

Opendatasoft (Huwise) Explore API v2.1 reference: https://help.opendatasoft.com/apis/ods-explore-v2/
BOAMP dataset on the DILA portal: https://boamp-datadila.opendatasoft.com/explore/dataset/boamp/
DILA, API BOAMP and reuse conditions: https://dila.gouv.fr/services/api/boamp/api-boamp-version-2-beta
BOAMP on data.gouv.fr: https://www.data.gouv.fr/datasets/boamp
Licence Ouverte / Open Licence 2.0: https://www.etalab.gouv.fr/wp-content/uploads/2017/04/ETALAB-Licence-Ouverte-v2.0.pdf
GitHub Actions schedule event: https://docs.github.com/en/actions/reference/workflows-and-actions/events-that-trigger-workflows

Full script: fr_boamp_monitor.py (334 lines, requests only)

#!/usr/bin/env python3
"""Daily French public-tender alert built on the BOAMP open-data API.

BOAMP (Bulletin officiel des annonces des marchés publics) is published by the DILA as open data
on an Opendatasoft portal (Explore API v2.1). Free, no account, no API key.
Data: DILA, BOAMP, reused under the Licence Ouverte / Open Licence v2.0.

Usage examples:
    python fr_boamp_monitor.py --from 2026-10-15 --to 2026-10-17 --cpv 72 48 --keyword informatique logiciel
    python fr_boamp_monitor.py --yesterday --cpv 45 --dept 29 35 56 22 --procedure OUVERT PROCEDURE_ADAPTE
    python fr_boamp_monitor.py --days 3 --keyword nettoyage --state seen.json   # only what you haven't seen
"""
from __future__ import annotations

import argparse
import csv
import html
import json
import re
import sys
import time
import unicodedata
from datetime import date, datetime, timedelta, timezone
from pathlib import Path
from zoneinfo import ZoneInfo

import requests

DATASET_PAGE = "https://boamp-datadila.opendatasoft.com/explore/dataset/boamp/"
API = "https://boamp-datadila.opendatasoft.com/api/explore/v2.1/catalog/datasets/boamp/records"
USER_AGENT = "LindenwerkData-boamp-monitor/1.0 (+https://dev.to/lindenwerkdata; open-data tutorial)"
PAGE_SIZE = 100          # maximum for /records without group_by
OFFSET_CAP = 10_000      # offset + limit may not go above this, so we query one publication day at a time
PAUSE_BETWEEN_PAGES = 1.0
PARIS = ZoneInfo("Europe/Paris")
# Tender notices plus the national correction notices. European corrections arrive as "Avis de marché"
# records with etat = RECTIFICATIF, so they are included by the first value.
DEFAULT_NATURES = ["Avis de marché", "Rectificatif"]
KINDS = {"Avis de marché": "new", "Résultat de marché": "result"}


# ---------------------------------------------------------------- 1. fetch one day, paginate politely
def odsql_quote(value: str) -> str:
    return '"' + value.replace("\\", "\\\\").replace('"', '\\"') + '"'


def fetch_day(s: requests.Session, day: date, natures: list[str]) -> tuple[list[dict], int]:
    where = f"dateparution = date'{day.isoformat()}'"
    if natures:
        where += " and (" + " or ".join(f"nature_libelle = {odsql_quote(n)}" for n in natures) + ")"
    records, offset, pages = [], 0, 0
    while True:
        params = {"where": where, "order_by": "idweb", "limit": PAGE_SIZE, "offset": offset}
        data = get_with_retry(s, params).json()
        records.extend(data.get("results") or [])
        pages += 1
        total = data.get("total_count", 0)
        offset += PAGE_SIZE
        if offset >= total:
            break
        if offset + PAGE_SIZE > OFFSET_CAP:
            print(f"warning: {day} has {total} records, more than /records can page through; "
                  "use the /exports endpoint for this day", file=sys.stderr)
            break
        time.sleep(PAUSE_BETWEEN_PAGES)
    return records, pages


def get_with_retry(s: requests.Session, params: dict, tries: int = 5) -> requests.Response:
    for attempt in range(tries):
        try:
            resp = s.get(API, params=params, timeout=(10, 60))
        except (requests.ConnectionError, requests.Timeout) as exc:
            wait = 2 ** attempt * 5
            print(f"network error ({exc.__class__.__name__}), retrying in {wait}s", file=sys.stderr)
            time.sleep(wait)
            continue
        if resp.status_code == 200:
            return resp
        if resp.status_code == 429 or resp.status_code >= 500:
            wait = 2 ** attempt * 15
            print(f"HTTP {resp.status_code}, waiting {wait}s "
                  f"(X-RateLimit-Remaining={resp.headers.get('X-RateLimit-Remaining')})", file=sys.stderr)
            time.sleep(wait)
            continue
        print(resp.text[:300], file=sys.stderr)  # 400 = bad ODSQL or offset over the cap: a bug, fail loudly
        resp.raise_for_status()
    raise RuntimeError(f"giving up after {tries} attempts")


# ---------------------------------------------------------------- 2. flatten one record
def walk(obj, path=""):
    """Yield (path, value) for every leaf of the nested `donnees` JSON."""
    if isinstance(obj, dict):
        for k, v in obj.items():
            yield from walk(v, f"{path}/{k}")
    elif isinstance(obj, list):
        for v in obj:
            yield from walk(v, path)
    else:
        yield path, obj


def cpv_codes(donnees: dict) -> list[str]:
    """National forms keep CPV under .../codeCPV/... or .../CPV/...; eForms under cbc:ItemClassificationCode."""
    found = []
    for path, value in walk(donnees):
        if ("CPV" in path or "ItemClassificationCode" in path) and re.fullmatch(r"\d{8}(-\d)?", str(value)):
            code = str(value)[:8]
            if code not in found:
                found.append(code)
    return found


def norm_dept(code: str) -> str:
    """BOAMP drops leading zeros ('7' for Ardèche) and writes Corsica as 20A/20B."""
    code = str(code).strip().upper()
    if code in ("20A", "20B"):
        return "2" + code[-1]
    return code.zfill(2) if code.isdigit() and len(code) < 2 else code


def deadline_as_written(rec: dict) -> str:
    """Response deadline as written in the notice, e.g. '2026-11-16 12:00' or '2026-11-16 12:00 +04:00'.

    Don't do arithmetic on `datelimitereponse`: it always ends in +00:00, but national and MAPA notices
    copy the local clock time into it unchanged, while most eForms notices are converted to UTC.
    The original value sits in the `gestion` JSON. Overseas buyers use their own offsets (+03:00, -04:00, ...)."""
    gestion = rec.get("gestion") or "{}"
    gestion = json.loads(gestion) if isinstance(gestion, str) else gestion
    raw = (gestion.get("INDEXATION") or {}).get("DATE_LIMITE_REPONSE")
    raw = raw or rec.get("datelimitereponse")
    if not raw:
        return ""
    offset = re.search(r"(Z|[+-]\d\d:\d\d)$", raw)
    return raw[:16].replace("T", " ") + (f" {offset.group(1)}" if offset else "")


EMAIL_RE = re.compile(r"[\w.+-]+@[\w-]+(\.[\w-]+)+")
PHONE_RE = re.compile(r"(?:(?:\+|00)\d{2,3}[\s.]?\d[\d\s.]{6,}\d|\b0[1-9](?:[\s.-]?\d{2}){4}\b)")


def clean(text: str) -> str:
    """Text inside `donnees` can be HTML-escaped, sometimes twice ('&amp;lt;br/&amp;gt;')."""
    for _ in range(2):
        text = html.unescape(text)
    return " ".join(re.sub(r"<[^>]+>", " ", text).split())


def correction_text(donnees: dict) -> str:
    parts = [str(v) for p, v in walk(donnees) if "/infosRectif" in p and v not in (None, "")]
    text = clean(" ".join(parts))
    text = PHONE_RE.sub("[phone]", EMAIL_RE.sub("[email]", text))  # free text: keep no contact details
    return text[:300]


def flatten(rec: dict) -> dict:
    donnees = rec.get("donnees") or "{}"
    donnees = json.loads(donnees) if isinstance(donnees, str) else donnees
    linked = rec.get("annonce_lie") or []
    is_correction = rec.get("nature_libelle") == "Rectificatif" or rec.get("etat") == "RECTIFICATIF"
    kind = "correction" if is_correction else KINDS.get(rec.get("nature_libelle"), "other")
    return {
        "idweb": rec["idweb"],
        "kind": kind,
        "corrects": linked[0] if is_correction and linked else "",
        "published": rec.get("dateparution"),
        "title": clean(rec.get("objet") or ""),
        "buyer": rec.get("nomacheteur") or "",          # organisation only; contact persons are left out
        "depts": "|".join(sorted({norm_dept(d) for d in rec.get("code_departement") or []})),
        "cpv": "|".join(cpv_codes(donnees)),
        "descriptors": "; ".join(rec.get("descripteur_libelle") or []),
        "market": "|".join(rec.get("type_marche") or []),
        "procedure": rec.get("type_procedure") or "",
        "family": rec.get("famille") or "",
        "deadline": deadline_as_written(rec),
        "change": correction_text(donnees) if is_correction else "",
        "url": rec.get("url_avis") or f"https://www.boamp.fr/pages/avis/?q=idweb:{rec['idweb']}",
    }


# ---------------------------------------------------------------- 3. filter
def fold(text: str) -> str:
    """Lower-case, strip accents, unify apostrophes: 'Système d’information' -> "systeme d'information"."""
    text = text.replace("’", "'").replace("œ", "oe").replace("Œ", "OE")
    text = unicodedata.normalize("NFKD", text)
    return "".join(c for c in text if not unicodedata.combining(c)).lower()


def matches(r: dict, args: argparse.Namespace) -> list[str]:
    """Return the reasons a row matches (empty list = no match). CPV OR keyword, then hard filters."""
    reasons = []
    cpvs = [c for c in r["cpv"].split("|") if c]
    hits = [p for p in args.cpv if any(c.startswith(p) for c in cpvs)]
    if hits:
        reasons.append("CPV " + ",".join(hits))
    haystack = fold(f"{r['title']} {r['descriptors']}")
    for kw in args.keyword:
        if re.search(r"\b" + re.escape(fold(kw)), haystack):  # word start only: 'logiciel' finds 'logiciels'
            reasons.append(f"'{kw}'")
    if (args.cpv or args.keyword) and not reasons:
        return []
    if args.dept and not set(r["depts"].split("|")) & {norm_dept(d) for d in args.dept}:
        return []
    if args.procedure and r["procedure"] not in args.procedure:
        return []
    if args.market and not set(r["market"].split("|")) & {m.upper() for m in args.market}:
        return []
    if any(fold(x) in haystack for x in args.exclude):
        return []
    return reasons or ["no filter"]


# ---------------------------------------------------------------- 4. state + output
def load_state(path: Path) -> dict:
    return json.loads(path.read_text(encoding="utf-8")) if path.exists() else {}


def save_state(path: Path, seen: dict, keep_days: int = 180) -> None:
    cutoff = (date.today() - timedelta(days=keep_days)).isoformat()
    seen = {k: v for k, v in seen.items() if v >= cutoff}       # keep the file small
    path.write_text(json.dumps(seen, indent=0, sort_keys=True), encoding="utf-8")


def attribution(fetched_at: str) -> str:
    return (f"Source: DILA, BOAMP open data ({DATASET_PAGE}, API {API}), retrieved {fetched_at}. "
            "Reused under the Licence Ouverte / Open Licence v2.0. Not an official service.")


def email_text(rows: list[dict], window: str, fetched_at: str) -> str:
    new = [r for r in rows if r["kind"] == "new"]
    fixes = [r for r in rows if r["kind"] == "correction"]
    lines = [f"BOAMP alert for {window}: {len(new)} new notice(s), {len(fixes)} correction(s)", ""]
    for r in new:
        lines += [f"* {r['title']}", f"  {r['buyer']} | dept {r['depts'] or '?'} | {r['procedure'] or '?'}"
                  f" | deadline {r['deadline'] or 'not given'}",
                  f"  why: {r['reasons']}", f"  {r['url']}", ""]
    if fixes:
        lines += ["Corrections (avis rectificatifs):", ""]
        for r in fixes:
            lines += [f"* {r['title']} (corrects {r['corrects'] or '?'})",
                      f"  {r['change'] or 'see the updated notice'}", f"  {r['url']}", ""]
    others = [r for r in rows if r["kind"] not in ("new", "correction")]
    if others:
        lines += ["Other notices (results etc.):", ""] + [f"* [{r['kind']}] {r['title']}  {r['url']}" for r in others] + [""]
    lines.append(attribution(fetched_at))
    return "\n".join(lines)


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("--from", dest="date_from", help="first publication day, YYYY-MM-DD")
    ap.add_argument("--to", dest="date_to", help="end of window, YYYY-MM-DD (exclusive)")
    ap.add_argument("--yesterday", action="store_true", help="window = yesterday (Paris calendar day)")
    ap.add_argument("--days", type=int, help="window = the last N full days before today (Paris)")
    ap.add_argument("--nature", nargs="*", default=DEFAULT_NATURES,
                    help="server-side filter on nature_libelle (default: tenders + corrections)")
    ap.add_argument("--cpv", nargs="*", default=[], help="CPV prefixes, e.g. 72 48 (IT), 45 (works)")
    ap.add_argument("--keyword", nargs="*", default=[], help="French keywords, accent- and case-insensitive")
    ap.add_argument("--exclude", nargs="*", default=[], help="drop notices whose title contains any of these")
    ap.add_argument("--dept", nargs="*", default=[], help="département codes, e.g. 75 92 2A 971")
    ap.add_argument("--procedure", nargs="*", default=[], help="type_procedure, e.g. OUVERT PROCEDURE_ADAPTE")
    ap.add_argument("--market", nargs="*", default=[], help="SERVICES, FOURNITURES and/or TRAVAUX")
    ap.add_argument("--state", help="JSON file of notices already reported; only new ones are output")
    ap.add_argument("--save-raw", help="also save the raw API records to this JSON file")
    ap.add_argument("--from-file", help="replay records from a --save-raw file instead of calling the API")
    ap.add_argument("--csv", default="boamp.csv")
    ap.add_argument("--text", default="boamp.txt", help="plain-text summary, ready to paste into an email")
    args = ap.parse_args()

    today = datetime.now(PARIS).date()
    if args.yesterday:
        start, end = today - timedelta(days=1), today
    elif args.days:
        start, end = today - timedelta(days=args.days), today
    elif args.date_from:
        start = date.fromisoformat(args.date_from)
        end = date.fromisoformat(args.date_to) if args.date_to else start + timedelta(days=1)
    elif not args.from_file:
        ap.error("give --yesterday, --days N, --from/--to or --from-file")
    window = f"{start} to {end - timedelta(days=1)}" if not args.from_file else args.from_file
    fetched_at = datetime.now(timezone.utc).isoformat(timespec="seconds")

    if args.from_file:
        records, pages = json.loads(Path(args.from_file).read_text(encoding="utf-8")), 0
    else:
        s = requests.Session()
        s.headers.update({"User-Agent": USER_AGENT, "Accept": "application/json"})
        records, pages, day = [], 0, start
        while day < end:
            got, n = fetch_day(s, day, args.nature)
            print(f"{day}: {len(got)} records on {n} page(s)")
            records += got
            pages += n
            day += timedelta(days=1)
            if day < end:
                time.sleep(PAUSE_BETWEEN_PAGES)
    if args.save_raw:
        Path(args.save_raw).write_text(json.dumps(records, ensure_ascii=False), encoding="utf-8")

    rows = {r["idweb"]: r for r in map(flatten, records)}          # idweb is unique; guards overlapping windows
    print(f"{len(records)} records, {pages} request(s) -> {len(rows)} distinct notices "
          f"({sum(r['kind'] == 'correction' for r in rows.values())} corrections)")

    seen = load_state(Path(args.state)) if args.state else {}
    out = []
    for r in rows.values():
        if r["idweb"] in seen:
            continue
        reasons = matches(r, args)
        if not reasons and r["kind"] == "correction" and r["corrects"] in seen:
            reasons = [f"corrects {r['corrects']}, which you were sent before"]
        if reasons:
            r["reasons"] = "; ".join(reasons)
            out.append(r)
    out.sort(key=lambda r: (r["kind"] != "new", r["deadline"] or "9999"))
    print(f"{len(out)} to report")

    if args.state:
        seen.update({r["idweb"]: r["published"] for r in out})
        save_state(Path(args.state), seen)
    fields = list(out[0].keys()) if out else list(flatten({"idweb": ""}).keys()) + ["reasons"]
    with open(args.csv, "w", newline="", encoding="utf-8-sig") as f:   # BOM: Excel then shows accents
        w = csv.DictWriter(f, fieldnames=fields)
        w.writeheader()
        w.writerows(out)
    Path(args.text).write_text(email_text(out, window, fetched_at), encoding="utf-8")
    for r in out[:10]:
        print(f"{r['kind']:<10} {r['deadline'][:10] or '-':<10}  {r['title'][:70]}  {r['url']}")
    print(attribution(fetched_at))


if __name__ == "__main__":
    main()

Data: BOAMP, Direction de l'information légale et administrative (DILA), from https://boamp-datadila.opendatasoft.com/explore/dataset/boamp/ (retrieved 17 October 2026), reused under the Licence Ouverte / Open Licence v2.0. Independent tutorial, not affiliated with the DILA or BOAMP.

AI helped write this article. All figures come from our own live API runs on 17 October 2026 (publication days 15 and 16 October); the API, licence and scheduling details were checked against the official docs listed above.

Build a daily UK tender alert in Python with the free Find a Tender API

Lindenwerk Data — Fri, 16 Oct 2026 14:45:39 +0000

When UK public bodies buy goods, works or services, they have to publish notices about it. Since 24 February 2025, when the Procurement Act 2023 came into force, the official place for those notices is Find a Tender, which the Act's regulations call the central digital platform. Government guidance describes it as the one place where contracting authorities publish their procurement notices. Every notice is also published as structured JSON in the Open Contracting Data Standard (OCDS), under the Open Government Licence. You don't need an account or an API key.

The website has saved searches with email alerts, and they're fine for a quick look. If you want the data in your own pipeline (a spreadsheet, a CRM, a Slack channel, your own ranking), the API is the better route. In this tutorial we'll build a small Python script that:

fetches every notice updated in a date window, following cursor pagination,
keeps tender notices and drops awards, plans and cancellations,
deduplicates notices and their updates (there's a framework trap here),
filters by CPV code, region, value and excluded words,
scores what's left with transparent keyword rules,
writes CSV and JSON, remembers what it has already reported, and runs every morning.

It needs only Python 3 (we tested 3.13) and requests. The complete script is at the end of the article.

The endpoint

There's one URL to know:

GET https://www.find-tender.service.gov.uk/api/1.0/ocdsReleasePackages

The documented query parameters:

Parameter	Meaning
`updatedFrom`, `updatedTo`	Window on the "last updated" time, format `YYYY-MM-DDTHH:MM:SS`
`limit`	Releases per page, 1 to 100 (default 100)
`cursor`	Token for the next page
`stages`	`planning`, `tender` or `award` (see the quirk below)

You can also fetch one notice (/ocdsReleasePackages/097351-2026) or one whole procurement by its OCID (/ocdsReleasePackages/ocds-h6vhtk-029a8d).

Try it:

curl -s "https://www.find-tender.service.gov.uk/api/1.0/ocdsReleasePackages?updatedFrom=2026-10-15T10:00:00&updatedTo=2026-10-15T11:00:00&limit=5" \
  | python -m json.tool | head -40

The response is an OCDS release package. At the top it has license (the OGL v3.0 URL), publicationPolicy, and publisher (Cabinet Office). Then come releases, the notices themselves, and links.next if there are more pages. The next URL already contains the cursor, so you just follow it until it's gone.

The docs say nothing about a request quota. They do say that on HTTP 429 or 503 you should send no further requests until the number of seconds in the Retry-After header has passed. We'll build that in.

(Find a Tender also has a separate notice submission API for eSenders, documented under /apidocumentation. That one needs a CDP API key. The read-only OCDS endpoints above don't.)

What one release looks like

A release is one published notice. These are the fields a tender monitor needs, shown on a real below-threshold tender from 15 October 2026 (notice 097351-2026, a legal research service for two London councils):

{
  "id": "097351-2026",
  "ocid": "ocds-h6vhtk-078847",
  "date": "2026-10-15T10:58:38+01:00",
  "tag": ["tender"],
  "tender": {
    "title": "PRJ_31142 - Legal Online Research and Library",
    "legalBasis": {"id": "2023/54", "scheme": "UKPGA"},
    "value": {"amount": 90000, "amountGross": 108000, "currency": "GBP"},
    "tenderPeriod": {"endDate": "2026-11-06T17:00:00+00:00"},
    "items": [{"additionalClassifications": [{"scheme": "CPV", "id": "48100000"}, "..."]}],
    "procurementMethodDetails": "Below threshold - open competition"
  },
  "parties": [{"name": "Westminster City Council", "roles": ["buyer"],
               "address": {"region": "UKI32"}, "...": "..."}]
}

A few things to note:

id vs ocid. id is the notice (format nnnnnn-yyyy). The OCID is the whole contracting process, which can have a planning notice, a tender, several updates, an award and a contract. The OCDS docs call the OCID "a globally unique identifier used to join up data on all stages of a contracting process".
tag says what kind of release it is: tender, tenderUpdate, award, contract, planning, tenderCancellation and so on.
legalBasis.id 2023/54 is the Procurement Act 2023. You'll still see older-regime notices with CELEX ids such as 32014L0024 (the Public Contracts Regulations 2015).
Value. The UK OCDS extension defines amountGross as "the amount including all taxes", so amount is the figure without VAT. Here that's £90,000 against £108,000.
CPV codes often aren't on tender.classification. In this notice they're only in items[].additionalClassifications.
Regions are ITL/NUTS-style codes such as UKI32 (inside London, UKI).
Contact data. parties[].contactPoint often holds a named officer with a direct email address and phone number. The OGL explicitly doesn't cover personal data, so our monitor keeps organisation names and leaves contact persons out.

Step 1: fetch a window and paginate politely

API = "https://www.find-tender.service.gov.uk/api/1.0/ocdsReleasePackages"
USER_AGENT = "LindenwerkData-fts-monitor/1.0 (+https://dev.to/lindenwerkdata; open-data tutorial)"

def fetch_releases(updated_from, updated_to, stages=None, session=None):
    s = session or requests.Session()
    s.headers.update({"User-Agent": USER_AGENT, "Accept": "application/json"})
    params = {"updatedFrom": updated_from, "updatedTo": updated_to, "limit": 100}
    if stages:
        params["stages"] = stages
    url, releases, pages = API, [], 0
    while url and pages < MAX_PAGES:
        resp = get_with_retry(s, url, params if pages == 0 else None)
        package = resp.json()
        releases.extend(package.get("releases") or [])
        pages += 1
        url = (package.get("links") or {}).get("next")  # already carries the cursor
        if url:
            time.sleep(PAUSE_BETWEEN_PAGES)              # 1 s between pages
    return releases, pages

def get_with_retry(s, url, params, tries=5):
    for attempt in range(tries):
        try:
            resp = s.get(url, params=params, timeout=(10, 60))
        except (requests.ConnectionError, requests.Timeout):
            time.sleep(2 ** attempt * 5)
            continue
        if resp.status_code == 200:
            return resp
        if resp.status_code in (429, 503):               # the docs: wait Retry-After seconds
            ra = resp.headers.get("Retry-After", "")
            time.sleep(int(ra) if ra.isdigit() else 30)
            continue
        resp.raise_for_status()                          # e.g. 400 for an unknown parameter
    raise RuntimeError(f"giving up on {url}")

Three habits worth keeping: identify your client in the User-Agent, always set a timeout, and pause between pages even though no limit is published. A whole day costs only a handful of requests anyway. On 16 October 2026 we fetched the window 2026-10-15T00:00:00 to 2026-10-16T00:00:00. It returned 443 releases on 5 pages in about 7 seconds.

The docs don't say which time zone updatedFrom/updatedTo use. Back-to-back daily windows still cover everything, and the deduplication and state file below absorb any overlap, so it's safest not to depend on it.

Step 2: don't rely on `stages=tender`, filter on `tag`

stages=tender looks like the obvious way to fetch only tenders. In our test it wasn't. For the same 15 October window:

stages=tender returned 10 releases.
The unfiltered feed had 60 releases tagged tender, plus 34 tenderUpdate.
All 10 releases from stages=tender were older-regime notices (PCR 2015 or UCR 2016 legal basis). The 50 Procurement Act 2023 tenders from that day weren't among them.

We haven't found documentation that explains the difference, so the script fetches everything and filters on the OCDS tag itself:

wanted = [r for r in releases if set(r.get("tag") or []) & {"tender", "tenderUpdate"}]

For context, here's how the 443 releases of that Thursday split by tag: 274 award+contract, 60 tender, 40 planning, 34 tenderUpdate, 17 planningUpdate, 11 award/contract updates, 4 cancellations, 2 contract amendments and 1 implementation. About a fifth of the feed is tender notices or tender updates (94 of 443). If you want competitor intelligence instead, switch the tags to award and contract.

Step 3: flatten the fields you care about

def cpv_codes(tender):
    found = []
    for b in [tender] + list(tender.get("items") or []) + list(tender.get("lots") or []):
        for c in [b.get("classification")] + list(b.get("additionalClassifications") or []):
            if c and str(c.get("scheme", "")).upper() == "CPV" and c.get("id"):
                code = str(c["id"]).split("-")[0]
                if code not in found:
                    found.append(code)
    return found

Look for CPV codes in all three places. Of the 94 tender releases, 78 had no tender.classification at all. A script that reads only that field would miss most of the day.

For regions, prefer where the work happens (items[].deliveryAddresses[].region) and fall back to the buyer's address. Mixing the two causes false hits. One framework notice that day had its buyer in London (UKI74) but delivery in Scotland (UKM), so a London filter shouldn't match it.

Expect gaps: 27 of the 94 had no tender.value.amount and 11 had no tenderPeriod.endDate. Treat missing values as unknown, not zero.

Step 4: deduplicate, and watch out for frameworks

A tender and its later updates share an OCID, so "keep the newest release per OCID" is the textbook approach. On Find a Tender it has a catch. Frameworks and dynamic purchasing systems can reuse one OCID for many separate competitions. The procurement ocds-h6vhtk-029a8d (a research and insights DPS) had 55 releases when we looked it up. Three of them were tenderUpdates on 15 October with three different titles: one for the DPS itself and two for separate research projects run under it.

So the key is OCID plus normalised title:

def dedupe_key(r):
    return f"{r['ocid'] or r['notice_id']}|{' '.join(r['title'].lower().split())}"

def latest_per_procurement(rows):
    best = {}
    for r in rows:
        k = dedupe_key(r)
        if k not in best or (r["release_date"] or "") > (best[k]["release_date"] or ""):
            best[k] = r
    return list(best.values())

On the test day this reduced 94 tender releases to 88 rows (86 distinct OCIDs). OCID alone would have given 86 and silently dropped two of those three. The trade-off: if a buyer renames a tender in an update, you'll see it twice. For an alert, a duplicate is cheaper than a miss.

Step 5: filter and score

Hard filters remove rows. The score only orders what's left:

def score(r, cpv_prefixes, keywords):
    points, reasons = 0, []
    cpvs = [c for c in r["cpv"].split("|") if c]
    hits = [p for p in cpv_prefixes if any(c.startswith(p) for c in cpvs)]
    if hits:
        points += 50
        reasons.append(f"CPV {','.join(hits)}")
    for kw in keywords:
        pattern = re.compile(rf"\b{re.escape(kw)}\b", re.IGNORECASE)
        if pattern.search(r["title"]):
            points += 25
            reasons.append(f"'{kw}' in title")
        elif pattern.search(r["description"]):
            points += 10
            reasons.append(f"'{kw}' in description")
    return min(points, 100), reasons

CPV codes are hierarchical, so a prefix match does a lot: 72 covers IT services, 48 software packages, 45 construction works. Use word boundaries for keywords, or data will also match database. Every point comes with a reason, so you can see why something ranked where it did.

On the test day, an IT profile (--cpv 72 48 --keyword software cloud cyber data) kept 12 of 88 notices. The top one was a cloud-hosted programme management platform for a council (60 points: CPV 72 plus "cloud" in the description). The two lowest, at 25 points, were keyword-only hits on "data" ("Electrical and Data Installation Services" and an energy-meter tender whose title mentions "Data Collector"). So use a short list of distinctive keywords, or raise --min-score to 50 so CPV evidence is required. A works profile (--cpv 45 --region UKI UKJ --min-value 100000) kept 3.

Step 6: output, attribution and "only new"

The script writes a CSV for humans and a JSON file whose first key is the attribution line. The OGL requires you to "acknowledge the source of the Information", and where the provider gives no specific attribution statement you must use: "Contains public sector information licensed under the Open Government Licence v3.0." Put that line wherever the data ends up, including emails and dashboards. The licence also says you must not suggest any official status or endorsement, so make it clear your alert isn't the government's.

For a daily alert, remember what you've already sent:

seen = set(json.loads(state_file.read_text())) if state_file.exists() else set()
rows = [r for r in rows if dedupe_key(r) not in seen]
state_file.write_text(json.dumps(sorted(seen | {dedupe_key(r) for r in rows})))

In our test, the first run reported 12 new matches. A second run over the same saved data reported 0.

During development, add --save-raw raw.json once and then tune your filters with --from-file raw.json. That way you're not downloading the same day again for every tweak.

Step 7: run it every morning

Cron (uses the machine's local time; % must be escaped in crontab):

17 6 * * 1-5  cd $HOME/fts-monitor && TZ=Europe/London ./venv/bin/python fts_monitor.py --yesterday --cpv 72 48 --keyword software cloud --state seen.json --csv "out/$(date +\%F).csv" >> monitor.log 2>&1

GitHub Actions (.github/workflows/fts-monitor.yml):

name: UK tender monitor
on:
  schedule:
    - cron: "17 6 * * 1-5"
      timezone: "Europe/London"
  workflow_dispatch: {}
jobs:
  monitor:
    runs-on: ubuntu-latest
    timeout-minutes: 15
    env:
      TZ: Europe/London            # so --yesterday means the UK calendar day
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with: {python-version: "3.12"}
      - run: pip install -r requirements.txt
      - run: python fts_monitor.py --yesterday --cpv 72 48 --keyword software cloud --csv tenders.csv --json tenders.json
      - uses: actions/upload-artifact@v4
        with: {name: "tenders-${{ github.run_id }}", path: "tenders.*", retention-days: 30}

Some GitHub details worth knowing:

Schedules run in UTC unless you set timezone.
Scheduled runs can be delayed at busy times, especially at the start of the hour, which is why the example runs at :17.
In a public repository, scheduled workflows are disabled automatically after 60 days without repository activity.

The Actions version is stateless on purpose. Each run covers exactly yesterday, so it doesn't need a state file. If you want overlapping windows there, you'll need to persist seen.json yourself.

Where to take it next

Send tenders.json to Slack or email (an incoming webhook takes a few lines of requests).
Track competitors: switch to --tags award contract and read the suppliers of each award.
Add more signals: aboveThreshold, lots[].suitability.sme / .vcse, tenderPeriod.endDate for "closing soon".
Always open the official notice before you bid. Your copy is only a convenience, and the official notice prevails.

Full script: fts_monitor.py (240 lines, requests only)

#!/usr/bin/env python3
"""Daily UK public tender monitor built on the Find a Tender Service (FTS) OCDS release API.

Free, official, no API key. Notice data: Open Government Licence v3.0.
Contains public sector information licensed under the Open Government Licence v3.0.

Usage examples:
    python fts_monitor.py --from 2026-10-15 --to 2026-10-16 --cpv 72 48 --keyword software cloud
    python fts_monitor.py --yesterday --cpv 45 --region UKD --min-value 100000 --csv out.csv
    python fts_monitor.py --yesterday --keyword cleaning --state seen.json   # only what you haven't seen
"""
from __future__ import annotations

import argparse
import csv
import json
import re
import sys
import time
from datetime import date, timedelta
from pathlib import Path

import requests

API = "https://www.find-tender.service.gov.uk/api/1.0/ocdsReleasePackages"
NOTICE_URL = "https://www.find-tender.service.gov.uk/Notice/{}"
USER_AGENT = "LindenwerkData-fts-monitor/1.0 (+https://dev.to/lindenwerkdata; open-data tutorial)"
ATTRIBUTION = "Contains public sector information licensed under the Open Government Licence v3.0."
PAUSE_BETWEEN_PAGES = 1.0  # seconds; be polite even though no limit is published
MAX_PAGES = 200            # safety stop (100 releases per page)


# ---------------------------------------------------------------- 1. fetch + paginate
def fetch_releases(updated_from: str, updated_to: str, stages: str | None = None,
                   session: requests.Session | None = None) -> tuple[list[dict], int]:
    """Return every OCDS release updated in [updated_from, updated_to]. Follows links.next (cursor pagination)."""
    s = session or requests.Session()
    s.headers.update({"User-Agent": USER_AGENT, "Accept": "application/json"})
    params = {"updatedFrom": updated_from, "updatedTo": updated_to, "limit": 100}
    if stages:
        params["stages"] = stages
    url, releases, pages = API, [], 0
    while url and pages < MAX_PAGES:
        resp = get_with_retry(s, url, params if pages == 0 else None)
        package = resp.json()
        releases.extend(package.get("releases") or [])
        pages += 1
        url = (package.get("links") or {}).get("next")  # the next URL already carries the cursor
        if url:
            time.sleep(PAUSE_BETWEEN_PAGES)
    if url:
        print(f"warning: stopped after {MAX_PAGES} pages, window is incomplete", file=sys.stderr)
    return releases, pages


def get_with_retry(s: requests.Session, url: str, params: dict | None, tries: int = 5) -> requests.Response:
    """GET with a timeout. On 429/503 wait the number of seconds in Retry-After, as the API docs ask."""
    for attempt in range(tries):
        try:
            resp = s.get(url, params=params, timeout=(10, 60))
        except (requests.ConnectionError, requests.Timeout) as exc:
            wait = 2 ** attempt * 5
            print(f"network error ({exc.__class__.__name__}), retrying in {wait}s", file=sys.stderr)
            time.sleep(wait)
            continue
        if resp.status_code == 200:
            return resp
        if resp.status_code in (429, 503):
            retry_after = resp.headers.get("Retry-After", "")
            wait = int(retry_after) if retry_after.isdigit() else 30
            print(f"HTTP {resp.status_code}, waiting {wait}s (Retry-After)", file=sys.stderr)
            time.sleep(wait)
            continue
        resp.raise_for_status()  # 400 = unknown parameter etc.: a bug, fail loudly
    raise RuntimeError(f"giving up on {url} after {tries} attempts")


# ---------------------------------------------------------------- 2. flatten one release
def cpv_codes(tender: dict) -> list[str]:
    """CPV codes can sit on tender.classification, items[].classification/additionalClassifications and lots."""
    found = []
    blocks = [tender] + list(tender.get("items") or []) + list(tender.get("lots") or [])
    for b in blocks:
        for c in [b.get("classification")] + list(b.get("additionalClassifications") or []):
            if c and str(c.get("scheme", "")).upper() == "CPV" and c.get("id"):
                code = str(c["id"]).split("-")[0]
                if code not in found:
                    found.append(code)
    return found


def flatten(rel: dict) -> dict:
    t = rel.get("tender") or {}
    buyers = [p for p in rel.get("parties") or [] if "buyer" in (p.get("roles") or [])]
    # Where the work happens (items[].deliveryAddresses) beats where the buyer sits (parties[].address).
    regions = [a.get("region") for it in t.get("items") or [] for a in it.get("deliveryAddresses") or []]
    if not any(regions):
        regions = [(b.get("address") or {}).get("region") for b in buyers]
    value = t.get("value") or {}
    return {
        "ocid": rel.get("ocid"),
        "notice_id": rel.get("id"),
        "release_date": rel.get("date"),
        "tags": "|".join(rel.get("tag") or []),
        "title": (t.get("title") or "").strip(),
        "description": " ".join((t.get("description") or "").split()),
        "buyer": "; ".join(b.get("name", "") for b in buyers),   # organisation names only, no contact persons
        "regions": "|".join(sorted({r for r in regions if r})),
        "cpv": "|".join(cpv_codes(t)),
        "value_gbp": value.get("amount"),                        # excl. VAT; amountGross = incl. VAT
        "currency": value.get("currency"),
        "deadline": (t.get("tenderPeriod") or {}).get("endDate"),
        "procedure": t.get("procurementMethodDetails"),
        "url": NOTICE_URL.format(rel.get("id")),
    }


# ---------------------------------------------------------------- 3. dedupe by OCID
def dedupe_key(r: dict) -> str:
    """OCID first. Frameworks and dynamic purchasing systems reuse one OCID for many separate
    competitions, so the title is part of the key: updates to the same competition collapse,
    different competitions under the same framework survive."""
    return f"{r['ocid'] or r['notice_id']}|{' '.join(r['title'].lower().split())}"


def latest_per_procurement(rows: list[dict]) -> list[dict]:
    """A tender notice and its updates share a key. Keep the newest release."""
    best: dict[str, dict] = {}
    for r in rows:
        key = dedupe_key(r)
        if key not in best or (r["release_date"] or "") > (best[key]["release_date"] or ""):
            best[key] = r
    return list(best.values())


# ---------------------------------------------------------------- 4. filter + score
def passes_filters(r: dict, args: argparse.Namespace) -> bool:
    if args.min_value and (r["value_gbp"] is None or r["value_gbp"] < args.min_value):
        return False
    if args.region and not any(code.startswith(tuple(args.region)) for code in r["regions"].split("|") if code):
        return False
    text = f"{r['title']} {r['description']}".lower()
    if any(x.lower() in text for x in args.exclude):
        return False
    return True


def score(r: dict, cpv_prefixes: list[str], keywords: list[str]) -> tuple[int, list[str]]:
    """Additive points, capped at 100. Transparent on purpose: every point has a reason."""
    points, reasons = 0, []
    cpvs = [c for c in r["cpv"].split("|") if c]
    hits = [p for p in cpv_prefixes if any(c.startswith(p) for c in cpvs)]
    if hits:
        points += 50
        reasons.append(f"CPV {','.join(hits)}")
    title, desc = r["title"], r["description"]
    for kw in keywords:
        pattern = re.compile(rf"\b{re.escape(kw)}\b", re.IGNORECASE)  # whole words: 'data' != 'database'
        if pattern.search(title):
            points += 25
            reasons.append(f"'{kw}' in title")
        elif pattern.search(desc):
            points += 10
            reasons.append(f"'{kw}' in description")
    return min(points, 100), reasons


# ---------------------------------------------------------------- 5. output
def write_outputs(rows: list[dict], csv_path: str | None, json_path: str | None) -> None:
    if csv_path:
        with open(csv_path, "w", newline="", encoding="utf-8") as f:
            w = csv.DictWriter(f, fieldnames=list(rows[0].keys()) if rows else ["ocid"])
            w.writeheader()
            w.writerows(rows)
    if json_path:
        Path(json_path).write_text(json.dumps({"attribution": ATTRIBUTION, "notices": rows},
                                              indent=2, ensure_ascii=False), encoding="utf-8")


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    ap.add_argument("--from", dest="date_from", help="YYYY-MM-DD (start of window)")
    ap.add_argument("--to", dest="date_to", help="YYYY-MM-DD (end of window, exclusive)")
    ap.add_argument("--yesterday", action="store_true", help="window = yesterday 00:00 to today 00:00")
    ap.add_argument("--stages", default="", help="server-side stages filter (default: none, see article)")
    ap.add_argument("--tags", nargs="*", default=["tender", "tenderUpdate"],
                    help="client-side filter on OCDS release tags (default: tender tenderUpdate)")
    ap.add_argument("--cpv", nargs="*", default=[], help="CPV prefixes, e.g. 72 48 (IT), 45 (works)")
    ap.add_argument("--keyword", nargs="*", default=[], help="keywords, case-insensitive")
    ap.add_argument("--exclude", nargs="*", default=[], help="drop notices containing any of these")
    ap.add_argument("--region", nargs="*", default=[], help="ITL/NUTS prefixes, e.g. UKI (London) UKD (North West)")
    ap.add_argument("--min-value", type=float, default=0, help="minimum estimated value in GBP excl. VAT")
    ap.add_argument("--min-score", type=int, default=25)
    ap.add_argument("--state", help="JSON file of procurements already reported; only new ones are output")
    ap.add_argument("--save-raw", help="also save the raw releases to this JSON file")
    ap.add_argument("--from-file", help="replay releases from a --save-raw file instead of calling the API")
    ap.add_argument("--csv", default="tenders.csv")
    ap.add_argument("--json", default="tenders.json")
    args = ap.parse_args()

    if args.from_file:  # tune filters offline without re-downloading
        releases, pages = json.loads(Path(args.from_file).read_text()), 0
    else:
        if args.yesterday:
            start, end = date.today() - timedelta(days=1), date.today()
        else:
            start, end = date.fromisoformat(args.date_from), date.fromisoformat(args.date_to)
        releases, pages = fetch_releases(f"{start}T00:00:00", f"{end}T00:00:00", args.stages or None)
    if args.save_raw:
        Path(args.save_raw).write_text(json.dumps(releases), encoding="utf-8")
    wanted = [r for r in releases if not args.tags or set(r.get("tag") or []) & set(args.tags)]
    rows = latest_per_procurement([flatten(r) for r in wanted])
    print(f"{len(releases)} releases on {pages} page(s), {len(wanted)} with tags {args.tags} "
          f"-> {len(rows)} after dedupe "
          f"({len({r['ocid'] for r in rows})} distinct OCIDs)")

    rows = [r for r in rows if passes_filters(r, args)]
    if args.cpv or args.keyword:
        for r in rows:
            r["score"], why = score(r, args.cpv, args.keyword)
            r["reasons"] = "; ".join(why)
        rows = [r for r in rows if r["score"] >= args.min_score]
        rows.sort(key=lambda r: (-r["score"], r["deadline"] or "9999"))
    print(f"{len(rows)} after filters/scoring")

    if args.state:
        state_file = Path(args.state)
        seen = set(json.loads(state_file.read_text())) if state_file.exists() else set()
        rows = [r for r in rows if dedupe_key(r) not in seen]
        state_file.write_text(json.dumps(sorted(seen | {dedupe_key(r) for r in rows}), indent=0))
        print(f"{len(rows)} not reported before")

    write_outputs(rows, args.csv, args.json)
    for r in rows[:10]:
        print(f"{r.get('score', '-'):>3}  {(r['deadline'] or '')[:10]}  {r['title'][:70]}  {r['url']}")
    print(ATTRIBUTION)


if __name__ == "__main__":
    main()

Data: Find a Tender Service, Cabinet Office. Contains public sector information licensed under the Open Government Licence v3.0. This is an independent tutorial, not an official service.

This article was written with the help of AI. Every number comes from our own runs against the live API on 16 October 2026, and the API, OCDS and licence details were checked against the official documentation linked above.

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