Phase 0B ran the upstream application on a network with no route out and the first turn died in tiktoken, which downloads its BPE table the first time anything counts a token. The browser separately fetched three font families from Google on every page load. Neither is visible on a machine that has been online once, which is why both now have tests. The tokenizer table is vendored at backend/app/context/vendor/cl100k_base.tiktoken and backend/app/context/encoding.py builds the encoding from it directly, verifying its SHA-256 against the digest tiktoken itself pins for that URL. No code path in the tokenizer can reach the network any more — not a warm cache, not an environment variable a deployment could forget. The encoding was checked token for token against tiktoken's own. The three font families are self-hosted as variable fonts under frontend/public/fonts/ (343 KiB, Latin and Latin Extended), declared in frontend/src/styles/fonts.css, and re-vendored by frontend/tools/vendor_fonts.py. Their OFL licences ship beside them. With no remote asset left, the CSP drops both Google hosts and gains object-src, base-uri and form-action; woff2 also gets its real media type, which Python's table lacks on a slim image. A trusted-LAN Ollama turned out not to work at all over HTTPS. httpx verifies against the certifi bundle, so an endpoint whose certificate comes from a CA the user installed on their own machines — a StartOS server's Ollama, for one — was refused with CERTIFICATE_VERIFY_FAILED while curl and the browser on the same host accepted it. app/tlstrust.py builds one context that unions the platform CA store with certifi's, and all four outbound clients use it. A union rather than a swap, so an image with an empty system store cannot start failing on endpoints that worked before. Verification itself is untouched: CERT_REQUIRED, hostname checking on, and no insecure escape hatch. The storyteller listener is now loopback by explicit statement rather than by inheriting uvicorn's default: start.sh, start.ps1, and docker-compose.yml, which publishes to 127.0.0.1 rather than every interface. Reaching an Ollama on another machine is outbound and needs none of that inbound exposure. backend/requirements.lock pins the exact tested closure; requirements.txt keeps the ranges. DEVELOPMENT.md covers setup, the same-host and trusted-LAN Ollama configurations, and how to re-run the offline proof. PROVENANCE.md records the upstream commit, the MIT terms, and both vendored assets. Verified, not just compiled. On an --internal Docker network with 1.1.1.1 unreachable and no name resolving, a campaign was created and played for six turns through same-host Ollama, restarted, and resumed. A second run played ten turns through Ollama on a separate physical machine on the LAN over verified HTTPS, summaries and embeddings included, with the storyteller's default route deleted so the LAN was reachable and the Internet was not. Its capture: 893 packets to the approved host, 730 loopback, zero anywhere else, and zero DNS queries. Two induced model failures left the accepted story bit-identical. The inherited SPA was opened in a browser and a campaign read back from it. Evidence is in planning/reports/M1-BASELINE-REPORT.md, along with the findings that did not belong in this change. 648 backend tests pass, up from the inherited 632; frontend lint and build are clean; the image builds. No M2 work is included: the hosted, cloud, analytics, Postgres and scripting surfaces are untouched. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_017foPNqFjAJa2Ngebf5mEfL
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
"""`cl100k_base` without a first-use download.
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Upstream called `tiktoken.get_encoding("cl100k_base")`, which fetches the BPE
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table from `openaipublic.blob.core.windows.net` the first time it is used and
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caches it under the system temp directory. That download is invisible on a
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developer machine that has already made it once, and fatal on a machine with
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outbound Internet blocked: the context builder counts tokens on *every* turn,
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so the first story turn died with a `ConnectionError` instead of narrating
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(Phase 0B offline report; acceptance tests A01 and H11).
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The table is vendored beside this module and the `Encoding` is constructed from
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it directly, so no code path inside the tokenizer can reach the network — not a
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cache that happens to be warm, and not an environment variable a deployment
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could forget to set.
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The vendored file's SHA-256 is verified against the digest `tiktoken` itself
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pins for that URL. A truncated checkout or a substituted table then fails
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loudly, rather than silently changing every token count the context budget is
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computed from.
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"""
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import base64
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import functools
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import hashlib
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from pathlib import Path
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import tiktoken
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ENCODING_NAME = "cl100k_base"
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#: Where the table came from, recorded so the vendored copy can be re-derived.
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SOURCE_URL = "https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken"
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#: The digest `tiktoken_ext.openai_public.cl100k_base()` pins for SOURCE_URL.
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BPE_SHA256 = "223921b76ee99bde995b7ff738513eef100fb51d18c93597a113bcffe865b2a7"
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BPE_PATH = Path(__file__).resolve().parent / "vendor" / "cl100k_base.tiktoken"
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# Both copied from `tiktoken_ext.openai_public.cl100k_base()`. They are part of
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# the encoding's identity: the same merge table with a different pattern is a
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# different tokenizer, so they are pinned here rather than imported, and the
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# round-trip test asserts this build agrees with tiktoken's own.
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_PAT_STR = r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}++|\p{N}{1,3}+| ?[^\s\p{L}\p{N}]++[\r\n]*+|\s++$|\s*[\r\n]|\s+(?!\S)|\s"""
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_SPECIAL_TOKENS = {
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"<|endoftext|>": 100257,
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"<|fim_prefix|>": 100258,
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"<|fim_middle|>": 100259,
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"<|fim_suffix|>": 100260,
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"<|endofprompt|>": 100276,
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}
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def _mergeable_ranks() -> dict[bytes, int]:
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try:
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data = BPE_PATH.read_bytes()
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except OSError as exc: # pragma: no cover - packaging fault, not a run fault
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raise RuntimeError(
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f"The vendored {ENCODING_NAME} table is missing at {BPE_PATH}. "
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f"Re-download it from {SOURCE_URL} (SHA-256 {BPE_SHA256})."
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) from exc
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digest = hashlib.sha256(data).hexdigest()
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if digest != BPE_SHA256:
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raise RuntimeError(
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f"The vendored {ENCODING_NAME} table at {BPE_PATH} has SHA-256 "
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f"{digest}, expected {BPE_SHA256}."
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)
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# Same parse as tiktoken.load.load_tiktoken_bpe, minus its fetch/cache step.
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ranks: dict[bytes, int] = {}
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for line in data.splitlines():
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if not line:
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continue
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try:
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token, rank = line.split()
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ranks[base64.b64decode(token)] = int(rank)
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except Exception as exc:
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raise ValueError(f"Error parsing line {line!r} in {BPE_PATH}") from exc
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return ranks
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@functools.lru_cache(maxsize=1)
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def get_encoding() -> tiktoken.Encoding:
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"""The `cl100k_base` encoding, built from the vendored table."""
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return tiktoken.Encoding(
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name=ENCODING_NAME,
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pat_str=_PAT_STR,
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mergeable_ranks=_mergeable_ranks(),
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special_tokens=_SPECIAL_TOKENS,
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)
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