mirror of
https://github.com/tiennm99/DocsGPT.git
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Alex
574f96341e
The backend import package is now docsgpt, the name it will carry on PyPI; application was far too generic to install into anyone's site-packages. git mv plus a mechanical rewrite of every import, dotted string and path reference: 734 Python files, the compose files, Dockerfile, workflows, docs, setup scripts, devcontainer, k8s manifests, vscode config, pytest and coverage config, .gitignore. Behaviour is unchanged. Kept for one release: - A top-level application package whose meta-path finder resolves application.x.y to the already-imported docsgpt.x.y object, so old imports and entry points (celery -A application.app.celery, uvicorn application.asgi:asgi_app) keep working with a FutureWarning. - Celery registers every application.* task name as an alias of its docsgpt.* task on start-up, so messages queued by the previous release still run. The redbeat key prefix moves to redbeat:docsgpt:v2: so schedule entries the previous release wrote are left unread instead of firing twice. The backend image builds from the repository root (docker build -f docsgpt/Dockerfile .) so it can ship the alias package; a root .dockerignore allow-lists docsgpt/ and application/ and keeps caches, local data, .env files, the sample index files and the Dockerfile out. Compose and the image workflows point at the new context.
114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
"""Download the model artifacts a fresh container would otherwise fetch.
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Run at image build time so a fresh container does not download on its first
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request, and an air-gapped install works at all. Two things are warmed:
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* Embedding models, into FastEmbed's cache. Both the legacy and the current
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default are baked: an upgraded deployment keeps using mpnet until it runs
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``reembed``, while a new one starts on granite.
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* tiktoken's ``cl100k_base`` encoding, which token accounting uses on every
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chat. tiktoken caches it under ``TIKTOKEN_CACHE_DIR`` (a temp dir when
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unset), so the image sets that variable and this warms it.
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Usage::
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python -m docsgpt.scripts.prefetch_models # the defaults
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python -m docsgpt.scripts.prefetch_models granite-311m # a subset
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"""
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from __future__ import annotations
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import logging
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import sys
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from typing import List, Optional, Sequence
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from docsgpt.vectorstore.model_registry import (
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DEFAULT_LEGACY,
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DEFAULT_NEW_INSTALL,
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known_names,
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resolve,
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)
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logger = logging.getLogger("prefetch_models")
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#: Fetched when no names are given.
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DEFAULT_MODELS = (DEFAULT_LEGACY, DEFAULT_NEW_INSTALL)
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#: tiktoken encodings the application loads (``docsgpt.utils.get_encoding``).
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TIKTOKEN_ENCODINGS = ("cl100k_base",)
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def prefetch_tiktoken(names: Sequence[str] = TIKTOKEN_ENCODINGS) -> List[str]:
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"""Warm tiktoken's cache for each encoding in ``names``.
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Returns:
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The encodings fetched.
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"""
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import tiktoken
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for name in names:
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logger.info("Fetching tiktoken encoding %s", name)
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tiktoken.get_encoding(name)
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return list(names)
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def prefetch(names: Sequence[str], cache_dir: Optional[str] = None) -> List[str]:
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"""Fetch each named model's artifacts.
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Args:
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names: Registry names or aliases.
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cache_dir: FastEmbed cache directory; its default when omitted.
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Returns:
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The repositories actually fetched.
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Raises:
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SystemExit: If a name is not in the registry, since a silent skip at
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build time becomes a download at run time on an offline host.
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"""
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from fastembed import TextEmbedding
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from fastembed.common.model_description import ModelSource, PoolingType
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pooling_types = {"cls": PoolingType.CLS, "mean": PoolingType.MEAN}
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fetched: List[str] = []
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for name in names:
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spec = resolve(name)
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if spec is None:
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raise SystemExit(
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f"Unknown embedding model {name!r}. Known: {', '.join(known_names())}"
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)
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if spec.provider != "fastembed":
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logger.info("Skipping %s: served remotely, nothing to cache.", spec.name)
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continue
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logger.info("Fetching %s", spec.repo)
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TextEmbedding.add_custom_model(
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model=spec.repo,
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pooling=pooling_types[spec.pooling],
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normalization=spec.normalize,
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sources=ModelSource(hf=spec.repo),
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dim=spec.dimension,
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model_file=spec.onnx_file,
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)
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kwargs = {"model_name": spec.repo}
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if cache_dir:
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kwargs["cache_dir"] = cache_dir
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TextEmbedding(**kwargs)
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fetched.append(spec.repo)
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return fetched
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def main(argv: Optional[Sequence[str]] = None) -> int:
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logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s")
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import os
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names = list(argv) if argv else list(DEFAULT_MODELS)
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fetched = prefetch(names, os.environ.get("EMBEDDINGS_CACHE_DIR"))
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logger.info("Cached %d model(s): %s", len(fetched), ", ".join(fetched))
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encodings = prefetch_tiktoken()
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logger.info("Cached tiktoken encoding(s): %s", ", ".join(encodings))
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return 0
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if __name__ == "__main__":
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sys.exit(main(sys.argv[1:]))
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