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.
30 lines
965 B
Python
30 lines
965 B
Python
"""The Celery task behind :mod:`docsgpt.vectorstore.embeddings_delegated`.
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Kept out of ``docsgpt.api.user.tasks`` deliberately: that module imports
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``docsgpt.worker`` and the whole parsing stack with it, which is the
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opposite of what delegation is for.
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"""
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from __future__ import annotations
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from typing import List, Optional
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from docsgpt.celery_init import celery
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from docsgpt.vectorstore.embeddings_delegated import EMBED_TASK
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@celery.task(name=EMBED_TASK, acks_late=False, ignore_result=False)
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def embed_texts(texts: List[str], embeddings_name: Optional[str] = None) -> List[List[float]]:
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"""Embed ``texts`` with the worker's local model.
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Args:
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texts: Strings to embed.
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embeddings_name: Model to use; the configured one when omitted.
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Returns:
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One vector per input, in input order.
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"""
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from docsgpt.vectorstore.base import get_embeddings
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return get_embeddings(embeddings_name).embed_documents(list(texts))
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