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.
25 lines
886 B
Python
25 lines
886 B
Python
from docsgpt.vectorstore.faiss import FaissStore
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from docsgpt.vectorstore.elasticsearch import ElasticsearchStore
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from docsgpt.vectorstore.milvus import MilvusStore
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from docsgpt.vectorstore.mongodb import MongoDBVectorStore
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from docsgpt.vectorstore.qdrant import QdrantStore
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from docsgpt.vectorstore.pgvector import PGVectorStore
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class VectorCreator:
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vectorstores = {
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"faiss": FaissStore,
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"elasticsearch": ElasticsearchStore,
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"mongodb": MongoDBVectorStore,
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"qdrant": QdrantStore,
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"milvus": MilvusStore,
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"pgvector": PGVectorStore
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}
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@classmethod
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def create_vectorstore(cls, type, *args, **kwargs):
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vectorstore_class = cls.vectorstores.get(type.lower())
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if not vectorstore_class:
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raise ValueError(f"No vectorstore class found for type {type}")
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return vectorstore_class(*args, **kwargs)
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