Merge branch 'langgenius:main' into feat/rediscluster-support-custom-user
homejim
8 months ago
committed by
GitHub
commit
3c9103bac8
@ -0,0 +1,14 @@
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from pydantic import BaseModel, Field
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class MatrixoneConfig(BaseModel):
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"""Matrixone vector database configuration."""
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MATRIXONE_HOST: str = Field(default="localhost", description="Host address of the Matrixone server")
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MATRIXONE_PORT: int = Field(default=6001, description="Port number of the Matrixone server")
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MATRIXONE_USER: str = Field(default="dump", description="Username for authenticating with Matrixone")
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MATRIXONE_PASSWORD: str = Field(default="111", description="Password for authenticating with Matrixone")
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MATRIXONE_DATABASE: str = Field(default="dify", description="Name of the Matrixone database to connect to")
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MATRIXONE_METRIC: str = Field(
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default="l2", description="Distance metric type for vector similarity search (cosine or l2)"
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)
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@ -0,0 +1,233 @@
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import json
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import logging
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import uuid
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from functools import wraps
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from typing import Any, Optional
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from mo_vector.client import MoVectorClient # type: ignore
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from pydantic import BaseModel, model_validator
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from configs import dify_config
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from core.rag.datasource.vdb.vector_base import BaseVector
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from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
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from core.rag.datasource.vdb.vector_type import VectorType
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from core.rag.embedding.embedding_base import Embeddings
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from core.rag.models.document import Document
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from extensions.ext_redis import redis_client
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from models.dataset import Dataset
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logger = logging.getLogger(__name__)
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class MatrixoneConfig(BaseModel):
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host: str = "localhost"
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port: int = 6001
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user: str = "dump"
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password: str = "111"
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database: str = "dify"
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metric: str = "l2"
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@model_validator(mode="before")
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@classmethod
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def validate_config(cls, values: dict) -> dict:
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if not values["host"]:
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raise ValueError("config host is required")
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if not values["port"]:
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raise ValueError("config port is required")
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if not values["user"]:
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raise ValueError("config user is required")
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if not values["password"]:
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raise ValueError("config password is required")
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if not values["database"]:
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raise ValueError("config database is required")
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return values
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def ensure_client(func):
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@wraps(func)
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def wrapper(self, *args, **kwargs):
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if self.client is None:
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self.client = self._get_client(None, False)
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return func(self, *args, **kwargs)
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return wrapper
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class MatrixoneVector(BaseVector):
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"""
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Matrixone vector storage implementation.
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"""
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def __init__(self, collection_name: str, config: MatrixoneConfig):
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super().__init__(collection_name)
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self.config = config
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self.collection_name = collection_name.lower()
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self.client = None
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@property
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def collection_name(self):
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return self._collection_name
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@collection_name.setter
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def collection_name(self, value):
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self._collection_name = value
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def get_type(self) -> str:
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return VectorType.MATRIXONE
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def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
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if self.client is None:
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self.client = self._get_client(len(embeddings[0]), True)
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return self.add_texts(texts, embeddings)
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def _get_client(self, dimension: Optional[int] = None, create_table: bool = False) -> MoVectorClient:
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"""
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Create a new client for the collection.
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The collection will be created if it doesn't exist.
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"""
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lock_name = f"vector_indexing_lock_{self._collection_name}"
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with redis_client.lock(lock_name, timeout=20):
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client = MoVectorClient(
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connection_string=f"mysql+pymysql://{self.config.user}:{self.config.password}@{self.config.host}:{self.config.port}/{self.config.database}",
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table_name=self.collection_name,
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vector_dimension=dimension,
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create_table=create_table,
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)
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collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
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if redis_client.get(collection_exist_cache_key):
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return client
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try:
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client.create_full_text_index()
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except Exception as e:
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logger.exception("Failed to create full text index")
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redis_client.set(collection_exist_cache_key, 1, ex=3600)
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return client
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def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
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if self.client is None:
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self.client = self._get_client(len(embeddings[0]), True)
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assert self.client is not None
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ids = []
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for _, doc in enumerate(documents):
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if doc.metadata is not None:
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doc_id = doc.metadata.get("doc_id", str(uuid.uuid4()))
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ids.append(doc_id)
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self.client.insert(
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texts=[doc.page_content for doc in documents],
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embeddings=embeddings,
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metadatas=[doc.metadata for doc in documents],
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ids=ids,
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)
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return ids
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@ensure_client
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def text_exists(self, id: str) -> bool:
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assert self.client is not None
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result = self.client.get(ids=[id])
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return len(result) > 0
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@ensure_client
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def delete_by_ids(self, ids: list[str]) -> None:
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assert self.client is not None
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if not ids:
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return
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self.client.delete(ids=ids)
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@ensure_client
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def get_ids_by_metadata_field(self, key: str, value: str):
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assert self.client is not None
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results = self.client.query_by_metadata(filter={key: value})
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return [result.id for result in results]
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@ensure_client
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def delete_by_metadata_field(self, key: str, value: str) -> None:
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assert self.client is not None
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self.client.delete(filter={key: value})
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@ensure_client
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def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
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assert self.client is not None
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top_k = kwargs.get("top_k", 5)
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document_ids_filter = kwargs.get("document_ids_filter")
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filter = None
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if document_ids_filter:
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filter = {"document_id": {"$in": document_ids_filter}}
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results = self.client.query(
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query_vector=query_vector,
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k=top_k,
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filter=filter,
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)
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docs = []
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# TODO: add the score threshold to the query
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for result in results:
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metadata = result.metadata
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docs.append(
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Document(
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page_content=result.document,
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metadata=metadata,
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)
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)
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return docs
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@ensure_client
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def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
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assert self.client is not None
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top_k = kwargs.get("top_k", 5)
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document_ids_filter = kwargs.get("document_ids_filter")
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filter = None
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if document_ids_filter:
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filter = {"document_id": {"$in": document_ids_filter}}
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score_threshold = float(kwargs.get("score_threshold", 0.0))
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results = self.client.full_text_query(
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keywords=[query],
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k=top_k,
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filter=filter,
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)
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docs = []
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for result in results:
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metadata = result.metadata
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if isinstance(metadata, str):
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import json
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metadata = json.loads(metadata)
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score = 1 - result.distance
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if score >= score_threshold:
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metadata["score"] = score
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docs.append(
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Document(
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page_content=result.document,
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metadata=metadata,
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)
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)
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return docs
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@ensure_client
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def delete(self) -> None:
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assert self.client is not None
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self.client.delete()
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class MatrixoneVectorFactory(AbstractVectorFactory):
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def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> MatrixoneVector:
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if dataset.index_struct_dict:
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class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
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collection_name = class_prefix
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else:
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dataset_id = dataset.id
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collection_name = Dataset.gen_collection_name_by_id(dataset_id)
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dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.MATRIXONE, collection_name))
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config = MatrixoneConfig(
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host=dify_config.MATRIXONE_HOST or "localhost",
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port=dify_config.MATRIXONE_PORT or 6001,
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user=dify_config.MATRIXONE_USER or "dump",
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password=dify_config.MATRIXONE_PASSWORD or "111",
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database=dify_config.MATRIXONE_DATABASE or "dify",
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metric=dify_config.MATRIXONE_METRIC or "l2",
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)
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return MatrixoneVector(collection_name=collection_name, config=config)
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@ -0,0 +1,42 @@
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import logging
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import sendgrid # type: ignore
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from python_http_client.exceptions import ForbiddenError, UnauthorizedError
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from sendgrid.helpers.mail import Content, Email, Mail, To # type: ignore
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class SendGridClient:
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def __init__(self, sendgrid_api_key: str, _from: str):
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self.sendgrid_api_key = sendgrid_api_key
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self._from = _from
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def send(self, mail: dict):
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logging.debug("Sending email with SendGrid")
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try:
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_to = mail["to"]
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if not _to:
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raise ValueError("SendGridClient: Cannot send email: recipient address is missing.")
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sg = sendgrid.SendGridAPIClient(api_key=self.sendgrid_api_key)
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from_email = Email(self._from)
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to_email = To(_to)
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subject = mail["subject"]
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content = Content("text/html", mail["html"])
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mail = Mail(from_email, to_email, subject, content)
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mail_json = mail.get() # type: ignore
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response = sg.client.mail.send.post(request_body=mail_json)
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logging.debug(response.status_code)
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logging.debug(response.body)
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logging.debug(response.headers)
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except TimeoutError as e:
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logging.exception("SendGridClient Timeout occurred while sending email")
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raise
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except (UnauthorizedError, ForbiddenError) as e:
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logging.exception("SendGridClient Authentication failed. Verify that your credentials and the 'from")
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raise
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except Exception as e:
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logging.exception(f"SendGridClient Unexpected error occurred while sending email to {_to}")
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raise
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@ -0,0 +1,66 @@
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"""remove sequence_number from workflow_runs
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Revision ID: 0ab65e1cc7fa
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Revises: 4474872b0ee6
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Create Date: 2025-06-19 16:33:13.377215
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"""
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from alembic import op
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import models as models
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import sqlalchemy as sa
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# revision identifiers, used by Alembic.
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revision = '0ab65e1cc7fa'
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down_revision = '4474872b0ee6'
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branch_labels = None
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depends_on = None
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def upgrade():
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# ### commands auto generated by Alembic - please adjust! ###
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with op.batch_alter_table('workflow_runs', schema=None) as batch_op:
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batch_op.drop_index(batch_op.f('workflow_run_tenant_app_sequence_idx'))
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batch_op.drop_column('sequence_number')
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# ### end Alembic commands ###
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def downgrade():
|
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# ### commands auto generated by Alembic - please adjust! ###
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# WARNING: This downgrade CANNOT recover the original sequence_number values!
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# The original sequence numbers are permanently lost after the upgrade.
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# This downgrade will regenerate sequence numbers based on created_at order,
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# which may result in different values than the original sequence numbers.
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#
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# If you need to preserve original sequence numbers, use the alternative
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# migration approach that creates a backup table before removal.
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# Step 1: Add sequence_number column as nullable first
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with op.batch_alter_table('workflow_runs', schema=None) as batch_op:
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batch_op.add_column(sa.Column('sequence_number', sa.INTEGER(), autoincrement=False, nullable=True))
|
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# Step 2: Populate sequence_number values based on created_at order within each app
|
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# NOTE: This recreates sequence numbering logic but values will be different
|
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# from the original sequence numbers that were removed in the upgrade
|
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connection = op.get_bind()
|
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connection.execute(sa.text("""
|
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UPDATE workflow_runs
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SET sequence_number = subquery.row_num
|
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FROM (
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SELECT id, ROW_NUMBER() OVER (
|
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PARTITION BY tenant_id, app_id
|
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ORDER BY created_at, id
|
||||
) as row_num
|
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FROM workflow_runs
|
||||
) subquery
|
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WHERE workflow_runs.id = subquery.id
|
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"""))
|
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|
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# Step 3: Make the column NOT NULL and add the index
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with op.batch_alter_table('workflow_runs', schema=None) as batch_op:
|
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batch_op.alter_column('sequence_number', nullable=False)
|
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batch_op.create_index(batch_op.f('workflow_run_tenant_app_sequence_idx'), ['tenant_id', 'app_id', 'sequence_number'], unique=False)
|
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|
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# ### end Alembic commands ###
|
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@ -0,0 +1,5 @@
|
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from services.errors.base import BaseServiceError
|
||||
|
||||
|
||||
class PluginInstallationForbiddenError(BaseServiceError):
|
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pass
|
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@ -0,0 +1,25 @@
|
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from core.rag.datasource.vdb.matrixone.matrixone_vector import MatrixoneConfig, MatrixoneVector
|
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from tests.integration_tests.vdb.test_vector_store import (
|
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AbstractVectorTest,
|
||||
get_example_text,
|
||||
setup_mock_redis,
|
||||
)
|
||||
|
||||
|
||||
class MatrixoneVectorTest(AbstractVectorTest):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.vector = MatrixoneVector(
|
||||
collection_name=self.collection_name,
|
||||
config=MatrixoneConfig(
|
||||
host="localhost", port=6001, user="dump", password="111", database="dify", metric="l2"
|
||||
),
|
||||
)
|
||||
|
||||
def get_ids_by_metadata_field(self):
|
||||
ids = self.vector.get_ids_by_metadata_field(key="document_id", value=self.example_doc_id)
|
||||
assert len(ids) == 1
|
||||
|
||||
|
||||
def test_matrixone_vector(setup_mock_redis):
|
||||
MatrixoneVectorTest().run_all_tests()
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,46 @@
|
||||
import { useGlobalPublicStore } from '@/context/global-public-context'
|
||||
import type { SystemFeatures } from '@/types/feature'
|
||||
import { InstallationScope } from '@/types/feature'
|
||||
import type { Plugin, PluginManifestInMarket } from '../../types'
|
||||
|
||||
type PluginProps = (Plugin | PluginManifestInMarket) & { from: 'github' | 'marketplace' | 'package' }
|
||||
|
||||
export function pluginInstallLimit(plugin: PluginProps, systemFeatures: SystemFeatures) {
|
||||
if (systemFeatures.plugin_installation_permission.restrict_to_marketplace_only) {
|
||||
if (plugin.from === 'github' || plugin.from === 'package')
|
||||
return { canInstall: false }
|
||||
}
|
||||
|
||||
if (systemFeatures.plugin_installation_permission.plugin_installation_scope === InstallationScope.ALL) {
|
||||
return {
|
||||
canInstall: true,
|
||||
}
|
||||
}
|
||||
if (systemFeatures.plugin_installation_permission.plugin_installation_scope === InstallationScope.NONE) {
|
||||
return {
|
||||
canInstall: false,
|
||||
}
|
||||
}
|
||||
const verification = plugin.verification || {}
|
||||
if (!plugin.verification || !plugin.verification.authorized_category)
|
||||
verification.authorized_category = 'langgenius'
|
||||
|
||||
if (systemFeatures.plugin_installation_permission.plugin_installation_scope === InstallationScope.OFFICIAL_ONLY) {
|
||||
return {
|
||||
canInstall: verification.authorized_category === 'langgenius',
|
||||
}
|
||||
}
|
||||
if (systemFeatures.plugin_installation_permission.plugin_installation_scope === InstallationScope.OFFICIAL_AND_PARTNER) {
|
||||
return {
|
||||
canInstall: verification.authorized_category === 'langgenius' || verification.authorized_category === 'partner',
|
||||
}
|
||||
}
|
||||
return {
|
||||
canInstall: true,
|
||||
}
|
||||
}
|
||||
|
||||
export default function usePluginInstallLimit(plugin: PluginProps) {
|
||||
const systemFeatures = useGlobalPublicStore(s => s.systemFeatures)
|
||||
return pluginInstallLimit(plugin, systemFeatures)
|
||||
}
|
||||
@ -1,30 +0,0 @@
|
||||
'use client'
|
||||
import type { FC } from 'react'
|
||||
import classNames from '@/utils/classnames'
|
||||
import { useGlobalPublicStore } from '@/context/global-public-context'
|
||||
import { useTheme } from 'next-themes'
|
||||
|
||||
type LoginLogoProps = {
|
||||
className?: string
|
||||
}
|
||||
|
||||
const LoginLogo: FC<LoginLogoProps> = ({
|
||||
className,
|
||||
}) => {
|
||||
const { systemFeatures } = useGlobalPublicStore()
|
||||
const { theme } = useTheme()
|
||||
|
||||
let src = theme === 'light' ? '/logo/logo-site.png' : `/logo/logo-site-${theme}.png`
|
||||
if (systemFeatures.branding.enabled)
|
||||
src = systemFeatures.branding.login_page_logo
|
||||
|
||||
return (
|
||||
<img
|
||||
src={src}
|
||||
className={classNames('block w-auto h-10', className)}
|
||||
alt='logo'
|
||||
/>
|
||||
)
|
||||
}
|
||||
|
||||
export default LoginLogo
|
||||
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