mirror of
https://github.com/langgenius/dify.git
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remove the weaviate and update dockerfiles
This commit is contained in:
Byron Wang
parent
32d6747ff2
commit
e1b7b0f81d
@ -86,7 +86,7 @@ COPY --chown=dify:dify . /app/api
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# Prepare entrypoint script
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COPY --chown=dify:dify --chmod=755 docker/entrypoint.sh /entrypoint.sh
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RUN uv sync --frozen --no-dev && pip3 install --no-cache-dir --upgrade "wheel==0.46.3" "jaraco.context==6.1.0"
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RUN uv sync --frozen --no-dev && pip3 install --no-cache-dir --upgrade "wheel==0.46.3" "jaraco.context==6.1.0" "setuptools==80.10.2"
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# Download nltk data
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RUN mkdir -p /usr/local/share/nltk_data \
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@ -119,10 +119,6 @@ class Vector:
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from core.rag.datasource.vdb.tidb_vector.tidb_vector import TiDBVectorFactory
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return TiDBVectorFactory
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case VectorType.WEAVIATE:
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from core.rag.datasource.vdb.weaviate.weaviate_vector import WeaviateVectorFactory
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return WeaviateVectorFactory
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case VectorType.TENCENT:
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from core.rag.datasource.vdb.tencent.tencent_vector import TencentVectorFactory
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@ -1,468 +0,0 @@
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"""
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Weaviate vector database implementation for Dify's RAG system.
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This module provides integration with Weaviate vector database for storing and retrieving
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document embeddings used in retrieval-augmented generation workflows.
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"""
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import datetime
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import json
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import logging
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import uuid as _uuid
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from typing import Any
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from urllib.parse import urlparse
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import weaviate
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import weaviate.classes.config as wc
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from pydantic import BaseModel, model_validator
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from weaviate.classes.data import DataObject
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from weaviate.classes.init import Auth
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from weaviate.classes.query import Filter, MetadataQuery
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from weaviate.exceptions import UnexpectedStatusCodeError
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from configs import dify_config
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from core.rag.datasource.vdb.field import Field
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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 WeaviateConfig(BaseModel):
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"""
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Configuration model for Weaviate connection settings.
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Attributes:
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endpoint: Weaviate server endpoint URL
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grpc_endpoint: Optional Weaviate gRPC server endpoint URL
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api_key: Optional API key for authentication
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batch_size: Number of objects to batch per insert operation
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"""
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endpoint: str
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grpc_endpoint: str | None = None
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api_key: str | None = None
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batch_size: int = 100
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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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"""Validates that required configuration values are present."""
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if not values["endpoint"]:
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raise ValueError("config WEAVIATE_ENDPOINT is required")
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return values
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class WeaviateVector(BaseVector):
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"""
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Weaviate vector database implementation for document storage and retrieval.
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Handles creation, insertion, deletion, and querying of document embeddings
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in a Weaviate collection.
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"""
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_DOCUMENT_ID_PROPERTY = "document_id"
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def __init__(self, collection_name: str, config: WeaviateConfig, attributes: list):
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"""
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Initializes the Weaviate vector store.
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Args:
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collection_name: Name of the Weaviate collection
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config: Weaviate configuration settings
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attributes: List of metadata attributes to store
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"""
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super().__init__(collection_name)
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self._client = self._init_client(config)
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self._attributes = attributes
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def __del__(self):
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"""
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Destructor to properly close the Weaviate client connection.
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Prevents connection leaks and resource warnings.
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"""
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if hasattr(self, "_client") and self._client is not None:
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try:
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self._client.close()
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except Exception as e:
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# Ignore errors during cleanup as object is being destroyed
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logger.warning("Error closing Weaviate client %s", e, exc_info=True)
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def _init_client(self, config: WeaviateConfig) -> weaviate.WeaviateClient:
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"""
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Initializes and returns a connected Weaviate client.
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Configures both HTTP and gRPC connections with proper authentication.
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"""
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p = urlparse(config.endpoint)
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host = p.hostname or config.endpoint.replace("https://", "").replace("http://", "")
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http_secure = p.scheme == "https"
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http_port = p.port or (443 if http_secure else 80)
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# Parse gRPC configuration
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if config.grpc_endpoint:
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# Urls without scheme won't be parsed correctly in some python versions,
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# see https://bugs.python.org/issue27657
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grpc_endpoint_with_scheme = (
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config.grpc_endpoint if "://" in config.grpc_endpoint else f"grpc://{config.grpc_endpoint}"
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)
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grpc_p = urlparse(grpc_endpoint_with_scheme)
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grpc_host = grpc_p.hostname or "localhost"
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grpc_port = grpc_p.port or (443 if grpc_p.scheme == "grpcs" else 50051)
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grpc_secure = grpc_p.scheme == "grpcs"
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else:
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# Infer from HTTP endpoint as fallback
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grpc_host = host
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grpc_secure = http_secure
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grpc_port = 443 if grpc_secure else 50051
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client = weaviate.connect_to_custom(
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http_host=host,
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http_port=http_port,
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http_secure=http_secure,
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grpc_host=grpc_host,
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grpc_port=grpc_port,
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grpc_secure=grpc_secure,
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auth_credentials=Auth.api_key(config.api_key) if config.api_key else None,
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skip_init_checks=True, # Skip PyPI version check to avoid unnecessary HTTP requests
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)
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if not client.is_ready():
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raise ConnectionError("Vector database is not ready")
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return client
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def get_type(self) -> str:
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"""Returns the vector database type identifier."""
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return VectorType.WEAVIATE
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def get_collection_name(self, dataset: Dataset) -> str:
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"""
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Retrieves or generates the collection name for a dataset.
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Uses existing index structure if available, otherwise generates from dataset ID.
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"""
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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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if not class_prefix.endswith("_Node"):
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class_prefix += "_Node"
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return class_prefix
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dataset_id = dataset.id
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return Dataset.gen_collection_name_by_id(dataset_id)
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def to_index_struct(self) -> dict:
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"""Returns the index structure dictionary for persistence."""
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return {"type": self.get_type(), "vector_store": {"class_prefix": self._collection_name}}
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def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
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"""
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Creates a new collection and adds initial documents with embeddings.
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"""
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self._create_collection()
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self.add_texts(texts, embeddings)
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def _create_collection(self):
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"""
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Creates the Weaviate collection with required schema if it doesn't exist.
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Uses Redis locking to prevent concurrent creation attempts.
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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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cache_key = f"vector_indexing_{self._collection_name}"
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if redis_client.get(cache_key):
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return
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try:
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if not self._client.collections.exists(self._collection_name):
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tokenization = (
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wc.Tokenization(dify_config.WEAVIATE_TOKENIZATION)
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if dify_config.WEAVIATE_TOKENIZATION
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else wc.Tokenization.WORD
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)
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self._client.collections.create(
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name=self._collection_name,
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properties=[
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wc.Property(
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name=Field.TEXT_KEY.value,
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data_type=wc.DataType.TEXT,
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tokenization=tokenization,
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),
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wc.Property(name="document_id", data_type=wc.DataType.TEXT),
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wc.Property(name="doc_id", data_type=wc.DataType.TEXT),
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wc.Property(name="chunk_index", data_type=wc.DataType.INT),
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],
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vector_config=wc.Configure.Vectors.self_provided(),
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)
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self._ensure_properties()
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redis_client.set(cache_key, 1, ex=3600)
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except Exception as e:
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logger.exception("Error creating collection %s", self._collection_name)
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raise
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def _ensure_properties(self) -> None:
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"""
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Ensures all required properties exist in the collection schema.
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Adds missing properties if the collection exists but lacks them.
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"""
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if not self._client.collections.exists(self._collection_name):
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return
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col = self._client.collections.use(self._collection_name)
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cfg = col.config.get()
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existing = {p.name for p in (cfg.properties or [])}
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to_add = []
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if "document_id" not in existing:
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to_add.append(wc.Property(name="document_id", data_type=wc.DataType.TEXT))
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if "doc_id" not in existing:
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to_add.append(wc.Property(name="doc_id", data_type=wc.DataType.TEXT))
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if "chunk_index" not in existing:
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to_add.append(wc.Property(name="chunk_index", data_type=wc.DataType.INT))
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for prop in to_add:
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try:
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col.config.add_property(prop)
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except Exception as e:
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logger.warning("Could not add property %s: %s", prop.name, e)
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def _get_uuids(self, documents: list[Document]) -> list[str]:
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"""
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Generates deterministic UUIDs for documents based on their content.
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Uses UUID5 with URL namespace to ensure consistent IDs for identical content.
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"""
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URL_NAMESPACE = _uuid.UUID("6ba7b811-9dad-11d1-80b4-00c04fd430c8")
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uuids = []
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for doc in documents:
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uuid_val = _uuid.uuid5(URL_NAMESPACE, doc.page_content)
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uuids.append(str(uuid_val))
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return uuids
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def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
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"""
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Adds documents with their embeddings to the collection.
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Batches insertions for efficiency and returns the list of inserted object IDs.
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"""
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uuids = self._get_uuids(documents)
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texts = [d.page_content for d in documents]
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metadatas = [d.metadata for d in documents]
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col = self._client.collections.use(self._collection_name)
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objs: list[DataObject] = []
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ids_out: list[str] = []
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for i, text in enumerate(texts):
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props: dict[str, Any] = {Field.TEXT_KEY.value: text}
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meta = metadatas[i] or {}
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for k, v in meta.items():
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props[k] = self._json_serializable(v)
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candidate = uuids[i] if uuids else None
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uid = candidate if (candidate and self._is_uuid(candidate)) else str(_uuid.uuid4())
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ids_out.append(uid)
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vec_payload = None
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if embeddings and i < len(embeddings) and embeddings[i]:
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vec_payload = {"default": embeddings[i]}
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objs.append(
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DataObject(
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uuid=uid,
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properties=props, # type: ignore[arg-type] # mypy incorrectly infers DataObject signature
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vector=vec_payload,
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)
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)
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with col.batch.dynamic() as batch:
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for obj in objs:
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batch.add_object(properties=obj.properties, uuid=obj.uuid, vector=obj.vector)
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return ids_out
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def _is_uuid(self, val: str) -> bool:
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"""Validates whether a string is a valid UUID format."""
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try:
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_uuid.UUID(str(val))
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return True
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except Exception:
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return False
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def delete_by_metadata_field(self, key: str, value: str) -> None:
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"""Deletes all objects matching a specific metadata field value."""
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if not self._client.collections.exists(self._collection_name):
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return
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col = self._client.collections.use(self._collection_name)
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col.data.delete_many(where=Filter.by_property(key).equal(value))
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def delete(self):
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"""Deletes the entire collection from Weaviate."""
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if self._client.collections.exists(self._collection_name):
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self._client.collections.delete(self._collection_name)
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def text_exists(self, id: str) -> bool:
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"""Checks if a document with the given doc_id exists in the collection."""
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if not self._client.collections.exists(self._collection_name):
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return False
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col = self._client.collections.use(self._collection_name)
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res = col.query.fetch_objects(
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filters=Filter.by_property("doc_id").equal(id),
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limit=1,
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return_properties=["doc_id"],
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)
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return len(res.objects) > 0
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def delete_by_ids(self, ids: list[str]) -> None:
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"""
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Deletes objects by their UUID identifiers.
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Silently ignores 404 errors for non-existent IDs.
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"""
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if not self._client.collections.exists(self._collection_name):
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return
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col = self._client.collections.use(self._collection_name)
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for uid in ids:
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try:
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col.data.delete_by_id(uid)
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except UnexpectedStatusCodeError as e:
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if getattr(e, "status_code", None) != 404:
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raise
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def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
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"""
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Performs vector similarity search using the provided query vector.
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Filters by document IDs if provided and applies score threshold.
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Returns documents sorted by relevance score.
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"""
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if not self._client.collections.exists(self._collection_name):
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return []
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col = self._client.collections.use(self._collection_name)
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props = list({*self._attributes, self._DOCUMENT_ID_PROPERTY, Field.TEXT_KEY.value})
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where = None
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doc_ids = kwargs.get("document_ids_filter") or []
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if doc_ids:
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where = Filter.by_property(self._DOCUMENT_ID_PROPERTY).contains_any(doc_ids)
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top_k = int(kwargs.get("top_k", 4))
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score_threshold = float(kwargs.get("score_threshold") or 0.0)
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res = col.query.near_vector(
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near_vector=query_vector,
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limit=top_k,
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return_properties=props,
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return_metadata=MetadataQuery(distance=True),
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include_vector=False,
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filters=where,
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target_vector="default",
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)
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docs: list[Document] = []
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for obj in res.objects:
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properties = dict(obj.properties or {})
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text = properties.pop(Field.TEXT_KEY.value, "")
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if obj.metadata and obj.metadata.distance is not None:
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distance = obj.metadata.distance
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else:
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distance = 1.0
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score = 1.0 - distance
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if score > score_threshold:
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properties["score"] = score
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docs.append(Document(page_content=text, metadata=properties))
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docs.sort(key=lambda d: d.metadata.get("score", 0.0), reverse=True)
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return docs
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def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
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"""
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Performs BM25 full-text search on document content.
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Filters by document IDs if provided and returns matching documents with vectors.
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"""
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if not self._client.collections.exists(self._collection_name):
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return []
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col = self._client.collections.use(self._collection_name)
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props = list({*self._attributes, Field.TEXT_KEY.value})
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where = None
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doc_ids = kwargs.get("document_ids_filter") or []
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if doc_ids:
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where = Filter.by_property(self._DOCUMENT_ID_PROPERTY).contains_any(doc_ids)
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top_k = int(kwargs.get("top_k", 4))
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res = col.query.bm25(
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query=query,
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query_properties=[Field.TEXT_KEY.value],
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limit=top_k,
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return_properties=props,
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include_vector=True,
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filters=where,
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)
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docs: list[Document] = []
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for obj in res.objects:
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properties = dict(obj.properties or {})
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text = properties.pop(Field.TEXT_KEY.value, "")
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vec = obj.vector
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if isinstance(vec, dict):
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vec = vec.get("default") or next(iter(vec.values()), None)
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docs.append(Document(page_content=text, vector=vec, metadata=properties))
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return docs
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def _json_serializable(self, value: Any) -> Any:
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"""Converts values to JSON-serializable format, handling datetime objects."""
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if isinstance(value, datetime.datetime):
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return value.isoformat()
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return value
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class WeaviateVectorFactory(AbstractVectorFactory):
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"""Factory class for creating WeaviateVector instances."""
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def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> WeaviateVector:
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"""
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Initializes a WeaviateVector instance for the given dataset.
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||||
Uses existing collection name from dataset index structure or generates a new one.
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Updates dataset index structure if not already set.
|
||||
"""
|
||||
if dataset.index_struct_dict:
|
||||
class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
|
||||
collection_name = class_prefix
|
||||
else:
|
||||
dataset_id = dataset.id
|
||||
collection_name = Dataset.gen_collection_name_by_id(dataset_id)
|
||||
dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.WEAVIATE, collection_name))
|
||||
return WeaviateVector(
|
||||
collection_name=collection_name,
|
||||
config=WeaviateConfig(
|
||||
endpoint=dify_config.WEAVIATE_ENDPOINT or "",
|
||||
grpc_endpoint=dify_config.WEAVIATE_GRPC_ENDPOINT or "",
|
||||
api_key=dify_config.WEAVIATE_API_KEY,
|
||||
batch_size=dify_config.WEAVIATE_BATCH_SIZE,
|
||||
),
|
||||
attributes=attributes,
|
||||
)
|
||||
@ -12,6 +12,7 @@ dependencies = [
|
||||
"cachetools~=6.2.4",
|
||||
"celery~=5.5.2",
|
||||
"charset-normalizer>=3.4.4",
|
||||
"cryptography~=46.0.5",
|
||||
"flask~=3.1.2",
|
||||
"flask-compress>=1.17,<1.18",
|
||||
"flask-cors~=6.0.0",
|
||||
@ -68,7 +69,7 @@ dependencies = [
|
||||
"pydantic~=2.12.5",
|
||||
"pydantic-extra-types~=2.10.3",
|
||||
"pydantic-settings~=2.11.0",
|
||||
"pyjwt~=2.10.1",
|
||||
"pyjwt~=2.11.0",
|
||||
"pypdfium2==5.2.0",
|
||||
"python-docx~=1.1.0",
|
||||
"python-dotenv==1.0.1",
|
||||
@ -90,9 +91,7 @@ dependencies = [
|
||||
"flask-restx~=1.3.2",
|
||||
"packaging~=23.2",
|
||||
"croniter>=6.0.0",
|
||||
"weaviate-client==4.19.2",
|
||||
"apscheduler>=3.11.0",
|
||||
"weave>=0.52.26",
|
||||
]
|
||||
# Before adding new dependency, consider place it in
|
||||
# alphabet order (a-z) and suitable group.
|
||||
@ -222,7 +221,6 @@ vdb = [
|
||||
"tidb-vector==0.0.9",
|
||||
"upstash-vector==0.6.0",
|
||||
"volcengine-compat~=1.0.0",
|
||||
"weaviate-client==4.19.2",
|
||||
"xinference-client~=1.2.2",
|
||||
"mo-vector~=0.1.13",
|
||||
"mysql-connector-python>=9.3.0",
|
||||
|
||||
351
api/uv.lock
generated
351
api/uv.lock
generated
@ -16,18 +16,6 @@ resolution-markers = [
|
||||
"python_full_version < '3.12' and platform_python_implementation == 'PyPy' and sys_platform != 'linux'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "abnf"
|
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version = "2.2.0"
|
||||
source = { registry = "https://pypi.org/simple" }
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dependencies = [
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{ name = "typing-extensions" },
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]
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wheels = [
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|
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]
|
||||
|
||||
[[package]]
|
||||
name = "aiofiles"
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|
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|
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[[package]]
|
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|
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|
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|
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|
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|
||||
[[package]]
|
||||
name = "azure-core"
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[[package]]
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wheels = [
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|
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|
||||
[[package]]
|
||||
name = "charset-normalizer"
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[[package]]
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[[package]]
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[[package]]
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||||
name = "stdlib-list"
|
||||
version = "0.11.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/5d/09/8d5c564931ae23bef17420a6c72618463a59222ca4291a7dd88de8a0d490/stdlib_list-0.11.1.tar.gz", hash = "sha256:95ebd1d73da9333bba03ccc097f5bac05e3aa03e6822a0c0290f87e1047f1857", size = 60442, upload-time = "2025-02-18T15:39:38.769Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/88/c7/4102536de33c19d090ed2b04e90e7452e2e3dc653cf3323208034eaaca27/stdlib_list-0.11.1-py3-none-any.whl", hash = "sha256:9029ea5e3dfde8cd4294cfd4d1797be56a67fc4693c606181730148c3fd1da29", size = 83620, upload-time = "2025-02-18T15:39:37.02Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "storage3"
|
||||
version = "0.12.1"
|
||||
@ -6686,7 +6496,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "unstructured"
|
||||
version = "0.18.31"
|
||||
version = "0.18.32"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "backoff" },
|
||||
@ -6712,9 +6522,9 @@ dependencies = [
|
||||
{ name = "unstructured-client" },
|
||||
{ name = "wrapt" },
|
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]
|
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sdist = { url = "https://files.pythonhosted.org/packages/a9/5f/64285bd69a538bc28753f1423fcaa9d64cd79a9e7c097171b1f0d27e9cdb/unstructured-0.18.31.tar.gz", hash = "sha256:af4bbe32d1894ae6e755f0da6fc0dd307a1d0adeebe0e7cc6278f6cf744339ca", size = 1707700, upload-time = "2026-01-27T15:33:05.378Z" }
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sdist = { url = "https://files.pythonhosted.org/packages/1c/65/b73d84ede08fc2defe9c59d85ebf91f78210a424986586c6e39784890c8e/unstructured-0.18.32.tar.gz", hash = "sha256:40a7cf4a4a7590350bedb8a447e37029d6e74b924692576627b4edb92d70e39d", size = 1707730, upload-time = "2026-02-10T22:28:22.332Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/c8/4a/9c43f39d9e443c9bc3f2e379b305bca27110adc653b071221b3132c18de5/unstructured-0.18.31-py3-none-any.whl", hash = "sha256:fab4641176cb9b192ed38048758aa0d9843121d03626d18f42275afb31e5b2d3", size = 1794889, upload-time = "2026-01-27T15:33:03.136Z" },
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{ url = "https://files.pythonhosted.org/packages/68/e7/35298355bdb917293dc3e179304e737ce3fe14247fb5edf09fddddc98409/unstructured-0.18.32-py3-none-any.whl", hash = "sha256:c832ecdf467f5a869cc5e91428459e4b9ed75a16156ce3fab8f41ff64d840bc7", size = 1794965, upload-time = "2026-02-10T22:28:20.301Z" },
|
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]
|
||||
|
||||
[package.optional-dependencies]
|
||||
@ -6835,15 +6645,6 @@ wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/99/39/6b3f7d234ba3964c428a6e40006340f53ba37993f46ed6e111c6e9141d18/uvloop-0.22.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:512fec6815e2dd45161054592441ef76c830eddaad55c8aa30952e6fe1ed07c0", size = 4296343, upload-time = "2025-10-16T22:16:35.149Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "validators"
|
||||
version = "0.35.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/53/66/a435d9ae49850b2f071f7ebd8119dd4e84872b01630d6736761e6e7fd847/validators-0.35.0.tar.gz", hash = "sha256:992d6c48a4e77c81f1b4daba10d16c3a9bb0dbb79b3a19ea847ff0928e70497a", size = 73399, upload-time = "2025-05-01T05:42:06.7Z" }
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wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/fa/6e/3e955517e22cbdd565f2f8b2e73d52528b14b8bcfdb04f62466b071de847/validators-0.35.0-py3-none-any.whl", hash = "sha256:e8c947097eae7892cb3d26868d637f79f47b4a0554bc6b80065dfe5aac3705dd", size = 44712, upload-time = "2025-05-01T05:42:04.203Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "vine"
|
||||
version = "5.1.0"
|
||||
@ -6871,35 +6672,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/37/da/7ccbe82470dc27e1cfd0466dc637248be906eb8447c28a40c1c74cf617ee/volcengine_compat-1.0.156-py3-none-any.whl", hash = "sha256:4abc149a7601ebad8fa2d28fab50c7945145cf74daecb71bca797b0bdc82c5a5", size = 677272, upload-time = "2024-10-13T09:17:19.944Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "wandb"
|
||||
version = "0.23.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "click" },
|
||||
{ name = "gitpython" },
|
||||
{ name = "packaging" },
|
||||
{ name = "platformdirs" },
|
||||
{ name = "protobuf" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "pyyaml" },
|
||||
{ name = "requests" },
|
||||
{ name = "sentry-sdk" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/0a/cc/770ae3aa7ae44f6792f7ecb81c14c0e38b672deb35235719bb1006519487/wandb-0.23.1.tar.gz", hash = "sha256:f6fb1e3717949b29675a69359de0eeb01e67d3360d581947d5b3f98c273567d6", size = 44298053, upload-time = "2025-12-03T02:25:10.79Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/12/0b/c3d7053dfd93fd259a63c7818d9c4ac2ba0642ff8dc8db98662ea0cf9cc0/wandb-0.23.1-py3-none-macosx_12_0_arm64.whl", hash = "sha256:358e15471d19b7d73fc464e37371c19d44d39e433252ac24df107aff993a286b", size = 21527293, upload-time = "2025-12-03T02:24:48.011Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/ee/9f/059420fa0cb6c511dc5c5a50184122b6aca7b178cb2aa210139e354020da/wandb-0.23.1-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:110304407f4b38f163bdd50ed5c5225365e4df3092f13089c30171a75257b575", size = 22745926, upload-time = "2025-12-03T02:24:50.519Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/96/b6/fd465827c14c64d056d30b4c9fcf4dac889a6969dba64489a88fc4ffa333/wandb-0.23.1-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:6cc984cf85feb2f8ee0451d76bc9fb7f39da94956bb8183e30d26284cf203b65", size = 21212973, upload-time = "2025-12-03T02:24:52.828Z" },
|
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{ url = "https://files.pythonhosted.org/packages/5c/ee/9a8bb9a39cc1f09c3060456cc79565110226dc4099a719af5c63432da21d/wandb-0.23.1-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:67431cd3168d79fdb803e503bd669c577872ffd5dadfa86de733b3274b93088e", size = 22887885, upload-time = "2025-12-03T02:24:55.281Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6d/4d/8d9e75add529142e037b05819cb3ab1005679272950128d69d218b7e5b2e/wandb-0.23.1-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:07be70c0baa97ea25fadc4a9d0097f7371eef6dcacc5ceb525c82491a31e9244", size = 21250967, upload-time = "2025-12-03T02:24:57.603Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/97/72/0b35cddc4e4168f03c759b96d9f671ad18aec8bdfdd84adfea7ecb3f5701/wandb-0.23.1-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:216c95b08e0a2ec6a6008373b056d597573d565e30b43a7a93c35a171485ee26", size = 22988382, upload-time = "2025-12-03T02:25:00.518Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/c0/6d/e78093d49d68afb26f5261a70fc7877c34c114af5c2ee0ab3b1af85f5e76/wandb-0.23.1-py3-none-win32.whl", hash = "sha256:fb5cf0f85692f758a5c36ab65fea96a1284126de64e836610f92ddbb26df5ded", size = 22150756, upload-time = "2025-12-03T02:25:02.734Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/05/27/4f13454b44c9eceaac3d6e4e4efa2230b6712d613ff9bf7df010eef4fd18/wandb-0.23.1-py3-none-win_amd64.whl", hash = "sha256:21c8c56e436eb707b7d54f705652e030d48e5cfcba24cf953823eb652e30e714", size = 22150760, upload-time = "2025-12-03T02:25:05.106Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/30/20/6c091d451e2a07689bfbfaeb7592d488011420e721de170884fedd68c644/wandb-0.23.1-py3-none-win_arm64.whl", hash = "sha256:8aee7f3bb573f2c0acf860f497ca9c684f9b35f2ca51011ba65af3d4592b77c1", size = 20137463, upload-time = "2025-12-03T02:25:08.317Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "watchfiles"
|
||||
version = "1.1.1"
|
||||
@ -6950,47 +6722,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/af/b5/123f13c975e9f27ab9c0770f514345bd406d0e8d3b7a0723af9d43f710af/wcwidth-0.2.14-py2.py3-none-any.whl", hash = "sha256:a7bb560c8aee30f9957e5f9895805edd20602f2d7f720186dfd906e82b4982e1", size = 37286, upload-time = "2025-09-22T16:29:51.641Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "weave"
|
||||
version = "0.52.26"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "cachetools" },
|
||||
{ name = "click" },
|
||||
{ name = "diskcache" },
|
||||
{ name = "gql", extra = ["httpx"] },
|
||||
{ name = "jsonschema" },
|
||||
{ name = "packaging" },
|
||||
{ name = "polyfile-weave" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "sentry-sdk" },
|
||||
{ name = "tenacity" },
|
||||
{ name = "tzdata", marker = "sys_platform == 'win32'" },
|
||||
{ name = "wandb" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c4/80/cab73a203df37e958ea4016627b6c784c1f3e747691d2b16e31c4e20d7ad/weave-0.52.26.tar.gz", hash = "sha256:9e54451d395ef46bd81d5503bb46cf0bbfe2000e6f6bc7705f358face25f63e5", size = 685093, upload-time = "2026-02-03T18:55:32.728Z" }
|
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wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/56/fb/ad243894530dc82cd103d93c2238209cff4d96f6e9a21ae74c0db71f3f83/weave-0.52.26-py3-none-any.whl", hash = "sha256:53310259574605fd2fd7de52f2bfa79ca2c5f51ef26248aa51b7b76365f12f16", size = 853946, upload-time = "2026-02-03T18:55:29.844Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "weaviate-client"
|
||||
version = "4.19.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "authlib" },
|
||||
{ name = "deprecation" },
|
||||
{ name = "grpcio" },
|
||||
{ name = "httpx" },
|
||||
{ name = "protobuf" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "validators" },
|
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]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/0b/57/f3201e5047d91aa9e7a06aa004b995bc8b05b2ea95dd178ace17eaaec16e/weaviate_client-4.19.2.tar.gz", hash = "sha256:99e76e912c95762436089cd5feedbfeea31e892aa13b6ad94729a2a54b316c45", size = 786448, upload-time = "2025-12-31T12:48:46.174Z" }
|
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wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/d5/09/636abb5ecfae2316b6584eaae7001a03abfe131f72d747fbec3419075524/weaviate_client-4.19.2-py3-none-any.whl", hash = "sha256:e78306d47c574c4035c87223e480bb77bd6e54142a21c4c58522dd43019fe493", size = 603652, upload-time = "2025-12-31T12:48:44.42Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "webencodings"
|
||||
version = "0.5.1"
|
||||
|
||||
@ -26,7 +26,7 @@ COPY package.json pnpm-lock.yaml /app/web/
|
||||
# Use packageManager from package.json
|
||||
RUN corepack install
|
||||
|
||||
RUN pnpm install --frozen-lockfile
|
||||
RUN pnpm install --frozen-lockfile --prod
|
||||
|
||||
# build resources
|
||||
FROM base AS builder
|
||||
|
||||
Loading…
Reference in New Issue
Block a user