feat: add upstash as a new vector database provider (#9644)
Zven
2 years ago
committed by
GitHub
parent
999d3f1539
commit
8e7a752b2a
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from typing import Optional
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from pydantic import Field
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from pydantic_settings import BaseSettings
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class UpstashConfig(BaseSettings):
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"""
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Configuration settings for Upstash vector database
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"""
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UPSTASH_VECTOR_URL: Optional[str] = Field(
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description="URL of the upstash server (e.g., 'https://vector.upstash.io')",
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default=None,
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)
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UPSTASH_VECTOR_TOKEN: Optional[str] = Field(
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description="Token for authenticating with the upstash server",
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default=None,
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)
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import json
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from typing import Any
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from uuid import uuid4
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from pydantic import BaseModel, model_validator
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from upstash_vector import Index, Vector
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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 models.dataset import Dataset
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class UpstashVectorConfig(BaseModel):
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url: str
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token: str
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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["url"]:
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raise ValueError("Upstash URL is required")
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if not values["token"]:
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raise ValueError("Upstash Token is required")
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return values
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class UpstashVector(BaseVector):
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def __init__(self, collection_name: str, config: UpstashVectorConfig):
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super().__init__(collection_name)
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self._table_name = collection_name
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self.index = Index(url=config.url, token=config.token)
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def _get_index_dimension(self) -> int:
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index_info = self.index.info()
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if index_info and index_info.dimension:
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return index_info.dimension
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else:
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return 1536
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def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
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self.add_texts(texts, embeddings)
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def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
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vectors = [
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Vector(
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id=str(uuid4()),
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vector=embedding,
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metadata=doc.metadata,
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data=doc.page_content,
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)
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for doc, embedding in zip(documents, embeddings)
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]
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self.index.upsert(vectors=vectors)
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def text_exists(self, id: str) -> bool:
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response = self.get_ids_by_metadata_field("doc_id", id)
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return len(response) > 0
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def delete_by_ids(self, ids: list[str]) -> None:
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item_ids = []
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for doc_id in ids:
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ids = self.get_ids_by_metadata_field("doc_id", doc_id)
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if id:
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item_ids += ids
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self._delete_by_ids(ids=item_ids)
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def _delete_by_ids(self, ids: list[str]) -> None:
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if ids:
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self.index.delete(ids=ids)
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def get_ids_by_metadata_field(self, key: str, value: str) -> list[str]:
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query_result = self.index.query(
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vector=[1.001 * i for i in range(self._get_index_dimension())],
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include_metadata=True,
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top_k=1000,
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filter=f"{key} = '{value}'",
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)
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return [result.id for result in query_result]
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def delete_by_metadata_field(self, key: str, value: str) -> None:
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ids = self.get_ids_by_metadata_field(key, value)
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if ids:
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self._delete_by_ids(ids)
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def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
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top_k = kwargs.get("top_k", 4)
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result = self.index.query(vector=query_vector, top_k=top_k, include_metadata=True, include_data=True)
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docs = []
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score_threshold = float(kwargs.get("score_threshold") or 0.0)
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for record in result:
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metadata = record.metadata
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text = record.data
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score = record.score
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metadata["score"] = score
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if score > score_threshold:
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docs.append(Document(page_content=text, metadata=metadata))
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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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return []
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def delete(self) -> None:
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self.index.reset()
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def get_type(self) -> str:
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return VectorType.UPSTASH
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class UpstashVectorFactory(AbstractVectorFactory):
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def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> UpstashVector:
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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.lower()
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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.UPSTASH, collection_name))
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return UpstashVector(
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collection_name=collection_name,
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config=UpstashVectorConfig(
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url=dify_config.UPSTASH_VECTOR_URL,
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token=dify_config.UPSTASH_VECTOR_TOKEN,
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),
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)
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@ -0,0 +1,75 @@
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import os
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from typing import Optional
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import pytest
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from _pytest.monkeypatch import MonkeyPatch
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from upstash_vector import Index
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# Mocking the Index class from upstash_vector
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class MockIndex:
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def __init__(self, url="", token=""):
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self.url = url
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self.token = token
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self.vectors = []
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def upsert(self, vectors):
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for vector in vectors:
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vector.score = 0.5
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self.vectors.append(vector)
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return {"code": 0, "msg": "operation success", "affectedCount": len(vectors)}
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def fetch(self, ids):
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return [vector for vector in self.vectors if vector.id in ids]
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def delete(self, ids):
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self.vectors = [vector for vector in self.vectors if vector.id not in ids]
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return {"code": 0, "msg": "Success"}
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def query(
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self,
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vector: None,
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top_k: int = 10,
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include_vectors: bool = False,
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include_metadata: bool = False,
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filter: str = "",
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data: Optional[str] = None,
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namespace: str = "",
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include_data: bool = False,
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):
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# Simple mock query, in real scenario you would calculate similarity
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mock_result = []
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for vector_data in self.vectors:
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mock_result.append(vector_data)
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return mock_result[:top_k]
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def reset(self):
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self.vectors = []
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def info(self):
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return AttrDict({"dimension": 1024})
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class AttrDict(dict):
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def __getattr__(self, item):
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return self.get(item)
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MOCK = os.getenv("MOCK_SWITCH", "false").lower() == "true"
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@pytest.fixture
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def setup_upstashvector_mock(request, monkeypatch: MonkeyPatch):
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if MOCK:
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monkeypatch.setattr(Index, "__init__", MockIndex.__init__)
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monkeypatch.setattr(Index, "upsert", MockIndex.upsert)
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monkeypatch.setattr(Index, "fetch", MockIndex.fetch)
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monkeypatch.setattr(Index, "delete", MockIndex.delete)
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monkeypatch.setattr(Index, "query", MockIndex.query)
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monkeypatch.setattr(Index, "reset", MockIndex.reset)
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monkeypatch.setattr(Index, "info", MockIndex.info)
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yield
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if MOCK:
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monkeypatch.undo()
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@ -0,0 +1,63 @@
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import time
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import uuid
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from core.rag.datasource.vdb.upstash.upstash_vector import UpstashVector, UpstashVectorConfig
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from core.rag.models.document import Document
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from tests.integration_tests.vdb.__mock.upstashvectordb import setup_upstashvector_mock
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from tests.integration_tests.vdb.test_vector_store import AbstractVectorTest
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def get_example_text() -> str:
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return "test_text"
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def get_example_document(doc_id: str) -> Document:
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doc = Document(
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page_content=get_example_text(),
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metadata={
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"doc_id": doc_id,
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"doc_hash": doc_id,
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"document_id": doc_id,
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"dataset_id": doc_id,
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},
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)
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return doc
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class UpstashVectorTest(AbstractVectorTest):
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def __init__(self):
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super().__init__()
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self.vector = UpstashVector(
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collection_name="test_collection",
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config=UpstashVectorConfig(
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url="your-server-url",
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token="your-access-token",
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),
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)
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self.example_embedding = [1.001 * i for i in range(self.vector._get_index_dimension())]
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def add_texts(self) -> list[str]:
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batch_size = 1
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documents = [get_example_document(doc_id=str(uuid.uuid4())) for _ in range(batch_size)]
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embeddings = [self.example_embedding] * batch_size
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self.vector.add_texts(documents=documents, embeddings=embeddings)
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return [doc.metadata["doc_id"] for doc in documents]
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def get_ids_by_metadata_field(self):
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print("doc_id", self.example_doc_id)
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ids = self.vector.get_ids_by_metadata_field(key="document_id", value=self.example_doc_id)
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assert len(ids) != 0
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def run_all_tests(self):
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self.create_vector()
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time.sleep(1)
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self.search_by_vector()
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self.text_exists()
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self.get_ids_by_metadata_field()
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added_doc_ids = self.add_texts()
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self.delete_by_ids(added_doc_ids + [self.example_doc_id])
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self.delete_vector()
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def test_upstash_vector(setup_upstashvector_mock):
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UpstashVectorTest().run_all_tests()
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