Merge branch 'langgenius:main' into feat-add-remote-file-upload
GuanMu
1 year ago
committed by
GitHub
commit
2348bea78c
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from typing import Optional
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from pydantic import Field, PositiveInt
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from pydantic_settings import BaseSettings
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class VastbaseVectorConfig(BaseSettings):
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"""
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Configuration settings for Vector (Vastbase with vector extension)
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"""
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VASTBASE_HOST: Optional[str] = Field(
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description="Hostname or IP address of the Vastbase server with Vector extension (e.g., 'localhost')",
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default=None,
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)
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VASTBASE_PORT: PositiveInt = Field(
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description="Port number on which the Vastbase server is listening (default is 5432)",
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default=5432,
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)
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VASTBASE_USER: Optional[str] = Field(
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description="Username for authenticating with the Vastbase database",
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default=None,
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)
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VASTBASE_PASSWORD: Optional[str] = Field(
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description="Password for authenticating with the Vastbase database",
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default=None,
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)
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VASTBASE_DATABASE: Optional[str] = Field(
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description="Name of the Vastbase database to connect to",
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default=None,
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)
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VASTBASE_MIN_CONNECTION: PositiveInt = Field(
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description="Min connection of the Vastbase database",
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default=1,
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)
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VASTBASE_MAX_CONNECTION: PositiveInt = Field(
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description="Max connection of the Vastbase database",
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default=5,
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)
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@ -0,0 +1,243 @@
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import json
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import uuid
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from contextlib import contextmanager
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from typing import Any
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import psycopg2.extras # type: ignore
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import psycopg2.pool # 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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class VastbaseVectorConfig(BaseModel):
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host: str
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port: int
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user: str
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password: str
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database: str
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min_connection: int
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max_connection: int
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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 VASTBASE_HOST is required")
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if not values["port"]:
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raise ValueError("config VASTBASE_PORT is required")
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if not values["user"]:
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raise ValueError("config VASTBASE_USER is required")
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if not values["password"]:
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raise ValueError("config VASTBASE_PASSWORD is required")
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if not values["database"]:
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raise ValueError("config VASTBASE_DATABASE is required")
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if not values["min_connection"]:
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raise ValueError("config VASTBASE_MIN_CONNECTION is required")
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if not values["max_connection"]:
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raise ValueError("config VASTBASE_MAX_CONNECTION is required")
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if values["min_connection"] > values["max_connection"]:
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raise ValueError("config VASTBASE_MIN_CONNECTION should less than VASTBASE_MAX_CONNECTION")
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return values
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SQL_CREATE_TABLE = """
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CREATE TABLE IF NOT EXISTS {table_name} (
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id UUID PRIMARY KEY,
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text TEXT NOT NULL,
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meta JSONB NOT NULL,
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embedding floatvector({dimension}) NOT NULL
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);
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"""
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SQL_CREATE_INDEX = """
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CREATE INDEX IF NOT EXISTS embedding_cosine_v1_idx ON {table_name}
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USING hnsw (embedding floatvector_cosine_ops) WITH (m = 16, ef_construction = 64);
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"""
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class VastbaseVector(BaseVector):
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def __init__(self, collection_name: str, config: VastbaseVectorConfig):
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super().__init__(collection_name)
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self.pool = self._create_connection_pool(config)
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self.table_name = f"embedding_{collection_name}"
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def get_type(self) -> str:
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return VectorType.VASTBASE
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def _create_connection_pool(self, config: VastbaseVectorConfig):
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return psycopg2.pool.SimpleConnectionPool(
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config.min_connection,
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config.max_connection,
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host=config.host,
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port=config.port,
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user=config.user,
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password=config.password,
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database=config.database,
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)
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@contextmanager
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def _get_cursor(self):
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conn = self.pool.getconn()
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cur = conn.cursor()
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try:
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yield cur
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finally:
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cur.close()
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conn.commit()
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self.pool.putconn(conn)
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def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
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dimension = len(embeddings[0])
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self._create_collection(dimension)
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return 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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values = []
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pks = []
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for i, 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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pks.append(doc_id)
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values.append(
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(
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doc_id,
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doc.page_content,
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json.dumps(doc.metadata),
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embeddings[i],
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)
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)
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with self._get_cursor() as cur:
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psycopg2.extras.execute_values(
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cur, f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES %s", values
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)
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return pks
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def text_exists(self, id: str) -> bool:
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with self._get_cursor() as cur:
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cur.execute(f"SELECT id FROM {self.table_name} WHERE id = %s", (id,))
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return cur.fetchone() is not None
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def get_by_ids(self, ids: list[str]) -> list[Document]:
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with self._get_cursor() as cur:
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cur.execute(f"SELECT meta, text FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
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docs = []
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for record in cur:
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docs.append(Document(page_content=record[1], metadata=record[0]))
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return docs
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def delete_by_ids(self, ids: list[str]) -> None:
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# Avoiding crashes caused by performing delete operations on empty lists in certain scenarios
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# Scenario 1: extract a document fails, resulting in a table not being created.
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# Then clicking the retry button triggers a delete operation on an empty list.
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if not ids:
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return
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with self._get_cursor() as cur:
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cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
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def delete_by_metadata_field(self, key: str, value: str) -> None:
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with self._get_cursor() as cur:
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cur.execute(f"DELETE FROM {self.table_name} WHERE meta->>%s = %s", (key, value))
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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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Search the nearest neighbors to a vector.
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:param query_vector: The input vector to search for similar items.
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:param top_k: The number of nearest neighbors to return, default is 5.
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:return: List of Documents that are nearest to the query vector.
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"""
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top_k = kwargs.get("top_k", 4)
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if not isinstance(top_k, int) or top_k <= 0:
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raise ValueError("top_k must be a positive integer")
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with self._get_cursor() as cur:
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cur.execute(
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f"SELECT meta, text, embedding <=> %s AS distance FROM {self.table_name}"
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f" ORDER BY distance LIMIT {top_k}",
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(json.dumps(query_vector),),
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)
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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 cur:
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metadata, text, distance = record
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score = 1 - distance
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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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top_k = kwargs.get("top_k", 5)
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if not isinstance(top_k, int) or top_k <= 0:
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raise ValueError("top_k must be a positive integer")
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with self._get_cursor() as cur:
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cur.execute(
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f"""SELECT meta, text, ts_rank(to_tsvector(coalesce(text, '')), plainto_tsquery(%s)) AS score
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FROM {self.table_name}
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WHERE to_tsvector(text) @@ plainto_tsquery(%s)
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ORDER BY score DESC
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LIMIT {top_k}""",
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# f"'{query}'" is required in order to account for whitespace in query
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(f"'{query}'", f"'{query}'"),
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)
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docs = []
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for record in cur:
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metadata, text, score = record
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metadata["score"] = score
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docs.append(Document(page_content=text, metadata=metadata))
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return docs
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def delete(self) -> None:
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with self._get_cursor() as cur:
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cur.execute(f"DROP TABLE IF EXISTS {self.table_name}")
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def _create_collection(self, dimension: int):
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cache_key = f"vector_indexing_{self._collection_name}"
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lock_name = f"{cache_key}_lock"
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with redis_client.lock(lock_name, timeout=20):
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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
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with self._get_cursor() as cur:
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cur.execute(SQL_CREATE_TABLE.format(table_name=self.table_name, dimension=dimension))
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# Vastbase 支持的向量维度取值范围为 [1,16000]
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if dimension <= 16000:
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cur.execute(SQL_CREATE_INDEX.format(table_name=self.table_name))
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redis_client.set(collection_exist_cache_key, 1, ex=3600)
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class VastbaseVectorFactory(AbstractVectorFactory):
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def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> VastbaseVector:
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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.VASTBASE, collection_name))
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return VastbaseVector(
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collection_name=collection_name,
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config=VastbaseVectorConfig(
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host=dify_config.VASTBASE_HOST or "localhost",
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port=dify_config.VASTBASE_PORT,
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user=dify_config.VASTBASE_USER or "dify",
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password=dify_config.VASTBASE_PASSWORD or "",
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database=dify_config.VASTBASE_DATABASE or "dify",
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min_connection=dify_config.VASTBASE_MIN_CONNECTION,
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max_connection=dify_config.VASTBASE_MAX_CONNECTION,
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),
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)
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@ -0,0 +1,27 @@
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from core.rag.datasource.vdb.pyvastbase.vastbase_vector import VastbaseVector, VastbaseVectorConfig
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from tests.integration_tests.vdb.test_vector_store import (
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AbstractVectorTest,
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get_example_text,
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setup_mock_redis,
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)
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class VastbaseVectorTest(AbstractVectorTest):
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def __init__(self):
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super().__init__()
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self.vector = VastbaseVector(
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collection_name=self.collection_name,
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config=VastbaseVectorConfig(
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host="localhost",
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port=5434,
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user="dify",
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password="Difyai123456",
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database="dify",
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min_connection=1,
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max_connection=5,
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),
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)
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def test_vastbase_vector(setup_mock_redis):
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VastbaseVectorTest().run_all_tests()
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Loading…
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