dep: bump pgvecto-rs client from 0.1.x to 0.2.x (#6891)

api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py

 from uuid import UUID, uuid4
 
 from numpy import ndarray
-from pgvecto_rs.sqlalchemy import Vector
+from pgvecto_rs.sqlalchemy import VECTOR
 from pydantic import BaseModel, model_validator
 from sqlalchemy import Float, String, create_engine, insert, select, text
 from sqlalchemy import text as sql_text
             )
             text: Mapped[str] = mapped_column(String)
             meta: Mapped[dict] = mapped_column(postgresql.JSONB)
-            vector: Mapped[ndarray] = mapped_column(Vector(dim))
+            vector: Mapped[ndarray] = mapped_column(VECTOR(dim))
 
         self._table = _Table
         self._distance_op = "<=>"

api/core/rag/datasource/vdb/relyt/relyt_vector.py

             redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
-        from pgvecto_rs.sqlalchemy import Vector
+        from pgvecto_rs.sqlalchemy import VECTOR
 
         ids = [str(uuid.uuid1()) for _ in documents]
         metadatas = [d.metadata for d in documents]
             self._collection_name,
             Base.metadata,
             Column("id", TEXT, primary_key=True),
-            Column("embedding", Vector(len(embeddings[0]))),
+            Column("embedding", VECTOR(len(embeddings[0]))),
             Column("document", String, nullable=True),
             Column("metadata", JSON, nullable=True),
             extend_existing=True,
         Args:
             ids: List of ids to delete.
         """
-        from pgvecto_rs.sqlalchemy import Vector
+        from pgvecto_rs.sqlalchemy import VECTOR
 
         if ids is None:
             raise ValueError("No ids provided to delete.")
             self._collection_name,
             Base.metadata,
             Column("id", TEXT, primary_key=True),
-            Column("embedding", Vector(self.embedding_dimension)),
+            Column("embedding", VECTOR(self.embedding_dimension)),
             Column("document", String, nullable=True),
             Column("metadata", JSON, nullable=True),
             extend_existing=True,

api/poetry.lock

 
 [[package]]
 name = "pgvecto-rs"
-version = "0.1.4"
+version = "0.2.1"
 description = "Python binding for pgvecto.rs"
 optional = false
-python-versions = ">=3.8"
+python-versions = "<3.13,>=3.8"
 files = [
-    {file = "pgvecto_rs-0.1.4-py3-none-any.whl", hash = "sha256:9b08a9e612f0cd65d1cc6e17a35b9bb5956187e0e3981bf6e997ff9e615c6116"},
-    {file = "pgvecto_rs-0.1.4.tar.gz", hash = "sha256:078b96cff1f3d417169ad46cacef7fc4d644978bbd6725a5c24c0675de5030ab"},
+    {file = "pgvecto_rs-0.2.1-py3-none-any.whl", hash = "sha256:b3ee2c465219469ad537b3efea2916477c6c576b3d6fd4298980d0733d12bb27"},
+    {file = "pgvecto_rs-0.2.1.tar.gz", hash = "sha256:07046eaad2c4f75745f76de9ba483541909f1c595aced8d3434224a4f933daca"},
 ]
 
 [package.dependencies]
 numpy = ">=1.23"
+SQLAlchemy = {version = ">=2.0.23", optional = true, markers = "extra == \"sqlalchemy\""}
 toml = ">=0.10"
 
 [package.extras]
+django = ["Django (>=4.2)"]
 psycopg3 = ["psycopg[binary] (>=3.1.12)"]
 sdk = ["openai (>=1.2.2)", "pgvecto_rs[sqlalchemy]"]
-sqlalchemy = ["SQLAlchemy (>=2.0.23)", "pgvecto_rs[psycopg3]"]
+sqlalchemy = ["SQLAlchemy (>=2.0.23)"]
 
 [[package]]
 name = "pgvector"
 [package.dependencies]
 annotated-types = ">=0.4.0"
 pydantic-core = "2.20.1"
-typing-extensions = [
-    {version = ">=4.6.1", markers = "python_version < \"3.13\""},
-    {version = ">=4.12.2", markers = "python_version >= \"3.13\""},
-]
+typing-extensions = {version = ">=4.6.1", markers = "python_version < \"3.13\""}
 
 [package.extras]
 email = ["email-validator (>=2.0.0)"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.10"
-content-hash = "ca55e4a4bb354fe969cc73c823557525c7598b0375e8791fcd77febc59e03b96"
+python-versions = ">=3.10,<3.13"
+content-hash = "50acbb78f2a273dfa8683d9d292596e89d13a420c6ecb1afad331f2c38dd1423"

api/pyproject.toml

 pydub = "~0.25.1"
 pyjwt = "~2.8.0"
 pypdfium2 = "~4.17.0"
-python = "^3.10"
+python = ">=3.10,<3.13"
 python-docx = "~1.1.0"
 python-dotenv = "1.0.0"
 pyyaml = "~6.0.1"
 [tool.poetry.group.vdb.dependencies]
 chromadb = "0.5.1"
 oracledb = "~2.2.1"
-pgvecto-rs = "0.1.4"
+pgvecto-rs = { version = "~0.2.1", extras = ['sqlalchemy'] }
 pgvector = "0.2.5"
 pymilvus = "~2.4.4"
 pymysql = "1.1.1"

docker/docker-compose.yaml

 
   # pgvecto-rs vector store
   pgvecto-rs:
-    image: tensorchord/pgvecto-rs:pg16-v0.2.0
+    image: tensorchord/pgvecto-rs:pg16-v0.3.0
     profiles:
       - pgvecto-rs
     restart: always