feat: add pgvector client

This commit is contained in:
Lanznx 2023-11-10 10:38:50 +08:00
commit f567a33883
3 changed files with 118 additions and 3 deletions

54
poetry.lock generated
View file

@ -1,4 +1,4 @@
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# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
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@ -7105,7 +7153,7 @@ files = [
]
[package.dependencies]
greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"AMD64\" or platform_machine == \"amd64\" or platform_machine == \"x86_64\" or platform_machine == \"ppc64le\" or platform_machine == \"aarch64\")"}
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[package.extras]
aiomysql = ["aiomysql", "greenlet (!=0.4.17)"]
@ -8715,4 +8763,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.11"
content-hash = "1ffee4719d2894fa3a888bfb36365e32de1f8553fd01003afd4e29c1d198d20c"
content-hash = "6ec484120db83c0829be5364af003e79837e3a04d4e819c197ccab1bbe555f82"

View file

@ -93,6 +93,7 @@ markupsafe = "^2.1.3"
boto3 = "^1.28.63"
numexpr = "^2.8.6"
qianfan = "0.0.5"
pgvector = "^0.2.3"
[tool.poetry.group.dev.dependencies]
types-redis = "^4.6.0.5"

View file

@ -0,0 +1,66 @@
from typing import Optional
from langflow import CustomComponent
from langchain.vectorstores.pgvector import PGVector
from langchain.schema import Document
from langchain.vectorstores.base import VectorStore
from langchain.embeddings.base import Embeddings
class PostgresqlVectorComponent(CustomComponent):
"""
A custom component for implementing a Vector Store using PostgreSQL.
"""
display_name: str = "PGVector"
description: str = "Implementation of Vector Store using PostgreSQL"
documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
beta = True
def build_config(self):
"""
Builds the configuration for the component.
Returns:
- dict: A dictionary containing the configuration options for the component.
"""
return {
"index_name": {"display_name": "Index Name", "value": "your_index"},
"code": {"show": True, "display_name": "Code"},
"documents": {"display_name": "Documents", "is_list": True},
"embedding": {"display_name": "Embedding"},
"pg_server_url": {
"display_name": "PostgreSQL Server Connection String",
"advanced": False,
},
"collection_name": {"display_name": "Table", "advanced": False},
}
def build(
self,
embedding: Embeddings,
pg_server_url: str,
collection_name: str,
documents: Optional[Document] = None,
) -> VectorStore:
"""
Builds the Vector Store or BaseRetriever object.
Args:
- embedding (Embeddings): The embeddings to use for the Vector Store.
- documents (Optional[Document]): The documents to use for the Vector Store.
- collection_name (str): The name of the PG table.
- pg_server_url (str): The URL for the PG server.
Returns:
- VectorStore: The Vector Store object.
"""
return PGVector.from_documents(
embedding=embedding,
documents=documents,
collection_name=collection_name,
connection_string=pg_server_url,
)