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Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. Hologres supports standard SQL syntax, is compatible with PostgreSQL, and supports most PostgreSQL functions. Hologres supports online analytical processing (OLAP) and ad hoc analysis for up to petabytes of data, and provides high-concurrency and low-latency online data services.
Hologres provides vector database functionality by adopting VSAG (from version r4.0.0) or Proxima (for versions earlier than r4.0.0). VSAG is an open-source vector indexing library developed by Ant Group for similarity search. Compared to Proxima, VSAG offers lower memory overhead, simplified configuration, improved query stability under concurrent writes, and the ability to handle ultra-large-scale vector datasets. Proxima is a high-performance software library developed by Alibaba DAMO Academy used in Hologres versions earlier than r4.0.0. It allows you to search for the nearest neighbors of vectors with high throughput and low latency.
This notebook shows how to use functionality related to the Hologres vector database. Deploy a Hologres cloud instance on Alibaba Cloud to get started. 
pip install -qU langchain_community
Install the Hologres SDK that matches your instance version:
  • If Hologres instance version is lower than r4.0.0, install hologres-vector.
  • If Hologres instance version is r4.0.0 or higher, install holo-search-sdk.
pip install hologres-vector
# or
pip install holo-search-sdk

from langchain_community.vectorstores import Hologres
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
Split documents and get embeddings by call OpenAI API 
from langchain_community.document_loaders import TextLoader

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
Connect to Hologres by setting related ENVIRONMENTS. 
export PG_HOST={host}
export PG_PORT={port} # Optional, default is 80
export PG_DATABASE={db_name} # Optional, default is postgres
export PG_USER={username}
export PG_PASSWORD={password}
Then store your embeddings and documents into Hologres 
import os

connection_string = Hologres.connection_string_from_db_params(
    host=os.environ.get("PGHOST", "localhost"),
    port=int(os.environ.get("PGPORT", "80")),
    database=os.environ.get("PGDATABASE", "postgres"),
    user=os.environ.get("PGUSER", "postgres"),
    password=os.environ.get("PGPASSWORD", "postgres"),
)

vector_db = Hologres.from_documents(
    docs,
    embeddings,
    connection_string=connection_string,
    table_name="langchain_example_embeddings",
)
Query and retrieve data 
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_db.similarity_search(query)

print(docs[0].page_content)

Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
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