viking DB
viking DB is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
This notebook shows how to use functionality related to the VikingDB vector database.
To run, you should have a viking DB instance up and running.
!pip install --upgrade volcengine
We want to use VikingDBEmbeddings so we have to get the VikingDB API Key.
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.document_loaders import TextLoader
from langchain_community.vectorstores.vikingdb import VikingDB, VikingDBConfig
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = TextLoader("./test.txt")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = VikingDB.from_documents(
docs,
embeddings,
connection_args=VikingDBConfig(
host="host", region="region", ak="ak", sk="sk", scheme="http"
),
drop_old=True,
)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
docs[0].page_content
Compartmentalize the data with viking DB Collectionsβ
You can store different unrelated documents in different collections within same viking DB instance to maintain the context
Hereβs how you can create a new collection
db = VikingDB.from_documents(
docs,
embeddings,
connection_args=VikingDBConfig(
host="host", region="region", ak="ak", sk="sk", scheme="http"
),
collection_name="collection_1",
drop_old=True,
)
And here is how you retrieve that stored collection
db = VikingDB.from_documents(
embeddings,
connection_args=VikingDBConfig(
host="host", region="region", ak="ak", sk="sk", scheme="http"
),
collection_name="collection_1",
)
After retrieval you can go on querying it as usual.