DashVector
DashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.
This notebook shows how to use functionality related to the DashVector
vector database.
To use DashVector, you must have an API key. Here are the installation instructions.
Install
%pip install --upgrade --quiet dashvector dashscope
We want to use DashScopeEmbeddings
so we also have to get the Dashscope API Key.
import getpass
import os
os.environ["DASHVECTOR_API_KEY"] = getpass.getpass("DashVector API Key:")
os.environ["DASHSCOPE_API_KEY"] = getpass.getpass("DashScope API Key:")
Example
from langchain_community.embeddings.dashscope import DashScopeEmbeddings
from langchain_community.vectorstores import DashVector
from langchain_text_splitters import CharacterTextSplitter
API Reference:
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 = DashScopeEmbeddings()
API Reference:
We can create DashVector from documents.
dashvector = DashVector.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = dashvector.similarity_search(query)
print(docs)
We can add texts with meta datas and ids, and search with meta filter.
texts = ["foo", "bar", "baz"]
metadatas = [{"key": i} for i in range(len(texts))]
ids = ["0", "1", "2"]
dashvector.add_texts(texts, metadatas=metadatas, ids=ids)
docs = dashvector.similarity_search("foo", filter="key = 2")
print(docs)
[Document(page_content='baz', metadata={'key': 2})]
Operating band partition
parameters
The partition
parameter defaults to default, and if a non-existent partition
parameter is passed in, the partition
will be created automatically.
texts = ["foo", "bar", "baz"]
metadatas = [{"key": i} for i in range(len(texts))]
ids = ["0", "1", "2"]
partition = "langchain"
# add texts
dashvector.add_texts(texts, metadatas=metadatas, ids=ids, partition=partition)
# similarity search
query = "What did the president say about Ketanji Brown Jackson"
docs = dashvector.similarity_search(query, partition=partition)
# delete
dashvector.delete(ids=ids, partition=partition)