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 langchain-community 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
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:TextLoader
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)