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Baidu Cloud ElasticSearch VectorSearch

Baidu Cloud VectorSearch is a fully managed, enterprise-level distributed search and analysis service which is 100% compatible to open source. Baidu Cloud VectorSearch provides low-cost, high-performance, and reliable retrieval and analysis platform level product services for structured/unstructured data. As a vector database , it supports multiple index types and similarity distance methods.

Baidu Cloud ElasticSearch provides a privilege management mechanism, for you to configure the cluster privileges freely, so as to further ensure data security.

This notebook shows how to use functionality related to the Baidu Cloud ElasticSearch VectorStore. To run, you should have an Baidu Cloud ElasticSearch instance up and running:

Read the help document to quickly familiarize and configure Baidu Cloud ElasticSearch instance.

After the instance is up and running, follow these steps to split documents, get embeddings, connect to the baidu cloud elasticsearch instance, index documents, and perform vector retrieval.

We need to install the following Python packages first.

%pip install --upgrade --quiet langchain-community elasticsearch == 7.11.0

First, we want to use QianfanEmbeddings so we have to get the Qianfan AK and SK. Details for QianFan is related to Baidu Qianfan Workshop

import getpass
import os

os.environ["QIANFAN_AK"] = getpass.getpass("Your Qianfan AK:")
os.environ["QIANFAN_SK"] = getpass.getpass("Your Qianfan SK:")

Secondly, split documents and get embeddings.

from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter

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

from langchain_community.embeddings import QianfanEmbeddingsEndpoint

embeddings = QianfanEmbeddingsEndpoint()

Then, create a Baidu ElasticeSearch accessable instance.

# Create a bes instance and index docs.
from langchain_community.vectorstores import BESVectorStore

bes = BESVectorStore.from_documents(
bes_url="your bes cluster url",
index_name="your vector index",
bes.client.indices.refresh(index="your vector index")
API Reference:BESVectorStore

Finally, Query and retrive data

query = "What did the president say about Ketanji Brown Jackson"
docs = bes.similarity_search(query)

Please feel free to contact or if you encounter any problems during use, and we will do our best to support you.

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