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Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Dan O'Donovan <dan.odonovan@gmail.com> Co-authored-by: Tom Daniel Grande <tomdgrande@gmail.com> Co-authored-by: Grande <Tom.Daniel.Grande@statsbygg.no> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com> Co-authored-by: ZhangShenao <15201440436@163.com> Co-authored-by: Friso H. Kingma <fhkingma@gmail.com> Co-authored-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Nuno Campos <nuno@langchain.dev> Co-authored-by: Morgante Pell <morgantep@google.com>
langchain-couchbase
This package contains the LangChain integration with Couchbase
Installation
pip install -U langchain-couchbase
Usage
The CouchbaseVectorStore class exposes the connection to the Couchbase vector store.
from langchain_couchbase.vectorstores import CouchbaseVectorStore
from couchbase.cluster import Cluster
from couchbase.auth import PasswordAuthenticator
from couchbase.options import ClusterOptions
from datetime import timedelta
auth = PasswordAuthenticator(username, password)
options = ClusterOptions(auth)
connect_string = "couchbases://localhost"
cluster = Cluster(connect_string, options)
# Wait until the cluster is ready for use.
cluster.wait_until_ready(timedelta(seconds=5))
embeddings = OpenAIEmbeddings()
vectorstore = CouchbaseVectorStore(
cluster=cluster,
bucket_name="",
scope_name="",
collection_name="",
embedding=embeddings,
index_name="vector-search-index",
)