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langchain/libs/partners/couchbase
Nithish Raghunandanan f2f0e0e13d
couchbase: Add the initial version of Couchbase partner package (#22087)
Co-authored-by: Nithish Raghunandanan <nithishr@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
3 months ago
..
langchain_couchbase couchbase: Add the initial version of Couchbase partner package (#22087) 3 months ago
scripts couchbase: Add the initial version of Couchbase partner package (#22087) 3 months ago
tests couchbase: Add the initial version of Couchbase partner package (#22087) 3 months ago
.gitignore couchbase: Add the initial version of Couchbase partner package (#22087) 3 months ago
LICENSE couchbase: Add the initial version of Couchbase partner package (#22087) 3 months ago
Makefile couchbase: Add the initial version of Couchbase partner package (#22087) 3 months ago
README.md couchbase: Add the initial version of Couchbase partner package (#22087) 3 months ago
poetry.lock couchbase: Add the initial version of Couchbase partner package (#22087) 3 months ago
pyproject.toml couchbase: Add the initial version of Couchbase partner package (#22087) 3 months ago

README.md

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",
)