jyapayne/langflow
1
0
Fork 0
Code Issues Pull requests Projects Releases 1 Packages Wiki Activity Actions

Add support for Couchbase vector store (#1901)

Browse source 
* add couchbase vector store support

* add docs + minor changes

* Fix lint issues

* remove stray lines

* Add required validation and minor changes

* Address Comments

---------

Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@langflow.org>
This commit is contained in:
Prajwal Pai 2024-05-23 18:36:21 +05:30 • committed by GitHub
commit 19680bb137
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
11 changed files with 281 additions and 4 deletions
Download patch file Download diff file Expand all files Collapse all files

73
src/backend/base/langflow/components/vectorsearch/CouchbaseSearch.py Normal file
View file

@ -0,0 +1,73 @@
from typing import List, Optional
from langflow.components.vectorstores.base.model import LCVectorStoreComponent
from langflow.components.vectorstores.Couchbase import CouchbaseComponent
from langflow.field_typing import Embeddings, NestedDict, Text
from langflow.schema import Record
class CouchbaseSearchComponent(LCVectorStoreComponent):
display_name = "Couchbase Search"
description = "Search a Couchbase Vector Store for similar documents."
documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
icon = "Couchbase"
field_order = [
"couchbase_connection_string",
"couchbase_username",
"couchbase_password",
"bucket_name",
"scope_name",
"collection_name",
"index_name",
]
def build_config(self):
return {
"input_value": {"display_name": "Input"},
"embedding": {"display_name": "Embedding"},
"couchbase_connection_string": {"display_name": "Couchbase Cluster connection string","required": True},
"couchbase_username": {"display_name": "Couchbase username","required": True},
"couchbase_password": {
"display_name": "Couchbase password",
"password": True,
"required": True
},
"bucket_name": {"display_name": "Bucket Name","required": True},
"scope_name": {"display_name": "Scope Name","required": True},
"collection_name": {"display_name": "Collection Name","required": True},
"index_name": {"display_name": "Index Name","required": True},
"number_of_results": {
"display_name": "Number of Results",
"info": "Number of results to return.",
"advanced": True,
},
}
def build( # type: ignore[override]
self,
input_value: Text,
embedding: Embeddings,
number_of_results: int = 4,
bucket_name: str = "",
scope_name: str = "",
collection_name: str = "",
index_name: str = "",
couchbase_connection_string: str = "",
couchbase_username: str = "",
couchbase_password: str = "",
) -> List[Record]:
vector_store = CouchbaseComponent().build(
couchbase_connection_string=couchbase_connection_string,
couchbase_username=couchbase_username,
couchbase_password=couchbase_password,
bucket_name=bucket_name,
scope_name=scope_name,
collection_name=collection_name,
embedding=embedding,
index_name=index_name,
)
if not vector_store:
raise ValueError("Failed to create Couchbase Vector Store")
return self.search_with_vector_store(
vector_store=vector_store, input_value=input_value, search_type="similarity", k=number_of_results
)

2
src/backend/base/langflow/components/vectorsearch/__init__.py
View file

@ -9,10 +9,12 @@ from .SupabaseVectorStoreSearch import SupabaseSearchComponent
from .VectaraSearch import VectaraSearchComponent
from .WeaviateSearch import WeaviateSearchVectorStore
from .pgvectorSearch import PGVectorSearchComponent
from .Couchbase import CouchbaseSearchComponent # type: ignore
__all__ = [
"AstraDBSearchComponent",
"ChromaSearchComponent",
"CouchbaseSearchComponent",
"FAISSSearchComponent",
"MongoDBAtlasSearchComponent",
"PineconeSearchComponent",

95
src/backend/base/langflow/components/vectorstores/Couchbase.py Normal file
View file

@ -0,0 +1,95 @@
from typing import List, Optional, Union
from langchain.schema import BaseRetriever
from langchain_community.vectorstores import CouchbaseVectorStore
from langflow.custom import CustomComponent
from langflow.field_typing import Embeddings, VectorStore
from langflow.schema import Record
from datetime import timedelta
from couchbase.auth import PasswordAuthenticator # type: ignore
from couchbase.cluster import Cluster # type: ignore
from couchbase.options import ClusterOptions # type: ignore
class CouchbaseComponent(CustomComponent):
display_name = "Couchbase"
description = "Construct a `Couchbase Vector Search` vector store from raw documents."
documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
icon = "Couchbase"
field_order = [
"couchbase_connection_string",
"couchbase_username",
"couchbase_password",
"bucket_name",
"scope_name",
"collection_name",
"index_name",
]
def build_config(self):
return {
"inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
"embedding": {"display_name": "Embedding"},
"couchbase_connection_string": {"display_name": "Couchbase Cluster connection string","required": True},
"couchbase_username": {"display_name": "Couchbase username","required": True},
"couchbase_password": {
"display_name": "Couchbase password",
"password": True,
"required": True
},
"bucket_name": {"display_name": "Bucket Name","required": True},
"scope_name": {"display_name": "Scope Name","required": True},
"collection_name": {"display_name": "Collection Name","required": True},
"index_name": {"display_name": "Index Name","required": True},
}
def build(
self,
embedding: Embeddings,
inputs: Optional[List[Record]] = None,
bucket_name: str = "",
scope_name: str = "",
collection_name: str = "",
index_name: str = "",
couchbase_connection_string: str = "",
couchbase_username: str = "",
couchbase_password: str = "",
) -> Union[VectorStore, BaseRetriever]:
try:
auth = PasswordAuthenticator(couchbase_username, couchbase_password)
options = ClusterOptions(auth)
cluster = Cluster(couchbase_connection_string, options)
cluster.wait_until_ready(timedelta(seconds=5))
except Exception as e:
raise ValueError(f"Failed to connect to Couchbase: {e}")
documents = []
for _input in inputs or []:
if isinstance(_input, Record):
documents.append(_input.to_lc_document())
else:
documents.append(_input)
if documents:
vector_store = CouchbaseVectorStore.from_documents(
documents=documents,
cluster=cluster,
bucket_name=bucket_name,
scope_name=scope_name,
collection_name=collection_name,
embedding=embedding,
index_name=index_name,
)
else:
vector_store = CouchbaseVectorStore(
cluster=cluster,
bucket_name=bucket_name,
scope_name=scope_name,
collection_name=collection_name,
embedding=embedding,
index_name=index_name,
)
return vector_store

2
src/backend/base/langflow/components/vectorstores/__init__.py
View file

@ -9,10 +9,12 @@ from .SupabaseVectorStore import SupabaseComponent
from .Vectara import VectaraComponent
from .Weaviate import WeaviateVectorStoreComponent
from .pgvector import PGVectorComponent
from .Couchbase import CouchbaseComponent
__all__ = [
"AstraDBVectorStoreComponent",
"ChromaComponent",
"CouchbaseComponent",
"FAISSComponent",
"MongoDBAtlasComponent",
"PineconeComponent",

17
src/frontend/src/icons/Couchbase/Couchbase.jsx Normal file
View file

@ -0,0 +1,17 @@
const SvgCouchbaseIcon = (props) => (
<svg
xmlns="http://www.w3.org/2000/svg"
width="1em"
height="1em"
preserveAspectRatio="xMidYMid"
viewBox="0 0 256 256"
{...props}
>
<path
fill="#ED2226"
d="M128 0C57.426 0 0 57.233 0 128c0 70.574 57.233 128 128 128 70.574 0 128-57.233 128-128S198.574 0 128 0zm86.429 150.429c0 7.734-4.447 14.502-13.148 16.048-15.082 2.707-46.792 4.254-73.281 4.254-26.49 0-58.2-1.547-73.281-4.254-8.7-1.546-13.148-8.314-13.148-16.048v-49.885c0-7.734 5.994-14.888 13.148-16.049 4.447-.773 14.888-1.546 23.01-1.546 3.093 0 5.606 2.32 5.606 5.994v34.997l44.858-.967 44.858.967V88.943c0-3.674 2.514-5.994 5.608-5.994 8.12 0 18.562.773 23.009 1.546 7.347 1.16 13.148 8.315 13.148 16.049-.387 16.435-.387 33.257-.387 49.885z"
/>
</svg>
);
export default SvgCouchbaseIcon;

1
src/frontend/src/icons/Couchbase/couchbase.svg Normal file
View file

@ -0,0 +1 @@
<svg xmlns="http://www.w3.org/2000/svg" width="2500" height="2500" preserveAspectRatio="xMidYMid" viewBox="0 0 256 256" id="couchbase"><path fill="#ED2226" d="M128 0C57.426 0 0 57.233 0 128c0 70.574 57.233 128 128 128 70.574 0 128-57.233 128-128S198.574 0 128 0zm86.429 150.429c0 7.734-4.447 14.502-13.148 16.048-15.082 2.707-46.792 4.254-73.281 4.254-26.49 0-58.2-1.547-73.281-4.254-8.7-1.546-13.148-8.314-13.148-16.048v-49.885c0-7.734 5.994-14.888 13.148-16.049 4.447-.773 14.888-1.546 23.01-1.546 3.093 0 5.606 2.32 5.606 5.994v34.997l44.858-.967 44.858.967V88.943c0-3.674 2.514-5.994 5.608-5.994 8.12 0 18.562.773 23.009 1.546 7.347 1.16 13.148 8.315 13.148 16.049-.387 16.435-.387 33.257-.387 49.885z"></path></svg>
Side by side

After

Width:  |  Height:  |  Size: 720 B

9
src/frontend/src/icons/Couchbase/index.tsx Normal file
View file

@ -0,0 +1,9 @@
import React, { forwardRef } from "react";
import SvgCouchbaseIcon from "./Couchbase";
export const CouchbaseIcon = forwardRef<
SVGSVGElement,
React.PropsWithChildren<{}>
>((props, ref) => {
return <SvgCouchbaseIcon ref={ref} {...props} />;
});

2
src/frontend/src/utils/styleUtils.ts
View file

@ -153,6 +153,7 @@ import { AzureIcon } from "../icons/Azure";
import { BingIcon } from "../icons/Bing";
import { BotMessageSquareIcon } from "../icons/BotMessageSquare";
import { ChromaIcon } from "../icons/ChromaIcon";
import { CouchbaseIcon } from "../icons/Couchbase";
import { CohereIcon } from "../icons/Cohere";
import { ElasticsearchIcon } from "../icons/ElasticsearchStore";
import { EvernoteIcon } from "../icons/Evernote";
@ -324,6 +325,7 @@ export const nodeIconsLucide: iconsType = {
Vectara: VectaraIcon,
ArrowUpToLine: ArrowUpToLine,
Chroma: ChromaIcon,
Couchbase: CouchbaseIcon,
AirbyteJSONLoader: AirbyteIcon,
AmazonBedrockEmbeddings: AWSIcon,
Amazon: AWSIcon,