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<feat>: update vectorstores to new format:

Browse source 
- CassandraVectorStoreComponent;
  - CouchbaseVectorStoreComponent;
  - FaissVectorStoreComponent;
  - MongoVectorStoreComponent;
  - PGVectorStoreComponent;
  - PineconeVectorStoreComponent;
  - QdrantVectorStoreComponent;
  - SupabaseVectorStoreComponent;
  - UpstashVectorStoreComponent;
  - VectaraVectorStoreComponent;
  - WeaviateVectorStoreComponent;

Fixes:
  - set token of CassandraVectorStoreComponent as secret;
  - set couchbase_password of CouchbaseVectorStoreComponent
as secret;
  - set mongodb_atlas_cluster_uri of MongoVectorStoreCompon
ent as secret;
  - set pg_server_url of PGVectorStoreComponent as secret;
  - set pinecone_api_key of PineconeVectorStoreComponent as
secret;
  - set api_key of QdrantVectorStoreComponent as secret;
  - set supabase_service_key of SupabaseVectorStoreComponen
t as secret;
  - set index_token of UpstashVectorStoreComponent as secre
t;
  - set vectara_api_key of VectaraVectorStoreComponent as s
ecret;
  - set api_key of WeaviateVectorStoreComponent as secret;
This commit is contained in:
berrytern 2024-06-21 16:41:59 -03:00 • committed by Gabriel Luiz Freitas Almeida
commit ab78d0aec6
14 changed files with 204 additions and 330 deletions
Download patch file Download diff file Expand all files Collapse all files

14
src/backend/base/langflow/components/vectorstores/AstraDB.py
View file

@ -1,7 +1,16 @@
from loguru import logger
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput
from langflow.io import (
BoolInput,
DropdownInput,
HandleInput,
IntInput,
MultilineInput,
SecretStrInput,
StrInput,
DataInput,
)
from langflow.schema import Data
@ -29,10 +38,9 @@ class AstraVectorStoreComponent(LCVectorStoreComponent):
info="API endpoint URL for the Astra DB service.",
value="ASTRA_DB_API_ENDPOINT",
),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
HandleInput(

40
src/backend/base/langflow/components/vectorstores/Cassandra.py
View file

@ -1,15 +1,23 @@
from typing import List
from langchain_community.vectorstores import Cassandra
from langchain_core.retrievers import BaseRetriever
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
from langflow.io import (
BoolInput,
DropdownInput,
HandleInput,
IntInput,
SecretStrInput,
TextInput,
DataInput,
MultilineInput,
)
from langflow.schema import Data
class CassandraVectorStoreComponent(Component):
class CassandraVectorStoreComponent(LCVectorStoreComponent):
display_name = "Cassandra"
description = "Cassandra Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/cassandra"
@ -22,14 +30,14 @@ class CassandraVectorStoreComponent(Component):
info="Authentication token for accessing Cassandra on Astra DB.",
required=True,
),
StrInput(name="database_id", display_name="Database ID", info="The Astra database ID.", required=True),
StrInput(
TextInput(name="database_id", display_name="Database ID", info="The Astra database ID.", required=True),
TextInput(
name="table_name",
display_name="Table Name",
info="The name of the table where vectors will be stored.",
required=True,
),
StrInput(
TextInput(
name="keyspace",
display_name="Keyspace",
info="Optional key space within Astra DB. The keyspace should already be created.",
@ -48,7 +56,7 @@ class CassandraVectorStoreComponent(Component):
value=16,
advanced=True,
),
StrInput(
TextInput(
name="body_index_options",
display_name="Body Index Options",
info="Optional options used to create the body index.",
@ -63,10 +71,9 @@ class CassandraVectorStoreComponent(Component):
advanced=True,
),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
@ -74,7 +81,7 @@ class CassandraVectorStoreComponent(Component):
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -84,17 +91,6 @@ class CassandraVectorStoreComponent(Component):
),
]
outputs = [
Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Cassandra),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> Cassandra:
return self._build_cassandra()

7
src/backend/base/langflow/components/vectorstores/Chroma.py
View file

@ -7,7 +7,7 @@ from loguru import logger
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.base.vectorstores.utils import chroma_collection_to_data
from langflow.io import BoolInput, DataInput, DropdownInput, HandleInput, IntInput, StrInput, TextInput
from langflow.io import BoolInput, DataInput, DropdownInput, HandleInput, IntInput, StrInput, MultilineInput
from langflow.schema import Data
if TYPE_CHECKING:
@ -34,13 +34,14 @@ class ChromaVectorStoreComponent(LCVectorStoreComponent):
name="persist_directory",
display_name="Persist Directory",
),
TextInput(
MultilineInput(
name="search_query",
display_name="Search Query",
),
DataInput(
name="ingest_data",
display_name="Ingest Data",
is_list=True,
),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
StrInput(
@ -96,7 +97,7 @@ class ChromaVectorStoreComponent(LCVectorStoreComponent):
),
]
def build_vector_store(self) -> "Chroma":
def build_vector_store(self) -> Chroma:
"""
Builds the Chroma object.
"""

28
src/backend/base/langflow/components/vectorstores/Couchbase.py
View file

@ -2,15 +2,14 @@ from datetime import timedelta
from typing import List
from langchain_community.vectorstores import CouchbaseVectorStore
from langchain_core.retrievers import BaseRetriever
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
from langflow.schema import Data
class CouchbaseVectorStoreComponent(Component):
class CouchbaseVectorStoreComponent(LCVectorStoreComponent):
display_name = "Couchbase"
description = "Couchbase Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/couchbase"
@ -25,10 +24,9 @@ class CouchbaseVectorStoreComponent(Component):
StrInput(name="collection_name", display_name="Collection Name", required=True),
StrInput(name="index_name", display_name="Index Name", required=True),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
@ -36,7 +34,7 @@ class CouchbaseVectorStoreComponent(Component):
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -46,22 +44,6 @@ class CouchbaseVectorStoreComponent(Component):
),
]
outputs = [
Output(
display_name="Vector Store",
name="vector_store",
method="build_vector_store",
output_type=CouchbaseVectorStore,
),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> CouchbaseVectorStore:
return self._build_couchbase()

65
src/backend/base/langflow/components/vectorstores/FAISS.py
View file

@ -6,7 +6,7 @@ from loguru import logger
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.field_typing import Text
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
from langflow.io import BoolInput, HandleInput, IntInput, StrInput, DataInput, MultilineInput
from langflow.schema import Data
@ -32,10 +32,9 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
value="langflow_index",
),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
StrInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
@ -48,9 +47,9 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
display_name="Allow Dangerous Deserialization",
info="Set to True to allow loading pickle files from untrusted sources. Only enable this if you trust the source of the data.",
advanced=True,
value=False,
value=True,
),
StrInput(
MultilineInput(
name="search_input",
display_name="Search Input",
),
@ -63,24 +62,6 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
),
]
outputs = [
Output(
display_name="Vector Store",
name="vector_store",
method="build_vector_store",
),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
),
Output(
display_name="Search Results",
name="search_results",
method="search_documents",
),
]
def build_vector_store(self) -> FAISS:
"""
Builds the FAISS object.
@ -100,19 +81,12 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
faiss = FAISS.from_documents(documents=documents, embedding=self.embedding)
faiss.save_local(Text(path), self.index_name)
else:
try:
faiss = FAISS.load_local(
folder_path=Text(path),
embeddings=self.embedding,
index_name=self.index_name,
allow_dangerous_deserialization=self.allow_dangerous_deserialization,
)
except Exception as e:
raise ValueError(
"Failed to load the FAISS index. Make sure the index was created with trusted data. "
"If you trust the data source, you can set `allow_dangerous_deserialization` to `True` "
"in the component's advanced settings to enable deserialization."
) from e
faiss = FAISS.load_local(
folder_path=Text(path),
embeddings=self.embedding,
index_name=self.index_name,
allow_dangerous_deserialization=self.allow_dangerous_deserialization,
)
return faiss
@ -124,19 +98,12 @@ class FaissVectorStoreComponent(LCVectorStoreComponent):
raise ValueError("Folder path is required to load the FAISS index.")
path = self.resolve_path(self.folder_path)
try:
vector_store = FAISS.load_local(
folder_path=Text(path),
embeddings=self.embedding,
index_name=self.index_name,
allow_dangerous_deserialization=self.allow_dangerous_deserialization,
)
except Exception as e:
raise ValueError(
"Failed to load the FAISS index. Make sure the index was created with trusted data. "
"If you trust the data source, you can set `allow_dangerous_deserialization` to `True` "
"in the component's advanced settings to enable deserialization."
) from e
vector_store = FAISS.load_local(
folder_path=Text(path),
embeddings=self.embedding,
index_name=self.index_name,
allow_dangerous_deserialization=self.allow_dangerous_deserialization,
)
if not vector_store:
raise ValueError("Failed to load the FAISS index.")

58
src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py
View file

@ -1,30 +1,28 @@
from typing import List
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_core.retrievers import BaseRetriever
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
from langflow.schema import Data
class MongoVectorStoreComponent(Component):
class MongoVectorStoreComponent(LCVectorStoreComponent):
display_name = "MongoDB Atlas"
description = "MongoDB Atlas Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/mongodb_atlas"
icon = "MongoDB"
inputs = [
StrInput(name="mongodb_atlas_cluster_uri", display_name="MongoDB Atlas Cluster URI", required=True),
SecretStrInput(name="mongodb_atlas_cluster_uri", display_name="MongoDB Atlas Cluster URI", required=True),
StrInput(name="db_name", display_name="Database Name", required=True),
StrInput(name="collection_name", display_name="Collection Name", required=True),
StrInput(name="index_name", display_name="Index Name", required=True),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
@ -32,7 +30,7 @@ class MongoVectorStoreComponent(Component):
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -42,22 +40,6 @@ class MongoVectorStoreComponent(Component):
),
]
outputs = [
Output(
display_name="Vector Store",
name="vector_store",
method="build_vector_store",
output_type=MongoDBAtlasVectorSearch,
),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> MongoDBAtlasVectorSearch:
return self._build_mongodb_atlas()
@ -83,12 +65,7 @@ class MongoVectorStoreComponent(Component):
if documents:
vector_store = MongoDBAtlasVectorSearch.from_documents(
documents=documents,
embedding=self.embedding,
collection=collection,
db_name=self.db_name,
index_name=self.index_name,
mongodb_atlas_cluster_uri=self.mongodb_atlas_cluster_uri,
documents=documents, embedding=self.embedding, collection=collection, index_name=self.index_name
)
else:
vector_store = MongoDBAtlasVectorSearch(
@ -106,14 +83,31 @@ class MongoVectorStoreComponent(Component):
return vector_store
def search_documents(self) -> List[Data]:
from typing import Union
from bson import ObjectId
from langchain.schema import Document
import json
vector_store = self._build_mongodb_atlas()
if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
class JSONEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, Union[ObjectId, Document]):
return str(o)
return super(JSONEncoder, self).default(o)
if self.search_input and isinstance(self.search_input, str):
docs = vector_store.similarity_search(
query=self.search_input,
k=self.number_of_results,
)
for index in range(len(docs)):
docs[index].metadata = {
key: str(docs[index].metadata[key])
if isinstance(docs[index].metadata[key], ObjectId)
else docs[index].metadata[key]
for key in docs[index].metadata
}
data = docs_to_data(docs)
self.status = data
return data

33
src/backend/base/langflow/components/vectorstores/Pinecone.py
View file

@ -1,15 +1,23 @@
from typing import List
from langchain_core.retrievers import BaseRetriever
from langchain_pinecone import Pinecone
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
from langflow.io import (
BoolInput,
DropdownInput,
HandleInput,
IntInput,
StrInput,
SecretStrInput,
DataInput,
MultilineInput,
)
from langflow.schema import Data
class PineconeVectorStoreComponent(Component):
class PineconeVectorStoreComponent(LCVectorStoreComponent):
display_name = "Pinecone"
description = "Pinecone Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone"
@ -34,18 +42,18 @@ class PineconeVectorStoreComponent(Component):
value="text",
advanced=True,
),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
name="add_to_vector_store",
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
value=True,
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -55,17 +63,6 @@ class PineconeVectorStoreComponent(Component):
),
]
outputs = [
Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Pinecone),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> Pinecone:
return self._build_pinecone()

37
src/backend/base/langflow/components/vectorstores/Qdrant.py
View file

@ -1,15 +1,23 @@
from typing import List
from langchain_community.vectorstores import Qdrant
from langchain_core.retrievers import BaseRetriever
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
from langflow.io import (
BoolInput,
DropdownInput,
HandleInput,
IntInput,
StrInput,
SecretStrInput,
DataInput,
MultilineInput,
)
from langflow.schema import Data
class QdrantVectorStoreComponent(Component):
class QdrantVectorStoreComponent(LCVectorStoreComponent):
display_name = "Qdrant"
description = "Qdrant Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant"
@ -35,10 +43,9 @@ class QdrantVectorStoreComponent(Component):
StrInput(name="content_payload_key", display_name="Content Payload Key", value="page_content", advanced=True),
StrInput(name="metadata_payload_key", display_name="Metadata Payload Key", value="metadata", advanced=True),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
@ -46,7 +53,7 @@ class QdrantVectorStoreComponent(Component):
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -56,17 +63,6 @@ class QdrantVectorStoreComponent(Component):
),
]
outputs = [
Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Qdrant),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> Qdrant:
return self._build_qdrant()
@ -91,7 +87,6 @@ class QdrantVectorStoreComponent(Component):
# Remove None values from server_kwargs
server_kwargs = {k: v for k, v in server_kwargs.items() if v is not None}
if self.add_to_vector_store:
documents = []
for _input in self.vector_store_inputs or []:
@ -101,9 +96,7 @@ class QdrantVectorStoreComponent(Component):
documents.append(_input)
if documents:
qdrant = Qdrant.from_documents(
documents, embedding=self.embedding, client_kwargs=server_kwargs, **qdrant_kwargs
)
qdrant = Qdrant.from_documents(documents, embedding=self.embedding, **qdrant_kwargs)
else:
from qdrant_client import QdrantClient

121
src/backend/base/langflow/components/vectorstores/Redis.py
View file

@ -1,14 +1,15 @@
from typing import Optional, cast
from typing import List
from langchain_community.vectorstores.redis import Redis
from langchain_core.embeddings import Embeddings
from langflow.custom import CustomComponent
from langflow.field_typing import VectorStore
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import HandleInput, IntInput, StrInput, SecretStrInput, MultilineInput, DataInput
from langflow.schema import Data
from langchain.text_splitter import CharacterTextSplitter
class RedisVectorStoreComponent(CustomComponent):
class RedisVectorStoreComponent(LCVectorStoreComponent):
"""
A custom component for implementing a Vector Store using Redis.
"""
@ -17,67 +18,77 @@ class RedisVectorStoreComponent(CustomComponent):
description: str = "Implementation of Vector Store using Redis"
documentation = "https://python.langchain.com/docs/integrations/vectorstores/redis"
def build_config(self):
"""
Builds the configuration for the component.
inputs = [
SecretStrInput(name="redis_server_url", display_name="Redis Server Connection String", required=True),
StrInput(
name="redis_index_name",
display_name="Redis Index",
),
StrInput(name="code", display_name="Code", advanced=True),
StrInput(
name="schema",
display_name="Schema",
field_type=[".yaml"],
),
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
is_list=True,
),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
info="Number of results to return.",
value=4,
advanced=True,
),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
]
Returns:
- dict: A dictionary containing the configuration options for the component.
"""
return {
"index_name": {"display_name": "Index Name", "value": "your_index"},
"code": {"show": False, "display_name": "Code"},
"inputs": {"display_name": "Input", "input_types": ["Document", "Data"]},
"embedding": {"display_name": "Embedding"},
"schema": {"display_name": "Schema", "file_types": [".yaml"]},
"redis_server_url": {
"display_name": "Redis Server Connection String",
"advanced": False,
},
"redis_index_name": {"display_name": "Redis Index", "advanced": False},
}
def build(
self,
embedding: Embeddings,
redis_server_url: str,
redis_index_name: str,
schema: Optional[str] = None,
inputs: Optional[Data] = None,
) -> VectorStore:
"""
Builds the Vector Store or BaseRetriever object.
Args:
- embedding (Embeddings): The embeddings to use for the Vector Store.
- documents (Optional[Document]): The documents to use for the Vector Store.
- redis_index_name (str): The name of the Redis index.
- redis_server_url (str): The URL for the Redis server.
Returns:
- VectorStore: The Vector Store object.
"""
def build_vector_store(self) -> Redis:
documents = []
for _input in inputs or []:
for _input in self.vector_store_inputs or []:
if isinstance(_input, Data):
documents.append(_input.to_lc_document())
else:
documents.append(_input)
with open("docuemnts.txt", "w") as f:
f.write(str(documents))
if not documents:
if schema is None:
if self.schema is None:
raise ValueError("If no documents are provided, a schema must be provided.")
redis_vs = Redis.from_existing_index(
embedding=embedding,
index_name=redis_index_name,
schema=schema,
embedding=self.embedding,
index_name=self.redis_index_name,
schema=self.schema,
key_prefix=None,
redis_url=redis_server_url,
redis_url=self.redis_server_url,
)
else:
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
redis_vs = Redis.from_documents(
documents=documents, # type: ignore
embedding=embedding,
redis_url=redis_server_url,
index_name=redis_index_name,
documents=docs,
embedding=self.embedding,
redis_url=self.redis_server_url,
index_name=self.redis_index_name,
)
return cast(VectorStore, redis_vs)
return redis_vs
def search_documents(self) -> List[Data]:
vector_store = self._build_redis()
if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
docs = vector_store.similarity_search(
query=self.search_input,
k=self.number_of_results,
)
data = docs_to_data(docs)
self.status = data
return data
else:
return []

30
src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py
View file

@ -1,16 +1,15 @@
from typing import List
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.retrievers import BaseRetriever
from supabase.client import Client, create_client
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import HandleInput, IntInput, Output, StrInput
from langflow.io import HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
from langflow.schema import Data
class SupabaseVectorStoreComponent(Component):
class SupabaseVectorStoreComponent(LCVectorStoreComponent):
display_name = "Supabase"
description = "Supabase Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase"
@ -18,17 +17,16 @@ class SupabaseVectorStoreComponent(Component):
inputs = [
StrInput(name="supabase_url", display_name="Supabase URL", required=True),
StrInput(name="supabase_service_key", display_name="Supabase Service Key", required=True),
SecretStrInput(name="supabase_service_key", display_name="Supabase Service Key", required=True),
StrInput(name="table_name", display_name="Table Name", advanced=True),
StrInput(name="query_name", display_name="Query Name"),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -38,22 +36,6 @@ class SupabaseVectorStoreComponent(Component):
),
]
outputs = [
Output(
display_name="Vector Store",
name="vector_store",
method="build_vector_store",
output_type=SupabaseVectorStore,
),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> SupabaseVectorStore:
return self._build_supabase()

30
src/backend/base/langflow/components/vectorstores/Upstash.py
View file

@ -1,15 +1,14 @@
from typing import List
from langchain_community.vectorstores import UpstashVectorStore
from langchain_core.retrievers import BaseRetriever
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
from langflow.schema import Data
class UpstashVectorStoreComponent(Component):
class UpstashVectorStoreComponent(LCVectorStoreComponent):
display_name = "Upstash"
description = "Upstash Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/upstash"
@ -17,7 +16,7 @@ class UpstashVectorStoreComponent(Component):
inputs = [
StrInput(name="index_url", display_name="Index URL", info="The URL of the Upstash index.", required=True),
StrInput(
SecretStrInput(
name="index_token", display_name="Index Token", info="The token for the Upstash index.", required=True
),
StrInput(
@ -33,10 +32,9 @@ class UpstashVectorStoreComponent(Component):
input_types=["Embeddings"],
info="To use Upstash's embeddings, don't provide an embedding.",
),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
@ -44,7 +42,7 @@ class UpstashVectorStoreComponent(Component):
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -54,22 +52,6 @@ class UpstashVectorStoreComponent(Component):
),
]
outputs = [
Output(
display_name="Vector Store",
name="vector_store",
method="build_vector_store",
output_type=UpstashVectorStore,
),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> UpstashVectorStore:
return self._build_upstash()

23
src/backend/base/langflow/components/vectorstores/Vectara.py
View file

@ -2,15 +2,14 @@ from typing import List
from langchain_community.embeddings import FakeEmbeddings
from langchain_community.vectorstores import Vectara
from langchain_core.retrievers import BaseRetriever
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
from langflow.io import BoolInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
from langflow.schema import Data
class VectaraVectorStoreComponent(Component):
class VectaraVectorStoreComponent(LCVectorStoreComponent):
display_name = "Vectara"
description = "Vectara Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/vectara"
@ -20,10 +19,9 @@ class VectaraVectorStoreComponent(Component):
StrInput(name="vectara_customer_id", display_name="Vectara Customer ID", required=True),
StrInput(name="vectara_corpus_id", display_name="Vectara Corpus ID", required=True),
SecretStrInput(name="vectara_api_key", display_name="Vectara API Key", required=True),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
@ -31,7 +29,7 @@ class VectaraVectorStoreComponent(Component):
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -41,17 +39,6 @@ class VectaraVectorStoreComponent(Component):
),
]
outputs = [
Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Vectara),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> Vectara:
return self._build_vectara()

23
src/backend/base/langflow/components/vectorstores/Weaviate.py
View file

@ -2,15 +2,14 @@ from typing import List
import weaviate # type: ignore
from langchain_community.vectorstores import Weaviate
from langchain_core.retrievers import BaseRetriever
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
from langflow.schema import Data
class WeaviateVectorStoreComponent(Component):
class WeaviateVectorStoreComponent(LCVectorStoreComponent):
display_name = "Weaviate"
description = "Weaviate Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/weaviate"
@ -22,10 +21,9 @@ class WeaviateVectorStoreComponent(Component):
StrInput(name="index_name", display_name="Index Name", required=True),
StrInput(name="text_key", display_name="Text Key", value="text", advanced=True),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
@ -33,7 +31,7 @@ class WeaviateVectorStoreComponent(Component):
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -44,17 +42,6 @@ class WeaviateVectorStoreComponent(Component):
BoolInput(name="search_by_text", display_name="Search By Text", advanced=True),
]
outputs = [
Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Weaviate),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> Weaviate:
return self._build_weaviate()

25
src/backend/base/langflow/components/vectorstores/pgvector.py
View file

@ -1,28 +1,26 @@
from typing import List
from langchain.schema import BaseRetriever
from langchain_community.vectorstores import PGVector
from langflow.custom import Component
from langflow.base.vectorstores.model import LCVectorStoreComponent
from langflow.helpers.data import docs_to_data
from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
from langflow.io import BoolInput, HandleInput, IntInput, StrInput, SecretStrInput, DataInput, MultilineInput
from langflow.schema import Data
class PGVectorStoreComponent(Component):
class PGVectorStoreComponent(LCVectorStoreComponent):
display_name = "PGVector"
description = "PGVector Vector Store with search capabilities"
documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pgvector"
icon = "PGVector"
inputs = [
StrInput(name="pg_server_url", display_name="PostgreSQL Server Connection String", required=True),
SecretStrInput(name="pg_server_url", display_name="PostgreSQL Server Connection String", required=True),
StrInput(name="collection_name", display_name="Table", required=True),
HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
HandleInput(
DataInput(
name="vector_store_inputs",
display_name="Vector Store Inputs",
input_types=["Document", "Data"],
is_list=True,
),
BoolInput(
@ -30,7 +28,7 @@ class PGVectorStoreComponent(Component):
display_name="Add to Vector Store",
info="If true, the Vector Store Inputs will be added to the Vector Store.",
),
StrInput(name="search_input", display_name="Search Input"),
MultilineInput(name="search_input", display_name="Search Input"),
IntInput(
name="number_of_results",
display_name="Number of Results",
@ -40,17 +38,6 @@ class PGVectorStoreComponent(Component):
),
]
outputs = [
Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=PGVector),
Output(
display_name="Base Retriever",
name="base_retriever",
method="build_base_retriever",
output_type=BaseRetriever,
),
Output(display_name="Search Results", name="search_results", method="search_documents"),
]
def build_vector_store(self) -> PGVector:
return self._build_pgvector()