chore: Remove deprecated vector search components
This commit is contained in:
Gabriel Luiz Freitas Almeida
parent
0f615e0b32
commit
0999ce5ae5
15 changed files with 0 additions and 1172 deletions
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@ -1,148 +0,0 @@
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from typing import List, Optional
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from langflow.base.vectorstores.model import LCVectorStoreComponent
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from langflow.components.vectorstores.AstraDB import AstraVectorStoreComponent
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from langflow.field_typing import Embeddings, Text
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from langflow.schema import Data
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class AstraDBSearchComponent(LCVectorStoreComponent):
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display_name = "Astra DB Search"
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description = "Searches an existing Astra DB Vector Store."
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icon = "AstraDB"
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field_order = ["token", "api_endpoint", "collection_name", "input_value", "embedding"]
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def build_config(self):
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return {
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"search_type": {
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"display_name": "Search Type",
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"options": ["Similarity", "MMR"],
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},
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"input_value": {
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"display_name": "Input Value",
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"info": "Input value to search",
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},
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"embedding": {"display_name": "Embedding", "info": "Embedding to use"},
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"collection_name": {
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"display_name": "Collection Name",
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"info": "The name of the collection within Astra DB where the vectors will be stored.",
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},
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"token": {
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"display_name": "Astra DB Application Token",
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"info": "Authentication token for accessing Astra DB.",
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"password": True,
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},
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"api_endpoint": {
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"display_name": "API Endpoint",
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"info": "API endpoint URL for the Astra DB service.",
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},
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"namespace": {
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"display_name": "Namespace",
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"info": "Optional namespace within Astra DB to use for the collection.",
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"advanced": True,
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},
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"metric": {
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"display_name": "Metric",
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"info": "Optional distance metric for vector comparisons in the vector store.",
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"advanced": True,
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},
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"batch_size": {
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"display_name": "Batch Size",
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"info": "Optional number of data to process in a single batch.",
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"advanced": True,
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},
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"bulk_insert_batch_concurrency": {
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"display_name": "Bulk Insert Batch Concurrency",
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"info": "Optional concurrency level for bulk insert operations.",
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"advanced": True,
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},
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"bulk_insert_overwrite_concurrency": {
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"display_name": "Bulk Insert Overwrite Concurrency",
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"info": "Optional concurrency level for bulk insert operations that overwrite existing data.",
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"advanced": True,
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},
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"bulk_delete_concurrency": {
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"display_name": "Bulk Delete Concurrency",
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"info": "Optional concurrency level for bulk delete operations.",
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"advanced": True,
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},
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"setup_mode": {
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"display_name": "Setup Mode",
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"info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.",
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"options": ["Sync", "Async", "Off"],
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"advanced": True,
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},
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"pre_delete_collection": {
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"display_name": "Pre Delete Collection",
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"info": "Boolean flag to determine whether to delete the collection before creating a new one.",
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"advanced": True,
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},
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"metadata_indexing_include": {
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"display_name": "Metadata Indexing Include",
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"info": "Optional list of metadata fields to include in the indexing.",
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"advanced": True,
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},
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"metadata_indexing_exclude": {
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"display_name": "Metadata Indexing Exclude",
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"info": "Optional list of metadata fields to exclude from the indexing.",
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"advanced": True,
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},
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"collection_indexing_policy": {
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"display_name": "Collection Indexing Policy",
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"info": "Optional dictionary defining the indexing policy for the collection.",
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"advanced": True,
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},
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"number_of_results": {
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"display_name": "Number of Results",
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"info": "Number of results to return.",
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"advanced": True,
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},
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}
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def build(
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self,
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embedding: Embeddings,
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collection_name: str,
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input_value: Text,
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token: str,
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api_endpoint: str,
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search_type: str = "Similarity",
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number_of_results: int = 4,
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namespace: Optional[str] = None,
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metric: Optional[str] = None,
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batch_size: Optional[int] = None,
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bulk_insert_batch_concurrency: Optional[int] = None,
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bulk_insert_overwrite_concurrency: Optional[int] = None,
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bulk_delete_concurrency: Optional[int] = None,
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setup_mode: str = "Sync",
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pre_delete_collection: bool = False,
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metadata_indexing_include: Optional[List[str]] = None,
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metadata_indexing_exclude: Optional[List[str]] = None,
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collection_indexing_policy: Optional[dict] = None,
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) -> List[Data]:
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vector_store = AstraVectorStoreComponent().build(
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embedding=embedding,
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collection_name=collection_name,
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token=token,
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api_endpoint=api_endpoint,
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namespace=namespace,
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metric=metric,
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batch_size=batch_size,
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bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,
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bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,
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bulk_delete_concurrency=bulk_delete_concurrency,
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setup_mode=setup_mode,
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pre_delete_collection=pre_delete_collection,
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metadata_indexing_include=metadata_indexing_include,
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metadata_indexing_exclude=metadata_indexing_exclude,
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collection_indexing_policy=collection_indexing_policy,
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)
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try:
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return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)
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except KeyError as e:
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if "content" in str(e):
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raise ValueError(
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"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'."
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)
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else:
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raise e
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@ -1,95 +0,0 @@
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from typing import Any, List, Optional, Tuple
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from langchain_community.utilities.cassandra import SetupMode
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from langflow.base.vectorstores.model import LCVectorStoreComponent
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from langflow.components.vectorstores.Cassandra import CassandraVectorStoreComponent
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from langflow.field_typing import Embeddings, Text
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from langflow.schema import Data
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class CassandraSearchComponent(LCVectorStoreComponent):
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display_name = "Cassandra Search"
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description = "Searches an existing Cassandra Vector Store."
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icon = "Cassandra"
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field_order = ["token", "database_id", "table_name", "input_value", "embedding"]
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def build_config(self):
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return {
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"search_type": {
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"display_name": "Search Type",
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"options": ["Similarity", "MMR"],
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},
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"input_value": {
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"display_name": "Input Value",
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"info": "Input value to search",
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},
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"embedding": {"display_name": "Embedding", "info": "Embedding to use"},
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"token": {
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"display_name": "Token",
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"info": "Authentication token for accessing Cassandra on Astra DB.",
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"password": True,
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},
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"database_id": {
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"display_name": "Database ID",
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"info": "The Astra database ID.",
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},
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"table_name": {
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"display_name": "Table Name",
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"info": "The name of the table where vectors will be stored.",
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},
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"keyspace": {
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"display_name": "Keyspace",
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"info": "Optional key space within Astra DB. The keyspace should already be created.",
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"advanced": True,
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},
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"body_index_options": {
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"display_name": "Body Index Options",
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"info": "Optional options used to create the body index.",
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"advanced": True,
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},
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"setup_mode": {
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"display_name": "Setup Mode",
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"info": "Configuration mode for setting up the Cassandra table, with options like 'Sync', 'Async', or 'Off'.",
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"options": ["Sync", "Async", "Off"],
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"advanced": True,
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},
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"number_of_results": {
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"display_name": "Number of Results",
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"info": "Number of results to return.",
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"advanced": True,
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},
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}
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def build(
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self,
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embedding: Embeddings,
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table_name: str,
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input_value: Text,
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token: str,
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database_id: str,
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search_type: str = "similarity",
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number_of_results: int = 4,
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keyspace: Optional[str] = None,
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body_index_options: Optional[List[Tuple[str, Any]]] = None,
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setup_mode: SetupMode = SetupMode.SYNC,
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) -> List[Data]:
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vector_store = CassandraVectorStoreComponent().build(
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embedding=embedding,
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table_name=table_name,
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token=token,
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database_id=database_id,
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keyspace=keyspace,
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body_index_options=body_index_options,
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setup_mode=setup_mode,
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)
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try:
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return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)
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except KeyError as e:
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if "content" in str(e):
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raise ValueError(
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"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'."
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)
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else:
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raise e
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@ -1,116 +0,0 @@
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from typing import List, Optional
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import chromadb
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from chromadb.config import Settings
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from langchain_chroma import Chroma
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from langflow.base.vectorstores.model import LCVectorStoreComponent
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from langflow.field_typing import Embeddings, Text
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from langflow.schema import Data
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class ChromaSearchComponent(LCVectorStoreComponent):
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display_name: str = "Chroma Search"
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description: str = "Search a Chroma collection for similar documents."
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icon = "Chroma"
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def build_config(self):
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"""
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Builds the configuration for the component.
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Returns:
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- dict: A dictionary containing the configuration options for the component.
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"""
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return {
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"input_value": {"display_name": "Input"},
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"search_type": {
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"display_name": "Search Type",
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"options": ["Similarity", "MMR"],
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},
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"collection_name": {"display_name": "Collection Name", "value": "langflow"},
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# "persist": {"display_name": "Persist"},
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"index_directory": {"display_name": "Index Directory"},
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"code": {"show": False, "display_name": "Code"},
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"embedding": {
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"display_name": "Embedding",
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"info": "Embedding model to vectorize inputs (make sure to use same as index)",
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},
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"chroma_server_cors_allow_origins": {
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"display_name": "Server CORS Allow Origins",
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"advanced": True,
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},
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"chroma_server_host": {"display_name": "Server Host", "advanced": True},
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"chroma_server_http_port": {"display_name": "Server HTTP Port", "advanced": True},
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"chroma_server_grpc_port": {
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"display_name": "Server gRPC Port",
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"advanced": True,
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},
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"chroma_server_ssl_enabled": {
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"display_name": "Server SSL Enabled",
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"advanced": True,
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},
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"number_of_results": {
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"display_name": "Number of Results",
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"info": "Number of results to return.",
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"advanced": True,
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},
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}
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def build(
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self,
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input_value: Text,
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search_type: str,
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collection_name: str,
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embedding: Embeddings,
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chroma_server_ssl_enabled: bool,
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number_of_results: int = 4,
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index_directory: Optional[str] = None,
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chroma_server_cors_allow_origins: List[str] = [],
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chroma_server_host: Optional[str] = None,
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chroma_server_http_port: Optional[int] = None,
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chroma_server_grpc_port: Optional[int] = None,
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) -> List[Data]:
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"""
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Builds the Vector Store or BaseRetriever object.
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Args:
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- input_value (Text): The input value.
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- search_type (str): The type of search.
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- collection_name (str): The name of the collection.
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- embedding (Embeddings): The embeddings to use for the Vector Store.
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- chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.
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- number_of_results (int, optional): The number of results to retrieve. Defaults to 4.
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- index_directory (str, optional): The directory to persist the Vector Store to. Defaults to None.
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- chroma_server_cors_allow_origins (List[str], optional): The CORS allow origins for the Chroma server. Defaults to [].
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- chroma_server_host (str, optional): The host for the Chroma server. Defaults to None.
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- chroma_server_http_port (int, optional): The HTTP port for the Chroma server. Defaults to None.
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- chroma_server_grpc_port (int, optional): The gRPC port for the Chroma server. Defaults to None.
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Returns:
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- List[Data]: The list of data.
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"""
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# Chroma settings
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chroma_settings = None
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client = None
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if chroma_server_host is not None:
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chroma_settings = Settings(
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chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or [],
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chroma_server_host=chroma_server_host,
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chroma_server_http_port=chroma_server_http_port or None,
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chroma_server_grpc_port=chroma_server_grpc_port or None,
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chroma_server_ssl_enabled=chroma_server_ssl_enabled,
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)
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client = chromadb.HttpClient(settings=chroma_settings)
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if index_directory:
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index_directory = self.resolve_path(index_directory)
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vector_store = Chroma(
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embedding_function=embedding,
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collection_name=collection_name,
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persist_directory=index_directory or None,
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client=client,
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)
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return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)
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from typing import List
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from langflow.base.vectorstores.model import LCVectorStoreComponent
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from langflow.components.vectorstores.Couchbase import CouchbaseVectorStoreComponent
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from langflow.field_typing import Embeddings, Text
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from langflow.schema import Data
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class CouchbaseSearchComponent(LCVectorStoreComponent):
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display_name = "Couchbase Search"
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description = "Search a Couchbase Vector Store for similar documents."
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documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
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icon = "Couchbase"
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field_order = [
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"couchbase_connection_string",
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"couchbase_username",
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"couchbase_password",
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"bucket_name",
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"scope_name",
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"collection_name",
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"index_name",
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]
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def build_config(self):
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return {
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"input_value": {"display_name": "Input"},
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"embedding": {"display_name": "Embedding"},
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"couchbase_connection_string": {"display_name": "Couchbase Cluster connection string", "required": True},
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"couchbase_username": {"display_name": "Couchbase username", "required": True},
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"couchbase_password": {"display_name": "Couchbase password", "password": True, "required": True},
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"bucket_name": {"display_name": "Bucket Name", "required": True},
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"scope_name": {"display_name": "Scope Name", "required": True},
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"collection_name": {"display_name": "Collection Name", "required": True},
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"index_name": {"display_name": "Index Name", "required": True},
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"number_of_results": {
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"display_name": "Number of Results",
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"info": "Number of results to return.",
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"advanced": True,
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},
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}
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def build( # type: ignore[override]
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self,
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input_value: Text,
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embedding: Embeddings,
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number_of_results: int = 4,
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bucket_name: str = "",
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scope_name: str = "",
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collection_name: str = "",
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index_name: str = "",
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couchbase_connection_string: str = "",
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couchbase_username: str = "",
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couchbase_password: str = "",
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) -> List[Data]:
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vector_store = CouchbaseVectorStoreComponent().build(
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couchbase_connection_string=couchbase_connection_string,
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couchbase_username=couchbase_username,
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couchbase_password=couchbase_password,
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bucket_name=bucket_name,
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scope_name=scope_name,
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collection_name=collection_name,
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embedding=embedding,
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index_name=index_name,
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)
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if not vector_store:
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raise ValueError("Failed to create Couchbase Vector Store")
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return self.search_with_vector_store(
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vector_store=vector_store, input_value=input_value, search_type="similarity", k=number_of_results
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)
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@ -1,48 +0,0 @@
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from typing import List
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from langchain_community.vectorstores.faiss import FAISS
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from langflow.base.vectorstores.model import LCVectorStoreComponent
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from langflow.field_typing import Embeddings, Text
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from langflow.schema import Data
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class FAISSSearchComponent(LCVectorStoreComponent):
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display_name = "FAISS Search"
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description = "Search a FAISS Vector Store for similar documents."
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documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
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def build_config(self):
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return {
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"embedding": {"display_name": "Embedding"},
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"folder_path": {
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"display_name": "Folder Path",
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"info": "Path to save the FAISS index. It will be relative to where Langflow is running.",
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},
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"input_value": {"display_name": "Input"},
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"index_name": {"display_name": "Index Name"},
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"number_of_results": {
|
||||
"display_name": "Number of Results",
|
||||
"info": "Number of results to return.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
input_value: Text,
|
||||
embedding: Embeddings,
|
||||
folder_path: str,
|
||||
number_of_results: int = 4,
|
||||
index_name: str = "langflow_index",
|
||||
) -> List[Data]:
|
||||
if not folder_path:
|
||||
raise ValueError("Folder path is required to save the FAISS index.")
|
||||
path = self.resolve_path(folder_path)
|
||||
vector_store = FAISS.load_local(folder_path=Text(path), embeddings=embedding, index_name=index_name)
|
||||
if not vector_store:
|
||||
raise ValueError("Failed to load the FAISS index.")
|
||||
|
||||
return self.search_with_vector_store(
|
||||
vector_store=vector_store, input_value=input_value, search_type="similarity", k=number_of_results
|
||||
)
|
||||
|
|
@ -1,57 +0,0 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langflow.base.vectorstores.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.MongoDBAtlasVector import MongoVectorStoreComponent
|
||||
from langflow.field_typing import Embeddings, NestedDict, Text
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class MongoDBAtlasSearchComponent(LCVectorStoreComponent):
|
||||
display_name = "MongoDB Atlas Search"
|
||||
description = "Search a MongoDB Atlas Vector Store for similar documents."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"search_type": {
|
||||
"display_name": "Search Type",
|
||||
"options": ["Similarity", "MMR"],
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"collection_name": {"display_name": "Collection Name"},
|
||||
"db_name": {"display_name": "Database Name"},
|
||||
"index_name": {"display_name": "Index Name"},
|
||||
"mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"},
|
||||
"search_kwargs": {"display_name": "Search Kwargs", "advanced": 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,
|
||||
search_type: str,
|
||||
embedding: Embeddings,
|
||||
number_of_results: int = 4,
|
||||
collection_name: str = "",
|
||||
db_name: str = "",
|
||||
index_name: str = "",
|
||||
mongodb_atlas_cluster_uri: str = "",
|
||||
search_kwargs: Optional[NestedDict] = None,
|
||||
) -> List[Data]:
|
||||
search_kwargs = search_kwargs or {}
|
||||
vector_store = MongoVectorStoreComponent().build(
|
||||
mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
|
||||
collection_name=collection_name,
|
||||
db_name=db_name,
|
||||
embedding=embedding,
|
||||
index_name=index_name,
|
||||
)
|
||||
if not vector_store:
|
||||
raise ValueError("Failed to create MongoDB Atlas Vector Store")
|
||||
return self.search_with_vector_store(
|
||||
vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results
|
||||
)
|
||||
|
|
@ -1,95 +0,0 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_pinecone._utilities import DistanceStrategy
|
||||
|
||||
from langflow.base.vectorstores.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Pinecone import PineconeVectorStoreComponent
|
||||
from langflow.field_typing import Embeddings, Text
|
||||
from langflow.field_typing.constants import NestedDict
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class PineconeSearchComponent(PineconeVectorStoreComponent, LCVectorStoreComponent):
|
||||
display_name = "Pinecone Search"
|
||||
description = "Search a Pinecone Vector Store for similar documents."
|
||||
icon = "Pinecone"
|
||||
field_order = ["index_name", "namespace", "distance_strategy", "pinecone_api_key", "input_value", "embedding"]
|
||||
|
||||
def build_config(self):
|
||||
distance_options = [e.value.title().replace("_", " ") for e in DistanceStrategy]
|
||||
distance_value = distance_options[0]
|
||||
return {
|
||||
"search_type": {
|
||||
"display_name": "Search Type",
|
||||
"options": ["Similarity", "MMR"],
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"index_name": {"display_name": "Index Name"},
|
||||
"namespace": {"display_name": "Namespace", "info": "Namespace for the index."},
|
||||
"distance_strategy": {
|
||||
"display_name": "Distance Strategy",
|
||||
# get values from enum
|
||||
# and make them title case for display
|
||||
"options": distance_options,
|
||||
"advanced": True,
|
||||
"value": distance_value,
|
||||
},
|
||||
"pinecone_api_key": {
|
||||
"display_name": "Pinecone API Key",
|
||||
"default": "",
|
||||
"password": True,
|
||||
},
|
||||
"pool_threads": {
|
||||
"display_name": "Pool Threads",
|
||||
"default": 1,
|
||||
"advanced": True,
|
||||
},
|
||||
"number_of_results": {
|
||||
"display_name": "Number of Results",
|
||||
"info": "Number of results to return.",
|
||||
"advanced": True,
|
||||
},
|
||||
"text_key": {
|
||||
"display_name": "Text Key",
|
||||
"info": "Key in the record to use as text.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build( # type: ignore[override]
|
||||
self,
|
||||
input_value: Text,
|
||||
embedding: Embeddings,
|
||||
distance_strategy: str,
|
||||
text_key: str = "text",
|
||||
number_of_results: int = 4,
|
||||
pool_threads: int = 4,
|
||||
index_name: Optional[str] = None,
|
||||
pinecone_api_key: Optional[str] = None,
|
||||
namespace: Optional[str] = "default",
|
||||
search_type: str = "similarity",
|
||||
search_kwargs: Optional[NestedDict] = None,
|
||||
) -> List[Data]: # type: ignore[override]
|
||||
vector_store = super().build(
|
||||
embedding=embedding,
|
||||
distance_strategy=distance_strategy,
|
||||
inputs=[],
|
||||
text_key=text_key,
|
||||
pool_threads=pool_threads,
|
||||
index_name=index_name,
|
||||
pinecone_api_key=pinecone_api_key,
|
||||
namespace=namespace,
|
||||
)
|
||||
if not vector_store:
|
||||
raise ValueError("Failed to load the Pinecone index.")
|
||||
if search_kwargs is None:
|
||||
search_kwargs = {}
|
||||
|
||||
return self.search_with_vector_store(
|
||||
vector_store=vector_store,
|
||||
input_value=input_value,
|
||||
search_type=search_type,
|
||||
k=number_of_results,
|
||||
**search_kwargs,
|
||||
)
|
||||
|
|
@ -1,103 +0,0 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langflow.base.vectorstores.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Qdrant import QdrantVectorStoreComponent
|
||||
from langflow.field_typing import Embeddings, NestedDict, Text
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class QdrantSearchComponent(QdrantVectorStoreComponent, LCVectorStoreComponent):
|
||||
display_name = "Qdrant Search"
|
||||
description = "Construct Qdrant wrapper from a list of texts."
|
||||
icon = "Qdrant"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"search_type": {
|
||||
"display_name": "Search Type",
|
||||
"options": ["Similarity", "MMR"],
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"api_key": {"display_name": "API Key", "password": True, "advanced": True},
|
||||
"collection_name": {"display_name": "Collection Name"},
|
||||
"content_payload_key": {
|
||||
"display_name": "Content Payload Key",
|
||||
"advanced": True,
|
||||
},
|
||||
"distance_func": {"display_name": "Distance Function", "advanced": True},
|
||||
"grpc_port": {"display_name": "gRPC Port", "advanced": True},
|
||||
"host": {"display_name": "Host", "advanced": True},
|
||||
"https": {"display_name": "HTTPS", "advanced": True},
|
||||
"location": {"display_name": "Location", "advanced": True},
|
||||
"metadata_payload_key": {
|
||||
"display_name": "Metadata Payload Key",
|
||||
"advanced": True,
|
||||
},
|
||||
"path": {"display_name": "Path", "advanced": True},
|
||||
"port": {"display_name": "Port", "advanced": True},
|
||||
"prefer_grpc": {"display_name": "Prefer gRPC", "advanced": True},
|
||||
"prefix": {"display_name": "Prefix", "advanced": True},
|
||||
"search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
|
||||
"timeout": {"display_name": "Timeout", "advanced": True},
|
||||
"url": {"display_name": "URL", "advanced": 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,
|
||||
collection_name: str,
|
||||
number_of_results: int = 4,
|
||||
search_type: str = "similarity",
|
||||
api_key: Optional[str] = None,
|
||||
content_payload_key: str = "page_content",
|
||||
distance_func: str = "Cosine",
|
||||
grpc_port: int = 6334,
|
||||
https: bool = False,
|
||||
host: Optional[str] = None,
|
||||
location: Optional[str] = None,
|
||||
metadata_payload_key: str = "metadata",
|
||||
path: Optional[str] = None,
|
||||
port: Optional[int] = 6333,
|
||||
prefer_grpc: bool = False,
|
||||
prefix: Optional[str] = None,
|
||||
search_kwargs: Optional[NestedDict] = None,
|
||||
timeout: Optional[int] = None,
|
||||
url: Optional[str] = None,
|
||||
) -> List[Data]: # type: ignore[override]
|
||||
vector_store = super().build(
|
||||
embedding=embedding,
|
||||
collection_name=collection_name,
|
||||
api_key=api_key,
|
||||
content_payload_key=content_payload_key,
|
||||
distance_func=distance_func,
|
||||
grpc_port=grpc_port,
|
||||
https=https,
|
||||
host=host,
|
||||
location=location,
|
||||
metadata_payload_key=metadata_payload_key,
|
||||
path=path,
|
||||
port=port,
|
||||
prefer_grpc=prefer_grpc,
|
||||
prefix=prefix,
|
||||
timeout=timeout,
|
||||
url=url,
|
||||
)
|
||||
if not vector_store:
|
||||
raise ValueError("Failed to load the Qdrant index.")
|
||||
if search_kwargs is None:
|
||||
search_kwargs = {}
|
||||
|
||||
return self.search_with_vector_store(
|
||||
vector_store=vector_store,
|
||||
input_value=input_value,
|
||||
search_type=search_type,
|
||||
k=number_of_results,
|
||||
**search_kwargs,
|
||||
)
|
||||
|
|
@ -1,82 +0,0 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.base.vectorstores.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Redis import RedisVectorStoreComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class RedisSearchComponent(RedisVectorStoreComponent, LCVectorStoreComponent):
|
||||
"""
|
||||
A custom component for implementing a Vector Store using Redis.
|
||||
"""
|
||||
|
||||
display_name: str = "Redis Search"
|
||||
description: str = "Search a Redis Vector Store for similar documents."
|
||||
documentation = "https://python.langchain.com/docs/integrations/vectorstores/redis"
|
||||
|
||||
def build_config(self):
|
||||
"""
|
||||
Builds the configuration for the component.
|
||||
|
||||
Returns:
|
||||
- dict: A dictionary containing the configuration options for the component.
|
||||
"""
|
||||
return {
|
||||
"search_type": {
|
||||
"display_name": "Search Type",
|
||||
"options": ["Similarity", "MMR"],
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"index_name": {"display_name": "Index Name", "value": "your_index"},
|
||||
"code": {"show": False, "display_name": "Code"},
|
||||
"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},
|
||||
"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,
|
||||
search_type: str,
|
||||
embedding: Embeddings,
|
||||
redis_server_url: str,
|
||||
redis_index_name: str,
|
||||
number_of_results: int = 4,
|
||||
schema: Optional[str] = None,
|
||||
) -> List[Data]:
|
||||
"""
|
||||
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.
|
||||
"""
|
||||
vector_store = super().build(
|
||||
embedding=embedding,
|
||||
redis_server_url=redis_server_url,
|
||||
redis_index_name=redis_index_name,
|
||||
schema=schema,
|
||||
)
|
||||
if not vector_store:
|
||||
raise ValueError("Failed to load the Redis index.")
|
||||
|
||||
return self.search_with_vector_store(
|
||||
input_value=input_value, search_type=search_type, vector_store=vector_store, k=number_of_results
|
||||
)
|
||||
|
|
@ -1,54 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
from langchain_community.vectorstores.supabase import SupabaseVectorStore
|
||||
from supabase.client import Client, create_client
|
||||
|
||||
from langflow.base.vectorstores.model import LCVectorStoreComponent
|
||||
from langflow.field_typing import Embeddings, Text
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class SupabaseSearchComponent(LCVectorStoreComponent):
|
||||
display_name = "Supabase Search"
|
||||
description = "Search a Supabase Vector Store for similar documents."
|
||||
icon = "Supabase"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"search_type": {
|
||||
"display_name": "Search Type",
|
||||
"options": ["Similarity", "MMR"],
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"query_name": {"display_name": "Query Name"},
|
||||
"search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
|
||||
"supabase_service_key": {"display_name": "Supabase Service Key"},
|
||||
"supabase_url": {"display_name": "Supabase URL"},
|
||||
"table_name": {"display_name": "Table Name", "advanced": True},
|
||||
"number_of_results": {
|
||||
"display_name": "Number of Results",
|
||||
"info": "Number of results to return.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
input_value: Text,
|
||||
search_type: str,
|
||||
embedding: Embeddings,
|
||||
number_of_results: int = 4,
|
||||
query_name: str = "",
|
||||
supabase_service_key: str = "",
|
||||
supabase_url: str = "",
|
||||
table_name: str = "",
|
||||
) -> List[Data]:
|
||||
supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)
|
||||
vector_store = SupabaseVectorStore(
|
||||
client=supabase,
|
||||
embedding=embedding,
|
||||
table_name=table_name,
|
||||
query_name=query_name,
|
||||
)
|
||||
return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)
|
||||
|
|
@ -1,79 +0,0 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.base.vectorstores.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Upstash import UpstashVectorStoreComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class UpstashSearchComponent(UpstashVectorStoreComponent, LCVectorStoreComponent):
|
||||
"""
|
||||
A custom component for implementing a Vector Store using Upstash.
|
||||
"""
|
||||
|
||||
display_name: str = "Upstash Search"
|
||||
description: str = "Search an Upstash Vector Store for similar documents."
|
||||
|
||||
def build_config(self):
|
||||
"""
|
||||
Builds the configuration for the component.
|
||||
|
||||
Returns:
|
||||
- dict: A dictionary containing the configuration options for the component.
|
||||
"""
|
||||
return {
|
||||
"search_type": {
|
||||
"display_name": "Search Type",
|
||||
"options": ["Similarity", "MMR"],
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"inputs": {"display_name": "Input", "input_types": ["Document", "Data"]},
|
||||
"embedding": {
|
||||
"display_name": "Embedding",
|
||||
"input_types": ["Embeddings"],
|
||||
"info": "To use Upstash's embeddings, don't provide an embedding.",
|
||||
},
|
||||
"index_url": {
|
||||
"display_name": "Index URL",
|
||||
"info": "The URL of the Upstash index.",
|
||||
},
|
||||
"index_token": {
|
||||
"display_name": "Index Token",
|
||||
"info": "The token for the Upstash index.",
|
||||
},
|
||||
"number_of_results": {
|
||||
"display_name": "Number of Results",
|
||||
"info": "Number of results to return.",
|
||||
"advanced": True,
|
||||
},
|
||||
"text_key": {
|
||||
"display_name": "Text Key",
|
||||
"info": "The key in the record to use as text.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build( # type: ignore[override]
|
||||
self,
|
||||
input_value: Text,
|
||||
search_type: str,
|
||||
text_key: str = "text",
|
||||
index_url: Optional[str] = None,
|
||||
index_token: Optional[str] = None,
|
||||
embedding: Optional[Embeddings] = None,
|
||||
number_of_results: int = 4,
|
||||
) -> List[Data]:
|
||||
vector_store = super().build(
|
||||
embedding=embedding,
|
||||
text_key=text_key,
|
||||
index_url=index_url,
|
||||
index_token=index_token,
|
||||
)
|
||||
if not vector_store:
|
||||
raise ValueError("Failed to load the Upstash Vector Store.")
|
||||
|
||||
return self.search_with_vector_store(
|
||||
input_value=input_value, search_type=search_type, vector_store=vector_store, k=number_of_results
|
||||
)
|
||||
|
|
@ -1,66 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
from langchain_community.vectorstores.vectara import Vectara
|
||||
|
||||
from langflow.base.vectorstores.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Vectara import VectaraVectorStoreComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class VectaraSearchComponent(VectaraVectorStoreComponent, LCVectorStoreComponent):
|
||||
display_name: str = "Vectara Search"
|
||||
description: str = "Search a Vectara Vector Store for similar documents."
|
||||
documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara"
|
||||
icon = "Vectara"
|
||||
|
||||
field_config = {
|
||||
"search_type": {
|
||||
"display_name": "Search Type",
|
||||
"options": ["Similarity", "MMR"],
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"vectara_customer_id": {
|
||||
"display_name": "Vectara Customer ID",
|
||||
},
|
||||
"vectara_corpus_id": {
|
||||
"display_name": "Vectara Corpus ID",
|
||||
},
|
||||
"vectara_api_key": {
|
||||
"display_name": "Vectara API Key",
|
||||
"password": True,
|
||||
},
|
||||
"files_url": {
|
||||
"display_name": "Files Url",
|
||||
"info": "Make vectara object using url of files (optional)",
|
||||
},
|
||||
"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,
|
||||
search_type: str,
|
||||
vectara_customer_id: str,
|
||||
vectara_corpus_id: str,
|
||||
vectara_api_key: str,
|
||||
number_of_results: int = 4,
|
||||
) -> List[Data]:
|
||||
source = "Langflow"
|
||||
vector_store = Vectara(
|
||||
vectara_customer_id=vectara_customer_id,
|
||||
vectara_corpus_id=vectara_corpus_id,
|
||||
vectara_api_key=vectara_api_key,
|
||||
source=source,
|
||||
)
|
||||
|
||||
if not vector_store:
|
||||
raise ValueError("Failed to create Vectara Vector Store")
|
||||
|
||||
return self.search_with_vector_store(
|
||||
vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results
|
||||
)
|
||||
|
|
@ -1,86 +0,0 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.base.vectorstores.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.Weaviate import WeaviateVectorStoreComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreComponent):
|
||||
display_name: str = "Weaviate Search"
|
||||
description: str = "Search a Weaviate Vector Store for similar documents."
|
||||
documentation = "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
|
||||
icon = "Weaviate"
|
||||
|
||||
field_config = {
|
||||
"search_type": {
|
||||
"display_name": "Search Type",
|
||||
"options": ["Similarity", "MMR"],
|
||||
},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"url": {"display_name": "Weaviate URL", "value": "http://localhost:8080"},
|
||||
"api_key": {
|
||||
"display_name": "API Key",
|
||||
"password": True,
|
||||
"required": False,
|
||||
},
|
||||
"index_name": {
|
||||
"display_name": "Index name",
|
||||
"required": False,
|
||||
},
|
||||
"text_key": {
|
||||
"display_name": "Text Key",
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
"value": "text",
|
||||
},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"attributes": {
|
||||
"display_name": "Attributes",
|
||||
"required": False,
|
||||
"is_list": True,
|
||||
"field_type": "str",
|
||||
"advanced": True,
|
||||
},
|
||||
"search_by_text": {
|
||||
"display_name": "Search By Text",
|
||||
"field_type": "bool",
|
||||
"advanced": 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,
|
||||
search_type: str,
|
||||
url: str,
|
||||
index_name: str,
|
||||
number_of_results: int = 4,
|
||||
search_by_text: bool = False,
|
||||
api_key: Optional[str] = None,
|
||||
text_key: str = "text",
|
||||
embedding: Optional[Embeddings] = None,
|
||||
attributes: Optional[list] = None,
|
||||
) -> List[Data]:
|
||||
vector_store = super().build(
|
||||
url=url,
|
||||
api_key=api_key,
|
||||
index_name=index_name,
|
||||
text_key=text_key,
|
||||
embedding=embedding,
|
||||
attributes=attributes,
|
||||
search_by_text=search_by_text,
|
||||
)
|
||||
if not vector_store:
|
||||
raise ValueError("Failed to load the Weaviate index.")
|
||||
|
||||
return self.search_with_vector_store(
|
||||
vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results
|
||||
)
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
from typing import List
|
||||
|
||||
from langchain_core.embeddings import Embeddings
|
||||
|
||||
from langflow.base.vectorstores.model import LCVectorStoreComponent
|
||||
from langflow.components.vectorstores.pgvector import PGVectorStoreComponent
|
||||
from langflow.field_typing import Text
|
||||
from langflow.schema import Data
|
||||
|
||||
|
||||
class PGVectorSearchComponent(PGVectorStoreComponent, LCVectorStoreComponent):
|
||||
display_name: str = "PGVector Search"
|
||||
description: str = "Search a PGVector Store for similar documents."
|
||||
documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
|
||||
|
||||
def build_config(self):
|
||||
"""
|
||||
Builds the configuration for the component.
|
||||
|
||||
Returns:
|
||||
- dict: A dictionary containing the configuration options for the component.
|
||||
"""
|
||||
return {
|
||||
"code": {"show": False},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"search_type": {
|
||||
"display_name": "Search Type",
|
||||
"options": ["Similarity", "MMR"],
|
||||
},
|
||||
"pg_server_url": {
|
||||
"display_name": "PostgreSQL Server Connection String",
|
||||
"advanced": False,
|
||||
},
|
||||
"collection_name": {"display_name": "Table", "advanced": False},
|
||||
"input_value": {"display_name": "Input"},
|
||||
"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,
|
||||
search_type: str,
|
||||
pg_server_url: str,
|
||||
collection_name: str,
|
||||
number_of_results: int = 4,
|
||||
) -> List[Data]:
|
||||
"""
|
||||
Builds the Vector Store or BaseRetriever object.
|
||||
|
||||
Args:
|
||||
- input_value (str): The input value to search for.
|
||||
- embedding (Embeddings): The embeddings to use for the Vector Store.
|
||||
- collection_name (str): The name of the PG table.
|
||||
- pg_server_url (str): The URL for the PG server.
|
||||
|
||||
Returns:
|
||||
- VectorStore: The Vector Store object.
|
||||
"""
|
||||
try:
|
||||
vector_store = super().build(
|
||||
embedding=embedding,
|
||||
pg_server_url=pg_server_url,
|
||||
collection_name=collection_name,
|
||||
)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to build PGVector: {e}")
|
||||
return self.search_with_vector_store(
|
||||
input_value=input_value, search_type=search_type, vector_store=vector_store, k=number_of_results
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue