feat: Generic Callback Dialog Input for Custom Component (#6236)
* force dialog * Reimplement backend dialog * Update astradb.py * Clean up dropdown options * Remove unused import * [autofix.ci] apply automated fixes * Update astradb.py * Ruff fixes * Update Vector Store RAG.json * [autofix.ci] apply automated fixes * fix: Conditionally render custom option dialog in dropdown * ✨ (NodeDialogComponent/index.tsx): Add support for passing 'name' prop to NodeDialog component to improve customization and flexibility 📝 (NodeDialogComponent/index.tsx): Update comments and remove unused import to improve code readability and maintainability 🔧 (dropdownComponent/index.tsx): Pass 'name' prop to Dropdown component to enhance customization and flexibility * ✨ Refactor NodeDialog component to improve state management and payload handling * Update astradb.py * [autofix.ci] apply automated fixes * ✨ Enhance NodeDialog and Dropdown components with improved payload handling and type safety * Add DB creation functionality * First version of create * Update astradb.py * Fix ruff errors * Update Vector Store RAG.json * [autofix.ci] apply automated fixes * Update astradb.py * [autofix.ci] apply automated fixes * Update astradb.py * [autofix.ci] apply automated fixes * Update astradb.py * Update astradb.py * Update astradb.py * Update Vector Store RAG.json * [autofix.ci] apply automated fixes * Update astradb.py * [autofix.ci] apply automated fixes * feat: Enhance dropdown and node dialog with loading states and improved UX * refactor: Improve error handling in NodeDialog component * refactor: Update default excluded keys in dropdown metadata filter * [autofix.ci] apply automated fixes * refactor: Update Vector Store RAG starter project JSON with formatting and connection ID corrections * Hide fields that aren't relevant yet * [autofix.ci] apply automated fixes * Update Vector Store RAG.json * [autofix.ci] apply automated fixes * Update astradb.py * feat: Improve dropdown component with loading states and enhanced UX * Update astradb.py * [autofix.ci] apply automated fixes * Update astradb.py * Simon feedback * [autofix.ci] apply automated fixes * feat: Enhance dropdown and UI components with status indicators and loading states * refactor: Update dropdown metadata filtering to exclude 'icon' key * fix: Conditionally render dropdown icon when available * fix: Improve dropdown icon rendering with null checks * chore: Remove debug console log in dropdown component * Add support for icons in the dropdowns * Update astradb.py * Update Vector Store RAG.json * [autofix.ci] apply automated fixes * feat: Enhance dropdown status display and color handling * feat: Add auto-close functionality to node dialog and expand status color handling * feat: Add real-time template refresh for node dialog fields * refactor: Improve node dialog component state management and naming * Async for create collection * [autofix.ci] apply automated fixes * Dynamic provider list generation * Update astradb.py * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * Update astradb.py * [autofix.ci] apply automated fixes --------- Co-authored-by: Eric Hare <ericrhare@gmail.com> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: cristhianzl <cristhian.lousa@gmail.com>
This commit is contained in:
Deon Sanchez
GitHub
parent
b8d2e63221
commit
c902fb9e11
11 changed files with 1314 additions and 538 deletions
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@ -1,8 +1,8 @@
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import os
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from collections import defaultdict
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from dataclasses import dataclass, field
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from dataclasses import asdict, dataclass, field
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from astrapy import AstraDBAdmin, DataAPIClient, Database
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from astrapy.info import CollectionDescriptor
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from langchain_astradb import AstraDBVectorStore, CollectionVectorServiceOptions
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from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
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@ -36,22 +36,24 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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default_factory=lambda: {
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"data": {
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"node": {
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"description": "Create a new database in Astra DB.",
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"display_name": "Create New Database",
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"name": "create_database",
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"description": "",
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"display_name": "Create new database",
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"field_order": ["new_database_name", "cloud_provider", "region"],
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"template": {
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"new_database_name": StrInput(
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name="new_database_name",
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display_name="New Database Name",
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display_name="Name",
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info="Name of the new database to create in Astra DB.",
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required=True,
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),
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"cloud_provider": DropdownInput(
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name="cloud_provider",
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display_name="Cloud Provider",
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display_name="Cloud provider",
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info="Cloud provider for the new database.",
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options=["Amazon Web Services", "Google Cloud Platform", "Microsoft Azure"],
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required=True,
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real_time_refresh=True,
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),
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"region": DropdownInput(
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name="region",
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@ -73,8 +75,9 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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default_factory=lambda: {
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"data": {
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"node": {
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"description": "Create a new collection in Astra DB.",
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"display_name": "Create New Collection",
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"name": "create_collection",
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"description": "",
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"display_name": "Create new collection",
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"field_order": [
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"new_collection_name",
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"embedding_generation_provider",
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@ -83,23 +86,31 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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"template": {
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"new_collection_name": StrInput(
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name="new_collection_name",
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display_name="New Collection Name",
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display_name="Name",
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info="Name of the new collection to create in Astra DB.",
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required=True,
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),
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"embedding_generation_provider": DropdownInput(
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name="embedding_generation_provider",
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display_name="Embedding Generation Provider",
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display_name="Embedding generation method",
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info="Provider to use for generating embeddings.",
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options=[],
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real_time_refresh=True,
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required=True,
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options=["Bring your own", "Nvidia"],
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),
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"embedding_generation_model": DropdownInput(
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name="embedding_generation_model",
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display_name="Embedding Generation Model",
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display_name="Embedding model",
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info="Model to use for generating embeddings.",
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options=[],
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required=True,
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options=[],
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),
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"dimension": IntInput(
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name="dimension",
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display_name="Dimensions (Required only for `Bring your own`)",
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info="Dimensions of the embeddings to generate.",
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required=False,
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value=1024,
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),
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},
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},
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@ -125,17 +136,18 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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real_time_refresh=True,
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),
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DropdownInput(
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name="api_endpoint",
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name="database_name",
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display_name="Database",
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info="The Database / API Endpoint for the Astra DB instance.",
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info="The Database name for the Astra DB instance.",
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required=True,
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refresh_button=True,
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real_time_refresh=True,
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dialog_inputs=asdict(NewDatabaseInput()),
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combobox=True,
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),
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StrInput(
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name="d_api_endpoint",
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display_name="Database API Endpoint",
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name="api_endpoint",
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display_name="Astra DB API Endpoint",
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info="The API Endpoint for the Astra DB instance. Supercedes database selection.",
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advanced=True,
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),
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@ -146,8 +158,9 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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required=True,
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refresh_button=True,
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real_time_refresh=True,
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# dialog_inputs=asdict(NewCollectionInput()),
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dialog_inputs=asdict(NewCollectionInput()),
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combobox=True,
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advanced=True,
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),
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StrInput(
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name="keyspace",
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@ -238,6 +251,7 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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@classmethod
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def map_cloud_providers(cls):
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# TODO: Programmatically fetch the regions for each cloud provider
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return {
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"Amazon Web Services": {
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"id": "aws",
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@ -254,54 +268,87 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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}
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@classmethod
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def create_database_api(
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def get_vectorize_providers(cls, token: str, environment: str | None = None, api_endpoint: str | None = None):
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try:
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# Get the admin object
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admin = AstraDBAdmin(token=token, environment=environment)
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db_admin = admin.get_database_admin(api_endpoint=api_endpoint)
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# Get the list of embedding providers
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embedding_providers = db_admin.find_embedding_providers().as_dict()
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vectorize_providers_mapping = {}
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# Map the provider display name to the provider key and models
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for provider_key, provider_data in embedding_providers["embeddingProviders"].items():
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# Get the provider display name and models
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display_name = provider_data["displayName"]
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models = [model["name"] for model in provider_data["models"]]
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# Build our mapping
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vectorize_providers_mapping[display_name] = [provider_key, models]
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# Sort the resulting dictionary
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return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))
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except Exception as e:
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msg = f"Error fetching vectorize providers: {e}"
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raise ValueError(msg) from e
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@classmethod
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async def create_database_api(
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cls,
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token: str,
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new_database_name: str,
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cloud_provider: str,
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region: str,
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token: str,
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environment: str | None = None,
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keyspace: str | None = None,
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):
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client = DataAPIClient(token=token)
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client = DataAPIClient(token=token, environment=environment)
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# Get the admin object
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admin_client = client.get_admin(token=token)
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# Call the create database function
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return admin_client.create_database(
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return await admin_client.async_create_database(
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name=new_database_name,
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cloud_provider=cloud_provider,
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cloud_provider=cls.map_cloud_providers()[cloud_provider]["id"],
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region=region,
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keyspace=keyspace,
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wait_until_active=False,
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)
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@classmethod
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def create_collection_api(
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async def create_collection_api(
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cls,
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token: str,
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database_name: str,
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new_collection_name: str,
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token: str,
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api_endpoint: str,
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environment: str | None = None,
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keyspace: str | None = None,
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dimension: int | None = None,
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embedding_generation_provider: str | None = None,
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embedding_generation_model: str | None = None,
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):
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# Create the data API client
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client = DataAPIClient(token=token)
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api_endpoint = cls.get_api_endpoint_static(token=token, database_name=database_name)
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# Get the database object
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database = client.get_database(api_endpoint=api_endpoint, token=token)
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database = client.get_async_database(api_endpoint=api_endpoint, token=token)
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# Build vectorize options, if needed
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vectorize_options = None
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if not dimension:
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vectorize_options = CollectionVectorServiceOptions(
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provider=embedding_generation_provider,
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provider=cls.get_vectorize_providers(
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token=token, environment=environment, api_endpoint=api_endpoint
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).get(embedding_generation_provider, [None, []])[0],
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model_name=embedding_generation_model,
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authentication=None,
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parameters=None,
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)
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# Create the collection
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return database.create_collection(
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return await database.create_collection(
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name=new_collection_name,
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keyspace=keyspace,
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dimension=dimension,
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service=vectorize_options,
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)
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@ -325,16 +372,28 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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db_info_dict = {}
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for db in db_list:
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try:
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# Get the API endpoint for the database
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api_endpoint = f"https://{db.info.id}-{db.info.region}.apps.astra{env_string}.datastax.com"
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db_info_dict[db.info.name] = {
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"api_endpoint": api_endpoint,
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"collections": len(
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# Get the number of collections
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try:
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num_collections = len(
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list(
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client.get_database(
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api_endpoint=api_endpoint, token=token, keyspace=db.info.keyspace
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).list_collection_names(keyspace=db.info.keyspace)
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)
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),
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)
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except Exception: # noqa: BLE001
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num_collections = 0
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if db.status != "PENDING":
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continue
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# Add the database to the dictionary
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db_info_dict[db.info.name] = {
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"api_endpoint": api_endpoint,
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"collections": num_collections,
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"status": db.status if db.status != "ACTIVE" else None,
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}
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except Exception: # noqa: BLE001, S110
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pass
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@ -364,15 +423,20 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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if not database_name:
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return None
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# Otherwise, get the URL from the database list
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return cls.get_database_list_static(token=token, environment=environment).get(database_name).get("api_endpoint")
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# Grab the database object
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db = cls.get_database_list_static(token=token, environment=environment).get(database_name)
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if not db:
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return None
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def get_api_endpoint(self, *, api_endpoint: str | None = None):
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# Otherwise, get the URL from the database list
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return db.get("api_endpoint")
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def get_api_endpoint(self):
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return self.get_api_endpoint_static(
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token=self.token,
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environment=self.environment,
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api_endpoint=api_endpoint or self.d_api_endpoint,
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database_name=self.api_endpoint,
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api_endpoint=self.api_endpoint,
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database_name=self.database_name,
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)
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def get_keyspace(self):
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@ -388,7 +452,7 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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client = DataAPIClient(token=self.token, environment=self.environment)
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return client.get_database(
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api_endpoint=self.get_api_endpoint(api_endpoint=api_endpoint),
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api_endpoint=api_endpoint or self.get_api_endpoint(),
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token=self.token,
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keyspace=self.get_keyspace(),
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)
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@ -415,40 +479,15 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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return None
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def get_vectorize_providers(self):
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try:
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self.log("Dynamically updating list of Vectorize providers.")
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# Get the admin object
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admin = AstraDBAdmin(token=self.token)
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db_admin = admin.get_database_admin(api_endpoint=self.get_api_endpoint())
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# Get the list of embedding providers
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embedding_providers = db_admin.find_embedding_providers().as_dict()
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vectorize_providers_mapping = {}
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# Map the provider display name to the provider key and models
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for provider_key, provider_data in embedding_providers["embeddingProviders"].items():
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display_name = provider_data["displayName"]
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models = [model["name"] for model in provider_data["models"]]
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# TODO: https://astra.datastax.com/api/v2/graphql
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vectorize_providers_mapping[display_name] = [provider_key, models]
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# Sort the resulting dictionary
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return defaultdict(list, dict(sorted(vectorize_providers_mapping.items())))
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except Exception as e: # noqa: BLE001
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self.log(f"Error fetching Vectorize providers: {e}")
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return {}
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def _initialize_database_options(self):
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try:
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return [
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{
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"name": name,
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"status": info["status"],
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"collections": info["collections"],
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"api_endpoint": info["api_endpoint"],
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"icon": "data",
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}
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for name, info in self.get_database_list().items()
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]
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@ -456,7 +495,35 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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msg = f"Error fetching database options: {e}"
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raise ValueError(msg) from e
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@classmethod
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def get_provider_icon(cls, collection: CollectionDescriptor | None = None, provider_name: str | None = None) -> str:
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# Get the provider name from the collection
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provider_name = provider_name or (
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collection.options.vector.service.provider
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if collection and collection.options and collection.options.vector and collection.options.vector.service
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else None
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)
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# If there is no provider, use the vector store icon
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if not provider_name or provider_name == "bring your own":
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return "vectorstores"
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# Special case for certain models
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# TODO: Add more icons
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if provider_name == "nvidia":
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return "NVIDIA"
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if provider_name == "openai":
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return "OpenAI"
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# Title case on the provider for the icon if no special case
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return provider_name.title()
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def _initialize_collection_options(self, api_endpoint: str | None = None):
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# Nothing to generate if we don't have an API endpoint yet
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api_endpoint = api_endpoint or self.get_api_endpoint()
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if not api_endpoint:
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return []
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# Retrieve the database object
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database = self.get_database_object(api_endpoint=api_endpoint)
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@ -471,7 +538,7 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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"provider": (
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col.options.vector.service.provider if col.options.vector and col.options.vector.service else None
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),
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"icon": "",
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"icon": self.get_provider_icon(collection=col),
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"model": (
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col.options.vector.service.model_name if col.options.vector and col.options.vector.service else None
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),
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@ -479,9 +546,53 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
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for col in collection_list
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]
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def reset_provider_options(self, build_config: dict):
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# Get the list of vectorize providers
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vectorize_providers = self.get_vectorize_providers(
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token=self.token,
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environment=self.environment,
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api_endpoint=build_config["api_endpoint"]["value"],
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)
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# If the collection is set, allow user to see embedding options
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build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
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"embedding_generation_provider"
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]["options"] = ["Bring your own", "Nvidia", *[key for key in vectorize_providers if key != "Nvidia"]]
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# For all not Bring your own or Nvidia providers, add metadata saying configure in Astra DB Portal
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provider_options = build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
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"embedding_generation_provider"
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]["options"]
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# Go over each possible provider and add metadata to configure in Astra DB Portal
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for provider in provider_options:
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# Skip Bring your own and Nvidia, automatically configured
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if provider in ["Bring your own", "Nvidia"]:
|
||||
build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
|
||||
"embedding_generation_provider"
|
||||
]["options_metadata"].append({"icon": self.get_provider_icon(provider_name=provider.lower())})
|
||||
continue
|
||||
|
||||
# Add metadata to configure in Astra DB Portal
|
||||
build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
|
||||
"embedding_generation_provider"
|
||||
]["options_metadata"].append({" ": "Configure in Astra DB Portal"})
|
||||
|
||||
# And allow the user to see the models based on a selected provider
|
||||
embedding_provider = build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
|
||||
"embedding_generation_provider"
|
||||
]["value"]
|
||||
|
||||
# Set the options for the embedding model based on the provider
|
||||
build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
|
||||
"embedding_generation_model"
|
||||
]["options"] = vectorize_providers.get(embedding_provider, [[], []])[1]
|
||||
|
||||
return build_config
|
||||
|
||||
def reset_collection_list(self, build_config: dict):
|
||||
# Get the list of options we have based on the token provided
|
||||
collection_options = self._initialize_collection_options()
|
||||
collection_options = self._initialize_collection_options(api_endpoint=build_config["api_endpoint"]["value"])
|
||||
|
||||
# If we retrieved options based on the token, show the dropdown
|
||||
build_config["collection_name"]["options"] = [col["name"] for col in collection_options]
|
||||
|
|
@ -490,7 +601,11 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
|
|||
]
|
||||
|
||||
# Reset the selected collection
|
||||
build_config["collection_name"]["value"] = ""
|
||||
if build_config["collection_name"]["value"] not in build_config["collection_name"]["options"]:
|
||||
build_config["collection_name"]["value"] = ""
|
||||
|
||||
# If we have a database, collection name should not be advanced
|
||||
build_config["collection_name"]["advanced"] = not build_config["database_name"]["value"]
|
||||
|
||||
return build_config
|
||||
|
||||
|
|
@ -499,84 +614,171 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
|
|||
database_options = self._initialize_database_options()
|
||||
|
||||
# If we retrieved options based on the token, show the dropdown
|
||||
build_config["api_endpoint"]["options"] = [db["name"] for db in database_options]
|
||||
build_config["api_endpoint"]["options_metadata"] = [
|
||||
build_config["database_name"]["options"] = [db["name"] for db in database_options]
|
||||
build_config["database_name"]["options_metadata"] = [
|
||||
{k: v for k, v in db.items() if k not in ["name"]} for db in database_options
|
||||
]
|
||||
|
||||
# Reset the selected database
|
||||
build_config["api_endpoint"]["value"] = ""
|
||||
if build_config["database_name"]["value"] not in build_config["database_name"]["options"]:
|
||||
build_config["database_name"]["value"] = ""
|
||||
build_config["api_endpoint"]["value"] = ""
|
||||
build_config["collection_name"]["advanced"] = True
|
||||
|
||||
# If we have a token, database name should not be advanced
|
||||
build_config["database_name"]["advanced"] = not build_config["token"]["value"]
|
||||
|
||||
return build_config
|
||||
|
||||
def reset_build_config(self, build_config: dict):
|
||||
# Reset the list of databases we have based on the token provided
|
||||
build_config["api_endpoint"]["options"] = []
|
||||
build_config["api_endpoint"]["options_metadata"] = []
|
||||
build_config["database_name"]["options"] = []
|
||||
build_config["database_name"]["options_metadata"] = []
|
||||
build_config["database_name"]["value"] = ""
|
||||
build_config["database_name"]["advanced"] = True
|
||||
build_config["api_endpoint"]["value"] = ""
|
||||
build_config["api_endpoint"]["name"] = "Database"
|
||||
|
||||
# Reset the list of collections and metadata associated
|
||||
build_config["collection_name"]["options"] = []
|
||||
build_config["collection_name"]["options_metadata"] = []
|
||||
build_config["collection_name"]["value"] = ""
|
||||
build_config["collection_name"]["advanced"] = True
|
||||
|
||||
return build_config
|
||||
|
||||
def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):
|
||||
# When the component first executes, this is the update refresh call
|
||||
first_run = field_name == "collection_name" and not field_value and not build_config["api_endpoint"]["options"]
|
||||
async def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None):
|
||||
# Callback for database creation
|
||||
if field_name == "database_name" and isinstance(field_value, dict) and "new_database_name" in field_value:
|
||||
try:
|
||||
await self.create_database_api(
|
||||
new_database_name=field_value["new_database_name"],
|
||||
token=self.token,
|
||||
keyspace=self.get_keyspace(),
|
||||
environment=self.environment,
|
||||
cloud_provider=field_value["cloud_provider"],
|
||||
region=field_value["region"],
|
||||
)
|
||||
except Exception as e:
|
||||
msg = f"Error creating database: {e}"
|
||||
raise ValueError(msg) from e
|
||||
|
||||
# If the token has not been provided, simply return
|
||||
# Add the new database to the list of options
|
||||
build_config["database_name"]["options"] = build_config["database_name"]["options"] + [
|
||||
field_value["new_database_name"]
|
||||
]
|
||||
build_config["database_name"]["options_metadata"] = build_config["database_name"]["options_metadata"] + [
|
||||
{"status": "PENDING"}
|
||||
]
|
||||
|
||||
return self.reset_collection_list(build_config)
|
||||
|
||||
# This is the callback required to update the list of regions for a cloud provider
|
||||
if field_name == "database_name" and isinstance(field_value, dict) and "new_database_name" not in field_value:
|
||||
cloud_provider = field_value["cloud_provider"]
|
||||
build_config["database_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"]["region"][
|
||||
"options"
|
||||
] = self.map_cloud_providers()[cloud_provider]["regions"]
|
||||
|
||||
return build_config
|
||||
|
||||
# Callback for the creation of collections
|
||||
if field_name == "collection_name" and isinstance(field_value, dict) and "new_collection_name" in field_value:
|
||||
try:
|
||||
# Get the dimension if its a BYO provider
|
||||
dimension = (
|
||||
field_value["dimension"]
|
||||
if field_value["embedding_generation_provider"] == "Bring your own"
|
||||
else None
|
||||
)
|
||||
|
||||
# Create the collection
|
||||
await self.create_collection_api(
|
||||
new_collection_name=field_value["new_collection_name"],
|
||||
token=self.token,
|
||||
api_endpoint=build_config["api_endpoint"]["value"],
|
||||
environment=self.environment,
|
||||
keyspace=self.get_keyspace(),
|
||||
dimension=dimension,
|
||||
embedding_generation_provider=field_value["embedding_generation_provider"],
|
||||
embedding_generation_model=field_value["embedding_generation_model"],
|
||||
)
|
||||
except Exception as e:
|
||||
msg = f"Error creating collection: {e}"
|
||||
raise ValueError(msg) from e
|
||||
|
||||
# Add the new collection to the list of options
|
||||
build_config["collection_name"]["value"] = field_value["new_collection_name"]
|
||||
build_config["collection_name"]["options"].append(field_value["new_collection_name"])
|
||||
|
||||
# Get the provider and model for the new collection
|
||||
generation_provider = field_value["embedding_generation_provider"]
|
||||
provider = generation_provider if generation_provider != "Bring your own" else None
|
||||
generation_model = field_value["embedding_generation_model"]
|
||||
model = generation_model if generation_model else None
|
||||
|
||||
# Add the new collection to the list of options
|
||||
icon = "NVIDIA" if provider == "Nvidia" else "vectorstores"
|
||||
build_config["collection_name"]["options_metadata"] = build_config["collection_name"][
|
||||
"options_metadata"
|
||||
] + [{"records": 0, "provider": provider, "icon": icon, "model": model}]
|
||||
|
||||
return build_config
|
||||
|
||||
# Callback to update the model list based on the embedding provider
|
||||
if (
|
||||
field_name == "collection_name"
|
||||
and isinstance(field_value, dict)
|
||||
and "new_collection_name" not in field_value
|
||||
):
|
||||
return self.reset_provider_options(build_config)
|
||||
|
||||
# When the component first executes, this is the update refresh call
|
||||
first_run = field_name == "collection_name" and not field_value and not build_config["database_name"]["options"]
|
||||
|
||||
# If the token has not been provided, simply return the empty build config
|
||||
if not self.token:
|
||||
return self.reset_build_config(build_config)
|
||||
|
||||
# If this is the first execution of the component, reset and build database list
|
||||
if first_run or field_name in ["token", "environment"]:
|
||||
# Reset the build config to ensure we are starting fresh
|
||||
build_config = self.reset_build_config(build_config)
|
||||
build_config = self.reset_database_list(build_config)
|
||||
|
||||
# Get list of regions for a given cloud provider
|
||||
"""
|
||||
cloud_provider = (
|
||||
build_config["api_endpoint"]["dialog_inputs"]["fields"]["data"]["node"]["template"]["cloud_provider"][
|
||||
"value"
|
||||
]
|
||||
or "Amazon Web Services"
|
||||
)
|
||||
build_config["api_endpoint"]["dialog_inputs"]["fields"]["data"]["node"]["template"]["region"][
|
||||
"options"
|
||||
] = self.map_cloud_providers()[cloud_provider]["regions"]
|
||||
"""
|
||||
|
||||
return build_config
|
||||
return self.reset_database_list(build_config)
|
||||
|
||||
# Refresh the collection name options
|
||||
if field_name == "api_endpoint":
|
||||
if field_name == "database_name" and not isinstance(field_value, dict):
|
||||
# If missing, refresh the database options
|
||||
if not build_config["api_endpoint"]["options"] or not field_value:
|
||||
return self.update_build_config(build_config, field_value=self.token, field_name="token")
|
||||
if field_value not in build_config["database_name"]["options"]:
|
||||
build_config = await self.update_build_config(build_config, field_value=self.token, field_name="token")
|
||||
build_config["database_name"]["value"] = ""
|
||||
else:
|
||||
# Find the position of the selected database to align with metadata
|
||||
index_of_name = build_config["database_name"]["options"].index(field_value)
|
||||
|
||||
# Set the underlying api endpoint value of the database
|
||||
if field_value in build_config["api_endpoint"]["options"]:
|
||||
index_of_name = build_config["api_endpoint"]["options"].index(field_value)
|
||||
build_config["d_api_endpoint"]["value"] = build_config["api_endpoint"]["options_metadata"][
|
||||
# Initializing database condition
|
||||
pending = build_config["database_name"]["options_metadata"][index_of_name]["status"] == "PENDING"
|
||||
if pending:
|
||||
return self.update_build_config(build_config, field_value=self.token, field_name="token")
|
||||
|
||||
# Set the API endpoint based on the selected database
|
||||
build_config["api_endpoint"]["value"] = build_config["database_name"]["options_metadata"][
|
||||
index_of_name
|
||||
]["api_endpoint"]
|
||||
else:
|
||||
build_config["d_api_endpoint"]["value"] = ""
|
||||
|
||||
# Reset the provider options
|
||||
build_config = self.reset_provider_options(build_config)
|
||||
|
||||
# Reset the list of collections we have based on the token provided
|
||||
return self.reset_collection_list(build_config)
|
||||
|
||||
# Hide embedding model option if opriona_metadata provider is not null
|
||||
if field_name == "collection_name" and field_value:
|
||||
if field_name == "collection_name" and not isinstance(field_value, dict):
|
||||
# Assume we will be autodetecting the collection:
|
||||
build_config["autodetect_collection"]["value"] = True
|
||||
|
||||
# Reload the collection list
|
||||
build_config = self.reset_collection_list(build_config)
|
||||
|
||||
# Set the options for collection name to be the field value if its a new collection
|
||||
if field_value not in build_config["collection_name"]["options"]:
|
||||
if field_value and field_value not in build_config["collection_name"]["options"]:
|
||||
# Add the new collection to the list of options
|
||||
build_config["collection_name"]["options"].append(field_value)
|
||||
build_config["collection_name"]["options_metadata"].append(
|
||||
|
|
@ -598,36 +800,8 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
|
|||
build_config["embedding_model"]["advanced"] = False
|
||||
build_config["embedding_choice"]["value"] = "Embedding Model"
|
||||
|
||||
# For the final step, get the list of vectorize providers
|
||||
"""
|
||||
vectorize_providers = self.get_vectorize_providers()
|
||||
if not vectorize_providers:
|
||||
return build_config
|
||||
|
||||
# Allow the user to see the embedding provider options
|
||||
provider_options = build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
|
||||
"embedding_generation_provider"
|
||||
]["options"]
|
||||
if not provider_options:
|
||||
# If the collection is set, allow user to see embedding options
|
||||
build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
|
||||
"embedding_generation_provider"
|
||||
]["options"] = ["Bring your own", "Nvidia", *[key for key in vectorize_providers if key != "Nvidia"]]
|
||||
|
||||
# And allow the user to see the models based on a selected provider
|
||||
model_options = build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
|
||||
"embedding_generation_model"
|
||||
]["options"]
|
||||
if not model_options:
|
||||
embedding_provider = build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
|
||||
"embedding_generation_provider"
|
||||
]["value"]
|
||||
|
||||
build_config["collection_name"]["dialog_inputs"]["fields"]["data"]["node"]["template"][
|
||||
"embedding_generation_model"
|
||||
]["options"] = vectorize_providers.get(embedding_provider, [[], []])[1]
|
||||
"""
|
||||
|
||||
return build_config
|
||||
|
||||
@check_cached_vector_store
|
||||
|
|
@ -654,11 +828,11 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
|
|||
# Get Langflow version and platform information
|
||||
__version__ = get_version_info()["version"]
|
||||
langflow_prefix = ""
|
||||
if os.getenv("AWS_EXECUTION_ENV") == "AWS_ECS_FARGATE": # TODO: More precise way of detecting
|
||||
langflow_prefix = "ds-"
|
||||
# if os.getenv("AWS_EXECUTION_ENV") == "AWS_ECS_FARGATE": # TODO: More precise way of detecting
|
||||
# langflow_prefix = "ds-"
|
||||
|
||||
# Get the database object
|
||||
database = self.get_database_object(api_endpoint=self.d_api_endpoint)
|
||||
database = self.get_database_object()
|
||||
autodetect = self.collection_name in database.list_collection_names() and self.autodetect_collection
|
||||
|
||||
# Bundle up the auto-detect parameters
|
||||
|
|
@ -714,7 +888,7 @@ class AstraDBVectorStoreComponent(LCVectorStoreComponent):
|
|||
if documents and self.deletion_field:
|
||||
self.log(f"Deleting documents where {self.deletion_field}")
|
||||
try:
|
||||
database = self.get_database_object(api_endpoint=self.d_api_endpoint)
|
||||
database = self.get_database_object()
|
||||
collection = database.get_collection(self.collection_name, keyspace=database.keyspace)
|
||||
delete_values = list({doc.metadata[self.deletion_field] for doc in documents})
|
||||
self.log(f"Deleting documents where {self.deletion_field} matches {delete_values}.")
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
Loading…
Add table
Add a link
Reference in a new issue