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feat: Support for Autodetect in AstraDBVectorStore settings (#4869)

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* feat: first pass at autodetect updates

* [autofix.ci] apply automated fixes

* Fully support autodetect

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
Eric Hare 2024-12-02 08:37:14 -08:00 • committed by GitHub
commit 19d2974904
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7 changed files with 280 additions and 93 deletions
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12
src/backend/tests/integration/components/astra/test_astra_component.py
View file

@ -4,7 +4,7 @@ import pytest
from astrapy.db import AstraDB
from langchain_core.documents import Document
from langflow.components.embeddings import OpenAIEmbeddingsComponent
from langflow.components.vectorstores import AstraVectorStoreComponent
from langflow.components.vectorstores import AstraDBVectorStoreComponent
from langflow.schema.data import Data
from tests.api_keys import get_astradb_api_endpoint, get_astradb_application_token, get_openai_api_key
@ -43,7 +43,7 @@ async def test_base(astradb_client: AstraDB):
api_endpoint = get_astradb_api_endpoint()
results = await run_single_component(
AstraVectorStoreComponent,
AstraDBVectorStoreComponent,
inputs={
"token": application_token,
"api_endpoint": api_endpoint,
@ -69,7 +69,7 @@ async def test_astra_embeds_and_search():
api_endpoint = get_astradb_api_endpoint()
results = await run_single_component(
AstraVectorStoreComponent,
AstraDBVectorStoreComponent,
inputs={
"token": application_token,
"api_endpoint": api_endpoint,
@ -111,7 +111,7 @@ def test_astra_vectorize():
documents = [Document(page_content="test1"), Document(page_content="test2")]
records = [Data.from_document(d) for d in documents]
component = AstraVectorStoreComponent()
component = AstraDBVectorStoreComponent()
vectorize_options = component.build_vectorize_options(**options_comp)
component.build(
@ -167,7 +167,7 @@ def test_astra_vectorize_with_provider_api_key():
documents = [Document(page_content="test1"), Document(page_content="test2")]
records = [Data.from_document(d) for d in documents]
component = AstraVectorStoreComponent()
component = AstraDBVectorStoreComponent()
vectorize_options = component.build_vectorize_options(**options_comp)
component.build(
@ -222,7 +222,7 @@ def test_astra_vectorize_passes_authentication():
documents = [Document(page_content="test1"), Document(page_content="test2")]
records = [Data.from_document(d) for d in documents]
component = AstraVectorStoreComponent()
component = AstraDBVectorStoreComponent()
vectorize_options = component.build_vectorize_options(**options_comp)
component.build(

6
src/backend/tests/unit/initial_setup/starter_projects/test_vector_store_rag.py
View file

@ -11,7 +11,7 @@ from langflow.components.outputs import ChatOutput
from langflow.components.processing import ParseDataComponent
from langflow.components.processing.split_text import SplitTextComponent
from langflow.components.prompts import PromptComponent
from langflow.components.vectorstores import AstraVectorStoreComponent
from langflow.components.vectorstores import AstraDBVectorStoreComponent
from langflow.graph import Graph
from langflow.graph.graph.constants import Finish
from langflow.schema import Data
@ -29,7 +29,7 @@ def ingestion_graph():
openai_embeddings.set(
openai_api_key="sk-123", openai_api_base="https://api.openai.com/v1", openai_api_type="openai"
)
vector_store = AstraVectorStoreComponent(_id="vector-store-123")
vector_store = AstraDBVectorStoreComponent(_id="vector-store-123")
vector_store.set(
embedding_model=openai_embeddings.build_embeddings,
ingest_data=text_splitter.split_text,
@ -48,7 +48,7 @@ def rag_graph():
openai_embeddings = OpenAIEmbeddingsComponent(_id="openai-embeddings-124")
chat_input = ChatInput(_id="chatinput-123")
chat_input.get_output("message").value = "What is the meaning of life?"
rag_vector_store = AstraVectorStoreComponent(_id="rag-vector-store-123")
rag_vector_store = AstraDBVectorStoreComponent(_id="rag-vector-store-123")
rag_vector_store.set(
search_input=chat_input.message_response,
api_endpoint="https://astra.example.com",