Tai Truong
fix readme
d202ada
from langflow.base.models.model import LCModelComponent
from langflow.field_typing import Embeddings
from langflow.io import BoolInput, FileInput, FloatInput, IntInput, MessageTextInput, Output
class VertexAIEmbeddingsComponent(LCModelComponent):
display_name = "VertexAI Embeddings"
description = "Generate embeddings using Google Cloud VertexAI models."
icon = "VertexAI"
name = "VertexAIEmbeddings"
inputs = [
FileInput(
name="credentials",
display_name="Credentials",
info="JSON credentials file. Leave empty to fallback to environment variables",
value="",
file_types=["json"],
),
MessageTextInput(name="location", display_name="Location", value="us-central1", advanced=True),
MessageTextInput(name="project", display_name="Project", info="The project ID.", advanced=True),
IntInput(name="max_output_tokens", display_name="Max Output Tokens", advanced=True),
IntInput(name="max_retries", display_name="Max Retries", value=1, advanced=True),
MessageTextInput(name="model_name", display_name="Model Name", value="textembedding-gecko"),
IntInput(name="n", display_name="N", value=1, advanced=True),
IntInput(name="request_parallelism", value=5, display_name="Request Parallelism", advanced=True),
MessageTextInput(name="stop_sequences", display_name="Stop", advanced=True, is_list=True),
BoolInput(name="streaming", display_name="Streaming", value=False, advanced=True),
FloatInput(name="temperature", value=0.0, display_name="Temperature"),
IntInput(name="top_k", display_name="Top K", advanced=True),
FloatInput(name="top_p", display_name="Top P", value=0.95, advanced=True),
]
outputs = [
Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
]
def build_embeddings(self) -> Embeddings:
try:
from langchain_google_vertexai import VertexAIEmbeddings
except ImportError as e:
msg = "Please install the langchain-google-vertexai package to use the VertexAIEmbeddings component."
raise ImportError(msg) from e
from google.oauth2 import service_account
if self.credentials:
gcloud_credentials = service_account.Credentials.from_service_account_file(self.credentials)
else:
# will fallback to environment variable or inferred from gcloud CLI
gcloud_credentials = None
return VertexAIEmbeddings(
credentials=gcloud_credentials,
location=self.location,
max_output_tokens=self.max_output_tokens or None,
max_retries=self.max_retries,
model_name=self.model_name,
n=self.n,
project=self.project,
request_parallelism=self.request_parallelism,
stop=self.stop_sequences or None,
streaming=self.streaming,
temperature=self.temperature,
top_k=self.top_k or None,
top_p=self.top_p,
)