from langflow.base.embeddings.model import LCEmbeddingsModel
from langflow.base.models.aiml_constants import AIML_EMBEDDING_MODELS
from langflow.components.embeddings.util.AIMLEmbeddingsImpl import AIMLEmbeddingsImpl
from langflow.field_typing import Embeddings
from langflow.inputs.inputs import DropdownInput
from langflow.io import SecretStrInput


class AIMLEmbeddingsComponent(LCEmbeddingsModel):
    display_name = "AI/ML Embeddings"
    description = "Generate embeddings using the AI/ML API."
    icon = "AI/ML"
    name = "AIMLEmbeddings"

    inputs = [
        DropdownInput(
            name="model_name",
            display_name="Model Name",
            options=AIML_EMBEDDING_MODELS,
            required=True,
        ),
        SecretStrInput(
            name="aiml_api_key",
            display_name="AI/ML API Key",
            value="AIML_API_KEY",
            required=True,
        ),
    ]

    def build_embeddings(self) -> Embeddings:
        return AIMLEmbeddingsImpl(
            api_key=self.aiml_api_key,
            model=self.model_name,
        )

from langchain_community.embeddings import BedrockEmbeddings

from langflow.base.models.model import LCModelComponent
from langflow.field_typing import Embeddings
from langflow.inputs import SecretStrInput
from langflow.io import DropdownInput, MessageTextInput, Output


class AmazonBedrockEmbeddingsComponent(LCModelComponent):
    display_name: str = "Amazon Bedrock Embeddings"
    description: str = "Generate embeddings using Amazon Bedrock models."
    documentation = "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock"
    icon = "Amazon"
    name = "AmazonBedrockEmbeddings"

    inputs = [
        DropdownInput(
            name="model_id",
            display_name="Model Id",
            options=["amazon.titan-embed-text-v1"],
            value="amazon.titan-embed-text-v1",
        ),
        SecretStrInput(name="aws_access_key", display_name="Access Key"),
        SecretStrInput(name="aws_secret_key", display_name="Secret Key"),
        MessageTextInput(
            name="credentials_profile_name",
            display_name="Credentials Profile Name",
            advanced=True,
        ),
        MessageTextInput(name="region_name", display_name="Region Name", value="us-east-1"),
        MessageTextInput(name="endpoint_url", display_name=" Endpoint URL", advanced=True),
    ]

    outputs = [
        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
    ]

    def build_embeddings(self) -> Embeddings:
        if self.aws_access_key:
            import boto3  # type: ignore

            session = boto3.Session(
                aws_access_key_id=self.aws_access_key,
                aws_secret_access_key=self.aws_secret_key,
            )
        elif self.credentials_profile_name:
            import boto3

            session = boto3.Session(profile_name=self.credentials_profile_name)
        else:
            import boto3

            session = boto3.Session()

        client_params = {}
        if self.endpoint_url:
            client_params["endpoint_url"] = self.endpoint_url
        if self.region_name:
            client_params["region_name"] = self.region_name

        boto3_client = session.client("bedrock-runtime", **client_params)
        output = BedrockEmbeddings(
            credentials_profile_name=self.credentials_profile_name,
            client=boto3_client,
            model_id=self.model_id,
            endpoint_url=self.endpoint_url,
            region_name=self.region_name,
        )  # type: ignore
        return output

from typing import Any

from langflow.custom import Component
from langflow.inputs.inputs import DictInput, DropdownInput, MessageTextInput, SecretStrInput
from langflow.template.field.base import Output


class AstraVectorizeComponent(Component):
    display_name: str = "Astra Vectorize"
    description: str = "Configuration options for Astra Vectorize server-side embeddings."
    documentation: str = "https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html"
    icon = "AstraDB"
    name = "AstraVectorize"

    VECTORIZE_PROVIDERS_MAPPING = {
        "Azure OpenAI": ["azureOpenAI", ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"]],
        "Hugging Face - Dedicated": ["huggingfaceDedicated", ["endpoint-defined-model"]],
        "Hugging Face - Serverless": [
            "huggingface",
            [
                "sentence-transformers/all-MiniLM-L6-v2",
                "intfloat/multilingual-e5-large",
                "intfloat/multilingual-e5-large-instruct",
                "BAAI/bge-small-en-v1.5",
                "BAAI/bge-base-en-v1.5",
                "BAAI/bge-large-en-v1.5",
            ],
        ],
        "Jina AI": [
            "jinaAI",
            [
                "jina-embeddings-v2-base-en",
                "jina-embeddings-v2-base-de",
                "jina-embeddings-v2-base-es",
                "jina-embeddings-v2-base-code",
                "jina-embeddings-v2-base-zh",
            ],
        ],
        "Mistral AI": ["mistral", ["mistral-embed"]],
        "NVIDIA": ["nvidia", ["NV-Embed-QA"]],
        "OpenAI": ["openai", ["text-embedding-3-small", "text-embedding-3-large", "text-embedding-ada-002"]],
        "Upstage": ["upstageAI", ["solar-embedding-1-large"]],
        "Voyage AI": [
            "voyageAI",
            ["voyage-large-2-instruct", "voyage-law-2", "voyage-code-2", "voyage-large-2", "voyage-2"],
        ],
    }
    VECTORIZE_MODELS_STR = "\n\n".join(
        [provider + ": " + (", ".join(models[1])) for provider, models in VECTORIZE_PROVIDERS_MAPPING.items()]
    )

    inputs = [
        DropdownInput(
            name="provider",
            display_name="Provider",
            options=VECTORIZE_PROVIDERS_MAPPING.keys(),
            value="",
            required=True,
        ),
        MessageTextInput(
            name="model_name",
            display_name="Model Name",
            info=f"The embedding model to use for the selected provider. Each provider has a different set of models "
            f"available (full list at https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html):\n\n{VECTORIZE_MODELS_STR}",
            required=True,
        ),
        MessageTextInput(
            name="api_key_name",
            display_name="API Key name",
            info="The name of the embeddings provider API key stored on Astra. If set, it will override the 'ProviderKey' in the authentication parameters.",
        ),
        DictInput(
            name="authentication",
            display_name="Authentication parameters",
            is_list=True,
            advanced=True,
        ),
        SecretStrInput(
            name="provider_api_key",
            display_name="Provider API Key",
            info="An alternative to the Astra Authentication that passes an API key for the provider with each request to Astra DB. This may be used when Vectorize is configured for the collection, but no corresponding provider secret is stored within Astra's key management system.",
            advanced=True,
        ),
        DictInput(
            name="authentication",
            display_name="Authentication Parameters",
            is_list=True,
            advanced=True,
        ),
        DictInput(
            name="model_parameters",
            display_name="Model Parameters",
            advanced=True,
            is_list=True,
        ),
    ]
    outputs = [
        Output(display_name="Vectorize", name="config", method="build_options", types=["dict"]),
    ]

    def build_options(self) -> dict[str, Any]:
        provider_value = self.VECTORIZE_PROVIDERS_MAPPING[self.provider][0]
        authentication = {**(self.authentication or {})}
        api_key_name = self.api_key_name
        if api_key_name:
            authentication["providerKey"] = api_key_name
        return {
            # must match astrapy.info.CollectionVectorServiceOptions
            "collection_vector_service_options": {
                "provider": provider_value,
                "modelName": self.model_name,
                "authentication": authentication,
                "parameters": self.model_parameters or {},
            },
            "collection_embedding_api_key": self.provider_api_key,
        }

from langchain_openai import AzureOpenAIEmbeddings

from langflow.base.models.model import LCModelComponent
from langflow.field_typing import Embeddings
from langflow.io import DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput


class AzureOpenAIEmbeddingsComponent(LCModelComponent):
    display_name: str = "Azure OpenAI Embeddings"
    description: str = "Generate embeddings using Azure OpenAI models."
    documentation: str = "https://python.langchain.com/docs/integrations/text_embedding/azureopenai"
    icon = "Azure"
    name = "AzureOpenAIEmbeddings"

    API_VERSION_OPTIONS = [
        "2022-12-01",
        "2023-03-15-preview",
        "2023-05-15",
        "2023-06-01-preview",
        "2023-07-01-preview",
        "2023-08-01-preview",
    ]

    inputs = [
        MessageTextInput(
            name="azure_endpoint",
            display_name="Azure Endpoint",
            required=True,
            info="Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`",
        ),
        MessageTextInput(
            name="azure_deployment",
            display_name="Deployment Name",
            required=True,
        ),
        DropdownInput(
            name="api_version",
            display_name="API Version",
            options=API_VERSION_OPTIONS,
            value=API_VERSION_OPTIONS[-1],
            advanced=True,
        ),
        SecretStrInput(
            name="api_key",
            display_name="API Key",
            required=True,
        ),
        IntInput(
            name="dimensions",
            display_name="Dimensions",
            info="The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
    ]

    def build_embeddings(self) -> Embeddings:
        try:
            embeddings = AzureOpenAIEmbeddings(
                azure_endpoint=self.azure_endpoint,
                azure_deployment=self.azure_deployment,
                api_version=self.api_version,
                api_key=self.api_key,
                dimensions=self.dimensions or None,
            )
        except Exception as e:
            raise ValueError(f"Could not connect to AzureOpenAIEmbeddings API: {str(e)}") from e

        return embeddings

from langchain_community.embeddings.cohere import CohereEmbeddings

from langflow.base.models.model import LCModelComponent
from langflow.field_typing import Embeddings
from langflow.io import DropdownInput, FloatInput, IntInput, MessageTextInput, Output, SecretStrInput


class CohereEmbeddingsComponent(LCModelComponent):
    display_name = "Cohere Embeddings"
    description = "Generate embeddings using Cohere models."
    icon = "Cohere"
    name = "CohereEmbeddings"

    inputs = [
        SecretStrInput(name="cohere_api_key", display_name="Cohere API Key"),
        DropdownInput(
            name="model",
            display_name="Model",
            advanced=True,
            options=[
                "embed-english-v2.0",
                "embed-multilingual-v2.0",
                "embed-english-light-v2.0",
                "embed-multilingual-light-v2.0",
            ],
            value="embed-english-v2.0",
        ),
        MessageTextInput(name="truncate", display_name="Truncate", advanced=True),
        IntInput(name="max_retries", display_name="Max Retries", value=3, advanced=True),
        MessageTextInput(name="user_agent", display_name="User Agent", advanced=True, value="langchain"),
        FloatInput(name="request_timeout", display_name="Request Timeout", advanced=True),
    ]

    outputs = [
        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
    ]

    def build_embeddings(self) -> Embeddings:
        return CohereEmbeddings(  # type: ignore
            cohere_api_key=self.cohere_api_key,
            model=self.model,
            truncate=self.truncate,
            max_retries=self.max_retries,
            user_agent=self.user_agent,
            request_timeout=self.request_timeout or None,
        )

from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
from pydantic.v1.types import SecretStr

from langflow.base.models.model import LCModelComponent
from langflow.field_typing import Embeddings
from langflow.io import MessageTextInput, Output, SecretStrInput


class HuggingFaceInferenceAPIEmbeddingsComponent(LCModelComponent):
    display_name = "HuggingFace Embeddings"
    description = "Generate embeddings using Hugging Face Inference API models."
    documentation = "https://github.com/huggingface/text-embeddings-inference"
    icon = "HuggingFace"
    name = "HuggingFaceInferenceAPIEmbeddings"

    inputs = [
        SecretStrInput(name="api_key", display_name="API Key"),
        MessageTextInput(name="api_url", display_name="API URL", advanced=True, value="http://localhost:8080"),
        MessageTextInput(name="model_name", display_name="Model Name", value="BAAI/bge-large-en-v1.5"),
    ]

    outputs = [
        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
    ]

    def build_embeddings(self) -> Embeddings:
        if not self.api_key:
            raise ValueError("API Key is required")

        api_key = SecretStr(self.api_key)

        return HuggingFaceInferenceAPIEmbeddings(api_key=api_key, api_url=self.api_url, model_name=self.model_name)

from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
from pydantic.v1.types import SecretStr

from langflow.base.models.model import LCModelComponent
from langflow.field_typing import Embeddings
from langflow.io import MessageTextInput, Output, SecretStrInput


class HuggingFaceInferenceAPIEmbeddingsComponent(LCModelComponent):
    display_name = "HuggingFace Embeddings"
    description = "Generate embeddings using Hugging Face Inference API models."
    documentation = "https://github.com/huggingface/text-embeddings-inference"
    icon = "HuggingFace"
    name = "HuggingFaceInferenceAPIEmbeddings"

    inputs = [
        SecretStrInput(name="api_key", display_name="API Key"),
        MessageTextInput(name="api_url", display_name="API URL", advanced=True, value="http://localhost:8080"),
        MessageTextInput(name="model_name", display_name="Model Name", value="BAAI/bge-large-en-v1.5"),
    ]

    outputs = [
        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
    ]

    def build_embeddings(self) -> Embeddings:
        if not self.api_key:
            raise ValueError("API Key is required")

        api_key = SecretStr(self.api_key)

        return HuggingFaceInferenceAPIEmbeddings(api_key=api_key, api_url=self.api_url, model_name=self.model_name)

from langchain_mistralai.embeddings import MistralAIEmbeddings
from pydantic.v1 import SecretStr

from langflow.base.models.model import LCModelComponent
from langflow.field_typing import Embeddings
from langflow.io import DropdownInput, IntInput, MessageTextInput, Output, SecretStrInput


class MistralAIEmbeddingsComponent(LCModelComponent):
    display_name = "MistralAI Embeddings"
    description = "Generate embeddings using MistralAI models."
    icon = "MistralAI"
    name = "MistalAIEmbeddings"

    inputs = [
        DropdownInput(
            name="model",
            display_name="Model",
            advanced=False,
            options=["mistral-embed"],
            value="mistral-embed",
        ),
        SecretStrInput(name="mistral_api_key", display_name="Mistral API Key"),
        IntInput(
            name="max_concurrent_requests",
            display_name="Max Concurrent Requests",
            advanced=True,
            value=64,
        ),
        IntInput(name="max_retries", display_name="Max Retries", advanced=True, value=5),
        IntInput(name="timeout", display_name="Request Timeout", advanced=True, value=120),
        MessageTextInput(
            name="endpoint",
            display_name="API Endpoint",
            advanced=True,
            value="https://api.mistral.ai/v1/",
        ),
    ]

    outputs = [
        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
    ]

    def build_embeddings(self) -> Embeddings:
        if not self.mistral_api_key:
            raise ValueError("Mistral API Key is required")

        api_key = SecretStr(self.mistral_api_key)

        return MistralAIEmbeddings(
            api_key=api_key,
            model=self.model,
            endpoint=self.endpoint,
            max_concurrent_requests=self.max_concurrent_requests,
            max_retries=self.max_retries,
            timeout=self.timeout,
        )

from typing import Any

from langflow.base.embeddings.model import LCEmbeddingsModel
from langflow.field_typing import Embeddings
from langflow.inputs.inputs import DropdownInput, SecretStrInput
from langflow.io import FloatInput, MessageTextInput
from langflow.schema.dotdict import dotdict


class NVIDIAEmbeddingsComponent(LCEmbeddingsModel):
    display_name: str = "NVIDIA Embeddings"
    description: str = "Generate embeddings using NVIDIA models."
    icon = "NVIDIA"

    inputs = [
        DropdownInput(
            name="model",
            display_name="Model",
            options=[
                "nvidia/nv-embed-v1",
                "snowflake/arctic-embed-I",
            ],
            value="nvidia/nv-embed-v1",
        ),
        MessageTextInput(
            name="base_url",
            display_name="NVIDIA Base URL",
            refresh_button=True,
            value="https://integrate.api.nvidia.com/v1",
        ),
        SecretStrInput(
            name="nvidia_api_key",
            display_name="NVIDIA API Key",
            info="The NVIDIA API Key.",
            advanced=False,
            value="NVIDIA_API_KEY",
        ),
        FloatInput(
            name="temperature",
            display_name="Model Temperature",
            value=0.1,
            advanced=True,
        ),
    ]

    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
        if field_name == "base_url" and field_value:
            try:
                build_model = self.build_embeddings()
                ids = [model.id for model in build_model.available_models]  # type: ignore
                build_config["model"]["options"] = ids
                build_config["model"]["value"] = ids[0]
            except Exception as e:
                raise ValueError(f"Error getting model names: {e}")
        return build_config

    def build_embeddings(self) -> Embeddings:
        try:
            from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
        except ImportError:
            raise ImportError("Please install langchain-nvidia-ai-endpoints to use the Nvidia model.")
        try:
            output = NVIDIAEmbeddings(
                model=self.model,
                base_url=self.base_url,
                temperature=self.temperature,
                nvidia_api_key=self.nvidia_api_key,
            )  # type: ignore
        except Exception as e:
            raise ValueError(f"Could not connect to NVIDIA API. Error: {e}") from e
        return output

from langchain_community.embeddings import OllamaEmbeddings

from langflow.base.models.model import LCModelComponent
from langflow.field_typing import Embeddings
from langflow.io import FloatInput, MessageTextInput, Output


class OllamaEmbeddingsComponent(LCModelComponent):
    display_name: str = "Ollama Embeddings"
    description: str = "Generate embeddings using Ollama models."
    documentation = "https://python.langchain.com/docs/integrations/text_embedding/ollama"
    icon = "Ollama"
    name = "OllamaEmbeddings"

    inputs = [
        MessageTextInput(
            name="model",
            display_name="Ollama Model",
            value="llama3.1",
        ),
        MessageTextInput(
            name="base_url",
            display_name="Ollama Base URL",
            value="http://localhost:11434",
        ),
        FloatInput(
            name="temperature",
            display_name="Model Temperature",
            value=0.1,
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
    ]

    def build_embeddings(self) -> Embeddings:
        try:
            output = OllamaEmbeddings(
                model=self.model,
                base_url=self.base_url,
                temperature=self.temperature,
            )  # type: ignore
        except Exception as e:
            raise ValueError("Could not connect to Ollama API.") from e
        return output

from langchain_openai.embeddings.base import OpenAIEmbeddings

from langflow.base.embeddings.model import LCEmbeddingsModel
from langflow.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES
from langflow.field_typing import Embeddings
from langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, MessageTextInput, SecretStrInput


class OpenAIEmbeddingsComponent(LCEmbeddingsModel):
    display_name = "OpenAI Embeddings"
    description = "Generate embeddings using OpenAI models."
    icon = "OpenAI"
    name = "OpenAIEmbeddings"

    inputs = [
        DictInput(
            name="default_headers",
            display_name="Default Headers",
            advanced=True,
            info="Default headers to use for the API request.",
        ),
        DictInput(
            name="default_query",
            display_name="Default Query",
            advanced=True,
            info="Default query parameters to use for the API request.",
        ),
        IntInput(name="chunk_size", display_name="Chunk Size", advanced=True, value=1000),
        MessageTextInput(name="client", display_name="Client", advanced=True),
        MessageTextInput(name="deployment", display_name="Deployment", advanced=True),
        IntInput(name="embedding_ctx_length", display_name="Embedding Context Length", advanced=True, value=1536),
        IntInput(name="max_retries", display_name="Max Retries", value=3, advanced=True),
        DropdownInput(
            name="model",
            display_name="Model",
            advanced=False,
            options=OPENAI_EMBEDDING_MODEL_NAMES,
            value="text-embedding-3-small",
        ),
        DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
        SecretStrInput(name="openai_api_base", display_name="OpenAI API Base", advanced=True),
        SecretStrInput(name="openai_api_key", display_name="OpenAI API Key", value="OPENAI_API_KEY"),
        SecretStrInput(name="openai_api_type", display_name="OpenAI API Type", advanced=True),
        MessageTextInput(name="openai_api_version", display_name="OpenAI API Version", advanced=True),
        MessageTextInput(
            name="openai_organization",
            display_name="OpenAI Organization",
            advanced=True,
        ),
        MessageTextInput(name="openai_proxy", display_name="OpenAI Proxy", advanced=True),
        FloatInput(name="request_timeout", display_name="Request Timeout", advanced=True),
        BoolInput(name="show_progress_bar", display_name="Show Progress Bar", advanced=True),
        BoolInput(name="skip_empty", display_name="Skip Empty", advanced=True),
        MessageTextInput(
            name="tiktoken_model_name",
            display_name="TikToken Model Name",
            advanced=True,
        ),
        BoolInput(
            name="tiktoken_enable",
            display_name="TikToken Enable",
            advanced=True,
            value=True,
            info="If False, you must have transformers installed.",
        ),
        IntInput(
            name="dimensions",
            display_name="Dimensions",
            info="The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
            advanced=True,
        ),
    ]

    def build_embeddings(self) -> Embeddings:
        return OpenAIEmbeddings(
            tiktoken_enabled=self.tiktoken_enable,
            default_headers=self.default_headers,
            default_query=self.default_query,
            allowed_special="all",
            disallowed_special="all",
            chunk_size=self.chunk_size,
            deployment=self.deployment,
            embedding_ctx_length=self.embedding_ctx_length,
            max_retries=self.max_retries,
            model=self.model,
            model_kwargs=self.model_kwargs,
            base_url=self.openai_api_base,
            api_key=self.openai_api_key,
            openai_api_type=self.openai_api_type,
            api_version=self.openai_api_version,
            organization=self.openai_organization,
            openai_proxy=self.openai_proxy,
            timeout=self.request_timeout or None,
            show_progress_bar=self.show_progress_bar,
            skip_empty=self.skip_empty,
            tiktoken_model_name=self.tiktoken_model_name,
            dimensions=self.dimensions or None,
        )

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:
            raise ImportError(
                "Please install the langchain-google-vertexai package to use the VertexAIEmbeddings component."
            )

        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,
        )