add third party embedding models (#1818)

backend/alembic/versions/44f856ae2a4a_add_cloud_embedding_model.py

@@ -0,0 +1,65 @@
+"""add cloud embedding model and update embedding_model
+
+Revision ID: 44f856ae2a4a
+Revises: d716b0791ddd
+Create Date: 2024-06-28 20:01:05.927647
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+# revision identifiers, used by Alembic.
+revision = "44f856ae2a4a"
+down_revision = "d716b0791ddd"
+branch_labels: None = None
+depends_on: None = None
+
+
+def upgrade() -> None:
+    # Create embedding_provider table
+    op.create_table(
+        "embedding_provider",
+        sa.Column("id", sa.Integer(), nullable=False),
+        sa.Column("name", sa.String(), nullable=False),
+        sa.Column("api_key", sa.LargeBinary(), nullable=True),
+        sa.Column("default_model_id", sa.Integer(), nullable=True),
+        sa.PrimaryKeyConstraint("id"),
+        sa.UniqueConstraint("name"),
+    )
+
+    # Add cloud_provider_id to embedding_model table
+    op.add_column(
+        "embedding_model", sa.Column("cloud_provider_id", sa.Integer(), nullable=True)
+    )
+
+    # Add foreign key constraints
+    op.create_foreign_key(
+        "fk_embedding_model_cloud_provider",
+        "embedding_model",
+        "embedding_provider",
+        ["cloud_provider_id"],
+        ["id"],
+    )
+    op.create_foreign_key(
+        "fk_embedding_provider_default_model",
+        "embedding_provider",
+        "embedding_model",
+        ["default_model_id"],
+        ["id"],
+    )
+
+
+def downgrade() -> None:
+    # Remove foreign key constraints
+    op.drop_constraint(
+        "fk_embedding_model_cloud_provider", "embedding_model", type_="foreignkey"
+    )
+    op.drop_constraint(
+        "fk_embedding_provider_default_model", "embedding_provider", type_="foreignkey"
+    )
+
+    # Remove cloud_provider_id column
+    op.drop_column("embedding_model", "cloud_provider_id")
+
+    # Drop embedding_provider table
+    op.drop_table("embedding_provider")

backend/alembic/versions/d716b0791ddd_combined_slack_id_fields.py

@@ -10,8 +10,8 @@ from alembic import op
 # revision identifiers, used by Alembic.
 revision = "d716b0791ddd"
 down_revision = "7aea705850d5"
-branch_labels = None
+branch_labels: None = None
-depends_on = None
+depends_on: None = None
 
 
 def upgrade() -> None:

backend/danswer/background/indexing/run_indexing.py

@@ -98,7 +98,6 @@ def _run_indexing(
     3. Updates Postgres to record the indexed documents + the outcome of this run
     """
     start_time = time.time()
-
     db_embedding_model = index_attempt.embedding_model
     index_name = db_embedding_model.index_name
 
@@ -116,6 +115,8 @@ def _run_indexing(
         normalize=db_embedding_model.normalize,
         query_prefix=db_embedding_model.query_prefix,
         passage_prefix=db_embedding_model.passage_prefix,
+        api_key=db_embedding_model.api_key,
+        provider_type=db_embedding_model.provider_type,
     )
 
     indexing_pipeline = build_indexing_pipeline(
@@ -287,6 +288,7 @@ def _prepare_index_attempt(db_session: Session, index_attempt_id: int) -> IndexA
         db_session=db_session,
         index_attempt_id=index_attempt_id,
     )
+
     if attempt is None:
         raise RuntimeError(f"Unable to find IndexAttempt for ID '{index_attempt_id}'")
 

backend/danswer/background/update.py

@@ -343,6 +343,8 @@ def update_loop(delay: int = 10, num_workers: int = NUM_INDEXING_WORKERS) -> Non
 
     # So that the first time users aren't surprised by really slow speed of first
     # batch of documents indexed
+
+    if db_embedding_model.cloud_provider_id is None:
         logger.info("Running a first inference to warm up embedding model")
         warm_up_encoders(
             model_name=db_embedding_model.model_name,

backend/danswer/danswerbot/slack/listener.py

@@ -469,7 +469,7 @@ if __name__ == "__main__":
                     # or the tokens have updated (set up for the first time)
                     with Session(get_sqlalchemy_engine()) as db_session:
                         embedding_model = get_current_db_embedding_model(db_session)
-
+                        if embedding_model.cloud_provider_id is None:
                             warm_up_encoders(
                                 model_name=embedding_model.model_name,
                                 normalize=embedding_model.normalize,

backend/danswer/db/embedding_model.py

@@ -10,10 +10,15 @@ from danswer.configs.model_configs import NORMALIZE_EMBEDDINGS
 from danswer.configs.model_configs import OLD_DEFAULT_DOCUMENT_ENCODER_MODEL
 from danswer.configs.model_configs import OLD_DEFAULT_MODEL_DOC_EMBEDDING_DIM
 from danswer.configs.model_configs import OLD_DEFAULT_MODEL_NORMALIZE_EMBEDDINGS
+from danswer.db.llm import fetch_embedding_provider
+from danswer.db.models import CloudEmbeddingProvider
 from danswer.db.models import EmbeddingModel
 from danswer.db.models import IndexModelStatus
 from danswer.indexing.models import EmbeddingModelDetail
 from danswer.search.search_nlp_models import clean_model_name
+from danswer.server.manage.embedding.models import (
+    CloudEmbeddingProvider as ServerCloudEmbeddingProvider,
+)
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -31,6 +36,7 @@ def create_embedding_model(
         query_prefix=model_details.query_prefix,
         passage_prefix=model_details.passage_prefix,
         status=status,
+        cloud_provider_id=model_details.cloud_provider_id,
         # Every single embedding model except the initial one from migrations has this name
         # The initial one from migration is called "danswer_chunk"
         index_name=f"danswer_chunk_{clean_model_name(model_details.model_name)}",
@@ -42,6 +48,42 @@ def create_embedding_model(
     return embedding_model
 
 
+def get_model_id_from_name(
+    db_session: Session, embedding_provider_name: str
+) -> int | None:
+    query = select(CloudEmbeddingProvider).where(
+        CloudEmbeddingProvider.name == embedding_provider_name
+    )
+    provider = db_session.execute(query).scalars().first()
+    return provider.id if provider else None
+
+
+def get_current_db_embedding_provider(
+    db_session: Session,
+) -> ServerCloudEmbeddingProvider | None:
+    current_embedding_model = EmbeddingModelDetail.from_model(
+        get_current_db_embedding_model(db_session=db_session)
+    )
+
+    if (
+        current_embedding_model is None
+        or current_embedding_model.cloud_provider_id is None
+    ):
+        return None
+
+    embedding_provider = fetch_embedding_provider(
+        db_session=db_session, provider_id=current_embedding_model.cloud_provider_id
+    )
+    if embedding_provider is None:
+        raise RuntimeError("No embedding provider exists for this model.")
+
+    current_embedding_provider = ServerCloudEmbeddingProvider.from_request(
+        cloud_provider_model=embedding_provider
+    )
+
+    return current_embedding_provider
+
+
 def get_current_db_embedding_model(db_session: Session) -> EmbeddingModel:
     query = (
         select(EmbeddingModel)

backend/danswer/db/llm.py

@@ -2,11 +2,34 @@ from sqlalchemy import delete
 from sqlalchemy import select
 from sqlalchemy.orm import Session
 
+from danswer.db.models import CloudEmbeddingProvider as CloudEmbeddingProviderModel
 from danswer.db.models import LLMProvider as LLMProviderModel
+from danswer.server.manage.embedding.models import CloudEmbeddingProvider
+from danswer.server.manage.embedding.models import CloudEmbeddingProviderCreationRequest
 from danswer.server.manage.llm.models import FullLLMProvider
 from danswer.server.manage.llm.models import LLMProviderUpsertRequest
 
 
+def upsert_cloud_embedding_provider(
+    db_session: Session, provider: CloudEmbeddingProviderCreationRequest
+) -> CloudEmbeddingProvider:
+    existing_provider = (
+        db_session.query(CloudEmbeddingProviderModel)
+        .filter_by(name=provider.name)
+        .first()
+    )
+    if existing_provider:
+        for key, value in provider.dict().items():
+            setattr(existing_provider, key, value)
+    else:
+        new_provider = CloudEmbeddingProviderModel(**provider.dict())
+        db_session.add(new_provider)
+        existing_provider = new_provider
+    db_session.commit()
+    db_session.refresh(existing_provider)
+    return CloudEmbeddingProvider.from_request(existing_provider)
+
+
 def upsert_llm_provider(
     db_session: Session, llm_provider: LLMProviderUpsertRequest
 ) -> FullLLMProvider:
@@ -26,7 +49,6 @@ def upsert_llm_provider(
         existing_llm_provider.model_names = llm_provider.model_names
         db_session.commit()
         return FullLLMProvider.from_model(existing_llm_provider)
-
     # if it does not exist, create a new entry
     llm_provider_model = LLMProviderModel(
         name=llm_provider.name,
@@ -46,10 +68,26 @@ def upsert_llm_provider(
     return FullLLMProvider.from_model(llm_provider_model)
 
 
+def fetch_existing_embedding_providers(
+    db_session: Session,
+) -> list[CloudEmbeddingProviderModel]:
+    return list(db_session.scalars(select(CloudEmbeddingProviderModel)).all())
+
+
 def fetch_existing_llm_providers(db_session: Session) -> list[LLMProviderModel]:
     return list(db_session.scalars(select(LLMProviderModel)).all())
 
 
+def fetch_embedding_provider(
+    db_session: Session, provider_id: int
+) -> CloudEmbeddingProviderModel | None:
+    return db_session.scalar(
+        select(CloudEmbeddingProviderModel).where(
+            CloudEmbeddingProviderModel.id == provider_id
+        )
+    )
+
+
 def fetch_default_provider(db_session: Session) -> FullLLMProvider | None:
     provider_model = db_session.scalar(
         select(LLMProviderModel).where(
@@ -70,6 +108,16 @@ def fetch_provider(db_session: Session, provider_name: str) -> FullLLMProvider |
     return FullLLMProvider.from_model(provider_model)
 
 
+def remove_embedding_provider(
+    db_session: Session, embedding_provider_name: str
+) -> None:
+    db_session.execute(
+        delete(CloudEmbeddingProviderModel).where(
+            CloudEmbeddingProviderModel.name == embedding_provider_name
+        )
+    )
+
+
 def remove_llm_provider(db_session: Session, provider_id: int) -> None:
     db_session.execute(
         delete(LLMProviderModel).where(LLMProviderModel.id == provider_id)

backend/danswer/db/models.py

@@ -130,6 +130,7 @@ class User(SQLAlchemyBaseUserTableUUID, Base):
     chat_folders: Mapped[list["ChatFolder"]] = relationship(
         "ChatFolder", back_populates="user"
     )
+
     prompts: Mapped[list["Prompt"]] = relationship("Prompt", back_populates="user")
     # Personas owned by this user
     personas: Mapped[list["Persona"]] = relationship("Persona", back_populates="user")
@@ -469,7 +470,7 @@ class Credential(Base):
 
 class EmbeddingModel(Base):
     __tablename__ = "embedding_model"
-    # ID is used also to indicate the order that the models are configured by the admin
+
     id: Mapped[int] = mapped_column(primary_key=True)
     model_name: Mapped[str] = mapped_column(String)
     model_dim: Mapped[int] = mapped_column(Integer)
@@ -481,6 +482,16 @@ class EmbeddingModel(Base):
     )
     index_name: Mapped[str] = mapped_column(String)
 
+    # New field for cloud provider relationship
+    cloud_provider_id: Mapped[int | None] = mapped_column(
+        ForeignKey("embedding_provider.id")
+    )
+    cloud_provider: Mapped["CloudEmbeddingProvider"] = relationship(
+        "CloudEmbeddingProvider",
+        back_populates="embedding_models",
+        foreign_keys=[cloud_provider_id],
+    )
+
     index_attempts: Mapped[list["IndexAttempt"]] = relationship(
         "IndexAttempt", back_populates="embedding_model"
     )
@@ -500,6 +511,18 @@ class EmbeddingModel(Base):
         ),
     )
 
+    def __repr__(self) -> str:
+        return f"<EmbeddingModel(model_name='{self.model_name}', status='{self.status}',\
+          cloud_provider='{self.cloud_provider.name if self.cloud_provider else 'None'}')>"
+
+    @property
+    def api_key(self) -> str | None:
+        return self.cloud_provider.api_key if self.cloud_provider else None
+
+    @property
+    def provider_type(self) -> str | None:
+        return self.cloud_provider.name if self.cloud_provider else None
+
 
 class IndexAttempt(Base):
     """
@@ -519,6 +542,7 @@ class IndexAttempt(Base):
         ForeignKey("credential.id"),
         nullable=True,
     )
+
     # Some index attempts that run from beginning will still have this as False
     # This is only for attempts that are explicitly marked as from the start via
     # the run once API
@@ -879,11 +903,6 @@ class ChatMessageFeedback(Base):
     )
 
 
-"""
-Structures, Organizational, Configurations Tables
-"""
-
-
 class LLMProvider(Base):
     __tablename__ = "llm_provider"
 
@@ -912,6 +931,29 @@ class LLMProvider(Base):
     is_default_provider: Mapped[bool | None] = mapped_column(Boolean, unique=True)
 
 
+class CloudEmbeddingProvider(Base):
+    __tablename__ = "embedding_provider"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    name: Mapped[str] = mapped_column(String, unique=True)
+    api_key: Mapped[str | None] = mapped_column(EncryptedString())
+    default_model_id: Mapped[int | None] = mapped_column(
+        Integer, ForeignKey("embedding_model.id"), nullable=True
+    )
+
+    embedding_models: Mapped[list["EmbeddingModel"]] = relationship(
+        "EmbeddingModel",
+        back_populates="cloud_provider",
+        foreign_keys="EmbeddingModel.cloud_provider_id",
+    )
+    default_model: Mapped["EmbeddingModel"] = relationship(
+        "EmbeddingModel", foreign_keys=[default_model_id]
+    )
+
+    def __repr__(self) -> str:
+        return f"<EmbeddingProvider(name='{self.name}')>"
+
+
 class DocumentSet(Base):
     __tablename__ = "document_set"
 
@@ -1194,6 +1236,7 @@ class SlackBotConfig(Base):
     response_type: Mapped[SlackBotResponseType] = mapped_column(
         Enum(SlackBotResponseType, native_enum=False), nullable=False
     )
+
     enable_auto_filters: Mapped[bool] = mapped_column(
         Boolean, nullable=False, default=False
     )

backend/danswer/indexing/embedder.py

@@ -50,6 +50,8 @@ class DefaultIndexingEmbedder(IndexingEmbedder):
         normalize: bool,
         query_prefix: str | None,
         passage_prefix: str | None,
+        api_key: str | None = None,
+        provider_type: str | None = None,
     ):
         super().__init__(model_name, normalize, query_prefix, passage_prefix)
         self.max_seq_length = DOC_EMBEDDING_CONTEXT_SIZE  # Currently not customizable
@@ -59,6 +61,8 @@ class DefaultIndexingEmbedder(IndexingEmbedder):
             query_prefix=query_prefix,
             passage_prefix=passage_prefix,
             normalize=normalize,
+            api_key=api_key,
+            provider_type=provider_type,
             # The below are globally set, this flow always uses the indexing one
             server_host=INDEXING_MODEL_SERVER_HOST,
             server_port=INDEXING_MODEL_SERVER_PORT,

backend/danswer/indexing/models.py

@@ -97,13 +97,19 @@ class EmbeddingModelDetail(BaseModel):
     normalize: bool
     query_prefix: str | None
     passage_prefix: str | None
+    cloud_provider_id: int | None = None
+    cloud_provider_name: str | None = None
 
     @classmethod
-    def from_model(cls, embedding_model: "EmbeddingModel") -> "EmbeddingModelDetail":
+    def from_model(
+        cls,
+        embedding_model: "EmbeddingModel",
+    ) -> "EmbeddingModelDetail":
         return cls(
             model_name=embedding_model.model_name,
             model_dim=embedding_model.model_dim,
             normalize=embedding_model.normalize,
             query_prefix=embedding_model.query_prefix,
             passage_prefix=embedding_model.passage_prefix,
+            cloud_provider_id=embedding_model.cloud_provider_id,
         )

backend/danswer/main.py

@@ -67,6 +67,8 @@ from danswer.server.features.tool.api import admin_router as admin_tool_router
 from danswer.server.features.tool.api import router as tool_router
 from danswer.server.gpts.api import router as gpts_router
 from danswer.server.manage.administrative import router as admin_router
+from danswer.server.manage.embedding.api import admin_router as embedding_admin_router
+from danswer.server.manage.embedding.api import basic_router as embedding_router
 from danswer.server.manage.get_state import router as state_router
 from danswer.server.manage.llm.api import admin_router as llm_admin_router
 from danswer.server.manage.llm.api import basic_router as llm_router
@@ -247,6 +249,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator:
                 time.sleep(wait_time)
 
     logger.info(f"Model Server: http://{MODEL_SERVER_HOST}:{MODEL_SERVER_PORT}")
+    if db_embedding_model.cloud_provider_id is None:
         warm_up_encoders(
             model_name=db_embedding_model.model_name,
             normalize=db_embedding_model.normalize,
@@ -291,6 +294,8 @@ def get_application() -> FastAPI:
     include_router_with_global_prefix_prepended(application, settings_admin_router)
     include_router_with_global_prefix_prepended(application, llm_admin_router)
     include_router_with_global_prefix_prepended(application, llm_router)
+    include_router_with_global_prefix_prepended(application, embedding_admin_router)
+    include_router_with_global_prefix_prepended(application, embedding_router)
     include_router_with_global_prefix_prepended(
         application, token_rate_limit_settings_router
     )

backend/danswer/one_shot_answer/answer_question.py

@@ -168,6 +168,7 @@ def stream_answer_objects(
         max_tokens=max_document_tokens,
         use_sections=query_req.chunks_above > 0 or query_req.chunks_below > 0,
     )
+
     search_tool = SearchTool(
         db_session=db_session,
         user=user,

backend/danswer/search/retrieval/search_runner.py

@@ -131,6 +131,8 @@ def doc_index_retrieval(
             query_prefix=db_embedding_model.query_prefix,
             passage_prefix=db_embedding_model.passage_prefix,
             normalize=db_embedding_model.normalize,
+            api_key=db_embedding_model.api_key,
+            provider_type=db_embedding_model.provider_type,
             # The below are globally set, this flow always uses the indexing one
             server_host=MODEL_SERVER_HOST,
             server_port=MODEL_SERVER_PORT,

backend/danswer/search/search_nlp_models.py

@@ -84,20 +84,24 @@ def build_model_server_url(
 class EmbeddingModel:
     def __init__(
         self,
-        model_name: str,
-        query_prefix: str | None,
-        passage_prefix: str | None,
-        normalize: bool,
         server_host: str,  # Changes depending on indexing or inference
         server_port: int,
+        model_name: str | None,
+        normalize: bool,
+        query_prefix: str | None,
+        passage_prefix: str | None,
+        api_key: str | None,
+        provider_type: str | None,
         # The following are globals are currently not configurable
         max_seq_length: int = DOC_EMBEDDING_CONTEXT_SIZE,
     ) -> None:
-        self.model_name = model_name
+        self.api_key = api_key
+        self.provider_type = provider_type
         self.max_seq_length = max_seq_length
         self.query_prefix = query_prefix
         self.passage_prefix = passage_prefix
         self.normalize = normalize
+        self.model_name = model_name
 
         model_server_url = build_model_server_url(server_host, server_port)
         self.embed_server_endpoint = f"{model_server_url}/encoder/bi-encoder-embed"
@@ -111,10 +115,13 @@ class EmbeddingModel:
             prefixed_texts = texts
 
         embed_request = EmbedRequest(
-            texts=prefixed_texts,
             model_name=self.model_name,
+            texts=prefixed_texts,
             max_context_length=self.max_seq_length,
             normalize_embeddings=self.normalize,
+            api_key=self.api_key,
+            provider_type=self.provider_type,
+            text_type=text_type,
         )
 
         response = requests.post(self.embed_server_endpoint, json=embed_request.dict())
@@ -187,6 +194,8 @@ def warm_up_encoders(
         passage_prefix=None,
         server_host=model_server_host,
         server_port=model_server_port,
+        api_key=None,
+        provider_type=None,
     )
 
     # First time downloading the models it may take even longer, but just in case,

backend/danswer/server/manage/embedding/api.py

@@ -0,0 +1,93 @@
+from fastapi import APIRouter
+from fastapi import Depends
+from fastapi import HTTPException
+from sqlalchemy.orm import Session
+
+from danswer.auth.users import current_admin_user
+from danswer.db.embedding_model import get_current_db_embedding_provider
+from danswer.db.engine import get_session
+from danswer.db.llm import fetch_existing_embedding_providers
+from danswer.db.llm import remove_embedding_provider
+from danswer.db.llm import upsert_cloud_embedding_provider
+from danswer.db.models import User
+from danswer.search.enums import EmbedTextType
+from danswer.search.search_nlp_models import EmbeddingModel
+from danswer.server.manage.embedding.models import CloudEmbeddingProvider
+from danswer.server.manage.embedding.models import CloudEmbeddingProviderCreationRequest
+from danswer.server.manage.embedding.models import TestEmbeddingRequest
+from danswer.utils.logger import setup_logger
+from shared_configs.configs import MODEL_SERVER_HOST
+from shared_configs.configs import MODEL_SERVER_PORT
+
+logger = setup_logger()
+
+
+admin_router = APIRouter(prefix="/admin/embedding")
+basic_router = APIRouter(prefix="/embedding")
+
+
+@admin_router.post("/test-embedding")
+def test_embedding_configuration(
+    test_llm_request: TestEmbeddingRequest,
+    _: User | None = Depends(current_admin_user),
+) -> None:
+    try:
+        test_model = EmbeddingModel(
+            server_host=MODEL_SERVER_HOST,
+            server_port=MODEL_SERVER_PORT,
+            api_key=test_llm_request.api_key,
+            provider_type=test_llm_request.provider,
+            normalize=False,
+            query_prefix=None,
+            passage_prefix=None,
+            model_name=None,
+        )
+        test_model.encode(["Test String"], text_type=EmbedTextType.QUERY)
+
+    except ValueError as e:
+        error_msg = f"Not a valid embedding model. Exception thrown: {e}"
+        logger.error(error_msg)
+        raise ValueError(error_msg)
+
+    except Exception as e:
+        error_msg = "An error occurred while testing your embedding model. Please check your configuration."
+        logger.error(f"{error_msg} Error message: {e}", exc_info=True)
+        raise HTTPException(status_code=400, detail=error_msg)
+
+
+@admin_router.get("/embedding-provider")
+def list_embedding_providers(
+    _: User | None = Depends(current_admin_user),
+    db_session: Session = Depends(get_session),
+) -> list[CloudEmbeddingProvider]:
+    return [
+        CloudEmbeddingProvider.from_request(embedding_provider_model)
+        for embedding_provider_model in fetch_existing_embedding_providers(db_session)
+    ]
+
+
+@admin_router.delete("/embedding-provider/{embedding_provider_name}")
+def delete_embedding_provider(
+    embedding_provider_name: str,
+    _: User | None = Depends(current_admin_user),
+    db_session: Session = Depends(get_session),
+) -> None:
+    embedding_provider = get_current_db_embedding_provider(db_session=db_session)
+    if (
+        embedding_provider is not None
+        and embedding_provider_name == embedding_provider.name
+    ):
+        raise HTTPException(
+            status_code=400, detail="You can't delete a currently active model"
+        )
+
+    remove_embedding_provider(db_session, embedding_provider_name)
+
+
+@admin_router.put("/embedding-provider")
+def put_cloud_embedding_provider(
+    provider: CloudEmbeddingProviderCreationRequest,
+    _: User = Depends(current_admin_user),
+    db_session: Session = Depends(get_session),
+) -> CloudEmbeddingProvider:
+    return upsert_cloud_embedding_provider(db_session, provider)

backend/danswer/server/manage/embedding/models.py

@@ -0,0 +1,35 @@
+from typing import TYPE_CHECKING
+
+from pydantic import BaseModel
+
+if TYPE_CHECKING:
+    from danswer.db.models import CloudEmbeddingProvider as CloudEmbeddingProviderModel
+
+
+class TestEmbeddingRequest(BaseModel):
+    provider: str
+    api_key: str | None = None
+
+
+class CloudEmbeddingProvider(BaseModel):
+    name: str
+    api_key: str | None = None
+    default_model_id: int | None = None
+    id: int
+
+    @classmethod
+    def from_request(
+        cls, cloud_provider_model: "CloudEmbeddingProviderModel"
+    ) -> "CloudEmbeddingProvider":
+        return cls(
+            id=cloud_provider_model.id,
+            name=cloud_provider_model.name,
+            api_key=cloud_provider_model.api_key,
+            default_model_id=cloud_provider_model.default_model_id,
+        )
+
+
+class CloudEmbeddingProviderCreationRequest(BaseModel):
+    name: str
+    api_key: str | None = None
+    default_model_id: int | None = None

backend/danswer/server/manage/llm/models.py

@@ -4,6 +4,7 @@ from pydantic import BaseModel
 
 from danswer.llm.llm_provider_options import fetch_models_for_provider
 
+
 if TYPE_CHECKING:
     from danswer.db.models import LLMProvider as LLMProviderModel
 

backend/danswer/server/manage/secondary_index.py

@@ -11,6 +11,7 @@ from danswer.db.connector_credential_pair import get_connector_credential_pairs
 from danswer.db.connector_credential_pair import resync_cc_pair
 from danswer.db.embedding_model import create_embedding_model
 from danswer.db.embedding_model import get_current_db_embedding_model
+from danswer.db.embedding_model import get_model_id_from_name
 from danswer.db.embedding_model import get_secondary_db_embedding_model
 from danswer.db.embedding_model import update_embedding_model_status
 from danswer.db.engine import get_session
@@ -38,6 +39,19 @@ def set_new_embedding_model(
     """
     current_model = get_current_db_embedding_model(db_session)
 
+    if embed_model_details.cloud_provider_name is not None:
+        cloud_id = get_model_id_from_name(
+            db_session, embed_model_details.cloud_provider_name
+        )
+
+        if cloud_id is None:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="No ID exists for given provider name",
+            )
+
+        embed_model_details.cloud_provider_id = cloud_id
+
     if embed_model_details.model_name == current_model.model_name:
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,

backend/model_server/constants.py

@@ -1 +1,38 @@
+from enum import Enum
+
+from danswer.search.enums import EmbedTextType
+
+
 MODEL_WARM_UP_STRING = "hi " * 512
+DEFAULT_OPENAI_MODEL = "text-embedding-3-small"
+DEFAULT_COHERE_MODEL = "embed-english-light-v3.0"
+DEFAULT_VOYAGE_MODEL = "voyage-large-2-instruct"
+DEFAULT_VERTEX_MODEL = "text-embedding-004"
+
+
+class EmbeddingProvider(Enum):
+    OPENAI = "openai"
+    COHERE = "cohere"
+    VOYAGE = "voyage"
+    GOOGLE = "google"
+
+
+class EmbeddingModelTextType:
+    PROVIDER_TEXT_TYPE_MAP = {
+        EmbeddingProvider.COHERE: {
+            EmbedTextType.QUERY: "search_query",
+            EmbedTextType.PASSAGE: "search_document",
+        },
+        EmbeddingProvider.VOYAGE: {
+            EmbedTextType.QUERY: "query",
+            EmbedTextType.PASSAGE: "document",
+        },
+        EmbeddingProvider.GOOGLE: {
+            EmbedTextType.QUERY: "RETRIEVAL_QUERY",
+            EmbedTextType.PASSAGE: "RETRIEVAL_DOCUMENT",
+        },
+    }
+
+    @staticmethod
+    def get_type(provider: EmbeddingProvider, text_type: EmbedTextType) -> str:
+        return EmbeddingModelTextType.PROVIDER_TEXT_TYPE_MAP[provider][text_type]

backend/model_server/encoders.py

@@ -1,12 +1,28 @@
 import gc
+import json
+from typing import Any
 from typing import Optional
 
+import openai
+import vertexai  # type: ignore
+import voyageai  # type: ignore
+from cohere import Client as CohereClient
 from fastapi import APIRouter
 from fastapi import HTTPException
+from google.oauth2 import service_account
 from sentence_transformers import CrossEncoder  # type: ignore
 from sentence_transformers import SentenceTransformer  # type: ignore
+from vertexai.language_models import TextEmbeddingInput  # type: ignore
+from vertexai.language_models import TextEmbeddingModel  # type: ignore
 
+from danswer.search.enums import EmbedTextType
 from danswer.utils.logger import setup_logger
+from model_server.constants import DEFAULT_COHERE_MODEL
+from model_server.constants import DEFAULT_OPENAI_MODEL
+from model_server.constants import DEFAULT_VERTEX_MODEL
+from model_server.constants import DEFAULT_VOYAGE_MODEL
+from model_server.constants import EmbeddingModelTextType
+from model_server.constants import EmbeddingProvider
 from model_server.constants import MODEL_WARM_UP_STRING
 from model_server.utils import simple_log_function_time
 from shared_configs.configs import CROSS_EMBED_CONTEXT_SIZE
@@ -17,6 +33,7 @@ from shared_configs.model_server_models import EmbedResponse
 from shared_configs.model_server_models import RerankRequest
 from shared_configs.model_server_models import RerankResponse
 
+
 logger = setup_logger()
 
 router = APIRouter(prefix="/encoder")
@@ -25,6 +42,117 @@ _GLOBAL_MODELS_DICT: dict[str, "SentenceTransformer"] = {}
 _RERANK_MODELS: Optional[list["CrossEncoder"]] = None
 
 
+class CloudEmbedding:
+    def __init__(self, api_key: str, provider: str, model: str | None = None):
+        self.api_key = api_key
+
+        # Only for Google as is needed on client setup
+        self.model = model
+        try:
+            self.provider = EmbeddingProvider(provider.lower())
+        except ValueError:
+            raise ValueError(f"Unsupported provider: {provider}")
+        self.client = self._initialize_client()
+
+    def _initialize_client(self) -> Any:
+        if self.provider == EmbeddingProvider.OPENAI:
+            return openai.OpenAI(api_key=self.api_key)
+        elif self.provider == EmbeddingProvider.COHERE:
+            return CohereClient(api_key=self.api_key)
+        elif self.provider == EmbeddingProvider.VOYAGE:
+            return voyageai.Client(api_key=self.api_key)
+        elif self.provider == EmbeddingProvider.GOOGLE:
+            credentials = service_account.Credentials.from_service_account_info(
+                json.loads(self.api_key)
+            )
+            project_id = json.loads(self.api_key)["project_id"]
+            vertexai.init(project=project_id, credentials=credentials)
+            return TextEmbeddingModel.from_pretrained(
+                self.model or DEFAULT_VERTEX_MODEL
+            )
+
+        else:
+            raise ValueError(f"Unsupported provider: {self.provider}")
+
+    def encode(
+        self, texts: list[str], model_name: str | None, text_type: EmbedTextType
+    ) -> list[list[float]]:
+        return [
+            self.embed(text=text, text_type=text_type, model=model_name)
+            for text in texts
+        ]
+
+    def embed(
+        self, *, text: str, text_type: EmbedTextType, model: str | None = None
+    ) -> list[float]:
+        logger.debug(f"Embedding text with provider: {self.provider}")
+        if self.provider == EmbeddingProvider.OPENAI:
+            return self._embed_openai(text, model)
+
+        embedding_type = EmbeddingModelTextType.get_type(self.provider, text_type)
+
+        if self.provider == EmbeddingProvider.COHERE:
+            return self._embed_cohere(text, model, embedding_type)
+        elif self.provider == EmbeddingProvider.VOYAGE:
+            return self._embed_voyage(text, model, embedding_type)
+        elif self.provider == EmbeddingProvider.GOOGLE:
+            return self._embed_vertex(text, model, embedding_type)
+        else:
+            raise ValueError(f"Unsupported provider: {self.provider}")
+
+    def _embed_openai(self, text: str, model: str | None) -> list[float]:
+        if model is None:
+            model = DEFAULT_OPENAI_MODEL
+
+        response = self.client.embeddings.create(input=text, model=model)
+        return response.data[0].embedding
+
+    def _embed_cohere(
+        self, text: str, model: str | None, embedding_type: str
+    ) -> list[float]:
+        if model is None:
+            model = DEFAULT_COHERE_MODEL
+
+        response = self.client.embed(
+            texts=[text],
+            model=model,
+            input_type=embedding_type,
+        )
+        return response.embeddings[0]
+
+    def _embed_voyage(
+        self, text: str, model: str | None, embedding_type: str
+    ) -> list[float]:
+        if model is None:
+            model = DEFAULT_VOYAGE_MODEL
+
+        response = self.client.embed(text, model=model, input_type=embedding_type)
+        return response.embeddings[0]
+
+    def _embed_vertex(
+        self, text: str, model: str | None, embedding_type: str
+    ) -> list[float]:
+        if model is None:
+            model = DEFAULT_VERTEX_MODEL
+
+        embedding = self.client.get_embeddings(
+            [
+                TextEmbeddingInput(
+                    text,
+                    embedding_type,
+                )
+            ]
+        )
+        return embedding[0].values
+
+    @staticmethod
+    def create(
+        api_key: str, provider: str, model: str | None = None
+    ) -> "CloudEmbedding":
+        logger.debug(f"Creating Embedding instance for provider: {provider}")
+        return CloudEmbedding(api_key, provider, model)
+
+
 def get_embedding_model(
     model_name: str,
     max_context_length: int,
@@ -78,18 +206,35 @@ def warm_up_cross_encoders() -> None:
 @simple_log_function_time()
 def embed_text(
     texts: list[str],
-    model_name: str,
+    text_type: EmbedTextType,
+    model_name: str | None,
     max_context_length: int,
     normalize_embeddings: bool,
+    api_key: str | None,
+    provider_type: str | None,
 ) -> list[list[float]]:
-    model = get_embedding_model(
+    if provider_type is not None:
+        if api_key is None:
+            raise RuntimeError("API key not provided for cloud model")
+
+        cloud_model = CloudEmbedding(
+            api_key=api_key, provider=provider_type, model=model_name
+        )
+        embeddings = cloud_model.encode(texts, model_name, text_type)
+
+    elif model_name is not None:
+        hosted_model = get_embedding_model(
             model_name=model_name, max_context_length=max_context_length
         )
-    embeddings = model.encode(texts, normalize_embeddings=normalize_embeddings)
+        embeddings = hosted_model.encode(
+            texts, normalize_embeddings=normalize_embeddings
+        )
+
+    if embeddings is None:
+        raise RuntimeError("Embeddings were not created")
 
     if not isinstance(embeddings, list):
         embeddings = embeddings.tolist()
-
     return embeddings
 
 
@@ -113,6 +258,9 @@ async def process_embed_request(
             model_name=embed_request.model_name,
             max_context_length=embed_request.max_context_length,
             normalize_embeddings=embed_request.normalize_embeddings,
+            api_key=embed_request.api_key,
+            provider_type=embed_request.provider_type,
+            text_type=embed_request.text_type,
         )
         return EmbedResponse(embeddings=embeddings)
     except Exception as e:

backend/requirements/model_server.txt

@@ -7,3 +7,7 @@ tensorflow==2.15.0
 torch==2.0.1
 transformers==4.39.2
 uvicorn==0.21.1
+voyageai==0.2.3
+openai==1.14.3
+cohere==5.5.8
+google-cloud-aiplatform==1.58.0

backend/shared_configs/model_server_models.py

@@ -1,12 +1,19 @@
 from pydantic import BaseModel
 
+from danswer.search.enums import EmbedTextType
+
 
 class EmbedRequest(BaseModel):
     # This already includes any prefixes, the text is just passed directly to the model
     texts: list[str]
-    model_name: str
+
+    # Can be none for cloud embedding model requests, error handling logic exists for other cases
+    model_name: str | None
     max_context_length: int
     normalize_embeddings: bool
+    api_key: str | None
+    provider_type: str | None
+    text_type: EmbedTextType
 
 
 class EmbedResponse(BaseModel):

web/public/Cohere.svg

@@ -0,0 +1,30 @@
+<svg version="1.1" id="Layer_1" xmlns:x="ns_extend;" xmlns:i="ns_ai;" xmlns:graph="ns_graphs;" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 75 75" style="enable-background:new 0 0 75 75;" xml:space="preserve">
+ <style type="text/css">
+  .st0{fill-rule:evenodd;clip-rule:evenodd;fill:#39594D;}
+	.st1{fill-rule:evenodd;clip-rule:evenodd;fill:#D18EE2;}
+	.st2{fill:#FF7759;}
+ </style>
+ <metadata>
+  <sfw xmlns="ns_sfw;">
+   <slices>
+   </slices>
+   <sliceSourceBounds bottomLeftOrigin="true" height="75" width="75" x="-347.6" y="0.5">
+   </sliceSourceBounds>
+  </sfw>
+ </metadata>
+ <g>
+  <g>
+   <g>
+    <path class="st0" d="M24.3,44.7c2,0,6-0.1,11.6-2.4c6.5-2.7,19.3-7.5,28.6-12.5c6.5-3.5,9.3-8.1,9.3-14.3C73.8,7,66.9,0,58.3,0
+				h-36C10,0,0,10,0,22.3S9.4,44.7,24.3,44.7z">
+    </path>
+    <path class="st1" d="M30.4,60c0-6,3.6-11.5,9.2-13.8l11.3-4.7C62.4,36.8,75,45.2,75,57.6C75,67.2,67.2,75,57.6,75l-12.3,0
+				C37.1,75,30.4,68.3,30.4,60z">
+    </path>
+    <path class="st2" d="M12.9,47.6L12.9,47.6C5.8,47.6,0,53.4,0,60.5v1.7C0,69.2,5.8,75,12.9,75h0c7.1,0,12.9-5.8,12.9-12.9v-1.7
+				C25.7,53.4,20,47.6,12.9,47.6z">
+    </path>
+   </g>
+  </g>
+ </g>
+</svg>

web/src/app/admin/models/embedding/CloudEmbeddingPage.tsx

@@ -0,0 +1,163 @@
+"use client";
+
+import { Text, Title } from "@tremor/react";
+
+import {
+  CloudEmbeddingProvider,
+  CloudEmbeddingModel,
+  AVAILABLE_CLOUD_PROVIDERS,
+  CloudEmbeddingProviderFull,
+  EmbeddingModelDescriptor,
+} from "./components/types";
+import { EmbeddingDetails } from "./page";
+import { FiInfo } from "react-icons/fi";
+import { HoverPopup } from "@/components/HoverPopup";
+import { Dispatch, SetStateAction } from "react";
+
+export default function CloudEmbeddingPage({
+  currentModel,
+  embeddingProviderDetails,
+  newEnabledProviders,
+  newUnenabledProviders,
+  setShowTentativeProvider,
+  setChangeCredentialsProvider,
+  setAlreadySelectedModel,
+  setShowTentativeModel,
+  setShowModelInQueue,
+}: {
+  setShowModelInQueue: Dispatch<SetStateAction<CloudEmbeddingModel | null>>;
+  setShowTentativeModel: Dispatch<SetStateAction<CloudEmbeddingModel | null>>;
+  currentModel: EmbeddingModelDescriptor | CloudEmbeddingModel;
+  setAlreadySelectedModel: Dispatch<SetStateAction<CloudEmbeddingModel | null>>;
+  newUnenabledProviders: string[];
+  embeddingProviderDetails?: EmbeddingDetails[];
+  newEnabledProviders: string[];
+  selectedModel: CloudEmbeddingProvider;
+
+  // create modal functions
+
+  setShowTentativeProvider: React.Dispatch<
+    React.SetStateAction<CloudEmbeddingProvider | null>
+  >;
+  setChangeCredentialsProvider: React.Dispatch<
+    React.SetStateAction<CloudEmbeddingProvider | null>
+  >;
+}) {
+  function hasNameInArray(
+    arr: Array<{ name: string }>,
+    searchName: string
+  ): boolean {
+    return arr.some(
+      (item) => item.name.toLowerCase() === searchName.toLowerCase()
+    );
+  }
+
+  let providers: CloudEmbeddingProviderFull[] = [];
+  AVAILABLE_CLOUD_PROVIDERS.forEach((model, ind) => {
+    let temporary_model: CloudEmbeddingProviderFull = {
+      ...model,
+      configured:
+        !newUnenabledProviders.includes(model.name) &&
+        (newEnabledProviders.includes(model.name) ||
+          (embeddingProviderDetails &&
+            hasNameInArray(embeddingProviderDetails, model.name))!),
+    };
+    providers.push(temporary_model);
+  });
+
+  return (
+    <div>
+      <Title className="mt-8">
+        Here are some cloud-based models to choose from.
+      </Title>
+      <Text className="mb-4">
+        They require API keys and run in the clouds of the respective providers.
+      </Text>
+
+      <div className="gap-4 mt-2 pb-10 flex content-start flex-wrap">
+        {providers.map((provider, ind) => (
+          <div
+            key={ind}
+            className="p-4 border border-border rounded-lg shadow-md bg-hover-light w-96 flex flex-col"
+          >
+            <div className="font-bold text-neutral-900 text-lg items-center py-1 gap-x-2 flex">
+              {provider.icon({ size: 40 })}
+              <p className="my-auto">{provider.name}</p>
+              <button
+                onClick={() => {
+                  setShowTentativeProvider(provider);
+                }}
+                className="cursor-pointer ml-auto"
+              >
+                <a className="my-auto hover:underline cursor-pointer">
+                  <HoverPopup
+                    mainContent={
+                      <FiInfo className="cusror-pointer" size={20} />
+                    }
+                    popupContent={
+                      <div className="text-sm text-neutral-800 w-52 flex">
+                        <div className="flex mx-auto">
+                          <div className="my-auto">{provider.description}</div>
+                        </div>
+                      </div>
+                    }
+                    direction="left-top"
+                    style="dark"
+                  />
+                </a>
+              </button>
+            </div>
+
+            <div>
+              {provider.embedding_models.map((model, index) => {
+                const enabled = model.model_name == currentModel.model_name;
+
+                return (
+                  <div
+                    key={index}
+                    className={`p-3 my-2 border-2 border-neutral-300 border-opacity-40 rounded-md rounded cursor-pointer  
+                    ${!provider.configured ? "opacity-80 hover:opacity-100" : enabled ? "bg-background-stronger" : "hover:bg-background-strong"}`}
+                    onClick={() => {
+                      if (enabled) {
+                        setAlreadySelectedModel(model);
+                      } else if (provider.configured) {
+                        setShowTentativeModel(model);
+                      } else {
+                        setShowModelInQueue(model);
+                        setShowTentativeProvider(provider);
+                      }
+                    }}
+                  >
+                    <div className="flex justify-between">
+                      <div className="font-medium text-sm">
+                        {model.model_name}
+                      </div>
+                      <p className="text-sm flex-none">
+                        ${model.pricePerMillion}/M tokens
+                      </p>
+                    </div>
+                    <div className="text-sm text-gray-600">
+                      {model.description}
+                    </div>
+                  </div>
+                );
+              })}
+            </div>
+            <button
+              onClick={() => {
+                if (!provider.configured) {
+                  setShowTentativeProvider(provider);
+                } else {
+                  setChangeCredentialsProvider(provider);
+                }
+              }}
+              className="hover:underline mb-1 text-sm mr-auto cursor-pointer"
+            >
+              {provider.configured && "Modify credentials"}
+            </button>
+          </div>
+        ))}
+      </div>
+    </div>
+  );
+}

web/src/app/admin/models/embedding/ModelSelectionConfirmation.tsx

@@ -1,74 +0,0 @@
-import { Modal } from "@/components/Modal";
-import { Button, Text, Callout } from "@tremor/react";
-import { EmbeddingModelDescriptor } from "./embeddingModels";
-
-export function ModelSelectionConfirmaion({
-  selectedModel,
-  isCustom,
-  onConfirm,
-}: {
-  selectedModel: EmbeddingModelDescriptor;
-  isCustom: boolean;
-  onConfirm: () => void;
-}) {
-  return (
-    <div className="mb-4">
-      <Text className="text-lg mb-4">
-        You have selected: <b>{selectedModel.model_name}</b>. Are you sure you
-        want to update to this new embedding model?
-      </Text>
-      <Text className="text-lg mb-2">
-        We will re-index all your documents in the background so you will be
-        able to continue to use Danswer as normal with the old model in the
-        meantime. Depending on how many documents you have indexed, this may
-        take a while.
-      </Text>
-      <Text className="text-lg mb-2">
-        <i>NOTE:</i> this re-indexing process will consume more resources than
-        normal. If you are self-hosting, we recommend that you allocate at least
-        16GB of RAM to Danswer during this process.
-      </Text>
-
-      {isCustom && (
-        <Callout title="IMPORTANT" color="yellow" className="mt-4">
-          We&apos;ve detected that this is a custom-specified embedding model.
-          Since we have to download the model files before verifying the
-          configuration&apos;s correctness, we won&apos;t be able to let you
-          know if the configuration is valid until <b>after</b> we start
-          re-indexing your documents. If there is an issue, it will show up on
-          this page as an indexing error on this page after clicking Confirm.
-        </Callout>
-      )}
-
-      <div className="flex mt-8">
-        <Button className="mx-auto" color="green" onClick={onConfirm}>
-          Confirm
-        </Button>
-      </div>
-    </div>
-  );
-}
-
-export function ModelSelectionConfirmaionModal({
-  selectedModel,
-  isCustom,
-  onConfirm,
-  onCancel,
-}: {
-  selectedModel: EmbeddingModelDescriptor;
-  isCustom: boolean;
-  onConfirm: () => void;
-  onCancel: () => void;
-}) {
-  return (
-    <Modal title="Update Embedding Model" onOutsideClick={onCancel}>
-      <div>
-        <ModelSelectionConfirmaion
-          selectedModel={selectedModel}
-          isCustom={isCustom}
-          onConfirm={onConfirm}
-        />
-      </div>
-    </Modal>
-  );
-}

web/src/app/admin/models/embedding/OpenEmbeddingPage.tsx

@@ -0,0 +1,55 @@
+"use client";
+import { Card, Text, Title } from "@tremor/react";
+import { ModelSelector } from "./components/ModelSelector";
+import {
+  AVAILABLE_MODELS,
+  EmbeddingModelDescriptor,
+  HostedEmbeddingModel,
+} from "./components/types";
+import { CustomModelForm } from "./components/CustomModelForm";
+
+export default function OpenEmbeddingPage({
+  onSelectOpenSource,
+  currentModelName,
+}: {
+  currentModelName: string;
+  onSelectOpenSource: (model: HostedEmbeddingModel) => Promise<void>;
+}) {
+  return (
+    <div>
+      <ModelSelector
+        modelOptions={AVAILABLE_MODELS.filter(
+          (modelOption) => modelOption.model_name !== currentModelName
+        )}
+        setSelectedModel={onSelectOpenSource}
+      />
+
+      <Text className="mt-6">
+        Alternatively, (if you know what you&apos;re doing) you can specify a{" "}
+        <a target="_blank" href="https://www.sbert.net/" className="text-link">
+          SentenceTransformers
+        </a>
+        -compatible model of your choice below. The rough list of supported
+        models can be found{" "}
+        <a
+          target="_blank"
+          href="https://huggingface.co/models?library=sentence-transformers&sort=trending"
+          className="text-link"
+        >
+          here
+        </a>
+        .
+        <br />
+        <b>NOTE:</b> not all models listed will work with Danswer, since some
+        have unique interfaces or special requirements. If in doubt, reach out
+        to the Danswer team.
+      </Text>
+
+      <div className="w-full flex">
+        <Card className="mt-4 2xl:w-4/6 mx-auto">
+          <CustomModelForm onSubmit={onSelectOpenSource} />
+        </Card>
+      </div>
+    </div>
+  );
+}

web/src/app/admin/models/embedding/components/CustomModelForm.tsx

@@ -2,16 +2,15 @@ import {
   BooleanFormField,
   TextFormField,
 } from "@/components/admin/connectors/Field";
-import { Button, Divider, Text } from "@tremor/react";
+import { Button } from "@tremor/react";
 import { Form, Formik } from "formik";
-
 import * as Yup from "yup";
-import { EmbeddingModelDescriptor } from "./embeddingModels";
+import { EmbeddingModelDescriptor, HostedEmbeddingModel } from "./types";
 
 export function CustomModelForm({
   onSubmit,
 }: {
-  onSubmit: (model: EmbeddingModelDescriptor) => void;
+  onSubmit: (model: HostedEmbeddingModel) => void;
 }) {
   return (
     <div>
@@ -21,6 +20,7 @@ export function CustomModelForm({
           model_dim: "",
           query_prefix: "",
           passage_prefix: "",
+          description: "",
           normalize: true,
         }}
         validationSchema={Yup.object().shape({
@@ -62,6 +62,13 @@ export function CustomModelForm({
                 }
               }}
             />
+            <TextFormField
+              name="description"
+              label="Description:"
+              subtext="Description of  your model"
+              placeholder=""
+              autoCompleteDisabled={true}
+            />
 
             <TextFormField
               name="query_prefix"
@@ -77,7 +84,6 @@ export function CustomModelForm({
               placeholder="E.g. 'query: '"
               autoCompleteDisabled={true}
             />
-
             <TextFormField
               name="passage_prefix"
               label="[Optional] Passage Prefix:"

web/src/app/admin/models/embedding/components/ModelSelector.tsx

@@ -1,18 +1,29 @@
-import { DefaultDropdown, StringOrNumberOption } from "@/components/Dropdown";
+import { EmbeddingModelDescriptor, HostedEmbeddingModel } from "./types";
-import { Title, Text, Divider, Card } from "@tremor/react";
-import {
-  EmbeddingModelDescriptor,
-  FullEmbeddingModelDescriptor,
-} from "./embeddingModels";
 import { FiStar } from "react-icons/fi";
-import { CustomModelForm } from "./CustomModelForm";
+
+export function ModelPreview({ model }: { model: EmbeddingModelDescriptor }) {
+  return (
+    <div
+      className={
+        "p-2 border border-border rounded shadow-md bg-hover-light w-96 flex flex-col"
+      }
+    >
+      <div className="font-bold text-lg flex">{model.model_name}</div>
+      <div className="text-sm mt-1 mx-1">
+        {model.description
+          ? model.description
+          : "Custom model—no description is available."}
+      </div>
+    </div>
+  );
+}
 
 export function ModelOption({
   model,
   onSelect,
 }: {
-  model: FullEmbeddingModelDescriptor;
+  model: HostedEmbeddingModel;
-  onSelect?: (model: EmbeddingModelDescriptor) => void;
+  onSelect?: (model: HostedEmbeddingModel) => void;
 }) {
   return (
     <div
@@ -68,8 +79,8 @@ export function ModelSelector({
   modelOptions,
   setSelectedModel,
 }: {
-  modelOptions: FullEmbeddingModelDescriptor[];
+  modelOptions: HostedEmbeddingModel[];
-  setSelectedModel: (model: EmbeddingModelDescriptor) => void;
+  setSelectedModel: (model: HostedEmbeddingModel) => void;
 }) {
   return (
     <div>

web/src/app/admin/models/embedding/components/types.ts

@@ -0,0 +1,286 @@
+import {
+  CohereIcon,
+  GoogleIcon,
+  IconProps,
+  OpenAIIcon,
+  VoyageIcon,
+} from "@/components/icons/icons";
+
+// Cloud Provider (not needed for hosted ones)
+
+export interface CloudEmbeddingProvider {
+  id: number;
+  name: string;
+  api_key?: string;
+  custom_config?: Record<string, string>;
+  docsLink?: string;
+
+  // Frontend-specific properties
+  website: string;
+  icon: ({ size, className }: IconProps) => JSX.Element;
+  description: string;
+  apiLink: string;
+  costslink?: string;
+
+  // Relationships
+  embedding_models: CloudEmbeddingModel[];
+  default_model?: CloudEmbeddingModel;
+}
+
+// Embedding Models
+export interface EmbeddingModelDescriptor {
+  model_name: string;
+  model_dim: number;
+  normalize: boolean;
+  query_prefix: string;
+  passage_prefix: string;
+  cloud_provider_name?: string | null;
+  description: string;
+}
+
+export interface CloudEmbeddingModel extends EmbeddingModelDescriptor {
+  cloud_provider_name: string | null;
+  pricePerMillion: number;
+  enabled?: boolean;
+  mtebScore: number;
+  maxContext: number;
+}
+
+export interface HostedEmbeddingModel extends EmbeddingModelDescriptor {
+  link?: string;
+  model_dim: number;
+  normalize: boolean;
+  query_prefix: string;
+  passage_prefix: string;
+  isDefault?: boolean;
+}
+
+// Responses
+export interface FullEmbeddingModelResponse {
+  current_model_name: string;
+  secondary_model_name: string | null;
+}
+
+export interface CloudEmbeddingProviderFull extends CloudEmbeddingProvider {
+  configured: boolean;
+}
+
+export const AVAILABLE_MODELS: HostedEmbeddingModel[] = [
+  {
+    model_name: "intfloat/e5-base-v2",
+    model_dim: 768,
+    normalize: true,
+    description:
+      "The recommended default for most situations. If you aren't sure which model to use, this is probably the one.",
+    isDefault: true,
+    link: "https://huggingface.co/intfloat/e5-base-v2",
+    query_prefix: "query: ",
+    passage_prefix: "passage: ",
+  },
+  {
+    model_name: "intfloat/e5-small-v2",
+    model_dim: 384,
+    normalize: true,
+    description:
+      "A smaller / faster version of the default model. If you're running Danswer on a resource constrained system, then this is a good choice.",
+    link: "https://huggingface.co/intfloat/e5-small-v2",
+    query_prefix: "query: ",
+    passage_prefix: "passage: ",
+  },
+  {
+    model_name: "intfloat/multilingual-e5-base",
+    model_dim: 768,
+    normalize: true,
+    description:
+      "If you have many documents in other languages besides English, this is the one to go for.",
+    link: "https://huggingface.co/intfloat/multilingual-e5-base",
+    query_prefix: "query: ",
+    passage_prefix: "passage: ",
+  },
+  {
+    model_name: "intfloat/multilingual-e5-small",
+    model_dim: 384,
+    normalize: true,
+    description:
+      "If you have many documents in other languages besides English, and you're running on a resource constrained system, then this is the one to go for.",
+    link: "https://huggingface.co/intfloat/multilingual-e5-base",
+    query_prefix: "query: ",
+    passage_prefix: "passage: ",
+  },
+];
+
+export const AVAILABLE_CLOUD_PROVIDERS: CloudEmbeddingProvider[] = [
+  {
+    id: 0,
+    name: "OpenAI",
+    website: "https://openai.com",
+    icon: OpenAIIcon,
+    description: "AI industry leader known for ChatGPT and DALL-E",
+    apiLink: "https://platform.openai.com/api-keys",
+    docsLink:
+      "https://docs.danswer.dev/guides/embedding_providers#openai-models",
+    costslink: "https://openai.com/pricing",
+    embedding_models: [
+      {
+        model_name: "text-embedding-3-large",
+        cloud_provider_name: "OpenAI",
+        description:
+          "OpenAI's large embedding model. Best performance, but more expensive.",
+        pricePerMillion: 0.13,
+        model_dim: 3072,
+        normalize: false,
+        query_prefix: "",
+        passage_prefix: "",
+        mtebScore: 64.6,
+        maxContext: 8191,
+        enabled: false,
+      },
+      {
+        model_name: "text-embedding-3-small",
+        cloud_provider_name: "OpenAI",
+        model_dim: 1536,
+        normalize: false,
+        query_prefix: "",
+        passage_prefix: "",
+        description:
+          "OpenAI's newer, more efficient embedding model. Good balance of performance and cost.",
+        pricePerMillion: 0.02,
+        enabled: false,
+        mtebScore: 62.3,
+        maxContext: 8191,
+      },
+    ],
+  },
+  {
+    id: 1,
+    name: "Cohere",
+    website: "https://cohere.ai",
+    icon: CohereIcon,
+    docsLink:
+      "https://docs.danswer.dev/guides/embedding_providers#cohere-models",
+    description:
+      "AI company specializing in NLP models for various text-based tasks",
+    apiLink: "https://dashboard.cohere.ai/api-keys",
+    costslink: "https://cohere.com/pricing",
+    embedding_models: [
+      {
+        model_name: "embed-english-v3.0",
+        cloud_provider_name: "Cohere",
+        description:
+          "Cohere's English embedding model. Good performance for English-language tasks.",
+        pricePerMillion: 0.1,
+        mtebScore: 64.5,
+        maxContext: 512,
+        enabled: false,
+        model_dim: 1024,
+        normalize: false,
+        query_prefix: "",
+        passage_prefix: "",
+      },
+      {
+        model_name: "embed-english-light-v3.0",
+        cloud_provider_name: "Cohere",
+        description:
+          "Cohere's lightweight English embedding model. Faster and more efficient for simpler tasks.",
+        pricePerMillion: 0.1,
+        mtebScore: 62,
+        maxContext: 512,
+        enabled: false,
+        model_dim: 384,
+        normalize: false,
+        query_prefix: "",
+        passage_prefix: "",
+      },
+    ],
+  },
+
+  {
+    id: 2,
+    name: "Google",
+    website: "https://ai.google",
+    icon: GoogleIcon,
+    docsLink:
+      "https://docs.danswer.dev/guides/embedding_providers#vertex-ai-google-model",
+    description:
+      "Offers a wide range of AI services including language and vision models",
+    apiLink: "https://console.cloud.google.com/apis/credentials",
+    costslink: "https://cloud.google.com/vertex-ai/pricing",
+    embedding_models: [
+      {
+        cloud_provider_name: "Google",
+        model_name: "text-embedding-004",
+        description: "Google's most recent text embedding model.",
+        pricePerMillion: 0.025,
+        mtebScore: 66.31,
+        maxContext: 2048,
+        enabled: false,
+        model_dim: 768,
+        normalize: false,
+        query_prefix: "",
+        passage_prefix: "",
+      },
+      {
+        cloud_provider_name: "Google",
+        model_name: "textembedding-gecko@003",
+        description: "Google's Gecko embedding model. Powerful and efficient.",
+        pricePerMillion: 0.025,
+        mtebScore: 66.31,
+        maxContext: 2048,
+        enabled: false,
+        model_dim: 768,
+        normalize: false,
+        query_prefix: "",
+        passage_prefix: "",
+      },
+    ],
+  },
+  {
+    id: 3,
+    name: "Voyage",
+    website: "https://www.voyageai.com",
+    icon: VoyageIcon,
+    description: "Advanced NLP research startup born from Stanford AI Labs",
+    docsLink:
+      "https://docs.danswer.dev/guides/embedding_providers#voyage-models",
+    apiLink: "https://www.voyageai.com/dashboard",
+    costslink: "https://www.voyageai.com/pricing",
+    embedding_models: [
+      {
+        cloud_provider_name: "Voyage",
+        model_name: "voyage-large-2-instruct",
+        description:
+          "Voyage's large embedding model. High performance with instruction fine-tuning.",
+        pricePerMillion: 0.12,
+        mtebScore: 68.28,
+        maxContext: 4000,
+        enabled: false,
+        model_dim: 1024,
+        normalize: false,
+        query_prefix: "",
+        passage_prefix: "",
+      },
+      {
+        cloud_provider_name: "Voyage",
+        model_name: "voyage-light-2-instruct",
+        description:
+          "Voyage's lightweight embedding model. Good balance of performance and efficiency.",
+        pricePerMillion: 0.12,
+        mtebScore: 67.13,
+        maxContext: 16000,
+        enabled: false,
+        model_dim: 1024,
+        normalize: false,
+        query_prefix: "",
+        passage_prefix: "",
+      },
+    ],
+  },
+];
+
+export const INVALID_OLD_MODEL = "thenlper/gte-small";
+
+export function checkModelNameIsValid(
+  modelName: string | undefined | null
+): boolean {
+  return !!modelName && modelName !== INVALID_OLD_MODEL;
+}

web/src/app/admin/models/embedding/embeddingModels.ts

@@ -1,87 +0,0 @@
-export interface EmbeddingModelResponse {
-  model_name: string | null;
-}
-
-export interface FullEmbeddingModelResponse {
-  current_model_name: string;
-  secondary_model_name: string | null;
-}
-
-export interface EmbeddingModelDescriptor {
-  model_name: string;
-  model_dim: number;
-  normalize: boolean;
-  query_prefix?: string;
-  passage_prefix?: string;
-}
-
-export interface FullEmbeddingModelDescriptor extends EmbeddingModelDescriptor {
-  description: string;
-  isDefault?: boolean;
-  link?: string;
-}
-
-export const AVAILABLE_MODELS: FullEmbeddingModelDescriptor[] = [
-  {
-    model_name: "intfloat/e5-base-v2",
-    model_dim: 768,
-    normalize: true,
-    description:
-      "The recommended default for most situations. If you aren't sure which model to use, this is probably the one.",
-    isDefault: true,
-    link: "https://huggingface.co/intfloat/e5-base-v2",
-    query_prefix: "query: ",
-    passage_prefix: "passage: ",
-  },
-  {
-    model_name: "intfloat/e5-small-v2",
-    model_dim: 384,
-    normalize: true,
-    description:
-      "A smaller / faster version of the default model. If you're running Danswer on a resource constrained system, then this is a good choice.",
-    link: "https://huggingface.co/intfloat/e5-small-v2",
-    query_prefix: "query: ",
-    passage_prefix: "passage: ",
-  },
-  {
-    model_name: "intfloat/multilingual-e5-base",
-    model_dim: 768,
-    normalize: true,
-    description:
-      "If you have many documents in other languages besides English, this is the one to go for.",
-    link: "https://huggingface.co/intfloat/multilingual-e5-base",
-    query_prefix: "query: ",
-    passage_prefix: "passage: ",
-  },
-  {
-    model_name: "intfloat/multilingual-e5-small",
-    model_dim: 384,
-    normalize: true,
-    description:
-      "If you have many documents in other languages besides English, and you're running on a resource constrained system, then this is the one to go for.",
-    link: "https://huggingface.co/intfloat/multilingual-e5-base",
-    query_prefix: "query: ",
-    passage_prefix: "passage: ",
-  },
-];
-
-export const INVALID_OLD_MODEL = "thenlper/gte-small";
-
-export function checkModelNameIsValid(modelName: string | undefined | null) {
-  if (!modelName) {
-    return false;
-  }
-  if (modelName === INVALID_OLD_MODEL) {
-    return false;
-  }
-  return true;
-}
-
-export function fillOutEmeddingModelDescriptor(
-  embeddingModel: EmbeddingModelDescriptor | FullEmbeddingModelDescriptor
-): FullEmbeddingModelDescriptor {
-  return {
-    ...embeddingModel,
-    description: "",
-  };
-}

web/src/app/admin/models/embedding/modals/AlreadyPickedModal.tsx

@@ -0,0 +1,31 @@
+import React from "react";
+import { Modal } from "@/components/Modal";
+import { Button, Text } from "@tremor/react";
+
+import { CloudEmbeddingModel } from "../components/types";
+
+export function AlreadyPickedModal({
+  model,
+  onClose,
+}: {
+  model: CloudEmbeddingModel;
+  onClose: () => void;
+}) {
+  return (
+    <Modal
+      title={`${model.model_name} already chosen`}
+      onOutsideClick={onClose}
+    >
+      <div className="mb-4">
+        <Text className="text-sm mb-2">
+          You can select a different one if you want!
+        </Text>
+        <div className="flex mt-8 justify-between">
+          <Button color="blue" onClick={onClose}>
+            Close
+          </Button>
+        </div>
+      </div>
+    </Modal>
+  );
+}

web/src/app/admin/models/embedding/modals/ChangeCredentialsModal.tsx

@@ -0,0 +1,244 @@
+import React, { useRef, useState } from "react";
+import { Modal } from "@/components/Modal";
+import { Button, Text, Callout, Subtitle, Divider } from "@tremor/react";
+import { Label, TextFormField } from "@/components/admin/connectors/Field";
+import { CloudEmbeddingProvider } from "../components/types";
+import {
+  EMBEDDING_PROVIDERS_ADMIN_URL,
+  LLM_PROVIDERS_ADMIN_URL,
+} from "../../llm/constants";
+import { mutate } from "swr";
+import { PopupSpec, usePopup } from "@/components/admin/connectors/Popup";
+import { Field } from "formik";
+
+export function ChangeCredentialsModal({
+  provider,
+  onConfirm,
+  onCancel,
+  onDeleted,
+  useFileUpload,
+}: {
+  provider: CloudEmbeddingProvider;
+  onConfirm: () => void;
+  onCancel: () => void;
+  onDeleted: () => void;
+  useFileUpload: boolean;
+}) {
+  const [apiKey, setApiKey] = useState("");
+  const [testError, setTestError] = useState<string>("");
+  const [fileName, setFileName] = useState<string>("");
+  const fileInputRef = useRef<HTMLInputElement>(null);
+  const [isProcessing, setIsProcessing] = useState(false);
+  const [deletionError, setDeletionError] = useState<string>("");
+
+  const clearFileInput = () => {
+    setFileName("");
+    if (fileInputRef.current) {
+      fileInputRef.current.value = "";
+    }
+  };
+
+  const handleFileUpload = async (
+    event: React.ChangeEvent<HTMLInputElement>
+  ) => {
+    const file = event.target.files?.[0];
+    setFileName("");
+
+    if (file) {
+      setFileName(file.name);
+      try {
+        setDeletionError("");
+        const fileContent = await file.text();
+        let jsonContent;
+        try {
+          jsonContent = JSON.parse(fileContent);
+          setApiKey(JSON.stringify(jsonContent));
+        } catch (parseError) {
+          throw new Error(
+            "Failed to parse JSON file. Please ensure it's a valid JSON."
+          );
+        }
+      } catch (error) {
+        setTestError(
+          error instanceof Error
+            ? error.message
+            : "An unknown error occurred while processing the file."
+        );
+        setApiKey("");
+        clearFileInput();
+      }
+    }
+  };
+
+  const handleDelete = async () => {
+    setDeletionError("");
+    setIsProcessing(true);
+
+    try {
+      const response = await fetch(
+        `${EMBEDDING_PROVIDERS_ADMIN_URL}/${provider.name}`,
+        {
+          method: "DELETE",
+        }
+      );
+
+      if (!response.ok) {
+        const errorData = await response.json();
+        setDeletionError(errorData.detail);
+        return;
+      }
+
+      mutate(LLM_PROVIDERS_ADMIN_URL);
+      onDeleted();
+    } catch (error) {
+      setDeletionError(
+        error instanceof Error ? error.message : "An unknown error occurred"
+      );
+    } finally {
+      setIsProcessing(false);
+    }
+  };
+
+  const handleSubmit = async () => {
+    setTestError("");
+
+    try {
+      const body = JSON.stringify({
+        api_key: apiKey,
+        provider: provider.name.toLowerCase().split(" ")[0],
+        default_model_id: provider.name,
+      });
+
+      const testResponse = await fetch("/api/admin/embedding/test-embedding", {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({
+          provider: provider.name.toLowerCase().split(" ")[0],
+          api_key: apiKey,
+        }),
+      });
+
+      if (!testResponse.ok) {
+        const errorMsg = (await testResponse.json()).detail;
+        throw new Error(errorMsg);
+      }
+
+      const updateResponse = await fetch(EMBEDDING_PROVIDERS_ADMIN_URL, {
+        method: "PUT",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({
+          name: provider.name,
+          api_key: apiKey,
+          is_default_provider: false,
+          is_configured: true,
+        }),
+      });
+
+      if (!updateResponse.ok) {
+        const errorData = await updateResponse.json();
+        throw new Error(
+          errorData.detail || "Failed to update provider- check your API key"
+        );
+      }
+
+      onConfirm();
+    } catch (error) {
+      setTestError(
+        error instanceof Error ? error.message : "An unknown error occurred"
+      );
+    }
+  };
+
+  return (
+    <Modal
+      icon={provider.icon}
+      title={`Modify your ${provider.name} key`}
+      onOutsideClick={onCancel}
+    >
+      <div className="mb-4">
+        <Subtitle className="mt-4 font-bold text-lg mb-2">
+          Want to swap out your key?
+        </Subtitle>
+
+        <div className="flex flex-col gap-y-2">
+          {useFileUpload ? (
+            <>
+              <Label>Upload JSON File</Label>
+              <input
+                ref={fileInputRef}
+                type="file"
+                accept=".json"
+                onChange={handleFileUpload}
+                className="text-lg w-full p-1"
+              />
+              {fileName && <p>Uploaded file: {fileName}</p>}
+            </>
+          ) : (
+            <>
+              <div className="flex gap-x-2 items-center">
+                <Label>New API Key</Label>
+              </div>
+              <input
+                className={`
+                    border 
+                    border-border 
+                    rounded 
+                    w-full 
+                    py-2 
+                    px-3 
+                    mt-1
+                    bg-background-emphasis
+                `}
+                value={apiKey}
+                onChange={(e: any) => setApiKey(e.target.value)}
+                placeholder="Paste your API key here"
+              />
+            </>
+          )}
+          <a
+            href={provider.apiLink}
+            target="_blank"
+            rel="noopener noreferrer"
+            className="underline cursor-pointer"
+          >
+            Visit API
+          </a>
+        </div>
+
+        {testError && (
+          <Callout title="Error" color="red" className="mt-4">
+            {testError}
+          </Callout>
+        )}
+
+        <div className="flex mt-8 justify-between">
+          <Button
+            color="blue"
+            onClick={() => handleSubmit()}
+            disabled={!apiKey}
+          >
+            Execute Key Swap
+          </Button>
+        </div>
+        <Divider />
+
+        <Subtitle className="mt-4 font-bold text-lg mb-2">
+          You can also delete your key.
+        </Subtitle>
+        <Text className="mb-2">
+          This is only possible if you have already switched to a different
+          embedding type!
+        </Text>
+
+        <Button onClick={handleDelete} color="red">
+          Delete key
+        </Button>
+        {deletionError && (
+          <Callout title="Error" color="red" className="mt-4">
+            {deletionError}
+          </Callout>
+        )}
+      </div>
+    </Modal>
+  );
+}

web/src/app/admin/models/embedding/modals/DeleteCredentialsModal.tsx

@@ -0,0 +1,41 @@
+import React from "react";
+import { Modal } from "@/components/Modal";
+import { Button, Text, Callout } from "@tremor/react";
+import { CloudEmbeddingProvider } from "../components/types";
+
+export function DeleteCredentialsModal({
+  modelProvider,
+  onConfirm,
+  onCancel,
+}: {
+  modelProvider: CloudEmbeddingProvider;
+  onConfirm: () => void;
+  onCancel: () => void;
+}) {
+  return (
+    <Modal
+      title={`Nuke ${modelProvider.name} Credentials?`}
+      onOutsideClick={onCancel}
+    >
+      <div className="mb-4">
+        <Text className="text-lg mb-2">
+          You&apos;re about to delete your {modelProvider.name} credentials. Are
+          you sure?
+        </Text>
+        <Callout
+          title="Point of No Return"
+          color="red"
+          className="mt-4"
+        ></Callout>
+        <div className="flex mt-8 justify-between">
+          <Button color="gray" onClick={onCancel}>
+            Keep Credentaisl
+          </Button>
+          <Button color="red" onClick={onConfirm}>
+            Delete Credentials
+          </Button>
+        </div>
+      </div>
+    </Modal>
+  );
+}

web/src/app/admin/models/embedding/modals/ModelSelectionModal.tsx

@@ -0,0 +1,61 @@
+import { Modal } from "@/components/Modal";
+import { Button, Text, Callout } from "@tremor/react";
+import {
+  EmbeddingModelDescriptor,
+  HostedEmbeddingModel,
+} from "../components/types";
+
+export function ModelSelectionConfirmationModal({
+  selectedModel,
+  isCustom,
+  onConfirm,
+  onCancel,
+}: {
+  selectedModel: HostedEmbeddingModel;
+  isCustom: boolean;
+  onConfirm: () => void;
+  onCancel: () => void;
+}) {
+  return (
+    <Modal title="Update Embedding Model" onOutsideClick={onCancel}>
+      <div>
+        <div className="mb-4">
+          <Text className="text-lg mb-4">
+            You have selected: <b>{selectedModel.model_name}</b>. Are you sure
+            you want to update to this new embedding model?
+          </Text>
+          <Text className="text-lg mb-2">
+            We will re-index all your documents in the background so you will be
+            able to continue to use Danswer as normal with the old model in the
+            meantime. Depending on how many documents you have indexed, this may
+            take a while.
+          </Text>
+          <Text className="text-lg mb-2">
+            <i>NOTE:</i> this re-indexing process will consume more resources
+            than normal. If you are self-hosting, we recommend that you allocate
+            at least 16GB of RAM to Danswer during this process.
+          </Text>
+
+          {/* TODO Change this back- ensure functional */}
+          {!isCustom && (
+            <Callout title="IMPORTANT" color="yellow" className="mt-4">
+              We&apos;ve detected that this is a custom-specified embedding
+              model. Since we have to download the model files before verifying
+              the configuration&apos;s correctness, we won&apos;t be able to let
+              you know if the configuration is valid until <b>after</b> we start
+              re-indexing your documents. If there is an issue, it will show up
+              on this page as an indexing error on this page after clicking
+              Confirm.
+            </Callout>
+          )}
+
+          <div className="flex mt-8">
+            <Button className="mx-auto" color="green" onClick={onConfirm}>
+              Confirm
+            </Button>
+          </div>
+        </div>
+      </div>
+    </Modal>
+  );
+}

web/src/app/admin/models/embedding/modals/ProviderCreationModal.tsx

@@ -0,0 +1,232 @@
+import React, { useRef, useState } from "react";
+import { Text, Button, Callout } from "@tremor/react";
+import { Formik, Form, Field } from "formik";
+import * as Yup from "yup";
+import { Label, TextFormField } from "@/components/admin/connectors/Field";
+import { LoadingAnimation } from "@/components/Loading";
+import { CloudEmbeddingProvider } from "../components/types";
+import { EMBEDDING_PROVIDERS_ADMIN_URL } from "../../llm/constants";
+import { Modal } from "@/components/Modal";
+
+export function ProviderCreationModal({
+  selectedProvider,
+  onConfirm,
+  onCancel,
+  existingProvider,
+}: {
+  selectedProvider: CloudEmbeddingProvider;
+  onConfirm: () => void;
+  onCancel: () => void;
+  existingProvider?: CloudEmbeddingProvider;
+}) {
+  const useFileUpload = selectedProvider.name == "Google";
+
+  const [isProcessing, setIsProcessing] = useState(false);
+  const [errorMsg, setErrorMsg] = useState<string>("");
+  const [fileName, setFileName] = useState<string>("");
+
+  const initialValues = {
+    name: existingProvider?.name || selectedProvider.name,
+    api_key: existingProvider?.api_key || "",
+    custom_config: existingProvider?.custom_config
+      ? Object.entries(existingProvider.custom_config)
+      : [],
+    default_model_name: "",
+    model_id: 0,
+  };
+
+  const validationSchema = Yup.object({
+    name: Yup.string().required("Name is required"),
+    api_key: useFileUpload
+      ? Yup.string()
+      : Yup.string().required("API Key is required"),
+    custom_config: Yup.array().of(Yup.array().of(Yup.string()).length(2)),
+  });
+
+  const fileInputRef = useRef<HTMLInputElement>(null);
+
+  const handleFileUpload = async (
+    event: React.ChangeEvent<HTMLInputElement>,
+    setFieldValue: (field: string, value: any) => void
+  ) => {
+    const file = event.target.files?.[0];
+    setFileName("");
+    if (file) {
+      setFileName(file.name);
+      try {
+        const fileContent = await file.text();
+        let jsonContent;
+        try {
+          jsonContent = JSON.parse(fileContent);
+        } catch (parseError) {
+          throw new Error(
+            "Failed to parse JSON file. Please ensure it's a valid JSON."
+          );
+        }
+        setFieldValue("api_key", JSON.stringify(jsonContent));
+      } catch (error) {
+        setFieldValue("api_key", "");
+      }
+    }
+  };
+
+  const handleSubmit = async (
+    values: any,
+    { setSubmitting }: { setSubmitting: (isSubmitting: boolean) => void }
+  ) => {
+    setIsProcessing(true);
+    setErrorMsg("");
+
+    try {
+      const customConfig = Object.fromEntries(values.custom_config);
+
+      const initialResponse = await fetch(
+        "/api/admin/embedding/test-embedding",
+        {
+          method: "POST",
+          headers: { "Content-Type": "application/json" },
+          body: JSON.stringify({
+            provider: values.name.toLowerCase().split(" ")[0],
+            api_key: values.api_key,
+          }),
+        }
+      );
+
+      if (!initialResponse.ok) {
+        const errorMsg = (await initialResponse.json()).detail;
+        setErrorMsg(errorMsg);
+        setIsProcessing(false);
+        setSubmitting(false);
+        return;
+      }
+
+      const response = await fetch(EMBEDDING_PROVIDERS_ADMIN_URL, {
+        method: "PUT",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({
+          ...values,
+          custom_config: customConfig,
+          is_default_provider: false,
+          is_configured: true,
+        }),
+      });
+
+      if (!response.ok) {
+        const errorData = await response.json();
+        throw new Error(
+          errorData.detail || "Failed to update provider- check your API key"
+        );
+      }
+
+      onConfirm();
+    } catch (error: unknown) {
+      if (error instanceof Error) {
+        setErrorMsg(error.message);
+      } else {
+        setErrorMsg("An unknown error occurred");
+      }
+    } finally {
+      setIsProcessing(false);
+      setSubmitting(false);
+    }
+  };
+
+  return (
+    <Modal
+      title={`Configure ${selectedProvider.name}`}
+      onOutsideClick={onCancel}
+      icon={selectedProvider.icon}
+    >
+      <div>
+        <Formik
+          initialValues={initialValues}
+          validationSchema={validationSchema}
+          onSubmit={handleSubmit}
+        >
+          {({
+            values,
+            errors,
+            touched,
+            isSubmitting,
+            handleSubmit,
+            setFieldValue,
+          }) => (
+            <Form onSubmit={handleSubmit} className="space-y-4">
+              <Text className="text-lg mb-2">
+                You are setting the credentials for this provider. To access
+                this information, follow the instructions{" "}
+                <a
+                  className="cursor-pointer underline"
+                  target="_blank"
+                  href={selectedProvider.docsLink}
+                >
+                  here
+                </a>{" "}
+                and gather your{" "}
+                <a
+                  className="cursor-pointer underline"
+                  target="_blank"
+                  href={selectedProvider.apiLink}
+                >
+                  API KEY
+                </a>
+              </Text>
+
+              <div className="flex flex-col gap-y-2">
+                {useFileUpload ? (
+                  <>
+                    <Label>Upload JSON File</Label>
+                    <input
+                      ref={fileInputRef}
+                      type="file"
+                      accept=".json"
+                      onChange={(e) => handleFileUpload(e, setFieldValue)}
+                      className="text-lg w-full p-1"
+                    />
+                    {fileName && <p>Uploaded file: {fileName}</p>}
+                  </>
+                ) : (
+                  <TextFormField
+                    name="api_key"
+                    label="API Key"
+                    placeholder="API Key"
+                    type="password"
+                  />
+                )}
+
+                <a
+                  href={selectedProvider.apiLink}
+                  target="_blank"
+                  className="underline cursor-pointer"
+                >
+                  Learn more here
+                </a>
+              </div>
+
+              {errorMsg && (
+                <Callout title="Error" color="red">
+                  {errorMsg}
+                </Callout>
+              )}
+
+              <Button
+                type="submit"
+                color="blue"
+                className="w-full"
+                disabled={isSubmitting}
+              >
+                {isProcessing ? (
+                  <LoadingAnimation />
+                ) : existingProvider ? (
+                  "Update"
+                ) : (
+                  "Create"
+                )}
+              </Button>
+            </Form>
+          )}
+        </Formik>
+      </div>
+    </Modal>
+  );
+}

web/src/app/admin/models/embedding/modals/SelectModelModal.tsx

@@ -0,0 +1,37 @@
+import React from "react";
+import { Modal } from "@/components/Modal";
+import { Button, Text, Callout } from "@tremor/react";
+import { CloudEmbeddingModel } from "../components/types";
+
+export function SelectModelModal({
+  model,
+  onConfirm,
+  onCancel,
+}: {
+  model: CloudEmbeddingModel;
+  onConfirm: () => void;
+  onCancel: () => void;
+}) {
+  return (
+    <Modal
+      title={`Elevate Your Game with ${model.model_name}`}
+      onOutsideClick={onCancel}
+    >
+      <div className="mb-4">
+        <Text className="text-lg mb-2">
+          You&apos;re about to set your embedding model to {model.model_name}.
+          <br />
+          Are you sure?
+        </Text>
+        <div className="flex mt-8 justify-between">
+          <Button color="gray" onClick={onCancel}>
+            Exit
+          </Button>
+          <Button color="green" onClick={onConfirm}>
+            Continue
+          </Button>
+        </div>
+      </div>
+    </Modal>
+  );
+}

web/src/app/admin/models/embedding/page.tsx

@@ -3,28 +3,75 @@
 import { ThreeDotsLoader } from "@/components/Loading";
 import { AdminPageTitle } from "@/components/admin/Title";
 import { errorHandlingFetcher } from "@/lib/fetcher";
-import { Button, Card, Text, Title } from "@tremor/react";
+import { Button, Text, Title } from "@tremor/react";
 import { FiPackage } from "react-icons/fi";
 import useSWR, { mutate } from "swr";
-import { ModelOption, ModelSelector } from "./ModelSelector";
+import { ModelOption, ModelPreview } from "./components/ModelSelector";
 import { useState } from "react";
-import { ModelSelectionConfirmaionModal } from "./ModelSelectionConfirmation";
+import { ReindexingProgressTable } from "./components/ReindexingProgressTable";
-import { ReindexingProgressTable } from "./ReindexingProgressTable";
 import { Modal } from "@/components/Modal";
 import {
+  CloudEmbeddingProvider,
+  CloudEmbeddingModel,
+  AVAILABLE_CLOUD_PROVIDERS,
   AVAILABLE_MODELS,
-  EmbeddingModelDescriptor,
   INVALID_OLD_MODEL,
-  fillOutEmeddingModelDescriptor,
+  HostedEmbeddingModel,
-} from "./embeddingModels";
+  EmbeddingModelDescriptor,
+} from "./components/types";
 import { ErrorCallout } from "@/components/ErrorCallout";
 import { Connector, ConnectorIndexingStatus } from "@/lib/types";
 import Link from "next/link";
-import { CustomModelForm } from "./CustomModelForm";
+import OpenEmbeddingPage from "./OpenEmbeddingPage";
+import CloudEmbeddingPage from "./CloudEmbeddingPage";
+import { ProviderCreationModal } from "./modals/ProviderCreationModal";
+
+import { DeleteCredentialsModal } from "./modals/DeleteCredentialsModal";
+import { SelectModelModal } from "./modals/SelectModelModal";
+import { ChangeCredentialsModal } from "./modals/ChangeCredentialsModal";
+import { ModelSelectionConfirmationModal } from "./modals/ModelSelectionModal";
+import { EMBEDDING_PROVIDERS_ADMIN_URL } from "../llm/constants";
+import { AlreadyPickedModal } from "./modals/AlreadyPickedModal";
+
+export interface EmbeddingDetails {
+  api_key: string;
+  custom_config: any;
+  default_model_id?: number;
+  name: string;
+}
 
 function Main() {
-  const [tentativeNewEmbeddingModel, setTentativeNewEmbeddingModel] =
+  const [openToggle, setOpenToggle] = useState(true);
-    useState<EmbeddingModelDescriptor | null>(null);
+
+  // Cloud Provider based modals
+  const [showTentativeProvider, setShowTentativeProvider] =
+    useState<CloudEmbeddingProvider | null>(null);
+  const [showUnconfiguredProvider, setShowUnconfiguredProvider] =
+    useState<CloudEmbeddingProvider | null>(null);
+  const [changeCredentialsProvider, setChangeCredentialsProvider] =
+    useState<CloudEmbeddingProvider | null>(null);
+
+  // Cloud Model based modals
+  const [alreadySelectedModel, setAlreadySelectedModel] =
+    useState<CloudEmbeddingModel | null>(null);
+  const [showTentativeModel, setShowTentativeModel] =
+    useState<CloudEmbeddingModel | null>(null);
+
+  const [showModelInQueue, setShowModelInQueue] =
+    useState<CloudEmbeddingModel | null>(null);
+
+  // Open Model based modals
+  const [showTentativeOpenProvider, setShowTentativeOpenProvider] =
+    useState<HostedEmbeddingModel | null>(null);
+
+  // Enabled / unenabled providers
+  const [newEnabledProviders, setNewEnabledProviders] = useState<string[]>([]);
+  const [newUnenabledProviders, setNewUnenabledProviders] = useState<string[]>(
+    []
+  );
+
+  const [showDeleteCredentialsModal, setShowDeleteCredentialsModal] =
+    useState<boolean>(false);
   const [isCancelling, setIsCancelling] = useState<boolean>(false);
   const [showAddConnectorPopup, setShowAddConnectorPopup] =
     useState<boolean>(false);
@@ -33,16 +80,22 @@ function Main() {
     data: currentEmeddingModel,
     isLoading: isLoadingCurrentModel,
     error: currentEmeddingModelError,
-  } = useSWR<EmbeddingModelDescriptor>(
+  } = useSWR<CloudEmbeddingModel | HostedEmbeddingModel | null>(
     "/api/secondary-index/get-current-embedding-model",
     errorHandlingFetcher,
     { refreshInterval: 5000 } // 5 seconds
   );
+
+  const { data: embeddingProviderDetails } = useSWR<EmbeddingDetails[]>(
+    EMBEDDING_PROVIDERS_ADMIN_URL,
+    errorHandlingFetcher
+  );
+
   const {
     data: futureEmbeddingModel,
     isLoading: isLoadingFutureModel,
     error: futureEmeddingModelError,
-  } = useSWR<EmbeddingModelDescriptor | null>(
+  } = useSWR<CloudEmbeddingModel | HostedEmbeddingModel | null>(
     "/api/secondary-index/get-secondary-embedding-model",
     errorHandlingFetcher,
     { refreshInterval: 5000 } // 5 seconds
@@ -61,27 +114,41 @@ function Main() {
     { refreshInterval: 5000 } // 5 seconds
   );
 
-  const onSelect = async (model: EmbeddingModelDescriptor) => {
+  const onConfirm = async (
-    if (currentEmeddingModel?.model_name === INVALID_OLD_MODEL) {
+    model: CloudEmbeddingModel | HostedEmbeddingModel
-      await onConfirm(model);
+  ) => {
-    } else {
+    let newModel: EmbeddingModelDescriptor;
-      setTentativeNewEmbeddingModel(model);
+
-    }
+    if ("cloud_provider_name" in model) {
-  };
+      // This is a CloudEmbeddingModel
+      newModel = {
+        ...model,
+        model_name: model.model_name,
+        cloud_provider_name: model.cloud_provider_name,
+        // cloud_provider_id: model.cloud_provider_id || 0,
+      };
+    } else {
+      // This is an EmbeddingModelDescriptor
+      newModel = {
+        ...model,
+        model_name: model.model_name!,
+        description: "",
+        cloud_provider_name: null,
+      };
+    }
 
-  const onConfirm = async (model: EmbeddingModelDescriptor) => {
     const response = await fetch(
       "/api/secondary-index/set-new-embedding-model",
       {
         method: "POST",
-        body: JSON.stringify(model),
+        body: JSON.stringify(newModel),
         headers: {
           "Content-Type": "application/json",
         },
       }
     );
     if (response.ok) {
-      setTentativeNewEmbeddingModel(null);
+      setShowTentativeModel(null);
       mutate("/api/secondary-index/get-secondary-embedding-model");
       if (!connectors || !connectors.length) {
         setShowAddConnectorPopup(true);
@@ -96,14 +163,13 @@ function Main() {
       method: "POST",
     });
     if (response.ok) {
-      setTentativeNewEmbeddingModel(null);
+      setShowTentativeModel(null);
       mutate("/api/secondary-index/get-secondary-embedding-model");
     } else {
       alert(
         `Failed to cancel embedding model update - ${await response.text()}`
       );
     }
-
     setIsCancelling(false);
   };
 
@@ -119,38 +185,235 @@ function Main() {
     return <ErrorCallout errorTitle="Failed to fetch embedding model status" />;
   }
 
-  const currentModelName = currentEmeddingModel.model_name;
+  const onConfirmSelection = async (model: EmbeddingModelDescriptor) => {
-  const currentModel =
+    const response = await fetch(
-    AVAILABLE_MODELS.find((model) => model.model_name === currentModelName) ||
+      "/api/secondary-index/set-new-embedding-model",
-    fillOutEmeddingModelDescriptor(currentEmeddingModel);
+      {
+        method: "POST",
+        body: JSON.stringify(model),
+        headers: {
+          "Content-Type": "application/json",
+        },
+      }
+    );
+    if (response.ok) {
+      setShowTentativeModel(null);
+      mutate("/api/secondary-index/get-secondary-embedding-model");
+      if (!connectors || !connectors.length) {
+        setShowAddConnectorPopup(true);
+      }
+    } else {
+      alert(`Failed to update embedding model - ${await response.text()}`);
+    }
+  };
 
-  const newModelSelection = futureEmbeddingModel
+  const currentModelName = currentEmeddingModel?.model_name;
-    ? AVAILABLE_MODELS.find(
+  const AVAILABLE_CLOUD_PROVIDERS_FLATTENED = AVAILABLE_CLOUD_PROVIDERS.flatMap(
-        (model) => model.model_name === futureEmbeddingModel.model_name
+    (provider) =>
-      ) || fillOutEmeddingModelDescriptor(futureEmbeddingModel)
+      provider.embedding_models.map((model) => ({
-    : null;
+        ...model,
+        cloud_provider_id: provider.id,
+        model_name: model.model_name, // Ensure model_name is set for consistency
+      }))
+  );
+
+  const currentModel: CloudEmbeddingModel | HostedEmbeddingModel =
+    AVAILABLE_MODELS.find((model) => model.model_name === currentModelName) ||
+    AVAILABLE_CLOUD_PROVIDERS_FLATTENED.find(
+      (model) => model.model_name === currentEmeddingModel.model_name
+    )!;
+  // ||
+  // fillOutEmeddingModelDescriptor(currentEmeddingModel);
+
+  const onSelectOpenSource = async (model: HostedEmbeddingModel) => {
+    if (currentEmeddingModel?.model_name === INVALID_OLD_MODEL) {
+      await onConfirmSelection(model);
+    } else {
+      setShowTentativeOpenProvider(model);
+    }
+  };
+
+  const selectedModel = AVAILABLE_CLOUD_PROVIDERS[0];
+  const clientsideAddProvider = (provider: CloudEmbeddingProvider) => {
+    const providerName = provider.name;
+    setNewEnabledProviders((newEnabledProviders) => [
+      ...newEnabledProviders,
+      providerName,
+    ]);
+    setNewUnenabledProviders((newUnenabledProviders) =>
+      newUnenabledProviders.filter(
+        (givenProvidername) => givenProvidername != providerName
+      )
+    );
+  };
+
+  const clientsideRemoveProvider = (provider: CloudEmbeddingProvider) => {
+    const providerName = provider.name;
+    setNewEnabledProviders((newEnabledProviders) =>
+      newEnabledProviders.filter(
+        (givenProvidername) => givenProvidername != providerName
+      )
+    );
+    setNewUnenabledProviders((newUnenabledProviders) => [
+      ...newUnenabledProviders,
+      providerName,
+    ]);
+  };
 
   return (
-    <div>
+    <div className="h-screen">
-      {tentativeNewEmbeddingModel && (
+      <Text>
-        <ModelSelectionConfirmaionModal
+        Embedding models are used to generate embeddings for your documents,
-          selectedModel={tentativeNewEmbeddingModel}
+        which then power Danswer&apos;s search.
+      </Text>
+
+      {alreadySelectedModel && (
+        <AlreadyPickedModal
+          model={alreadySelectedModel}
+          onClose={() => setAlreadySelectedModel(null)}
+        />
+      )}
+      {showTentativeOpenProvider && (
+        <ModelSelectionConfirmationModal
+          selectedModel={showTentativeOpenProvider}
           isCustom={
             AVAILABLE_MODELS.find(
               (model) =>
-                model.model_name === tentativeNewEmbeddingModel.model_name
+                model.model_name === showTentativeOpenProvider.model_name
             ) === undefined
           }
-          onConfirm={() => onConfirm(tentativeNewEmbeddingModel)}
+          onConfirm={() => onConfirm(showTentativeOpenProvider)}
-          onCancel={() => setTentativeNewEmbeddingModel(null)}
+          onCancel={() => setShowTentativeOpenProvider(null)}
         />
       )}
 
+      {showTentativeProvider && (
+        <ProviderCreationModal
+          selectedProvider={showTentativeProvider}
+          onConfirm={() => {
+            setShowTentativeProvider(showUnconfiguredProvider);
+            clientsideAddProvider(showTentativeProvider);
+            if (showModelInQueue) {
+              setShowTentativeModel(showModelInQueue);
+            }
+          }}
+          onCancel={() => {
+            setShowModelInQueue(null);
+            setShowTentativeProvider(null);
+          }}
+        />
+      )}
+      {changeCredentialsProvider && (
+        <ChangeCredentialsModal
+          // setPopup={setPopup}
+          useFileUpload={changeCredentialsProvider.name == "Google"}
+          onDeleted={() => {
+            clientsideRemoveProvider(changeCredentialsProvider);
+            setChangeCredentialsProvider(null);
+          }}
+          provider={changeCredentialsProvider}
+          onConfirm={() => setChangeCredentialsProvider(null)}
+          onCancel={() => setChangeCredentialsProvider(null)}
+        />
+      )}
+
+      {showTentativeModel && (
+        <SelectModelModal
+          model={showTentativeModel}
+          onConfirm={() => {
+            setShowModelInQueue(null);
+            onConfirm(showTentativeModel);
+          }}
+          onCancel={() => {
+            setShowModelInQueue(null);
+            setShowTentativeModel(null);
+          }}
+        />
+      )}
+
+      {showDeleteCredentialsModal && (
+        <DeleteCredentialsModal
+          modelProvider={showTentativeProvider!}
+          onConfirm={() => {
+            setShowDeleteCredentialsModal(false);
+          }}
+          onCancel={() => setShowDeleteCredentialsModal(false)}
+        />
+      )}
+
+      {currentModel ? (
+        <>
+          <Title className="mt-8 mb-2">Current Embedding Model</Title>
+          <Text>
+            <ModelPreview model={currentModel} />
+          </Text>
+        </>
+      ) : (
+        <Title className="mt-8 mb-4">Choose your Embedding Model</Title>
+      )}
+
+      {!(futureEmbeddingModel && connectors && connectors.length > 0) && (
+        <>
+          <Title className="mt-8">Switch your Embedding Model</Title>
+          <Text className="mb-4">
+            If the current model is not working for you, you can update your
+            model choice below. Note that this will require a complete
+            re-indexing of all your documents across every connected source. We
+            will take care of this in the background, but depending on the size
+            of your corpus, this could take hours, day, or even weeks. You can
+            monitor the progress of the re-indexing on this page.
+          </Text>
+
+          <div className="mt-8 text-sm  mr-auto  mb-12 divide-x-2  flex   ">
+            <button
+              onClick={() => setOpenToggle(true)}
+              className={` mx-2 p-2 font-bold  ${openToggle ? "rounded bg-neutral-900 text-neutral-100 underline" : "hover:underline"}`}
+            >
+              Self-hosted
+            </button>
+            <div className="px-2 ">
+              <button
+                onClick={() => setOpenToggle(false)}
+                className={`mx-2 p-2 font-bold  ${!openToggle ? "rounded bg-neutral-900   text-neutral-100 underline" : " hover:underline"}`}
+              >
+                Cloud-based
+              </button>
+            </div>
+          </div>
+        </>
+      )}
+
+      {!showAddConnectorPopup &&
+        !futureEmbeddingModel &&
+        (openToggle ? (
+          <OpenEmbeddingPage
+            onSelectOpenSource={onSelectOpenSource}
+            currentModelName={currentModelName!}
+          />
+        ) : (
+          <CloudEmbeddingPage
+            setShowModelInQueue={setShowModelInQueue}
+            setShowTentativeModel={setShowTentativeModel}
+            currentModel={currentModel}
+            setAlreadySelectedModel={setAlreadySelectedModel}
+            embeddingProviderDetails={embeddingProviderDetails}
+            newEnabledProviders={newEnabledProviders}
+            newUnenabledProviders={newUnenabledProviders}
+            setShowTentativeProvider={setShowTentativeProvider}
+            selectedModel={selectedModel}
+            setChangeCredentialsProvider={setChangeCredentialsProvider}
+          />
+        ))}
+
+      {openToggle && (
+        <>
           {showAddConnectorPopup && (
             <Modal>
               <div>
                 <div>
-              <b className="text-base">Embeding model successfully selected</b>{" "}
+                  <b className="text-base">
+                    Embedding model successfully selected
+                  </b>{" "}
                   🙌
                   <br />
                   <br />
@@ -158,12 +421,16 @@ function Main() {
                   <br />
                   <br />
                   Connectors are the way that Danswer gets data from your
-              organization&apos;s various data sources. Once setup, we&apos;ll
+                  organization&apos;s various data sources. Once setup,
-              automatically sync data from your apps and docs into Danswer, so
+                  we&apos;ll automatically sync data from your apps and docs
-              you can search all through all of them in one place.
+                  into Danswer, so you can search all through all of them in one
+                  place.
                 </div>
                 <div className="flex">
-              <Link className="mx-auto mt-2 w-fit" href="/admin/add-connector">
+                  <Link
+                    className="mx-auto mt-2 w-fit"
+                    href="/admin/add-connector"
+                  >
                     <Button className="mt-3 mx-auto" size="xs">
                       Add Connector
                     </Button>
@@ -183,121 +450,33 @@ function Main() {
                   Are you sure you want to cancel?
                   <br />
                   <br />
-              Cancelling will revert to the previous model and all progress will
+                  Cancelling will revert to the previous model and all progress
-              be lost.
+                  will be lost.
                 </div>
                 <div className="flex">
-              <Button onClick={onCancel} className="mt-3 mx-auto" color="green">
+                  <Button
+                    onClick={onCancel}
+                    className="mt-3 mx-auto"
+                    color="green"
+                  >
                     Confirm
                   </Button>
                 </div>
               </div>
             </Modal>
           )}
-
-      <Text>
-        Embedding models are used to generate embeddings for your documents,
-        which then power Danswer&apos;s search.
-      </Text>
-
-      {currentModel ? (
-        <>
-          <Title className="mt-8 mb-2">Current Embedding Model</Title>
-
-          <Text>
-            <ModelOption model={currentModel} />
-          </Text>
-        </>
-      ) : (
-        newModelSelection &&
-        (!connectors || !connectors.length) && (
-          <>
-            <Title className="mt-8 mb-2">Current Embedding Model</Title>
-
-            <Text>
-              <ModelOption model={newModelSelection} />
-            </Text>
-          </>
-        )
-      )}
-
-      {!showAddConnectorPopup &&
-        (!newModelSelection ? (
-          <div>
-            {currentModel ? (
-              <>
-                <Title className="mt-8">Switch your Embedding Model</Title>
-
-                <Text className="mb-4">
-                  If the current model is not working for you, you can update
-                  your model choice below. Note that this will require a
-                  complete re-indexing of all your documents across every
-                  connected source. We will take care of this in the background,
-                  but depending on the size of your corpus, this could take
-                  hours, day, or even weeks. You can monitor the progress of the
-                  re-indexing on this page.
-                </Text>
-              </>
-            ) : (
-              <>
-                <Title className="mt-8 mb-4">Choose your Embedding Model</Title>
         </>
       )}
 
-            <Text className="mb-4">
+      {futureEmbeddingModel && connectors && connectors.length > 0 && (
-              Below are a curated selection of quality models that we recommend
-              you choose from.
-            </Text>
-
-            <ModelSelector
-              modelOptions={AVAILABLE_MODELS.filter(
-                (modelOption) => modelOption.model_name !== currentModelName
-              )}
-              setSelectedModel={onSelect}
-            />
-
-            <Text className="mt-6">
-              Alternatively, (if you know what you&apos;re doing) you can
-              specify a{" "}
-              <a
-                target="_blank"
-                href="https://www.sbert.net/"
-                className="text-link"
-              >
-                SentenceTransformers
-              </a>
-              -compatible model of your choice below. The rough list of
-              supported models can be found{" "}
-              <a
-                target="_blank"
-                href="https://huggingface.co/models?library=sentence-transformers&sort=trending"
-                className="text-link"
-              >
-                here
-              </a>
-              .
-              <br />
-              <b>NOTE:</b> not all models listed will work with Danswer, since
-              some have unique interfaces or special requirements. If in doubt,
-              reach out to the Danswer team.
-            </Text>
-
-            <div className="w-full flex">
-              <Card className="mt-4 2xl:w-4/6 mx-auto">
-                <CustomModelForm onSubmit={onSelect} />
-              </Card>
-            </div>
-          </div>
-        ) : (
-          connectors &&
-          connectors.length > 0 && (
         <div>
           <Title className="mt-8">Current Upgrade Status</Title>
           <div className="mt-4">
-                <div className="italic text-sm mb-2">
+            <div className="italic text-lg mb-2">
-                  Currently in the process of switching to:
+              Currently in the process of switching to:{" "}
+              {futureEmbeddingModel.model_name}
             </div>
-                <ModelOption model={newModelSelection} />
+            {/* <ModelOption model={futureEmbeddingModel} /> */}
 
             <Button
               color="red"
@@ -310,10 +489,10 @@ function Main() {
 
             <Text className="my-4">
               The table below shows the re-indexing progress of all existing
-                  connectors. Once all connectors have been re-indexed
+              connectors. Once all connectors have been re-indexed successfully,
-                  successfully, the new model will be used for all search
+              the new model will be used for all search queries. Until then, we
-                  queries. Until then, we will use the old model so that no
+              will use the old model so that no downtime is necessary during
-                  downtime is necessary during this transition.
+              this transition.
             </Text>
 
             {isLoadingOngoingReIndexingStatus ? (
@@ -327,8 +506,7 @@ function Main() {
             )}
           </div>
         </div>
-          )
+      )}
-        ))}
     </div>
   );
 }

web/src/app/admin/models/llm/constants.ts

@@ -1 +1,4 @@
 export const LLM_PROVIDERS_ADMIN_URL = "/api/admin/llm/provider";
+
+export const EMBEDDING_PROVIDERS_ADMIN_URL =
+  "/api/admin/embedding/embedding-provider";

web/src/app/search/page.tsx

@@ -20,7 +20,7 @@ import {
 import { unstable_noStore as noStore } from "next/cache";
 import { InstantSSRAutoRefresh } from "@/components/SSRAutoRefresh";
 import { personaComparator } from "../admin/assistants/lib";
-import { FullEmbeddingModelResponse } from "../admin/models/embedding/embeddingModels";
+import { FullEmbeddingModelResponse } from "../admin/models/embedding/components/types";
 import { NoSourcesModal } from "@/components/initialSetup/search/NoSourcesModal";
 import { NoCompleteSourcesModal } from "@/components/initialSetup/search/NoCompleteSourceModal";
 import { ChatPopup } from "../chat/ChatPopup";

web/src/components/Modal.tsx

@@ -1,7 +1,9 @@
 import { Divider } from "@tremor/react";
 import { FiX } from "react-icons/fi";
+import { IconProps } from "./icons/icons";
 
 interface ModalProps {
+  icon?: ({ size, className }: IconProps) => JSX.Element;
   children: JSX.Element | string;
   title?: JSX.Element | string;
   onOutsideClick?: () => void;
@@ -13,6 +15,7 @@ interface ModalProps {
 }
 
 export function Modal({
+  icon,
   children,
   title,
   onOutsideClick,
@@ -44,10 +47,15 @@ export function Modal({
             <>
               <div className="flex mb-4">
                 <h2
-                  className={"my-auto font-bold " + (titleSize || "text-2xl")}
+                  className={
+                    "my-auto flex content-start gap-x-4 font-bold " +
+                    (titleSize || "text-2xl")
+                  }
                 >
                   {title}
+                  {icon && icon({ size: 30 })}
                 </h2>
+
                 {onOutsideClick && (
                   <div
                     onClick={onOutsideClick}

web/src/components/icons/icons.tsx

@@ -72,11 +72,16 @@ import sharepointIcon from "../../../public/Sharepoint.png";
 import teamsIcon from "../../../public/Teams.png";
 import mediawikiIcon from "../../../public/MediaWiki.svg";
 import wikipediaIcon from "../../../public/Wikipedia.svg";
+
 import discourseIcon from "../../../public/Discourse.png";
 import clickupIcon from "../../../public/Clickup.svg";
+import cohereIcon from "../../../public/Cohere.svg";
+import voyageIcon from "../../../public/Voyage.png";
+import googleIcon from "../../../public/Google.webp";
+
 import { FaRobot } from "react-icons/fa";
 
-interface IconProps {
+export interface IconProps {
   size?: number;
   className?: string;
 }
@@ -84,20 +89,6 @@ interface IconProps {
 export const defaultTailwindCSS = "my-auto flex flex-shrink-0 text-default";
 export const defaultTailwindCSSBlue = "my-auto flex flex-shrink-0 text-link";
 
-export const OpenAIIcon = ({
-  size = 16,
-  className = defaultTailwindCSS,
-}: IconProps) => {
-  return (
-    <div
-      style={{ width: `${size + 4}px`, height: `${size + 4}px` }}
-      className={`w-[${size + 4}px] h-[${size + 4}px] -m-0.5 ` + className}
-    >
-      <Image src={openAISVG} alt="Logo" width="96" height="96" />
-    </div>
-  );
-};
-
 export const OpenSourceIcon = ({
   size = 16,
   className = defaultTailwindCSS,
@@ -528,6 +519,62 @@ export const ZulipIcon = ({
   );
 };
 
+export const OpenAIIcon = ({
+  size = 16,
+  className = defaultTailwindCSS,
+}: IconProps) => {
+  return (
+    <div
+      style={{ width: `${size}px`, height: `${size}px` }}
+      className={`w-[${size}px] h-[${size}px] ` + className}
+    >
+      <Image src={openAISVG} alt="Logo" width="96" height="96" />
+    </div>
+  );
+};
+
+export const VoyageIcon = ({
+  size = 16,
+  className = defaultTailwindCSS,
+}: IconProps) => {
+  return (
+    <div
+      style={{ width: `${size}px`, height: `${size}px` }}
+      className={`w-[${size}px] h-[${size}px] ` + className}
+    >
+      <Image src={voyageIcon} alt="Logo" width="96" height="96" />
+    </div>
+  );
+};
+
+export const GoogleIcon = ({
+  size = 16,
+  className = defaultTailwindCSS,
+}: IconProps) => {
+  return (
+    <div
+      style={{ width: `${size}px`, height: `${size}px` }}
+      className={`w-[${size}px] h-[${size}px] ` + className}
+    >
+      <Image src={googleIcon} alt="Logo" width="96" height="96" />
+    </div>
+  );
+};
+
+export const CohereIcon = ({
+  size = 16,
+  className = defaultTailwindCSS,
+}: IconProps) => {
+  return (
+    <div
+      style={{ width: `${size}px`, height: `${size}px` }}
+      className={`w-[${size}px] h-[${size}px] ` + className}
+    >
+      <Image src={cohereIcon} alt="Logo" width="96" height="96" />
+    </div>
+  );
+};
+
 export const GoogleStorageIcon = ({
   size = 16,
   className = defaultTailwindCSS,

web/src/lib/chat/fetchChatData.ts

@@ -13,7 +13,7 @@ import {
 } from "@/lib/types";
 import { ChatSession } from "@/app/chat/interfaces";
 import { Persona } from "@/app/admin/assistants/interfaces";
-import { FullEmbeddingModelResponse } from "@/app/admin/models/embedding/embeddingModels";
+import { FullEmbeddingModelResponse } from "@/app/admin/models/embedding/components/types";
 import { Settings } from "@/app/admin/settings/interfaces";
 import { fetchLLMProvidersSS } from "@/lib/llm/fetchLLMs";
 import { LLMProviderDescriptor } from "@/app/admin/models/llm/interfaces";