Merge branch 'main' into add-user-on-slack-bot-invoke

backend/Dockerfile

@@ -75,8 +75,8 @@ Tokenizer.from_pretrained('nomic-ai/nomic-embed-text-v1')"
 # Pre-downloading NLTK for setups with limited egress
 RUN python -c "import nltk; \
 nltk.download('stopwords', quiet=True); \
-nltk.download('wordnet', quiet=True); \
 nltk.download('punkt', quiet=True);"
+# nltk.download('wordnet', quiet=True); introduce this back if lemmatization is needed
 
 # Set up application files
 WORKDIR /app

backend/alembic/versions/ba98eba0f66a_add_support_for_litellm_proxy_in_.py

@@ -0,0 +1,26 @@
+"""add support for litellm proxy in reranking
+
+Revision ID: ba98eba0f66a
+Revises: bceb1e139447
+Create Date: 2024-09-06 10:36:04.507332
+
+"""
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision = "ba98eba0f66a"
+down_revision = "bceb1e139447"
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "search_settings", sa.Column("rerank_api_url", sa.String(), nullable=True)
+    )
+
+
+def downgrade() -> None:
+    op.drop_column("search_settings", "rerank_api_url")

backend/danswer/configs/chat_configs.py

@@ -88,3 +88,6 @@ HARD_DELETE_CHATS = False
 
 # Internet Search
 BING_API_KEY = os.environ.get("BING_API_KEY") or None
+
+# Enable in-house model for detecting connector-based filtering in queries
+ENABLE_CONNECTOR_CLASSIFIER = os.environ.get("ENABLE_CONNECTOR_CLASSIFIER", False)

backend/danswer/connectors/danswer_jira/connector.py

@@ -45,10 +45,15 @@ def extract_jira_project(url: str) -> tuple[str, str]:
     return jira_base, jira_project
 
 
-def extract_text_from_content(content: dict) -> str:
+def extract_text_from_adf(adf: dict | None) -> str:
+    """Extracts plain text from Atlassian Document Format:
+    https://developer.atlassian.com/cloud/jira/platform/apis/document/structure/
+
+    WARNING: This function is incomplete and will e.g. skip lists!
+    """
     texts = []
-    if "content" in content:
-        for block in content["content"]:
+    if adf is not None and "content" in adf:
+        for block in adf["content"]:
             if "content" in block:
                 for item in block["content"]:
                     if item["type"] == "text":
@@ -72,18 +77,15 @@ def _get_comment_strs(
     comment_strs = []
     for comment in jira.fields.comment.comments:
         try:
-            if hasattr(comment, "body"):
-                body_text = extract_text_from_content(comment.raw["body"])
-            elif hasattr(comment, "raw"):
-                body = comment.raw.get("body", "No body content available")
-                body_text = (
-                    extract_text_from_content(body) if isinstance(body, dict) else body
-                )
-            else:
-                body_text = "No body attribute found"
+            body_text = (
+                comment.body
+                if JIRA_API_VERSION == "2"
+                else extract_text_from_adf(comment.raw["body"])
+            )
 
             if (
                 hasattr(comment, "author")
+                and hasattr(comment.author, "emailAddress")
                 and comment.author.emailAddress in comment_email_blacklist
             ):
                 continue  # Skip adding comment if author's email is in blacklist
@@ -126,11 +128,14 @@ def fetch_jira_issues_batch(
             )
             continue
 
+        description = (
+            jira.fields.description
+            if JIRA_API_VERSION == "2"
+            else extract_text_from_adf(jira.raw["fields"]["description"])
+        )
         comments = _get_comment_strs(jira, comment_email_blacklist)
-        semantic_rep = (
-            f"{jira.fields.description}\n"
-            if jira.fields.description
-            else "" + "\n".join([f"Comment: {comment}" for comment in comments])
+        semantic_rep = f"{description}\n" + "\n".join(
+            [f"Comment: {comment}" for comment in comments if comment]
         )
 
         page_url = f"{jira_client.client_info()}/browse/{jira.key}"

backend/danswer/connectors/google_drive/connector.py

@@ -327,7 +327,7 @@ def extract_text(file: dict[str, str], service: discovery.Resource) -> str:
             export_mime_type = "text/plain"
         elif mime_type in [
             GDriveMimeType.PLAIN_TEXT.value,
-            GDriveMimeType.MARKDOWN.value
+            GDriveMimeType.MARKDOWN.value,
         ]:
             export_mime_type = mime_type
 

backend/danswer/connectors/notion/connector.py

@@ -237,6 +237,14 @@ class NotionConnector(LoadConnector, PollConnector):
                     )
                     continue
 
+                if result_type == "external_object_instance_page":
+                    logger.warning(
+                        f"Skipping 'external_object_instance_page' ('{result_block_id}') for base block '{base_block_id}': "
+                        f"Notion API does not currently support reading external blocks (as of 24/07/03) "
+                        f"(discussion: https://github.com/danswer-ai/danswer/issues/1761)"
+                    )
+                    continue
+
                 cur_result_text_arr = []
                 if "rich_text" in result_obj:
                     for rich_text in result_obj["rich_text"]:

backend/danswer/connectors/sharepoint/connector.py

@@ -25,7 +25,6 @@ from danswer.connectors.models import Section
 from danswer.file_processing.extract_file_text import extract_file_text
 from danswer.utils.logger import setup_logger
 
-
 logger = setup_logger()
 
 
@@ -137,7 +136,7 @@ class SharepointConnector(LoadConnector, PollConnector):
                     .execute_query()
                 ]
         else:
-            sites = self.graph_client.sites.get().execute_query()
+            sites = self.graph_client.sites.get_all().execute_query()
             self.site_data = [
                 SiteData(url=None, folder=None, sites=sites, driveitems=[])
             ]

backend/danswer/connectors/slack/connector.py

@@ -29,6 +29,7 @@ from danswer.connectors.slack.utils import make_slack_api_rate_limited
 from danswer.connectors.slack.utils import SlackTextCleaner
 from danswer.utils.logger import setup_logger
 
+
 logger = setup_logger()
 
 

backend/danswer/connectors/web/connector.py

@@ -1,6 +1,8 @@
 import io
 import ipaddress
 import socket
+from datetime import datetime
+from datetime import timezone
 from enum import Enum
 from typing import Any
 from typing import cast
@@ -203,6 +205,15 @@ def _read_urls_file(location: str) -> list[str]:
     return urls
 
 
+def _get_datetime_from_last_modified_header(last_modified: str) -> datetime | None:
+    try:
+        return datetime.strptime(last_modified, "%a, %d %b %Y %H:%M:%S %Z").replace(
+            tzinfo=timezone.utc
+        )
+    except (ValueError, TypeError):
+        return None
+
+
 class WebConnector(LoadConnector):
     def __init__(
         self,
@@ -288,6 +299,7 @@ class WebConnector(LoadConnector):
                     page_text, metadata = read_pdf_file(
                         file=io.BytesIO(response.content)
                     )
+                    last_modified = response.headers.get("Last-Modified")
 
                     doc_batch.append(
                         Document(
@@ -296,12 +308,22 @@ class WebConnector(LoadConnector):
                             source=DocumentSource.WEB,
                             semantic_identifier=current_url.split("/")[-1],
                             metadata=metadata,
+                            doc_updated_at=_get_datetime_from_last_modified_header(
+                                last_modified
+                            )
+                            if last_modified
+                            else None,
                         )
                     )
                     continue
 
                 page = context.new_page()
                 page_response = page.goto(current_url)
+                last_modified = (
+                    page_response.header_value("Last-Modified")
+                    if page_response
+                    else None
+                )
                 final_page = page.url
                 if final_page != current_url:
                     logger.info(f"Redirected to {final_page}")
@@ -337,6 +359,11 @@ class WebConnector(LoadConnector):
                         source=DocumentSource.WEB,
                         semantic_identifier=parsed_html.title or current_url,
                         metadata={},
+                        doc_updated_at=_get_datetime_from_last_modified_header(
+                            last_modified
+                        )
+                        if last_modified
+                        else None,
                     )
                 )
 

backend/danswer/db/models.py

@@ -61,7 +61,7 @@ from shared_configs.enums import RerankerProvider
 
 
 class Base(DeclarativeBase):
-    pass
+    __abstract__ = True
 
 
 class EncryptedString(TypeDecorator):
@@ -450,7 +450,7 @@ class Document(Base):
     )
     tags = relationship(
         "Tag",
-        secondary="document__tag",
+        secondary=Document__Tag.__table__,
         back_populates="documents",
     )
 
@@ -467,7 +467,7 @@ class Tag(Base):
 
     documents = relationship(
         "Document",
-        secondary="document__tag",
+        secondary=Document__Tag.__table__,
         back_populates="tags",
     )
 
@@ -578,6 +578,8 @@ class SearchSettings(Base):
         Enum(RerankerProvider, native_enum=False), nullable=True
     )
     rerank_api_key: Mapped[str | None] = mapped_column(String, nullable=True)
+    rerank_api_url: Mapped[str | None] = mapped_column(String, nullable=True)
+
     num_rerank: Mapped[int] = mapped_column(Integer, default=NUM_POSTPROCESSED_RESULTS)
 
     cloud_provider: Mapped["CloudEmbeddingProvider"] = relationship(
@@ -816,7 +818,7 @@ class SearchDoc(Base):
 
     chat_messages = relationship(
         "ChatMessage",
-        secondary="chat_message__search_doc",
+        secondary=ChatMessage__SearchDoc.__table__,
         back_populates="search_docs",
     )
 
@@ -959,7 +961,7 @@ class ChatMessage(Base):
     )
     search_docs: Mapped[list["SearchDoc"]] = relationship(
         "SearchDoc",
-        secondary="chat_message__search_doc",
+        secondary=ChatMessage__SearchDoc.__table__,
         back_populates="chat_messages",
     )
     # NOTE: Should always be attached to the `assistant` message.

backend/danswer/file_processing/extract_file_text.py

@@ -240,9 +240,43 @@ def read_pdf_file(
 
 
 def docx_to_text(file: IO[Any]) -> str:
+    def is_simple_table(table: docx.table.Table) -> bool:
+        for row in table.rows:
+            # No omitted cells
+            if row.grid_cols_before > 0 or row.grid_cols_after > 0:
+                return False
+
+            # No nested tables
+            if any(cell.tables for cell in row.cells):
+                return False
+
+        return True
+
+    def extract_cell_text(cell: docx.table._Cell) -> str:
+        cell_paragraphs = [para.text.strip() for para in cell.paragraphs]
+        return " ".join(p for p in cell_paragraphs if p) or "N/A"
+
+    paragraphs = []
     doc = docx.Document(file)
-    full_text = [para.text for para in doc.paragraphs]
-    return TEXT_SECTION_SEPARATOR.join(full_text)
+    for item in doc.iter_inner_content():
+        if isinstance(item, docx.text.paragraph.Paragraph):
+            paragraphs.append(item.text)
+
+        elif isinstance(item, docx.table.Table):
+            if not item.rows or not is_simple_table(item):
+                continue
+
+            # Every row is a new line, joined with a single newline
+            table_content = "\n".join(
+                [
+                    ",\t".join(extract_cell_text(cell) for cell in row.cells)
+                    for row in item.rows
+                ]
+            )
+            paragraphs.append(table_content)
+
+    # Docx already has good spacing between paragraphs
+    return "\n".join(paragraphs)
 
 
 def pptx_to_text(file: IO[Any]) -> str:

backend/danswer/main.py

@@ -392,8 +392,11 @@ async def lifespan(app: FastAPI) -> AsyncGenerator:
                 logger.notice(
                     f"Multilingual query expansion is enabled with {search_settings.multilingual_expansion}."
                 )
-
-        if search_settings.rerank_model_name and not search_settings.provider_type:
+        if (
+            search_settings.rerank_model_name
+            and not search_settings.provider_type
+            and not search_settings.rerank_provider_type
+        ):
             warm_up_cross_encoder(search_settings.rerank_model_name)
 
         logger.notice("Verifying query preprocessing (NLTK) data is downloaded")

backend/danswer/natural_language_processing/search_nlp_models.py

@@ -24,6 +24,8 @@ from shared_configs.configs import MODEL_SERVER_PORT
 from shared_configs.enums import EmbeddingProvider
 from shared_configs.enums import EmbedTextType
 from shared_configs.enums import RerankerProvider
+from shared_configs.model_server_models import ConnectorClassificationRequest
+from shared_configs.model_server_models import ConnectorClassificationResponse
 from shared_configs.model_server_models import Embedding
 from shared_configs.model_server_models import EmbedRequest
 from shared_configs.model_server_models import EmbedResponse
@@ -240,6 +242,7 @@ class RerankingModel:
         model_name: str,
         provider_type: RerankerProvider | None,
         api_key: str | None,
+        api_url: str | None,
         model_server_host: str = MODEL_SERVER_HOST,
         model_server_port: int = MODEL_SERVER_PORT,
     ) -> None:
@@ -248,6 +251,7 @@ class RerankingModel:
         self.model_name = model_name
         self.provider_type = provider_type
         self.api_key = api_key
+        self.api_url = api_url
 
     def predict(self, query: str, passages: list[str]) -> list[float]:
         rerank_request = RerankRequest(
@@ -256,6 +260,7 @@ class RerankingModel:
             model_name=self.model_name,
             provider_type=self.provider_type,
             api_key=self.api_key,
+            api_url=self.api_url,
         )
 
         response = requests.post(
@@ -301,6 +306,37 @@ class QueryAnalysisModel:
         return response_model.is_keyword, response_model.keywords
 
 
+class ConnectorClassificationModel:
+    def __init__(
+        self,
+        model_server_host: str = MODEL_SERVER_HOST,
+        model_server_port: int = MODEL_SERVER_PORT,
+    ):
+        model_server_url = build_model_server_url(model_server_host, model_server_port)
+        self.connector_classification_endpoint = (
+            model_server_url + "/custom/connector-classification"
+        )
+
+    def predict(
+        self,
+        query: str,
+        available_connectors: list[str],
+    ) -> list[str]:
+        connector_classification_request = ConnectorClassificationRequest(
+            available_connectors=available_connectors,
+            query=query,
+        )
+        response = requests.post(
+            self.connector_classification_endpoint,
+            json=connector_classification_request.dict(),
+        )
+        response.raise_for_status()
+
+        response_model = ConnectorClassificationResponse(**response.json())
+
+        return response_model.connectors
+
+
 def warm_up_retry(
     func: Callable[..., Any],
     tries: int = 20,
@@ -367,6 +403,7 @@ def warm_up_cross_encoder(
     reranking_model = RerankingModel(
         model_name=rerank_model_name,
         provider_type=None,
+        api_url=None,
         api_key=None,
     )
 

backend/danswer/search/models.py

@@ -26,6 +26,7 @@ MAX_METRICS_CONTENT = (
 class RerankingDetails(BaseModel):
     # If model is None (or num_rerank is 0), then reranking is turned off
     rerank_model_name: str | None
+    rerank_api_url: str | None
     rerank_provider_type: RerankerProvider | None
     rerank_api_key: str | None = None
 
@@ -42,6 +43,7 @@ class RerankingDetails(BaseModel):
             rerank_provider_type=search_settings.rerank_provider_type,
             rerank_api_key=search_settings.rerank_api_key,
             num_rerank=search_settings.num_rerank,
+            rerank_api_url=search_settings.rerank_api_url,
         )
 
 
@@ -81,7 +83,7 @@ class SavedSearchSettings(InferenceSettings, IndexingSetting):
             num_rerank=search_settings.num_rerank,
             # Multilingual Expansion
             multilingual_expansion=search_settings.multilingual_expansion,
-            api_url=search_settings.api_url,
+            rerank_api_url=search_settings.rerank_api_url,
         )
 
 

backend/danswer/search/postprocessing/postprocessing.py

@@ -100,6 +100,7 @@ def semantic_reranking(
         model_name=rerank_settings.rerank_model_name,
         provider_type=rerank_settings.rerank_provider_type,
         api_key=rerank_settings.rerank_api_key,
+        api_url=rerank_settings.rerank_api_url,
     )
 
     passages = [

backend/danswer/search/retrieval/search_runner.py

@@ -3,7 +3,6 @@ from collections.abc import Callable
 
 import nltk  # type:ignore
 from nltk.corpus import stopwords  # type:ignore
-from nltk.stem import WordNetLemmatizer  # type:ignore
 from nltk.tokenize import word_tokenize  # type:ignore
 from sqlalchemy.orm import Session
 
@@ -40,7 +39,7 @@ logger = setup_logger()
 def download_nltk_data() -> None:
     resources = {
         "stopwords": "corpora/stopwords",
-        "wordnet": "corpora/wordnet",
+        # "wordnet": "corpora/wordnet",  # Not in use
         "punkt": "tokenizers/punkt",
     }
 
@@ -58,15 +57,16 @@ def download_nltk_data() -> None:
 
 
 def lemmatize_text(keywords: list[str]) -> list[str]:
-    try:
-        query = " ".join(keywords)
-        lemmatizer = WordNetLemmatizer()
-        word_tokens = word_tokenize(query)
-        lemmatized_words = [lemmatizer.lemmatize(word) for word in word_tokens]
-        combined_keywords = list(set(keywords + lemmatized_words))
-        return combined_keywords
-    except Exception:
-        return keywords
+    raise NotImplementedError("Lemmatization should not be used currently")
+    # try:
+    #     query = " ".join(keywords)
+    #     lemmatizer = WordNetLemmatizer()
+    #     word_tokens = word_tokenize(query)
+    #     lemmatized_words = [lemmatizer.lemmatize(word) for word in word_tokens]
+    #     combined_keywords = list(set(keywords + lemmatized_words))
+    #     return combined_keywords
+    # except Exception:
+    #     return keywords
 
 
 def remove_stop_words_and_punctuation(keywords: list[str]) -> list[str]:

backend/danswer/secondary_llm_flows/source_filter.py

@@ -3,12 +3,16 @@ import random
 
 from sqlalchemy.orm import Session
 
+from danswer.configs.chat_configs import ENABLE_CONNECTOR_CLASSIFIER
 from danswer.configs.constants import DocumentSource
 from danswer.db.connector import fetch_unique_document_sources
 from danswer.db.engine import get_sqlalchemy_engine
 from danswer.llm.interfaces import LLM
 from danswer.llm.utils import dict_based_prompt_to_langchain_prompt
 from danswer.llm.utils import message_to_string
+from danswer.natural_language_processing.search_nlp_models import (
+    ConnectorClassificationModel,
+)
 from danswer.prompts.constants import SOURCES_KEY
 from danswer.prompts.filter_extration import FILE_SOURCE_WARNING
 from danswer.prompts.filter_extration import SOURCE_FILTER_PROMPT
@@ -42,11 +46,38 @@ def _sample_document_sources(
         return random.sample(valid_sources, num_sample)
 
 
+def _sample_documents_using_custom_connector_classifier(
+    query: str,
+    valid_sources: list[DocumentSource],
+) -> list[DocumentSource] | None:
+    query_joined = "".join(ch for ch in query.lower() if ch.isalnum())
+    available_connectors = list(
+        filter(
+            lambda conn: conn.lower() in query_joined,
+            [item.value for item in valid_sources],
+        )
+    )
+
+    if not available_connectors:
+        return None
+
+    connectors = ConnectorClassificationModel().predict(query, available_connectors)
+
+    return strings_to_document_sources(connectors) if connectors else None
+
+
 def extract_source_filter(
     query: str, llm: LLM, db_session: Session
 ) -> list[DocumentSource] | None:
     """Returns a list of valid sources for search or None if no specific sources were detected"""
 
+    valid_sources = fetch_unique_document_sources(db_session)
+    if not valid_sources:
+        return None
+
+    if ENABLE_CONNECTOR_CLASSIFIER:
+        return _sample_documents_using_custom_connector_classifier(query, valid_sources)
+
     def _get_source_filter_messages(
         query: str,
         valid_sources: list[DocumentSource],
@@ -146,10 +177,6 @@ def extract_source_filter(
             logger.warning("LLM failed to provide a valid Source Filter output")
             return None
 
-    valid_sources = fetch_unique_document_sources(db_session)
-    if not valid_sources:
-        return None
-
     messages = _get_source_filter_messages(query=query, valid_sources=valid_sources)
     filled_llm_prompt = dict_based_prompt_to_langchain_prompt(messages)
     model_output = message_to_string(llm.invoke(filled_llm_prompt))

backend/danswer/server/documents/models.py

@@ -4,6 +4,7 @@ from uuid import UUID
 
 from pydantic import BaseModel
 from pydantic import Field
+from pydantic import model_validator
 
 from danswer.configs.app_configs import MASK_CREDENTIAL_PREFIX
 from danswer.configs.constants import DocumentSource
@@ -346,8 +347,18 @@ class GoogleServiceAccountKey(BaseModel):
 
 
 class GoogleServiceAccountCredentialRequest(BaseModel):
-    google_drive_delegated_user: str | None  # email of user to impersonate
-    gmail_delegated_user: str | None  # email of user to impersonate
+    google_drive_delegated_user: str | None = None  # email of user to impersonate
+    gmail_delegated_user: str | None = None  # email of user to impersonate
+
+    @model_validator(mode="after")
+    def check_user_delegation(self) -> "GoogleServiceAccountCredentialRequest":
+        if (self.google_drive_delegated_user is None) == (
+            self.gmail_delegated_user is None
+        ):
+            raise ValueError(
+                "Exactly one of google_drive_delegated_user or gmail_delegated_user must be set"
+            )
+        return self
 
 
 class FileUploadResponse(BaseModel):

backend/model_server/custom_models.py

@@ -3,15 +3,21 @@ import torch.nn.functional as F
 from fastapi import APIRouter
 from huggingface_hub import snapshot_download  # type: ignore
 from transformers import AutoTokenizer  # type: ignore
-from transformers import BatchEncoding
+from transformers import BatchEncoding  # type: ignore
+from transformers import PreTrainedTokenizer  # type: ignore
 
 from danswer.utils.logger import setup_logger
 from model_server.constants import MODEL_WARM_UP_STRING
+from model_server.danswer_torch_model import ConnectorClassifier
 from model_server.danswer_torch_model import HybridClassifier
 from model_server.utils import simple_log_function_time
+from shared_configs.configs import CONNECTOR_CLASSIFIER_MODEL_REPO
+from shared_configs.configs import CONNECTOR_CLASSIFIER_MODEL_TAG
 from shared_configs.configs import INDEXING_ONLY
 from shared_configs.configs import INTENT_MODEL_TAG
 from shared_configs.configs import INTENT_MODEL_VERSION
+from shared_configs.model_server_models import ConnectorClassificationRequest
+from shared_configs.model_server_models import ConnectorClassificationResponse
 from shared_configs.model_server_models import IntentRequest
 from shared_configs.model_server_models import IntentResponse
 
@@ -19,10 +25,55 @@ logger = setup_logger()
 
 router = APIRouter(prefix="/custom")
 
+_CONNECTOR_CLASSIFIER_TOKENIZER: AutoTokenizer | None = None
+_CONNECTOR_CLASSIFIER_MODEL: ConnectorClassifier | None = None
+
 _INTENT_TOKENIZER: AutoTokenizer | None = None
 _INTENT_MODEL: HybridClassifier | None = None
 
 
+def get_connector_classifier_tokenizer() -> AutoTokenizer:
+    global _CONNECTOR_CLASSIFIER_TOKENIZER
+    if _CONNECTOR_CLASSIFIER_TOKENIZER is None:
+        # The tokenizer details are not uploaded to the HF hub since it's just the
+        # unmodified distilbert tokenizer.
+        _CONNECTOR_CLASSIFIER_TOKENIZER = AutoTokenizer.from_pretrained(
+            "distilbert-base-uncased"
+        )
+    return _CONNECTOR_CLASSIFIER_TOKENIZER
+
+
+def get_local_connector_classifier(
+    model_name_or_path: str = CONNECTOR_CLASSIFIER_MODEL_REPO,
+    tag: str = CONNECTOR_CLASSIFIER_MODEL_TAG,
+) -> ConnectorClassifier:
+    global _CONNECTOR_CLASSIFIER_MODEL
+    if _CONNECTOR_CLASSIFIER_MODEL is None:
+        try:
+            # Calculate where the cache should be, then load from local if available
+            local_path = snapshot_download(
+                repo_id=model_name_or_path, revision=tag, local_files_only=True
+            )
+            _CONNECTOR_CLASSIFIER_MODEL = ConnectorClassifier.from_pretrained(
+                local_path
+            )
+        except Exception as e:
+            logger.warning(f"Failed to load model directly: {e}")
+            try:
+                # Attempt to download the model snapshot
+                logger.info(f"Downloading model snapshot for {model_name_or_path}")
+                local_path = snapshot_download(repo_id=model_name_or_path, revision=tag)
+                _CONNECTOR_CLASSIFIER_MODEL = ConnectorClassifier.from_pretrained(
+                    local_path
+                )
+            except Exception as e:
+                logger.error(
+                    f"Failed to load model even after attempted snapshot download: {e}"
+                )
+                raise
+    return _CONNECTOR_CLASSIFIER_MODEL
+
+
 def get_intent_model_tokenizer() -> AutoTokenizer:
     global _INTENT_TOKENIZER
     if _INTENT_TOKENIZER is None:
@@ -61,6 +112,74 @@ def get_local_intent_model(
     return _INTENT_MODEL
 
 
+def tokenize_connector_classification_query(
+    connectors: list[str],
+    query: str,
+    tokenizer: PreTrainedTokenizer,
+    connector_token_end_id: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Tokenize the connectors & user query into one prompt for the forward pass of ConnectorClassifier models
+
+    The attention mask is just all 1s. The prompt is CLS + each connector name suffixed with the connector end
+    token and then the user query.
+    """
+
+    input_ids = torch.tensor([tokenizer.cls_token_id], dtype=torch.long)
+
+    for connector in connectors:
+        connector_token_ids = tokenizer(
+            connector,
+            add_special_tokens=False,
+            return_tensors="pt",
+        )
+
+        input_ids = torch.cat(
+            (
+                input_ids,
+                connector_token_ids["input_ids"].squeeze(dim=0),
+                torch.tensor([connector_token_end_id], dtype=torch.long),
+            ),
+            dim=-1,
+        )
+    query_token_ids = tokenizer(
+        query,
+        add_special_tokens=False,
+        return_tensors="pt",
+    )
+
+    input_ids = torch.cat(
+        (
+            input_ids,
+            query_token_ids["input_ids"].squeeze(dim=0),
+            torch.tensor([tokenizer.sep_token_id], dtype=torch.long),
+        ),
+        dim=-1,
+    )
+    attention_mask = torch.ones(input_ids.numel(), dtype=torch.long)
+
+    return input_ids.unsqueeze(0), attention_mask.unsqueeze(0)
+
+
+def warm_up_connector_classifier_model() -> None:
+    logger.info(
+        f"Warming up connector_classifier model {CONNECTOR_CLASSIFIER_MODEL_TAG}"
+    )
+    connector_classifier_tokenizer = get_connector_classifier_tokenizer()
+    connector_classifier = get_local_connector_classifier()
+
+    input_ids, attention_mask = tokenize_connector_classification_query(
+        ["GitHub"],
+        "danswer classifier query google doc",
+        connector_classifier_tokenizer,
+        connector_classifier.connector_end_token_id,
+    )
+    input_ids = input_ids.to(connector_classifier.device)
+    attention_mask = attention_mask.to(connector_classifier.device)
+
+    connector_classifier(input_ids, attention_mask)
+
+
 def warm_up_intent_model() -> None:
     logger.notice(f"Warming up Intent Model: {INTENT_MODEL_VERSION}")
     intent_tokenizer = get_intent_model_tokenizer()
@@ -157,6 +276,35 @@ def clean_keywords(keywords: list[str]) -> list[str]:
     return cleaned_words
 
 
+def run_connector_classification(req: ConnectorClassificationRequest) -> list[str]:
+    tokenizer = get_connector_classifier_tokenizer()
+    model = get_local_connector_classifier()
+
+    connector_names = req.available_connectors
+
+    input_ids, attention_mask = tokenize_connector_classification_query(
+        connector_names,
+        req.query,
+        tokenizer,
+        model.connector_end_token_id,
+    )
+    input_ids = input_ids.to(model.device)
+    attention_mask = attention_mask.to(model.device)
+
+    global_confidence, classifier_confidence = model(input_ids, attention_mask)
+
+    if global_confidence.item() < 0.5:
+        return []
+
+    passed_connectors = []
+
+    for i, connector_name in enumerate(connector_names):
+        if classifier_confidence.view(-1)[i].item() > 0.5:
+            passed_connectors.append(connector_name)
+
+    return passed_connectors
+
+
 def run_analysis(intent_req: IntentRequest) -> tuple[bool, list[str]]:
     tokenizer = get_intent_model_tokenizer()
     model_input = tokenizer(
@@ -189,6 +337,22 @@ def run_analysis(intent_req: IntentRequest) -> tuple[bool, list[str]]:
     return is_keyword_sequence, cleaned_keywords
 
 
+@router.post("/connector-classification")
+async def process_connector_classification_request(
+    classification_request: ConnectorClassificationRequest,
+) -> ConnectorClassificationResponse:
+    if INDEXING_ONLY:
+        raise RuntimeError(
+            "Indexing model server should not call connector classification endpoint"
+        )
+
+    if len(classification_request.available_connectors) == 0:
+        return ConnectorClassificationResponse(connectors=[])
+
+    connectors = run_connector_classification(classification_request)
+    return ConnectorClassificationResponse(connectors=connectors)
+
+
 @router.post("/query-analysis")
 async def process_analysis_request(
     intent_request: IntentRequest,

backend/model_server/danswer_torch_model.py

@@ -4,7 +4,8 @@ import os
 import torch
 import torch.nn as nn
 from transformers import DistilBertConfig  # type: ignore
-from transformers import DistilBertModel
+from transformers import DistilBertModel  # type: ignore
+from transformers import DistilBertTokenizer  # type: ignore
 
 
 class HybridClassifier(nn.Module):
@@ -21,7 +22,6 @@ class HybridClassifier(nn.Module):
             self.distilbert.config.dim, self.distilbert.config.dim
         )
         self.intent_classifier = nn.Linear(self.distilbert.config.dim, 2)
-        self.dropout = nn.Dropout(self.distilbert.config.seq_classif_dropout)
 
         self.device = torch.device("cpu")
 
@@ -36,8 +36,7 @@ class HybridClassifier(nn.Module):
         # Intent classification on the CLS token
         cls_token_state = sequence_output[:, 0, :]
         pre_classifier_out = self.pre_classifier(cls_token_state)
-        dropout_out = self.dropout(pre_classifier_out)
-        intent_logits = self.intent_classifier(dropout_out)
+        intent_logits = self.intent_classifier(pre_classifier_out)
 
         # Keyword classification on all tokens
         token_logits = self.keyword_classifier(sequence_output)
@@ -72,3 +71,70 @@ class HybridClassifier(nn.Module):
             param.requires_grad = False
 
         return model
+
+
+class ConnectorClassifier(nn.Module):
+    def __init__(self, config: DistilBertConfig) -> None:
+        super().__init__()
+
+        self.config = config
+        self.distilbert = DistilBertModel(config)
+        self.connector_global_classifier = nn.Linear(self.distilbert.config.dim, 1)
+        self.connector_match_classifier = nn.Linear(self.distilbert.config.dim, 1)
+        self.tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+
+        # Token indicating end of connector name, and on which classifier is used
+        self.connector_end_token_id = self.tokenizer.get_vocab()[
+            self.config.connector_end_token
+        ]
+
+        self.device = torch.device("cpu")
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        hidden_states = self.distilbert(
+            input_ids=input_ids, attention_mask=attention_mask
+        ).last_hidden_state
+
+        cls_hidden_states = hidden_states[
+            :, 0, :
+        ]  # Take leap of faith that first token is always [CLS]
+        global_logits = self.connector_global_classifier(cls_hidden_states).view(-1)
+        global_confidence = torch.sigmoid(global_logits).view(-1)
+
+        connector_end_position_ids = input_ids == self.connector_end_token_id
+        connector_end_hidden_states = hidden_states[connector_end_position_ids]
+        classifier_output = self.connector_match_classifier(connector_end_hidden_states)
+        classifier_confidence = torch.nn.functional.sigmoid(classifier_output).view(-1)
+
+        return global_confidence, classifier_confidence
+
+    @classmethod
+    def from_pretrained(cls, repo_dir: str) -> "ConnectorClassifier":
+        config = DistilBertConfig.from_pretrained(os.path.join(repo_dir, "config.json"))
+        device = (
+            torch.device("cuda")
+            if torch.cuda.is_available()
+            else torch.device("mps")
+            if torch.backends.mps.is_available()
+            else torch.device("cpu")
+        )
+        state_dict = torch.load(
+            os.path.join(repo_dir, "pytorch_model.pt"),
+            map_location=device,
+            weights_only=True,
+        )
+
+        model = cls(config)
+        model.load_state_dict(state_dict)
+        model.to(device)
+        model.device = device
+        model.eval()
+
+        for param in model.parameters():
+            param.requires_grad = False
+
+        return model

backend/model_server/encoders.py

@@ -362,6 +362,28 @@ def cohere_rerank(
     return [result.relevance_score for result in sorted_results]
 
 
+def litellm_rerank(
+    query: str, docs: list[str], api_url: str, model_name: str, api_key: str | None
+) -> list[float]:
+    headers = {} if not api_key else {"Authorization": f"Bearer {api_key}"}
+    with httpx.Client() as client:
+        response = client.post(
+            api_url,
+            json={
+                "model": model_name,
+                "query": query,
+                "documents": docs,
+            },
+            headers=headers,
+        )
+        response.raise_for_status()
+        result = response.json()
+        return [
+            item["relevance_score"]
+            for item in sorted(result["results"], key=lambda x: x["index"])
+        ]
+
+
 @router.post("/bi-encoder-embed")
 async def process_embed_request(
     embed_request: EmbedRequest,
@@ -418,6 +440,20 @@ async def process_rerank_request(rerank_request: RerankRequest) -> RerankRespons
                 model_name=rerank_request.model_name,
             )
             return RerankResponse(scores=sim_scores)
+        elif rerank_request.provider_type == RerankerProvider.LITELLM:
+            if rerank_request.api_url is None:
+                raise ValueError("API URL is required for LiteLLM reranking.")
+
+            sim_scores = litellm_rerank(
+                query=rerank_request.query,
+                docs=rerank_request.documents,
+                api_url=rerank_request.api_url,
+                model_name=rerank_request.model_name,
+                api_key=rerank_request.api_key,
+            )
+
+            return RerankResponse(scores=sim_scores)
+
         elif rerank_request.provider_type == RerankerProvider.COHERE:
             if rerank_request.api_key is None:
                 raise RuntimeError("Cohere Rerank Requires an API Key")

backend/requirements/default.txt

@@ -1,4 +1,4 @@
-aiohttp==3.9.4
+aiohttp==3.10.2
 alembic==1.10.4
 asyncpg==0.27.0
 atlassian-python-api==3.37.0
@@ -26,13 +26,12 @@ huggingface-hub==0.20.1
 jira==3.5.1
 jsonref==1.1.0
 langchain==0.1.17
-langchain-community==0.0.36
 langchain-core==0.1.50
 langchain-text-splitters==0.0.1
 litellm==1.43.18
 llama-index==0.9.45
 Mako==1.2.4
-msal==1.26.0
+msal==1.28.0
 nltk==3.8.1
 Office365-REST-Python-Client==2.5.9
 oauthlib==3.2.2
@@ -50,7 +49,7 @@ python-pptx==0.6.23
 pypdf==3.17.0
 pytest-mock==3.12.0
 pytest-playwright==0.3.2
-python-docx==1.1.0
+python-docx==1.1.2
 python-dotenv==1.0.0
 python-multipart==0.0.7
 pywikibot==9.0.0

backend/requirements/model_server.txt

@@ -8,7 +8,7 @@ pydantic==2.8.2
 retry==0.9.2
 safetensors==0.4.2
 sentence-transformers==2.6.1
-torch==2.0.1
+torch==2.2.0
 transformers==4.39.2
 uvicorn==0.21.1
 voyageai==0.2.3

backend/shared_configs/configs.py

@@ -16,9 +16,12 @@ INDEXING_MODEL_SERVER_PORT = int(
 )
 
 # Danswer custom Deep Learning Models
+CONNECTOR_CLASSIFIER_MODEL_REPO = "Danswer/filter-extraction-model"
+CONNECTOR_CLASSIFIER_MODEL_TAG = "1.0.0"
 INTENT_MODEL_VERSION = "danswer/hybrid-intent-token-classifier"
 INTENT_MODEL_TAG = "v1.0.3"
 
+
 # Bi-Encoder, other details
 DOC_EMBEDDING_CONTEXT_SIZE = 512
 

backend/shared_configs/enums.py

@@ -11,6 +11,7 @@ class EmbeddingProvider(str, Enum):
 
 class RerankerProvider(str, Enum):
     COHERE = "cohere"
+    LITELLM = "litellm"
 
 
 class EmbedTextType(str, Enum):

backend/shared_configs/model_server_models.py

@@ -7,6 +7,15 @@ from shared_configs.enums import RerankerProvider
 Embedding = list[float]
 
 
+class ConnectorClassificationRequest(BaseModel):
+    available_connectors: list[str]
+    query: str
+
+
+class ConnectorClassificationResponse(BaseModel):
+    connectors: list[str]
+
+
 class EmbedRequest(BaseModel):
     texts: list[str]
     # Can be none for cloud embedding model requests, error handling logic exists for other cases
@@ -34,6 +43,7 @@ class RerankRequest(BaseModel):
     model_name: str
     provider_type: RerankerProvider | None = None
     api_key: str | None = None
+    api_url: str | None = None
 
     # This disables the "model_" protected namespace for pydantic
     model_config = {"protected_namespaces": ()}

deployment/docker_compose/docker-compose.dev.yml

@@ -1,4 +1,3 @@
-version: '3'
 services:
   api_server:
     image: danswer/danswer-backend:${IMAGE_TAG:-latest}

deployment/docker_compose/docker-compose.gpu-dev.yml

@@ -1,4 +1,3 @@
-version: '3'
 services:
   api_server:
     image: danswer/danswer-backend:${IMAGE_TAG:-latest}

deployment/docker_compose/docker-compose.prod-no-letsencrypt.yml

@@ -1,4 +1,3 @@
-version: '3'
 services:
   api_server:
     image: danswer/danswer-backend:${IMAGE_TAG:-latest}

deployment/docker_compose/docker-compose.prod.yml

@@ -1,4 +1,3 @@
-version: '3'
 services:
   api_server:
     image: danswer/danswer-backend:${IMAGE_TAG:-latest}

deployment/docker_compose/docker-compose.search-testing.yml

@@ -1,4 +1,3 @@
-version: '3'
 services:
   api_server:
     image: danswer/danswer-backend:${IMAGE_TAG:-latest}

deployment/docker_compose/env.multilingual.template

@@ -1,33 +1,8 @@
-# This env template shows how to configure Danswer for multilingual use
-# In this case, it is configured for French and English
-# To use it, copy it to .env in the docker_compose directory.
-# Feel free to combine it with the other templates to suit your needs
+# This env template shows how to configure Danswer for custom multilingual use
+# Note that for most use cases it will be enough to configure Danswer multilingual purely through the UI
+# See "Search Settings" -> "Advanced" for UI options.
+# To use it, copy it to .env in the docker_compose directory (or the equivalent environment settings file for your deployment)
 
-
-# Rephrase the user query in specified languages using LLM, use comma separated values
-MULTILINGUAL_QUERY_EXPANSION="English, French"
-# Change the below to suit your specific needs, can be more explicit about the language of the response
-LANGUAGE_HINT="IMPORTANT: Respond in the same language as my query!"
+# The following is included with the user prompt. Here's one example but feel free to customize it to your needs:
+LANGUAGE_HINT="IMPORTANT: ALWAYS RESPOND IN FRENCH! Even if the documents and the user query are in English, your response must be in French."
 LANGUAGE_CHAT_NAMING_HINT="The name of the conversation must be in the same language as the user query."
-
-# A recent MIT license multilingual model: https://huggingface.co/intfloat/multilingual-e5-small
-DOCUMENT_ENCODER_MODEL="intfloat/multilingual-e5-small"
-
-# The model above is trained with the following prefix for queries and passages to improve retrieval
-# by letting the model know which of the two type is currently being embedded
-ASYM_QUERY_PREFIX="query: "
-ASYM_PASSAGE_PREFIX="passage: "
-
-# Depends model by model, the one shown above is tuned with this as True
-NORMALIZE_EMBEDDINGS="True"
-
-# Use LLM to determine if chunks are relevant to the query
-# May not work well for languages that do not have much training data in the LLM training set
-# If using a common language like Spanish, French, Chinese, etc. this can be kept turned on
-DISABLE_LLM_DOC_RELEVANCE="True"
-
-# Enables fine-grained embeddings for better retrieval
-# At the cost of indexing speed (~5x slower), query time is same speed
-# Since reranking is turned off and multilingual retrieval is generally harder
-# it is advised to turn this one on
-ENABLE_MULTIPASS_INDEXING="True"

web/next.config.js

@@ -8,47 +8,6 @@ const version = env_version || package_version;
 const nextConfig = {
   output: "standalone",
   swcMinify: true,
-  rewrites: async () => {
-    // In production, something else (nginx in the one box setup) should take
-    // care of this rewrite. TODO (chris): better support setups where
-    // web_server and api_server are on different machines.
-    if (process.env.NODE_ENV === "production") return [];
-
-    return [
-      {
-        source: "/api/:path*",
-        destination: "http://127.0.0.1:8080/:path*", // Proxy to Backend
-      },
-    ];
-  },
-  redirects: async () => {
-    // In production, something else (nginx in the one box setup) should take
-    // care of this redirect. TODO (chris): better support setups where
-    // web_server and api_server are on different machines.
-    const defaultRedirects = [];
-
-    if (process.env.NODE_ENV === "production") return defaultRedirects;
-
-    return defaultRedirects.concat([
-      {
-        source: "/api/chat/send-message:params*",
-        destination: "http://127.0.0.1:8080/chat/send-message:params*", // Proxy to Backend
-        permanent: true,
-      },
-      {
-        source: "/api/query/stream-answer-with-quote:params*",
-        destination:
-          "http://127.0.0.1:8080/query/stream-answer-with-quote:params*", // Proxy to Backend
-        permanent: true,
-      },
-      {
-        source: "/api/query/stream-query-validation:params*",
-        destination:
-          "http://127.0.0.1:8080/query/stream-query-validation:params*", // Proxy to Backend
-        permanent: true,
-      },
-    ]);
-  },
   publicRuntimeConfig: {
     version,
   },

web/package-lock.json

@@ -2555,11 +2555,11 @@
       }
     },
     "node_modules/braces": {
-      "version": "3.0.2",
-      "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.2.tgz",
-      "integrity": "sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==",
+      "version": "3.0.3",
+      "resolved": "https://registry.npmjs.org/braces/-/braces-3.0.3.tgz",
+      "integrity": "sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA==",
       "dependencies": {
-        "fill-range": "^7.0.1"
+        "fill-range": "^7.1.1"
       },
       "engines": {
         "node": ">=8"
@@ -4061,9 +4061,9 @@
       }
     },
     "node_modules/fill-range": {
-      "version": "7.0.1",
-      "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.0.1.tgz",
-      "integrity": "sha512-qOo9F+dMUmC2Lcb4BbVvnKJxTPjCm+RRpe4gDuGrzkL7mEVl/djYSu2OdQ2Pa302N4oqkSg9ir6jaLWJ2USVpQ==",
+      "version": "7.1.1",
+      "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.1.1.tgz",
+      "integrity": "sha512-YsGpe3WHLK8ZYi4tWDg2Jy3ebRz2rXowDxnld4bkQB00cc/1Zw9AWnC0i9ztDJitivtQvaI9KaLyKrc+hBW0yg==",
       "dependencies": {
         "to-regex-range": "^5.0.1"
       },

web/src/app/admin/configuration/llm/LLMProviderUpdateForm.tsx

@@ -31,11 +31,13 @@ export function LLMProviderUpdateForm({
   existingLlmProvider,
   shouldMarkAsDefault,
   setPopup,
+  hideAdvanced,
 }: {
   llmProviderDescriptor: WellKnownLLMProviderDescriptor;
   onClose: () => void;
   existingLlmProvider?: FullLLMProvider;
   shouldMarkAsDefault?: boolean;
+  hideAdvanced?: boolean;
   setPopup?: (popup: PopupSpec) => void;
 }) {
   const { mutate } = useSWRConfig();
@@ -52,7 +54,7 @@ export function LLMProviderUpdateForm({
 
   // Define the initial values based on the provider's requirements
   const initialValues = {
-    name: existingLlmProvider?.name ?? "",
+    name: existingLlmProvider?.name || (hideAdvanced ? "Default" : ""),
     api_key: existingLlmProvider?.api_key ?? "",
     api_base: existingLlmProvider?.api_base ?? "",
     api_version: existingLlmProvider?.api_version ?? "",
@@ -218,17 +220,20 @@ export function LLMProviderUpdateForm({
       }}
     >
       {({ values, setFieldValue }) => (
-        <Form className="gap-y-6 items-stretch mt-8">
-          <TextFormField
-            name="name"
-            label="Display Name"
-            subtext="A name which you can use to identify this provider when selecting it in the UI."
-            placeholder="Display Name"
-            disabled={existingLlmProvider ? true : false}
-          />
+        <Form className="gap-y-4 items-stretch mt-6">
+          {!hideAdvanced && (
+            <TextFormField
+              name="name"
+              label="Display Name"
+              subtext="A name which you can use to identify this provider when selecting it in the UI."
+              placeholder="Display Name"
+              disabled={existingLlmProvider ? true : false}
+            />
+          )}
 
           {llmProviderDescriptor.api_key_required && (
             <TextFormField
+              small={hideAdvanced}
               name="api_key"
               label="API Key"
               placeholder="API Key"
@@ -238,6 +243,7 @@ export function LLMProviderUpdateForm({
 
           {llmProviderDescriptor.api_base_required && (
             <TextFormField
+              small={hideAdvanced}
               name="api_base"
               label="API Base"
               placeholder="API Base"
@@ -246,6 +252,7 @@ export function LLMProviderUpdateForm({
 
           {llmProviderDescriptor.api_version_required && (
             <TextFormField
+              small={hideAdvanced}
               name="api_version"
               label="API Version"
               placeholder="API Version"
@@ -255,6 +262,7 @@ export function LLMProviderUpdateForm({
           {llmProviderDescriptor.custom_config_keys?.map((customConfigKey) => (
             <div key={customConfigKey.name}>
               <TextFormField
+                small={hideAdvanced}
                 name={`custom_config.${customConfigKey.name}`}
                 label={
                   customConfigKey.is_required
@@ -266,134 +274,144 @@ export function LLMProviderUpdateForm({
             </div>
           ))}
 
-          <Divider />
-
-          {llmProviderDescriptor.llm_names.length > 0 ? (
-            <SelectorFormField
-              name="default_model_name"
-              subtext="The model to use by default for this provider unless otherwise specified."
-              label="Default Model"
-              options={llmProviderDescriptor.llm_names.map((name) => ({
-                name: getDisplayNameForModel(name),
-                value: name,
-              }))}
-              maxHeight="max-h-56"
-            />
-          ) : (
-            <TextFormField
-              name="default_model_name"
-              subtext="The model to use by default for this provider unless otherwise specified."
-              label="Default Model"
-              placeholder="E.g. gpt-4"
-            />
-          )}
-
-          {llmProviderDescriptor.llm_names.length > 0 ? (
-            <SelectorFormField
-              name="fast_default_model_name"
-              subtext={`The model to use for lighter flows like \`LLM Chunk Filter\` 
-                for this provider. If \`Default\` is specified, will use 
-                the Default Model configured above.`}
-              label="[Optional] Fast Model"
-              options={llmProviderDescriptor.llm_names.map((name) => ({
-                name: getDisplayNameForModel(name),
-                value: name,
-              }))}
-              includeDefault
-              maxHeight="max-h-56"
-            />
-          ) : (
-            <TextFormField
-              name="fast_default_model_name"
-              subtext={`The model to use for lighter flows like \`LLM Chunk Filter\` 
-                for this provider. If \`Default\` is specified, will use 
-                the Default Model configured above.`}
-              label="[Optional] Fast Model"
-              placeholder="E.g. gpt-4"
-            />
-          )}
-
-          <Divider />
-
-          {llmProviderDescriptor.name != "azure" && (
-            <AdvancedOptionsToggle
-              showAdvancedOptions={showAdvancedOptions}
-              setShowAdvancedOptions={setShowAdvancedOptions}
-            />
-          )}
-
-          {showAdvancedOptions && (
+          {!hideAdvanced && (
             <>
-              {llmProviderDescriptor.llm_names.length > 0 && (
-                <div className="w-full">
-                  <MultiSelectField
-                    selectedInitially={values.display_model_names}
-                    name="display_model_names"
-                    label="Display Models"
-                    subtext="Select the models to make available to users. Unselected models will not be available."
-                    options={llmProviderDescriptor.llm_names.map((name) => ({
-                      value: name,
-                      label: getDisplayNameForModel(name),
-                    }))}
-                    onChange={(selected) =>
-                      setFieldValue("display_model_names", selected)
-                    }
-                  />
-                </div>
+              <Divider />
+
+              {llmProviderDescriptor.llm_names.length > 0 ? (
+                <SelectorFormField
+                  name="default_model_name"
+                  subtext="The model to use by default for this provider unless otherwise specified."
+                  label="Default Model"
+                  options={llmProviderDescriptor.llm_names.map((name) => ({
+                    name: getDisplayNameForModel(name),
+                    value: name,
+                  }))}
+                  maxHeight="max-h-56"
+                />
+              ) : (
+                <TextFormField
+                  name="default_model_name"
+                  subtext="The model to use by default for this provider unless otherwise specified."
+                  label="Default Model"
+                  placeholder="E.g. gpt-4"
+                />
               )}
 
-              {isPaidEnterpriseFeaturesEnabled && userGroups && (
-                <>
-                  <BooleanFormField
-                    small
-                    removeIndent
-                    alignTop
-                    name="is_public"
-                    label="Is Public?"
-                    subtext="If set, this LLM Provider will be available to all users. If not, only the specified User Groups will be able to use it."
-                  />
+              {llmProviderDescriptor.llm_names.length > 0 ? (
+                <SelectorFormField
+                  name="fast_default_model_name"
+                  subtext={`The model to use for lighter flows like \`LLM Chunk Filter\` 
+                for this provider. If \`Default\` is specified, will use 
+                the Default Model configured above.`}
+                  label="[Optional] Fast Model"
+                  options={llmProviderDescriptor.llm_names.map((name) => ({
+                    name: getDisplayNameForModel(name),
+                    value: name,
+                  }))}
+                  includeDefault
+                  maxHeight="max-h-56"
+                />
+              ) : (
+                <TextFormField
+                  name="fast_default_model_name"
+                  subtext={`The model to use for lighter flows like \`LLM Chunk Filter\` 
+                for this provider. If \`Default\` is specified, will use 
+                the Default Model configured above.`}
+                  label="[Optional] Fast Model"
+                  placeholder="E.g. gpt-4"
+                />
+              )}
 
-                  {userGroups && userGroups.length > 0 && !values.is_public && (
-                    <div>
-                      <Text>
-                        Select which User Groups should have access to this LLM
-                        Provider.
-                      </Text>
-                      <div className="flex flex-wrap gap-2 mt-2">
-                        {userGroups.map((userGroup) => {
-                          const isSelected = values.groups.includes(
-                            userGroup.id
-                          );
-                          return (
-                            <Bubble
-                              key={userGroup.id}
-                              isSelected={isSelected}
-                              onClick={() => {
-                                if (isSelected) {
-                                  setFieldValue(
-                                    "groups",
-                                    values.groups.filter(
-                                      (id) => id !== userGroup.id
-                                    )
-                                  );
-                                } else {
-                                  setFieldValue("groups", [
-                                    ...values.groups,
-                                    userGroup.id,
-                                  ]);
-                                }
-                              }}
-                            >
-                              <div className="flex">
-                                <GroupsIcon />
-                                <div className="ml-1">{userGroup.name}</div>
-                              </div>
-                            </Bubble>
-                          );
-                        })}
-                      </div>
+              <Divider />
+
+              {llmProviderDescriptor.name != "azure" && (
+                <AdvancedOptionsToggle
+                  showAdvancedOptions={showAdvancedOptions}
+                  setShowAdvancedOptions={setShowAdvancedOptions}
+                />
+              )}
+
+              {showAdvancedOptions && (
+                <>
+                  {llmProviderDescriptor.llm_names.length > 0 && (
+                    <div className="w-full">
+                      <MultiSelectField
+                        selectedInitially={values.display_model_names}
+                        name="display_model_names"
+                        label="Display Models"
+                        subtext="Select the models to make available to users. Unselected models will not be available."
+                        options={llmProviderDescriptor.llm_names.map(
+                          (name) => ({
+                            value: name,
+                            label: getDisplayNameForModel(name),
+                          })
+                        )}
+                        onChange={(selected) =>
+                          setFieldValue("display_model_names", selected)
+                        }
+                      />
                     </div>
                   )}
+
+                  {isPaidEnterpriseFeaturesEnabled && userGroups && (
+                    <>
+                      <BooleanFormField
+                        small
+                        removeIndent
+                        alignTop
+                        name="is_public"
+                        label="Is Public?"
+                        subtext="If set, this LLM Provider will be available to all users. If not, only the specified User Groups will be able to use it."
+                      />
+
+                      {userGroups &&
+                        userGroups.length > 0 &&
+                        !values.is_public && (
+                          <div>
+                            <Text>
+                              Select which User Groups should have access to
+                              this LLM Provider.
+                            </Text>
+                            <div className="flex flex-wrap gap-2 mt-2">
+                              {userGroups.map((userGroup) => {
+                                const isSelected = values.groups.includes(
+                                  userGroup.id
+                                );
+                                return (
+                                  <Bubble
+                                    key={userGroup.id}
+                                    isSelected={isSelected}
+                                    onClick={() => {
+                                      if (isSelected) {
+                                        setFieldValue(
+                                          "groups",
+                                          values.groups.filter(
+                                            (id) => id !== userGroup.id
+                                          )
+                                        );
+                                      } else {
+                                        setFieldValue("groups", [
+                                          ...values.groups,
+                                          userGroup.id,
+                                        ]);
+                                      }
+                                    }}
+                                  >
+                                    <div className="flex">
+                                      <GroupsIcon />
+                                      <div className="ml-1">
+                                        {userGroup.name}
+                                      </div>
+                                    </div>
+                                  </Bubble>
+                                );
+                              })}
+                            </div>
+                          </div>
+                        )}
+                    </>
+                  )}
                 </>
               )}
             </>
@@ -432,6 +450,27 @@ export function LLMProviderUpdateForm({
                       return;
                     }
 
+                    // If the deleted provider was the default, set the first remaining provider as default
+                    const remainingProvidersResponse = await fetch(
+                      LLM_PROVIDERS_ADMIN_URL
+                    );
+                    if (remainingProvidersResponse.ok) {
+                      const remainingProviders =
+                        await remainingProvidersResponse.json();
+
+                      if (remainingProviders.length > 0) {
+                        const setDefaultResponse = await fetch(
+                          `${LLM_PROVIDERS_ADMIN_URL}/${remainingProviders[0].id}/default`,
+                          {
+                            method: "POST",
+                          }
+                        );
+                        if (!setDefaultResponse.ok) {
+                          console.error("Failed to set new default provider");
+                        }
+                      }
+                    }
+
                     mutate(LLM_PROVIDERS_ADMIN_URL);
                     onClose();
                   }}

web/src/app/admin/connectors/[connector]/pages/gdrive/Credential.tsx

@@ -17,6 +17,8 @@ import {
   GoogleDriveServiceAccountCredentialJson,
 } from "@/lib/connectors/credentials";
 
+import { Button as TremorButton } from "@tremor/react";
+
 type GoogleDriveCredentialJsonTypes = "authorized_user" | "service_account";
 
 export const DriveJsonUpload = ({
@@ -344,7 +346,7 @@ export const DriveOAuthSection = ({
   if (serviceAccountKeyData?.service_account_email) {
     return (
       <div>
-        <p className="text-sm mb-2">
+        <p className="text-sm mb-6">
           When using a Google Drive Service Account, you can either have Danswer
           act as the service account itself OR you can specify an account for
           the service account to impersonate.
@@ -356,70 +358,59 @@ export const DriveOAuthSection = ({
           the documents you want to index with the service account.
         </p>
 
-        <Card>
-          <Formik
-            initialValues={{
-              google_drive_delegated_user: "",
-            }}
-            validationSchema={Yup.object().shape({
-              google_drive_delegated_user: Yup.string().optional(),
-            })}
-            onSubmit={async (values, formikHelpers) => {
-              formikHelpers.setSubmitting(true);
-
-              const response = await fetch(
-                "/api/manage/admin/connector/google-drive/service-account-credential",
-                {
-                  method: "PUT",
-                  headers: {
-                    "Content-Type": "application/json",
-                  },
-                  body: JSON.stringify({
-                    google_drive_delegated_user:
-                      values.google_drive_delegated_user,
-                  }),
-                }
-              );
-
-              if (response.ok) {
-                setPopup({
-                  message: "Successfully created service account credential",
-                  type: "success",
-                });
-              } else {
-                const errorMsg = await response.text();
-                setPopup({
-                  message: `Failed to create service account credential - ${errorMsg}`,
-                  type: "error",
-                });
+        <Formik
+          initialValues={{
+            google_drive_delegated_user: "",
+          }}
+          validationSchema={Yup.object().shape({
+            google_drive_delegated_user: Yup.string().optional(),
+          })}
+          onSubmit={async (values, formikHelpers) => {
+            formikHelpers.setSubmitting(true);
+            const response = await fetch(
+              "/api/manage/admin/connector/google-drive/service-account-credential",
+              {
+                method: "PUT",
+                headers: {
+                  "Content-Type": "application/json",
+                },
+                body: JSON.stringify({
+                  google_drive_delegated_user:
+                    values.google_drive_delegated_user,
+                }),
               }
-              refreshCredentials();
-            }}
-          >
-            {({ isSubmitting }) => (
-              <Form>
-                <TextFormField
-                  name="google_drive_delegated_user"
-                  label="[Optional] User email to impersonate:"
-                  subtext="If left blank, Danswer will use the service account itself."
-                />
-                <div className="flex">
-                  <button
-                    type="submit"
-                    disabled={isSubmitting}
-                    className={
-                      "bg-slate-500 hover:bg-slate-700 text-white " +
-                      "font-bold py-2 px-4 rounded focus:outline-none " +
-                      "focus:shadow-outline w-full max-w-sm mx-auto"
-                    }
-                  >
-                    Submit
-                  </button>
-                </div>
-              </Form>
-            )}
-          </Formik>
-        </Card>
+            );
+
+            if (response.ok) {
+              setPopup({
+                message: "Successfully created service account credential",
+                type: "success",
+              });
+            } else {
+              const errorMsg = await response.text();
+              setPopup({
+                message: `Failed to create service account credential - ${errorMsg}`,
+                type: "error",
+              });
+            }
+            refreshCredentials();
+          }}
+        >
+          {({ isSubmitting }) => (
+            <Form>
+              <TextFormField
+                name="google_drive_delegated_user"
+                label="[Optional] User email to impersonate:"
+                subtext="If left blank, Danswer will use the service account itself."
+              />
+              <div className="flex">
+                <TremorButton type="submit" disabled={isSubmitting}>
+                  Create Credential
+                </TremorButton>
+              </div>
+            </Form>
+          )}
+        </Formik>
       </div>
     );
   }

web/src/app/admin/connectors/[connector]/pages/gdrive/GoogleDrivePage.tsx

@@ -8,8 +8,6 @@ import { ErrorCallout } from "@/components/ErrorCallout";
 import { LoadingAnimation } from "@/components/Loading";
 import { usePopup } from "@/components/admin/connectors/Popup";
 import { ConnectorIndexingStatus } from "@/lib/types";
-import { getCurrentUser } from "@/lib/user";
-import { User, UserRole } from "@/lib/types";
 import { usePublicCredentials } from "@/lib/hooks";
 import { Title } from "@tremor/react";
 import { DriveJsonUploadSection, DriveOAuthSection } from "./Credential";
@@ -109,6 +107,7 @@ const GDriveMain = ({}: {}) => {
     | undefined = credentialsData.find(
     (credential) => credential.credential_json?.google_drive_service_account_key
   );
+
   const googleDriveConnectorIndexingStatuses: ConnectorIndexingStatus<
     GoogleDriveConfig,
     GoogleDriveCredentialJson

web/src/app/admin/embeddings/RerankingFormPage.tsx

@@ -7,7 +7,11 @@ import {
   rerankingModels,
 } from "./interfaces";
 import { FiExternalLink } from "react-icons/fi";
-import { CohereIcon, MixedBreadIcon } from "@/components/icons/icons";
+import {
+  CohereIcon,
+  LiteLLMIcon,
+  MixedBreadIcon,
+} from "@/components/icons/icons";
 import { Modal } from "@/components/Modal";
 import { Button } from "@tremor/react";
 import { TextFormField } from "@/components/admin/connectors/Field";
@@ -35,6 +39,8 @@ const RerankingDetailsForm = forwardRef<
     ref
   ) => {
     const [isApiKeyModalOpen, setIsApiKeyModalOpen] = useState(false);
+    const [showLiteLLMConfigurationModal, setShowLiteLLMConfigurationModal] =
+      useState(false);
 
     return (
       <Formik
@@ -48,13 +54,17 @@ const RerankingDetailsForm = forwardRef<
             .optional(),
           api_key: Yup.string().nullable(),
           num_rerank: Yup.number().min(1, "Must be at least 1"),
+          rerank_api_url: Yup.string()
+            .url("Must be a valid URL")
+            .matches(/^https?:\/\//, "URL must start with http:// or https://")
+            .nullable(),
         })}
         onSubmit={async (_, { setSubmitting }) => {
           setSubmitting(false);
         }}
         enableReinitialize={true}
       >
-        {({ values, setFieldValue }) => {
+        {({ values, setFieldValue, resetForm }) => {
           const resetRerankingValues = () => {
             setRerankingDetails({
               ...values,
@@ -131,14 +141,22 @@ const RerankingDetailsForm = forwardRef<
                       )
                     : rerankingModels.filter(
                         (modelCard) =>
-                          modelCard.modelName ==
-                          originalRerankingDetails.rerank_model_name
+                          (modelCard.modelName ==
+                            originalRerankingDetails.rerank_model_name &&
+                            modelCard.rerank_provider_type ==
+                              originalRerankingDetails.rerank_provider_type) ||
+                          (modelCard.rerank_provider_type ==
+                            RerankerProvider.LITELLM &&
+                            originalRerankingDetails.rerank_provider_type ==
+                              RerankerProvider.LITELLM)
                       )
                   ).map((card) => {
                     const isSelected =
                       values.rerank_provider_type ===
                         card.rerank_provider_type &&
-                      values.rerank_model_name === card.modelName;
+                      (card.modelName == null ||
+                        values.rerank_model_name === card.modelName);
+
                     return (
                       <div
                         key={`${card.rerank_provider_type}-${card.modelName}`}
@@ -148,26 +166,39 @@ const RerankingDetailsForm = forwardRef<
                             : "border-gray-200 hover:border-blue-300 hover:shadow-sm"
                         }`}
                         onClick={() => {
-                          if (card.rerank_provider_type) {
+                          if (
+                            card.rerank_provider_type == RerankerProvider.COHERE
+                          ) {
                             setIsApiKeyModalOpen(true);
+                          } else if (
+                            card.rerank_provider_type ==
+                            RerankerProvider.LITELLM
+                          ) {
+                            setShowLiteLLMConfigurationModal(true);
+                          }
+
+                          if (!isSelected) {
+                            setRerankingDetails({
+                              ...values,
+                              rerank_provider_type: card.rerank_provider_type!,
+                              rerank_model_name: card.modelName || null,
+                              rerank_api_key: null,
+                              rerank_api_url: null,
+                            });
+                            setFieldValue(
+                              "rerank_provider_type",
+                              card.rerank_provider_type
+                            );
+                            setFieldValue("rerank_model_name", card.modelName);
                           }
-                          setRerankingDetails({
-                            ...values,
-                            rerank_provider_type: card.rerank_provider_type!,
-                            rerank_model_name: card.modelName,
-                            rerank_api_key: null,
-                          });
-                          setFieldValue(
-                            "rerank_provider_type",
-                            card.rerank_provider_type
-                          );
-                          setFieldValue("rerank_model_name", card.modelName);
                         }}
                       >
                         <div className="flex items-center justify-between mb-3">
                           <div className="flex items-center">
                             {card.rerank_provider_type ===
-                            RerankerProvider.COHERE ? (
+                            RerankerProvider.LITELLM ? (
+                              <LiteLLMIcon size={24} className="mr-2" />
+                            ) : RerankerProvider.COHERE ? (
                               <CohereIcon size={24} className="mr-2" />
                             ) : (
                               <MixedBreadIcon size={24} className="mr-2" />
@@ -199,6 +230,88 @@ const RerankingDetailsForm = forwardRef<
                   })}
                 </div>
 
+                {showLiteLLMConfigurationModal && (
+                  <Modal
+                    onOutsideClick={() => {
+                      resetForm();
+                      setShowLiteLLMConfigurationModal(false);
+                    }}
+                    width="w-[800px]"
+                    title="API Key Configuration"
+                  >
+                    <div className="w-full  flex flex-col gap-y-4 px-4">
+                      <TextFormField
+                        subtext="Set the URL at which your LiteLLM Proxy is hosted"
+                        placeholder={values.rerank_api_url || undefined}
+                        onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
+                          const value = e.target.value;
+                          setRerankingDetails({
+                            ...values,
+                            rerank_api_url: value,
+                          });
+                          setFieldValue("rerank_api_url", value);
+                        }}
+                        type="text"
+                        label="LiteLLM Proxy  URL"
+                        name="rerank_api_url"
+                      />
+
+                      <TextFormField
+                        subtext="Set the key to access your LiteLLM Proxy"
+                        placeholder={
+                          values.rerank_api_key
+                            ? "*".repeat(values.rerank_api_key.length)
+                            : undefined
+                        }
+                        onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
+                          const value = e.target.value;
+                          setRerankingDetails({
+                            ...values,
+                            rerank_api_key: value,
+                          });
+                          setFieldValue("rerank_api_key", value);
+                        }}
+                        type="password"
+                        label="LiteLLM Proxy Key"
+                        name="rerank_api_key"
+                        optional
+                      />
+
+                      <TextFormField
+                        subtext="Set the model name to use for LiteLLM Proxy"
+                        placeholder={
+                          values.rerank_model_name
+                            ? "*".repeat(values.rerank_model_name.length)
+                            : undefined
+                        }
+                        onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
+                          const value = e.target.value;
+                          setRerankingDetails({
+                            ...values,
+                            rerank_model_name: value,
+                          });
+                          setFieldValue("rerank_model_name", value);
+                        }}
+                        label="LiteLLM Model Name"
+                        name="rerank_model_name"
+                        optional
+                      />
+
+                      <div className="flex w-full justify-end mt-4">
+                        <Button
+                          onClick={() => {
+                            setShowLiteLLMConfigurationModal(false);
+                          }}
+                          color="blue"
+                          size="xs"
+                        >
+                          Update
+                        </Button>
+                      </div>
+                    </div>
+                  </Modal>
+                )}
+
                 {isApiKeyModalOpen && (
                   <Modal
                     onOutsideClick={() => {
@@ -218,7 +331,11 @@ const RerankingDetailsForm = forwardRef<
                   >
                     <div className="w-full px-4">
                       <TextFormField
-                        placeholder={values.rerank_api_key || undefined}
+                        placeholder={
+                          values.rerank_api_key
+                            ? "*".repeat(values.rerank_api_key.length)
+                            : undefined
+                        }
                         onChange={(e: React.ChangeEvent<HTMLInputElement>) => {
                           const value = e.target.value;
                           setRerankingDetails({

web/src/app/admin/embeddings/interfaces.ts

@@ -5,11 +5,13 @@ export interface RerankingDetails {
   rerank_model_name: string | null;
   rerank_provider_type: RerankerProvider | null;
   rerank_api_key: string | null;
+  rerank_api_url: string | null;
   num_rerank: number;
 }
 
 export enum RerankerProvider {
   COHERE = "cohere",
+  LITELLM = "litellm",
 }
 export interface AdvancedSearchConfiguration {
   model_name: string;
@@ -40,7 +42,7 @@ export interface SavedSearchSettings extends RerankingDetails {
 
 export interface RerankingModel {
   rerank_provider_type: RerankerProvider | null;
-  modelName: string;
+  modelName?: string;
   displayName: string;
   description: string;
   link: string;
@@ -48,6 +50,13 @@ export interface RerankingModel {
 }
 
 export const rerankingModels: RerankingModel[] = [
+  {
+    rerank_provider_type: RerankerProvider.LITELLM,
+    cloud: true,
+    displayName: "LiteLLM",
+    description: "Host your own reranker or router with LiteLLM proxy",
+    link: "https://docs.litellm.ai/docs/proxy",
+  },
   {
     rerank_provider_type: null,
     cloud: false,

web/src/app/admin/embeddings/pages/AdvancedEmbeddingFormPage.tsx

@@ -4,7 +4,7 @@ import * as Yup from "yup";
 import CredentialSubText from "@/components/credentials/CredentialFields";
 import { TrashIcon } from "@/components/icons/icons";
 import { FaPlus } from "react-icons/fa";
-import { AdvancedSearchConfiguration, RerankingDetails } from "../interfaces";
+import { AdvancedSearchConfiguration } from "../interfaces";
 import { BooleanFormField } from "@/components/admin/connectors/Field";
 import NumberInput from "../../connectors/[connector]/pages/ConnectorInput/NumberInput";
 

web/src/app/admin/embeddings/pages/EmbeddingFormPage.tsx

@@ -10,7 +10,7 @@ import {
   CloudEmbeddingModel,
   EmbeddingProvider,
   HostedEmbeddingModel,
-} from "../../../../components/embedding/interfaces";
+} from "@/components/embedding/interfaces";
 import { errorHandlingFetcher } from "@/lib/fetcher";
 import { ErrorCallout } from "@/components/ErrorCallout";
 import useSWR, { mutate } from "swr";
@@ -18,7 +18,6 @@ import { ThreeDotsLoader } from "@/components/Loading";
 import AdvancedEmbeddingFormPage from "./AdvancedEmbeddingFormPage";
 import {
   AdvancedSearchConfiguration,
-  RerankerProvider,
   RerankingDetails,
   SavedSearchSettings,
 } from "../interfaces";
@@ -49,6 +48,7 @@ export default function EmbeddingForm() {
     num_rerank: 0,
     rerank_provider_type: null,
     rerank_model_name: "",
+    rerank_api_url: null,
   });
 
   const updateAdvancedEmbeddingDetails = (
@@ -124,6 +124,7 @@ export default function EmbeddingForm() {
         num_rerank: searchSettings.num_rerank,
         rerank_provider_type: searchSettings.rerank_provider_type,
         rerank_model_name: searchSettings.rerank_model_name,
+        rerank_api_url: searchSettings.rerank_api_url,
       });
     }
   }, [searchSettings]);
@@ -134,12 +135,14 @@ export default function EmbeddingForm() {
         num_rerank: searchSettings.num_rerank,
         rerank_provider_type: searchSettings.rerank_provider_type,
         rerank_model_name: searchSettings.rerank_model_name,
+        rerank_api_url: searchSettings.rerank_api_url,
       }
     : {
         rerank_api_key: "",
         num_rerank: 0,
         rerank_provider_type: null,
         rerank_model_name: "",
+        rerank_api_url: null,
       };
 
   useEffect(() => {

web/src/app/api/[...path]/route.ts

@@ -0,0 +1,116 @@
+import { INTERNAL_URL } from "@/lib/constants";
+import { NextRequest, NextResponse } from "next/server";
+
+/* NextJS is annoying and makes use use a separate function for 
+each request type >:( */
+
+export async function GET(
+  request: NextRequest,
+  { params }: { params: { path: string[] } }
+) {
+  return handleRequest(request, params.path);
+}
+
+export async function POST(
+  request: NextRequest,
+  { params }: { params: { path: string[] } }
+) {
+  return handleRequest(request, params.path);
+}
+
+export async function PUT(
+  request: NextRequest,
+  { params }: { params: { path: string[] } }
+) {
+  return handleRequest(request, params.path);
+}
+
+export async function PATCH(
+  request: NextRequest,
+  { params }: { params: { path: string[] } }
+) {
+  return handleRequest(request, params.path);
+}
+
+export async function DELETE(
+  request: NextRequest,
+  { params }: { params: { path: string[] } }
+) {
+  return handleRequest(request, params.path);
+}
+
+export async function HEAD(
+  request: NextRequest,
+  { params }: { params: { path: string[] } }
+) {
+  return handleRequest(request, params.path);
+}
+
+export async function OPTIONS(
+  request: NextRequest,
+  { params }: { params: { path: string[] } }
+) {
+  return handleRequest(request, params.path);
+}
+
+async function handleRequest(request: NextRequest, path: string[]) {
+  if (process.env.NODE_ENV !== "development") {
+    return NextResponse.json(
+      {
+        message:
+          "This API is only available in development mode. In production, something else (e.g. nginx) should handle this.",
+      },
+      { status: 404 }
+    );
+  }
+
+  try {
+    const backendUrl = new URL(`${INTERNAL_URL}/${path.join("/")}`);
+
+    // Get the URL parameters from the request
+    const urlParams = new URLSearchParams(request.url.split("?")[1]);
+
+    // Append the URL parameters to the backend URL
+    urlParams.forEach((value, key) => {
+      backendUrl.searchParams.append(key, value);
+    });
+
+    const response = await fetch(backendUrl, {
+      method: request.method,
+      headers: request.headers,
+      body: request.body,
+      // @ts-ignore
+      duplex: "half",
+    });
+
+    // Check if the response is a stream
+    if (
+      response.headers.get("Transfer-Encoding") === "chunked" ||
+      response.headers.get("Content-Type")?.includes("stream")
+    ) {
+      // If it's a stream, create a TransformStream to pass the data through
+      const { readable, writable } = new TransformStream();
+      response.body?.pipeTo(writable);
+
+      return new NextResponse(readable, {
+        status: response.status,
+        headers: response.headers,
+      });
+    } else {
+      return new NextResponse(response.body, {
+        status: response.status,
+        headers: response.headers,
+      });
+    }
+  } catch (error: unknown) {
+    console.error("Proxy error:", error);
+    return NextResponse.json(
+      {
+        message: "Proxy error",
+        error:
+          error instanceof Error ? error.message : "An unknown error occurred",
+      },
+      { status: 500 }
+    );
+  }
+}

web/src/app/chat/ChatPage.tsx

@@ -98,6 +98,7 @@ import ExceptionTraceModal from "@/components/modals/ExceptionTraceModal";
 
 import { SEARCH_TOOL_NAME } from "./tools/constants";
 import { useUser } from "@/components/user/UserProvider";
+import { ApiKeyModal } from "@/components/llm/ApiKeyModal";
 
 const TEMP_USER_MESSAGE_ID = -1;
 const TEMP_ASSISTANT_MESSAGE_ID = -2;
@@ -106,12 +107,10 @@ const SYSTEM_MESSAGE_ID = -3;
 export function ChatPage({
   toggle,
   documentSidebarInitialWidth,
-  defaultSelectedAssistantId,
   toggledSidebar,
 }: {
   toggle: (toggled?: boolean) => void;
   documentSidebarInitialWidth?: number;
-  defaultSelectedAssistantId?: number;
   toggledSidebar: boolean;
 }) {
   const router = useRouter();
@@ -126,8 +125,13 @@ export function ChatPage({
     folders,
     openedFolders,
     userInputPrompts,
+    defaultAssistantId,
+    shouldShowWelcomeModal,
+    refreshChatSessions,
   } = useChatContext();
 
+  const [showApiKeyModal, setShowApiKeyModal] = useState(true);
+
   const { user, refreshUser, isLoadingUser } = useUser();
 
   // chat session
@@ -162,9 +166,9 @@ export function ChatPage({
       ? availableAssistants.find(
           (assistant) => assistant.id === existingChatSessionAssistantId
         )
-      : defaultSelectedAssistantId !== undefined
+      : defaultAssistantId !== undefined
         ? availableAssistants.find(
-            (assistant) => assistant.id === defaultSelectedAssistantId
+            (assistant) => assistant.id === defaultAssistantId
           )
         : undefined
   );
@@ -327,8 +331,8 @@ export function ChatPage({
     async function initialSessionFetch() {
       if (existingChatSessionId === null) {
         setIsFetchingChatMessages(false);
-        if (defaultSelectedAssistantId !== undefined) {
-          setSelectedAssistantFromId(defaultSelectedAssistantId);
+        if (defaultAssistantId !== undefined) {
+          setSelectedAssistantFromId(defaultAssistantId);
         } else {
           setSelectedAssistant(undefined);
         }
@@ -402,7 +406,7 @@ export function ChatPage({
         // force re-name if the chat session doesn't have one
         if (!chatSession.description) {
           await nameChatSession(existingChatSessionId, seededMessage);
-          router.refresh(); // need to refresh to update name on sidebar
+          refreshChatSessions();
         }
       }
     }
@@ -676,12 +680,10 @@ export function ChatPage({
   useEffect(() => {
     if (messageHistory.length === 0 && chatSessionIdRef.current === null) {
       setSelectedAssistant(
-        filteredAssistants.find(
-          (persona) => persona.id === defaultSelectedAssistantId
-        )
+        filteredAssistants.find((persona) => persona.id === defaultAssistantId)
       );
     }
-  }, [defaultSelectedAssistantId]);
+  }, [defaultAssistantId]);
 
   const [
     selectedDocuments,
@@ -1111,6 +1113,12 @@ export function ChatPage({
               console.error(
                 "First packet should contain message response info "
               );
+              if (Object.hasOwn(packet, "error")) {
+                const error = (packet as StreamingError).error;
+                setLoadingError(error);
+                updateChatState("input");
+                return;
+              }
               continue;
             }
 
@@ -1330,6 +1338,7 @@ export function ChatPage({
       if (!searchParamBasedChatSessionName) {
         await new Promise((resolve) => setTimeout(resolve, 200));
         await nameChatSession(currChatSessionId, currMessage);
+        refreshChatSessions();
       }
 
       // NOTE: don't switch pages if the user has navigated away from the chat
@@ -1465,6 +1474,7 @@ export function ChatPage({
 
   // Used to maintain a "time out" for history sidebar so our existing refs can have time to process change
   const [untoggled, setUntoggled] = useState(false);
+  const [loadingError, setLoadingError] = useState<string | null>(null);
 
   const explicitlyUntoggle = () => {
     setShowDocSidebar(false);
@@ -1588,6 +1598,11 @@ export function ChatPage({
   return (
     <>
       <HealthCheckBanner />
+
+      {showApiKeyModal && !shouldShowWelcomeModal && (
+        <ApiKeyModal hide={() => setShowApiKeyModal(false)} />
+      )}
+
       {/* ChatPopup is a custom popup that displays a admin-specified message on initial user visit. 
       Only used in the EE version of the app. */}
       {popup}
@@ -1760,7 +1775,6 @@ export function ChatPage({
                         className={`h-full w-full relative flex-auto transition-margin duration-300 overflow-x-auto mobile:pb-12 desktop:pb-[100px]`}
                         {...getRootProps()}
                       >
-                        {/* <input {...getInputProps()} /> */}
                         <div
                           className={`w-full h-full  flex flex-col overflow-y-auto include-scrollbar overflow-x-hidden relative`}
                           ref={scrollableDivRef}
@@ -1770,7 +1784,8 @@ export function ChatPage({
 
                           {messageHistory.length === 0 &&
                             !isFetchingChatMessages &&
-                            currentSessionChatState == "input" && (
+                            currentSessionChatState == "input" &&
+                            !loadingError && (
                               <ChatIntro
                                 availableSources={finalAvailableSources}
                                 selectedPersona={liveAssistant}
@@ -2078,16 +2093,17 @@ export function ChatPage({
                               }
                             })}
 
-                            {currentSessionChatState == "loading" &&
-                              !currentSessionRegenerationState?.regenerating &&
-                              messageHistory[messageHistory.length - 1]?.type !=
-                                "user" && (
-                                <HumanMessage
-                                  key={-2}
-                                  messageId={-1}
-                                  content={submittedMessage}
-                                />
-                              )}
+                            {currentSessionChatState == "loading" ||
+                              (loadingError &&
+                                !currentSessionRegenerationState?.regenerating &&
+                                messageHistory[messageHistory.length - 1]
+                                  ?.type != "user" && (
+                                  <HumanMessage
+                                    key={-2}
+                                    messageId={-1}
+                                    content={submittedMessage}
+                                  />
+                                ))}
 
                             {currentSessionChatState == "loading" && (
                               <div
@@ -2116,6 +2132,20 @@ export function ChatPage({
                               </div>
                             )}
 
+                            {loadingError && (
+                              <div key={-1}>
+                                <AIMessage
+                                  currentPersona={liveAssistant}
+                                  messageId={-1}
+                                  personaName={liveAssistant.name}
+                                  content={
+                                    <p className="text-red-700 text-sm my-auto">
+                                      {loadingError}
+                                    </p>
+                                  }
+                                />
+                              </div>
+                            )}
                             {currentPersona &&
                               currentPersona.starter_messages &&
                               currentPersona.starter_messages.length > 0 &&
@@ -2177,6 +2207,9 @@ export function ChatPage({
                               </div>
                             )}
                             <ChatInputBar
+                              showConfigureAPIKey={() =>
+                                setShowApiKeyModal(true)
+                              }
                               chatState={currentSessionChatState}
                               stopGenerating={stopGenerating}
                               openModelSettings={() => setSettingsToggled(true)}

web/src/app/chat/WrappedChat.tsx

@@ -3,21 +3,15 @@ import { ChatPage } from "./ChatPage";
 import FunctionalWrapper from "./shared_chat_search/FunctionalWrapper";
 
 export default function WrappedChat({
-  defaultAssistantId,
   initiallyToggled,
 }: {
-  defaultAssistantId?: number;
   initiallyToggled: boolean;
 }) {
   return (
     <FunctionalWrapper
       initiallyToggled={initiallyToggled}
       content={(toggledSidebar, toggle) => (
-        <ChatPage
-          toggle={toggle}
-          defaultSelectedAssistantId={defaultAssistantId}
-          toggledSidebar={toggledSidebar}
-        />
+        <ChatPage toggle={toggle} toggledSidebar={toggledSidebar} />
       )}
     />
   );

web/src/app/chat/input/ChatInputBar.tsx

@@ -33,12 +33,15 @@ import { Tooltip } from "@/components/tooltip/Tooltip";
 import { Hoverable } from "@/components/Hoverable";
 import { SettingsContext } from "@/components/settings/SettingsProvider";
 import { ChatState } from "../types";
+import UnconfiguredProviderText from "@/components/chat_search/UnconfiguredProviderText";
+import { useSearchContext } from "@/components/context/SearchContext";
 
 const MAX_INPUT_HEIGHT = 200;
 
 export function ChatInputBar({
   openModelSettings,
   showDocs,
+  showConfigureAPIKey,
   selectedDocuments,
   message,
   setMessage,
@@ -62,6 +65,7 @@ export function ChatInputBar({
   chatSessionId,
   inputPrompts,
 }: {
+  showConfigureAPIKey: () => void;
   openModelSettings: () => void;
   chatState: ChatState;
   stopGenerating: () => void;
@@ -111,6 +115,7 @@ export function ChatInputBar({
       }
     }
   };
+
   const settings = useContext(SettingsContext);
 
   const { llmProviders } = useChatContext();
@@ -364,6 +369,9 @@ export function ChatInputBar({
           <div>
             <SelectedFilterDisplay filterManager={filterManager} />
           </div>
+
+          <UnconfiguredProviderText showConfigureAPIKey={showConfigureAPIKey} />
+
           <div
             className="
               opacity-100

web/src/app/chat/page.tsx

@@ -2,10 +2,10 @@ import { redirect } from "next/navigation";
 import { unstable_noStore as noStore } from "next/cache";
 import { InstantSSRAutoRefresh } from "@/components/SSRAutoRefresh";
 import { WelcomeModal } from "@/components/initialSetup/welcome/WelcomeModalWrapper";
-import { ApiKeyModal } from "@/components/llm/ApiKeyModal";
 import { ChatProvider } from "@/components/context/ChatContext";
 import { fetchChatData } from "@/lib/chat/fetchChatData";
 import WrappedChat from "./WrappedChat";
+import { ProviderContextProvider } from "@/components/chat_search/ProviderContext";
 
 export default async function Page({
   searchParams,
@@ -23,7 +23,6 @@ export default async function Page({
   const {
     user,
     chatSessions,
-    ccPairs,
     availableSources,
     documentSets,
     assistants,
@@ -33,9 +32,7 @@ export default async function Page({
     toggleSidebar,
     openedFolders,
     defaultAssistantId,
-    finalDocumentSidebarInitialWidth,
     shouldShowWelcomeModal,
-    shouldDisplaySourcesIncompleteModal,
     userInputPrompts,
   } = data;
 
@@ -43,9 +40,7 @@ export default async function Page({
     <>
       <InstantSSRAutoRefresh />
       {shouldShowWelcomeModal && <WelcomeModal user={user} />}
-      {!shouldShowWelcomeModal && !shouldDisplaySourcesIncompleteModal && (
-        <ApiKeyModal user={user} />
-      )}
+
       <ChatProvider
         value={{
           chatSessions,
@@ -57,12 +52,13 @@ export default async function Page({
           folders,
           openedFolders,
           userInputPrompts,
+          shouldShowWelcomeModal,
+          defaultAssistantId,
         }}
       >
-        <WrappedChat
-          defaultAssistantId={defaultAssistantId}
-          initiallyToggled={toggleSidebar}
-        />
+        <ProviderContextProvider>
+          <WrappedChat initiallyToggled={toggleSidebar} />
+        </ProviderContextProvider>
       </ChatProvider>
     </>
   );

web/src/app/layout.tsx

@@ -6,6 +6,7 @@ import {
 } from "@/components/settings/lib";
 import {
   CUSTOM_ANALYTICS_ENABLED,
+  EE_ENABLED,
   SERVER_SIDE_ONLY__PAID_ENTERPRISE_FEATURES_ENABLED,
 } from "@/lib/constants";
 import { SettingsProvider } from "@/components/settings/SettingsProvider";
@@ -53,6 +54,7 @@ export default async function RootLayout({
   children: React.ReactNode;
 }) {
   const combinedSettings = await fetchSettingsSS();
+
   if (!combinedSettings) {
     // Just display a simple full page error if fetching fails.
 
@@ -72,8 +74,34 @@ export default async function RootLayout({
               <h1 className="text-2xl font-bold mb-4 text-error">Error</h1>
               <p className="text-text-500">
                 Your Danswer instance was not configured properly and your
-                settings could not be loaded. Please contact your admin to fix
-                this error.
+                settings could not be loaded. This could be due to an admin
+                configuration issue or an incomplete setup.
+              </p>
+              <p className="mt-4">
+                If you&apos;re an admin, please check{" "}
+                <a
+                  className="text-link"
+                  href="https://docs.danswer.dev/introduction?utm_source=app&utm_medium=error_page&utm_campaign=config_error"
+                  target="_blank"
+                  rel="noopener noreferrer"
+                >
+                  our docs
+                </a>{" "}
+                to see how to configure Danswer properly. If you&apos;re a user,
+                please contact your admin to fix this error.
+              </p>
+              <p className="mt-4">
+                For additional support and guidance, you can reach out to our
+                community on{" "}
+                <a
+                  className="text-link"
+                  href="https://danswer.ai?utm_source=app&utm_medium=error_page&utm_campaign=config_error"
+                  target="_blank"
+                  rel="noopener noreferrer"
+                >
+                  Slack
+                </a>
+                .
               </p>
             </Card>
           </div>

web/src/app/search/WrappedSearch.tsx

@@ -1,31 +1,12 @@
 "use client";
 import { SearchSection } from "@/components/search/SearchSection";
 import FunctionalWrapper from "../chat/shared_chat_search/FunctionalWrapper";
-import { CCPairBasicInfo, DocumentSet, Tag, User } from "@/lib/types";
-import { Persona } from "../admin/assistants/interfaces";
-import { ChatSession } from "../chat/interfaces";
 
 export default function WrappedSearch({
-  querySessions,
-  ccPairs,
-  documentSets,
-  personas,
   searchTypeDefault,
-  tags,
-  user,
-  agenticSearchEnabled,
   initiallyToggled,
-  disabledAgentic,
 }: {
-  disabledAgentic: boolean;
-  querySessions: ChatSession[];
-  ccPairs: CCPairBasicInfo[];
-  documentSets: DocumentSet[];
-  personas: Persona[];
   searchTypeDefault: string;
-  tags: Tag[];
-  user: User | null;
-  agenticSearchEnabled: boolean;
   initiallyToggled: boolean;
 }) {
   return (
@@ -33,16 +14,8 @@ export default function WrappedSearch({
       initiallyToggled={initiallyToggled}
       content={(toggledSidebar, toggle) => (
         <SearchSection
-          disabledAgentic={disabledAgentic}
-          agenticSearchEnabled={agenticSearchEnabled}
           toggle={toggle}
           toggledSidebar={toggledSidebar}
-          querySessions={querySessions}
-          user={user}
-          ccPairs={ccPairs}
-          documentSets={documentSets}
-          personas={personas}
-          tags={tags}
           defaultSearchType={searchTypeDefault}
         />
       )}

web/src/app/search/page.tsx

@@ -5,7 +5,6 @@ import {
 } from "@/lib/userSS";
 import { redirect } from "next/navigation";
 import { HealthCheckBanner } from "@/components/health/healthcheck";
-import { ApiKeyModal } from "@/components/llm/ApiKeyModal";
 import { fetchSS } from "@/lib/utilsSS";
 import { CCPairBasicInfo, DocumentSet, Tag, User } from "@/lib/types";
 import { cookies } from "next/headers";
@@ -34,6 +33,8 @@ import {
   DISABLE_LLM_DOC_RELEVANCE,
 } from "@/lib/constants";
 import WrappedSearch from "./WrappedSearch";
+import { SearchProvider } from "@/components/context/SearchContext";
+import { ProviderContextProvider } from "@/components/chat_search/ProviderContext";
 
 export default async function Home() {
   // Disable caching so we always get the up to date connector / document set / persona info
@@ -185,10 +186,6 @@ export default async function Home() {
       {shouldShowWelcomeModal && <WelcomeModal user={user} />}
       <InstantSSRAutoRefresh />
 
-      {!shouldShowWelcomeModal &&
-        !shouldDisplayNoSourcesModal &&
-        !shouldDisplaySourcesIncompleteModal && <ApiKeyModal user={user} />}
-
       {shouldDisplayNoSourcesModal && <NoSourcesModal />}
 
       {shouldDisplaySourcesIncompleteModal && (
@@ -199,18 +196,27 @@ export default async function Home() {
       Only used in the EE version of the app. */}
       <ChatPopup />
 
-      <WrappedSearch
-        disabledAgentic={DISABLE_LLM_DOC_RELEVANCE}
-        initiallyToggled={toggleSidebar}
-        querySessions={querySessions}
-        user={user}
-        ccPairs={ccPairs}
-        documentSets={documentSets}
-        personas={assistants}
-        tags={tags}
-        searchTypeDefault={searchTypeDefault}
-        agenticSearchEnabled={agenticSearchEnabled}
-      />
+      <SearchProvider
+        value={{
+          querySessions,
+          ccPairs,
+          documentSets,
+          assistants,
+          tags,
+          agenticSearchEnabled,
+          disabledAgentic: DISABLE_LLM_DOC_RELEVANCE,
+          initiallyToggled: toggleSidebar,
+          shouldShowWelcomeModal,
+          shouldDisplayNoSources: shouldDisplayNoSourcesModal,
+        }}
+      >
+        <ProviderContextProvider>
+          <WrappedSearch
+            initiallyToggled={toggleSidebar}
+            searchTypeDefault={searchTypeDefault}
+          />
+        </ProviderContextProvider>
+      </SearchProvider>
     </>
   );
 }

web/src/components/admin/connectors/Field.tsx

@@ -198,7 +198,7 @@ export function TextFormField({
           rounded-lg
           w-full 
           py-2 
-          px-3 
+          px-3
           mt-1
           placeholder:font-description 
           placeholder:text-base 

web/src/components/chat_search/ProviderContext.tsx

@@ -0,0 +1,70 @@
+"use client";
+import { WellKnownLLMProviderDescriptor } from "@/app/admin/configuration/llm/interfaces";
+import React, { createContext, useContext, useState, useEffect } from "react";
+import { useUser } from "../user/UserProvider";
+import { useRouter } from "next/navigation";
+import { checkLlmProvider } from "../initialSetup/welcome/lib";
+
+interface ProviderContextType {
+  shouldShowConfigurationNeeded: boolean;
+  providerOptions: WellKnownLLMProviderDescriptor[];
+  refreshProviderInfo: () => Promise<void>; // Add this line
+}
+
+const ProviderContext = createContext<ProviderContextType | undefined>(
+  undefined
+);
+
+export function ProviderContextProvider({
+  children,
+}: {
+  children: React.ReactNode;
+}) {
+  const { user } = useUser();
+  const router = useRouter();
+
+  const [validProviderExists, setValidProviderExists] = useState<boolean>(true);
+  const [providerOptions, setProviderOptions] = useState<
+    WellKnownLLMProviderDescriptor[]
+  >([]);
+
+  const fetchProviderInfo = async () => {
+    const { providers, options, defaultCheckSuccessful } =
+      await checkLlmProvider(user);
+    setValidProviderExists(providers.length > 0 && defaultCheckSuccessful);
+    setProviderOptions(options);
+  };
+
+  useEffect(() => {
+    fetchProviderInfo();
+  }, [router, user]);
+
+  const shouldShowConfigurationNeeded =
+    !validProviderExists && providerOptions.length > 0;
+
+  const refreshProviderInfo = async () => {
+    await fetchProviderInfo();
+  };
+
+  return (
+    <ProviderContext.Provider
+      value={{
+        shouldShowConfigurationNeeded,
+        providerOptions,
+        refreshProviderInfo, // Add this line
+      }}
+    >
+      {children}
+    </ProviderContext.Provider>
+  );
+}
+
+export function useProviderStatus() {
+  const context = useContext(ProviderContext);
+  if (context === undefined) {
+    throw new Error(
+      "useProviderStatus must be used within a ProviderContextProvider"
+    );
+  }
+  return context;
+}

web/src/components/chat_search/UnconfiguredProviderText.tsx

@@ -0,0 +1,27 @@
+import { useProviderStatus } from "./ProviderContext";
+
+export default function CredentialNotConfigured({
+  showConfigureAPIKey,
+}: {
+  showConfigureAPIKey: () => void;
+}) {
+  const { shouldShowConfigurationNeeded } = useProviderStatus();
+
+  if (!shouldShowConfigurationNeeded) {
+    return null;
+  }
+
+  return (
+    <p className="text-base text-center w-full text-subtle">
+      Please note that you have not yet configured an LLM provider. You can
+      configure one{" "}
+      <button
+        onClick={showConfigureAPIKey}
+        className="text-link hover:underline cursor-pointer"
+      >
+        here
+      </button>
+      .
+    </p>
+  );
+}

web/src/components/chat_search/hooks.ts

@@ -1,4 +1,4 @@
-import { Dispatch, SetStateAction, useEffect, useRef, useState } from "react";
+import { Dispatch, SetStateAction, useEffect, useRef } from "react";
 
 interface UseSidebarVisibilityProps {
   toggledSidebar: boolean;

web/src/components/context/ChatContext.tsx

@@ -1,6 +1,6 @@
 "use client";
 
-import React, { createContext, useContext } from "react";
+import React, { createContext, useContext, useState } from "react";
 import { DocumentSet, Tag, User, ValidSources } from "@/lib/types";
 import { ChatSession } from "@/app/chat/interfaces";
 import { Persona } from "@/app/admin/assistants/interfaces";
@@ -18,15 +18,40 @@ interface ChatContextProps {
   folders: Folder[];
   openedFolders: Record<string, boolean>;
   userInputPrompts: InputPrompt[];
+  shouldShowWelcomeModal?: boolean;
+  shouldDisplaySourcesIncompleteModal?: boolean;
+  defaultAssistantId?: number;
+  refreshChatSessions: () => Promise<void>;
 }
 
 const ChatContext = createContext<ChatContextProps | undefined>(undefined);
 
+// We use Omit to exclude 'refreshChatSessions' from the value prop type
+// because we're defining it within the component
 export const ChatProvider: React.FC<{
-  value: ChatContextProps;
+  value: Omit<ChatContextProps, "refreshChatSessions">;
   children: React.ReactNode;
 }> = ({ value, children }) => {
-  return <ChatContext.Provider value={value}>{children}</ChatContext.Provider>;
+  const [chatSessions, setChatSessions] = useState(value?.chatSessions || []);
+
+  const refreshChatSessions = async () => {
+    try {
+      const response = await fetch("/api/chat/get-user-chat-sessions");
+      if (!response.ok) throw new Error("Failed to fetch chat sessions");
+      const { sessions } = await response.json();
+      setChatSessions(sessions);
+    } catch (error) {
+      console.error("Error refreshing chat sessions:", error);
+    }
+  };
+
+  return (
+    <ChatContext.Provider
+      value={{ ...value, chatSessions, refreshChatSessions }}
+    >
+      {children}
+    </ChatContext.Provider>
+  );
 };
 
 export const useChatContext = (): ChatContextProps => {

web/src/components/context/SearchContext.tsx

@@ -0,0 +1,38 @@
+"use client";
+
+import React, { createContext, useContext } from "react";
+import { CCPairBasicInfo, DocumentSet, Tag } from "@/lib/types";
+import { Persona } from "@/app/admin/assistants/interfaces";
+import { ChatSession } from "@/app/chat/interfaces";
+
+interface SearchContextProps {
+  querySessions: ChatSession[];
+  ccPairs: CCPairBasicInfo[];
+  documentSets: DocumentSet[];
+  assistants: Persona[];
+  tags: Tag[];
+  agenticSearchEnabled: boolean;
+  disabledAgentic: boolean;
+  initiallyToggled: boolean;
+  shouldShowWelcomeModal: boolean;
+  shouldDisplayNoSources: boolean;
+}
+
+const SearchContext = createContext<SearchContextProps | undefined>(undefined);
+
+export const SearchProvider: React.FC<{
+  value: SearchContextProps;
+  children: React.ReactNode;
+}> = ({ value, children }) => {
+  return (
+    <SearchContext.Provider value={value}>{children}</SearchContext.Provider>
+  );
+};
+
+export const useSearchContext = (): SearchContextProps => {
+  const context = useContext(SearchContext);
+  if (!context) {
+    throw new Error("useSearchContext must be used within a SearchProvider");
+  }
+  return context;
+};

web/src/components/initialSetup/welcome/WelcomeModal.tsx

@@ -27,13 +27,11 @@ function UsageTypeSection({
   title,
   description,
   callToAction,
-  icon,
   onClick,
 }: {
   title: string;
   description: string | JSX.Element;
   callToAction: string;
-  icon?: React.ElementType;
   onClick: () => void;
 }) {
   return (
@@ -243,7 +241,6 @@ export function _WelcomeModal({ user }: { user: User | null }) {
                 this is the option for you!
               </Text>
             }
-            icon={FiMessageSquare}
             callToAction="Get Started"
             onClick={() => {
               setSelectedFlow("chat");

web/src/components/llm/ApiKeyForm.tsx

@@ -55,6 +55,7 @@ export const ApiKeyForm = ({
             return (
               <TabPanel key={provider.name}>
                 <LLMProviderUpdateForm
+                  hideAdvanced
                   llmProviderDescriptor={provider}
                   onClose={() => onSuccess()}
                   shouldMarkAsDefault

web/src/components/llm/ApiKeyModal.tsx

@@ -1,60 +1,38 @@
 "use client";
 
-import { useState, useEffect } from "react";
 import { ApiKeyForm } from "./ApiKeyForm";
 import { Modal } from "../Modal";
-import { WellKnownLLMProviderDescriptor } from "@/app/admin/configuration/llm/interfaces";
-import { checkLlmProvider } from "../initialSetup/welcome/lib";
-import { User } from "@/lib/types";
 import { useRouter } from "next/navigation";
+import { useProviderStatus } from "../chat_search/ProviderContext";
 
-export const ApiKeyModal = ({ user }: { user: User | null }) => {
+export const ApiKeyModal = ({ hide }: { hide: () => void }) => {
   const router = useRouter();
 
-  const [forceHidden, setForceHidden] = useState<boolean>(false);
-  const [validProviderExists, setValidProviderExists] = useState<boolean>(true);
-  const [providerOptions, setProviderOptions] = useState<
-    WellKnownLLMProviderDescriptor[]
-  >([]);
+  const {
+    shouldShowConfigurationNeeded,
+    providerOptions,
+    refreshProviderInfo,
+  } = useProviderStatus();
 
-  useEffect(() => {
-    async function fetchProviderInfo() {
-      const { providers, options, defaultCheckSuccessful } =
-        await checkLlmProvider(user);
-      setValidProviderExists(providers.length > 0 && defaultCheckSuccessful);
-      setProviderOptions(options);
-    }
-
-    fetchProviderInfo();
-  }, []);
-
-  // don't show if
-  //  (1) a valid provider has been setup or
-  //  (2) there are no provider options (e.g. user isn't an admin)
-  //  (3) user explicitly hides the modal
-  if (validProviderExists || !providerOptions.length || forceHidden) {
+  if (!shouldShowConfigurationNeeded) {
     return null;
   }
 
   return (
     <Modal
-      title="LLM Key Setup"
-      className="max-w-4xl"
-      onOutsideClick={() => setForceHidden(true)}
+      title="Set an API Key!"
+      className="max-w-3xl"
+      onOutsideClick={() => hide()}
     >
       <div className="max-h-[75vh] overflow-y-auto flex flex-col px-4">
         <div>
           <div className="mb-5 text-sm">
-            Please setup an LLM below in order to start using Danswer Search or
-            Danswer Chat. Don&apos;t worry, you can always change this later in
-            the Admin Panel.
+            Please provide an API Key below in order to start using
+            Danswer – you can always change this later.
             <br />
-            <br />
-            Or if you&apos;d rather look around first,{" "}
-            <strong
-              onClick={() => setForceHidden(true)}
-              className="text-link cursor-pointer"
-            >
+            If you&apos;d rather look around first, you can
+            <strong onClick={() => hide()} className="text-link cursor-pointer">
+              {" "}
               skip this step
             </strong>
             .
@@ -63,7 +41,8 @@ export const ApiKeyModal = ({ user }: { user: User | null }) => {
           <ApiKeyForm
             onSuccess={() => {
               router.refresh();
-              setForceHidden(true);
+              refreshProviderInfo();
+              hide();
             }}
             providerOptions={providerOptions}
           />

web/src/components/search/SearchSection.tsx

@@ -37,6 +37,10 @@ import { FeedbackModal } from "@/app/chat/modal/FeedbackModal";
 import { deleteChatSession, handleChatFeedback } from "@/app/chat/lib";
 import SearchAnswer from "./SearchAnswer";
 import { DeleteEntityModal } from "../modals/DeleteEntityModal";
+import { ApiKeyModal } from "../llm/ApiKeyModal";
+import { useSearchContext } from "../context/SearchContext";
+import { useUser } from "../user/UserProvider";
+import UnconfiguredProviderText from "../chat_search/UnconfiguredProviderText";
 
 export type searchState =
   | "input"
@@ -58,33 +62,28 @@ const VALID_QUESTION_RESPONSE_DEFAULT: ValidQuestionResponse = {
 };
 
 interface SearchSectionProps {
-  disabledAgentic: boolean;
-  ccPairs: CCPairBasicInfo[];
-  documentSets: DocumentSet[];
-  personas: Persona[];
-  tags: Tag[];
   toggle: () => void;
-  querySessions: ChatSession[];
   defaultSearchType: SearchType;
-  user: User | null;
   toggledSidebar: boolean;
-  agenticSearchEnabled: boolean;
 }
 
 export const SearchSection = ({
-  ccPairs,
   toggle,
-  disabledAgentic,
-  documentSets,
-  agenticSearchEnabled,
-  personas,
-  user,
-  tags,
-  querySessions,
   toggledSidebar,
   defaultSearchType,
 }: SearchSectionProps) => {
-  // Search Bar
+  const {
+    querySessions,
+    ccPairs,
+    documentSets,
+    assistants,
+    tags,
+    shouldShowWelcomeModal,
+    agenticSearchEnabled,
+    disabledAgentic,
+    shouldDisplayNoSources,
+  } = useSearchContext();
+
   const [query, setQuery] = useState<string>("");
   const [comments, setComments] = useState<any>(null);
   const [contentEnriched, setContentEnriched] = useState(false);
@@ -100,6 +99,8 @@ export const SearchSection = ({
     messageId: null,
   });
 
+  const [showApiKeyModal, setShowApiKeyModal] = useState(true);
+
   const [agentic, setAgentic] = useState(agenticSearchEnabled);
 
   const toggleAgentic = () => {
@@ -147,7 +148,7 @@ export const SearchSection = ({
     useState<SearchType>(defaultSearchType);
 
   const [selectedPersona, setSelectedPersona] = useState<number>(
-    personas[0]?.id || 0
+    assistants[0]?.id || 0
   );
 
   // Used for search state display
@@ -158,8 +159,8 @@ export const SearchSection = ({
   const availableSources = ccPairs.map((ccPair) => ccPair.source);
   const [finalAvailableSources, finalAvailableDocumentSets] =
     computeAvailableFilters({
-      selectedPersona: personas.find(
-        (persona) => persona.id === selectedPersona
+      selectedPersona: assistants.find(
+        (assistant) => assistant.id === selectedPersona
       ),
       availableSources: availableSources,
       availableDocumentSets: documentSets,
@@ -362,6 +363,7 @@ export const SearchSection = ({
       setSearchState("input");
     }
   };
+  const { user } = useUser();
   const [searchAnswerExpanded, setSearchAnswerExpanded] = useState(false);
 
   const resetInput = (finalized?: boolean) => {
@@ -403,8 +405,8 @@ export const SearchSection = ({
       documentSets: filterManager.selectedDocumentSets,
       timeRange: filterManager.timeRange,
       tags: filterManager.selectedTags,
-      persona: personas.find(
-        (persona) => persona.id === selectedPersona
+      persona: assistants.find(
+        (assistant) => assistant.id === selectedPersona
       ) as Persona,
       updateCurrentAnswer: cancellable({
         cancellationToken: lastSearchCancellationToken.current,
@@ -595,6 +597,12 @@ export const SearchSection = ({
       <div className="flex relative pr-[8px] h-full text-default">
         {popup}
 
+        {!shouldDisplayNoSources &&
+          showApiKeyModal &&
+          !shouldShowWelcomeModal && (
+            <ApiKeyModal hide={() => setShowApiKeyModal(false)} />
+          )}
+
         {deletingChatSession && (
           <DeleteEntityModal
             entityType="search"
@@ -747,6 +755,11 @@ export const SearchSection = ({
                         </div>
                       </div>
                     </div>
+
+                    <UnconfiguredProviderText
+                      showConfigureAPIKey={() => setShowApiKeyModal(true)}
+                    />
+
                     <FullSearchBar
                       toggleAgentic={
                         disabledAgentic ? undefined : toggleAgentic

web/src/lib/chat/fetchChatData.ts

@@ -44,7 +44,6 @@ interface FetchChatDataResult {
   toggleSidebar: boolean;
   finalDocumentSidebarInitialWidth?: number;
   shouldShowWelcomeModal: boolean;
-  shouldDisplaySourcesIncompleteModal: boolean;
   userInputPrompts: InputPrompt[];
 }
 
@@ -242,7 +241,6 @@ export async function fetchChatData(searchParams: {
     finalDocumentSidebarInitialWidth,
     toggleSidebar,
     shouldShowWelcomeModal,
-    shouldDisplaySourcesIncompleteModal,
     userInputPrompts,
   };
 }