clarity

* update props

* update documents

* nit

* update chat processing

* k

* k

* nit

* minor nit

* minor nits

* k

* nits

backend/danswer/auth/users.py

@@ -58,7 +58,6 @@ from danswer.auth.schemas import UserRole
 from danswer.auth.schemas import UserUpdate
 from danswer.configs.app_configs import AUTH_TYPE
 from danswer.configs.app_configs import DISABLE_AUTH
-from danswer.configs.app_configs import DISABLE_VERIFICATION
 from danswer.configs.app_configs import EMAIL_FROM
 from danswer.configs.app_configs import REQUIRE_EMAIL_VERIFICATION
 from danswer.configs.app_configs import SESSION_EXPIRE_TIME_SECONDS
@@ -132,11 +131,12 @@ def get_display_email(email: str | None, space_less: bool = False) -> str:
 
 
 def user_needs_to_be_verified() -> bool:
-    # all other auth types besides basic should require users to be
-    # verified
-    return not DISABLE_VERIFICATION and (
-        AUTH_TYPE != AuthType.BASIC or REQUIRE_EMAIL_VERIFICATION
-    )
+    if AUTH_TYPE == AuthType.BASIC:
+        return REQUIRE_EMAIL_VERIFICATION
+
+    # For other auth types, if the user is authenticated it's assumed that
+    # the user is already verified via the external IDP
+    return False
 
 
 def verify_email_is_invited(email: str) -> None:

backend/danswer/chat/answer.py

@@ -6,27 +6,27 @@ from langchain.schema.messages import BaseMessage
 from langchain_core.messages import AIMessageChunk
 from langchain_core.messages import ToolCall
 
+from danswer.chat.llm_response_handler import LLMResponseHandlerManager
 from danswer.chat.models import AnswerQuestionPossibleReturn
+from danswer.chat.models import AnswerStyleConfig
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import DanswerAnswerPiece
-from danswer.file_store.utils import InMemoryChatFile
-from danswer.llm.answering.llm_response_handler import LLMCall
-from danswer.llm.answering.llm_response_handler import LLMResponseHandlerManager
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
-from danswer.llm.answering.prompts.build import default_build_system_message
-from danswer.llm.answering.prompts.build import default_build_user_message
-from danswer.llm.answering.stream_processing.answer_response_handler import (
+from danswer.chat.models import PromptConfig
+from danswer.chat.prompt_builder.build import AnswerPromptBuilder
+from danswer.chat.prompt_builder.build import default_build_system_message
+from danswer.chat.prompt_builder.build import default_build_user_message
+from danswer.chat.prompt_builder.build import LLMCall
+from danswer.chat.stream_processing.answer_response_handler import (
     CitationResponseHandler,
 )
-from danswer.llm.answering.stream_processing.answer_response_handler import (
+from danswer.chat.stream_processing.answer_response_handler import (
     DummyAnswerResponseHandler,
 )
-from danswer.llm.answering.stream_processing.utils import map_document_id_order
-from danswer.llm.answering.tool.tool_response_handler import ToolResponseHandler
+from danswer.chat.stream_processing.utils import map_document_id_order
+from danswer.chat.tool_handling.tool_response_handler import ToolResponseHandler
+from danswer.file_store.utils import InMemoryChatFile
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.natural_language_processing.utils import get_tokenizer
 from danswer.tools.force import ForceUseTool
 from danswer.tools.models import ToolResponse

backend/danswer/chat/chat_utils.py

@@ -26,7 +26,7 @@ from danswer.db.models import Prompt
 from danswer.db.models import Tool
 from danswer.db.models import User
 from danswer.db.persona import get_prompts_by_ids
-from danswer.llm.answering.models import PreviousMessage
+from danswer.llm.models import PreviousMessage
 from danswer.natural_language_processing.utils import BaseTokenizer
 from danswer.server.query_and_chat.models import CreateChatMessageRequest
 from danswer.tools.tool_implementations.custom.custom_tool import (

backend/danswer/chat/llm_response_handler.py

@@ -1,58 +1,22 @@
 from collections.abc import Callable
 from collections.abc import Generator
 from collections.abc import Iterator
-from typing import TYPE_CHECKING
 
 from langchain_core.messages import BaseMessage
-from pydantic.v1 import BaseModel as BaseModel__v1
 
-from danswer.chat.models import CitationInfo
-from danswer.chat.models import DanswerAnswerPiece
+from danswer.chat.models import ResponsePart
 from danswer.chat.models import StreamStopInfo
 from danswer.chat.models import StreamStopReason
-from danswer.file_store.models import InMemoryChatFile
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
-from danswer.tools.force import ForceUseTool
-from danswer.tools.models import ToolCallFinalResult
-from danswer.tools.models import ToolCallKickoff
-from danswer.tools.models import ToolResponse
-from danswer.tools.tool import Tool
-
-
-if TYPE_CHECKING:
-    from danswer.llm.answering.stream_processing.answer_response_handler import (
-        AnswerResponseHandler,
-    )
-    from danswer.llm.answering.tool.tool_response_handler import ToolResponseHandler
-
-
-ResponsePart = (
-    DanswerAnswerPiece
-    | CitationInfo
-    | ToolCallKickoff
-    | ToolResponse
-    | ToolCallFinalResult
-    | StreamStopInfo
-)
-
-
-class LLMCall(BaseModel__v1):
-    prompt_builder: AnswerPromptBuilder
-    tools: list[Tool]
-    force_use_tool: ForceUseTool
-    files: list[InMemoryChatFile]
-    tool_call_info: list[ToolCallKickoff | ToolResponse | ToolCallFinalResult]
-    using_tool_calling_llm: bool
-
-    class Config:
-        arbitrary_types_allowed = True
+from danswer.chat.prompt_builder.build import LLMCall
+from danswer.chat.stream_processing.answer_response_handler import AnswerResponseHandler
+from danswer.chat.tool_handling.tool_response_handler import ToolResponseHandler
 
 
 class LLMResponseHandlerManager:
     def __init__(
         self,
-        tool_handler: "ToolResponseHandler",
-        answer_handler: "AnswerResponseHandler",
+        tool_handler: ToolResponseHandler,
+        answer_handler: AnswerResponseHandler,
         is_cancelled: Callable[[], bool],
     ):
         self.tool_handler = tool_handler

backend/danswer/chat/models.py

@@ -1,10 +1,14 @@
+from collections.abc import Callable
 from collections.abc import Iterator
 from datetime import datetime
 from enum import Enum
 from typing import Any
+from typing import TYPE_CHECKING
 
 from pydantic import BaseModel
+from pydantic import ConfigDict
 from pydantic import Field
+from pydantic import model_validator
 
 from danswer.configs.constants import DocumentSource
 from danswer.configs.constants import MessageType
@@ -12,8 +16,15 @@ from danswer.context.search.enums import QueryFlow
 from danswer.context.search.enums import RecencyBiasSetting
 from danswer.context.search.enums import SearchType
 from danswer.context.search.models import RetrievalDocs
+from danswer.llm.override_models import PromptOverride
+from danswer.tools.models import ToolCallFinalResult
+from danswer.tools.models import ToolCallKickoff
+from danswer.tools.models import ToolResponse
 from danswer.tools.tool_implementations.custom.base_tool_types import ToolResultType
 
+if TYPE_CHECKING:
+    from danswer.db.models import Prompt
+
 
 class LlmDoc(BaseModel):
     """This contains the minimal set information for the LLM portion including citations"""
@@ -210,3 +221,109 @@ AnswerQuestionStreamReturn = Iterator[AnswerQuestionPossibleReturn]
 class LLMMetricsContainer(BaseModel):
     prompt_tokens: int
     response_tokens: int
+
+
+StreamProcessor = Callable[[Iterator[str]], AnswerQuestionStreamReturn]
+
+
+class DocumentPruningConfig(BaseModel):
+    max_chunks: int | None = None
+    max_window_percentage: float | None = None
+    max_tokens: int | None = None
+    # different pruning behavior is expected when the
+    # user manually selects documents they want to chat with
+    # e.g. we don't want to truncate each document to be no more
+    # than one chunk long
+    is_manually_selected_docs: bool = False
+    # If user specifies to include additional context Chunks for each match, then different pruning
+    # is used. As many Sections as possible are included, and the last Section is truncated
+    # If this is false, all of the Sections are truncated if they are longer than the expected Chunk size.
+    # Sections are often expected to be longer than the maximum Chunk size but Chunks should not be.
+    use_sections: bool = True
+    # If using tools, then we need to consider the tool length
+    tool_num_tokens: int = 0
+    # If using a tool message to represent the docs, then we have to JSON serialize
+    # the document content, which adds to the token count.
+    using_tool_message: bool = False
+
+
+class ContextualPruningConfig(DocumentPruningConfig):
+    num_chunk_multiple: int
+
+    @classmethod
+    def from_doc_pruning_config(
+        cls, num_chunk_multiple: int, doc_pruning_config: DocumentPruningConfig
+    ) -> "ContextualPruningConfig":
+        return cls(num_chunk_multiple=num_chunk_multiple, **doc_pruning_config.dict())
+
+
+class CitationConfig(BaseModel):
+    all_docs_useful: bool = False
+
+
+class QuotesConfig(BaseModel):
+    pass
+
+
+class AnswerStyleConfig(BaseModel):
+    citation_config: CitationConfig | None = None
+    quotes_config: QuotesConfig | None = None
+    document_pruning_config: DocumentPruningConfig = Field(
+        default_factory=DocumentPruningConfig
+    )
+    # forces the LLM to return a structured response, see
+    # https://platform.openai.com/docs/guides/structured-outputs/introduction
+    # right now, only used by the simple chat API
+    structured_response_format: dict | None = None
+
+    @model_validator(mode="after")
+    def check_quotes_and_citation(self) -> "AnswerStyleConfig":
+        if self.citation_config is None and self.quotes_config is None:
+            raise ValueError(
+                "One of `citation_config` or `quotes_config` must be provided"
+            )
+
+        if self.citation_config is not None and self.quotes_config is not None:
+            raise ValueError(
+                "Only one of `citation_config` or `quotes_config` must be provided"
+            )
+
+        return self
+
+
+class PromptConfig(BaseModel):
+    """Final representation of the Prompt configuration passed
+    into the `Answer` object."""
+
+    system_prompt: str
+    task_prompt: str
+    datetime_aware: bool
+    include_citations: bool
+
+    @classmethod
+    def from_model(
+        cls, model: "Prompt", prompt_override: PromptOverride | None = None
+    ) -> "PromptConfig":
+        override_system_prompt = (
+            prompt_override.system_prompt if prompt_override else None
+        )
+        override_task_prompt = prompt_override.task_prompt if prompt_override else None
+
+        return cls(
+            system_prompt=override_system_prompt or model.system_prompt,
+            task_prompt=override_task_prompt or model.task_prompt,
+            datetime_aware=model.datetime_aware,
+            include_citations=model.include_citations,
+        )
+
+    model_config = ConfigDict(frozen=True)
+
+
+ResponsePart = (
+    DanswerAnswerPiece
+    | CitationInfo
+    | ToolCallKickoff
+    | ToolResponse
+    | ToolCallFinalResult
+    | StreamStopInfo
+)

backend/danswer/chat/process_message.py

@@ -6,19 +6,24 @@ from typing import cast
 
 from sqlalchemy.orm import Session
 
+from danswer.chat.answer import Answer
 from danswer.chat.chat_utils import create_chat_chain
 from danswer.chat.chat_utils import create_temporary_persona
 from danswer.chat.models import AllCitations
+from danswer.chat.models import AnswerStyleConfig
 from danswer.chat.models import ChatDanswerBotResponse
+from danswer.chat.models import CitationConfig
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import CustomToolResponse
 from danswer.chat.models import DanswerAnswerPiece
 from danswer.chat.models import DanswerContexts
+from danswer.chat.models import DocumentPruningConfig
 from danswer.chat.models import FileChatDisplay
 from danswer.chat.models import FinalUsedContextDocsResponse
 from danswer.chat.models import LLMRelevanceFilterResponse
 from danswer.chat.models import MessageResponseIDInfo
 from danswer.chat.models import MessageSpecificCitations
+from danswer.chat.models import PromptConfig
 from danswer.chat.models import QADocsResponse
 from danswer.chat.models import StreamingError
 from danswer.chat.models import StreamStopInfo
@@ -57,16 +62,11 @@ from danswer.document_index.factory import get_default_document_index
 from danswer.file_store.models import ChatFileType
 from danswer.file_store.models import FileDescriptor
 from danswer.file_store.utils import load_all_chat_files
-from danswer.file_store.utils import save_files_from_urls
-from danswer.llm.answering.answer import Answer
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import CitationConfig
-from danswer.llm.answering.models import DocumentPruningConfig
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.models import PromptConfig
+from danswer.file_store.utils import save_files
 from danswer.llm.exceptions import GenAIDisabledException
 from danswer.llm.factory import get_llms_for_persona
 from danswer.llm.factory import get_main_llm_from_tuple
+from danswer.llm.models import PreviousMessage
 from danswer.llm.utils import litellm_exception_to_error_msg
 from danswer.natural_language_processing.utils import get_tokenizer
 from danswer.server.query_and_chat.models import ChatMessageDetail
@@ -119,6 +119,7 @@ from danswer.utils.logger import setup_logger
 from danswer.utils.long_term_log import LongTermLogger
 from danswer.utils.timing import log_function_time
 from danswer.utils.timing import log_generator_function_time
+from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
 
 
 logger = setup_logger()
@@ -302,6 +303,7 @@ def stream_chat_message_objects(
     3. [always] A set of streamed LLM tokens or an error anywhere along the line if something fails
     4. [always] Details on the final AI response message that is created
     """
+    tenant_id = CURRENT_TENANT_ID_CONTEXTVAR.get()
     use_existing_user_message = new_msg_req.use_existing_user_message
     existing_assistant_message_id = new_msg_req.existing_assistant_message_id
 
@@ -678,7 +680,8 @@ def stream_chat_message_objects(
 
         reference_db_search_docs = None
         qa_docs_response = None
-        ai_message_files = None  # any files to associate with the AI message e.g. dall-e generated images
+        # any files to associate with the AI message e.g. dall-e generated images
+        ai_message_files = []
         dropped_indices = None
         tool_result = None
 
@@ -733,8 +736,14 @@ def stream_chat_message_objects(
                         list[ImageGenerationResponse], packet.response
                     )
 
-                    file_ids = save_files_from_urls(
-                        [img.url for img in img_generation_response]
+                    file_ids = save_files(
+                        urls=[img.url for img in img_generation_response if img.url],
+                        base64_files=[
+                            img.image_data
+                            for img in img_generation_response
+                            if img.image_data
+                        ],
+                        tenant_id=tenant_id,
                     )
                     ai_message_files = [
                         FileDescriptor(id=str(file_id), type=ChatFileType.IMAGE)
@@ -760,15 +769,19 @@ def stream_chat_message_objects(
                         or custom_tool_response.response_type == "csv"
                     ):
                         file_ids = custom_tool_response.tool_result.file_ids
-                        ai_message_files = [
-                            FileDescriptor(
-                                id=str(file_id),
-                                type=ChatFileType.IMAGE
-                                if custom_tool_response.response_type == "image"
-                                else ChatFileType.CSV,
-                            )
-                            for file_id in file_ids
-                        ]
+                        ai_message_files.extend(
+                            [
+                                FileDescriptor(
+                                    id=str(file_id),
+                                    type=(
+                                        ChatFileType.IMAGE
+                                        if custom_tool_response.response_type == "image"
+                                        else ChatFileType.CSV
+                                    ),
+                                )
+                                for file_id in file_ids
+                            ]
+                        )
                         yield FileChatDisplay(
                             file_ids=[str(file_id) for file_id in file_ids]
                         )
@@ -818,7 +831,8 @@ def stream_chat_message_objects(
                 citations_list=answer.citations,
                 db_docs=reference_db_search_docs,
             )
-            yield AllCitations(citations=answer.citations)
+            if not answer.is_cancelled():
+                yield AllCitations(citations=answer.citations)
 
         # Saving Gen AI answer and responding with message info
         tool_name_to_tool_id: dict[str, int] = {}

backend/danswer/chat/prompt_builder/build.py

@@ -4,20 +4,26 @@ from typing import cast
 from langchain_core.messages import BaseMessage
 from langchain_core.messages import HumanMessage
 from langchain_core.messages import SystemMessage
+from pydantic.v1 import BaseModel as BaseModel__v1
 
+from danswer.chat.models import PromptConfig
+from danswer.chat.prompt_builder.citations_prompt import compute_max_llm_input_tokens
+from danswer.chat.prompt_builder.utils import translate_history_to_basemessages
 from danswer.file_store.models import InMemoryChatFile
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.citations_prompt import compute_max_llm_input_tokens
 from danswer.llm.interfaces import LLMConfig
+from danswer.llm.models import PreviousMessage
 from danswer.llm.utils import build_content_with_imgs
 from danswer.llm.utils import check_message_tokens
 from danswer.llm.utils import message_to_prompt_and_imgs
-from danswer.llm.utils import translate_history_to_basemessages
 from danswer.natural_language_processing.utils import get_tokenizer
 from danswer.prompts.chat_prompts import CHAT_USER_CONTEXT_FREE_PROMPT
 from danswer.prompts.prompt_utils import add_date_time_to_prompt
 from danswer.prompts.prompt_utils import drop_messages_history_overflow
+from danswer.tools.force import ForceUseTool
+from danswer.tools.models import ToolCallFinalResult
+from danswer.tools.models import ToolCallKickoff
+from danswer.tools.models import ToolResponse
+from danswer.tools.tool import Tool
 
 
 def default_build_system_message(
@@ -139,3 +145,15 @@ class AnswerPromptBuilder:
         return drop_messages_history_overflow(
             final_messages_with_tokens, self.max_tokens
         )
+
+
+class LLMCall(BaseModel__v1):
+    prompt_builder: AnswerPromptBuilder
+    tools: list[Tool]
+    force_use_tool: ForceUseTool
+    files: list[InMemoryChatFile]
+    tool_call_info: list[ToolCallKickoff | ToolResponse | ToolCallFinalResult]
+    using_tool_calling_llm: bool
+
+    class Config:
+        arbitrary_types_allowed = True

backend/danswer/chat/prompt_builder/citations_prompt.py

@@ -2,12 +2,12 @@ from langchain.schema.messages import HumanMessage
 from langchain.schema.messages import SystemMessage
 
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PromptConfig
 from danswer.configs.model_configs import GEN_AI_SINGLE_USER_MESSAGE_EXPECTED_MAX_TOKENS
 from danswer.context.search.models import InferenceChunk
 from danswer.db.models import Persona
 from danswer.db.persona import get_default_prompt__read_only
 from danswer.db.search_settings import get_multilingual_expansion
-from danswer.llm.answering.models import PromptConfig
 from danswer.llm.factory import get_llms_for_persona
 from danswer.llm.factory import get_main_llm_from_tuple
 from danswer.llm.interfaces import LLMConfig

backend/danswer/chat/prompt_builder/quotes_prompt.py

@@ -1,10 +1,10 @@
 from langchain.schema.messages import HumanMessage
 
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PromptConfig
 from danswer.configs.chat_configs import LANGUAGE_HINT
 from danswer.context.search.models import InferenceChunk
 from danswer.db.search_settings import get_multilingual_expansion
-from danswer.llm.answering.models import PromptConfig
 from danswer.llm.utils import message_to_prompt_and_imgs
 from danswer.prompts.direct_qa_prompts import CONTEXT_BLOCK
 from danswer.prompts.direct_qa_prompts import HISTORY_BLOCK

backend/danswer/chat/prompt_builder/utils.py

@@ -0,0 +1,62 @@
+from langchain.schema.messages import AIMessage
+from langchain.schema.messages import BaseMessage
+from langchain.schema.messages import HumanMessage
+
+from danswer.configs.constants import MessageType
+from danswer.db.models import ChatMessage
+from danswer.file_store.models import InMemoryChatFile
+from danswer.llm.models import PreviousMessage
+from danswer.llm.utils import build_content_with_imgs
+from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT
+from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT_WITHOUT_CONTEXT
+
+
+def build_dummy_prompt(
+    system_prompt: str, task_prompt: str, retrieval_disabled: bool
+) -> str:
+    if retrieval_disabled:
+        return PARAMATERIZED_PROMPT_WITHOUT_CONTEXT.format(
+            user_query="<USER_QUERY>",
+            system_prompt=system_prompt,
+            task_prompt=task_prompt,
+        ).strip()
+
+    return PARAMATERIZED_PROMPT.format(
+        context_docs_str="<CONTEXT_DOCS>",
+        user_query="<USER_QUERY>",
+        system_prompt=system_prompt,
+        task_prompt=task_prompt,
+    ).strip()
+
+
+def translate_danswer_msg_to_langchain(
+    msg: ChatMessage | PreviousMessage,
+) -> BaseMessage:
+    files: list[InMemoryChatFile] = []
+
+    # If the message is a `ChatMessage`, it doesn't have the downloaded files
+    # attached. Just ignore them for now.
+    if not isinstance(msg, ChatMessage):
+        files = msg.files
+    content = build_content_with_imgs(msg.message, files, message_type=msg.message_type)
+
+    if msg.message_type == MessageType.SYSTEM:
+        raise ValueError("System messages are not currently part of history")
+    if msg.message_type == MessageType.ASSISTANT:
+        return AIMessage(content=content)
+    if msg.message_type == MessageType.USER:
+        return HumanMessage(content=content)
+
+    raise ValueError(f"New message type {msg.message_type} not handled")
+
+
+def translate_history_to_basemessages(
+    history: list[ChatMessage] | list["PreviousMessage"],
+) -> tuple[list[BaseMessage], list[int]]:
+    history_basemessages = [
+        translate_danswer_msg_to_langchain(msg)
+        for msg in history
+        if msg.token_count != 0
+    ]
+    history_token_counts = [msg.token_count for msg in history if msg.token_count != 0]
+    return history_basemessages, history_token_counts

backend/danswer/chat/prune_and_merge.py

@@ -5,16 +5,16 @@ from typing import TypeVar
 
 from pydantic import BaseModel
 
+from danswer.chat.models import ContextualPruningConfig
 from danswer.chat.models import (
     LlmDoc,
 )
+from danswer.chat.models import PromptConfig
+from danswer.chat.prompt_builder.citations_prompt import compute_max_document_tokens
 from danswer.configs.constants import IGNORE_FOR_QA
 from danswer.configs.model_configs import DOC_EMBEDDING_CONTEXT_SIZE
 from danswer.context.search.models import InferenceChunk
 from danswer.context.search.models import InferenceSection
-from danswer.llm.answering.models import ContextualPruningConfig
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.citations_prompt import compute_max_document_tokens
 from danswer.llm.interfaces import LLMConfig
 from danswer.natural_language_processing.utils import get_tokenizer
 from danswer.natural_language_processing.utils import tokenizer_trim_content

backend/danswer/chat/stream_processing/answer_response_handler.py

@@ -3,13 +3,11 @@ from collections.abc import Generator
 
 from langchain_core.messages import BaseMessage
 
+from danswer.chat.llm_response_handler import ResponsePart
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import LlmDoc
-from danswer.llm.answering.llm_response_handler import ResponsePart
-from danswer.llm.answering.stream_processing.citation_processing import (
-    CitationProcessor,
-)
-from danswer.llm.answering.stream_processing.utils import DocumentIdOrderMapping
+from danswer.chat.stream_processing.citation_processing import CitationProcessor
+from danswer.chat.stream_processing.utils import DocumentIdOrderMapping
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()

backend/danswer/chat/stream_processing/citation_processing.py

@@ -4,8 +4,8 @@ from collections.abc import Generator
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import DanswerAnswerPiece
 from danswer.chat.models import LlmDoc
+from danswer.chat.stream_processing.utils import DocumentIdOrderMapping
 from danswer.configs.chat_configs import STOP_STREAM_PAT
-from danswer.llm.answering.stream_processing.utils import DocumentIdOrderMapping
 from danswer.prompts.constants import TRIPLE_BACKTICK
 from danswer.utils.logger import setup_logger
 

backend/danswer/chat/tool_handling/tool_response_handler.py

@@ -4,8 +4,8 @@ from langchain_core.messages import AIMessageChunk
 from langchain_core.messages import BaseMessage
 from langchain_core.messages import ToolCall
 
-from danswer.llm.answering.llm_response_handler import LLMCall
-from danswer.llm.answering.llm_response_handler import ResponsePart
+from danswer.chat.models import ResponsePart
+from danswer.chat.prompt_builder.build import LLMCall
 from danswer.llm.interfaces import LLM
 from danswer.tools.force import ForceUseTool
 from danswer.tools.message import build_tool_message

backend/danswer/configs/app_configs.py

@@ -43,9 +43,6 @@ WEB_DOMAIN = os.environ.get("WEB_DOMAIN") or "http://localhost:3000"
 AUTH_TYPE = AuthType((os.environ.get("AUTH_TYPE") or AuthType.DISABLED.value).lower())
 DISABLE_AUTH = AUTH_TYPE == AuthType.DISABLED
 
-# Necessary for cloud integration tests
-DISABLE_VERIFICATION = os.environ.get("DISABLE_VERIFICATION", "").lower() == "true"
-
 # Encryption key secret is used to encrypt connector credentials, api keys, and other sensitive
 # information. This provides an extra layer of security on top of Postgres access controls
 # and is available in Danswer EE
@@ -85,6 +82,7 @@ OAUTH_CLIENT_SECRET = (
 )
 
 USER_AUTH_SECRET = os.environ.get("USER_AUTH_SECRET", "")
+
 # for basic auth
 REQUIRE_EMAIL_VERIFICATION = (
     os.environ.get("REQUIRE_EMAIL_VERIFICATION", "").lower() == "true"
@@ -118,6 +116,8 @@ VESPA_HOST = os.environ.get("VESPA_HOST") or "localhost"
 VESPA_CONFIG_SERVER_HOST = os.environ.get("VESPA_CONFIG_SERVER_HOST") or VESPA_HOST
 VESPA_PORT = os.environ.get("VESPA_PORT") or "8081"
 VESPA_TENANT_PORT = os.environ.get("VESPA_TENANT_PORT") or "19071"
+# the number of times to try and connect to vespa on startup before giving up
+VESPA_NUM_ATTEMPTS_ON_STARTUP = int(os.environ.get("NUM_RETRIES_ON_STARTUP") or 10)
 
 VESPA_CLOUD_URL = os.environ.get("VESPA_CLOUD_URL", "")
 

backend/danswer/configs/tool_configs.py

@@ -2,6 +2,8 @@ import json
 import os
 
 
+IMAGE_GENERATION_OUTPUT_FORMAT = os.environ.get("IMAGE_GENERATION_OUTPUT_FORMAT", "url")
+
 # if specified, will pass through request headers to the call to API calls made by custom tools
 CUSTOM_TOOL_PASS_THROUGH_HEADERS: list[str] | None = None
 _CUSTOM_TOOL_PASS_THROUGH_HEADERS_RAW = os.environ.get(

backend/danswer/connectors/confluence/connector.py

@@ -15,6 +15,7 @@ from danswer.connectors.confluence.utils import attachment_to_content
 from danswer.connectors.confluence.utils import build_confluence_document_id
 from danswer.connectors.confluence.utils import datetime_from_string
 from danswer.connectors.confluence.utils import extract_text_from_confluence_html
+from danswer.connectors.confluence.utils import validate_attachment_filetype
 from danswer.connectors.interfaces import GenerateDocumentsOutput
 from danswer.connectors.interfaces import GenerateSlimDocumentOutput
 from danswer.connectors.interfaces import LoadConnector
@@ -276,9 +277,11 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
         ):
             # If the page has restrictions, add them to the perm_sync_data
             # These will be used by doc_sync.py to sync permissions
-            perm_sync_data = {
-                "restrictions": page.get("restrictions", {}),
-                "space_key": page.get("space", {}).get("key"),
+            page_restrictions = page.get("restrictions")
+            page_space_key = page.get("space", {}).get("key")
+            page_perm_sync_data = {
+                "restrictions": page_restrictions or {},
+                "space_key": page_space_key,
             }
 
             doc_metadata_list.append(
@@ -288,7 +291,7 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
                         page["_links"]["webui"],
                         self.is_cloud,
                     ),
-                    perm_sync_data=perm_sync_data,
+                    perm_sync_data=page_perm_sync_data,
                 )
             )
             attachment_cql = f"type=attachment and container='{page['id']}'"
@@ -298,6 +301,21 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
                 expand=restrictions_expand,
                 limit=_SLIM_DOC_BATCH_SIZE,
             ):
+                if not validate_attachment_filetype(attachment):
+                    continue
+                attachment_restrictions = attachment.get("restrictions")
+                if not attachment_restrictions:
+                    attachment_restrictions = page_restrictions
+
+                attachment_space_key = attachment.get("space", {}).get("key")
+                if not attachment_space_key:
+                    attachment_space_key = page_space_key
+
+                attachment_perm_sync_data = {
+                    "restrictions": attachment_restrictions or {},
+                    "space_key": attachment_space_key,
+                }
+
                 doc_metadata_list.append(
                     SlimDocument(
                         id=build_confluence_document_id(
@@ -305,7 +323,7 @@ class ConfluenceConnector(LoadConnector, PollConnector, SlimConnector):
                             attachment["_links"]["webui"],
                             self.is_cloud,
                         ),
-                        perm_sync_data=perm_sync_data,
+                        perm_sync_data=attachment_perm_sync_data,
                     )
                 )
             if len(doc_metadata_list) > _SLIM_DOC_BATCH_SIZE:

backend/danswer/connectors/confluence/utils.py

@@ -177,19 +177,23 @@ def extract_text_from_confluence_html(
     return format_document_soup(soup)
 
 
-def attachment_to_content(
-    confluence_client: OnyxConfluence,
-    attachment: dict[str, Any],
-) -> str | None:
-    """If it returns None, assume that we should skip this attachment."""
-    if attachment["metadata"]["mediaType"] in [
+def validate_attachment_filetype(attachment: dict[str, Any]) -> bool:
+    return attachment["metadata"]["mediaType"] not in [
         "image/jpeg",
         "image/png",
         "image/gif",
         "image/svg+xml",
         "video/mp4",
         "video/quicktime",
-    ]:
+    ]
+
+
+def attachment_to_content(
+    confluence_client: OnyxConfluence,
+    attachment: dict[str, Any],
+) -> str | None:
+    """If it returns None, assume that we should skip this attachment."""
+    if not validate_attachment_filetype(attachment):
         return None
 
     download_link = confluence_client.url + attachment["_links"]["download"]
@@ -245,7 +249,7 @@ def build_confluence_document_id(
     return f"{base_url}{content_url}"
 
 
-def extract_referenced_attachment_names(page_text: str) -> list[str]:
+def _extract_referenced_attachment_names(page_text: str) -> list[str]:
     """Parse a Confluence html page to generate a list of current
         attachments in use
 

backend/danswer/context/search/pipeline.py

@@ -5,7 +5,11 @@ from typing import cast
 
 from sqlalchemy.orm import Session
 
+from danswer.chat.models import PromptConfig
 from danswer.chat.models import SectionRelevancePiece
+from danswer.chat.prune_and_merge import _merge_sections
+from danswer.chat.prune_and_merge import ChunkRange
+from danswer.chat.prune_and_merge import merge_chunk_intervals
 from danswer.configs.chat_configs import DISABLE_LLM_DOC_RELEVANCE
 from danswer.context.search.enums import LLMEvaluationType
 from danswer.context.search.enums import QueryFlow
@@ -27,10 +31,6 @@ from danswer.db.models import User
 from danswer.db.search_settings import get_current_search_settings
 from danswer.document_index.factory import get_default_document_index
 from danswer.document_index.interfaces import VespaChunkRequest
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prune_and_merge import _merge_sections
-from danswer.llm.answering.prune_and_merge import ChunkRange
-from danswer.llm.answering.prune_and_merge import merge_chunk_intervals
 from danswer.llm.interfaces import LLM
 from danswer.secondary_llm_flows.agentic_evaluation import evaluate_inference_section
 from danswer.utils.logger import setup_logger

backend/danswer/document_index/vespa/app_config/schemas/danswer_chunk.sd

@@ -4,6 +4,8 @@ schema DANSWER_CHUNK_NAME {
         # Not to be confused with the UUID generated for this chunk which is called documentid by default
         field document_id type string {
             indexing: summary | attribute
+            attribute: fast-search
+            rank: filter
         }
         field chunk_id type int {
             indexing: summary | attribute

backend/danswer/file_processing/extract_file_text.py

@@ -6,6 +6,7 @@ import zipfile
 from collections.abc import Callable
 from collections.abc import Iterator
 from email.parser import Parser as EmailParser
+from io import BytesIO
 from pathlib import Path
 from typing import Any
 from typing import Dict
@@ -15,13 +16,17 @@ import chardet
 import docx  # type: ignore
 import openpyxl  # type: ignore
 import pptx  # type: ignore
+from docx import Document
+from fastapi import UploadFile
 from pypdf import PdfReader
 from pypdf.errors import PdfStreamError
 
 from danswer.configs.constants import DANSWER_METADATA_FILENAME
+from danswer.configs.constants import FileOrigin
 from danswer.file_processing.html_utils import parse_html_page_basic
 from danswer.file_processing.unstructured import get_unstructured_api_key
 from danswer.file_processing.unstructured import unstructured_to_text
+from danswer.file_store.file_store import FileStore
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
@@ -375,3 +380,35 @@ def extract_file_text(
             ) from e
         logger.warning(f"Failed to process file {file_name or 'Unknown'}: {str(e)}")
         return ""
+
+
+def convert_docx_to_txt(
+    file: UploadFile, file_store: FileStore, file_path: str
+) -> None:
+    file.file.seek(0)
+    docx_content = file.file.read()
+    doc = Document(BytesIO(docx_content))
+
+    # Extract text from the document
+    full_text = []
+    for para in doc.paragraphs:
+        full_text.append(para.text)
+
+    # Join the extracted text
+    text_content = "\n".join(full_text)
+
+    txt_file_path = docx_to_txt_filename(file_path)
+    file_store.save_file(
+        file_name=txt_file_path,
+        content=BytesIO(text_content.encode("utf-8")),
+        display_name=file.filename,
+        file_origin=FileOrigin.CONNECTOR,
+        file_type="text/plain",
+    )
+
+
+def docx_to_txt_filename(file_path: str) -> str:
+    """
+    Convert a .docx file path to its corresponding .txt file path.
+    """
+    return file_path.rsplit(".", 1)[0] + ".txt"

backend/danswer/file_store/utils.py

@@ -1,6 +1,6 @@
+import base64
 from collections.abc import Callable
 from io import BytesIO
-from typing import Any
 from typing import cast
 from uuid import uuid4
 
@@ -13,8 +13,8 @@ from danswer.db.models import ChatMessage
 from danswer.file_store.file_store import get_default_file_store
 from danswer.file_store.models import FileDescriptor
 from danswer.file_store.models import InMemoryChatFile
+from danswer.utils.b64 import get_image_type
 from danswer.utils.threadpool_concurrency import run_functions_tuples_in_parallel
-from shared_configs.contextvars import CURRENT_TENANT_ID_CONTEXTVAR
 
 
 def load_chat_file(
@@ -75,11 +75,58 @@ def save_file_from_url(url: str, tenant_id: str) -> str:
         return unique_id
 
 
-def save_files_from_urls(urls: list[str]) -> list[str]:
-    tenant_id = CURRENT_TENANT_ID_CONTEXTVAR.get()
+def save_file_from_base64(base64_string: str, tenant_id: str) -> str:
+    with get_session_with_tenant(tenant_id) as db_session:
+        unique_id = str(uuid4())
+        file_store = get_default_file_store(db_session)
+        file_store.save_file(
+            file_name=unique_id,
+            content=BytesIO(base64.b64decode(base64_string)),
+            display_name="GeneratedImage",
+            file_origin=FileOrigin.CHAT_IMAGE_GEN,
+            file_type=get_image_type(base64_string),
+        )
+        return unique_id
 
-    funcs: list[tuple[Callable[..., Any], tuple[Any, ...]]] = [
-        (save_file_from_url, (url, tenant_id)) for url in urls
+
+def save_file(
+    tenant_id: str,
+    url: str | None = None,
+    base64_data: str | None = None,
+) -> str:
+    """Save a file from either a URL or base64 encoded string.
+
+    Args:
+        tenant_id: The tenant ID to save the file under
+        url: URL to download file from
+        base64_data: Base64 encoded file data
+
+    Returns:
+        The unique ID of the saved file
+
+    Raises:
+        ValueError: If neither url nor base64_data is provided, or if both are provided
+    """
+    if url is not None and base64_data is not None:
+        raise ValueError("Cannot specify both url and base64_data")
+
+    if url is not None:
+        return save_file_from_url(url, tenant_id)
+    elif base64_data is not None:
+        return save_file_from_base64(base64_data, tenant_id)
+    else:
+        raise ValueError("Must specify either url or base64_data")
+
+
+def save_files(urls: list[str], base64_files: list[str], tenant_id: str) -> list[str]:
+    # NOTE: be explicit about typing so that if we change things, we get notified
+    funcs: list[
+        tuple[
+            Callable[[str, str | None, str | None], str],
+            tuple[str, str | None, str | None],
+        ]
+    ] = [(save_file, (tenant_id, url, None)) for url in urls] + [
+        (save_file, (tenant_id, None, base64_file)) for base64_file in base64_files
     ]
-    # Must pass in tenant_id here, since this is called by multithreading
+
     return run_functions_tuples_in_parallel(funcs)

backend/danswer/llm/answering/models.py

@@ -1,163 +0,0 @@
-from collections.abc import Callable
-from collections.abc import Iterator
-from typing import TYPE_CHECKING
-
-from langchain.schema.messages import AIMessage
-from langchain.schema.messages import BaseMessage
-from langchain.schema.messages import HumanMessage
-from langchain.schema.messages import SystemMessage
-from pydantic import BaseModel
-from pydantic import ConfigDict
-from pydantic import Field
-from pydantic import model_validator
-
-from danswer.chat.models import AnswerQuestionStreamReturn
-from danswer.configs.constants import MessageType
-from danswer.file_store.models import InMemoryChatFile
-from danswer.llm.override_models import PromptOverride
-from danswer.llm.utils import build_content_with_imgs
-from danswer.tools.models import ToolCallFinalResult
-
-if TYPE_CHECKING:
-    from danswer.db.models import ChatMessage
-    from danswer.db.models import Prompt
-
-
-StreamProcessor = Callable[[Iterator[str]], AnswerQuestionStreamReturn]
-
-
-class PreviousMessage(BaseModel):
-    """Simplified version of `ChatMessage`"""
-
-    message: str
-    token_count: int
-    message_type: MessageType
-    files: list[InMemoryChatFile]
-    tool_call: ToolCallFinalResult | None
-
-    @classmethod
-    def from_chat_message(
-        cls, chat_message: "ChatMessage", available_files: list[InMemoryChatFile]
-    ) -> "PreviousMessage":
-        message_file_ids = (
-            [file["id"] for file in chat_message.files] if chat_message.files else []
-        )
-        return cls(
-            message=chat_message.message,
-            token_count=chat_message.token_count,
-            message_type=chat_message.message_type,
-            files=[
-                file
-                for file in available_files
-                if str(file.file_id) in message_file_ids
-            ],
-            tool_call=ToolCallFinalResult(
-                tool_name=chat_message.tool_call.tool_name,
-                tool_args=chat_message.tool_call.tool_arguments,
-                tool_result=chat_message.tool_call.tool_result,
-            )
-            if chat_message.tool_call
-            else None,
-        )
-
-    def to_langchain_msg(self) -> BaseMessage:
-        content = build_content_with_imgs(self.message, self.files)
-        if self.message_type == MessageType.USER:
-            return HumanMessage(content=content)
-        elif self.message_type == MessageType.ASSISTANT:
-            return AIMessage(content=content)
-        else:
-            return SystemMessage(content=content)
-
-
-class DocumentPruningConfig(BaseModel):
-    max_chunks: int | None = None
-    max_window_percentage: float | None = None
-    max_tokens: int | None = None
-    # different pruning behavior is expected when the
-    # user manually selects documents they want to chat with
-    # e.g. we don't want to truncate each document to be no more
-    # than one chunk long
-    is_manually_selected_docs: bool = False
-    # If user specifies to include additional context Chunks for each match, then different pruning
-    # is used. As many Sections as possible are included, and the last Section is truncated
-    # If this is false, all of the Sections are truncated if they are longer than the expected Chunk size.
-    # Sections are often expected to be longer than the maximum Chunk size but Chunks should not be.
-    use_sections: bool = True
-    # If using tools, then we need to consider the tool length
-    tool_num_tokens: int = 0
-    # If using a tool message to represent the docs, then we have to JSON serialize
-    # the document content, which adds to the token count.
-    using_tool_message: bool = False
-
-
-class ContextualPruningConfig(DocumentPruningConfig):
-    num_chunk_multiple: int
-
-    @classmethod
-    def from_doc_pruning_config(
-        cls, num_chunk_multiple: int, doc_pruning_config: DocumentPruningConfig
-    ) -> "ContextualPruningConfig":
-        return cls(num_chunk_multiple=num_chunk_multiple, **doc_pruning_config.dict())
-
-
-class CitationConfig(BaseModel):
-    all_docs_useful: bool = False
-
-
-class QuotesConfig(BaseModel):
-    pass
-
-
-class AnswerStyleConfig(BaseModel):
-    citation_config: CitationConfig | None = None
-    quotes_config: QuotesConfig | None = None
-    document_pruning_config: DocumentPruningConfig = Field(
-        default_factory=DocumentPruningConfig
-    )
-    # forces the LLM to return a structured response, see
-    # https://platform.openai.com/docs/guides/structured-outputs/introduction
-    # right now, only used by the simple chat API
-    structured_response_format: dict | None = None
-
-    @model_validator(mode="after")
-    def check_quotes_and_citation(self) -> "AnswerStyleConfig":
-        if self.citation_config is None and self.quotes_config is None:
-            raise ValueError(
-                "One of `citation_config` or `quotes_config` must be provided"
-            )
-
-        if self.citation_config is not None and self.quotes_config is not None:
-            raise ValueError(
-                "Only one of `citation_config` or `quotes_config` must be provided"
-            )
-
-        return self
-
-
-class PromptConfig(BaseModel):
-    """Final representation of the Prompt configuration passed
-    into the `Answer` object."""
-
-    system_prompt: str
-    task_prompt: str
-    datetime_aware: bool
-    include_citations: bool
-
-    @classmethod
-    def from_model(
-        cls, model: "Prompt", prompt_override: PromptOverride | None = None
-    ) -> "PromptConfig":
-        override_system_prompt = (
-            prompt_override.system_prompt if prompt_override else None
-        )
-        override_task_prompt = prompt_override.task_prompt if prompt_override else None
-
-        return cls(
-            system_prompt=override_system_prompt or model.system_prompt,
-            task_prompt=override_task_prompt or model.task_prompt,
-            datetime_aware=model.datetime_aware,
-            include_citations=model.include_citations,
-        )
-
-    model_config = ConfigDict(frozen=True)

backend/danswer/llm/answering/prompts/utils.py

@@ -1,20 +0,0 @@
-from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT
-from danswer.prompts.direct_qa_prompts import PARAMATERIZED_PROMPT_WITHOUT_CONTEXT
-
-
-def build_dummy_prompt(
-    system_prompt: str, task_prompt: str, retrieval_disabled: bool
-) -> str:
-    if retrieval_disabled:
-        return PARAMATERIZED_PROMPT_WITHOUT_CONTEXT.format(
-            user_query="<USER_QUERY>",
-            system_prompt=system_prompt,
-            task_prompt=task_prompt,
-        ).strip()
-
-    return PARAMATERIZED_PROMPT.format(
-        context_docs_str="<CONTEXT_DOCS>",
-        user_query="<USER_QUERY>",
-        system_prompt=system_prompt,
-        task_prompt=task_prompt,
-    ).strip()

backend/danswer/llm/models.py

@@ -0,0 +1,59 @@
+from typing import TYPE_CHECKING
+
+from langchain.schema.messages import AIMessage
+from langchain.schema.messages import BaseMessage
+from langchain.schema.messages import HumanMessage
+from langchain.schema.messages import SystemMessage
+from pydantic import BaseModel
+
+from danswer.configs.constants import MessageType
+from danswer.file_store.models import InMemoryChatFile
+from danswer.llm.utils import build_content_with_imgs
+from danswer.tools.models import ToolCallFinalResult
+
+if TYPE_CHECKING:
+    from danswer.db.models import ChatMessage
+
+
+class PreviousMessage(BaseModel):
+    """Simplified version of `ChatMessage`"""
+
+    message: str
+    token_count: int
+    message_type: MessageType
+    files: list[InMemoryChatFile]
+    tool_call: ToolCallFinalResult | None
+
+    @classmethod
+    def from_chat_message(
+        cls, chat_message: "ChatMessage", available_files: list[InMemoryChatFile]
+    ) -> "PreviousMessage":
+        message_file_ids = (
+            [file["id"] for file in chat_message.files] if chat_message.files else []
+        )
+        return cls(
+            message=chat_message.message,
+            token_count=chat_message.token_count,
+            message_type=chat_message.message_type,
+            files=[
+                file
+                for file in available_files
+                if str(file.file_id) in message_file_ids
+            ],
+            tool_call=ToolCallFinalResult(
+                tool_name=chat_message.tool_call.tool_name,
+                tool_args=chat_message.tool_call.tool_arguments,
+                tool_result=chat_message.tool_call.tool_result,
+            )
+            if chat_message.tool_call
+            else None,
+        )
+
+    def to_langchain_msg(self) -> BaseMessage:
+        content = build_content_with_imgs(self.message, self.files)
+        if self.message_type == MessageType.USER:
+            return HumanMessage(content=content)
+        elif self.message_type == MessageType.ASSISTANT:
+            return AIMessage(content=content)
+        else:
+            return SystemMessage(content=content)

backend/danswer/llm/utils.py

@@ -5,8 +5,6 @@ from collections.abc import Callable
 from collections.abc import Iterator
 from typing import Any
 from typing import cast
-from typing import TYPE_CHECKING
-from typing import Union
 
 import litellm  # type: ignore
 import pandas as pd
@@ -36,17 +34,15 @@ from danswer.configs.constants import MessageType
 from danswer.configs.model_configs import GEN_AI_MAX_TOKENS
 from danswer.configs.model_configs import GEN_AI_MODEL_FALLBACK_MAX_TOKENS
 from danswer.configs.model_configs import GEN_AI_NUM_RESERVED_OUTPUT_TOKENS
-from danswer.db.models import ChatMessage
 from danswer.file_store.models import ChatFileType
 from danswer.file_store.models import InMemoryChatFile
 from danswer.llm.interfaces import LLM
 from danswer.prompts.constants import CODE_BLOCK_PAT
+from danswer.utils.b64 import get_image_type
+from danswer.utils.b64 import get_image_type_from_bytes
 from danswer.utils.logger import setup_logger
 from shared_configs.configs import LOG_LEVEL
 
-if TYPE_CHECKING:
-    from danswer.llm.answering.models import PreviousMessage
-
 logger = setup_logger()
 
 
@@ -104,39 +100,6 @@ def litellm_exception_to_error_msg(
     return error_msg
 
 
-def translate_danswer_msg_to_langchain(
-    msg: Union[ChatMessage, "PreviousMessage"],
-) -> BaseMessage:
-    files: list[InMemoryChatFile] = []
-
-    # If the message is a `ChatMessage`, it doesn't have the downloaded files
-    # attached. Just ignore them for now.
-    if not isinstance(msg, ChatMessage):
-        files = msg.files
-    content = build_content_with_imgs(msg.message, files, message_type=msg.message_type)
-
-    if msg.message_type == MessageType.SYSTEM:
-        raise ValueError("System messages are not currently part of history")
-    if msg.message_type == MessageType.ASSISTANT:
-        return AIMessage(content=content)
-    if msg.message_type == MessageType.USER:
-        return HumanMessage(content=content)
-
-    raise ValueError(f"New message type {msg.message_type} not handled")
-
-
-def translate_history_to_basemessages(
-    history: list[ChatMessage] | list["PreviousMessage"],
-) -> tuple[list[BaseMessage], list[int]]:
-    history_basemessages = [
-        translate_danswer_msg_to_langchain(msg)
-        for msg in history
-        if msg.token_count != 0
-    ]
-    history_token_counts = [msg.token_count for msg in history if msg.token_count != 0]
-    return history_basemessages, history_token_counts
-
-
 #  Processes CSV files to show the first 5 rows and max_columns (default 40) columns
 def _process_csv_file(file: InMemoryChatFile, max_columns: int = 40) -> str:
     df = pd.read_csv(io.StringIO(file.content.decode("utf-8")))
@@ -190,6 +153,7 @@ def build_content_with_imgs(
     message: str,
     files: list[InMemoryChatFile] | None = None,
     img_urls: list[str] | None = None,
+    b64_imgs: list[str] | None = None,
     message_type: MessageType = MessageType.USER,
 ) -> str | list[str | dict[str, Any]]:  # matching Langchain's BaseMessage content type
     files = files or []
@@ -202,6 +166,7 @@ def build_content_with_imgs(
     )
 
     img_urls = img_urls or []
+    b64_imgs = b64_imgs or []
 
     message_main_content = _build_content(message, files)
 
@@ -220,11 +185,22 @@ def build_content_with_imgs(
             {
                 "type": "image_url",
                 "image_url": {
-                    "url": f"data:image/jpeg;base64,{file.to_base64()}",
+                    "url": (
+                        f"data:{get_image_type_from_bytes(file.content)};"
+                        f"base64,{file.to_base64()}"
+                    ),
                 },
             }
-            for file in files
-            if file.file_type == "image"
+            for file in img_files
+        ]
+        + [
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:{get_image_type(b64_img)};base64,{b64_img}",
+                },
+            }
+            for b64_img in b64_imgs
         ]
         + [
             {

backend/danswer/prompts/prompt_utils.py

@@ -5,11 +5,11 @@ from typing import cast
 from langchain_core.messages import BaseMessage
 
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PromptConfig
 from danswer.configs.chat_configs import LANGUAGE_HINT
 from danswer.configs.constants import DocumentSource
 from danswer.context.search.models import InferenceChunk
 from danswer.db.models import Prompt
-from danswer.llm.answering.models import PromptConfig
 from danswer.prompts.chat_prompts import ADDITIONAL_INFO
 from danswer.prompts.chat_prompts import CITATION_REMINDER
 from danswer.prompts.constants import CODE_BLOCK_PAT

backend/danswer/secondary_llm_flows/choose_search.py

@@ -3,14 +3,14 @@ from langchain.schema import HumanMessage
 from langchain.schema import SystemMessage
 
 from danswer.chat.chat_utils import combine_message_chain
+from danswer.chat.prompt_builder.utils import translate_danswer_msg_to_langchain
 from danswer.configs.chat_configs import DISABLE_LLM_CHOOSE_SEARCH
 from danswer.configs.model_configs import GEN_AI_HISTORY_CUTOFF
 from danswer.db.models import ChatMessage
-from danswer.llm.answering.models import PreviousMessage
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.llm.utils import dict_based_prompt_to_langchain_prompt
 from danswer.llm.utils import message_to_string
-from danswer.llm.utils import translate_danswer_msg_to_langchain
 from danswer.prompts.chat_prompts import AGGRESSIVE_SEARCH_TEMPLATE
 from danswer.prompts.chat_prompts import NO_SEARCH
 from danswer.prompts.chat_prompts import REQUIRE_SEARCH_HINT

backend/danswer/secondary_llm_flows/query_expansion.py

@@ -4,10 +4,10 @@ from danswer.chat.chat_utils import combine_message_chain
 from danswer.configs.chat_configs import DISABLE_LLM_QUERY_REPHRASE
 from danswer.configs.model_configs import GEN_AI_HISTORY_CUTOFF
 from danswer.db.models import ChatMessage
-from danswer.llm.answering.models import PreviousMessage
 from danswer.llm.exceptions import GenAIDisabledException
 from danswer.llm.factory import get_default_llms
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.llm.utils import dict_based_prompt_to_langchain_prompt
 from danswer.llm.utils import message_to_string
 from danswer.prompts.chat_prompts import HISTORY_QUERY_REPHRASE

backend/danswer/server/documents/connector.py

@@ -86,6 +86,7 @@ from danswer.db.models import SearchSettings
 from danswer.db.models import User
 from danswer.db.search_settings import get_current_search_settings
 from danswer.db.search_settings import get_secondary_search_settings
+from danswer.file_processing.extract_file_text import convert_docx_to_txt
 from danswer.file_store.file_store import get_default_file_store
 from danswer.key_value_store.interface import KvKeyNotFoundError
 from danswer.redis.redis_connector import RedisConnector
@@ -393,6 +394,12 @@ def upload_files(
                 file_origin=FileOrigin.CONNECTOR,
                 file_type=file.content_type or "text/plain",
             )
+
+            if file.content_type and file.content_type.startswith(
+                "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+            ):
+                convert_docx_to_txt(file, file_store, file_path)
+
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     return FileUploadResponse(file_paths=deduped_file_paths)
@@ -1010,37 +1017,18 @@ def get_connector_by_id(
 
 
 class BasicCCPairInfo(BaseModel):
-    docs_indexed: int
     has_successful_run: bool
     source: DocumentSource
 
 
-@router.get("/indexing-status")
+@router.get("/connector-status")
 def get_basic_connector_indexing_status(
     _: User = Depends(current_user),
     db_session: Session = Depends(get_session),
 ) -> list[BasicCCPairInfo]:
     cc_pairs = get_connector_credential_pairs(db_session)
-    cc_pair_identifiers = [
-        ConnectorCredentialPairIdentifier(
-            connector_id=cc_pair.connector_id, credential_id=cc_pair.credential_id
-        )
-        for cc_pair in cc_pairs
-    ]
-    document_count_info = get_document_counts_for_cc_pairs(
-        db_session=db_session,
-        cc_pair_identifiers=cc_pair_identifiers,
-    )
-    cc_pair_to_document_cnt = {
-        (connector_id, credential_id): cnt
-        for connector_id, credential_id, cnt in document_count_info
-    }
     return [
         BasicCCPairInfo(
-            docs_indexed=cc_pair_to_document_cnt.get(
-                (cc_pair.connector_id, cc_pair.credential_id)
-            )
-            or 0,
             has_successful_run=cc_pair.last_successful_index_time is not None,
             source=cc_pair.connector.source,
         )

backend/danswer/server/features/persona/api.py

@@ -13,6 +13,7 @@ from danswer.auth.users import current_admin_user
 from danswer.auth.users import current_curator_or_admin_user
 from danswer.auth.users import current_limited_user
 from danswer.auth.users import current_user
+from danswer.chat.prompt_builder.utils import build_dummy_prompt
 from danswer.configs.constants import FileOrigin
 from danswer.configs.constants import NotificationType
 from danswer.db.engine import get_session
@@ -33,7 +34,6 @@ from danswer.db.persona import update_persona_shared_users
 from danswer.db.persona import update_persona_visibility
 from danswer.file_store.file_store import get_default_file_store
 from danswer.file_store.models import ChatFileType
-from danswer.llm.answering.prompts.utils import build_dummy_prompt
 from danswer.server.features.persona.models import CreatePersonaRequest
 from danswer.server.features.persona.models import ImageGenerationToolStatus
 from danswer.server.features.persona.models import PersonaCategoryCreate

backend/danswer/server/manage/users.py

@@ -194,11 +194,11 @@ def bulk_invite_users(
         )
 
     tenant_id = CURRENT_TENANT_ID_CONTEXTVAR.get()
-    normalized_emails = []
+    new_invited_emails = []
     try:
         for email in emails:
             email_info = validate_email(email)
-            normalized_emails.append(email_info.normalized)  # type: ignore
+            new_invited_emails.append(email_info.normalized)
 
     except (EmailUndeliverableError, EmailNotValidError) as e:
         raise HTTPException(
@@ -210,7 +210,7 @@ def bulk_invite_users(
         try:
             fetch_ee_implementation_or_noop(
                 "danswer.server.tenants.provisioning", "add_users_to_tenant", None
-            )(normalized_emails, tenant_id)
+            )(new_invited_emails, tenant_id)
 
         except IntegrityError as e:
             if isinstance(e.orig, UniqueViolation):
@@ -224,7 +224,7 @@ def bulk_invite_users(
 
     initial_invited_users = get_invited_users()
 
-    all_emails = list(set(normalized_emails) | set(initial_invited_users))
+    all_emails = list(set(new_invited_emails) | set(initial_invited_users))
     number_of_invited_users = write_invited_users(all_emails)
 
     if not MULTI_TENANT:
@@ -236,7 +236,7 @@ def bulk_invite_users(
         )(CURRENT_TENANT_ID_CONTEXTVAR.get(), get_total_users_count(db_session))
         if ENABLE_EMAIL_INVITES:
             try:
-                for email in all_emails:
+                for email in new_invited_emails:
                     send_user_email_invite(email, current_user)
             except Exception as e:
                 logger.error(f"Error sending email invite to invited users: {e}")
@@ -250,7 +250,7 @@ def bulk_invite_users(
         write_invited_users(initial_invited_users)  # Reset to original state
         fetch_ee_implementation_or_noop(
             "danswer.server.tenants.user_mapping", "remove_users_from_tenant", None
-        )(normalized_emails, tenant_id)
+        )(new_invited_emails, tenant_id)
         raise e
 
 

backend/danswer/server/query_and_chat/chat_backend.py

@@ -1,6 +1,7 @@
 import asyncio
 import io
 import json
+import os
 import uuid
 from collections.abc import Callable
 from collections.abc import Generator
@@ -23,6 +24,9 @@ from danswer.auth.users import current_user
 from danswer.chat.chat_utils import create_chat_chain
 from danswer.chat.chat_utils import extract_headers
 from danswer.chat.process_message import stream_chat_message
+from danswer.chat.prompt_builder.citations_prompt import (
+    compute_max_document_tokens_for_persona,
+)
 from danswer.configs.app_configs import WEB_DOMAIN
 from danswer.configs.constants import FileOrigin
 from danswer.configs.constants import MessageType
@@ -47,13 +51,11 @@ from danswer.db.models import User
 from danswer.db.persona import get_persona_by_id
 from danswer.document_index.document_index_utils import get_both_index_names
 from danswer.document_index.factory import get_default_document_index
+from danswer.file_processing.extract_file_text import docx_to_txt_filename
 from danswer.file_processing.extract_file_text import extract_file_text
 from danswer.file_store.file_store import get_default_file_store
 from danswer.file_store.models import ChatFileType
 from danswer.file_store.models import FileDescriptor
-from danswer.llm.answering.prompts.citations_prompt import (
-    compute_max_document_tokens_for_persona,
-)
 from danswer.llm.exceptions import GenAIDisabledException
 from danswer.llm.factory import get_default_llms
 from danswer.llm.factory import get_llms_for_persona
@@ -718,6 +720,18 @@ def fetch_chat_file(
     if not file_record:
         raise HTTPException(status_code=404, detail="File not found")
 
+    original_file_name = file_record.display_name
+    if file_record.file_type.startswith(
+        "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+    ):
+        # Check if a converted text file exists for .docx files
+        txt_file_name = docx_to_txt_filename(original_file_name)
+        txt_file_id = os.path.join(os.path.dirname(file_id), txt_file_name)
+        txt_file_record = file_store.read_file_record(txt_file_id)
+        if txt_file_record:
+            file_record = txt_file_record
+            file_id = txt_file_id
+
     media_type = file_record.file_type
     file_io = file_store.read_file(file_id, mode="b")
 

backend/danswer/server/query_and_chat/models.py

@@ -1,5 +1,6 @@
 from datetime import datetime
 from typing import Any
+from typing import TYPE_CHECKING
 from uuid import UUID
 
 from pydantic import BaseModel
@@ -22,6 +23,9 @@ from danswer.llm.override_models import LLMOverride
 from danswer.llm.override_models import PromptOverride
 from danswer.tools.models import ToolCallFinalResult
 
+if TYPE_CHECKING:
+    pass
+
 
 class SourceTag(Tag):
     source: DocumentSource

backend/danswer/setup.py

@@ -4,6 +4,7 @@ from sqlalchemy.orm import Session
 
 from danswer.configs.app_configs import DISABLE_INDEX_UPDATE_ON_SWAP
 from danswer.configs.app_configs import MANAGED_VESPA
+from danswer.configs.app_configs import VESPA_NUM_ATTEMPTS_ON_STARTUP
 from danswer.configs.constants import KV_REINDEX_KEY
 from danswer.configs.constants import KV_SEARCH_SETTINGS
 from danswer.configs.model_configs import FAST_GEN_AI_MODEL_VERSION
@@ -221,13 +222,13 @@ def setup_vespa(
     document_index: DocumentIndex,
     index_setting: IndexingSetting,
     secondary_index_setting: IndexingSetting | None,
+    num_attempts: int = VESPA_NUM_ATTEMPTS_ON_STARTUP,
 ) -> bool:
     # Vespa startup is a bit slow, so give it a few seconds
     WAIT_SECONDS = 5
-    VESPA_ATTEMPTS = 5
-    for x in range(VESPA_ATTEMPTS):
+    for x in range(num_attempts):
         try:
-            logger.notice(f"Setting up Vespa (attempt {x+1}/{VESPA_ATTEMPTS})...")
+            logger.notice(f"Setting up Vespa (attempt {x+1}/{num_attempts})...")
             document_index.ensure_indices_exist(
                 index_embedding_dim=index_setting.model_dim,
                 secondary_index_embedding_dim=secondary_index_setting.model_dim
@@ -244,7 +245,7 @@ def setup_vespa(
             time.sleep(WAIT_SECONDS)
 
     logger.error(
-        f"Vespa setup did not succeed. Attempt limit reached. ({VESPA_ATTEMPTS})"
+        f"Vespa setup did not succeed. Attempt limit reached. ({num_attempts})"
     )
     return False
 

backend/danswer/tools/base_tool.py

@@ -7,7 +7,7 @@ from danswer.llm.utils import message_to_prompt_and_imgs
 from danswer.tools.tool import Tool
 
 if TYPE_CHECKING:
-    from danswer.llm.answering.prompts.build import AnswerPromptBuilder
+    from danswer.chat.prompt_builder.build import AnswerPromptBuilder
     from danswer.tools.tool_implementations.custom.custom_tool import (
         CustomToolCallSummary,
     )

backend/danswer/tools/tool.py

@@ -3,13 +3,13 @@ from collections.abc import Generator
 from typing import Any
 from typing import TYPE_CHECKING
 
-from danswer.llm.answering.models import PreviousMessage
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.utils.special_types import JSON_ro
 
 
 if TYPE_CHECKING:
-    from danswer.llm.answering.prompts.build import AnswerPromptBuilder
+    from danswer.chat.prompt_builder.build import AnswerPromptBuilder
     from danswer.tools.message import ToolCallSummary
     from danswer.tools.models import ToolResponse
 

backend/danswer/tools/tool_constructor.py

@@ -5,6 +5,10 @@ from pydantic import BaseModel
 from pydantic import Field
 from sqlalchemy.orm import Session
 
+from danswer.chat.models import AnswerStyleConfig
+from danswer.chat.models import CitationConfig
+from danswer.chat.models import DocumentPruningConfig
+from danswer.chat.models import PromptConfig
 from danswer.configs.app_configs import AZURE_DALLE_API_BASE
 from danswer.configs.app_configs import AZURE_DALLE_API_KEY
 from danswer.configs.app_configs import AZURE_DALLE_API_VERSION
@@ -19,10 +23,6 @@ from danswer.db.llm import fetch_existing_llm_providers
 from danswer.db.models import Persona
 from danswer.db.models import User
 from danswer.file_store.models import InMemoryChatFile
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import CitationConfig
-from danswer.llm.answering.models import DocumentPruningConfig
-from danswer.llm.answering.models import PromptConfig
 from danswer.llm.interfaces import LLM
 from danswer.llm.interfaces import LLMConfig
 from danswer.natural_language_processing.utils import get_tokenizer

backend/danswer/tools/tool_implementations/custom/custom_tool.py

@@ -15,14 +15,14 @@ from langchain_core.messages import SystemMessage
 from pydantic import BaseModel
 from requests import JSONDecodeError
 
+from danswer.chat.prompt_builder.build import AnswerPromptBuilder
 from danswer.configs.constants import FileOrigin
 from danswer.db.engine import get_session_with_default_tenant
 from danswer.file_store.file_store import get_default_file_store
 from danswer.file_store.models import ChatFileType
 from danswer.file_store.models import InMemoryChatFile
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.tools.base_tool import BaseTool
 from danswer.tools.message import ToolCallSummary
 from danswer.tools.models import CHAT_SESSION_ID_PLACEHOLDER

backend/danswer/tools/tool_implementations/images/image_generation_tool.py

@@ -4,14 +4,16 @@ from enum import Enum
 from typing import Any
 from typing import cast
 
+import requests
 from litellm import image_generation  # type: ignore
 from pydantic import BaseModel
 
 from danswer.chat.chat_utils import combine_message_chain
+from danswer.chat.prompt_builder.build import AnswerPromptBuilder
 from danswer.configs.model_configs import GEN_AI_HISTORY_CUTOFF
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
+from danswer.configs.tool_configs import IMAGE_GENERATION_OUTPUT_FORMAT
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.llm.utils import build_content_with_imgs
 from danswer.llm.utils import message_to_string
 from danswer.prompts.constants import GENERAL_SEP_PAT
@@ -56,9 +58,18 @@ Follow Up Input:
 """.strip()
 
 
+class ImageFormat(str, Enum):
+    URL = "url"
+    BASE64 = "b64_json"
+
+
+_DEFAULT_OUTPUT_FORMAT = ImageFormat(IMAGE_GENERATION_OUTPUT_FORMAT)
+
+
 class ImageGenerationResponse(BaseModel):
     revised_prompt: str
-    url: str
+    url: str | None
+    image_data: str | None
 
 
 class ImageShape(str, Enum):
@@ -80,6 +91,7 @@ class ImageGenerationTool(Tool):
         model: str = "dall-e-3",
         num_imgs: int = 2,
         additional_headers: dict[str, str] | None = None,
+        output_format: ImageFormat = _DEFAULT_OUTPUT_FORMAT,
     ) -> None:
         self.api_key = api_key
         self.api_base = api_base
@@ -89,6 +101,7 @@ class ImageGenerationTool(Tool):
         self.num_imgs = num_imgs
 
         self.additional_headers = additional_headers
+        self.output_format = output_format
 
     @property
     def name(self) -> str:
@@ -168,7 +181,7 @@ class ImageGenerationTool(Tool):
         )
 
         return build_content_with_imgs(
-            json.dumps(
+            message=json.dumps(
                 [
                     {
                         "revised_prompt": image_generation.revised_prompt,
@@ -177,13 +190,10 @@ class ImageGenerationTool(Tool):
                     for image_generation in image_generations
                 ]
             ),
-            # NOTE: we can't pass in the image URLs here, since OpenAI doesn't allow
-            # Tool messages to contain images
-            # img_urls=[image_generation.url for image_generation in image_generations],
         )
 
     def _generate_image(
-        self, prompt: str, shape: ImageShape
+        self, prompt: str, shape: ImageShape, format: ImageFormat
     ) -> ImageGenerationResponse:
         if shape == ImageShape.LANDSCAPE:
             size = "1792x1024"
@@ -197,20 +207,32 @@ class ImageGenerationTool(Tool):
                 prompt=prompt,
                 model=self.model,
                 api_key=self.api_key,
-                # need to pass in None rather than empty str
                 api_base=self.api_base or None,
                 api_version=self.api_version or None,
                 size=size,
                 n=1,
+                response_format=format,
                 extra_headers=build_llm_extra_headers(self.additional_headers),
             )
+
+            if format == ImageFormat.URL:
+                url = response.data[0]["url"]
+                image_data = None
+            else:
+                url = None
+                image_data = response.data[0]["b64_json"]
+
             return ImageGenerationResponse(
                 revised_prompt=response.data[0]["revised_prompt"],
-                url=response.data[0]["url"],
+                url=url,
+                image_data=image_data,
             )
 
+        except requests.RequestException as e:
+            logger.error(f"Error fetching or converting image: {e}")
+            raise ValueError("Failed to fetch or convert the generated image")
         except Exception as e:
-            logger.debug(f"Error occured during image generation: {e}")
+            logger.debug(f"Error occurred during image generation: {e}")
 
             error_message = str(e)
             if "OpenAIException" in str(type(e)):
@@ -235,9 +257,8 @@ class ImageGenerationTool(Tool):
     def run(self, **kwargs: str) -> Generator[ToolResponse, None, None]:
         prompt = cast(str, kwargs["prompt"])
         shape = ImageShape(kwargs.get("shape", ImageShape.SQUARE))
+        format = self.output_format
 
-        # dalle3 only supports 1 image at a time, which is why we have to
-        # parallelize this via threading
         results = cast(
             list[ImageGenerationResponse],
             run_functions_tuples_in_parallel(
@@ -247,6 +268,7 @@ class ImageGenerationTool(Tool):
                         (
                             prompt,
                             shape,
+                            format,
                         ),
                     )
                     for _ in range(self.num_imgs)
@@ -288,11 +310,17 @@ class ImageGenerationTool(Tool):
         if img_generation_response is None:
             raise ValueError("No image generation response found")
 
-        img_urls = [img.url for img in img_generation_response]
+        img_urls = [img.url for img in img_generation_response if img.url is not None]
+        b64_imgs = [
+            img.image_data
+            for img in img_generation_response
+            if img.image_data is not None
+        ]
         prompt_builder.update_user_prompt(
             build_image_generation_user_prompt(
                 query=prompt_builder.get_user_message_content(),
                 img_urls=img_urls,
+                b64_imgs=b64_imgs,
             )
         )
 

backend/danswer/tools/tool_implementations/images/prompt.py

@@ -11,11 +11,14 @@ Can you please summarize them in a sentence or two? Do NOT include image urls or
 
 
 def build_image_generation_user_prompt(
-    query: str, img_urls: list[str] | None = None
+    query: str,
+    img_urls: list[str] | None = None,
+    b64_imgs: list[str] | None = None,
 ) -> HumanMessage:
     return HumanMessage(
         content=build_content_with_imgs(
             message=IMG_GENERATION_SUMMARY_PROMPT.format(query=query).strip(),
+            b64_imgs=b64_imgs,
             img_urls=img_urls,
         )
     )

backend/danswer/tools/tool_implementations/internet_search/internet_search_tool.py

@@ -7,15 +7,15 @@ from typing import cast
 import httpx
 
 from danswer.chat.chat_utils import combine_message_chain
+from danswer.chat.models import AnswerStyleConfig
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PromptConfig
+from danswer.chat.prompt_builder.build import AnswerPromptBuilder
 from danswer.configs.constants import DocumentSource
 from danswer.configs.model_configs import GEN_AI_HISTORY_CUTOFF
 from danswer.context.search.models import SearchDoc
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.llm.utils import message_to_string
 from danswer.prompts.chat_prompts import INTERNET_SEARCH_QUERY_REPHRASE
 from danswer.prompts.constants import GENERAL_SEP_PAT

backend/danswer/tools/tool_implementations/search/search_tool.py

@@ -7,10 +7,19 @@ from pydantic import BaseModel
 from sqlalchemy.orm import Session
 
 from danswer.chat.chat_utils import llm_doc_from_inference_section
+from danswer.chat.llm_response_handler import LLMCall
+from danswer.chat.models import AnswerStyleConfig
+from danswer.chat.models import ContextualPruningConfig
 from danswer.chat.models import DanswerContext
 from danswer.chat.models import DanswerContexts
+from danswer.chat.models import DocumentPruningConfig
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PromptConfig
 from danswer.chat.models import SectionRelevancePiece
+from danswer.chat.prompt_builder.build import AnswerPromptBuilder
+from danswer.chat.prompt_builder.citations_prompt import compute_max_llm_input_tokens
+from danswer.chat.prune_and_merge import prune_and_merge_sections
+from danswer.chat.prune_and_merge import prune_sections
 from danswer.configs.chat_configs import CONTEXT_CHUNKS_ABOVE
 from danswer.configs.chat_configs import CONTEXT_CHUNKS_BELOW
 from danswer.configs.model_configs import GEN_AI_MODEL_FALLBACK_MAX_TOKENS
@@ -25,17 +34,8 @@ from danswer.context.search.models import SearchRequest
 from danswer.context.search.pipeline import SearchPipeline
 from danswer.db.models import Persona
 from danswer.db.models import User
-from danswer.llm.answering.llm_response_handler import LLMCall
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import ContextualPruningConfig
-from danswer.llm.answering.models import DocumentPruningConfig
-from danswer.llm.answering.models import PreviousMessage
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
-from danswer.llm.answering.prompts.citations_prompt import compute_max_llm_input_tokens
-from danswer.llm.answering.prune_and_merge import prune_and_merge_sections
-from danswer.llm.answering.prune_and_merge import prune_sections
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.secondary_llm_flows.choose_search import check_if_need_search
 from danswer.secondary_llm_flows.query_expansion import history_based_query_rephrase
 from danswer.tools.message import ToolCallSummary

backend/danswer/tools/tool_implementations/search_like_tool_utils.py

@@ -2,15 +2,15 @@ from typing import cast
 
 from langchain_core.messages import HumanMessage
 
+from danswer.chat.models import AnswerStyleConfig
 from danswer.chat.models import LlmDoc
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
-from danswer.llm.answering.prompts.citations_prompt import (
+from danswer.chat.models import PromptConfig
+from danswer.chat.prompt_builder.build import AnswerPromptBuilder
+from danswer.chat.prompt_builder.citations_prompt import (
     build_citations_system_message,
 )
-from danswer.llm.answering.prompts.citations_prompt import build_citations_user_message
-from danswer.llm.answering.prompts.quotes_prompt import build_quotes_user_message
+from danswer.chat.prompt_builder.citations_prompt import build_citations_user_message
+from danswer.chat.prompt_builder.quotes_prompt import build_quotes_user_message
 from danswer.tools.message import ToolCallSummary
 from danswer.tools.models import ToolResponse
 

backend/danswer/tools/tool_runner.py

@@ -2,8 +2,8 @@ from collections.abc import Callable
 from collections.abc import Generator
 from typing import Any
 
-from danswer.llm.answering.models import PreviousMessage
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.tools.models import ToolCallFinalResult
 from danswer.tools.models import ToolCallKickoff
 from danswer.tools.models import ToolResponse

backend/danswer/tools/tool_selection.py

@@ -3,8 +3,8 @@ from typing import Any
 
 from danswer.chat.chat_utils import combine_message_chain
 from danswer.configs.model_configs import GEN_AI_HISTORY_CUTOFF
-from danswer.llm.answering.models import PreviousMessage
 from danswer.llm.interfaces import LLM
+from danswer.llm.models import PreviousMessage
 from danswer.llm.utils import message_to_string
 from danswer.prompts.constants import GENERAL_SEP_PAT
 from danswer.tools.tool import Tool

backend/danswer/utils/b64.py

@@ -0,0 +1,25 @@
+import base64
+
+
+def get_image_type_from_bytes(raw_b64_bytes: bytes) -> str:
+    magic_number = raw_b64_bytes[:4]
+
+    if magic_number.startswith(b"\x89PNG"):
+        mime_type = "image/png"
+    elif magic_number.startswith(b"\xFF\xD8"):
+        mime_type = "image/jpeg"
+    elif magic_number.startswith(b"GIF8"):
+        mime_type = "image/gif"
+    elif magic_number.startswith(b"RIFF") and raw_b64_bytes[8:12] == b"WEBP":
+        mime_type = "image/webp"
+    else:
+        raise ValueError(
+            "Unsupported image format - only PNG, JPEG, " "GIF, and WEBP are supported."
+        )
+
+    return mime_type
+
+
+def get_image_type(raw_b64_string: str) -> str:
+    binary_data = base64.b64decode(raw_b64_string)
+    return get_image_type_from_bytes(binary_data)

backend/ee/danswer/configs/app_configs.py

@@ -11,6 +11,14 @@ SAML_CONF_DIR = os.environ.get("SAML_CONF_DIR") or "/app/ee/danswer/configs/saml
 #####
 # Auto Permission Sync
 #####
+# In seconds, default is 5 minutes
+CONFLUENCE_PERMISSION_GROUP_SYNC_FREQUENCY = int(
+    os.environ.get("CONFLUENCE_PERMISSION_GROUP_SYNC_FREQUENCY") or 5 * 60
+)
+# In seconds, default is 5 minutes
+CONFLUENCE_PERMISSION_DOC_SYNC_FREQUENCY = int(
+    os.environ.get("CONFLUENCE_PERMISSION_DOC_SYNC_FREQUENCY") or 5 * 60
+)
 NUM_PERMISSION_WORKERS = int(os.environ.get("NUM_PERMISSION_WORKERS") or 2)
 
 

backend/ee/danswer/db/analytics.py

@@ -170,3 +170,67 @@ def fetch_danswerbot_analytics(
     )
 
     return results
+
+
+def fetch_persona_message_analytics(
+    db_session: Session,
+    persona_id: int,
+    start: datetime.datetime,
+    end: datetime.datetime,
+) -> list[tuple[int, datetime.date]]:
+    """Gets the daily message counts for a specific persona within the given time range."""
+    query = (
+        select(
+            func.count(ChatMessage.id),
+            cast(ChatMessage.time_sent, Date),
+        )
+        .join(
+            ChatSession,
+            ChatMessage.chat_session_id == ChatSession.id,
+        )
+        .where(
+            or_(
+                ChatMessage.alternate_assistant_id == persona_id,
+                ChatSession.persona_id == persona_id,
+            ),
+            ChatMessage.time_sent >= start,
+            ChatMessage.time_sent <= end,
+            ChatMessage.message_type == MessageType.ASSISTANT,
+        )
+        .group_by(cast(ChatMessage.time_sent, Date))
+        .order_by(cast(ChatMessage.time_sent, Date))
+    )
+
+    return [tuple(row) for row in db_session.execute(query).all()]
+
+
+def fetch_persona_unique_users(
+    db_session: Session,
+    persona_id: int,
+    start: datetime.datetime,
+    end: datetime.datetime,
+) -> list[tuple[int, datetime.date]]:
+    """Gets the daily unique user counts for a specific persona within the given time range."""
+    query = (
+        select(
+            func.count(func.distinct(ChatSession.user_id)),
+            cast(ChatMessage.time_sent, Date),
+        )
+        .join(
+            ChatSession,
+            ChatMessage.chat_session_id == ChatSession.id,
+        )
+        .where(
+            or_(
+                ChatMessage.alternate_assistant_id == persona_id,
+                ChatSession.persona_id == persona_id,
+            ),
+            ChatMessage.time_sent >= start,
+            ChatMessage.time_sent <= end,
+            ChatMessage.message_type == MessageType.ASSISTANT,
+        )
+        .group_by(cast(ChatMessage.time_sent, Date))
+        .order_by(cast(ChatMessage.time_sent, Date))
+    )
+
+    return [tuple(row) for row in db_session.execute(query).all()]

backend/ee/danswer/db/external_perm.py

@@ -10,6 +10,9 @@ from danswer.access.utils import prefix_group_w_source
 from danswer.configs.constants import DocumentSource
 from danswer.db.models import User__ExternalUserGroupId
 from danswer.db.users import batch_add_ext_perm_user_if_not_exists
+from danswer.utils.logger import setup_logger
+
+logger = setup_logger()
 
 
 class ExternalUserGroup(BaseModel):
@@ -73,7 +76,13 @@ def replace_user__ext_group_for_cc_pair(
     new_external_permissions = []
     for external_group in group_defs:
         for user_email in external_group.user_emails:
-            user_id = email_id_map[user_email]
+            user_id = email_id_map.get(user_email)
+            if user_id is None:
+                logger.warning(
+                    f"User in group {external_group.id}"
+                    f" with email {user_email} not found"
+                )
+                continue
             new_external_permissions.append(
                 User__ExternalUserGroupId(
                     user_id=user_id,

backend/ee/danswer/external_permissions/confluence/doc_sync.py

@@ -195,6 +195,7 @@ def _fetch_all_page_restrictions_for_space(
     confluence_client: OnyxConfluence,
     slim_docs: list[SlimDocument],
     space_permissions_by_space_key: dict[str, ExternalAccess],
+    is_cloud: bool,
 ) -> list[DocExternalAccess]:
     """
     For all pages, if a page has restrictions, then use those restrictions.
@@ -222,27 +223,50 @@ def _fetch_all_page_restrictions_for_space(
             continue
 
         space_key = slim_doc.perm_sync_data.get("space_key")
-        if space_permissions := space_permissions_by_space_key.get(space_key):
-            # If there are no restrictions, then use the space's restrictions
-            document_restrictions.append(
-                DocExternalAccess(
-                    doc_id=slim_doc.id,
-                    external_access=space_permissions,
-                )
+        if not (space_permissions := space_permissions_by_space_key.get(space_key)):
+            logger.debug(
+                f"Individually fetching space permissions for space {space_key}"
             )
-            if (
-                not space_permissions.is_public
-                and not space_permissions.external_user_emails
-                and not space_permissions.external_user_group_ids
-            ):
+            try:
+                # If the space permissions are not in the cache, then fetch them
+                if is_cloud:
+                    retrieved_space_permissions = _get_cloud_space_permissions(
+                        confluence_client=confluence_client, space_key=space_key
+                    )
+                else:
+                    retrieved_space_permissions = _get_server_space_permissions(
+                        confluence_client=confluence_client, space_key=space_key
+                    )
+                space_permissions_by_space_key[space_key] = retrieved_space_permissions
+                space_permissions = retrieved_space_permissions
+            except Exception as e:
                 logger.warning(
-                    f"Permissions are empty for document: {slim_doc.id}\n"
-                    "This means space permissions are may be wrong for"
-                    f" Space key: {space_key}"
+                    f"Error fetching space permissions for space {space_key}: {e}"
                 )
+
+        if not space_permissions:
+            logger.warning(
+                f"No permissions found for document {slim_doc.id} in space {space_key}"
+            )
             continue
 
-        logger.warning(f"No permissions found for document {slim_doc.id}")
+        # If there are no restrictions, then use the space's restrictions
+        document_restrictions.append(
+            DocExternalAccess(
+                doc_id=slim_doc.id,
+                external_access=space_permissions,
+            )
+        )
+        if (
+            not space_permissions.is_public
+            and not space_permissions.external_user_emails
+            and not space_permissions.external_user_group_ids
+        ):
+            logger.warning(
+                f"Permissions are empty for document: {slim_doc.id}\n"
+                "This means space permissions are may be wrong for"
+                f" Space key: {space_key}"
+            )
 
     logger.debug("Finished fetching all page restrictions for space")
     return document_restrictions
@@ -281,4 +305,5 @@ def confluence_doc_sync(
         confluence_client=confluence_connector.confluence_client,
         slim_docs=slim_docs,
         space_permissions_by_space_key=space_permissions_by_space_key,
+        is_cloud=is_cloud,
     )

backend/ee/danswer/external_permissions/sync_params.py

@@ -3,6 +3,8 @@ from collections.abc import Callable
 from danswer.access.models import DocExternalAccess
 from danswer.configs.constants import DocumentSource
 from danswer.db.models import ConnectorCredentialPair
+from ee.danswer.configs.app_configs import CONFLUENCE_PERMISSION_DOC_SYNC_FREQUENCY
+from ee.danswer.configs.app_configs import CONFLUENCE_PERMISSION_GROUP_SYNC_FREQUENCY
 from ee.danswer.db.external_perm import ExternalUserGroup
 from ee.danswer.external_permissions.confluence.doc_sync import confluence_doc_sync
 from ee.danswer.external_permissions.confluence.group_sync import confluence_group_sync
@@ -56,7 +58,7 @@ GROUP_PERMISSIONS_IS_CC_PAIR_AGNOSTIC: set[DocumentSource] = {
 # If nothing is specified here, we run the doc_sync every time the celery beat runs
 DOC_PERMISSION_SYNC_PERIODS: dict[DocumentSource, int] = {
     # Polling is not supported so we fetch all doc permissions every 5 minutes
-    DocumentSource.CONFLUENCE: 5 * 60,
+    DocumentSource.CONFLUENCE: CONFLUENCE_PERMISSION_DOC_SYNC_FREQUENCY,
     DocumentSource.SLACK: 5 * 60,
 }
 
@@ -64,7 +66,7 @@ DOC_PERMISSION_SYNC_PERIODS: dict[DocumentSource, int] = {
 EXTERNAL_GROUP_SYNC_PERIODS: dict[DocumentSource, int] = {
     # Polling is not supported so we fetch all group permissions every 30 minutes
     DocumentSource.GOOGLE_DRIVE: 5 * 60,
-    DocumentSource.CONFLUENCE: 30 * 60,
+    DocumentSource.CONFLUENCE: CONFLUENCE_PERMISSION_GROUP_SYNC_FREQUENCY,
 }
 
 

backend/ee/danswer/server/analytics/api.py

@@ -11,11 +11,16 @@ from danswer.db.engine import get_session
 from danswer.db.models import User
 from ee.danswer.db.analytics import fetch_danswerbot_analytics
 from ee.danswer.db.analytics import fetch_per_user_query_analytics
+from ee.danswer.db.analytics import fetch_persona_message_analytics
+from ee.danswer.db.analytics import fetch_persona_unique_users
 from ee.danswer.db.analytics import fetch_query_analytics
 
 router = APIRouter(prefix="/analytics")
 
 
+_DEFAULT_LOOKBACK_DAYS = 30
+
+
 class QueryAnalyticsResponse(BaseModel):
     total_queries: int
     total_likes: int
@@ -33,7 +38,7 @@ def get_query_analytics(
     daily_query_usage_info = fetch_query_analytics(
         start=start
         or (
-            datetime.datetime.utcnow() - datetime.timedelta(days=30)
+            datetime.datetime.utcnow() - datetime.timedelta(days=_DEFAULT_LOOKBACK_DAYS)
         ),  # default is 30d lookback
         end=end or datetime.datetime.utcnow(),
         db_session=db_session,
@@ -64,7 +69,7 @@ def get_user_analytics(
     daily_query_usage_info_per_user = fetch_per_user_query_analytics(
         start=start
         or (
-            datetime.datetime.utcnow() - datetime.timedelta(days=30)
+            datetime.datetime.utcnow() - datetime.timedelta(days=_DEFAULT_LOOKBACK_DAYS)
         ),  # default is 30d lookback
         end=end or datetime.datetime.utcnow(),
         db_session=db_session,
@@ -98,7 +103,7 @@ def get_danswerbot_analytics(
     daily_danswerbot_info = fetch_danswerbot_analytics(
         start=start
         or (
-            datetime.datetime.utcnow() - datetime.timedelta(days=30)
+            datetime.datetime.utcnow() - datetime.timedelta(days=_DEFAULT_LOOKBACK_DAYS)
         ),  # default is 30d lookback
         end=end or datetime.datetime.utcnow(),
         db_session=db_session,
@@ -115,3 +120,74 @@ def get_danswerbot_analytics(
     ]
 
     return resolution_results
+
+
+class PersonaMessageAnalyticsResponse(BaseModel):
+    total_messages: int
+    date: datetime.date
+    persona_id: int
+
+
+@router.get("/admin/persona/messages")
+def get_persona_messages(
+    persona_id: int,
+    start: datetime.datetime | None = None,
+    end: datetime.datetime | None = None,
+    _: User | None = Depends(current_admin_user),
+    db_session: Session = Depends(get_session),
+) -> list[PersonaMessageAnalyticsResponse]:
+    """Fetch daily message counts for a single persona within the given time range."""
+    start = start or (
+        datetime.datetime.utcnow() - datetime.timedelta(days=_DEFAULT_LOOKBACK_DAYS)
+    )
+    end = end or datetime.datetime.utcnow()
+
+    persona_message_counts = []
+    for count, date in fetch_persona_message_analytics(
+        db_session=db_session,
+        persona_id=persona_id,
+        start=start,
+        end=end,
+    ):
+        persona_message_counts.append(
+            PersonaMessageAnalyticsResponse(
+                total_messages=count,
+                date=date,
+                persona_id=persona_id,
+            )
+        )
+
+    return persona_message_counts
+
+
+class PersonaUniqueUsersResponse(BaseModel):
+    unique_users: int
+    date: datetime.date
+    persona_id: int
+
+
+@router.get("/admin/persona/unique-users")
+def get_persona_unique_users(
+    persona_id: int,
+    start: datetime.datetime,
+    end: datetime.datetime,
+    _: User | None = Depends(current_admin_user),
+    db_session: Session = Depends(get_session),
+) -> list[PersonaUniqueUsersResponse]:
+    """Get unique users per day for a single persona."""
+    unique_user_counts = []
+    daily_counts = fetch_persona_unique_users(
+        db_session=db_session,
+        persona_id=persona_id,
+        start=start,
+        end=end,
+    )
+    for count, date in daily_counts:
+        unique_user_counts.append(
+            PersonaUniqueUsersResponse(
+                unique_users=count,
+                date=date,
+                persona_id=persona_id,
+            )
+        )
+    return unique_user_counts

backend/shared_configs/configs.py

@@ -61,7 +61,7 @@ LOG_FILE_NAME = os.environ.get("LOG_FILE_NAME") or "danswer"
 # Enable generating persistent log files for local dev environments
 DEV_LOGGING_ENABLED = os.environ.get("DEV_LOGGING_ENABLED", "").lower() == "true"
 # notset, debug, info, notice, warning, error, or critical
-LOG_LEVEL = os.environ.get("LOG_LEVEL", "notice")
+LOG_LEVEL = os.environ.get("LOG_LEVEL", "info")
 
 # Timeout for API-based embedding models
 # NOTE: does not apply for Google VertexAI, since the python client doesn't

backend/tests/daily/connectors/confluence/test_confluence_permissions_basic.py

@@ -0,0 +1,39 @@
+import os
+
+import pytest
+
+from danswer.connectors.confluence.connector import ConfluenceConnector
+
+
+@pytest.fixture
+def confluence_connector() -> ConfluenceConnector:
+    connector = ConfluenceConnector(
+        wiki_base="https://danswerai.atlassian.net",
+        is_cloud=True,
+    )
+    connector.load_credentials(
+        {
+            "confluence_access_token": os.environ["CONFLUENCE_ACCESS_TOKEN"],
+            "confluence_username": os.environ["CONFLUENCE_USER_NAME"],
+        }
+    )
+    return connector
+
+
+# This should never fail because even if the docs in the cloud change,
+# the full doc ids retrieved should always be a subset of the slim doc ids
+def test_confluence_connector_permissions(
+    confluence_connector: ConfluenceConnector,
+) -> None:
+    # Get all doc IDs from the full connector
+    all_full_doc_ids = set()
+    for doc_batch in confluence_connector.load_from_state():
+        all_full_doc_ids.update([doc.id for doc in doc_batch])
+
+    # Get all doc IDs from the slim connector
+    all_slim_doc_ids = set()
+    for slim_doc_batch in confluence_connector.retrieve_all_slim_documents():
+        all_slim_doc_ids.update([doc.id for doc in slim_doc_batch])
+
+    # The set of full doc IDs should be always be a subset of the slim doc IDs
+    assert all_full_doc_ids.issubset(all_slim_doc_ids)

backend/tests/unit/danswer/chat/conftest.py

@@ -5,12 +5,12 @@ from unittest.mock import MagicMock
 import pytest
 from langchain_core.messages import SystemMessage
 
+from danswer.chat.models import AnswerStyleConfig
+from danswer.chat.models import CitationConfig
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PromptConfig
+from danswer.chat.prompt_builder.build import AnswerPromptBuilder
 from danswer.configs.constants import DocumentSource
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import CitationConfig
-from danswer.llm.answering.models import PromptConfig
-from danswer.llm.answering.prompts.build import AnswerPromptBuilder
 from danswer.llm.interfaces import LLMConfig
 from danswer.tools.models import ToolResponse
 from danswer.tools.tool_implementations.search.search_tool import SearchTool

backend/tests/unit/danswer/chat/stream_processing/test_citation_processing.py

@@ -5,11 +5,9 @@ import pytest
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import DanswerAnswerPiece
 from danswer.chat.models import LlmDoc
+from danswer.chat.stream_processing.citation_processing import CitationProcessor
+from danswer.chat.stream_processing.utils import DocumentIdOrderMapping
 from danswer.configs.constants import DocumentSource
-from danswer.llm.answering.stream_processing.citation_processing import (
-    CitationProcessor,
-)
-from danswer.llm.answering.stream_processing.utils import DocumentIdOrderMapping
 
 
 """

backend/tests/unit/danswer/chat/stream_processing/test_quotes_processing.py

@@ -2,14 +2,10 @@ import textwrap
 
 import pytest
 
+from danswer.chat.stream_processing.quotes_processing import match_quotes_to_docs
+from danswer.chat.stream_processing.quotes_processing import separate_answer_quotes
 from danswer.configs.constants import DocumentSource
 from danswer.context.search.models import InferenceChunk
-from danswer.llm.answering.stream_processing.quotes_processing import (
-    match_quotes_to_docs,
-)
-from danswer.llm.answering.stream_processing.quotes_processing import (
-    separate_answer_quotes,
-)
 
 
 def test_passed_in_quotes() -> None:

backend/tests/unit/danswer/chat/test_answer.py

@@ -11,21 +11,21 @@ from langchain_core.messages import SystemMessage
 from langchain_core.messages import ToolCall
 from langchain_core.messages import ToolCallChunk
 
+from danswer.chat.answer import Answer
+from danswer.chat.models import AnswerStyleConfig
 from danswer.chat.models import CitationInfo
 from danswer.chat.models import DanswerAnswerPiece
 from danswer.chat.models import LlmDoc
+from danswer.chat.models import PromptConfig
 from danswer.chat.models import StreamStopInfo
 from danswer.chat.models import StreamStopReason
-from danswer.llm.answering.answer import Answer
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import PromptConfig
 from danswer.llm.interfaces import LLM
 from danswer.tools.force import ForceUseTool
 from danswer.tools.models import ToolCallFinalResult
 from danswer.tools.models import ToolCallKickoff
 from danswer.tools.models import ToolResponse
-from tests.unit.danswer.llm.answering.conftest import DEFAULT_SEARCH_ARGS
-from tests.unit.danswer.llm.answering.conftest import QUERY
+from tests.unit.danswer.chat.conftest import DEFAULT_SEARCH_ARGS
+from tests.unit.danswer.chat.conftest import QUERY
 
 
 @pytest.fixture

backend/tests/unit/danswer/chat/test_prune_and_merge.py

@@ -1,9 +1,9 @@
 import pytest
 
+from danswer.chat.prune_and_merge import _merge_sections
 from danswer.configs.constants import DocumentSource
 from danswer.context.search.models import InferenceChunk
 from danswer.context.search.models import InferenceSection
-from danswer.llm.answering.prune_and_merge import _merge_sections
 
 
 # This large test accounts for all of the following:

backend/tests/unit/danswer/chat/test_skip_gen_ai.py

@@ -5,10 +5,10 @@ from unittest.mock import Mock
 import pytest
 from pytest_mock import MockerFixture
 
-from danswer.llm.answering.answer import Answer
-from danswer.llm.answering.answer import AnswerStream
-from danswer.llm.answering.models import AnswerStyleConfig
-from danswer.llm.answering.models import PromptConfig
+from danswer.chat.answer import Answer
+from danswer.chat.answer import AnswerStream
+from danswer.chat.models import AnswerStyleConfig
+from danswer.chat.models import PromptConfig
 from danswer.tools.force import ForceUseTool
 from danswer.tools.tool_implementations.search.search_tool import SearchTool
 from tests.regression.answer_quality.run_qa import _process_and_write_query_results

deployment/cloud_kubernetes/workers/beat.yaml

@@ -14,7 +14,7 @@ spec:
     spec:
       containers:
         - name: celery-beat
-          image: danswer/danswer-backend-cloud:v0.15.0-cloud.beta.0
+          image: danswer/danswer-backend-cloud:v0.14.0-cloud.beta.4
           imagePullPolicy: IfNotPresent
           command:
             [

deployment/cloud_kubernetes/workers/heavy_worker.yaml

@@ -14,7 +14,7 @@ spec:
     spec:
       containers:
         - name: celery-worker-heavy
-          image: danswer/danswer-backend-cloud:v0.15.0-cloud.beta.0
+          image: danswer/danswer-backend-cloud:v0.14.0-cloud.beta.4
           imagePullPolicy: IfNotPresent
           command:
             [

deployment/cloud_kubernetes/workers/indexing_worker.yaml

@@ -14,7 +14,7 @@ spec:
     spec:
       containers:
         - name: celery-worker-indexing
-          image: danswer/danswer-backend-cloud:v0.15.0-cloud.beta.0
+          image: danswer/danswer-backend-cloud:v0.14.0-cloud.beta.4
           imagePullPolicy: IfNotPresent
           command:
             [

deployment/cloud_kubernetes/workers/light_worker.yaml

@@ -14,7 +14,7 @@ spec:
     spec:
       containers:
         - name: celery-worker-light
-          image: danswer/danswer-backend-cloud:v0.15.0-cloud.beta.0
+          image: danswer/danswer-backend-cloud:v0.14.0-cloud.beta.4
           imagePullPolicy: IfNotPresent
           command:
             [

deployment/cloud_kubernetes/workers/primary.yaml

@@ -14,7 +14,7 @@ spec:
     spec:
       containers:
         - name: celery-worker-primary
-          image: danswer/danswer-backend-cloud:v0.15.0-cloud.beta.0
+          image: danswer/danswer-backend-cloud:v0.14.0-cloud.beta.4
           imagePullPolicy: IfNotPresent
           command:
             [

web/package-lock.json

@@ -73,7 +73,6 @@
       },
       "devDependencies": {
         "@chromatic-com/playwright": "^0.10.0",
-        "@playwright/test": "^1.49.0",
         "@tailwindcss/typography": "^0.5.10",
         "chromatic": "^11.18.1",
         "eslint": "^8.48.0",
@@ -2574,13 +2573,14 @@
       }
     },
     "node_modules/@playwright/test": {
-      "version": "1.49.0",
-      "resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.49.0.tgz",
-      "integrity": "sha512-DMulbwQURa8rNIQrf94+jPJQ4FmOVdpE5ZppRNvWVjvhC+6sOeo28r8MgIpQRYouXRtt/FCCXU7zn20jnHR4Qw==",
+      "version": "1.48.2",
+      "resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.48.2.tgz",
+      "integrity": "sha512-54w1xCWfXuax7dz4W2M9uw0gDyh+ti/0K/MxcCUxChFh37kkdxPdfZDw5QBbuPUJHr1CiHJ1hXgSs+GgeQc5Zw==",
       "devOptional": true,
       "license": "Apache-2.0",
+      "peer": true,
       "dependencies": {
-        "playwright": "1.49.0"
+        "playwright": "1.48.2"
       },
       "bin": {
         "playwright": "cli.js"
@@ -13329,13 +13329,14 @@
       }
     },
     "node_modules/playwright": {
-      "version": "1.49.0",
-      "resolved": "https://registry.npmjs.org/playwright/-/playwright-1.49.0.tgz",
-      "integrity": "sha512-eKpmys0UFDnfNb3vfsf8Vx2LEOtflgRebl0Im2eQQnYMA4Aqd+Zw8bEOB+7ZKvN76901mRnqdsiOGKxzVTbi7A==",
+      "version": "1.48.2",
+      "resolved": "https://registry.npmjs.org/playwright/-/playwright-1.48.2.tgz",
+      "integrity": "sha512-NjYvYgp4BPmiwfe31j4gHLa3J7bD2WiBz8Lk2RoSsmX38SVIARZ18VYjxLjAcDsAhA+F4iSEXTSGgjua0rrlgQ==",
       "devOptional": true,
       "license": "Apache-2.0",
+      "peer": true,
       "dependencies": {
-        "playwright-core": "1.49.0"
+        "playwright-core": "1.48.2"
       },
       "bin": {
         "playwright": "cli.js"
@@ -13348,11 +13349,12 @@
       }
     },
     "node_modules/playwright-core": {
-      "version": "1.49.0",
-      "resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.49.0.tgz",
-      "integrity": "sha512-R+3KKTQF3npy5GTiKH/T+kdhoJfJojjHESR1YEWhYuEKRVfVaxH3+4+GvXE5xyCngCxhxnykk0Vlah9v8fs3jA==",
+      "version": "1.48.2",
+      "resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.48.2.tgz",
+      "integrity": "sha512-sjjw+qrLFlriJo64du+EK0kJgZzoQPsabGF4lBvsid+3CNIZIYLgnMj9V6JY5VhM2Peh20DJWIVpVljLLnlawA==",
       "devOptional": true,
       "license": "Apache-2.0",
+      "peer": true,
       "bin": {
         "playwright-core": "cli.js"
       },
@@ -13371,6 +13373,7 @@
       "os": [
         "darwin"
       ],
+      "peer": true,
       "engines": {
         "node": "^8.16.0 || ^10.6.0 || >=11.0.0"
       }

web/package.json

@@ -75,7 +75,6 @@
   },
   "devDependencies": {
     "@chromatic-com/playwright": "^0.10.0",
-    "@playwright/test": "^1.49.0",
     "@tailwindcss/typography": "^0.5.10",
     "chromatic": "^11.18.1",
     "eslint": "^8.48.0",

web/src/app/admin/bots/SlackBotCreationForm.tsx

@@ -7,6 +7,7 @@ import { useState } from "react";
 import { SlackTokensForm } from "./SlackTokensForm";
 import { SourceIcon } from "@/components/SourceIcon";
 import { AdminPageTitle } from "@/components/admin/Title";
+import { ValidSources } from "@/lib/types";
 
 export const NewSlackBotForm = ({}: {}) => {
   const [formValues] = useState({
@@ -21,7 +22,7 @@ export const NewSlackBotForm = ({}: {}) => {
   return (
     <div>
       <AdminPageTitle
-        icon={<SourceIcon iconSize={36} sourceType={"slack"} />}
+        icon={<SourceIcon iconSize={36} sourceType={ValidSources.Slack} />}
         title="New Slack Bot"
       />
       <CardSection>

web/src/app/admin/bots/SlackBotUpdateForm.tsx

@@ -1,7 +1,7 @@
 "use client";
 
 import { usePopup } from "@/components/admin/connectors/Popup";
-import { SlackBot } from "@/lib/types";
+import { SlackBot, ValidSources } from "@/lib/types";
 import { useRouter } from "next/navigation";
 import { ChevronDown, ChevronRight } from "lucide-react";
 import { useState, useEffect, useRef } from "react";
@@ -78,7 +78,7 @@ export const ExistingSlackBotForm = ({
       <div className="flex items-center justify-between h-14">
         <div className="flex items-center gap-2">
           <div className="my-auto">
-            <SourceIcon iconSize={32} sourceType={"slack"} />
+            <SourceIcon iconSize={32} sourceType={ValidSources.Slack} />
           </div>
           <div className="ml-1">
             <EditableStringFieldDisplay

web/src/app/admin/bots/[bot-id]/channels/SlackChannelConfigCreationForm.tsx

@@ -276,13 +276,7 @@ export const SlackChannelConfigCreationForm = ({
 
                 {showAdvancedOptions && (
                   <div className="mt-4">
-                    <BooleanFormField
-                      name="show_continue_in_web_ui"
-                      removeIndent
-                      label="Show Continue in Web UI button"
-                      tooltip="If set, will show a button at the bottom of the response that allows the user to continue the conversation in the Danswer Web UI"
-                    />
-                    <div className="w-64 mb-4 mt-4">
+                    <div className="w-64 mb-4">
                       <SelectorFormField
                         name="response_type"
                         label="Answer Type"
@@ -294,6 +288,12 @@ export const SlackChannelConfigCreationForm = ({
                       />
                     </div>
 
+                    <BooleanFormField
+                      name="show_continue_in_web_ui"
+                      removeIndent
+                      label="Show Continue in Web UI button"
+                      tooltip="If set, will show a button at the bottom of the response that allows the user to continue the conversation in the Danswer Web UI"
+                    />
                     <div className="flex flex-col space-y-3 mt-2">
                       <BooleanFormField
                         name="still_need_help_enabled"
@@ -325,8 +325,8 @@ export const SlackChannelConfigCreationForm = ({
                       <BooleanFormField
                         name="answer_validity_check_enabled"
                         removeIndent
-                        label="Hide Non-Answers"
-                        tooltip="If set, will only answer questions that the model determines it can answer"
+                        label="Only respond if citations found"
+                        tooltip="If set, will only answer questions where the model successfully produces citations"
                       />
                       <BooleanFormField
                         name="questionmark_prefilter_enabled"

web/src/app/admin/bots/[bot-id]/channels/[id]/page.tsx

@@ -3,7 +3,7 @@ import { SourceIcon } from "@/components/SourceIcon";
 import { SlackChannelConfigCreationForm } from "../SlackChannelConfigCreationForm";
 import { fetchSS } from "@/lib/utilsSS";
 import { ErrorCallout } from "@/components/ErrorCallout";
-import { DocumentSet, SlackChannelConfig } from "@/lib/types";
+import { DocumentSet, SlackChannelConfig, ValidSources } from "@/lib/types";
 import { BackButton } from "@/components/BackButton";
 import { InstantSSRAutoRefresh } from "@/components/SSRAutoRefresh";
 import {
@@ -84,7 +84,7 @@ async function EditslackChannelConfigPage(props: {
 
       <BackButton />
       <AdminPageTitle
-        icon={<SourceIcon sourceType={"slack"} iconSize={32} />}
+        icon={<SourceIcon sourceType={ValidSources.Slack} iconSize={32} />}
         title="Edit Slack Channel Config"
       />
 

web/src/app/admin/bots/[bot-id]/channels/new/page.tsx

@@ -2,7 +2,7 @@ import { AdminPageTitle } from "@/components/admin/Title";
 import { SlackChannelConfigCreationForm } from "../SlackChannelConfigCreationForm";
 import { fetchSS } from "@/lib/utilsSS";
 import { ErrorCallout } from "@/components/ErrorCallout";
-import { DocumentSet } from "@/lib/types";
+import { DocumentSet, ValidSources } from "@/lib/types";
 import { BackButton } from "@/components/BackButton";
 import { fetchAssistantsSS } from "@/lib/assistants/fetchAssistantsSS";
 import {
@@ -59,7 +59,7 @@ async function NewChannelConfigPage(props: {
     <div className="container mx-auto">
       <BackButton />
       <AdminPageTitle
-        icon={<SourceIcon iconSize={32} sourceType={"slack"} />}
+        icon={<SourceIcon iconSize={32} sourceType={ValidSources.Slack} />}
         title="Configure DanswerBot for Slack Channel"
       />
 

web/src/app/admin/bots/page.tsx

@@ -10,6 +10,7 @@ import Link from "next/link";
 import { SourceIcon } from "@/components/SourceIcon";
 import { SlackBotTable } from "./SlackBotTable";
 import { useSlackBots } from "./[bot-id]/hooks";
+import { ValidSources } from "@/lib/types";
 
 const Main = () => {
   const {
@@ -103,7 +104,7 @@ const Page = () => {
   return (
     <div className="container mx-auto">
       <AdminPageTitle
-        icon={<SourceIcon iconSize={36} sourceType={"slack"} />}
+        icon={<SourceIcon iconSize={36} sourceType={ValidSources.Slack} />}
         title="Slack Bots"
       />
       <InstantSSRAutoRefresh />

web/src/app/admin/connector/[ccPairId]/ConfigDisplay.tsx

@@ -25,13 +25,13 @@ function buildConfigEntries(
   obj: any,
   sourceType: ValidSources
 ): { [key: string]: string } {
-  if (sourceType === "file") {
+  if (sourceType === ValidSources.File) {
     return obj.file_locations
       ? {
           file_names: obj.file_locations.map(getNameFromPath),
         }
       : {};
-  } else if (sourceType === "google_sites") {
+  } else if (sourceType === ValidSources.GoogleSites) {
     return {
       base_url: obj.base_url,
     };

web/src/app/admin/connector/[ccPairId]/page.tsx

@@ -30,7 +30,7 @@ import { Button } from "@/components/ui/button";
 // since the uploaded files are cleaned up after some period of time
 // re-indexing will not work for the file connector. Also, it would not
 // make sense to re-index, since the files will not have changed.
-const CONNECTOR_TYPES_THAT_CANT_REINDEX: ValidSources[] = ["file"];
+const CONNECTOR_TYPES_THAT_CANT_REINDEX: ValidSources[] = [ValidSources.File];
 
 function Main({ ccPairId }: { ccPairId: number }) {
   const router = useRouter(); // Initialize the router

web/src/app/admin/connectors/[connector]/pages/utils/files.ts

@@ -2,7 +2,7 @@ import { PopupSpec } from "@/components/admin/connectors/Popup";
 import { createConnector, runConnector } from "@/lib/connector";
 import { createCredential, linkCredential } from "@/lib/credential";
 import { FileConfig } from "@/lib/connectors/connectors";
-import { AccessType } from "@/lib/types";
+import { AccessType, ValidSources } from "@/lib/types";
 
 export const submitFiles = async (
   selectedFiles: File[],
@@ -34,7 +34,7 @@ export const submitFiles = async (
 
   const [connectorErrorMsg, connector] = await createConnector<FileConfig>({
     name: "FileConnector-" + Date.now(),
-    source: "file",
+    source: ValidSources.File,
     input_type: "load_state",
     connector_specific_config: {
       file_locations: filePaths,
@@ -60,7 +60,7 @@ export const submitFiles = async (
   const createCredentialResponse = await createCredential({
     credential_json: {},
     admin_public: true,
-    source: "file",
+    source: ValidSources.File,
     curator_public: true,
     groups: groups,
     name,

web/src/app/admin/connectors/[connector]/pages/utils/google_site.ts

@@ -2,6 +2,7 @@ import { PopupSpec } from "@/components/admin/connectors/Popup";
 import { createConnector, runConnector } from "@/lib/connector";
 import { linkCredential } from "@/lib/credential";
 import { GoogleSitesConfig } from "@/lib/connectors/connectors";
+import { ValidSources } from "@/lib/types";
 
 export const submitGoogleSite = async (
   selectedFiles: File[],
@@ -38,7 +39,7 @@ export const submitGoogleSite = async (
     const [connectorErrorMsg, connector] =
       await createConnector<GoogleSitesConfig>({
         name: name ? name : `GoogleSitesConnector-${base_url}`,
-        source: "google_sites",
+        source: ValidSources.GoogleSites,
         input_type: "load_state",
         connector_specific_config: {
           base_url: base_url,

web/src/app/admin/indexing/status/CCPairIndexingStatusTable.tsx

@@ -384,7 +384,7 @@ export function CCPairIndexingStatusTable({
             last_status: "success",
             connector: {
               name: "Sample File Connector",
-              source: "file",
+              source: ValidSources.File,
               input_type: "poll",
               connector_specific_config: {
                 file_locations: ["/path/to/sample/file.txt"],
@@ -401,7 +401,7 @@ export function CCPairIndexingStatusTable({
             credential: {
               id: 1,
               name: "Sample Credential",
-              source: "file",
+              source: ValidSources.File,
               user_id: "1",
               time_created: "2023-07-01T12:00:00Z",
               time_updated: "2023-07-01T12:00:00Z",

web/src/app/chat/ChatPage.tsx

@@ -982,7 +982,7 @@ export function ChatPage({
     ) {
       setDocumentSidebarToggled(false);
     }
-  }, [selectedDocuments, filtersToggled]);
+  }, [chatSessionIdRef.current]);
 
   useEffect(() => {
     adjustDocumentSidebarWidth(); // Adjust the width on initial render
@@ -1610,14 +1610,14 @@ export function ChatPage({
       }
     });
   };
-  const [showDocSidebar, setShowDocSidebar] = useState(false); // State to track if sidebar is open
+  const [showHistorySidebar, setShowHistorySidebar] = useState(false); // State to track if sidebar is open
 
   // 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);
+    setShowHistorySidebar(false);
 
     setUntoggled(true);
     setTimeout(() => {
@@ -1636,7 +1636,7 @@ export function ChatPage({
     toggle();
   };
   const removeToggle = () => {
-    setShowDocSidebar(false);
+    setShowHistorySidebar(false);
     toggle(false);
   };
 
@@ -1646,8 +1646,8 @@ export function ChatPage({
   useSidebarVisibility({
     toggledSidebar,
     sidebarElementRef,
-    showDocSidebar,
-    setShowDocSidebar,
+    showDocSidebar: showHistorySidebar,
+    setShowDocSidebar: setShowHistorySidebar,
     setToggled: removeToggle,
     mobile: settings?.isMobile,
   });
@@ -1923,6 +1923,7 @@ export function ChatPage({
   interface RegenerationRequest {
     messageId: number;
     parentMessage: Message;
+    forceSearch?: boolean;
   }
 
   function createRegenerator(regenerationRequest: RegenerationRequest) {
@@ -1932,6 +1933,7 @@ export function ChatPage({
         modelOverRide,
         messageIdToResend: regenerationRequest.parentMessage.messageId,
         regenerationRequest,
+        forceSearch: regenerationRequest.forceSearch,
       });
     };
   }
@@ -2098,7 +2100,7 @@ export function ChatPage({
                 duration-300
                 ease-in-out
                 ${
-                  !untoggled && (showDocSidebar || toggledSidebar)
+                  !untoggled && (showHistorySidebar || toggledSidebar)
                     ? "opacity-100 w-[250px] translate-x-0"
                     : "opacity-0 w-[200px] pointer-events-none -translate-x-10"
                 }`}
@@ -2112,7 +2114,7 @@ export function ChatPage({
                   ref={innerSidebarElementRef}
                   toggleSidebar={toggleSidebar}
                   toggled={toggledSidebar && !settings?.isMobile}
-                  backgroundToggled={toggledSidebar || showDocSidebar}
+                  backgroundToggled={toggledSidebar || showHistorySidebar}
                   existingChats={chatSessions}
                   currentChatSession={selectedChatSession}
                   folders={folders}
@@ -2171,7 +2173,7 @@ export function ChatPage({
           )}
 
           <BlurBackground
-            visible={!untoggled && (showDocSidebar || toggledSidebar)}
+            visible={!untoggled && (showHistorySidebar || toggledSidebar)}
           />
 
           <div
@@ -2592,13 +2594,11 @@ export function ChatPage({
                                           previousMessage &&
                                           previousMessage.messageId
                                         ) {
-                                          onSubmit({
-                                            messageIdToResend:
-                                              previousMessage.messageId,
+                                          createRegenerator({
+                                            messageId: message.messageId,
+                                            parentMessage: parentMessage!,
                                             forceSearch: true,
-                                            alternativeAssistantOverride:
-                                              currentAlternativeAssistant,
-                                          });
+                                          })(llmOverrideManager.llmOverride);
                                         } else {
                                           setPopup({
                                             type: "error",
@@ -2831,7 +2831,7 @@ export function ChatPage({
               )}
             </div>
           </div>
-          <FixedLogo backgroundToggled={toggledSidebar || showDocSidebar} />
+          <FixedLogo backgroundToggled={toggledSidebar || showHistorySidebar} />
         </div>
         {/* Right Sidebar - DocumentSidebar */}
       </div>

web/src/app/chat/documentSidebar/ChatDocumentDisplay.tsx

@@ -7,6 +7,7 @@ import { DocumentUpdatedAtBadge } from "@/components/search/DocumentUpdatedAtBad
 import { MetadataBadge } from "@/components/MetadataBadge";
 import { WebResultIcon } from "@/components/WebResultIcon";
 import { Dispatch, SetStateAction } from "react";
+import { ValidSources } from "@/lib/types";
 
 interface DocumentDisplayProps {
   closeSidebar: () => void;
@@ -73,19 +74,15 @@ export function ChatDocumentDisplay({
   }
 
   const handleViewFile = async () => {
-    if (document.link) {
+    if (document.source_type == ValidSources.File && setPresentingDocument) {
+      setPresentingDocument(document);
+    } else if (document.link) {
       window.open(document.link, "_blank");
-    } else {
-      closeSidebar();
-
-      setTimeout(async () => {
-        setPresentingDocument(document);
-      }, 100);
     }
   };
 
   return (
-    <div className={`opacity-100   ${modal ? "w-[90vw]" : "w-full"}`}>
+    <div className={`opacity-100 ${modal ? "w-[90vw]" : "w-full"}`}>
       <div
         className={`flex relative flex-col gap-0.5  rounded-xl mx-2 my-1 ${
           isSelected ? "bg-gray-200" : "hover:bg-background-125"

web/src/app/chat/documentSidebar/DocumentSelector.tsx

@@ -12,7 +12,8 @@ export function DocumentSelector({
 }) {
   const [popupDisabled, setPopupDisabled] = useState(false);
 
-  function onClick() {
+  function onClick(e: React.MouseEvent<HTMLInputElement>) {
+    e.stopPropagation();
     if (!isDisabled) {
       setPopupDisabled(true);
       handleSelect();

web/src/app/chat/message/Messages.tsx

@@ -468,7 +468,13 @@ export const AIMessage = ({
                               docs
                                 .slice(0, 2)
                                 .map((doc, ind) => (
-                                  <SourceCard doc={doc} key={ind} />
+                                  <SourceCard
+                                    doc={doc}
+                                    key={ind}
+                                    setPresentingDocument={
+                                      setPresentingDocument
+                                    }
+                                  />
                                 ))}
                             <SeeMoreBlock
                               documentSelectionToggled={

web/src/app/chat/message/SkippedSearch.tsx

@@ -1,26 +1,6 @@
 import { EmphasizedClickable } from "@/components/BasicClickable";
 import { FiBook } from "react-icons/fi";
 
-function ForceSearchButton({
-  messageId,
-  handleShowRetrieved,
-}: {
-  messageId: number | null;
-  isCurrentlyShowingRetrieved: boolean;
-  handleShowRetrieved: (messageId: number | null) => void;
-}) {
-  return (
-    <div
-      className="ml-auto my-auto"
-      onClick={() => handleShowRetrieved(messageId)}
-    >
-      <EmphasizedClickable>
-        <div className="w-24 text-xs">Force Search</div>
-      </EmphasizedClickable>
-    </div>
-  );
-}
-
 export function SkippedSearch({
   handleForceSearch,
 }: {

web/src/app/ee/admin/performance/lib.ts

@@ -97,3 +97,69 @@ export function getDatesList(startDate: Date): string[] {
 
   return datesList;
 }
+
+export interface PersonaMessageAnalytics {
+  total_messages: number;
+  date: string;
+  persona_id: number;
+}
+
+export interface PersonaSnapshot {
+  id: number;
+  name: string;
+  description: string;
+  is_visible: boolean;
+  is_public: boolean;
+}
+
+export const usePersonaMessages = (
+  personaId: number | undefined,
+  timeRange: DateRangePickerValue
+) => {
+  const url = buildApiPath(`/api/analytics/admin/persona/messages`, {
+    persona_id: personaId?.toString(),
+    start: convertDateToStartOfDay(timeRange.from)?.toISOString(),
+    end: convertDateToEndOfDay(timeRange.to)?.toISOString(),
+  });
+
+  const { data, error, isLoading } = useSWR<PersonaMessageAnalytics[]>(
+    personaId !== undefined ? url : null,
+    errorHandlingFetcher
+  );
+
+  return {
+    data,
+    error,
+    isLoading,
+    refreshPersonaMessages: () => mutate(url),
+  };
+};
+
+export interface PersonaUniqueUserAnalytics {
+  unique_users: number;
+  date: string;
+  persona_id: number;
+}
+
+export const usePersonaUniqueUsers = (
+  personaId: number | undefined,
+  timeRange: DateRangePickerValue
+) => {
+  const url = buildApiPath(`/api/analytics/admin/persona/unique-users`, {
+    persona_id: personaId?.toString(),
+    start: convertDateToStartOfDay(timeRange.from)?.toISOString(),
+    end: convertDateToEndOfDay(timeRange.to)?.toISOString(),
+  });
+
+  const { data, error, isLoading } = useSWR<PersonaUniqueUserAnalytics[]>(
+    personaId !== undefined ? url : null,
+    errorHandlingFetcher
+  );
+
+  return {
+    data,
+    error,
+    isLoading,
+    refreshPersonaUniqueUsers: () => mutate(url),
+  };
+};

web/src/app/ee/admin/performance/usage/PersonaMessagesChart.tsx

@@ -0,0 +1,231 @@
+import { ThreeDotsLoader } from "@/components/Loading";
+import { X, Search } from "lucide-react";
+import {
+  getDatesList,
+  usePersonaMessages,
+  usePersonaUniqueUsers,
+} from "../lib";
+import { useAssistants } from "@/components/context/AssistantsContext";
+import { DateRangePickerValue } from "@/app/ee/admin/performance/DateRangeSelector";
+import Text from "@/components/ui/text";
+import Title from "@/components/ui/title";
+import CardSection from "@/components/admin/CardSection";
+import { AreaChartDisplay } from "@/components/ui/areaChart";
+import {
+  Select,
+  SelectContent,
+  SelectItem,
+  SelectTrigger,
+  SelectValue,
+} from "@/components/ui/select";
+import { useState, useMemo, useEffect } from "react";
+
+export function PersonaMessagesChart({
+  timeRange,
+}: {
+  timeRange: DateRangePickerValue;
+}) {
+  const [selectedPersonaId, setSelectedPersonaId] = useState<
+    number | undefined
+  >(undefined);
+  const [searchQuery, setSearchQuery] = useState("");
+  const [highlightedIndex, setHighlightedIndex] = useState(-1);
+  const { allAssistants: personaList } = useAssistants();
+
+  const {
+    data: personaMessagesData,
+    isLoading: isPersonaMessagesLoading,
+    error: personaMessagesError,
+  } = usePersonaMessages(selectedPersonaId, timeRange);
+
+  const {
+    data: personaUniqueUsersData,
+    isLoading: isPersonaUniqueUsersLoading,
+    error: personaUniqueUsersError,
+  } = usePersonaUniqueUsers(selectedPersonaId, timeRange);
+
+  const isLoading = isPersonaMessagesLoading || isPersonaUniqueUsersLoading;
+  const hasError = personaMessagesError || personaUniqueUsersError;
+
+  const filteredPersonaList = useMemo(() => {
+    if (!personaList) return [];
+    return personaList.filter((persona) =>
+      persona.name.toLowerCase().includes(searchQuery.toLowerCase())
+    );
+  }, [personaList, searchQuery]);
+
+  const handleKeyDown = (e: React.KeyboardEvent) => {
+    e.stopPropagation();
+
+    switch (e.key) {
+      case "ArrowDown":
+        e.preventDefault();
+        setHighlightedIndex((prev) =>
+          prev < filteredPersonaList.length - 1 ? prev + 1 : prev
+        );
+        break;
+      case "ArrowUp":
+        e.preventDefault();
+        setHighlightedIndex((prev) => (prev > 0 ? prev - 1 : prev));
+        break;
+      case "Enter":
+        if (
+          highlightedIndex >= 0 &&
+          highlightedIndex < filteredPersonaList.length
+        ) {
+          setSelectedPersonaId(filteredPersonaList[highlightedIndex].id);
+          setSearchQuery("");
+          setHighlightedIndex(-1);
+        }
+        break;
+      case "Escape":
+        setSearchQuery("");
+        setHighlightedIndex(-1);
+        break;
+    }
+  };
+
+  // Reset highlight when search query changes
+  useEffect(() => {
+    setHighlightedIndex(-1);
+  }, [searchQuery]);
+
+  const chartData = useMemo(() => {
+    if (
+      !personaMessagesData?.length ||
+      !personaUniqueUsersData?.length ||
+      selectedPersonaId === undefined
+    ) {
+      return null;
+    }
+
+    const initialDate =
+      timeRange.from ||
+      new Date(
+        Math.min(
+          ...personaMessagesData.map((entry) => new Date(entry.date).getTime())
+        )
+      );
+    const dateRange = getDatesList(initialDate);
+
+    // Create maps for messages and unique users data
+    const messagesMap = new Map(
+      personaMessagesData.map((entry) => [entry.date, entry])
+    );
+    const uniqueUsersMap = new Map(
+      personaUniqueUsersData.map((entry) => [entry.date, entry])
+    );
+
+    return dateRange.map((dateStr) => {
+      const messageData = messagesMap.get(dateStr);
+      const uniqueUserData = uniqueUsersMap.get(dateStr);
+      return {
+        Day: dateStr,
+        Messages: messageData?.total_messages || 0,
+        "Unique Users": uniqueUserData?.unique_users || 0,
+      };
+    });
+  }, [
+    personaMessagesData,
+    personaUniqueUsersData,
+    timeRange.from,
+    selectedPersonaId,
+  ]);
+
+  let content;
+  if (isLoading) {
+    content = (
+      <div className="h-80 flex flex-col">
+        <ThreeDotsLoader />
+      </div>
+    );
+  } else if (!personaList || hasError) {
+    content = (
+      <div className="h-80 text-red-600 text-bold flex flex-col">
+        <p className="m-auto">Failed to fetch data...</p>
+      </div>
+    );
+  } else if (selectedPersonaId === undefined) {
+    content = (
+      <div className="h-80 text-gray-500 flex flex-col">
+        <p className="m-auto">Select a persona to view analytics</p>
+      </div>
+    );
+  } else if (!personaMessagesData?.length) {
+    content = (
+      <div className="h-80 text-gray-500 flex flex-col">
+        <p className="m-auto">
+          No data found for selected persona in the selected time range
+        </p>
+      </div>
+    );
+  } else if (chartData) {
+    content = (
+      <AreaChartDisplay
+        className="mt-4"
+        data={chartData}
+        categories={["Messages", "Unique Users"]}
+        index="Day"
+        colors={["indigo", "fuchsia"]}
+        yAxisWidth={60}
+      />
+    );
+  }
+
+  const selectedPersona = personaList?.find((p) => p.id === selectedPersonaId);
+
+  return (
+    <CardSection className="mt-8">
+      <Title>Persona Analytics</Title>
+      <div className="flex flex-col gap-4">
+        <Text>Messages and unique users per day for selected persona</Text>
+        <div className="flex items-center gap-4">
+          <Select
+            value={selectedPersonaId?.toString() ?? ""}
+            onValueChange={(value) => {
+              setSelectedPersonaId(parseInt(value));
+            }}
+          >
+            <SelectTrigger className="flex w-full max-w-xs">
+              <SelectValue placeholder="Select a persona to display" />
+            </SelectTrigger>
+            <SelectContent>
+              <div className="flex items-center px-2 pb-2 sticky top-0 bg-background border-b">
+                <Search className="h-4 w-4 mr-2 shrink-0 opacity-50" />
+                <input
+                  className="flex h-8 w-full rounded-sm bg-transparent py-3 text-sm outline-none placeholder:text-muted-foreground disabled:cursor-not-allowed disabled:opacity-50"
+                  placeholder="Search personas..."
+                  value={searchQuery}
+                  onChange={(e) => setSearchQuery(e.target.value)}
+                  onClick={(e) => e.stopPropagation()}
+                  onMouseDown={(e) => e.stopPropagation()}
+                  onKeyDown={handleKeyDown}
+                />
+                {searchQuery && (
+                  <X
+                    className="h-4 w-4 shrink-0 opacity-50 cursor-pointer hover:opacity-100"
+                    onClick={() => {
+                      setSearchQuery("");
+                      setHighlightedIndex(-1);
+                    }}
+                  />
+                )}
+              </div>
+              {filteredPersonaList.map((persona, index) => (
+                <SelectItem
+                  key={persona.id}
+                  value={persona.id.toString()}
+                  className={`${highlightedIndex === index ? "hover" : ""}`}
+                  onMouseEnter={() => setHighlightedIndex(index)}
+                >
+                  {persona.name}
+                </SelectItem>
+              ))}
+            </SelectContent>
+          </Select>
+        </div>
+      </div>
+      {content}
+    </CardSection>
+  );
+}

web/src/app/ee/admin/performance/usage/page.tsx

@@ -4,6 +4,7 @@ import { DateRangeSelector } from "../DateRangeSelector";
 import { DanswerBotChart } from "./DanswerBotChart";
 import { FeedbackChart } from "./FeedbackChart";
 import { QueryPerformanceChart } from "./QueryPerformanceChart";
+import { PersonaMessagesChart } from "./PersonaMessagesChart";
 import { useTimeRange } from "../lib";
 import { AdminPageTitle } from "@/components/admin/Title";
 import { FiActivity } from "react-icons/fi";
@@ -26,6 +27,7 @@ export default function AnalyticsPage() {
       <QueryPerformanceChart timeRange={timeRange} />
       <FeedbackChart timeRange={timeRange} />
       <DanswerBotChart timeRange={timeRange} />
+      <PersonaMessagesChart timeRange={timeRange} />
       <Separator />
       <UsageReports />
     </main>

web/src/app/not-found.tsx

@@ -0,0 +1,5 @@
+import { redirect } from "next/navigation";
+
+export default function NotFound() {
+  redirect("/chat");
+}

web/src/components/SearchResultIcon.tsx

@@ -1,6 +1,7 @@
 import { useState, useEffect } from "react";
 import faviconFetch from "favicon-fetch";
 import { SourceIcon } from "./SourceIcon";
+import { ValidSources } from "@/lib/types";
 
 const CACHE_DURATION = 24 * 60 * 60 * 1000;
 
@@ -45,7 +46,7 @@ export function SearchResultIcon({ url }: { url: string }) {
   }, [url]);
 
   if (!faviconUrl) {
-    return <SourceIcon sourceType="web" iconSize={18} />;
+    return <SourceIcon sourceType={ValidSources.Web} iconSize={18} />;
   }
 
   return (

web/src/components/WebResultIcon.tsx

@@ -1,3 +1,4 @@
+import { ValidSources } from "@/lib/types";
 import { SourceIcon } from "./SourceIcon";
 
 export function WebResultIcon({ url }: { url: string }) {
@@ -11,6 +12,6 @@ export function WebResultIcon({ url }: { url: string }) {
       width={18}
     />
   ) : (
-    <SourceIcon sourceType="web" iconSize={18} />
+    <SourceIcon sourceType={ValidSources.Web} iconSize={18} />
   );
 }

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

@@ -1,7 +1,7 @@
 import { DefaultDropdown } from "@/components/Dropdown";
 import {
   AccessType,
-  ValidAutoSyncSources,
+  ValidAutoSyncSource,
   ConfigurableSources,
   validAutoSyncSources,
 } from "@/lib/types";
@@ -13,8 +13,8 @@ import { useEffect } from "react";
 
 function isValidAutoSyncSource(
   value: ConfigurableSources
-): value is ValidAutoSyncSources {
-  return validAutoSyncSources.includes(value as ValidAutoSyncSources);
+): value is ValidAutoSyncSource {
+  return validAutoSyncSources.includes(value as ValidAutoSyncSource);
 }
 
 export function AccessTypeForm({
@@ -92,9 +92,7 @@ export function AccessTypeForm({
           />
 
           {access_type.value === "sync" && isAutoSyncSupported && (
-            <AutoSyncOptions
-              connectorType={connector as ValidAutoSyncSources}
-            />
+            <AutoSyncOptions connectorType={connector as ValidAutoSyncSource} />
           )}
         </>
       )}

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

@@ -8,7 +8,7 @@ import { UserGroup, UserRole } from "@/lib/types";
 import { useUserGroups } from "@/lib/hooks";
 import {
   AccessType,
-  ValidAutoSyncSources,
+  ValidAutoSyncSource,
   ConfigurableSources,
   validAutoSyncSources,
 } from "@/lib/types";
@@ -16,8 +16,8 @@ import { useUser } from "@/components/user/UserProvider";
 
 function isValidAutoSyncSource(
   value: ConfigurableSources
-): value is ValidAutoSyncSources {
-  return validAutoSyncSources.includes(value as ValidAutoSyncSources);
+): value is ValidAutoSyncSource {
+  return validAutoSyncSources.includes(value as ValidAutoSyncSource);
 }
 
 // This should be included for all forms that require groups / public access

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

@@ -1,12 +1,12 @@
 import { TextFormField } from "@/components/admin/connectors/Field";
-import { ValidAutoSyncSources } from "@/lib/types";
+import { ValidAutoSyncSource } from "@/lib/types";
 import { Separator } from "@/components/ui/separator";
 import { autoSyncConfigBySource } from "@/lib/connectors/AutoSyncOptionFields";
 
 export function AutoSyncOptions({
   connectorType,
 }: {
-  connectorType: ValidAutoSyncSources;
+  connectorType: ValidAutoSyncSource;
 }) {
   const autoSyncConfig = autoSyncConfigBySource[connectorType];
 

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

@@ -109,7 +109,7 @@ export function ToolTipDetails({
   return (
     <TooltipProvider>
       <Tooltip>
-        <TooltipTrigger>
+        <TooltipTrigger type="button">
           <FiInfo size={12} />
         </TooltipTrigger>
         <TooltipContent side="top" align="center">

web/src/components/chat_search/AssistantSelector.tsx

@@ -1,4 +1,10 @@
-import React, { useState, useRef, useCallback, useEffect } from "react";
+import React, {
+  useState,
+  useRef,
+  useCallback,
+  useEffect,
+  useContext,
+} from "react";
 import { useAssistants } from "@/components/context/AssistantsContext";
 import { useChatContext } from "@/components/context/ChatContext";
 import { useUser } from "@/components/user/UserProvider";
@@ -35,6 +41,7 @@ import { restrictToVerticalAxis } from "@dnd-kit/modifiers";
 import { restrictToParentElement } from "@dnd-kit/modifiers";
 import { Drawer, DrawerContent, DrawerHeader, DrawerTitle } from "../ui/drawer";
 import { truncateString } from "@/lib/utils";
+import { SettingsContext } from "../settings/SettingsProvider";
 
 const AssistantSelector = ({
   liveAssistant,
@@ -96,6 +103,8 @@ const AssistantSelector = ({
     localStorage.setItem("assistantSelectorSelectedTab", index.toString());
   };
 
+  const settings = useContext(SettingsContext);
+
   // Get the user's default model
   const userDefaultModel = user?.preferences.default_model;
 
@@ -280,7 +289,13 @@ const AssistantSelector = ({
 
   return (
     <div className="pointer-events-auto relative" ref={dropdownRef}>
-      <div className="flex justify-center">
+      <div
+        className={
+          (settings?.enterpriseSettings?.two_lines_for_chat_header
+            ? "mt-20 "
+            : "mt-8") + " flex justify-center"
+        }
+      >
         <div
           onClick={() => {
             setIsOpen(!isOpen);

web/src/components/chat_search/Header.tsx

@@ -119,7 +119,6 @@ export default function FunctionalHeader({
               <FiShare2 size="18" />
             </div>
           )}
-
           <div className="mobile:hidden flex my-auto">
             <UserDropdown page={page} toggleUserSettings={toggleUserSettings} />
           </div>
@@ -137,11 +136,12 @@ export default function FunctionalHeader({
               <NewChatIcon size={20} />
             </div>
           </Link>
-          <div
-            style={{ transition: "width 0.30s ease-out" }}
-            className={`
-            mobile:hidden
-            flex-none 
+        </div>
+        <div
+          style={{ transition: "width 0.30s ease-out" }}
+          className={`
+            hidden
+            md:flex 
             mx-auto
             overflow-y-hidden 
             transition-all 
@@ -150,8 +150,7 @@ export default function FunctionalHeader({
             h-full
             ${documentSidebarToggled ? "w-[400px]" : "w-[0px]"}
             `}
-          />
-        </div>
+        />
 
         {page != "assistants" && (
           <div

web/src/components/chat_search/sources/SourceCard.tsx

@@ -2,15 +2,28 @@ import { WebResultIcon } from "@/components/WebResultIcon";
 import { SourceIcon } from "@/components/SourceIcon";
 import { DanswerDocument } from "@/lib/search/interfaces";
 import { truncateString } from "@/lib/utils";
+import { SetStateAction } from "react";
+import { Dispatch } from "react";
+import { ValidSources } from "@/lib/types";
 
-export default function SourceCard({ doc }: { doc: DanswerDocument }) {
+export default function SourceCard({
+  doc,
+  setPresentingDocument,
+}: {
+  doc: DanswerDocument;
+  setPresentingDocument?: (document: DanswerDocument) => void;
+}) {
   return (
-    <a
+    <div
       key={doc.document_id}
-      href={doc.link || undefined}
-      target="_blank"
-      rel="noopener"
-      className="flex flex-col gap-0.5 rounded-sm px-3 py-2.5 hover:bg-background-125 bg-background-100 w-[200px]"
+      onClick={() => {
+        if (doc.source_type == ValidSources.File && setPresentingDocument) {
+          setPresentingDocument(doc);
+        } else if (doc.link) {
+          window.open(doc.link, "_blank");
+        }
+      }}
+      className="cursor-pointer text-left overflow-hidden flex flex-col gap-0.5 rounded-sm px-3 py-2.5 hover:bg-background-125 bg-background-100 w-[200px]"
     >
       <div className="line-clamp-1 font-semibold text-ellipsis  text-text-900  flex h-6 items-center gap-2 text-sm">
         {doc.is_internet || doc.source_type === "web" ? (
@@ -24,7 +37,7 @@ export default function SourceCard({ doc }: { doc: DanswerDocument }) {
       <div className="line-clamp-2 text-sm font-normal leading-snug text-text-700">
         {doc.blurb}
       </div>
-    </a>
+    </div>
   );
 }
 

web/src/components/initialSetup/search/NoCompleteSourceModal.tsx

@@ -1,71 +0,0 @@
-"use client";
-
-import { Modal } from "../../Modal";
-import Link from "next/link";
-import { useEffect, useState } from "react";
-import { CCPairBasicInfo } from "@/lib/types";
-import { useRouter } from "next/navigation";
-
-export function NoCompleteSourcesModal({
-  ccPairs,
-}: {
-  ccPairs: CCPairBasicInfo[];
-}) {
-  const router = useRouter();
-  const [isHidden, setIsHidden] = useState(false);
-
-  useEffect(() => {
-    const interval = setInterval(() => {
-      router.refresh();
-    }, 5000);
-
-    return () => clearInterval(interval);
-  }, [router]);
-
-  if (isHidden) {
-    return null;
-  }
-
-  const totalDocs = ccPairs.reduce(
-    (acc, ccPair) => acc + ccPair.docs_indexed,
-    0
-  );
-
-  return (
-    <Modal
-      className="max-w-4xl"
-      title="⏳ None of your connectors have finished a full sync yet"
-      onOutsideClick={() => setIsHidden(true)}
-    >
-      <div className="text-sm">
-        <div>
-          <div>
-            You&apos;ve connected some sources, but none of them have finished
-            syncing. Depending on the size of the knowledge base(s) you&apos;ve
-            connected to Danswer, it can take anywhere between 30 seconds to a
-            few days for the initial sync to complete. So far we&apos;ve synced{" "}
-            <b>{totalDocs}</b> documents.
-            <br />
-            <br />
-            To view the status of your syncing connectors, head over to the{" "}
-            <Link className="text-link" href="admin/indexing/status">
-              Existing Connectors page
-            </Link>
-            .
-            <br />
-            <br />
-            <p
-              className="text-link cursor-pointer inline"
-              onClick={() => {
-                setIsHidden(true);
-              }}
-            >
-              Or, click here to continue and ask questions on the partially
-              synced knowledge set.
-            </p>
-          </div>
-        </div>
-      </div>
-    </Modal>
-  );
-}