New assistants api (#3097)

backend/alembic/versions/b156fa702355_chat_reworked.py

@@ -288,6 +288,15 @@ def upgrade() -> None:
 
 
 def downgrade() -> None:
+    # NOTE: you will lose all chat history. This is to satisfy the non-nullable constraints
+    # below
+    op.execute("DELETE FROM chat_feedback")
+    op.execute("DELETE FROM chat_message__search_doc")
+    op.execute("DELETE FROM document_retrieval_feedback")
+    op.execute("DELETE FROM document_retrieval_feedback")
+    op.execute("DELETE FROM chat_message")
+    op.execute("DELETE FROM chat_session")
+
     op.drop_constraint(
         "chat_feedback__chat_message_fk", "chat_feedback", type_="foreignkey"
     )

backend/alembic/versions/c99d76fcd298_add_nullable_to_persona_id_in_chat_.py

@@ -23,6 +23,56 @@ def upgrade() -> None:
 
 
 def downgrade() -> None:
+    # Delete chat messages and feedback first since they reference chat sessions
+    # Get chat messages from sessions with null persona_id
+    chat_messages_query = """
+        SELECT id
+        FROM chat_message
+        WHERE chat_session_id IN (
+            SELECT id
+            FROM chat_session
+            WHERE persona_id IS NULL
+        )
+    """
+
+    # Delete dependent records first
+    op.execute(
+        f"""
+        DELETE FROM document_retrieval_feedback
+        WHERE chat_message_id IN (
+            {chat_messages_query}
+        )
+    """
+    )
+    op.execute(
+        f"""
+        DELETE FROM chat_message__search_doc
+        WHERE chat_message_id IN (
+            {chat_messages_query}
+        )
+    """
+    )
+
+    # Delete chat messages
+    op.execute(
+        """
+        DELETE FROM chat_message
+        WHERE chat_session_id IN (
+            SELECT id
+            FROM chat_session
+            WHERE persona_id IS NULL
+        )
+    """
+    )
+
+    # Now we can safely delete the chat sessions
+    op.execute(
+        """
+        DELETE FROM chat_session
+        WHERE persona_id IS NULL
+    """
+    )
+
     op.alter_column(
         "chat_session",
         "persona_id",

backend/danswer/chat/process_message.py

@@ -19,16 +19,10 @@ from danswer.chat.models import MessageSpecificCitations
 from danswer.chat.models import QADocsResponse
 from danswer.chat.models import StreamingError
 from danswer.chat.models import StreamStopInfo
-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
-from danswer.configs.app_configs import AZURE_DALLE_DEPLOYMENT_NAME
-from danswer.configs.chat_configs import BING_API_KEY
 from danswer.configs.chat_configs import CHAT_TARGET_CHUNK_PERCENTAGE
 from danswer.configs.chat_configs import DISABLE_LLM_CHOOSE_SEARCH
 from danswer.configs.chat_configs import MAX_CHUNKS_FED_TO_CHAT
 from danswer.configs.constants import MessageType
-from danswer.configs.model_configs import GEN_AI_TEMPERATURE
 from danswer.db.chat import attach_files_to_chat_message
 from danswer.db.chat import create_db_search_doc
 from danswer.db.chat import create_new_chat_message
@@ -41,7 +35,6 @@ from danswer.db.chat import reserve_message_id
 from danswer.db.chat import translate_db_message_to_chat_message_detail
 from danswer.db.chat import translate_db_search_doc_to_server_search_doc
 from danswer.db.engine import get_session_context_manager
-from danswer.db.llm import fetch_existing_llm_providers
 from danswer.db.models import SearchDoc as DbSearchDoc
 from danswer.db.models import ToolCall
 from danswer.db.models import User
@@ -61,14 +54,13 @@ from danswer.llm.answering.models import PromptConfig
 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.interfaces import LLMConfig
 from danswer.llm.utils import litellm_exception_to_error_msg
 from danswer.natural_language_processing.utils import get_tokenizer
-from danswer.search.enums import LLMEvaluationType
 from danswer.search.enums import OptionalSearchSetting
 from danswer.search.enums import QueryFlow
 from danswer.search.enums import SearchType
 from danswer.search.models import InferenceSection
+from danswer.search.models import RetrievalDetails
 from danswer.search.retrieval.search_runner import inference_sections_from_ids
 from danswer.search.utils import chunks_or_sections_to_search_docs
 from danswer.search.utils import dedupe_documents
@@ -77,14 +69,14 @@ from danswer.search.utils import relevant_sections_to_indices
 from danswer.server.query_and_chat.models import ChatMessageDetail
 from danswer.server.query_and_chat.models import CreateChatMessageRequest
 from danswer.server.utils import get_json_line
-from danswer.tools.built_in_tools import get_built_in_tool_by_id
 from danswer.tools.force import ForceUseTool
-from danswer.tools.models import DynamicSchemaInfo
 from danswer.tools.models import ToolResponse
 from danswer.tools.tool import Tool
-from danswer.tools.tool_implementations.custom.custom_tool import (
+from danswer.tools.tool_constructor import construct_tools
-    build_custom_tools_from_openapi_schema_and_headers,
+from danswer.tools.tool_constructor import CustomToolConfig
-)
+from danswer.tools.tool_constructor import ImageGenerationToolConfig
+from danswer.tools.tool_constructor import InternetSearchToolConfig
+from danswer.tools.tool_constructor import SearchToolConfig
 from danswer.tools.tool_implementations.custom.custom_tool import (
     CUSTOM_TOOL_RESPONSE_ID,
 )
@@ -95,9 +87,6 @@ from danswer.tools.tool_implementations.images.image_generation_tool import (
 from danswer.tools.tool_implementations.images.image_generation_tool import (
     ImageGenerationResponse,
 )
-from danswer.tools.tool_implementations.images.image_generation_tool import (
-    ImageGenerationTool,
-)
 from danswer.tools.tool_implementations.internet_search.internet_search_tool import (
     INTERNET_SEARCH_RESPONSE_ID,
 )
@@ -122,9 +111,6 @@ from danswer.tools.tool_implementations.search.search_tool import (
     SECTION_RELEVANCE_LIST_ID,
 )
 from danswer.tools.tool_runner import ToolCallFinalResult
-from danswer.tools.utils import compute_all_tool_tokens
-from danswer.tools.utils import explicit_tool_calling_supported
-from danswer.utils.headers import header_dict_to_header_list
 from danswer.utils.logger import setup_logger
 from danswer.utils.timing import log_generator_function_time
 
@@ -295,7 +281,6 @@ def stream_chat_message_objects(
     max_document_percentage: float = CHAT_TARGET_CHUNK_PERCENTAGE,
     # if specified, uses the last user message and does not create a new user message based
     # on the `new_msg_req.message`. Currently, requires a state where the last message is a
-    use_existing_user_message: bool = False,
     litellm_additional_headers: dict[str, str] | None = None,
     custom_tool_additional_headers: dict[str, str] | None = None,
     is_connected: Callable[[], bool] | None = None,
@@ -307,6 +292,9 @@ 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
     """
+    use_existing_user_message = new_msg_req.use_existing_user_message
+    existing_assistant_message_id = new_msg_req.existing_assistant_message_id
+
     # Currently surrounding context is not supported for chat
     # Chat is already token heavy and harder for the model to process plus it would roll history over much faster
     new_msg_req.chunks_above = 0
@@ -428,12 +416,20 @@ def stream_chat_message_objects(
             final_msg, history_msgs = create_chat_chain(
                 chat_session_id=chat_session_id, db_session=db_session
             )
+            if existing_assistant_message_id is None:
                 if final_msg.message_type != MessageType.USER:
                     raise RuntimeError(
                         "The last message was not a user message. Cannot call "
                         "`stream_chat_message_objects` with `is_regenerate=True` "
                         "when the last message is not a user message."
                     )
+            else:
+                if final_msg.id != existing_assistant_message_id:
+                    raise RuntimeError(
+                        "The last message was not the existing assistant message. "
+                        f"Final message id: {final_msg.id}, "
+                        f"existing assistant message id: {existing_assistant_message_id}"
+                    )
 
         # Disable Query Rephrasing for the first message
         # This leads to a better first response since the LLM rephrasing the question
@@ -504,7 +500,12 @@ def stream_chat_message_objects(
                 ),
                 max_window_percentage=max_document_percentage,
             )
-        reserved_message_id = reserve_message_id(
+
+        # we don't need to reserve a message id if we're using an existing assistant message
+        reserved_message_id = (
+            final_msg.id
+            if existing_assistant_message_id is not None
+            else reserve_message_id(
                 db_session=db_session,
                 chat_session_id=chat_session_id,
                 parent_message=user_message.id
@@ -512,6 +513,7 @@ def stream_chat_message_objects(
                 else parent_message.id,
                 message_type=MessageType.ASSISTANT,
             )
+        )
         yield MessageResponseIDInfo(
             user_message_id=user_message.id if user_message else None,
             reserved_assistant_message_id=reserved_message_id,
@@ -525,7 +527,13 @@ def stream_chat_message_objects(
         partial_response = partial(
             create_new_chat_message,
             chat_session_id=chat_session_id,
-            parent_message=final_msg,
+            # if we're using an existing assistant message, then this will just be an
+            # update operation, in which case the parent should be the parent of
+            # the latest. If we're creating a new assistant message, then the parent
+            # should be the latest message (latest user message)
+            parent_message=(
+                final_msg if existing_assistant_message_id is None else parent_message
+            ),
             prompt_id=prompt_id,
             overridden_model=overridden_model,
             # message=,
@@ -537,6 +545,7 @@ def stream_chat_message_objects(
             # reference_docs=,
             db_session=db_session,
             commit=False,
+            reserved_message_id=reserved_message_id,
         )
 
         if not final_msg.prompt:
@@ -560,142 +569,39 @@ def stream_chat_message_objects(
             structured_response_format=new_msg_req.structured_response_format,
         )
 
-        # find out what tools to use
+        tool_dict = construct_tools(
-        search_tool: SearchTool | None = None
+            persona=persona,
-        tool_dict: dict[int, list[Tool]] = {}  # tool_id to tool
+            prompt_config=prompt_config,
-        for db_tool_model in persona.tools:
-            # handle in-code tools specially
-            if db_tool_model.in_code_tool_id:
-                tool_cls = get_built_in_tool_by_id(db_tool_model.id, db_session)
-                if tool_cls.__name__ == SearchTool.__name__ and not latest_query_files:
-                    search_tool = SearchTool(
             db_session=db_session,
             user=user,
-                        persona=persona,
-                        retrieval_options=retrieval_options,
-                        prompt_config=prompt_config,
             llm=llm,
             fast_llm=fast_llm,
-                        pruning_config=document_pruning_config,
+            search_tool_config=SearchToolConfig(
                 answer_style_config=answer_style_config,
+                document_pruning_config=document_pruning_config,
+                retrieval_options=retrieval_options or RetrievalDetails(),
                 selected_sections=selected_sections,
                 chunks_above=new_msg_req.chunks_above,
                 chunks_below=new_msg_req.chunks_below,
                 full_doc=new_msg_req.full_doc,
-                        evaluation_type=(
+                latest_query_files=latest_query_files,
-                            LLMEvaluationType.BASIC
-                            if persona.llm_relevance_filter
-                            else LLMEvaluationType.SKIP
             ),
-                    )
+            internet_search_tool_config=InternetSearchToolConfig(
-                    tool_dict[db_tool_model.id] = [search_tool]
-                elif tool_cls.__name__ == ImageGenerationTool.__name__:
-                    img_generation_llm_config: LLMConfig | None = None
-                    if (
-                        llm
-                        and llm.config.api_key
-                        and llm.config.model_provider == "openai"
-                    ):
-                        img_generation_llm_config = LLMConfig(
-                            model_provider=llm.config.model_provider,
-                            model_name="dall-e-3",
-                            temperature=GEN_AI_TEMPERATURE,
-                            api_key=llm.config.api_key,
-                            api_base=llm.config.api_base,
-                            api_version=llm.config.api_version,
-                        )
-                    elif (
-                        llm.config.model_provider == "azure"
-                        and AZURE_DALLE_API_KEY is not None
-                    ):
-                        img_generation_llm_config = LLMConfig(
-                            model_provider="azure",
-                            model_name=f"azure/{AZURE_DALLE_DEPLOYMENT_NAME}",
-                            temperature=GEN_AI_TEMPERATURE,
-                            api_key=AZURE_DALLE_API_KEY,
-                            api_base=AZURE_DALLE_API_BASE,
-                            api_version=AZURE_DALLE_API_VERSION,
-                        )
-                    else:
-                        llm_providers = fetch_existing_llm_providers(db_session)
-                        openai_provider = next(
-                            iter(
-                                [
-                                    llm_provider
-                                    for llm_provider in llm_providers
-                                    if llm_provider.provider == "openai"
-                                ]
-                            ),
-                            None,
-                        )
-                        if not openai_provider or not openai_provider.api_key:
-                            raise ValueError(
-                                "Image generation tool requires an OpenAI API key"
-                            )
-                        img_generation_llm_config = LLMConfig(
-                            model_provider=openai_provider.provider,
-                            model_name="dall-e-3",
-                            temperature=GEN_AI_TEMPERATURE,
-                            api_key=openai_provider.api_key,
-                            api_base=openai_provider.api_base,
-                            api_version=openai_provider.api_version,
-                        )
-                    tool_dict[db_tool_model.id] = [
-                        ImageGenerationTool(
-                            api_key=cast(str, img_generation_llm_config.api_key),
-                            api_base=img_generation_llm_config.api_base,
-                            api_version=img_generation_llm_config.api_version,
-                            additional_headers=litellm_additional_headers,
-                            model=img_generation_llm_config.model_name,
-                        )
-                    ]
-                elif tool_cls.__name__ == InternetSearchTool.__name__:
-                    bing_api_key = BING_API_KEY
-                    if not bing_api_key:
-                        raise ValueError(
-                            "Internet search tool requires a Bing API key, please contact your Danswer admin to get it added!"
-                        )
-                    tool_dict[db_tool_model.id] = [
-                        InternetSearchTool(
-                            api_key=bing_api_key,
                 answer_style_config=answer_style_config,
-                            prompt_config=prompt_config,
+            ),
-                        )
+            image_generation_tool_config=ImageGenerationToolConfig(
-                    ]
+                additional_headers=litellm_additional_headers,
-
+            ),
-                continue
+            custom_tool_config=CustomToolConfig(
-
-            # handle all custom tools
-            if db_tool_model.openapi_schema:
-                tool_dict[db_tool_model.id] = cast(
-                    list[Tool],
-                    build_custom_tools_from_openapi_schema_and_headers(
-                        db_tool_model.openapi_schema,
-                        dynamic_schema_info=DynamicSchemaInfo(
                 chat_session_id=chat_session_id,
                 message_id=user_message.id if user_message else None,
-                        ),
+                additional_headers=custom_tool_additional_headers,
-                        custom_headers=(db_tool_model.custom_headers or [])
-                        + (
-                            header_dict_to_header_list(
-                                custom_tool_additional_headers or {}
-                            )
-                        ),
             ),
         )
-
         tools: list[Tool] = []
         for tool_list in tool_dict.values():
             tools.extend(tool_list)
 
-        # factor in tool definition size when pruning
-        document_pruning_config.tool_num_tokens = compute_all_tool_tokens(
-            tools, llm_tokenizer
-        )
-        document_pruning_config.using_tool_message = explicit_tool_calling_supported(
-            llm_provider, llm_model_name
-        )
-
         # LLM prompt building, response capturing, etc.
         answer = Answer(
             is_connected=is_connected,
@@ -871,7 +777,6 @@ def stream_chat_message_objects(
                 tool_name_to_tool_id[tool.name] = tool_id
 
         gen_ai_response_message = partial_response(
-            reserved_message_id=reserved_message_id,
             message=answer.llm_answer,
             rephrased_query=(
                 qa_docs_response.rephrased_query if qa_docs_response else None
@@ -879,9 +784,11 @@ def stream_chat_message_objects(
             reference_docs=reference_db_search_docs,
             files=ai_message_files,
             token_count=len(llm_tokenizer_encode_func(answer.llm_answer)),
-            citations=message_specific_citations.citation_map
+            citations=(
+                message_specific_citations.citation_map
                 if message_specific_citations
-            else None,
+                else None
+            ),
             error=None,
             tool_call=(
                 ToolCall(
@@ -915,7 +822,6 @@ def stream_chat_message_objects(
 def stream_chat_message(
     new_msg_req: CreateChatMessageRequest,
     user: User | None,
-    use_existing_user_message: bool = False,
     litellm_additional_headers: dict[str, str] | None = None,
     custom_tool_additional_headers: dict[str, str] | None = None,
     is_connected: Callable[[], bool] | None = None,
@@ -925,7 +831,6 @@ def stream_chat_message(
             new_msg_req=new_msg_req,
             user=user,
             db_session=db_session,
-            use_existing_user_message=use_existing_user_message,
             litellm_additional_headers=litellm_additional_headers,
             custom_tool_additional_headers=custom_tool_additional_headers,
             is_connected=is_connected,

backend/danswer/db/tools.py

@@ -24,6 +24,13 @@ def get_tool_by_id(tool_id: int, db_session: Session) -> Tool:
     return tool
 
 
+def get_tool_by_name(tool_name: str, db_session: Session) -> Tool:
+    tool = db_session.scalar(select(Tool).where(Tool.name == tool_name))
+    if not tool:
+        raise ValueError("Tool by specified name does not exist")
+    return tool
+
+
 def create_tool(
     name: str,
     description: str | None,
@@ -37,7 +44,7 @@ def create_tool(
         description=description,
         in_code_tool_id=None,
         openapi_schema=openapi_schema,
-        custom_headers=[header.dict() for header in custom_headers]
+        custom_headers=[header.model_dump() for header in custom_headers]
         if custom_headers
         else [],
         user_id=user_id,

backend/danswer/main.py

@@ -74,6 +74,9 @@ from danswer.server.manage.search_settings import router as search_settings_rout
 from danswer.server.manage.slack_bot import router as slack_bot_management_router
 from danswer.server.manage.users import router as user_router
 from danswer.server.middleware.latency_logging import add_latency_logging_middleware
+from danswer.server.openai_assistants_api.full_openai_assistants_api import (
+    get_full_openai_assistants_api_router,
+)
 from danswer.server.query_and_chat.chat_backend import router as chat_router
 from danswer.server.query_and_chat.query_backend import (
     admin_router as admin_query_router,
@@ -270,6 +273,9 @@ def get_application() -> FastAPI:
         application, token_rate_limit_settings_router
     )
     include_router_with_global_prefix_prepended(application, indexing_router)
+    include_router_with_global_prefix_prepended(
+        application, get_full_openai_assistants_api_router()
+    )
 
     if AUTH_TYPE == AuthType.DISABLED:
         # Server logs this during auth setup verification step

backend/danswer/server/openai_assistants_api/asssistants_api.py

@@ -0,0 +1,273 @@
+from typing import Any
+from typing import Optional
+from uuid import uuid4
+
+from fastapi import APIRouter
+from fastapi import Depends
+from fastapi import HTTPException
+from fastapi import Query
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+
+from danswer.auth.users import current_user
+from danswer.db.engine import get_session
+from danswer.db.models import Persona
+from danswer.db.models import User
+from danswer.db.persona import get_persona_by_id
+from danswer.db.persona import get_personas
+from danswer.db.persona import mark_persona_as_deleted
+from danswer.db.persona import upsert_persona
+from danswer.db.persona import upsert_prompt
+from danswer.db.tools import get_tool_by_name
+from danswer.search.enums import RecencyBiasSetting
+from danswer.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+router = APIRouter(prefix="/assistants")
+
+
+# Base models
+class AssistantObject(BaseModel):
+    id: int
+    object: str = "assistant"
+    created_at: int
+    name: Optional[str] = None
+    description: Optional[str] = None
+    model: str
+    instructions: Optional[str] = None
+    tools: list[dict[str, Any]]
+    file_ids: list[str]
+    metadata: Optional[dict[str, Any]] = None
+
+
+class CreateAssistantRequest(BaseModel):
+    model: str
+    name: Optional[str] = None
+    description: Optional[str] = None
+    instructions: Optional[str] = None
+    tools: Optional[list[dict[str, Any]]] = None
+    file_ids: Optional[list[str]] = None
+    metadata: Optional[dict[str, Any]] = None
+
+
+class ModifyAssistantRequest(BaseModel):
+    model: Optional[str] = None
+    name: Optional[str] = None
+    description: Optional[str] = None
+    instructions: Optional[str] = None
+    tools: Optional[list[dict[str, Any]]] = None
+    file_ids: Optional[list[str]] = None
+    metadata: Optional[dict[str, Any]] = None
+
+
+class DeleteAssistantResponse(BaseModel):
+    id: int
+    object: str = "assistant.deleted"
+    deleted: bool
+
+
+class ListAssistantsResponse(BaseModel):
+    object: str = "list"
+    data: list[AssistantObject]
+    first_id: Optional[int] = None
+    last_id: Optional[int] = None
+    has_more: bool
+
+
+def persona_to_assistant(persona: Persona) -> AssistantObject:
+    return AssistantObject(
+        id=persona.id,
+        created_at=0,
+        name=persona.name,
+        description=persona.description,
+        model=persona.llm_model_version_override or "gpt-3.5-turbo",
+        instructions=persona.prompts[0].system_prompt if persona.prompts else None,
+        tools=[
+            {
+                "type": tool.display_name,
+                "function": {
+                    "name": tool.name,
+                    "description": tool.description,
+                    "schema": tool.openapi_schema,
+                },
+            }
+            for tool in persona.tools
+        ],
+        file_ids=[],  # Assuming no file support for now
+        metadata={},  # Assuming no metadata for now
+    )
+
+
+# API endpoints
+@router.post("")
+def create_assistant(
+    request: CreateAssistantRequest,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> AssistantObject:
+    prompt = None
+    if request.instructions:
+        prompt = upsert_prompt(
+            user=user,
+            name=f"Prompt for {request.name or 'New Assistant'}",
+            description="Auto-generated prompt",
+            system_prompt=request.instructions,
+            task_prompt="",
+            include_citations=True,
+            datetime_aware=True,
+            personas=[],
+            db_session=db_session,
+        )
+
+    tool_ids = []
+    for tool in request.tools or []:
+        tool_type = tool.get("type")
+        if not tool_type:
+            continue
+
+        try:
+            tool_db = get_tool_by_name(tool_type, db_session)
+            tool_ids.append(tool_db.id)
+        except ValueError:
+            # Skip tools that don't exist in the database
+            logger.error(f"Tool {tool_type} not found in database")
+            raise HTTPException(
+                status_code=404, detail=f"Tool {tool_type} not found in database"
+            )
+
+    persona = upsert_persona(
+        user=user,
+        name=request.name or f"Assistant-{uuid4()}",
+        description=request.description or "",
+        num_chunks=25,
+        llm_relevance_filter=True,
+        llm_filter_extraction=True,
+        recency_bias=RecencyBiasSetting.AUTO,
+        llm_model_provider_override=None,
+        llm_model_version_override=request.model,
+        starter_messages=None,
+        is_public=False,
+        db_session=db_session,
+        prompt_ids=[prompt.id] if prompt else [0],
+        document_set_ids=[],
+        tool_ids=tool_ids,
+        icon_color=None,
+        icon_shape=None,
+        is_visible=True,
+    )
+
+    if prompt:
+        prompt.personas = [persona]
+        db_session.commit()
+
+    return persona_to_assistant(persona)
+
+
+""
+
+
+@router.get("/{assistant_id}")
+def retrieve_assistant(
+    assistant_id: int,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> AssistantObject:
+    try:
+        persona = get_persona_by_id(
+            persona_id=assistant_id,
+            user=user,
+            db_session=db_session,
+            is_for_edit=False,
+        )
+    except ValueError:
+        persona = None
+
+    if not persona:
+        raise HTTPException(status_code=404, detail="Assistant not found")
+    return persona_to_assistant(persona)
+
+
+@router.post("/{assistant_id}")
+def modify_assistant(
+    assistant_id: int,
+    request: ModifyAssistantRequest,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> AssistantObject:
+    persona = get_persona_by_id(
+        persona_id=assistant_id,
+        user=user,
+        db_session=db_session,
+        is_for_edit=True,
+    )
+    if not persona:
+        raise HTTPException(status_code=404, detail="Assistant not found")
+
+    update_data = request.model_dump(exclude_unset=True)
+    for key, value in update_data.items():
+        setattr(persona, key, value)
+
+    if "instructions" in update_data and persona.prompts:
+        persona.prompts[0].system_prompt = update_data["instructions"]
+
+    db_session.commit()
+    return persona_to_assistant(persona)
+
+
+@router.delete("/{assistant_id}")
+def delete_assistant(
+    assistant_id: int,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> DeleteAssistantResponse:
+    try:
+        mark_persona_as_deleted(
+            persona_id=int(assistant_id),
+            user=user,
+            db_session=db_session,
+        )
+        return DeleteAssistantResponse(id=assistant_id, deleted=True)
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Assistant not found")
+
+
+@router.get("")
+def list_assistants(
+    limit: int = Query(20, le=100),
+    order: str = Query("desc", regex="^(asc|desc)$"),
+    after: Optional[int] = None,
+    before: Optional[int] = None,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> ListAssistantsResponse:
+    personas = list(
+        get_personas(
+            user=user,
+            db_session=db_session,
+            get_editable=False,
+            joinedload_all=True,
+        )
+    )
+
+    # Apply filtering based on after and before
+    if after:
+        personas = [p for p in personas if p.id > int(after)]
+    if before:
+        personas = [p for p in personas if p.id < int(before)]
+
+    # Apply ordering
+    personas.sort(key=lambda p: p.id, reverse=(order == "desc"))
+
+    # Apply limit
+    personas = personas[:limit]
+
+    assistants = [persona_to_assistant(p) for p in personas]
+
+    return ListAssistantsResponse(
+        data=assistants,
+        first_id=assistants[0].id if assistants else None,
+        last_id=assistants[-1].id if assistants else None,
+        has_more=len(personas) == limit,
+    )

backend/danswer/server/openai_assistants_api/full_openai_assistants_api.py

@@ -0,0 +1,19 @@
+from fastapi import APIRouter
+
+from danswer.server.openai_assistants_api.asssistants_api import (
+    router as assistants_router,
+)
+from danswer.server.openai_assistants_api.messages_api import router as messages_router
+from danswer.server.openai_assistants_api.runs_api import router as runs_router
+from danswer.server.openai_assistants_api.threads_api import router as threads_router
+
+
+def get_full_openai_assistants_api_router() -> APIRouter:
+    router = APIRouter(prefix="/openai-assistants")
+
+    router.include_router(assistants_router)
+    router.include_router(runs_router)
+    router.include_router(threads_router)
+    router.include_router(messages_router)
+
+    return router

backend/danswer/server/openai_assistants_api/messages_api.py

@@ -0,0 +1,235 @@
+import uuid
+from datetime import datetime
+from typing import Any
+from typing import Literal
+from typing import Optional
+
+from fastapi import APIRouter
+from fastapi import Depends
+from fastapi import HTTPException
+from pydantic import BaseModel
+from pydantic import Field
+from sqlalchemy.orm import Session
+
+from danswer.auth.users import current_user
+from danswer.configs.constants import MessageType
+from danswer.db.chat import create_new_chat_message
+from danswer.db.chat import get_chat_message
+from danswer.db.chat import get_chat_messages_by_session
+from danswer.db.chat import get_chat_session_by_id
+from danswer.db.chat import get_or_create_root_message
+from danswer.db.engine import get_session
+from danswer.db.models import User
+from danswer.llm.utils import check_number_of_tokens
+
+router = APIRouter(prefix="")
+
+
+Role = Literal["user", "assistant"]
+
+
+class MessageContent(BaseModel):
+    type: Literal["text"]
+    text: str
+
+
+class Message(BaseModel):
+    id: str = Field(default_factory=lambda: f"msg_{uuid.uuid4()}")
+    object: Literal["thread.message"] = "thread.message"
+    created_at: int = Field(default_factory=lambda: int(datetime.now().timestamp()))
+    thread_id: str
+    role: Role
+    content: list[MessageContent]
+    file_ids: list[str] = []
+    assistant_id: Optional[str] = None
+    run_id: Optional[str] = None
+    metadata: Optional[dict[str, Any]] = None  # Change this line to use dict[str, Any]
+
+
+class CreateMessageRequest(BaseModel):
+    role: Role
+    content: str
+    file_ids: list[str] = []
+    metadata: Optional[dict] = None
+
+
+class ListMessagesResponse(BaseModel):
+    object: Literal["list"] = "list"
+    data: list[Message]
+    first_id: str
+    last_id: str
+    has_more: bool
+
+
+@router.post("/threads/{thread_id}/messages")
+def create_message(
+    thread_id: str,
+    message: CreateMessageRequest,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> Message:
+    user_id = user.id if user else None
+
+    try:
+        chat_session = get_chat_session_by_id(
+            chat_session_id=uuid.UUID(thread_id),
+            user_id=user_id,
+            db_session=db_session,
+        )
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Chat session not found")
+
+    chat_messages = get_chat_messages_by_session(
+        chat_session_id=chat_session.id,
+        user_id=user.id if user else None,
+        db_session=db_session,
+    )
+    latest_message = (
+        chat_messages[-1]
+        if chat_messages
+        else get_or_create_root_message(chat_session.id, db_session)
+    )
+
+    new_message = create_new_chat_message(
+        chat_session_id=chat_session.id,
+        parent_message=latest_message,
+        message=message.content,
+        prompt_id=chat_session.persona.prompts[0].id,
+        token_count=check_number_of_tokens(message.content),
+        message_type=(
+            MessageType.USER if message.role == "user" else MessageType.ASSISTANT
+        ),
+        db_session=db_session,
+    )
+
+    return Message(
+        id=str(new_message.id),
+        thread_id=thread_id,
+        role="user",
+        content=[MessageContent(type="text", text=message.content)],
+        file_ids=message.file_ids,
+        metadata=message.metadata,
+    )
+
+
+@router.get("/threads/{thread_id}/messages")
+def list_messages(
+    thread_id: str,
+    limit: int = 20,
+    order: Literal["asc", "desc"] = "desc",
+    after: Optional[str] = None,
+    before: Optional[str] = None,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> ListMessagesResponse:
+    user_id = user.id if user else None
+
+    try:
+        chat_session = get_chat_session_by_id(
+            chat_session_id=uuid.UUID(thread_id),
+            user_id=user_id,
+            db_session=db_session,
+        )
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Chat session not found")
+
+    messages = get_chat_messages_by_session(
+        chat_session_id=chat_session.id,
+        user_id=user_id,
+        db_session=db_session,
+    )
+
+    # Apply filtering based on after and before
+    if after:
+        messages = [m for m in messages if str(m.id) >= after]
+    if before:
+        messages = [m for m in messages if str(m.id) <= before]
+
+    # Apply ordering
+    messages = sorted(messages, key=lambda m: m.id, reverse=(order == "desc"))
+
+    # Apply limit
+    messages = messages[:limit]
+
+    data = [
+        Message(
+            id=str(m.id),
+            thread_id=thread_id,
+            role="user" if m.message_type == "user" else "assistant",
+            content=[MessageContent(type="text", text=m.message)],
+            created_at=int(m.time_sent.timestamp()),
+        )
+        for m in messages
+    ]
+
+    return ListMessagesResponse(
+        data=data,
+        first_id=str(data[0].id) if data else "",
+        last_id=str(data[-1].id) if data else "",
+        has_more=len(messages) == limit,
+    )
+
+
+@router.get("/threads/{thread_id}/messages/{message_id}")
+def retrieve_message(
+    thread_id: str,
+    message_id: int,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> Message:
+    user_id = user.id if user else None
+
+    try:
+        chat_message = get_chat_message(
+            chat_message_id=message_id,
+            user_id=user_id,
+            db_session=db_session,
+        )
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Message not found")
+
+    return Message(
+        id=str(chat_message.id),
+        thread_id=thread_id,
+        role="user" if chat_message.message_type == "user" else "assistant",
+        content=[MessageContent(type="text", text=chat_message.message)],
+        created_at=int(chat_message.time_sent.timestamp()),
+    )
+
+
+class ModifyMessageRequest(BaseModel):
+    metadata: dict
+
+
+@router.post("/threads/{thread_id}/messages/{message_id}")
+def modify_message(
+    thread_id: str,
+    message_id: int,
+    request: ModifyMessageRequest,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> Message:
+    user_id = user.id if user else None
+
+    try:
+        chat_message = get_chat_message(
+            chat_message_id=message_id,
+            user_id=user_id,
+            db_session=db_session,
+        )
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Message not found")
+
+    # Update metadata
+    # TODO: Uncomment this once we have metadata in the chat message
+    # chat_message.metadata = request.metadata
+    # db_session.commit()
+
+    return Message(
+        id=str(chat_message.id),
+        thread_id=thread_id,
+        role="user" if chat_message.message_type == "user" else "assistant",
+        content=[MessageContent(type="text", text=chat_message.message)],
+        created_at=int(chat_message.time_sent.timestamp()),
+        metadata=request.metadata,
+    )

backend/danswer/server/openai_assistants_api/runs_api.py

@@ -0,0 +1,344 @@
+from typing import Literal
+from typing import Optional
+from uuid import UUID
+
+from fastapi import APIRouter
+from fastapi import BackgroundTasks
+from fastapi import Depends
+from fastapi import HTTPException
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+
+from danswer.auth.users import current_user
+from danswer.chat.process_message import stream_chat_message_objects
+from danswer.configs.constants import MessageType
+from danswer.db.chat import create_new_chat_message
+from danswer.db.chat import get_chat_message
+from danswer.db.chat import get_chat_messages_by_session
+from danswer.db.chat import get_chat_session_by_id
+from danswer.db.chat import get_or_create_root_message
+from danswer.db.engine import get_session
+from danswer.db.models import ChatMessage
+from danswer.db.models import User
+from danswer.search.models import RetrievalDetails
+from danswer.server.query_and_chat.models import ChatMessageDetail
+from danswer.server.query_and_chat.models import CreateChatMessageRequest
+from danswer.tools.tool_implementations.search.search_tool import SearchTool
+from danswer.utils.logger import setup_logger
+
+
+logger = setup_logger()
+
+
+router = APIRouter()
+
+
+class RunRequest(BaseModel):
+    assistant_id: int
+    model: Optional[str] = None
+    instructions: Optional[str] = None
+    additional_instructions: Optional[str] = None
+    tools: Optional[list[dict]] = None
+    metadata: Optional[dict] = None
+
+
+RunStatus = Literal[
+    "queued",
+    "in_progress",
+    "requires_action",
+    "cancelling",
+    "cancelled",
+    "failed",
+    "completed",
+    "expired",
+]
+
+
+class RunResponse(BaseModel):
+    id: str
+    object: Literal["thread.run"]
+    created_at: int
+    assistant_id: int
+    thread_id: UUID
+    status: RunStatus
+    started_at: Optional[int] = None
+    expires_at: Optional[int] = None
+    cancelled_at: Optional[int] = None
+    failed_at: Optional[int] = None
+    completed_at: Optional[int] = None
+    last_error: Optional[dict] = None
+    model: str
+    instructions: str
+    tools: list[dict]
+    file_ids: list[str]
+    metadata: Optional[dict] = None
+
+
+def process_run_in_background(
+    message_id: int,
+    parent_message_id: int,
+    chat_session_id: UUID,
+    assistant_id: int,
+    instructions: str,
+    tools: list[dict],
+    user: User | None,
+    db_session: Session,
+) -> None:
+    # Get the latest message in the chat session
+    chat_session = get_chat_session_by_id(
+        chat_session_id=chat_session_id,
+        user_id=user.id if user else None,
+        db_session=db_session,
+    )
+
+    search_tool_retrieval_details = RetrievalDetails()
+    for tool in tools:
+        if tool["type"] == SearchTool.__name__ and (
+            retrieval_details := tool.get("retrieval_details")
+        ):
+            search_tool_retrieval_details = RetrievalDetails.model_validate(
+                retrieval_details
+            )
+            break
+
+    new_msg_req = CreateChatMessageRequest(
+        chat_session_id=chat_session_id,
+        parent_message_id=int(parent_message_id) if parent_message_id else None,
+        message=instructions,
+        file_descriptors=[],
+        prompt_id=chat_session.persona.prompts[0].id,
+        search_doc_ids=None,
+        retrieval_options=search_tool_retrieval_details,  # Adjust as needed
+        query_override=None,
+        regenerate=None,
+        llm_override=None,
+        prompt_override=None,
+        alternate_assistant_id=assistant_id,
+        use_existing_user_message=True,
+        existing_assistant_message_id=message_id,
+    )
+
+    run_message = get_chat_message(message_id, user.id if user else None, db_session)
+    try:
+        for packet in stream_chat_message_objects(
+            new_msg_req=new_msg_req,
+            user=user,
+            db_session=db_session,
+        ):
+            if isinstance(packet, ChatMessageDetail):
+                # Update the run status and message content
+                run_message = get_chat_message(
+                    message_id, user.id if user else None, db_session
+                )
+                if run_message:
+                    # this handles cancelling
+                    if run_message.error:
+                        return
+
+                    run_message.message = packet.message
+                    run_message.message_type = MessageType.ASSISTANT
+                    db_session.commit()
+    except Exception as e:
+        logger.exception("Error processing run in background")
+        run_message.error = str(e)
+        db_session.commit()
+        return
+
+    db_session.refresh(run_message)
+    if run_message.token_count == 0:
+        run_message.error = "No tokens generated"
+        db_session.commit()
+
+
+@router.post("/threads/{thread_id}/runs")
+def create_run(
+    thread_id: UUID,
+    run_request: RunRequest,
+    background_tasks: BackgroundTasks,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> RunResponse:
+    try:
+        chat_session = get_chat_session_by_id(
+            chat_session_id=thread_id,
+            user_id=user.id if user else None,
+            db_session=db_session,
+        )
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Thread not found")
+
+    chat_messages = get_chat_messages_by_session(
+        chat_session_id=chat_session.id,
+        user_id=user.id if user else None,
+        db_session=db_session,
+    )
+    latest_message = (
+        chat_messages[-1]
+        if chat_messages
+        else get_or_create_root_message(chat_session.id, db_session)
+    )
+
+    # Create a new "run" (chat message) in the session
+    new_message = create_new_chat_message(
+        chat_session_id=chat_session.id,
+        parent_message=latest_message,
+        message="",
+        prompt_id=chat_session.persona.prompts[0].id,
+        token_count=0,
+        message_type=MessageType.ASSISTANT,
+        db_session=db_session,
+        commit=False,
+    )
+    db_session.flush()
+    latest_message.latest_child_message = new_message.id
+    db_session.commit()
+
+    # Schedule the background task
+    background_tasks.add_task(
+        process_run_in_background,
+        new_message.id,
+        latest_message.id,
+        chat_session.id,
+        run_request.assistant_id,
+        run_request.instructions or "",
+        run_request.tools or [],
+        user,
+        db_session,
+    )
+
+    return RunResponse(
+        id=str(new_message.id),
+        object="thread.run",
+        created_at=int(new_message.time_sent.timestamp()),
+        assistant_id=run_request.assistant_id,
+        thread_id=chat_session.id,
+        status="queued",
+        model=run_request.model or "default_model",
+        instructions=run_request.instructions or "",
+        tools=run_request.tools or [],
+        file_ids=[],
+        metadata=run_request.metadata,
+    )
+
+
+@router.get("/threads/{thread_id}/runs/{run_id}")
+def retrieve_run(
+    thread_id: UUID,
+    run_id: str,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> RunResponse:
+    # Retrieve the chat message (which represents a "run" in DAnswer)
+    chat_message = get_chat_message(
+        chat_message_id=int(run_id),  # Convert string run_id to int
+        user_id=user.id if user else None,
+        db_session=db_session,
+    )
+    if not chat_message:
+        raise HTTPException(status_code=404, detail="Run not found")
+
+    chat_session = chat_message.chat_session
+
+    # Map DAnswer status to OpenAI status
+    run_status: RunStatus = "queued"
+    if chat_message.message:
+        run_status = "in_progress"
+    if chat_message.token_count != 0:
+        run_status = "completed"
+    if chat_message.error:
+        run_status = "cancelled"
+
+    return RunResponse(
+        id=run_id,
+        object="thread.run",
+        created_at=int(chat_message.time_sent.timestamp()),
+        assistant_id=chat_session.persona_id or 0,
+        thread_id=chat_session.id,
+        status=run_status,
+        started_at=int(chat_message.time_sent.timestamp()),
+        completed_at=(
+            int(chat_message.time_sent.timestamp()) if chat_message.message else None
+        ),
+        model=chat_session.current_alternate_model or "default_model",
+        instructions="",  # DAnswer doesn't store per-message instructions
+        tools=[],  # DAnswer doesn't have a direct equivalent for tools
+        file_ids=(
+            [file["id"] for file in chat_message.files] if chat_message.files else []
+        ),
+        metadata=None,  # DAnswer doesn't store metadata for individual messages
+    )
+
+
+@router.post("/threads/{thread_id}/runs/{run_id}/cancel")
+def cancel_run(
+    thread_id: UUID,
+    run_id: str,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> RunResponse:
+    # In DAnswer, we don't have a direct equivalent to cancelling a run
+    # We'll simulate it by marking the message as "cancelled"
+    chat_message = (
+        db_session.query(ChatMessage).filter(ChatMessage.id == run_id).first()
+    )
+    if not chat_message:
+        raise HTTPException(status_code=404, detail="Run not found")
+
+    chat_message.error = "Cancelled"
+    db_session.commit()
+
+    return retrieve_run(thread_id, run_id, user, db_session)
+
+
+@router.get("/threads/{thread_id}/runs")
+def list_runs(
+    thread_id: UUID,
+    limit: int = 20,
+    order: Literal["asc", "desc"] = "desc",
+    after: Optional[str] = None,
+    before: Optional[str] = None,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> list[RunResponse]:
+    # In DAnswer, we'll treat each message in a chat session as a "run"
+    chat_messages = get_chat_messages_by_session(
+        chat_session_id=thread_id,
+        user_id=user.id if user else None,
+        db_session=db_session,
+    )
+
+    # Apply pagination
+    if after:
+        chat_messages = [msg for msg in chat_messages if str(msg.id) > after]
+    if before:
+        chat_messages = [msg for msg in chat_messages if str(msg.id) < before]
+
+    # Apply ordering
+    chat_messages = sorted(
+        chat_messages, key=lambda msg: msg.time_sent, reverse=(order == "desc")
+    )
+
+    # Apply limit
+    chat_messages = chat_messages[:limit]
+
+    return [
+        retrieve_run(thread_id, str(msg.id), user, db_session) for msg in chat_messages
+    ]
+
+
+@router.get("/threads/{thread_id}/runs/{run_id}/steps")
+def list_run_steps(
+    run_id: str,
+    limit: int = 20,
+    order: Literal["asc", "desc"] = "desc",
+    after: Optional[str] = None,
+    before: Optional[str] = None,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> list[dict]:  # You may want to create a specific model for run steps
+    # DAnswer doesn't have an equivalent to run steps
+    # We'll return an empty list to maintain API compatibility
+    return []
+
+
+# Additional helper functions can be added here if needed

backend/danswer/server/openai_assistants_api/threads_api.py

@@ -0,0 +1,156 @@
+from typing import Optional
+from uuid import UUID
+
+from fastapi import APIRouter
+from fastapi import Depends
+from fastapi import HTTPException
+from pydantic import BaseModel
+from sqlalchemy.orm import Session
+
+from danswer.auth.users import current_user
+from danswer.db.chat import create_chat_session
+from danswer.db.chat import delete_chat_session
+from danswer.db.chat import get_chat_session_by_id
+from danswer.db.chat import get_chat_sessions_by_user
+from danswer.db.chat import update_chat_session
+from danswer.db.engine import get_session
+from danswer.db.models import User
+from danswer.server.query_and_chat.models import ChatSessionDetails
+from danswer.server.query_and_chat.models import ChatSessionsResponse
+
+router = APIRouter(prefix="/threads")
+
+
+# Models
+class Thread(BaseModel):
+    id: UUID
+    object: str = "thread"
+    created_at: int
+    metadata: Optional[dict[str, str]] = None
+
+
+class CreateThreadRequest(BaseModel):
+    messages: Optional[list[dict]] = None
+    metadata: Optional[dict[str, str]] = None
+
+
+class ModifyThreadRequest(BaseModel):
+    metadata: Optional[dict[str, str]] = None
+
+
+# API Endpoints
+@router.post("")
+def create_thread(
+    request: CreateThreadRequest,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> Thread:
+    user_id = user.id if user else None
+    new_chat_session = create_chat_session(
+        db_session=db_session,
+        description="",  # Leave the naming till later to prevent delay
+        user_id=user_id,
+        persona_id=0,
+    )
+
+    return Thread(
+        id=new_chat_session.id,
+        created_at=int(new_chat_session.time_created.timestamp()),
+        metadata=request.metadata,
+    )
+
+
+@router.get("/{thread_id}")
+def retrieve_thread(
+    thread_id: UUID,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> Thread:
+    user_id = user.id if user else None
+    try:
+        chat_session = get_chat_session_by_id(
+            chat_session_id=thread_id,
+            user_id=user_id,
+            db_session=db_session,
+        )
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Thread not found")
+
+    return Thread(
+        id=chat_session.id,
+        created_at=int(chat_session.time_created.timestamp()),
+        metadata=None,  # Assuming we don't store metadata in our current implementation
+    )
+
+
+@router.post("/{thread_id}")
+def modify_thread(
+    thread_id: UUID,
+    request: ModifyThreadRequest,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> Thread:
+    user_id = user.id if user else None
+    try:
+        chat_session = update_chat_session(
+            db_session=db_session,
+            user_id=user_id,
+            chat_session_id=thread_id,
+            description=None,  # Not updating description
+            sharing_status=None,  # Not updating sharing status
+        )
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Thread not found")
+
+    return Thread(
+        id=chat_session.id,
+        created_at=int(chat_session.time_created.timestamp()),
+        metadata=request.metadata,
+    )
+
+
+@router.delete("/{thread_id}")
+def delete_thread(
+    thread_id: UUID,
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> dict:
+    user_id = user.id if user else None
+    try:
+        delete_chat_session(
+            user_id=user_id,
+            chat_session_id=thread_id,
+            db_session=db_session,
+        )
+    except ValueError:
+        raise HTTPException(status_code=404, detail="Thread not found")
+
+    return {"id": str(thread_id), "object": "thread.deleted", "deleted": True}
+
+
+@router.get("")
+def list_threads(
+    user: User | None = Depends(current_user),
+    db_session: Session = Depends(get_session),
+) -> ChatSessionsResponse:
+    user_id = user.id if user else None
+    chat_sessions = get_chat_sessions_by_user(
+        user_id=user_id,
+        deleted=False,
+        db_session=db_session,
+    )
+
+    return ChatSessionsResponse(
+        sessions=[
+            ChatSessionDetails(
+                id=chat.id,
+                name=chat.description,
+                persona_id=chat.persona_id,
+                time_created=chat.time_created.isoformat(),
+                shared_status=chat.shared_status,
+                folder_id=chat.folder_id,
+                current_alternate_model=chat.current_alternate_model,
+            )
+            for chat in chat_sessions
+        ]
+    )

backend/danswer/server/query_and_chat/chat_backend.py

@@ -347,7 +347,6 @@ def handle_new_chat_message(
             for packet in stream_chat_message(
                 new_msg_req=chat_message_req,
                 user=user,
-                use_existing_user_message=chat_message_req.use_existing_user_message,
                 litellm_additional_headers=extract_headers(
                     request.headers, LITELLM_PASS_THROUGH_HEADERS
                 ),

backend/danswer/server/query_and_chat/models.py

@@ -108,6 +108,9 @@ class CreateChatMessageRequest(ChunkContext):
     # used for seeded chats to kick off the generation of an AI answer
     use_existing_user_message: bool = False
 
+    # used for "OpenAI Assistants API"
+    existing_assistant_message_id: int | None = None
+
     # forces the LLM to return a structured response, see
     # https://platform.openai.com/docs/guides/structured-outputs/introduction
     structured_response_format: dict | None = None

backend/danswer/tools/tool_constructor.py

@@ -0,0 +1,255 @@
+from typing import cast
+from uuid import UUID
+
+from pydantic import BaseModel
+from pydantic import Field
+from sqlalchemy.orm import Session
+
+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
+from danswer.configs.app_configs import AZURE_DALLE_DEPLOYMENT_NAME
+from danswer.configs.chat_configs import BING_API_KEY
+from danswer.configs.model_configs import GEN_AI_TEMPERATURE
+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
+from danswer.search.enums import LLMEvaluationType
+from danswer.search.models import InferenceSection
+from danswer.search.models import RetrievalDetails
+from danswer.tools.built_in_tools import get_built_in_tool_by_id
+from danswer.tools.models import DynamicSchemaInfo
+from danswer.tools.tool import Tool
+from danswer.tools.tool_implementations.custom.custom_tool import (
+    build_custom_tools_from_openapi_schema_and_headers,
+)
+from danswer.tools.tool_implementations.images.image_generation_tool import (
+    ImageGenerationTool,
+)
+from danswer.tools.tool_implementations.internet_search.internet_search_tool import (
+    InternetSearchTool,
+)
+from danswer.tools.tool_implementations.search.search_tool import SearchTool
+from danswer.tools.utils import compute_all_tool_tokens
+from danswer.tools.utils import explicit_tool_calling_supported
+from danswer.utils.headers import header_dict_to_header_list
+from danswer.utils.logger import setup_logger
+
+logger = setup_logger()
+
+
+def _get_image_generation_config(llm: LLM, db_session: Session) -> LLMConfig:
+    """Helper function to get image generation LLM config based on available providers"""
+    if llm and llm.config.api_key and llm.config.model_provider == "openai":
+        return LLMConfig(
+            model_provider=llm.config.model_provider,
+            model_name="dall-e-3",
+            temperature=GEN_AI_TEMPERATURE,
+            api_key=llm.config.api_key,
+            api_base=llm.config.api_base,
+            api_version=llm.config.api_version,
+        )
+
+    if llm.config.model_provider == "azure" and AZURE_DALLE_API_KEY is not None:
+        return LLMConfig(
+            model_provider="azure",
+            model_name=f"azure/{AZURE_DALLE_DEPLOYMENT_NAME}",
+            temperature=GEN_AI_TEMPERATURE,
+            api_key=AZURE_DALLE_API_KEY,
+            api_base=AZURE_DALLE_API_BASE,
+            api_version=AZURE_DALLE_API_VERSION,
+        )
+
+    # Fallback to checking for OpenAI provider in database
+    llm_providers = fetch_existing_llm_providers(db_session)
+    openai_provider = next(
+        iter(
+            [
+                llm_provider
+                for llm_provider in llm_providers
+                if llm_provider.provider == "openai"
+            ]
+        ),
+        None,
+    )
+
+    if not openai_provider or not openai_provider.api_key:
+        raise ValueError("Image generation tool requires an OpenAI API key")
+
+    return LLMConfig(
+        model_provider=openai_provider.provider,
+        model_name="dall-e-3",
+        temperature=GEN_AI_TEMPERATURE,
+        api_key=openai_provider.api_key,
+        api_base=openai_provider.api_base,
+        api_version=openai_provider.api_version,
+    )
+
+
+class SearchToolConfig(BaseModel):
+    answer_style_config: AnswerStyleConfig = Field(
+        default_factory=lambda: AnswerStyleConfig(citation_config=CitationConfig())
+    )
+    document_pruning_config: DocumentPruningConfig = Field(
+        default_factory=DocumentPruningConfig
+    )
+    retrieval_options: RetrievalDetails = Field(default_factory=RetrievalDetails)
+    selected_sections: list[InferenceSection] | None = None
+    chunks_above: int = 0
+    chunks_below: int = 0
+    full_doc: bool = False
+    latest_query_files: list[InMemoryChatFile] | None = None
+
+
+class InternetSearchToolConfig(BaseModel):
+    answer_style_config: AnswerStyleConfig = Field(
+        default_factory=lambda: AnswerStyleConfig(
+            citation_config=CitationConfig(all_docs_useful=True)
+        )
+    )
+
+
+class ImageGenerationToolConfig(BaseModel):
+    additional_headers: dict[str, str] | None = None
+
+
+class CustomToolConfig(BaseModel):
+    chat_session_id: UUID | None = None
+    message_id: int | None = None
+    additional_headers: dict[str, str] | None = None
+
+
+def construct_tools(
+    persona: Persona,
+    prompt_config: PromptConfig,
+    db_session: Session,
+    user: User | None,
+    llm: LLM,
+    fast_llm: LLM,
+    search_tool_config: SearchToolConfig | None = None,
+    internet_search_tool_config: InternetSearchToolConfig | None = None,
+    image_generation_tool_config: ImageGenerationToolConfig | None = None,
+    custom_tool_config: CustomToolConfig | None = None,
+) -> dict[int, list[Tool]]:
+    """Constructs tools based on persona configuration and available APIs"""
+    tool_dict: dict[int, list[Tool]] = {}
+
+    for db_tool_model in persona.tools:
+        if db_tool_model.in_code_tool_id:
+            tool_cls = get_built_in_tool_by_id(db_tool_model.id, db_session)
+
+            # Handle Search Tool
+            if tool_cls.__name__ == SearchTool.__name__:
+                if not search_tool_config:
+                    search_tool_config = SearchToolConfig()
+
+                search_tool = SearchTool(
+                    db_session=db_session,
+                    user=user,
+                    persona=persona,
+                    retrieval_options=search_tool_config.retrieval_options,
+                    prompt_config=prompt_config,
+                    llm=llm,
+                    fast_llm=fast_llm,
+                    pruning_config=search_tool_config.document_pruning_config,
+                    answer_style_config=search_tool_config.answer_style_config,
+                    selected_sections=search_tool_config.selected_sections,
+                    chunks_above=search_tool_config.chunks_above,
+                    chunks_below=search_tool_config.chunks_below,
+                    full_doc=search_tool_config.full_doc,
+                    evaluation_type=(
+                        LLMEvaluationType.BASIC
+                        if persona.llm_relevance_filter
+                        else LLMEvaluationType.SKIP
+                    ),
+                )
+                tool_dict[db_tool_model.id] = [search_tool]
+
+            # Handle Image Generation Tool
+            elif tool_cls.__name__ == ImageGenerationTool.__name__:
+                if not image_generation_tool_config:
+                    image_generation_tool_config = ImageGenerationToolConfig()
+
+                img_generation_llm_config = _get_image_generation_config(
+                    llm, db_session
+                )
+
+                tool_dict[db_tool_model.id] = [
+                    ImageGenerationTool(
+                        api_key=cast(str, img_generation_llm_config.api_key),
+                        api_base=img_generation_llm_config.api_base,
+                        api_version=img_generation_llm_config.api_version,
+                        additional_headers=image_generation_tool_config.additional_headers,
+                        model=img_generation_llm_config.model_name,
+                    )
+                ]
+
+            # Handle Internet Search Tool
+            elif tool_cls.__name__ == InternetSearchTool.__name__:
+                if not internet_search_tool_config:
+                    internet_search_tool_config = InternetSearchToolConfig()
+
+                if not BING_API_KEY:
+                    raise ValueError(
+                        "Internet search tool requires a Bing API key, please contact your Danswer admin to get it added!"
+                    )
+                tool_dict[db_tool_model.id] = [
+                    InternetSearchTool(
+                        api_key=BING_API_KEY,
+                        answer_style_config=internet_search_tool_config.answer_style_config,
+                        prompt_config=prompt_config,
+                    )
+                ]
+
+        # Handle custom tools
+        elif db_tool_model.openapi_schema:
+            if not custom_tool_config:
+                custom_tool_config = CustomToolConfig()
+
+            tool_dict[db_tool_model.id] = cast(
+                list[Tool],
+                build_custom_tools_from_openapi_schema_and_headers(
+                    db_tool_model.openapi_schema,
+                    dynamic_schema_info=DynamicSchemaInfo(
+                        chat_session_id=custom_tool_config.chat_session_id,
+                        message_id=custom_tool_config.message_id,
+                    ),
+                    custom_headers=(db_tool_model.custom_headers or [])
+                    + (
+                        header_dict_to_header_list(
+                            custom_tool_config.additional_headers or {}
+                        )
+                    ),
+                ),
+            )
+
+    tools: list[Tool] = []
+    for tool_list in tool_dict.values():
+        tools.extend(tool_list)
+
+    # factor in tool definition size when pruning
+    if search_tool_config:
+        search_tool_config.document_pruning_config.tool_num_tokens = (
+            compute_all_tool_tokens(
+                tools,
+                get_tokenizer(
+                    model_name=llm.config.model_name,
+                    provider_type=llm.config.model_provider,
+                ),
+            )
+        )
+        search_tool_config.document_pruning_config.using_tool_message = (
+            explicit_tool_calling_supported(
+                llm.config.model_provider, llm.config.model_name
+            )
+        )
+
+    return tool_dict

backend/tests/integration/common_utils/managers/user.py

@@ -13,6 +13,14 @@ from tests.integration.common_utils.constants import GENERAL_HEADERS
 from tests.integration.common_utils.test_models import DATestUser
 
 
+DOMAIN = "test.com"
+DEFAULT_PASSWORD = "test"
+
+
+def build_email(name: str) -> str:
+    return f"{name}@test.com"
+
+
 class UserManager:
     @staticmethod
     def create(
@@ -23,9 +31,9 @@ class UserManager:
             name = f"test{str(uuid4())}"
 
         if email is None:
-            email = f"{name}@test.com"
+            email = build_email(name)
 
-        password = "test"
+        password = DEFAULT_PASSWORD
 
         body = {
             "email": email,

backend/tests/integration/openai_assistants_api/conftest.py

@@ -0,0 +1,55 @@
+from typing import Optional
+from uuid import UUID
+
+import pytest
+import requests
+
+from tests.integration.common_utils.constants import API_SERVER_URL
+from tests.integration.common_utils.constants import GENERAL_HEADERS
+from tests.integration.common_utils.managers.llm_provider import LLMProviderManager
+from tests.integration.common_utils.managers.user import build_email
+from tests.integration.common_utils.managers.user import DEFAULT_PASSWORD
+from tests.integration.common_utils.managers.user import UserManager
+from tests.integration.common_utils.test_models import DATestLLMProvider
+from tests.integration.common_utils.test_models import DATestUser
+
+BASE_URL = f"{API_SERVER_URL}/openai-assistants"
+
+
+@pytest.fixture
+def admin_user() -> DATestUser | None:
+    try:
+        return UserManager.create("admin_user")
+    except Exception:
+        pass
+
+    try:
+        return UserManager.login_as_user(
+            DATestUser(
+                id="",
+                email=build_email("admin_user"),
+                password=DEFAULT_PASSWORD,
+                headers=GENERAL_HEADERS,
+            )
+        )
+    except Exception:
+        pass
+
+    return None
+
+
+@pytest.fixture
+def llm_provider(admin_user: DATestUser | None) -> DATestLLMProvider:
+    return LLMProviderManager.create(user_performing_action=admin_user)
+
+
+@pytest.fixture
+def thread_id(admin_user: Optional[DATestUser]) -> UUID:
+    # Create a thread to use in the tests
+    response = requests.post(
+        f"{BASE_URL}/threads",  # Updated endpoint path
+        json={},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    return UUID(response.json()["id"])

backend/tests/integration/openai_assistants_api/test_assistants.py

@@ -0,0 +1,151 @@
+import requests
+
+from tests.integration.common_utils.constants import API_SERVER_URL
+from tests.integration.common_utils.constants import GENERAL_HEADERS
+from tests.integration.common_utils.test_models import DATestUser
+
+ASSISTANTS_URL = f"{API_SERVER_URL}/openai-assistants/assistants"
+
+
+def test_create_assistant(admin_user: DATestUser | None) -> None:
+    response = requests.post(
+        ASSISTANTS_URL,
+        json={
+            "model": "gpt-3.5-turbo",
+            "name": "Test Assistant",
+            "description": "A test assistant",
+            "instructions": "You are a helpful assistant.",
+        },
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert data["name"] == "Test Assistant"
+    assert data["description"] == "A test assistant"
+    assert data["model"] == "gpt-3.5-turbo"
+    assert data["instructions"] == "You are a helpful assistant."
+
+
+def test_retrieve_assistant(admin_user: DATestUser | None) -> None:
+    # First, create an assistant
+    create_response = requests.post(
+        ASSISTANTS_URL,
+        json={"model": "gpt-3.5-turbo", "name": "Retrieve Test"},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert create_response.status_code == 200
+    assistant_id = create_response.json()["id"]
+
+    # Now, retrieve the assistant
+    response = requests.get(
+        f"{ASSISTANTS_URL}/{assistant_id}",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert data["id"] == assistant_id
+    assert data["name"] == "Retrieve Test"
+
+
+def test_modify_assistant(admin_user: DATestUser | None) -> None:
+    # First, create an assistant
+    create_response = requests.post(
+        ASSISTANTS_URL,
+        json={"model": "gpt-3.5-turbo", "name": "Modify Test"},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert create_response.status_code == 200
+    assistant_id = create_response.json()["id"]
+
+    # Now, modify the assistant
+    response = requests.post(
+        f"{ASSISTANTS_URL}/{assistant_id}",
+        json={"name": "Modified Assistant", "instructions": "New instructions"},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert data["id"] == assistant_id
+    assert data["name"] == "Modified Assistant"
+    assert data["instructions"] == "New instructions"
+
+
+def test_delete_assistant(admin_user: DATestUser | None) -> None:
+    # First, create an assistant
+    create_response = requests.post(
+        ASSISTANTS_URL,
+        json={"model": "gpt-3.5-turbo", "name": "Delete Test"},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert create_response.status_code == 200
+    assistant_id = create_response.json()["id"]
+
+    # Now, delete the assistant
+    response = requests.delete(
+        f"{ASSISTANTS_URL}/{assistant_id}",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert data["id"] == assistant_id
+    assert data["deleted"] is True
+
+
+def test_list_assistants(admin_user: DATestUser | None) -> None:
+    # Create multiple assistants
+    for i in range(3):
+        requests.post(
+            ASSISTANTS_URL,
+            json={"model": "gpt-3.5-turbo", "name": f"List Test {i}"},
+            headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+        )
+
+    # Now, list the assistants
+    response = requests.get(
+        ASSISTANTS_URL,
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert data["object"] == "list"
+    assert len(data["data"]) >= 3  # At least the 3 we just created
+    assert all(assistant["object"] == "assistant" for assistant in data["data"])
+
+
+def test_list_assistants_pagination(admin_user: DATestUser | None) -> None:
+    # Create 5 assistants
+    for i in range(5):
+        requests.post(
+            ASSISTANTS_URL,
+            json={"model": "gpt-3.5-turbo", "name": f"Pagination Test {i}"},
+            headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+        )
+
+    # List assistants with limit
+    response = requests.get(
+        f"{ASSISTANTS_URL}?limit=2",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert len(data["data"]) == 2
+    assert data["has_more"] is True
+
+    # Get next page
+    before = data["last_id"]
+    response = requests.get(
+        f"{ASSISTANTS_URL}?limit=2&before={before}",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert len(data["data"]) == 2
+
+
+def test_assistant_not_found(admin_user: DATestUser | None) -> None:
+    non_existent_id = -99
+    response = requests.get(
+        f"{ASSISTANTS_URL}/{non_existent_id}",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 404

backend/tests/integration/openai_assistants_api/test_messages.py

@@ -0,0 +1,133 @@
+import uuid
+from typing import Optional
+
+import pytest
+import requests
+
+from tests.integration.common_utils.constants import API_SERVER_URL
+from tests.integration.common_utils.constants import GENERAL_HEADERS
+from tests.integration.common_utils.test_models import DATestUser
+
+BASE_URL = f"{API_SERVER_URL}/openai-assistants/threads"
+
+
+@pytest.fixture
+def thread_id(admin_user: Optional[DATestUser]) -> str:
+    response = requests.post(
+        BASE_URL,
+        json={},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    return response.json()["id"]
+
+
+def test_create_message(admin_user: Optional[DATestUser], thread_id: str) -> None:
+    response = requests.post(
+        f"{BASE_URL}/{thread_id}/messages",  # URL structure matches API
+        json={
+            "role": "user",
+            "content": "Hello, world!",
+            "file_ids": [],
+            "metadata": {"key": "value"},
+        },
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+
+    response_json = response.json()
+    assert "id" in response_json
+    assert response_json["thread_id"] == thread_id
+    assert response_json["role"] == "user"
+    assert response_json["content"] == [{"type": "text", "text": "Hello, world!"}]
+    assert response_json["metadata"] == {"key": "value"}
+
+
+def test_list_messages(admin_user: Optional[DATestUser], thread_id: str) -> None:
+    # Create a message first
+    requests.post(
+        f"{BASE_URL}/{thread_id}/messages",
+        json={"role": "user", "content": "Test message"},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+
+    # Now, list the messages
+    response = requests.get(
+        f"{BASE_URL}/{thread_id}/messages",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+
+    response_json = response.json()
+    assert response_json["object"] == "list"
+    assert isinstance(response_json["data"], list)
+    assert len(response_json["data"]) > 0
+    assert "first_id" in response_json
+    assert "last_id" in response_json
+    assert "has_more" in response_json
+
+
+def test_retrieve_message(admin_user: Optional[DATestUser], thread_id: str) -> None:
+    # Create a message first
+    create_response = requests.post(
+        f"{BASE_URL}/{thread_id}/messages",
+        json={"role": "user", "content": "Test message"},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    message_id = create_response.json()["id"]
+
+    # Now, retrieve the message
+    response = requests.get(
+        f"{BASE_URL}/{thread_id}/messages/{message_id}",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+
+    response_json = response.json()
+    assert response_json["id"] == message_id
+    assert response_json["thread_id"] == thread_id
+    assert response_json["role"] == "user"
+    assert response_json["content"] == [{"type": "text", "text": "Test message"}]
+
+
+def test_modify_message(admin_user: Optional[DATestUser], thread_id: str) -> None:
+    # Create a message first
+    create_response = requests.post(
+        f"{BASE_URL}/{thread_id}/messages",
+        json={"role": "user", "content": "Test message"},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    message_id = create_response.json()["id"]
+
+    # Now, modify the message
+    response = requests.post(
+        f"{BASE_URL}/{thread_id}/messages/{message_id}",
+        json={"metadata": {"new_key": "new_value"}},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+
+    response_json = response.json()
+    assert response_json["id"] == message_id
+    assert response_json["thread_id"] == thread_id
+    assert response_json["metadata"] == {"new_key": "new_value"}
+
+
+def test_error_handling(admin_user: Optional[DATestUser]) -> None:
+    non_existent_thread_id = str(uuid.uuid4())
+    non_existent_message_id = -99
+
+    # Test with non-existent thread
+    response = requests.post(
+        f"{BASE_URL}/{non_existent_thread_id}/messages",
+        json={"role": "user", "content": "Test message"},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 404
+
+    # Test with non-existent message
+    response = requests.get(
+        f"{BASE_URL}/{non_existent_thread_id}/messages/{non_existent_message_id}",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 404

backend/tests/integration/openai_assistants_api/test_runs.py

@@ -0,0 +1,137 @@
+from uuid import UUID
+
+import pytest
+import requests
+
+from tests.integration.common_utils.constants import API_SERVER_URL
+from tests.integration.common_utils.constants import GENERAL_HEADERS
+from tests.integration.common_utils.test_models import DATestLLMProvider
+from tests.integration.common_utils.test_models import DATestUser
+
+BASE_URL = f"{API_SERVER_URL}/openai-assistants"
+
+
+@pytest.fixture
+def run_id(admin_user: DATestUser | None, thread_id: UUID) -> str:
+    """Create a run and return its ID."""
+    response = requests.post(
+        f"{BASE_URL}/threads/{thread_id}/runs",
+        json={
+            "assistant_id": 0,
+        },
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+    return response.json()["id"]
+
+
+def test_create_run(
+    admin_user: DATestUser | None, thread_id: UUID, llm_provider: DATestLLMProvider
+) -> None:
+    response = requests.post(
+        f"{BASE_URL}/threads/{thread_id}/runs",
+        json={
+            "assistant_id": 0,
+            "model": "gpt-3.5-turbo",
+            "instructions": "Test instructions",
+        },
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+
+    response_json = response.json()
+    assert "id" in response_json
+    assert response_json["object"] == "thread.run"
+    assert "created_at" in response_json
+    assert response_json["assistant_id"] == 0
+    assert UUID(response_json["thread_id"]) == thread_id
+    assert response_json["status"] == "queued"
+    assert response_json["model"] == "gpt-3.5-turbo"
+    assert response_json["instructions"] == "Test instructions"
+
+
+def test_retrieve_run(
+    admin_user: DATestUser | None,
+    thread_id: UUID,
+    run_id: str,
+    llm_provider: DATestLLMProvider,
+) -> None:
+    retrieve_response = requests.get(
+        f"{BASE_URL}/threads/{thread_id}/runs/{run_id}",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert retrieve_response.status_code == 200
+
+    response_json = retrieve_response.json()
+    assert response_json["id"] == run_id
+    assert response_json["object"] == "thread.run"
+    assert "created_at" in response_json
+    assert UUID(response_json["thread_id"]) == thread_id
+
+
+def test_cancel_run(
+    admin_user: DATestUser | None,
+    thread_id: UUID,
+    run_id: str,
+    llm_provider: DATestLLMProvider,
+) -> None:
+    cancel_response = requests.post(
+        f"{BASE_URL}/threads/{thread_id}/runs/{run_id}/cancel",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert cancel_response.status_code == 200
+
+    response_json = cancel_response.json()
+    assert response_json["id"] == run_id
+    assert response_json["status"] == "cancelled"
+
+
+def test_list_runs(
+    admin_user: DATestUser | None, thread_id: UUID, llm_provider: DATestLLMProvider
+) -> None:
+    # Create a few runs
+    for _ in range(3):
+        requests.post(
+            f"{BASE_URL}/threads/{thread_id}/runs",
+            json={
+                "assistant_id": 0,
+            },
+            headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+        )
+
+    # Now, list the runs
+    list_response = requests.get(
+        f"{BASE_URL}/threads/{thread_id}/runs",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert list_response.status_code == 200
+
+    response_json = list_response.json()
+    assert isinstance(response_json, list)
+    assert len(response_json) >= 3
+
+    for run in response_json:
+        assert "id" in run
+        assert run["object"] == "thread.run"
+        assert "created_at" in run
+        assert UUID(run["thread_id"]) == thread_id
+        assert "status" in run
+        assert "model" in run
+
+
+def test_list_run_steps(
+    admin_user: DATestUser | None,
+    thread_id: UUID,
+    run_id: str,
+    llm_provider: DATestLLMProvider,
+) -> None:
+    steps_response = requests.get(
+        f"{BASE_URL}/threads/{thread_id}/runs/{run_id}/steps",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert steps_response.status_code == 200
+
+    response_json = steps_response.json()
+    assert isinstance(response_json, list)
+    # Since DAnswer doesn't have an equivalent to run steps, we expect an empty list
+    assert len(response_json) == 0

backend/tests/integration/openai_assistants_api/test_threads.py

@@ -0,0 +1,132 @@
+from uuid import UUID
+
+import requests
+
+from danswer.db.models import ChatSessionSharedStatus
+from tests.integration.common_utils.constants import API_SERVER_URL
+from tests.integration.common_utils.constants import GENERAL_HEADERS
+from tests.integration.common_utils.test_models import DATestUser
+
+THREADS_URL = f"{API_SERVER_URL}/openai-assistants/threads"
+
+
+def test_create_thread(admin_user: DATestUser | None) -> None:
+    response = requests.post(
+        THREADS_URL,
+        json={"messages": None, "metadata": {"key": "value"}},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert response.status_code == 200
+
+    response_json = response.json()
+    assert "id" in response_json
+    assert response_json["object"] == "thread"
+    assert "created_at" in response_json
+    assert response_json["metadata"] == {"key": "value"}
+
+
+def test_retrieve_thread(admin_user: DATestUser | None) -> None:
+    # First, create a thread
+    create_response = requests.post(
+        THREADS_URL,
+        json={},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert create_response.status_code == 200
+    thread_id = create_response.json()["id"]
+
+    # Now, retrieve the thread
+    retrieve_response = requests.get(
+        f"{THREADS_URL}/{thread_id}",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert retrieve_response.status_code == 200
+
+    response_json = retrieve_response.json()
+    assert response_json["id"] == thread_id
+    assert response_json["object"] == "thread"
+    assert "created_at" in response_json
+
+
+def test_modify_thread(admin_user: DATestUser | None) -> None:
+    # First, create a thread
+    create_response = requests.post(
+        THREADS_URL,
+        json={},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert create_response.status_code == 200
+    thread_id = create_response.json()["id"]
+
+    # Now, modify the thread
+    modify_response = requests.post(
+        f"{THREADS_URL}/{thread_id}",
+        json={"metadata": {"new_key": "new_value"}},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert modify_response.status_code == 200
+
+    response_json = modify_response.json()
+    assert response_json["id"] == thread_id
+    assert response_json["metadata"] == {"new_key": "new_value"}
+
+
+def test_delete_thread(admin_user: DATestUser | None) -> None:
+    # First, create a thread
+    create_response = requests.post(
+        THREADS_URL,
+        json={},
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert create_response.status_code == 200
+    thread_id = create_response.json()["id"]
+
+    # Now, delete the thread
+    delete_response = requests.delete(
+        f"{THREADS_URL}/{thread_id}",
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert delete_response.status_code == 200
+
+    response_json = delete_response.json()
+    assert response_json["id"] == thread_id
+    assert response_json["object"] == "thread.deleted"
+    assert response_json["deleted"] is True
+
+
+def test_list_threads(admin_user: DATestUser | None) -> None:
+    # Create a few threads
+    for _ in range(3):
+        requests.post(
+            THREADS_URL,
+            json={},
+            headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+        )
+
+    # Now, list the threads
+    list_response = requests.get(
+        THREADS_URL,
+        headers=admin_user.headers if admin_user else GENERAL_HEADERS,
+    )
+    assert list_response.status_code == 200
+
+    response_json = list_response.json()
+    assert "sessions" in response_json
+    assert len(response_json["sessions"]) >= 3
+
+    for session in response_json["sessions"]:
+        assert "id" in session
+        assert "name" in session
+        assert "persona_id" in session
+        assert "time_created" in session
+        assert "shared_status" in session
+        assert "folder_id" in session
+        assert "current_alternate_model" in session
+
+        # Validate UUID
+        UUID(session["id"])
+
+        # Validate shared_status
+        assert session["shared_status"] in [
+            status.value for status in ChatSessionSharedStatus
+        ]

examples/assistants-api/topics_analyzer.py

@@ -0,0 +1,125 @@
+import argparse
+import os
+import time
+from datetime import datetime
+from datetime import timedelta
+from datetime import timezone
+
+from openai import OpenAI
+
+
+ASSISTANT_NAME = "Topic Analyzer"
+SYSTEM_PROMPT = """
+You are a helpful assistant that analyzes topics by searching through available \
+documents and providing insights. These available documents come from common \
+workplace tools like Slack, emails, Confluence, Google Drive, etc.
+
+When analyzing a topic:
+1. Search for relevant information using the search tool
+2. Synthesize the findings into clear insights
+3. Highlight key trends, patterns, or notable developments
+4. Maintain objectivity and cite sources where relevant
+"""
+USER_PROMPT = """
+Please analyze and provide insights about this topic: {topic}.
+
+IMPORTANT: do not mention things that are not relevant to the specified topic. \
+If there is no relevant information, just say "No relevant information found."
+"""
+
+
+def wait_on_run(client: OpenAI, run, thread):
+    while run.status == "queued" or run.status == "in_progress":
+        run = client.beta.threads.runs.retrieve(
+            thread_id=thread.id,
+            run_id=run.id,
+        )
+        time.sleep(0.5)
+    return run
+
+
+def show_response(messages) -> None:
+    # Get only the assistant's response text
+    for message in messages.data[::-1]:
+        if message.role == "assistant":
+            for content in message.content:
+                if content.type == "text":
+                    print(content.text)
+                    break
+
+
+def analyze_topics(topics: list[str]) -> None:
+    openai_api_key = os.environ.get(
+        "OPENAI_API_KEY", "<your OpenAI API key if not set as env var>"
+    )
+    danswer_api_key = os.environ.get(
+        "DANSWER_API_KEY", "<your Danswer API key if not set as env var>"
+    )
+    client = OpenAI(
+        api_key=openai_api_key,
+        base_url="http://localhost:8080/openai-assistants",
+        default_headers={
+            "Authorization": f"Bearer {danswer_api_key}",
+        },
+    )
+
+    # Create an assistant if it doesn't exist
+    try:
+        assistants = client.beta.assistants.list(limit=100)
+        # Find the Topic Analyzer assistant if it exists
+        assistant = next((a for a in assistants.data if a.name == ASSISTANT_NAME))
+        client.beta.assistants.delete(assistant.id)
+    except Exception:
+        pass
+
+    assistant = client.beta.assistants.create(
+        name=ASSISTANT_NAME,
+        instructions=SYSTEM_PROMPT,
+        tools=[{"type": "SearchTool"}],  # type: ignore
+        model="gpt-4o",
+    )
+
+    # Process each topic individually
+    for topic in topics:
+        thread = client.beta.threads.create()
+        message = client.beta.threads.messages.create(
+            thread_id=thread.id,
+            role="user",
+            content=USER_PROMPT.format(topic=topic),
+        )
+
+        run = client.beta.threads.runs.create(
+            thread_id=thread.id,
+            assistant_id=assistant.id,
+            tools=[
+                {  # type: ignore
+                    "type": "SearchTool",
+                    "retrieval_details": {
+                        "run_search": "always",
+                        "filters": {
+                            "time_cutoff": str(
+                                datetime.now(timezone.utc) - timedelta(days=7)
+                            )
+                        },
+                    },
+                }
+            ],
+        )
+
+        run = wait_on_run(client, run, thread)
+        messages = client.beta.threads.messages.list(
+            thread_id=thread.id, order="asc", after=message.id
+        )
+        print(f"\nAnalysis for topic: {topic}")
+        print("-" * 40)
+        show_response(messages)
+        print()
+
+
+# Example usage
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Analyze specific topics")
+    parser.add_argument("topics", nargs="+", help="Topics to analyze (one or more)")
+
+    args = parser.parse_args()
+    analyze_topics(args.topics)