mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-04-25 00:36:31 +02:00
add stream_resume_chat and POST /threads/{id}/resume endpoint
This commit is contained in:
CREDO23
parent
9751918e41
commit
39ee4742d2
2 changed files with 697 additions and 1 deletions
|
|
@ -1268,3 +1268,623 @@ async def stream_new_chat(
|
|||
finally:
|
||||
# Clear AI responding state for live collaboration
|
||||
await clear_ai_responding(session, chat_id)
|
||||
|
||||
|
||||
async def stream_resume_chat(
|
||||
chat_id: int,
|
||||
search_space_id: int,
|
||||
decisions: list[dict],
|
||||
session: AsyncSession,
|
||||
user_id: str | None = None,
|
||||
llm_config_id: int = -1,
|
||||
thread_visibility: ChatVisibility | None = None,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
streaming_service = VercelStreamingService()
|
||||
current_text_id: str | None = None
|
||||
|
||||
try:
|
||||
if user_id:
|
||||
await set_ai_responding(session, chat_id, UUID(user_id))
|
||||
|
||||
agent_config: AgentConfig | None = None
|
||||
if llm_config_id >= 0:
|
||||
agent_config = await load_agent_config(
|
||||
session=session,
|
||||
config_id=llm_config_id,
|
||||
search_space_id=search_space_id,
|
||||
)
|
||||
if not agent_config:
|
||||
yield streaming_service.format_error(
|
||||
f"Failed to load NewLLMConfig with id {llm_config_id}"
|
||||
)
|
||||
yield streaming_service.format_done()
|
||||
return
|
||||
llm = create_chat_litellm_from_agent_config(agent_config)
|
||||
else:
|
||||
llm_config = load_llm_config_from_yaml(llm_config_id=llm_config_id)
|
||||
if not llm_config:
|
||||
yield streaming_service.format_error(
|
||||
f"Failed to load LLM config with id {llm_config_id}"
|
||||
)
|
||||
yield streaming_service.format_done()
|
||||
return
|
||||
llm = create_chat_litellm_from_config(llm_config)
|
||||
agent_config = AgentConfig.from_yaml_config(llm_config)
|
||||
|
||||
if not llm:
|
||||
yield streaming_service.format_error("Failed to create LLM instance")
|
||||
yield streaming_service.format_done()
|
||||
return
|
||||
|
||||
connector_service = ConnectorService(session, search_space_id=search_space_id)
|
||||
|
||||
from app.db import SearchSourceConnectorType
|
||||
|
||||
firecrawl_api_key = None
|
||||
webcrawler_connector = await connector_service.get_connector_by_type(
|
||||
SearchSourceConnectorType.WEBCRAWLER_CONNECTOR, search_space_id
|
||||
)
|
||||
if webcrawler_connector and webcrawler_connector.config:
|
||||
firecrawl_api_key = webcrawler_connector.config.get("FIRECRAWL_API_KEY")
|
||||
|
||||
checkpointer = await get_checkpointer()
|
||||
visibility = thread_visibility or ChatVisibility.PRIVATE
|
||||
|
||||
agent = await create_surfsense_deep_agent(
|
||||
llm=llm,
|
||||
search_space_id=search_space_id,
|
||||
db_session=session,
|
||||
connector_service=connector_service,
|
||||
checkpointer=checkpointer,
|
||||
user_id=user_id,
|
||||
thread_id=chat_id,
|
||||
agent_config=agent_config,
|
||||
firecrawl_api_key=firecrawl_api_key,
|
||||
thread_visibility=visibility,
|
||||
)
|
||||
|
||||
from langgraph.types import Command
|
||||
|
||||
config = {
|
||||
"configurable": {"thread_id": str(chat_id)},
|
||||
"recursion_limit": 80,
|
||||
}
|
||||
|
||||
yield streaming_service.format_message_start()
|
||||
yield streaming_service.format_start_step()
|
||||
|
||||
accumulated_text = ""
|
||||
thinking_step_counter = 0
|
||||
tool_step_ids: dict[str, str] = {}
|
||||
completed_step_ids: set[str] = set()
|
||||
last_active_step_id: str | None = None
|
||||
last_active_step_title = ""
|
||||
last_active_step_items: list[str] = []
|
||||
just_finished_tool = False
|
||||
|
||||
def next_thinking_step_id() -> str:
|
||||
nonlocal thinking_step_counter
|
||||
thinking_step_counter += 1
|
||||
return f"thinking-resume-{thinking_step_counter}"
|
||||
|
||||
def complete_current_step() -> str | None:
|
||||
nonlocal last_active_step_id
|
||||
if last_active_step_id and last_active_step_id not in completed_step_ids:
|
||||
completed_step_ids.add(last_active_step_id)
|
||||
event = streaming_service.format_thinking_step(
|
||||
step_id=last_active_step_id,
|
||||
title=last_active_step_title,
|
||||
status="completed",
|
||||
items=last_active_step_items,
|
||||
)
|
||||
last_active_step_id = None
|
||||
return event
|
||||
return None
|
||||
|
||||
async for event in agent.astream_events(
|
||||
Command(resume={"decisions": decisions}), config=config, version="v2"
|
||||
):
|
||||
event_type = event.get("event", "")
|
||||
|
||||
if event_type == "on_chat_model_stream":
|
||||
chunk = event.get("data", {}).get("chunk")
|
||||
if chunk and hasattr(chunk, "content"):
|
||||
content = chunk.content
|
||||
if content and isinstance(content, str):
|
||||
if current_text_id is None:
|
||||
completion_event = complete_current_step()
|
||||
if completion_event:
|
||||
yield completion_event
|
||||
if just_finished_tool:
|
||||
last_active_step_id = None
|
||||
last_active_step_title = ""
|
||||
last_active_step_items = []
|
||||
just_finished_tool = False
|
||||
current_text_id = streaming_service.generate_text_id()
|
||||
yield streaming_service.format_text_start(current_text_id)
|
||||
yield streaming_service.format_text_delta(
|
||||
current_text_id, content
|
||||
)
|
||||
accumulated_text += content
|
||||
|
||||
elif event_type == "on_tool_start":
|
||||
tool_name = event.get("name", "unknown_tool")
|
||||
run_id = event.get("run_id", "")
|
||||
tool_input = event.get("data", {}).get("input", {})
|
||||
|
||||
if current_text_id is not None:
|
||||
yield streaming_service.format_text_end(current_text_id)
|
||||
current_text_id = None
|
||||
|
||||
if last_active_step_title != "Synthesizing response":
|
||||
completion_event = complete_current_step()
|
||||
if completion_event:
|
||||
yield completion_event
|
||||
|
||||
just_finished_tool = False
|
||||
tool_step_id = next_thinking_step_id()
|
||||
tool_step_ids[run_id] = tool_step_id
|
||||
last_active_step_id = tool_step_id
|
||||
|
||||
if tool_name == "search_knowledge_base":
|
||||
query = (
|
||||
tool_input.get("query", "")
|
||||
if isinstance(tool_input, dict)
|
||||
else str(tool_input)
|
||||
)
|
||||
last_active_step_title = "Searching knowledge base"
|
||||
last_active_step_items = [
|
||||
f"Query: {query[:100]}{'...' if len(query) > 100 else ''}"
|
||||
]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=tool_step_id,
|
||||
title="Searching knowledge base",
|
||||
status="in_progress",
|
||||
items=last_active_step_items,
|
||||
)
|
||||
elif tool_name == "link_preview":
|
||||
url = (
|
||||
tool_input.get("url", "")
|
||||
if isinstance(tool_input, dict)
|
||||
else str(tool_input)
|
||||
)
|
||||
last_active_step_title = "Fetching link preview"
|
||||
last_active_step_items = [
|
||||
f"URL: {url[:80]}{'...' if len(url) > 80 else ''}"
|
||||
]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=tool_step_id,
|
||||
title="Fetching link preview",
|
||||
status="in_progress",
|
||||
items=last_active_step_items,
|
||||
)
|
||||
elif tool_name == "display_image":
|
||||
src = (
|
||||
tool_input.get("src", "")
|
||||
if isinstance(tool_input, dict)
|
||||
else str(tool_input)
|
||||
)
|
||||
title = (
|
||||
tool_input.get("title", "")
|
||||
if isinstance(tool_input, dict)
|
||||
else ""
|
||||
)
|
||||
last_active_step_title = "Analyzing the image"
|
||||
last_active_step_items = [
|
||||
f"Analyzing: {title[:50] if title else src[:50]}{'...' if len(title or src) > 50 else ''}"
|
||||
]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=tool_step_id,
|
||||
title="Analyzing the image",
|
||||
status="in_progress",
|
||||
items=last_active_step_items,
|
||||
)
|
||||
elif tool_name == "scrape_webpage":
|
||||
url = (
|
||||
tool_input.get("url", "")
|
||||
if isinstance(tool_input, dict)
|
||||
else str(tool_input)
|
||||
)
|
||||
last_active_step_title = "Scraping webpage"
|
||||
last_active_step_items = [
|
||||
f"URL: {url[:80]}{'...' if len(url) > 80 else ''}"
|
||||
]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=tool_step_id,
|
||||
title="Scraping webpage",
|
||||
status="in_progress",
|
||||
items=last_active_step_items,
|
||||
)
|
||||
elif tool_name == "generate_podcast":
|
||||
podcast_title = (
|
||||
tool_input.get("podcast_title", "SurfSense Podcast")
|
||||
if isinstance(tool_input, dict)
|
||||
else "SurfSense Podcast"
|
||||
)
|
||||
content_len = len(
|
||||
tool_input.get("source_content", "")
|
||||
if isinstance(tool_input, dict)
|
||||
else ""
|
||||
)
|
||||
last_active_step_title = "Generating podcast"
|
||||
last_active_step_items = [
|
||||
f"Title: {podcast_title}",
|
||||
f"Content: {content_len:,} characters",
|
||||
"Preparing audio generation...",
|
||||
]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=tool_step_id,
|
||||
title="Generating podcast",
|
||||
status="in_progress",
|
||||
items=last_active_step_items,
|
||||
)
|
||||
else:
|
||||
last_active_step_title = f"Using {tool_name.replace('_', ' ')}"
|
||||
last_active_step_items = []
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=tool_step_id,
|
||||
title=last_active_step_title,
|
||||
status="in_progress",
|
||||
)
|
||||
|
||||
tool_call_id = (
|
||||
f"call_{run_id[:32]}"
|
||||
if run_id
|
||||
else streaming_service.generate_tool_call_id()
|
||||
)
|
||||
yield streaming_service.format_tool_input_start(tool_call_id, tool_name)
|
||||
yield streaming_service.format_tool_input_available(
|
||||
tool_call_id,
|
||||
tool_name,
|
||||
tool_input
|
||||
if isinstance(tool_input, dict)
|
||||
else {"input": tool_input},
|
||||
)
|
||||
|
||||
elif event_type == "on_tool_end":
|
||||
run_id = event.get("run_id", "")
|
||||
tool_name = event.get("name", "unknown_tool")
|
||||
raw_output = event.get("data", {}).get("output", "")
|
||||
|
||||
if hasattr(raw_output, "content"):
|
||||
content = raw_output.content
|
||||
if isinstance(content, str):
|
||||
try:
|
||||
tool_output = json.loads(content)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
tool_output = {"result": content}
|
||||
elif isinstance(content, dict):
|
||||
tool_output = content
|
||||
else:
|
||||
tool_output = {"result": str(content)}
|
||||
elif isinstance(raw_output, dict):
|
||||
tool_output = raw_output
|
||||
else:
|
||||
tool_output = {
|
||||
"result": str(raw_output) if raw_output else "completed"
|
||||
}
|
||||
|
||||
tool_call_id = f"call_{run_id[:32]}" if run_id else "call_unknown"
|
||||
original_step_id = tool_step_ids.get(
|
||||
run_id, f"thinking-unknown-{run_id[:8]}"
|
||||
)
|
||||
completed_step_ids.add(original_step_id)
|
||||
|
||||
if tool_name == "search_knowledge_base":
|
||||
result_info = "Search completed"
|
||||
if isinstance(tool_output, dict):
|
||||
result_len = tool_output.get("result_length", 0)
|
||||
if result_len > 0:
|
||||
result_info = (
|
||||
f"Found relevant information ({result_len} chars)"
|
||||
)
|
||||
completed_items = [*last_active_step_items, result_info]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=original_step_id,
|
||||
title="Searching knowledge base",
|
||||
status="completed",
|
||||
items=completed_items,
|
||||
)
|
||||
elif tool_name == "link_preview":
|
||||
if isinstance(tool_output, dict):
|
||||
title = tool_output.get("title", "Link")
|
||||
domain = tool_output.get("domain", "")
|
||||
has_error = "error" in tool_output
|
||||
if has_error:
|
||||
completed_items = [
|
||||
*last_active_step_items,
|
||||
f"Error: {tool_output.get('error', 'Failed to fetch')}",
|
||||
]
|
||||
else:
|
||||
completed_items = [
|
||||
*last_active_step_items,
|
||||
f"Title: {title[:60]}{'...' if len(title) > 60 else ''}",
|
||||
f"Domain: {domain}" if domain else "Preview loaded",
|
||||
]
|
||||
else:
|
||||
completed_items = [*last_active_step_items, "Preview loaded"]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=original_step_id,
|
||||
title="Fetching link preview",
|
||||
status="completed",
|
||||
items=completed_items,
|
||||
)
|
||||
elif tool_name == "display_image":
|
||||
if isinstance(tool_output, dict):
|
||||
title = tool_output.get("title", "")
|
||||
alt = tool_output.get("alt", "Image")
|
||||
display_name = title or alt
|
||||
completed_items = [
|
||||
*last_active_step_items,
|
||||
f"Analyzed: {display_name[:50]}{'...' if len(display_name) > 50 else ''}",
|
||||
]
|
||||
else:
|
||||
completed_items = [*last_active_step_items, "Image analyzed"]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=original_step_id,
|
||||
title="Analyzing the image",
|
||||
status="completed",
|
||||
items=completed_items,
|
||||
)
|
||||
elif tool_name == "scrape_webpage":
|
||||
if isinstance(tool_output, dict):
|
||||
title = tool_output.get("title", "Webpage")
|
||||
word_count = tool_output.get("word_count", 0)
|
||||
has_error = "error" in tool_output
|
||||
if has_error:
|
||||
completed_items = [
|
||||
*last_active_step_items,
|
||||
f"Error: {tool_output.get('error', 'Failed to scrape')[:50]}",
|
||||
]
|
||||
else:
|
||||
completed_items = [
|
||||
*last_active_step_items,
|
||||
f"Title: {title[:50]}{'...' if len(title) > 50 else ''}",
|
||||
f"Extracted: {word_count:,} words",
|
||||
]
|
||||
else:
|
||||
completed_items = [*last_active_step_items, "Content extracted"]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=original_step_id,
|
||||
title="Scraping webpage",
|
||||
status="completed",
|
||||
items=completed_items,
|
||||
)
|
||||
elif tool_name == "generate_podcast":
|
||||
podcast_status = (
|
||||
tool_output.get("status", "unknown")
|
||||
if isinstance(tool_output, dict)
|
||||
else "unknown"
|
||||
)
|
||||
podcast_title = (
|
||||
tool_output.get("title", "Podcast")
|
||||
if isinstance(tool_output, dict)
|
||||
else "Podcast"
|
||||
)
|
||||
if podcast_status == "processing":
|
||||
completed_items = [
|
||||
f"Title: {podcast_title}",
|
||||
"Audio generation started",
|
||||
"Processing in background...",
|
||||
]
|
||||
elif podcast_status == "already_generating":
|
||||
completed_items = [
|
||||
f"Title: {podcast_title}",
|
||||
"Podcast already in progress",
|
||||
"Please wait for it to complete",
|
||||
]
|
||||
elif podcast_status == "error":
|
||||
error_msg = (
|
||||
tool_output.get("error", "Unknown error")
|
||||
if isinstance(tool_output, dict)
|
||||
else "Unknown error"
|
||||
)
|
||||
completed_items = [
|
||||
f"Title: {podcast_title}",
|
||||
f"Error: {error_msg[:50]}",
|
||||
]
|
||||
else:
|
||||
completed_items = last_active_step_items
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=original_step_id,
|
||||
title="Generating podcast",
|
||||
status="completed",
|
||||
items=completed_items,
|
||||
)
|
||||
elif tool_name == "ls":
|
||||
if isinstance(tool_output, dict):
|
||||
result = tool_output.get("result", "")
|
||||
elif isinstance(tool_output, str):
|
||||
result = tool_output
|
||||
else:
|
||||
result = str(tool_output) if tool_output else ""
|
||||
file_names = []
|
||||
if result:
|
||||
for line in result.strip().split("\n"):
|
||||
line = line.strip()
|
||||
if line:
|
||||
name = line.rstrip("/").split("/")[-1]
|
||||
if name and len(name) <= 40:
|
||||
file_names.append(name)
|
||||
elif name:
|
||||
file_names.append(name[:37] + "...")
|
||||
if file_names:
|
||||
if len(file_names) <= 5:
|
||||
completed_items = [f"[{name}]" for name in file_names]
|
||||
else:
|
||||
completed_items = [f"[{name}]" for name in file_names[:4]]
|
||||
completed_items.append(f"(+{len(file_names) - 4} more)")
|
||||
else:
|
||||
completed_items = ["No files found"]
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=original_step_id,
|
||||
title="Exploring files",
|
||||
status="completed",
|
||||
items=completed_items,
|
||||
)
|
||||
else:
|
||||
yield streaming_service.format_thinking_step(
|
||||
step_id=original_step_id,
|
||||
title=f"Using {tool_name.replace('_', ' ')}",
|
||||
status="completed",
|
||||
items=last_active_step_items,
|
||||
)
|
||||
|
||||
just_finished_tool = True
|
||||
last_active_step_id = None
|
||||
last_active_step_title = ""
|
||||
last_active_step_items = []
|
||||
|
||||
if tool_name == "generate_podcast":
|
||||
yield streaming_service.format_tool_output_available(
|
||||
tool_call_id,
|
||||
tool_output
|
||||
if isinstance(tool_output, dict)
|
||||
else {"result": tool_output},
|
||||
)
|
||||
if (
|
||||
isinstance(tool_output, dict)
|
||||
and tool_output.get("status") == "success"
|
||||
):
|
||||
yield streaming_service.format_terminal_info(
|
||||
f"Podcast generated successfully: {tool_output.get('title', 'Podcast')}",
|
||||
"success",
|
||||
)
|
||||
else:
|
||||
error_msg = (
|
||||
tool_output.get("error", "Unknown error")
|
||||
if isinstance(tool_output, dict)
|
||||
else "Unknown error"
|
||||
)
|
||||
yield streaming_service.format_terminal_info(
|
||||
f"Podcast generation failed: {error_msg}",
|
||||
"error",
|
||||
)
|
||||
elif tool_name == "link_preview":
|
||||
yield streaming_service.format_tool_output_available(
|
||||
tool_call_id,
|
||||
tool_output
|
||||
if isinstance(tool_output, dict)
|
||||
else {"result": tool_output},
|
||||
)
|
||||
if isinstance(tool_output, dict) and "error" not in tool_output:
|
||||
title = tool_output.get("title", "Link")
|
||||
yield streaming_service.format_terminal_info(
|
||||
f"Link preview loaded: {title[:50]}{'...' if len(title) > 50 else ''}",
|
||||
"success",
|
||||
)
|
||||
else:
|
||||
error_msg = (
|
||||
tool_output.get("error", "Failed to fetch")
|
||||
if isinstance(tool_output, dict)
|
||||
else "Failed to fetch"
|
||||
)
|
||||
yield streaming_service.format_terminal_info(
|
||||
f"Link preview failed: {error_msg}",
|
||||
"error",
|
||||
)
|
||||
elif tool_name == "display_image":
|
||||
yield streaming_service.format_tool_output_available(
|
||||
tool_call_id,
|
||||
tool_output
|
||||
if isinstance(tool_output, dict)
|
||||
else {"result": tool_output},
|
||||
)
|
||||
if isinstance(tool_output, dict):
|
||||
title = tool_output.get("title") or tool_output.get(
|
||||
"alt", "Image"
|
||||
)
|
||||
yield streaming_service.format_terminal_info(
|
||||
f"Image analyzed: {title[:40]}{'...' if len(title) > 40 else ''}",
|
||||
"success",
|
||||
)
|
||||
elif tool_name == "scrape_webpage":
|
||||
if isinstance(tool_output, dict):
|
||||
display_output = {
|
||||
k: v for k, v in tool_output.items() if k != "content"
|
||||
}
|
||||
if "content" in tool_output:
|
||||
content = tool_output.get("content", "")
|
||||
display_output["content_preview"] = (
|
||||
content[:500] + "..." if len(content) > 500 else content
|
||||
)
|
||||
yield streaming_service.format_tool_output_available(
|
||||
tool_call_id,
|
||||
display_output,
|
||||
)
|
||||
else:
|
||||
yield streaming_service.format_tool_output_available(
|
||||
tool_call_id,
|
||||
{"result": tool_output},
|
||||
)
|
||||
if isinstance(tool_output, dict) and "error" not in tool_output:
|
||||
title = tool_output.get("title", "Webpage")
|
||||
word_count = tool_output.get("word_count", 0)
|
||||
yield streaming_service.format_terminal_info(
|
||||
f"Scraped: {title[:40]}{'...' if len(title) > 40 else ''} ({word_count:,} words)",
|
||||
"success",
|
||||
)
|
||||
else:
|
||||
error_msg = (
|
||||
tool_output.get("error", "Failed to scrape")
|
||||
if isinstance(tool_output, dict)
|
||||
else "Failed to scrape"
|
||||
)
|
||||
yield streaming_service.format_terminal_info(
|
||||
f"Scrape failed: {error_msg}",
|
||||
"error",
|
||||
)
|
||||
elif tool_name == "search_knowledge_base":
|
||||
yield streaming_service.format_tool_output_available(
|
||||
tool_call_id,
|
||||
{"status": "completed", "result_length": len(str(tool_output))},
|
||||
)
|
||||
yield streaming_service.format_terminal_info(
|
||||
"Knowledge base search completed", "success"
|
||||
)
|
||||
else:
|
||||
yield streaming_service.format_tool_output_available(
|
||||
tool_call_id,
|
||||
{"status": "completed", "result_length": len(str(tool_output))},
|
||||
)
|
||||
yield streaming_service.format_terminal_info(
|
||||
f"Tool {tool_name} completed", "success"
|
||||
)
|
||||
|
||||
elif event_type in ("on_chain_end", "on_agent_end"):
|
||||
if current_text_id is not None:
|
||||
yield streaming_service.format_text_end(current_text_id)
|
||||
current_text_id = None
|
||||
|
||||
if current_text_id is not None:
|
||||
yield streaming_service.format_text_end(current_text_id)
|
||||
|
||||
completion_event = complete_current_step()
|
||||
if completion_event:
|
||||
yield completion_event
|
||||
|
||||
state = await agent.aget_state(config)
|
||||
is_interrupted = state.tasks and any(task.interrupts for task in state.tasks)
|
||||
if is_interrupted:
|
||||
interrupt_value = state.tasks[0].interrupts[0].value
|
||||
yield streaming_service.format_interrupt_request(interrupt_value)
|
||||
|
||||
yield streaming_service.format_finish_step()
|
||||
yield streaming_service.format_finish()
|
||||
yield streaming_service.format_done()
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
|
||||
error_message = f"Error during resume: {e!s}"
|
||||
print(f"[stream_resume_chat] {error_message}")
|
||||
print(f"[stream_resume_chat] Traceback:\n{traceback.format_exc()}")
|
||||
if current_text_id is not None:
|
||||
yield streaming_service.format_text_end(current_text_id)
|
||||
yield streaming_service.format_error(error_message)
|
||||
yield streaming_service.format_finish_step()
|
||||
yield streaming_service.format_finish()
|
||||
yield streaming_service.format_done()
|
||||
|
||||
finally:
|
||||
await clear_ai_responding(session, chat_id)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue