Display actions in frontend (needs to add manifest files to LibreChat)
This commit is contained in:
185
agent/agent.py
185
agent/agent.py
@@ -2,7 +2,7 @@
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import json
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import logging
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from typing import Any
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from typing import Any, AsyncGenerator
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from infrastructure.persistence import get_memory
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@@ -183,3 +183,186 @@ class Agent:
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memory = get_memory()
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memory.episodic.add_error(tool_name, str(e))
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return {"error": "execution_failed", "message": str(e), "tool": tool_name}
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async def step_streaming(
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self, user_input: str, completion_id: str, created_ts: int, model: str
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) -> AsyncGenerator[dict[str, Any], None]:
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"""
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Execute agent step with streaming support for LibreChat.
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Yields SSE chunks for tool calls and final response.
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Args:
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user_input: User's message
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completion_id: Completion ID for the response
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created_ts: Timestamp for the response
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model: Model name
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Yields:
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SSE chunks in OpenAI format
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"""
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memory = get_memory()
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# Add user message to history
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memory.stm.add_message("user", user_input)
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memory.save()
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# Build initial messages
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system_prompt = self.prompt_builder.build_system_prompt()
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messages: list[dict[str, Any]] = [{"role": "system", "content": system_prompt}]
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# Add conversation history
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history = memory.stm.get_recent_history(settings.max_history_messages)
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messages.extend(history)
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# Add unread events if any
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unread_events = memory.episodic.get_unread_events()
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if unread_events:
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events_text = "\n".join(
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[f"- {e['type']}: {e['data']}" for e in unread_events]
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)
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messages.append(
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{"role": "system", "content": f"Background events:\n{events_text}"}
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)
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# Get tools specification for OpenAI format
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tools_spec = self.prompt_builder.build_tools_spec()
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# Tool execution loop
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for iteration in range(self.max_tool_iterations):
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# Call LLM with tools
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llm_result = self.llm.complete(messages, tools=tools_spec)
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# Handle both tuple (response, usage) and dict response
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if isinstance(llm_result, tuple):
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response_message, usage = llm_result
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else:
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response_message = llm_result
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# Check if there are tool calls
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tool_calls = response_message.get("tool_calls")
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if not tool_calls:
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# No tool calls, this is the final response
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final_content = response_message.get("content", "")
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memory.stm.add_message("assistant", final_content)
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memory.save()
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# Stream the final response
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yield {
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"id": completion_id,
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"object": "chat.completion.chunk",
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"created": created_ts,
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {"role": "assistant", "content": final_content},
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"finish_reason": "stop",
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}
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],
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}
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return
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# Stream tool calls
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for tool_call in tool_calls:
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function = tool_call.get("function", {})
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tool_name = function.get("name", "")
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tool_args = function.get("arguments", "{}")
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# Yield chunk indicating tool call
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yield {
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"id": completion_id,
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"object": "chat.completion.chunk",
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"created": created_ts,
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {
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"tool_calls": [
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{
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"index": 0,
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"id": tool_call.get("id"),
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"type": "function",
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"function": {
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"name": tool_name,
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"arguments": tool_args,
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},
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}
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]
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},
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"finish_reason": None,
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}
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],
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}
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# Add assistant message with tool calls to conversation
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messages.append(response_message)
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# Execute each tool call and stream results
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for tool_call in tool_calls:
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tool_result = self._execute_tool_call(tool_call)
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function = tool_call.get("function", {})
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tool_name = function.get("name", "")
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# Add tool result to messages
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messages.append(
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{
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"tool_call_id": tool_call.get("id"),
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"role": "tool",
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"name": tool_name,
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"content": json.dumps(tool_result, ensure_ascii=False),
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}
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)
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# Stream tool result as content
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result_text = f"\n🔧 {tool_name}: {json.dumps(tool_result, ensure_ascii=False)}\n"
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yield {
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"id": completion_id,
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"object": "chat.completion.chunk",
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"created": created_ts,
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {"content": result_text},
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"finish_reason": None,
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}
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],
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}
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# Max iterations reached, force final response
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messages.append(
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{
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"role": "system",
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"content": "Please provide a final response to the user without using any more tools.",
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}
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)
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llm_result = self.llm.complete(messages)
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if isinstance(llm_result, tuple):
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final_message, usage = llm_result
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else:
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final_message = llm_result
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final_response = final_message.get(
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"content", "I've completed the requested actions."
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)
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memory.stm.add_message("assistant", final_response)
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memory.save()
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# Stream final response
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yield {
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"id": completion_id,
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"object": "chat.completion.chunk",
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"created": created_ts,
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {"content": final_response},
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"finish_reason": "stop",
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}
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],
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}
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72
app.py
72
app.py
@@ -170,19 +170,19 @@ async def chat_completions(chat_request: ChatCompletionRequest):
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f"Chat request - stream={chat_request.stream}, input_length={len(user_input)}"
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)
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try:
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answer = agent.step(user_input)
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except LLMAPIError as e:
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logger.error(f"LLM API error: {e}")
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raise HTTPException(status_code=502, detail=f"LLM API error: {e}")
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except Exception as e:
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logger.error(f"Agent error: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail="Internal agent error")
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created_ts = int(time.time())
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completion_id = f"chatcmpl-{uuid.uuid4().hex}"
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if not chat_request.stream:
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try:
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answer = agent.step(user_input)
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except LLMAPIError as e:
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logger.error(f"LLM API error: {e}")
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raise HTTPException(status_code=502, detail=f"LLM API error: {e}")
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except Exception as e:
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logger.error(f"Agent error: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail="Internal agent error")
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return JSONResponse(
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{
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"id": completion_id,
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@@ -205,20 +205,44 @@ async def chat_completions(chat_request: ChatCompletionRequest):
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)
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async def event_generator():
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chunk = {
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"id": completion_id,
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"object": "chat.completion.chunk",
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"created": created_ts,
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"model": chat_request.model,
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"choices": [
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{
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"index": 0,
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"delta": {"role": "assistant", "content": answer or ""},
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"finish_reason": "stop",
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}
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],
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}
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yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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try:
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# Stream the agent execution
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async for chunk in agent.step_streaming(user_input, completion_id, created_ts, chat_request.model):
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yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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except LLMAPIError as e:
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logger.error(f"LLM API error: {e}")
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error_chunk = {
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"id": completion_id,
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"object": "chat.completion.chunk",
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"created": created_ts,
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"model": chat_request.model,
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"choices": [
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{
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"index": 0,
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"delta": {"role": "assistant", "content": f"Error: {e}"},
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"finish_reason": "stop",
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}
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],
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}
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yield f"data: {json.dumps(error_chunk, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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except Exception as e:
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logger.error(f"Agent error: {e}", exc_info=True)
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error_chunk = {
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"id": completion_id,
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"object": "chat.completion.chunk",
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"created": created_ts,
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"model": chat_request.model,
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"choices": [
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{
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"index": 0,
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"delta": {"role": "assistant", "content": "Internal agent error"},
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"finish_reason": "stop",
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}
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],
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}
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yield f"data: {json.dumps(error_chunk, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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return StreamingResponse(event_generator(), media_type="text/event-stream")
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