unify chat.py file

templates/components/multiagent/python/app/api/routers/chat.py

-import logging
-
-from fastapi import APIRouter, BackgroundTasks, HTTPException, Request, status
-
-from app.api.callbacks.llamacloud import LlamaCloudFileDownload
-from app.api.callbacks.next_question import SuggestNextQuestions
-from app.api.callbacks.stream_handler import StreamHandler
-from app.api.callbacks.source_nodes import AddNodeUrl
-from app.api.routers.models import (
-    ChatData,
-)
-from app.engine.query_filter import generate_filters
-from app.workflows import create_workflow
-
-chat_router = r = APIRouter()
-
-logger = logging.getLogger("uvicorn")
-
-
-@r.post("")
-async def chat(
-    request: Request,
-    data: ChatData,
-    background_tasks: BackgroundTasks,
-):
-    try:
-        last_message_content = data.get_last_message_content()
-        messages = data.get_history_messages(include_agent_messages=True)
-
-        doc_ids = data.get_chat_document_ids()
-        filters = generate_filters(doc_ids)
-        params = data.data or {}
-
-        workflow = create_workflow(
-            params=params,
-            filters=filters,
-        )
-
-        handler = workflow.run(
-            user_msg=last_message_content,
-            chat_history=messages,
-            stream=True,
-        )
-        return StreamHandler.from_default(
-            handler=handler,
-            callbacks=[
-                LlamaCloudFileDownload.from_default(background_tasks),
-                SuggestNextQuestions.from_default(data),
-                AddNodeUrl.from_default(),
-            ],
-        ).vercel_stream()
-    except Exception as e:
-        logger.exception("Error in chat engine", exc_info=True)
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=f"Error in chat engine: {e}",
-        ) from e

templates/types/streaming/fastapi/app/api/routers/chat.py

-import json
 import logging
 
 from fastapi import APIRouter, BackgroundTasks, HTTPException, Request, status
-from llama_index.core.agent.workflow import AgentOutput
-from llama_index.core.llms import MessageRole
 
 from app.api.callbacks.llamacloud import LlamaCloudFileDownload
 from app.api.callbacks.next_question import SuggestNextQuestions
-from app.api.callbacks.source_nodes import AddNodeUrl
 from app.api.callbacks.stream_handler import StreamHandler
+from app.api.callbacks.source_nodes import AddNodeUrl
 from app.api.routers.models import (
     ChatData,
-    Message,
-    Result,
 )
-from app.engine.engine import get_engine
 from app.engine.query_filter import generate_filters
+from app.workflows import create_workflow
 
 chat_router = r = APIRouter()
 
 logger = logging.getLogger("uvicorn")
 
 
-# streaming endpoint - delete if not needed
 @r.post("")
 async def chat(
     request: Request,
@@ -31,16 +25,18 @@ async def chat(
 ):
     try:
         last_message_content = data.get_last_message_content()
-        messages = data.get_history_messages()
+        messages = data.get_history_messages(include_agent_messages=True)
 
         doc_ids = data.get_chat_document_ids()
         filters = generate_filters(doc_ids)
         params = data.data or {}
-        logger.info(
-            f"Creating chat engine with filters: {str(filters)}",
+
+        workflow = create_workflow(
+            params=params,
+            filters=filters,
         )
-        engine = get_engine(filters=filters, params=params)
-        handler = engine.run(
+
+        handler = workflow.run(
             user_msg=last_message_content,
             chat_history=messages,
             stream=True,
@@ -59,35 +55,3 @@ async def chat(
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             detail=f"Error in chat engine: {e}",
         ) from e
-
-
-# non-streaming endpoint - delete if not needed
-@r.post("/request")
-async def chat_request(
-    data: ChatData,
-) -> Result:
-    last_message_content = data.get_last_message_content()
-    messages = data.get_history_messages()
-
-    doc_ids = data.get_chat_document_ids()
-    filters = generate_filters(doc_ids)
-    params = data.data or {}
-    logger.info(
-        f"Creating chat engine with filters: {str(filters)}",
-    )
-    engine = get_engine(filters=filters, params=params)
-
-    response = await engine.run(
-        user_msg=last_message_content,
-        chat_history=messages,
-        stream=False,
-    )
-    output = response
-    if isinstance(output, AgentOutput):
-        content = output.response.content
-    else:
-        content = json.dumps(output)
-
-    return Result(
-        result=Message(role=MessageRole.ASSISTANT, content=content),
-    )

templates/types/streaming/fastapi/app/workflows/__init__.py

+from .agent import create_workflow

templates/types/streaming/fastapi/app/engine/engine.py → templates/types/streaming/fastapi/app/workflows/agent.py

@@ -2,7 +2,6 @@ import os
 from typing import List
 
 from llama_index.core.agent.workflow import AgentWorkflow
-
 from llama_index.core.settings import Settings
 from llama_index.core.tools import BaseTool
 
@@ -11,7 +10,7 @@ from app.engine.tools import ToolFactory
 from app.engine.tools.query_engine import get_query_engine_tool
 
 
-def get_engine(params=None, **kwargs):
+def create_workflow(params=None, **kwargs):
     if params is None:
         params = {}
     system_prompt = os.getenv("SYSTEM_PROMPT")