242 lines
7.5 KiB
Python
242 lines
7.5 KiB
Python
from fastapi import APIRouter, Depends, Request
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from fastapi.responses import StreamingResponse
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from openai import OpenAI
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from pydantic import BaseModel
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import json
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from api.dependencies import get_llm_client, get_agent_graph
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from agents import get as get_agent, list_all as list_all_agents
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from core.state import AgentState
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router = APIRouter()
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class ChatRequest(BaseModel):
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message: str
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session_id: str | None = None
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agent_id: str | None = None
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class ChatCompletionRequest(BaseModel):
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messages: list[dict]
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stream: bool = False
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model: str = "deepseek-chat"
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# ---------------------------------------------------------------------------
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# Agent resolution
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# ---------------------------------------------------------------------------
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def _resolve_agent(agent_id: str | None = None, model: str | None = None):
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"""
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1. explicit agent_id
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2. model field (OpenWebUI sends this — maps to agent_id if registered)
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3. fallback to "naked"
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"""
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lookup = agent_id or model
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if lookup is None:
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return get_agent("naked")
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agent = get_agent(lookup)
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return agent if agent else get_agent("naked")
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# ---------------------------------------------------------------------------
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# LangGraph helpers
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# ---------------------------------------------------------------------------
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async def _invoke_graph(graph, messages: list[dict]) -> str:
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"""Run the graph synchronously (non-streaming) and return the final text."""
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state: AgentState = {"messages": messages}
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result = await graph.ainvoke(state)
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last_msg = result["messages"][-1]
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return last_msg.content or ""
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async def _stream_graph(graph, messages: list[dict]):
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"""
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Run the graph and stream the final response token-by-token.
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LangGraph's astream_events would require langchain-openai's ChatOpenAI
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to intercept LLM chunks. Instead we run the graph to completion (tools
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execute silently) and then stream the final text content character by
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character — this gives the client a real SSE stream without adding new
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dependencies.
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"""
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state: AgentState = {"messages": messages}
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result = await graph.ainvoke(state)
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content = result["messages"][-1].content or ""
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# Yield token-by-token so the SSE client sees incremental output
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for token in content:
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yield token
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# ---------------------------------------------------------------------------
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# Non-streaming run (kept for /chat/sync and sync completions)
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# ---------------------------------------------------------------------------
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async def run_agent_with_tools(
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request: Request,
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messages: list[dict],
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agent_id: str | None = None,
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model: str | None = None,
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) -> str:
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"""Send messages through the agent's LangGraph. Non-streaming."""
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agent = _resolve_agent(agent_id, model)
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graph = get_agent_graph(agent.agent_id, request)
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return await _invoke_graph(graph, messages)
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# ---------------------------------------------------------------------------
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# Streaming generator (kept for /chat and stream completions)
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# ---------------------------------------------------------------------------
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async def run_agent_stream(
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request: Request,
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messages: list[dict],
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agent_id: str | None = None,
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model: str | None = None,
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):
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"""Async generator — yields tokens via the agent's LangGraph."""
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agent = _resolve_agent(agent_id, model)
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graph = get_agent_graph(agent.agent_id, request)
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async for token in _stream_graph(graph, messages):
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yield token
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# ---------------------------------------------------------------------------
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# Endpoints
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# ---------------------------------------------------------------------------
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@router.get("/")
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def root():
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return {"status": "ok"}
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@router.post("/chat")
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async def chat(
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req: ChatRequest,
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request: Request,
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client: OpenAI = Depends(get_llm_client),
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):
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"""Streaming chat — single message, no history."""
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messages = [{"role": "user", "content": req.message}]
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async def event_stream():
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async for token in run_agent_stream(request, messages, req.agent_id):
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payload = json.dumps({"token": token, "session_id": req.session_id})
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yield f"data: {payload}\n\n"
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yield f"data: {json.dumps({'done': True, 'session_id': req.session_id})}\n\n"
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return StreamingResponse(
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event_stream(),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no",
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},
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)
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@router.post("/chat/sync")
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async def chat_sync(
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req: ChatRequest,
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request: Request,
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client: OpenAI = Depends(get_llm_client),
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):
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"""Non-streaming chat — single message."""
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messages = [{"role": "user", "content": req.message}]
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response = await run_agent_with_tools(request, messages, req.agent_id)
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return {"response": response, "session_id": req.session_id}
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@router.get("/agents")
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def list_agents():
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"""Return all registered agents."""
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return {
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"agents": [
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{
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"agent_id": a.agent_id,
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"description": a.description,
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"skills": a.skills,
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}
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for a in list_all_agents().values()
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]
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}
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@router.get("/models")
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def list_models():
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"""Return agents as selectable models for OpenWebUI."""
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return {
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"object": "list",
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"data": [
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{
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"id": a.agent_id,
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"object": "model",
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"created": 0,
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"owned_by": "local-agent",
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}
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for a in list_all_agents().values()
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],
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}
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@router.post("/chat/completions")
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async def chat_completions(
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req: ChatCompletionRequest,
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request: Request,
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client: OpenAI = Depends(get_llm_client),
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):
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"""OpenAI-compatible /chat/completions — supports stream=True.
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Multi-turn: req.messages contains the FULL conversation history.
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Agent resolved from the model field (OpenWebUI sends this).
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"""
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agent = _resolve_agent(model=req.model)
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if req.stream:
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async def sse_stream():
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async for token in run_agent_stream(
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request, req.messages, agent_id=agent.agent_id,
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):
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chunk = {
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"id": "chatcmpl-local",
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"object": "chat.completion.chunk",
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"choices": [
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{"index": 0, "delta": {"content": token}, "finish_reason": None}
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],
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}
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yield f"data: {json.dumps(chunk)}\n\n"
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final_chunk = {
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"id": "chatcmpl-local",
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"object": "chat.completion.chunk",
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"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
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}
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yield f"data: {json.dumps(final_chunk)}\n\n"
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yield "data: [DONE]\n\n"
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return StreamingResponse(
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sse_stream(),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
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)
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# Non-streaming — full history, LangGraph agent
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response = await run_agent_with_tools(
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request, req.messages, agent_id=agent.agent_id,
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)
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return {
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"id": "chatcmpl-local",
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"object": "chat.completion",
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"created": 0,
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"model": req.model,
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"choices": [
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{
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"index": 0,
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"message": {"role": "assistant", "content": response},
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"finish_reason": "stop",
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}
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],
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}
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