alex
21954df3ee
- Fetch models dynamically from OpenCode Zen API (36 models vs 5 hardcoded) - Add function calling support check before using tools - Add retry logic (num_retries=2) for transient failures - Improve logging for debugging API calls and errors - Update system prompt for multi-stop itinerary context - Clean up unused imports in frontend components - Remove deleted views.py (moved to views/__init__.py)
496 lines
18 KiB
Python
496 lines
18 KiB
Python
import asyncio
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import json
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import logging
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from asgiref.sync import sync_to_async
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from adventures.models import Collection
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from django.http import StreamingHttpResponse
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from rest_framework import status, viewsets
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from rest_framework.decorators import action
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from rest_framework.permissions import IsAuthenticated
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from rest_framework.response import Response
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from ..agent_tools import AGENT_TOOLS, execute_tool, serialize_tool_result
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from ..llm_client import (
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get_provider_catalog,
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get_system_prompt,
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is_chat_provider_available,
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stream_chat_completion,
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)
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from ..models import ChatConversation, ChatMessage
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from ..serializers import ChatConversationSerializer
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logger = logging.getLogger(__name__)
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class ChatViewSet(viewsets.ModelViewSet):
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serializer_class = ChatConversationSerializer
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permission_classes = [IsAuthenticated]
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def get_queryset(self):
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return ChatConversation.objects.filter(user=self.request.user).prefetch_related(
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"messages"
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)
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def list(self, request, *args, **kwargs):
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conversations = self.get_queryset().only("id", "title", "updated_at")
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data = [
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{
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"id": str(conversation.id),
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"title": conversation.title,
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"updated_at": conversation.updated_at,
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}
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for conversation in conversations
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]
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return Response(data)
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def create(self, request, *args, **kwargs):
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conversation = ChatConversation.objects.create(
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user=request.user,
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title=(request.data.get("title") or "").strip(),
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)
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serializer = self.get_serializer(conversation)
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return Response(serializer.data, status=status.HTTP_201_CREATED)
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def _build_llm_messages(self, conversation, user, system_prompt=None):
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messages = [
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{
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"role": "system",
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"content": system_prompt or get_system_prompt(user),
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}
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]
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for message in conversation.messages.all().order_by("created_at"):
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payload = {
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"role": message.role,
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"content": message.content,
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}
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if message.role == "assistant" and message.tool_calls:
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payload["tool_calls"] = message.tool_calls
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if message.role == "tool":
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payload["tool_call_id"] = message.tool_call_id
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payload["name"] = message.name
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messages.append(payload)
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return messages
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def _async_to_sync_generator(self, async_gen):
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loop = asyncio.new_event_loop()
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try:
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while True:
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try:
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yield loop.run_until_complete(async_gen.__anext__())
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except StopAsyncIteration:
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break
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finally:
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loop.run_until_complete(loop.shutdown_asyncgens())
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loop.close()
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@staticmethod
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def _merge_tool_call_delta(accumulator, tool_calls_delta):
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for idx, tool_call in enumerate(tool_calls_delta or []):
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idx = tool_call.get("index", idx)
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while len(accumulator) <= idx:
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accumulator.append(
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{
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"id": None,
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"type": "function",
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"function": {"name": "", "arguments": ""},
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}
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)
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current = accumulator[idx]
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if tool_call.get("id"):
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current["id"] = tool_call.get("id")
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if tool_call.get("type"):
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current["type"] = tool_call.get("type")
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function_data = tool_call.get("function") or {}
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if function_data.get("name"):
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current["function"]["name"] = function_data.get("name")
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if function_data.get("arguments"):
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current["function"]["arguments"] += function_data.get("arguments")
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@action(detail=True, methods=["post"])
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def send_message(self, request, pk=None):
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# Auto-learn preferences from user's travel history
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from integrations.utils.auto_profile import update_auto_preference_profile
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try:
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update_auto_preference_profile(request.user)
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except Exception as exc:
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logger.warning("Auto-profile update failed: %s", exc)
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# Continue anyway - not critical
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conversation = self.get_object()
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user_content = (request.data.get("message") or "").strip()
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if not user_content:
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return Response(
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{"error": "message is required"},
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status=status.HTTP_400_BAD_REQUEST,
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)
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provider = (request.data.get("provider") or "openai").strip().lower()
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model = (request.data.get("model") or "").strip() or None
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collection_id = request.data.get("collection_id")
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collection_name = request.data.get("collection_name")
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start_date = request.data.get("start_date")
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end_date = request.data.get("end_date")
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destination = request.data.get("destination")
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if not is_chat_provider_available(provider):
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return Response(
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{"error": f"Provider is not available for chat: {provider}."},
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status=status.HTTP_400_BAD_REQUEST,
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)
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context_parts = []
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if collection_name:
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context_parts.append(f"Trip: {collection_name}")
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if destination:
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context_parts.append(f"Destination: {destination}")
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if start_date and end_date:
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context_parts.append(f"Dates: {start_date} to {end_date}")
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collection = None
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if collection_id:
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try:
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requested_collection = Collection.objects.get(id=collection_id)
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if (
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requested_collection.user == request.user
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or requested_collection.shared_with.filter(
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id=request.user.id
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).exists()
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):
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collection = requested_collection
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except Collection.DoesNotExist:
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pass
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if collection:
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itinerary_stops = []
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seen_stops = set()
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for location in collection.locations.select_related(
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"city", "country"
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).all():
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city_name = (getattr(location.city, "name", "") or "").strip()
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country_name = (getattr(location.country, "name", "") or "").strip()
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if city_name or country_name:
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stop_label = (
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f"{city_name}, {country_name}"
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if city_name and country_name
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else city_name or country_name
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)
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stop_key = f"geo:{city_name.lower()}|{country_name.lower()}"
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else:
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fallback_name = (location.location or location.name or "").strip()
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if not fallback_name:
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continue
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stop_label = fallback_name
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stop_key = f"name:{fallback_name.lower()}"
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if stop_key in seen_stops:
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continue
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seen_stops.add(stop_key)
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itinerary_stops.append(stop_label)
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if len(itinerary_stops) >= 8:
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break
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if itinerary_stops:
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context_parts.append(f"Itinerary stops: {'; '.join(itinerary_stops)}")
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system_prompt = get_system_prompt(request.user, collection)
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if context_parts:
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system_prompt += "\n\n## Trip Context\n" + "\n".join(context_parts)
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ChatMessage.objects.create(
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conversation=conversation,
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role="user",
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content=user_content,
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)
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conversation.save(update_fields=["updated_at"])
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if not conversation.title:
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conversation.title = user_content[:120]
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conversation.save(update_fields=["title", "updated_at"])
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llm_messages = self._build_llm_messages(
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conversation,
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request.user,
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system_prompt=system_prompt,
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)
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MAX_TOOL_ITERATIONS = 10
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async def event_stream():
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current_messages = list(llm_messages)
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encountered_error = False
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tool_iterations = 0
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while tool_iterations < MAX_TOOL_ITERATIONS:
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content_chunks = []
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tool_calls_accumulator = []
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async for chunk in stream_chat_completion(
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request.user,
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current_messages,
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provider,
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tools=AGENT_TOOLS,
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model=model,
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):
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if not chunk.startswith("data: "):
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yield chunk
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continue
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payload = chunk[len("data: ") :].strip()
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if payload == "[DONE]":
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continue
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yield chunk
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try:
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data = json.loads(payload)
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except json.JSONDecodeError:
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continue
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if data.get("error"):
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encountered_error = True
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break
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if data.get("content"):
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content_chunks.append(data["content"])
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if data.get("tool_calls"):
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self._merge_tool_call_delta(
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tool_calls_accumulator,
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data["tool_calls"],
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)
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if encountered_error:
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break
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assistant_content = "".join(content_chunks)
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if tool_calls_accumulator:
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assistant_with_tools = {
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"role": "assistant",
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"content": assistant_content,
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"tool_calls": tool_calls_accumulator,
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}
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current_messages.append(assistant_with_tools)
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await sync_to_async(
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ChatMessage.objects.create, thread_sensitive=True
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)(
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conversation=conversation,
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role="assistant",
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content=assistant_content,
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tool_calls=tool_calls_accumulator,
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)
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await sync_to_async(conversation.save, thread_sensitive=True)(
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update_fields=["updated_at"]
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)
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for tool_call in tool_calls_accumulator:
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function_payload = tool_call.get("function") or {}
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function_name = function_payload.get("name") or ""
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raw_arguments = function_payload.get("arguments") or "{}"
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try:
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arguments = json.loads(raw_arguments)
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except json.JSONDecodeError:
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arguments = {}
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if not isinstance(arguments, dict):
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arguments = {}
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result = await sync_to_async(
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execute_tool, thread_sensitive=True
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)(
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function_name,
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request.user,
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**arguments,
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)
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result_content = serialize_tool_result(result)
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current_messages.append(
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{
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"role": "tool",
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"tool_call_id": tool_call.get("id"),
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"name": function_name,
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"content": result_content,
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}
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)
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await sync_to_async(
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ChatMessage.objects.create, thread_sensitive=True
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)(
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conversation=conversation,
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role="tool",
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content=result_content,
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tool_call_id=tool_call.get("id"),
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name=function_name,
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)
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await sync_to_async(conversation.save, thread_sensitive=True)(
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update_fields=["updated_at"]
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)
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tool_event = {
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"tool_result": {
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"tool_call_id": tool_call.get("id"),
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"name": function_name,
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"result": result,
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}
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}
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yield f"data: {json.dumps(tool_event)}\n\n"
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continue
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await sync_to_async(ChatMessage.objects.create, thread_sensitive=True)(
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conversation=conversation,
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role="assistant",
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content=assistant_content,
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)
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await sync_to_async(conversation.save, thread_sensitive=True)(
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update_fields=["updated_at"]
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)
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yield "data: [DONE]\n\n"
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break
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response = StreamingHttpResponse(
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streaming_content=self._async_to_sync_generator(event_stream()),
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content_type="text/event-stream",
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)
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response["Cache-Control"] = "no-cache"
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response["X-Accel-Buffering"] = "no"
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return response
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class ChatProviderCatalogViewSet(viewsets.ViewSet):
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permission_classes = [IsAuthenticated]
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def list(self, request):
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return Response(get_provider_catalog(user=request.user))
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@action(detail=True, methods=["get"])
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def models(self, request, pk=None):
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"""Fetch available models from a provider's API."""
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from chat.llm_client import CHAT_PROVIDER_CONFIG, get_llm_api_key
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provider = (pk or "").lower()
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api_key = get_llm_api_key(request.user, provider)
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if not api_key:
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return Response(
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{"error": "No API key configured for this provider"},
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status=status.HTTP_403_FORBIDDEN,
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)
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try:
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if provider == "openai":
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import openai
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client = openai.OpenAI(api_key=api_key)
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models = client.models.list()
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chat_models = [
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model.id
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for model in models
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if any(prefix in model.id for prefix in ["gpt-", "o1-", "chatgpt"])
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]
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return Response({"models": sorted(set(chat_models), reverse=True)})
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if provider in ["anthropic", "claude"]:
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return Response(
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{
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"models": [
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"claude-sonnet-4-20250514",
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"claude-opus-4-20250514",
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"claude-3-5-sonnet-20241022",
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"claude-3-5-haiku-20241022",
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"claude-3-haiku-20240307",
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]
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}
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)
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if provider in ["gemini", "google"]:
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return Response(
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{
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"models": [
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"gemini-2.0-flash",
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"gemini-1.5-pro",
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"gemini-1.5-flash",
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"gemini-1.5-flash-8b",
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]
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}
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)
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if provider in ["groq"]:
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return Response(
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{
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"models": [
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"llama-3.3-70b-versatile",
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"llama-3.1-70b-versatile",
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"llama-3.1-8b-instant",
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"mixtral-8x7b-32768",
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]
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}
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)
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if provider in ["ollama"]:
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import requests
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try:
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response = requests.get(
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"http://localhost:11434/api/tags", timeout=5
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)
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if response.ok:
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data = response.json()
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models = [item["name"] for item in data.get("models", [])]
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return Response({"models": models})
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except Exception:
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pass
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return Response({"models": []})
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if provider == "opencode_zen":
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import requests
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config = CHAT_PROVIDER_CONFIG.get("opencode_zen", {})
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api_base = config.get("api_base", "https://opencode.ai/zen/v1")
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response = requests.get(
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f"{api_base}/models",
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headers={"Authorization": f"Bearer {api_key}"},
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timeout=10,
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)
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if response.ok:
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data = response.json()
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raw_models = (
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data.get("data", data) if isinstance(data, dict) else data
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)
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model_ids = []
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for model_entry in raw_models:
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if not isinstance(model_entry, dict):
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continue
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model_id = model_entry.get("id") or model_entry.get("model_id")
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if model_id:
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model_ids.append(model_id)
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return Response({"models": sorted(set(model_ids))})
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logger.warning(
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"OpenCode Zen models fetch failed with status %s",
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response.status_code,
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)
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return Response({"models": []})
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return Response({"models": []})
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except Exception as exc:
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logger.error("Failed to fetch models for %s: %s", provider, exc)
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return Response(
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{"error": f"Failed to fetch models: {str(exc)}"},
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status=status.HTTP_500_INTERNAL_SERVER_ERROR,
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)
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from .capabilities import CapabilitiesView
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from .day_suggestions import DaySuggestionsView
|