efd69cc134
- Introduced a new chat endpoint for handling user messages and generating responses. - Added models for chat message requests and responses. - Implemented a conversation store to manage chat history. - Integrated memory layer for retrieving presentation context. - Created tools for accessing presentation outlines and searching slides. - Updated dependencies to include jsonschema for validation. - Enhanced the API router to include the new chat functionality.
26 lines
892 B
Python
26 lines
892 B
Python
from fastapi import APIRouter, Depends
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from sqlalchemy.ext.asyncio import AsyncSession
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from models.chat import ChatMessageRequest, ChatMessageResponse
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from services.chat import PresentationChatService
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from services.database import get_async_session
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CHAT_ROUTER = APIRouter(prefix="/chat", tags=["Chat"])
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@CHAT_ROUTER.post("/message", response_model=ChatMessageResponse)
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async def chat_message(
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payload: ChatMessageRequest,
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sql_session: AsyncSession = Depends(get_async_session),
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):
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service = PresentationChatService(
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sql_session=sql_session,
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presentation_id=payload.presentation_id,
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conversation_id=payload.conversation_id,
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)
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result = await service.generate_reply(payload.message)
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return ChatMessageResponse(
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conversation_id=result.conversation_id,
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response=result.response_text,
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tool_calls=result.tool_calls,
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)
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