mirror of
https://github.com/MODSetter/SurfSense.git
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76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
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"""
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Utilities for working with message content.
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Message content in new_chat_messages can be stored in various formats:
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- String: Simple text content
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- List: Array of content parts [{"type": "text", "text": "..."}, {"type": "tool-call", ...}]
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- Dict: Single content object
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These utilities help extract and transform content for different use cases.
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"""
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from langchain_core.messages import AIMessage, HumanMessage
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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def extract_text_content(content: str | dict | list) -> str:
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"""Extract plain text content from various message formats."""
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if isinstance(content, str):
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return content
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if isinstance(content, dict):
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# Handle dict with 'text' key
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if "text" in content:
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return content["text"]
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return str(content)
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if isinstance(content, list):
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# Handle list of parts (e.g., [{"type": "text", "text": "..."}])
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texts = []
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for part in content:
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if isinstance(part, dict) and part.get("type") == "text":
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texts.append(part.get("text", ""))
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elif isinstance(part, str):
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texts.append(part)
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return "\n".join(texts) if texts else ""
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return ""
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async def bootstrap_history_from_db(
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session: AsyncSession,
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thread_id: int,
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) -> list[HumanMessage | AIMessage]:
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"""
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Load message history from database and convert to LangChain format.
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Used for cloned chats where the LangGraph checkpointer has no state,
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but we have messages in the database that should be used as context.
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Args:
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session: Database session
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thread_id: The chat thread ID
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Returns:
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List of LangChain messages (HumanMessage/AIMessage)
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"""
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from app.db import NewChatMessage
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result = await session.execute(
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select(NewChatMessage)
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.filter(NewChatMessage.thread_id == thread_id)
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.order_by(NewChatMessage.created_at)
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)
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db_messages = result.scalars().all()
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langchain_messages: list[HumanMessage | AIMessage] = []
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for msg in db_messages:
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text_content = extract_text_content(msg.content)
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if not text_content:
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continue
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if msg.role == "user":
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langchain_messages.append(HumanMessage(content=text_content))
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elif msg.role == "assistant":
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langchain_messages.append(AIMessage(content=text_content))
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return langchain_messages
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