mirror of
https://github.com/lfnovo/open-notebook.git
synced 2026-04-28 19:40:50 +00:00
ab5560c9a2
Changes: - Move migrations/ under open_notebook/database/migrations/ - Extract AI models to open_notebook/ai/ (Model, ModelManager, provision) - Extract podcasts to open_notebook/podcasts/ (EpisodeProfile, SpeakerProfile, PodcastEpisode) - Reorganize prompts to mirror graphs structure (chat/, source_chat/) This improves code organization by: - Consolidating database concerns (migrations now with database code) - Separating AI infrastructure from domain entities - Isolating podcast feature into its own module - Creating consistent prompt/graph naming conventions All 52 tests pass.
89 lines
3 KiB
Python
89 lines
3 KiB
Python
import asyncio
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import sqlite3
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from typing import Annotated, Optional
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from ai_prompter import Prompter
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from langchain_core.messages import AIMessage, SystemMessage
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from langchain_core.runnables import RunnableConfig
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from langgraph.checkpoint.sqlite import SqliteSaver
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from langgraph.graph import END, START, StateGraph
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from langgraph.graph.message import add_messages
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from typing_extensions import TypedDict
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from open_notebook.ai.provision import provision_langchain_model
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from open_notebook.config import LANGGRAPH_CHECKPOINT_FILE
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from open_notebook.domain.notebook import Notebook
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from open_notebook.utils import clean_thinking_content
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class ThreadState(TypedDict):
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messages: Annotated[list, add_messages]
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notebook: Optional[Notebook]
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context: Optional[str]
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context_config: Optional[dict]
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model_override: Optional[str]
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def call_model_with_messages(state: ThreadState, config: RunnableConfig) -> dict:
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system_prompt = Prompter(prompt_template="chat/system").render(data=state) # type: ignore[arg-type]
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payload = [SystemMessage(content=system_prompt)] + state.get("messages", [])
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model_id = config.get("configurable", {}).get("model_id") or state.get(
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"model_override"
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)
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# Handle async model provisioning from sync context
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def run_in_new_loop():
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"""Run the async function in a new event loop"""
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new_loop = asyncio.new_event_loop()
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try:
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asyncio.set_event_loop(new_loop)
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return new_loop.run_until_complete(
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provision_langchain_model(
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str(payload), model_id, "chat", max_tokens=8192
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)
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)
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finally:
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new_loop.close()
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asyncio.set_event_loop(None)
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try:
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# Try to get the current event loop
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asyncio.get_running_loop()
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# If we're in an event loop, run in a thread with a new loop
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import concurrent.futures
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with concurrent.futures.ThreadPoolExecutor() as executor:
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future = executor.submit(run_in_new_loop)
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model = future.result()
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except RuntimeError:
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# No event loop running, safe to use asyncio.run()
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model = asyncio.run(
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provision_langchain_model(
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str(payload),
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model_id,
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"chat",
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max_tokens=8192,
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)
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)
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ai_message = model.invoke(payload)
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# Clean thinking content from AI response (e.g., <think>...</think> tags)
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content = ai_message.content if isinstance(ai_message.content, str) else str(ai_message.content)
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cleaned_content = clean_thinking_content(content)
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cleaned_message = ai_message.model_copy(update={"content": cleaned_content})
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return {"messages": cleaned_message}
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conn = sqlite3.connect(
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LANGGRAPH_CHECKPOINT_FILE,
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check_same_thread=False,
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)
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memory = SqliteSaver(conn)
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agent_state = StateGraph(ThreadState)
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agent_state.add_node("agent", call_model_with_messages)
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agent_state.add_edge(START, "agent")
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agent_state.add_edge("agent", END)
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graph = agent_state.compile(checkpointer=memory)
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