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.
65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
from ai_prompter import Prompter
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from langchain_core.messages import HumanMessage, SystemMessage
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from langchain_core.runnables import RunnableConfig
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from langgraph.graph import END, START, StateGraph
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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.domain.notebook import Source
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from open_notebook.domain.transformation import DefaultPrompts, Transformation
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from open_notebook.utils import clean_thinking_content
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class TransformationState(TypedDict):
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input_text: str
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source: Source
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transformation: Transformation
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output: str
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async def run_transformation(state: dict, config: RunnableConfig) -> dict:
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source_obj = state.get("source")
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source: Source = source_obj if isinstance(source_obj, Source) else None # type: ignore[assignment]
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content = state.get("input_text")
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assert source or content, "No content to transform"
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transformation: Transformation = state["transformation"]
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if not content:
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content = source.full_text
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transformation_template_text = transformation.prompt
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default_prompts: DefaultPrompts = DefaultPrompts(transformation_instructions=None)
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if default_prompts.transformation_instructions:
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transformation_template_text = f"{default_prompts.transformation_instructions}\n\n{transformation_template_text}"
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transformation_template_text = f"{transformation_template_text}\n\n# INPUT"
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system_prompt = Prompter(template_text=transformation_template_text).render(
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data=state
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)
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content_str = str(content) if content else ""
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payload = [SystemMessage(content=system_prompt), HumanMessage(content=content_str)]
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chain = await provision_langchain_model(
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str(payload),
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config.get("configurable", {}).get("model_id"),
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"transformation",
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max_tokens=5055,
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)
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response = await chain.ainvoke(payload)
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# Clean thinking content from the response
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response_content = response.content if isinstance(response.content, str) else str(response.content)
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cleaned_content = clean_thinking_content(response_content)
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if source:
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await source.add_insight(transformation.title, cleaned_content)
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return {
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"output": cleaned_content,
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}
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agent_state = StateGraph(TransformationState)
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agent_state.add_node("agent", run_transformation) # type: ignore[type-var]
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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()
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