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57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
from executing import Source
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from langchain_core.messages import HumanMessage, SystemMessage
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from langchain_core.runnables import (
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RunnableConfig,
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)
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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.domain.transformation import DefaultPrompts, Transformation
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from open_notebook.graphs.utils import provision_langchain_model
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from open_notebook.prompter import Prompter
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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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def run_transformation(state: dict, config: RunnableConfig) -> dict:
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source: Source = state.get("source")
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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_prompt_text = transformation.prompt
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default_prompts: DefaultPrompts = DefaultPrompts().load()
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if default_prompts.transformation_instructions:
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transformation_prompt_text = f"{default_prompts.transformation_instructions}\n\n{transformation_prompt_text}"
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transformation_prompt_text = f"{transformation_prompt_text}\n\n# INPUT"
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system_prompt = Prompter(prompt_text=transformation_prompt_text).render(data=state)
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payload = [SystemMessage(content=system_prompt)] + [HumanMessage(content=content)]
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chain = 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=5000,
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)
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response = chain.invoke(payload)
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if source:
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source.add_insight(transformation.title, response.content)
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return {
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"output": response.content,
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}
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agent_state = StateGraph(TransformationState)
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agent_state.add_node("agent", run_transformation)
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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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