refactor transformation, add graph and admin

open_notebook/graphs/transformation.py

@@ -0,0 +1,57 @@
+from executing import Source
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.runnables import (
+    RunnableConfig,
+)
+from langgraph.graph import END, START, StateGraph
+from typing_extensions import TypedDict
+
+from open_notebook.domain.transformation import DefaultPrompts, Transformation
+from open_notebook.graphs.utils import provision_langchain_model
+from open_notebook.prompter import Prompter
+
+
+class TransformationState(TypedDict):
+    input_text: str
+    source: Source
+    transformation: Transformation
+    output: str
+
+
+def run_transformation(state: dict, config: RunnableConfig) -> dict:
+    source: Source = state.get("source")
+    content = state.get("input_text")
+    assert source or content, "No content to transform"
+    transformation: Transformation = state["transformation"]
+    if not content:
+        content = source.full_text
+    transformation_prompt_text = transformation.prompt
+    default_prompts: DefaultPrompts = DefaultPrompts().load()
+    if default_prompts.transformation_instructions:
+        transformation_prompt_text = f"{default_prompts.transformation_instructions}\n\n{transformation_prompt_text}"
+
+    transformation_prompt_text = f"{transformation_prompt_text}\n\n# INPUT"
+
+    system_prompt = Prompter(prompt_text=transformation_prompt_text).render(data=state)
+    payload = [SystemMessage(content=system_prompt)] + [HumanMessage(content=content)]
+    chain = provision_langchain_model(
+        str(payload),
+        config.get("configurable", {}).get("model_id"),
+        "transformation",
+        max_tokens=5000,
+    )
+
+    response = chain.invoke(payload)
+    if source:
+        source.add_insight(transformation.title, response.content)
+
+    return {
+        "output": response.content,
+    }
+
+
+agent_state = StateGraph(TransformationState)
+agent_state.add_node("agent", run_transformation)
+agent_state.add_edge(START, "agent")
+agent_state.add_edge("agent", END)
+graph = agent_state.compile()