mirror of
https://github.com/lfnovo/open-notebook.git
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b7e656a319
New front-end Launch Chat API Manage Sources Enable re-embedding of all contents Sources can be added without a notebook now Improved settings Enable model selector on all chats Background processing for better experience Dark mode Improved Notes Improved Docs: - Remove all Streamlit references from documentation - Update deployment guides with React frontend setup - Fix Docker environment variables format (SURREAL_URL, SURREAL_PASSWORD) - Update docker image tag from :latest to :v1-latest - Change navigation references (Settings → Models to just Models) - Update development setup to include frontend npm commands - Add MIGRATION.md guide for users upgrading from Streamlit - Update quick-start guide with correct environment variables - Add port 5055 documentation for API access - Update project structure to reflect frontend/ directory - Remove outdated source-chat documentation files
42 lines
1.2 KiB
Python
42 lines
1.2 KiB
Python
from typing import Any, Optional
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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.graphs.utils import provision_langchain_model
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class PatternChainState(TypedDict):
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prompt: str
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parser: Optional[Any]
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input_text: str
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output: str
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async def call_model(state: dict, config: RunnableConfig) -> dict:
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content = state["input_text"]
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system_prompt = Prompter(
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template_text=state["prompt"], parser=state.get("parser")
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).render(data=state)
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payload = [SystemMessage(content=system_prompt)] + [HumanMessage(content=content)]
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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=5000,
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)
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response = await chain.ainvoke(payload)
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return {"output": response.content}
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agent_state = StateGraph(PatternChainState)
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agent_state.add_node("agent", call_model) # 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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