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1a6fe4723b
Some LLM providers (notably Gemini, DeepSeek via OpenAI-compatible
proxies) return ai_message.content as a list of content parts:
[{'type': 'text', 'text': '...', 'extras': {...}}]
The current code uses str() on non-string content, which produces the
Python repr of the entire list — not valid JSON. This breaks
PydanticOutputParser.parse() with OutputParserException.
This adds extract_text_content() to properly unwrap text from both
string and structured content formats, applied in ask.py, chat.py,
and prompt.py.
Fixes #329
45 lines
1.4 KiB
Python
45 lines
1.4 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.ai.provision import provision_langchain_model
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from open_notebook.utils.text_utils import clean_thinking_content, extract_text_content
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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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# Clean thinking tags from response (handles extended thinking models)
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output = clean_thinking_content(extract_text_content(response.content))
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return {"output": output}
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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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