Luis Novo
GitHub
commit
17b3ad010b
7 changed files with 130 additions and 15 deletions
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@ -3,9 +3,7 @@ from typing import Annotated, List
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from ai_prompter import Prompter
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from langchain_core.output_parsers.pydantic import PydanticOutputParser
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from langchain_core.runnables import (
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RunnableConfig,
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)
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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 langgraph.types import Send
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from pydantic import BaseModel, Field
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@ -13,6 +11,7 @@ from typing_extensions import TypedDict
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from open_notebook.domain.notebook import vector_search
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from open_notebook.graphs.utils import provision_langchain_model
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from open_notebook.utils import clean_thinking_content
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class SubGraphState(TypedDict):
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@ -59,10 +58,19 @@ async def call_model_with_messages(state: ThreadState, config: RunnableConfig) -
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config.get("configurable", {}).get("strategy_model"),
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"tools",
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max_tokens=2000,
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structured=dict(type="json"),
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)
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# model = model.bind_tools(tools)
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ai_message = (model | parser).invoke(system_prompt)
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return {"strategy": ai_message}
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# First get the raw response from the model
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ai_message = model.invoke(system_prompt)
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# Clean the thinking content from the response
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cleaned_content = clean_thinking_content(ai_message.content)
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# Parse the cleaned JSON content
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strategy = parser.parse(cleaned_content)
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return {"strategy": strategy}
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async def trigger_queries(state: ThreadState, config: RunnableConfig):
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@ -99,7 +107,7 @@ async def provide_answer(state: SubGraphState, config: RunnableConfig) -> dict:
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max_tokens=2000,
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)
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ai_message = model.invoke(system_prompt)
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return {"answers": [ai_message.content]}
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return {"answers": [clean_thinking_content(ai_message.content)]}
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async def write_final_answer(state: ThreadState, config: RunnableConfig) -> dict:
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@ -111,7 +119,7 @@ async def write_final_answer(state: ThreadState, config: RunnableConfig) -> dict
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max_tokens=2000,
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)
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ai_message = model.invoke(system_prompt)
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return {"final_answer": ai_message.content}
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return {"final_answer": clean_thinking_content(ai_message.content)}
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agent_state = StateGraph(ThreadState)
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@ -7,6 +7,7 @@ from typing_extensions import TypedDict
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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.graphs.utils import provision_langchain_model
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from open_notebook.utils import clean_thinking_content
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class TransformationState(TypedDict):
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@ -42,11 +43,15 @@ def run_transformation(state: dict, config: RunnableConfig) -> dict:
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)
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response = chain.invoke(payload)
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# Clean thinking content from the response
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cleaned_content = clean_thinking_content(response.content)
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if source:
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source.add_insight(transformation.title, response.content)
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source.add_insight(transformation.title, cleaned_content)
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return {
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"output": response.content,
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"output": cleaned_content,
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}
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@ -1,6 +1,7 @@
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import re
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import unicodedata
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from importlib.metadata import PackageNotFoundError, version
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from typing import Tuple
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from urllib.parse import urlparse
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import requests
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@ -217,3 +218,75 @@ def compare_versions(version1: str, version2: str) -> int:
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return 1
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else:
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return 0
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# Compile regex pattern once for better performance
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THINK_PATTERN = re.compile(r'<think>(.*?)</think>', re.DOTALL)
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def parse_thinking_content(content: str) -> Tuple[str, str]:
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"""
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Parse message content to extract thinking content from <think> tags.
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Args:
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content (str): The original message content
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Returns:
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Tuple[str, str]: (thinking_content, cleaned_content)
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- thinking_content: Content from within <think> tags
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- cleaned_content: Original content with <think> blocks removed
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Example:
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>>> content = "<think>Let me analyze this</think>Here's my answer"
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>>> thinking, cleaned = parse_thinking_content(content)
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>>> print(thinking)
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"Let me analyze this"
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>>> print(cleaned)
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"Here's my answer"
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"""
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# Input validation
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if not isinstance(content, str):
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return "", str(content) if content is not None else ""
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# Limit processing for very large content (100KB limit)
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if len(content) > 100000:
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return "", content
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# Find all thinking blocks
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thinking_matches = THINK_PATTERN.findall(content)
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if not thinking_matches:
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return "", content
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# Join all thinking content with double newlines
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thinking_content = "\n\n".join(match.strip() for match in thinking_matches)
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# Remove all <think>...</think> blocks from the original content
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cleaned_content = THINK_PATTERN.sub("", content)
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# Clean up extra whitespace
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cleaned_content = re.sub(r'\n\s*\n\s*\n', '\n\n', cleaned_content).strip()
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return thinking_content, cleaned_content
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def clean_thinking_content(content: str) -> str:
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"""
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Remove thinking content from AI responses, returning only the cleaned content.
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This is a convenience function for cases where you only need the cleaned
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content and don't need access to the thinking process.
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Args:
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content (str): The original message content with potential <think> tags
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Returns:
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str: Content with <think> blocks removed and whitespace cleaned
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Example:
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>>> content = "<think>Let me think...</think>Here's the answer"
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>>> clean_thinking_content(content)
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"Here's the answer"
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"""
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_, cleaned_content = parse_thinking_content(content)
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return cleaned_content
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@ -40,10 +40,9 @@ async def process_ask_query(question, strategy_model, answer_model, final_answer
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def results_card(item):
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score = item.get("relevance", item.get("similarity", item.get("score", 0)))
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with st.container(border=True):
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st.markdown(
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f"[{score:.2f}] **[{item['title']}](/?object_id={item['parent_id']})**"
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f"[{item['final_score']:.2f}] **[{item['title']}](/?object_id={item['parent_id']})**"
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)
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if "matches" in item:
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with st.expander("Matches"):
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@ -160,5 +159,15 @@ with search_tab:
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st.session_state["search_results"] = vector_search(
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search_term, 100, search_sources, search_notes
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)
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for item in st.session_state["search_results"]:
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search_results = st.session_state["search_results"].copy()
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for item in search_results:
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item["final_score"] = item.get(
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"relevance", item.get("similarity", item.get("score", 0))
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)
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# Sort search results by final_score in descending order
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search_results.sort(key=lambda x: x["final_score"], reverse=True)
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for item in search_results:
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results_card(item)
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@ -14,6 +14,8 @@ from pages.stream_app.utils import (
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create_session_for_notebook,
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)
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from open_notebook.utils import parse_thinking_content
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from .note import make_note_from_chat
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@ -186,11 +188,28 @@ def chat_sidebar(current_notebook: Notebook, current_session: ChatSession):
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continue
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with st.chat_message(name=msg.type):
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st.markdown(convert_source_references(msg.content))
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if msg.type == "ai":
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# Parse thinking content for AI messages
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thinking_content, cleaned_content = parse_thinking_content(msg.content)
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# Show thinking content in expander if present
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if thinking_content:
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with st.expander("🤔 AI Reasoning", expanded=False):
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st.markdown(thinking_content)
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# Show the cleaned regular content
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if cleaned_content:
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st.markdown(convert_source_references(cleaned_content))
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elif msg.content: # Fallback to original if cleaning resulted in empty content
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st.markdown(convert_source_references(msg.content))
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# New Note button for AI messages
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if st.button("💾 New Note", key=f"render_save_{msg.id}"):
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make_note_from_chat(
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content=msg.content,
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notebook_id=current_notebook.id,
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)
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st.rerun()
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else:
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# Human messages - display normally
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st.markdown(convert_source_references(msg.content))
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@ -1,6 +1,6 @@
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[project]
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name = "open-notebook"
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version = "0.2.2"
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version = "0.2.3"
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description = "An open source implementation of a research assistant, inspired by Google Notebook LM"
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authors = [
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{name = "Luis Novo", email = "lfnovo@gmail.com"}
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3
uv.lock
3
uv.lock
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@ -2716,7 +2716,7 @@ wheels = [
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[[package]]
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name = "open-notebook"
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version = "0.2.2"
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version = "0.2.3"
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source = { editable = "." }
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dependencies = [
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{ name = "ai-prompter" },
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@ -3460,6 +3460,7 @@ sdist = { url = "https://files.pythonhosted.org/packages/bd/62/d29612ca33b7844e7
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/32/5a/3399a2caf51c91db650de57464465b830c2d4ea15b23d24a98182202b704/pymupdf-1.26.1-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:32296f12a7c7f36febd59cee77823a54490313bcaba9879b17def6518186f94e", size = 23054640 },
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