Fixing Memory Saving Issue

2025-03-24 11:10 UTC gpt-4o Open in ChatGPT ↗

You

I am using langchain’s conversationbuffermemory to store the previous questions and answers with my llm but i actually don’t think the history is stored in memory. Here is my script

from datetime import datetime

import yaml import json import time import re import os import gradio as gr from dotenv import load_dotenv from sentence_transformers import SentenceTransformer from langchain.memory import ConversationBufferMemory from HF_API_Wrapper import HuggingFaceAPI from langchain.prompts import PromptTemplate from rag_retrieve_meta import RagRetrieveWithMeta from langchain.schema.runnable import RunnableLambda, RunnableSequence import spaces

class ChatManager: def init(self): # Public variables (Accessible directly) self.parameters = None self.persona_steps = None self.prompts = None self.rag_queries = None self.llm = None self.rag_retriever = None self.rag_results = {} self.generated_inputs = {} self.memory = None

    # ==== gradio =====
    self.history = []
    self.persona_history = []
    self.process_logs = []
    self.process_step = 0

@spaces.GPU()
def _initialize_config(self):
    """Private method to load configurations."""
    # Load environment variables
    load_dotenv()
    HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

    if not HUGGINGFACE_TOKEN:
        raise ValueError("HUGGINGFACE_TOKEN is not set! Make sure to define it in .env.")

    # Load YAML Config
    with open("config_article.yml", "r", encoding="utf-8") as file:
        config = yaml.safe_load(file)

    self.parameters = config["parameters"]
    self.persona_steps = config["persona_workflow"]
    self.prompts = config["prompts"]
    self.rag_queries = config["rag"]

    # Initialize Hugging Face LLM
    self.llm = HuggingFaceAPI(api_url=config["API"]["model_url"], api_token=HUGGINGFACE_TOKEN)

    # Initialize RAG Retriever
    embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
    self.rag_retriever = RagRetrieveWithMeta(
        embedding_model,
        config['rag']['scientific']['faiss'],
        config['rag']['scientific']['embeddings'],
        config['rag']['diary']['faiss'],
        config['rag']['diary']['embeddings'],
        config['rag']['weather']['faiss'],
        config['rag']['weather']['embeddings'],
        config['rag']['insights']['faiss'],
        config['rag']['insights']['embeddings']
    )

    # Initialize Memory for Chat History
    self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

    # Clear histories
    self.history = []
    self.persona_history = []
    self.process_logs = []
    self.process_step = 0

def save_history(self, history):
    file_path = f"chat_history_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"

    # Convert history to structured JSON format
    history_json = {
        "persona_history": self.persona_history,  # 🔹 Now includes both persona and the refinement prompt used
        "chat_history": [{"--HUMAN--": msg[0], "--TREE--": msg[1]} for msg in history],
        "process_logs": self.process_logs
    }

    # Save the JSON data to a file
    with open(file_path, "w", encoding="utf-8") as file:
        json.dump(history_json, file, indent=4)

    return file_path  # Returns the file path for download

def get_step_inputs(self, step_inputs):
    """
    Fetches and formats the required inputs for a step.
    - Uses dictionary-style input definitions from `parameters` in `config.yaml`.
    - If an input isn't found, it checks `generated_inputs` for dynamically created values.
    """
    input_data = {}

    for item in step_inputs:
        # Check in parameters first
        for category in self.parameters.values():  # `parameters` now has grouped dictionaries
            if item in category:
                input_data[item] = category[item]
                break
        # If not found, check in dynamically generated inputs
        if item in self.generated_inputs:
            input_data[item] = self.generated_inputs[item]

    return input_data

# ======== FUNCTION to store and return llm outputs ===============
# =================================================================
def store_result_and_return(self, response, _output=None):
    # 🛑 Debug: Check if step_output is set correctly
    if _output is None:
        raise ValueError("⚠️ ERROR: `step_output` is missing in store_result_and_return()!")

    # ✅ Store response in generated_inputs dictionary
    self.generated_inputs[_output] = response

    # 📝 Debugging Log
    print(f"\n📝 Stored Result -> `{_output}`:\n{response}\n")

    return {_output: response}

# ======== FUNCTION to format llm response ========================
# =================================================================
# def extract_response(self, response: str):
#     # Use regex to find everything AFTER '</think>'
#     match = re.search(r"</think>\s*(.*)", response, re.DOTALL)
#
#     # If there's a match, return only the actual response (after '</think>')
#     return match.group(1).strip() if match else response

@spaces.GPU()
def generate_persona_step(self, _step_index):
    _inputs = self.persona_steps[_step_index]["inputs"]
    _output = self.persona_steps[_step_index]["output"]
    _prompt = self.prompts[self.persona_steps[_step_index]["prompt"]]["template"]
    required_inputs = self.get_step_inputs(_inputs)
    formatted_prompt = _prompt.format(**required_inputs)
    response = self.llm.invoke(formatted_prompt)
    clean_response = self.extract_response(response)

    # Store the base persona
    self.persona_history.append({
        _output: clean_response
    })

    return self.store_result_and_return(clean_response, _output=_output)

def get_refinement_prompt(self):
    return self.prompts[self.persona_steps[1]["prompt"]]["template"]

def refine_persona(self):
    persona_data = self.generate_persona_step(1)
    return persona_data["refined_persona"]

# ===== QUESTION CLASSIFICATION ==========================
# ========================================================
# Function to format response - eliminate reasoning
def extract_response(self,response: str):
    # Use regex to find everything AFTER '</think>'
    match = re.search(r"</think>\s*(.*)", response, re.DOTALL)

    # If there's a match, return only the actual response (after '</think>')
    return match.group(1).strip() if match else response

# Function to classify question
@spaces.GPU()
def classify_question(self,question, classifier_prompt_template):
    # classifier_prompt_template = prompts["hop_classification"]["template"]
    prompt = classifier_prompt_template.format(question=question)
    classification_raw = self.extract_response(self.llm.invoke(prompt).strip().lower())
    classification = self.extract_classification(classification_raw)
    results = [c.strip() for c in classification.split(",")]
    return results

def extract_classification(self, classification_raw):
    classification = []
    if "historical" in classification_raw:
        classification.append("historical")
    if "scientific" in classification_raw:
        classification.append("scientific")
    if "weather" in classification_raw:
        classification.append("weather")
    if "insights" in classification_raw:
        classification.append("insights")
    if "no_rag" in classification_raw:
        classification.append("no_rag")
    results = ", ".join(classification) #[c.strip() for c in classification.split(",")]
    return results

# ===== RAG RETRIEVAL ====================================
# ========================================================
# Function to Handle Multi-Hop RAG Queries
@spaces.GPU()
def retrieve_information(self, query, classifications):
    if "no_rag" in classifications:
        return {},{}  # Skip retrieval

    retrieved_docs = {}  # ✅ Store results by category
    retrieved_metadata = {}

    for mode in classifications:
        print(f"----> current mode: {mode}")
        retrieved_texts = self.rag_retriever.rag_wrapper(query, mode=mode)

        if retrieved_texts:
            plain_texts = [doc['content'] for doc in retrieved_texts]
            metadata = [doc['metadata'] for doc in retrieved_texts]
            query_segment = " ".join(query[:5])
            key = query_segment.replace(" ", "_")
            retrieved_docs[mode] = plain_texts  # ✅ Organized by mode
            retrieved_metadata[mode] = metadata

    return retrieved_docs, retrieved_metadata

# ===== GENERATE TREE RESPONSE ===========================
# ========================================================
@spaces.GPU()
def generate_tree_response(self,instruction, question, history):
    # init dictionary to hold full llm process logs
    process_log = {}

    print (instruction + self.prompts['tree_response']['template'])

    # Add question to process log
    process_log['journalist_question'] = question

    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = self.classify_question(question, self.prompts["hop_classification"]["template"])
    print(f"1. CLASSIFICATION -- {classifications}")
    # Add to process log
    process_log['classification']=classifications

    # Step 2: Retrieve Relevant Knowledge (Categorized)
    retrieved_info, retrieved_meta = self.retrieve_information(question, classifications)
    print(f"2. RAG RESULTS -- {retrieved_info}")
    print(f"2. RAG METADATA -- {retrieved_meta}")
    # Add to process log
    process_log['retrieved_data'] = retrieved_info
    process_log['retrieved_metadata'] = retrieved_meta

    # Step 3: Get Chat History for Context
    chat_history = self.memory.load_memory_variables({})["chat_history"]

    # Step 4: Tree Generates Thoughts Before Answering (Always Challenges & Asks a Question)
    tree_thoughts = self.generate_tree_thoughts(instruction, question, retrieved_info, chat_history)
    print(f"3. INTERNAL THOUGHTS -- {tree_thoughts}")
    process_log['tree_thoughts'] = tree_thoughts

    tree_response_prompt_template = instruction + self.prompts['tree_response']['template']

    # Step 5: Format Final Response Prompt
    formatted_prompt = tree_response_prompt_template.format(
        chat_history=chat_history,
        question=question,
        retrieved_info="\n".join(retrieved_info) if retrieved_info else "No external knowledge needed.",
        tree_thoughts=tree_thoughts
    )

    # Step 6: Invoke HuggingFaceAPI for Response
    raw = self.llm.invoke(formatted_prompt)
    print(f"================== RAW DEEPSEEK ANSWER - {raw}")
    response = self.extract_response(self.llm.invoke(formatted_prompt).strip().lower())
    print(f"4. RAW RESPONSE -- {response}")
    process_log['tree_response'] = response

    # add process log to history
    self.process_logs.append({
        f"_process_step_{self.process_step}": process_log
    })
    self.process_step += 1
    # print(f"TREE: {stylized_response}")
    return history + [(question, response)], "" #response

@spaces.GPU()
def generate_tree_thoughts(self,instruction,question, retrieved_info, chat_history):
    """Before answering, the tree decides how to challenge and what question to ask."""
    #instruction = self.parameters['general_instruction']
    thought_prompt_template = instruction + self.prompts['tree_thought']['template']
    formatted_thought_prompt = thought_prompt_template.format(
        chat_history=chat_history,
        retrieved_info=retrieved_info,
        question=question
    )
    print(formatted_thought_prompt)
    # ✅ Now we correctly invoke the LLM using the full thought template
    tree_thoughts = self.extract_response(
        self.llm.invoke(formatted_thought_prompt).strip().lower())  # llm.invoke(thought_prompt)
    return tree_thoughts

@spaces.GPU()
def reset_chat(self):
    """Resets the conversation history in memory."""
    self.memory.clear()
    # Clear histories
    self.history = []
    self.persona_history = []
    self.process_logs = []
    self.process_step = 0
    return []  # Return empty history to reset UI

ChatGPT

Thanks for sharing the full script — it’s really detailed and helpful! After going through your code, it looks like you’re correctly initializing ConversationBufferMemory, but you’re not actually saving anything to memory during the chat.

🔍 Here’s what’s happening:

• You initialize the memory here:

  self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

• And you correctly load the memory here:

  chat_history = self.memory.load_memory_variables({})["chat_history"]

❌ But you’re never saving the new user inputs and assistant responses into memory.

That means your memory object stays empty no matter how many turns the conversation has. That’s why chat_history is likely returning [].

---

✅ Fix: Save each interaction into memory

You need to manually save the conversation turns like this:

Somewhere near the end of generate_tree_response, after getting the response, add:

# Save the current exchange to memory
self.memory.chat_memory.add_user_message(question)
self.memory.chat_memory.add_ai_message(response)

Full snippet inside generate_tree_response:

# Step 6: Invoke HuggingFaceAPI for Response
raw = self.llm.invoke(formatted_prompt)
print(f"================== RAW DEEPSEEK ANSWER - {raw}")
response = self.extract_response(self.llm.invoke(formatted_prompt).strip().lower())
print(f"4. RAW RESPONSE -- {response}")
process_log['tree_response'] = response

# ✅ Save to memory
self.memory.chat_memory.add_user_message(question)
self.memory.chat_memory.add_ai_message(response)

---

🔁 Optional: Debug your memory

You can print what’s inside the memory like this to confirm it’s working:

print("🔁 Memory now contains:")
for m in self.memory.chat_memory.messages:
    print(f"- {m.type.upper()}: {m.content}")

---

Let me know if you want to also persist that history somewhere or hook it into your Gradio interface!

You

ok thanks for that. Now i ma trying to include an extra step where i can factcheck that the information written in the llm’s paragraphs is actually coming from the RAG retrieved knowledge. I have altered the generate response fuinction to this

def generate_tree_response(self,instruction, question, history):
    # init dictionary to hold full llm process logs
    process_log = {}

    print (instruction + self.prompts['tree_response']['template'])

    # Add question to process log
    process_log['journalist_question'] = question

    # Step 0: Get prompt type
    prompt_type = self.filter_question_type(question)

    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = self.classify_question(question, self.prompts["hop_classification"]["template"])
    print(f"1. CLASSIFICATION -- {classifications}")
    # Add to process log
    process_log['classification']=classifications

    # Step 2: Retrieve Relevant Knowledge (Categorized)
    retrieved_info, retrieved_meta = self.retrieve_information(question, classifications)
    print(f"2. RAG RESULTS -- {retrieved_info}")
    print(f"2. RAG METADATA -- {retrieved_meta}")
    # Add to process log
    process_log['retrieved_data'] = retrieved_info
    process_log['retrieved_metadata'] = retrieved_meta

    # Step 3: Get Chat History for Context
    chat_history = self.memory.load_memory_variables({})["chat_history"]

    # ====================================================================
    # ========= PROCEED ACCORDING TO PROMPT TYPE =========================

    if prompt_type == "factcheck":
        fact_check_prompt_template = self.prompts['tree_validate']['template']
        clean_prompt = question.split("FACTCHECK")[1]
        formatted_fact_check_prompt = fact_check_prompt_template.format(
            paragraph=clean_prompt,
            retrieved_info="\n".join(retrieved_info) if retrieved_info else "No external knowledge needed.",
        )
        rag_source_list = self.extract_response(self.llm.invoke(formatted_fact_check_prompt).strip().lower())
        print(rag_source_list)
        return history + [(question, rag_source_list)], ""
    else:
        # Step 4: Tree Generates Thoughts Before Answering (Always Challenges & Asks a Question)
        tree_thoughts = self.generate_tree_thoughts(instruction, question, retrieved_info, chat_history)
        print(f"3. INTERNAL THOUGHTS -- {tree_thoughts}")
        process_log['tree_thoughts'] = tree_thoughts

        tree_response_prompt_template = instruction + self.prompts['tree_response']['template']

        # Step 5: Format Final Response Prompt
        formatted_prompt = tree_response_prompt_template.format(
            chat_history=chat_history,
            question=question,
            retrieved_info="\n".join(retrieved_info) if retrieved_info else "No external knowledge needed.",
            tree_thoughts=tree_thoughts
        )

        # Step 6: Invoke HuggingFaceAPI for Response
        raw = self.llm.invoke(formatted_prompt)
        print(f"================== RAW DEEPSEEK ANSWER - {raw}")
        response = self.extract_response(self.llm.invoke(formatted_prompt).strip().lower())
        print(f"4. RAW RESPONSE -- {response}")
        process_log['tree_response'] = response

        # Step 7: Save chat memory
        # ✅ Save to memory
        self.memory.chat_memory.add_user_message(question)
        self.memory.chat_memory.add_ai_message(response)

        # add process log to history
        self.process_logs.append({
            f"_process_step_{self.process_step}": process_log
        })
        self.process_step += 1
        # print(f"TREE: {stylized_response}")
        return history + [(question, response)], "" #response

and this is the relevant prompt:

tree_validate: template: | You are a scientific assistant performing a precise fact checking task. You need to ensure that the information included in the Given Paragraph is contained in the Retrieved Scientific Knowledge.

  Given Paragraph:
  {paragraph}

  Retrieved Scientific Knowledge:
  {retrieved_info}

  Your task is to relate the content of the Paragraph to the Scientific Knowledge.
  Of course not all sentences can be backed with facts from the retrieved knowledge.
  Clearly List where available sentences from the Retrieved Scientific Knowledge that justify the content of specific sentences from the Given Paragraph.
  Make sure the sentences from the Retrieved Scientific Knowledge are listed verbatim.

  Strictly respond with a list in the following format:
  - Paragraph sentence
  - Retrieved Knowledge

however when i tested it i got back this

1. CLASSIFICATION — [‘scientific’] ----> current mode: scientific
2. RAG RESULTS — {‘scientific’: [‘National Adaptation Policies:\nKorea undertook the task of establishing an adaptation base starting with the 3rd Comprehensive Plan on Countermeasures to Climate Change (2005–2007). Previously, the policy focused solely on reducing greenhouse gas emissions. Established comprehensive measures to cope with climate change were to reflect the national climate change response strategy. On 1 July 2009, Korea’s only research institute specializing in adaptation policies, the Korea Adaptation Centre for Climate Change (KACCC), was established at the Korea Environment Institute (KEI).\nKorea does not have a national UHI or heatwave policy, so adaptation strategies related to urban heat islands or heatwaves are spread across various climate change policies (Table 2). The national-level legislation that has provisions related to urban heat islands and increased temperatures include the Framework Act on Low Carbon Green Growth, a law promoting climate change impact assessment and adaptation measures; the Urban Parks and Green Areas Act; and the Act on the Creation and Management of Urban Forests. The central government of Korea establishes detailed implementation plans every five years to mitigate the climate change effect, which are implemented in all metropolitan cities.\nKorea’s top strategy for responding to climate change and the basis for climate change adaptation measures, the Framework Act on Low Carbon Green Growth (Clause 4, Article 48 and Article 38 of Enforcement Decree), was enacted in 2010 [23]. Following the Act, the government introduced the first 5-year National Climate Change Adaptation Plan (2011–2015). The vision of the plan was the establishment of a safe society and support for green growth through climate change adaptation and it consists of seven sectoral adaptation plans including health with the aim of protecting people from heatwaves and air pollution. The National Plan is a mandatory document that provides guidance for the central and local governments to map out detailed plans for implementation [23]. The second National Plan kept the public health sectoral plan aiming to establish countermeasures to protect the citizen’s lives and health from heatwaves and air pollution.’, ‘Seoul Special City:\nKorea’s capital Seoul is administratively a special city. It is highly dense, housing 18 percent of the nation’s population, and is about three times larger than the second-largest city, Busan. Due to rapid urbanisation and a rapidly aging population, Seoul is particularly vulnerable to the impacts of climate change [36]. Over the past 100 years, the average temperature of Seoul has increased by 2.3 °C from 10.7 °C to 13 °C, while the number of heatwave days has risen from 5 days per year to 18 days per year between 2005 and 2021. The Korea Meteorological Administration predicts that heatwaves will increase to 69 days per year by 2071 if greenhouse gas emissions continue at current levels [36].\nFollowing the National Low Carbon Green Growth Framework Act, Seoul has developed detailed implementation plans for climate change adaptation every five years, the current being Detailed Implementation Plan for Climate Change Adaptation (2022–2026). The city also set a new target for climate change adaptation by 2050 in line with the Climate Action Plan (CAP) [37]. Seoul plans to prepare climate change scenarios, evaluate climate risks, and establish short, medium, and long-term goals and implementation schedules related to climate change adaptation [38]. The UHI and heatwave-related legislation exists only in three of the twenty-five districts of Seoul which include the Yangcheon-gu, Mapo-gu, Nowon-gu, and Eunpyeong-gu. Seoul Special City Regulation 2022 does not include a comprehensive regulation for heatwave damage prevention and response. The Seoul Metropolitan City Climate Action Plan 2050 envisions Seoul becoming a low-carbon climate-safe city, and its objective is healthy and safe cities by boosting climate change adaptation capabilities.\nSeoul Climate Action Plan includes the following strategies: green remodeling of old buildings, speeding up the supply of sustainable cars, expanding urban forests, building heat evacuation facilities and safety systems, intensifying monitoring, and expanding urban green areas. Currently, Seoul is raising funds through the Regulation on the Establishment and Operation of the Climate Change Fund, financed from various sources such as general account contributions, fund operations revenue, and dividends from entities like the Korea District Heating Corporation. In 2021, Seoul established its own climate budget classification system and piloted a climate budget statement in 2022 across three departments. By 2023, the climate budget system was fully implemented across all city departments [39]. Outside sources may include central government support and corporate and civil society funding, while internal funding can be secured through climate policy budgets. In addition, the city’s policies include green infrastructures such as planting street trees, implementing green roofs, green façades, urban agriculture revitalization, etc. The Seoul Metropolitan Government Regulation on Support for the Green Roof was enacted in 2019. Table 3 provides an overview of Seoul Special City’s relevant laws and regulations related to UHI effect mitigation and heatwave adaptation.’, ‘Fragmented Responsibility Results in Fragmented Action:\nDespite the serious impacts of UHI in the densely populated Korean cities and the increasing vulnerability of their population, policies and strategies that directly target the impacts of rising temperatures are limited. While there are strategies that can help with heatwaves and UHI in general climate adaptation policies or projects, these are not explicitly linked to climate adaptation. Their main aim may be sustainable transport or some urban greening programs that target, for example, water quality. Response to heatwaves is a co-benefit. As it is not part of the aim, opportunities to maximize the cooling benefits may be missed.\nThe key informant interview confirmed the overall findings of the policy review. Heat island-related responsibilities and policies are scattered across several ministries, including the Ministry of Environment, Construction and Transportation, Korea Forest Service, etc. Most of Korea’s heatwave policies protect the weak by providing shelter and cooling centres for vulnerable groups. Each ministry and local government in Korea established measures to adapt to climate change. While this is as it should be, better coordination is necessary for effective outcomes.’, ‘Seoul Special City 2:\nGreen areas have been steadily decreasing in all three case study cities and nationally since 2008 (Figure 4). Seoul has the smallest green area among the three cities and the heat island effect is higher than that of comparable cities [40]. There is a slight increase in urban parks. In 2020, the total urban park area was 42,365 m2, growing to 43,468 m2 by 2023. A study on the history of Seoul’s parks and greenspace policies found that in parallel with increasing urbanization in South Korea the parks and greenspace policy has focused on quantitative expansion from 1960 to the mid-1990s [41]. However, after moving to a system of popularly elected mayors in 1995 urban greenspace policies in Seoul have moved from quantitative expansion to qualitative improvement and an increase of greenspaces around residential areas.\nSeoul’s most ambitious green infrastructure strategy is the 30 million tree-planting project to cope with the impacts of the city’s deteriorated climate environment, such as heatwaves and fine dust. The project was introduced in 2014 and aimed to plant 30 million trees by 2022, and it successfully exceeded this goal, with a total of 31,284,436 trees planted. An analysis shows that 30 million additional trees will reduce fine dust emitted by 64,000 old diesel cars a year and lower the city’s temperature by equivalent to operating 24 million air conditioners for 5 hours [42]. The Urban Park Protection Campaign of 2013, Seoul’s urban greening campaign, is a civic-led urban greening campaign that supports citizens to plant and grow flowers and trees in their daily living spaces to expand greenspaces. Seoul has protected most of its areas designated under the ‘Urban Park Expiration System’ by compensating landowners and ensuring these areas are developed into functional parks to prevent removal of the designation and loss of the function of urban parks if the park was not built or developed for 20 years after it was designated. A study by Hwang, et al. [43] examining change in vegetated areas in Seoul over a 32-year period (1987–2018) found significant greening in approximately 39 percent of the city. The main driver of this greening is suggested to be an expansion of vegetation due to planting trees along streets and in residential areas and creating urban parks. On the other hand, a 7 percent browning trend was observed in the city due to the conversion of vegetated areas to impervious surfaces. At the city scale, a greening trend at a rate of 0.002 yr−1 was observed. This is important because a trend of lower heat-mortality associations is observed in areas of higher urban vegetation in Seoul while higher heat-mortality associations are observed particularly in women and people aged over 65 in areas of lower levels of urban vegetation [44]. Furthermore, a study examining the effectiveness of extreme heat adaptation strategies in Seoul found that green walls, sidewalk greenways, and reduced-albedo sidewalks were only effective under low-emission scenarios. Only street trees were found to be effective in reducing the extreme heat impacts under high-emission scenarios [45].’, ‘Abstract:\nRising temperatures due to climate change are exacerbating the Urban Heat Island effect (UHI). UHI has serious impacts on ecosystems and human health, causes the deterioration of infrastructure and economic loss, decreases air quality, and increases energy consumption. The problem is heightened in densely populated cities. Temperatures are projected to rise more steeply in Korea than global temperatures. This paper focuses on evaluating UHI adaptation strategies of densely populated Korean cities Seoul, Busan, and Daegu through a policy review. The comparative analysis shows that, in terms of heatwave and UHI policies, Daegu is ahead as the only city with a dedicated heatwave plan. As the hottest city in Korea, it is not surprising that it is leading in heatwave policy and research. The study finds that blue–green infrastructures were the most common strategies for heatwave mitigation in these cities, but transformative adaptation is still largely absent. Despite the severity of the UHI phenomenon, particularly in densely populated Asian cities, the response so far has been limited. While there is evidence of genuine efforts towards UHI mitigation and increasing the quality and quantity of green infrastructure, many policies are too general and do not include specific details or measurable targets. Investment in expensive projects has fallen well short of the need and scale of the problem. The findings show that Korea is in the early stages of policy development for adaptation to the effects of heatwaves and UHI, and much more action is needed in the future.’]}
3. RAG METADATA — {‘scientific’: [{‘title’: ‘Tulip Tree Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 143, ‘item_name’: ‘The Heat Is on: How Well Are Densely Populated Korean Cities Adapting to Increased Temperatures and Urban Heat Island Effect?’, ‘item_author’: ‘Suhyun An and Aysin Dedekorkut-Howes’, ‘item_year’: None, ‘item_type’: ‘Paper’}, {‘title’: ‘Tulip Tree Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 143, ‘item_name’: ‘The Heat Is on: How Well Are Densely Populated Korean Cities Adapting to Increased Temperatures and Urban Heat Island Effect?’, ‘item_author’: ‘Suhyun An and Aysin Dedekorkut-Howes’, ‘item_year’: None, ‘item_type’: ‘Paper’}, {‘title’: ‘Tulip Tree Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 143, ‘item_name’: ‘The Heat Is on: How Well Are Densely Populated Korean Cities Adapting to Increased Temperatures and Urban Heat Island Effect?’, ‘item_author’: ‘Suhyun An and Aysin Dedekorkut-Howes’, ‘item_year’: None, ‘item_type’: ‘Paper’}, {‘title’: ‘Tulip Tree Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 143, ‘item_name’: ‘The Heat Is on: How Well Are Densely Populated Korean Cities Adapting to Increased Temperatures and Urban Heat Island Effect?’, ‘item_author’: ‘Suhyun An and Aysin Dedekorkut-Howes’, ‘item_year’: None, ‘item_type’: ‘Paper’}, {‘title’: ‘Tulip Tree Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 143, ‘item_name’: ‘The Heat Is on: How Well Are Densely Populated Korean Cities Adapting to Increased Temperatures and Urban Heat Island Effect?’, ‘item_author’: ‘Suhyun An and Aysin Dedekorkut-Howes’, ‘item_year’: None, ‘item_type’: ‘Paper’}]}

• “seoul, korea’s bustling capital, faces escalating challenges from climate change, exacerbated by rapid urbanization and an aging population.”

• no supporting knowledge found.

• “over the past century, the city’s average temperature has surged by 2.3°c, with heatwave days increasing from 5 to 18 annually.”

• no supporting knowledge found.

• “this alarming trend is mirrored in the weather data from hongcheon, where rising temperatures and extreme weather events reflect broader climate shifts.”

• no supporting knowledge found.

• “to combat these impacts, seoul has implemented strategies such as green infrastructure expansion, including the green remodeling of buildings and the expansion of urban forests, and the establishment of heat evacuation facilities.”

• no supporting knowledge found.

• “additionally, the city has introduced funding mechanisms like the climate change fund and the climate budget system across city departments.”

• no supporting knowledge found.

• “despite these efforts, gaps remain in national and local policies, with responsibilities scattered across various ministries and local governments, leading to fragmented actions and missed opportunities for cooling benefits.”

• no supporting knowledge found.

• “the resilience of local flora, like the tulip tree, underscores the need for integrated and adaptive strategies.”

• no supporting knowledge found.

• “as the city strives to achieve its goal of becoming a low-carbon, climate-safe city by 2050, the urgency for coordinated efforts and the importance of learning from past policies to enhance future adaptation strategies cannot be overstated.”

• no supporting knowledge found.

i wonder why that is

ChatGPT

Great question — and kudos for implementing a structured fact-checking step like this! You’re on the right track, and your setup looks solid. Based on what you’ve shown, the fact-checking step is technically working, but the LLM is saying “no supporting knowledge found” for every sentence, even though we know the retrieved RAG content does include that information.

Let’s troubleshoot why this is happening.

---

🧠 Likely Reason: Prompt formatting & cognitive overload

Here’s what might be going wrong:

• You’re passing the entire retrieved info as one massive blob into {retrieved_info}.
• That blob contains many unrelated or semi-related sentences.
• The fact-checking LLM now has to:
  1. Parse the paragraph
  2. Break it into sentences
  3. Look for semantic matches across possibly hundreds of lines of text

That’s a ton of cognitive load for the LLM, especially if the match isn’t verbatim or close enough.

---

✅ Fix 1: Chunk the RAG data into smaller, searchable pieces

Instead of passing a huge "\n".join(retrieved_info) (i.e., a multi-paragraph dump), try:

• Breaking retrieved_info into smaller segments
• Doing sentence-level or paragraph-level matching

You could even turn this into a loop, comparing each sentence from the paragraph against the RAG docs.

Example (pseudo-pseudocode):

from nltk.tokenize import sent_tokenize

fact_sentences = sent_tokenize(clean_prompt)
match_results = []

for sentence in fact_sentences:
    check_prompt = fact_check_prompt_template.format(
        paragraph=sentence,
        retrieved_info="\n".join(retrieved_info)[:3000]  # limit token size
    )
    check_result = self.extract_response(self.llm.invoke(check_prompt))
    match_results.append((sentence, check_result))

---

✅ Fix 2: Make your prompt more explicit about fuzzy semantic matches

Right now, you’re asking for verbatim matches only:

“Make sure the sentences from the Retrieved Scientific Knowledge are listed verbatim.”

That sets the LLM up to say “no supporting knowledge” if the sentence isn’t an exact copy, even if it’s semantically equivalent.

➕ Prompt tweak idea:

Replace this line:

“Make sure the sentences from the Retrieved Scientific Knowledge are listed verbatim.”

With something like:

“Where exact matches exist, list them. Otherwise, identify the most semantically similar statements from the Retrieved Scientific Knowledge.”

This gives the LLM permission to do fuzzy matching — much more aligned with how retrieval-based systems actually work.

---

✅ Fix 3: Explicitly pass metadata to help grounding

Your RAG results include source metadata (title, author, etc.), but you’re not feeding that into the LLM. You could embed those into the RAG content like:

[Source: Tulip Tree Papers]
Korea undertook the task of establishing...

[Source: Seoul Special City]
Green areas have been steadily decreasing...

This helps the LLM anchor its responses more clearly, especially if you later want it to output which source supports which sentence.

---

🧪 Bonus: Add a baseline “debug check” prompt

If you’re ever unsure whether the LLM understood the content, you can ask it first:

“Summarize the key claims in the Retrieved Scientific Knowledge.”

That’ll show if it can even parse what you’ve fed it before attempting fact-checking.

---

📌 Summary

Issue	Solution
RAG blob too big	Chunk or truncate retrieved_info
Prompt too strict	Allow semantic similarity, not just verbatim
No grounding context	Embed metadata in RAG
Can’t parse the paragraph	Break into individual sentences and check each one

---

If you want, I can help you refactor the fact-checking logic into a loop that gives detailed sentence-by-sentence trace with source snippets. Just let me know!

You

for fix 2 i wrote verbatim to make sure that the sentences from the sources are listed verbatim. I didn’t mean to look for verbatim matches

ChatGPT

Ah, gotcha! That makes total sense — you’re asking for the paragraph sentence to be listed as-is, and then the LLM should pull verbatim support from the retrieved knowledge, not the other way around. Thanks for clarifying.

So the problem isn’t about too-strict matching, but rather that:

• The retrieved knowledge contains relevant info, but
• The LLM isn’t surfacing any of it as justification.

Which means… the issue is probably a reasoning gap or formatting overload.

---

🔍 Updated Diagnosis

Here are the most likely causes, given your clarified goal:

1. Prompt not emphasizing the relationship

Your prompt says:

“Clearly list where available sentences from the Retrieved Scientific Knowledge that justify the content of specific sentences…”

But that doesn’t strongly prompt the model to:

• Actually extract relevant RAG lines
• Pair them with each paragraph sentence

So, it might understand the task, but not execute it properly — especially if the LLM you’re using has limited instruction-following ability compared to GPT-4 or Claude.

---

✅ Fix: Prompt tweak for better alignment

Here’s a modified version of your prompt that preserves your structure but improves the LLM’s ability to follow through:

---

✍️ Improved Prompt (tree_validate)

tree_validate:
  template: |
    You are a scientific assistant performing a precise fact-checking task.
    Your job is to verify if each sentence in the Given Paragraph is supported by any of the sentences in the Retrieved Scientific Knowledge.

    - For **each sentence** in the Given Paragraph, check if there is a **closely related or supporting sentence** in the Retrieved Scientific Knowledge.
    - If one or more supporting sentences exist, **list them verbatim** directly underneath the paragraph sentence.
    - If none exist, write: `"No supporting knowledge found."`

    Format:
    - Paragraph sentence
    - Retrieved Knowledge

    Do this for every sentence in the paragraph.

    ---
    Given Paragraph:
    {paragraph}

    Retrieved Scientific Knowledge:
    {retrieved_info}

---

This version:

• Clarifies you want every sentence in the paragraph checked.
• Emphasizes verbatim listing of supporting content.
• Is LLM-friendly (uses bullets, steps, clear format expectations).

---

✅ Fix: Split the paragraph manually (if needed)

If you want to be absolutely certain, consider pre-tokenizing the paragraph before sending it, and asking the LLM to process each sentence separately. For instance:

from nltk.tokenize import sent_tokenize

paragraph_sentences = sent_tokenize(paragraph)

fact_check_prompt = tree_validate_prompt_template.format(
    paragraph="\n".join(paragraph_sentences),
    retrieved_info="\n".join(retrieved_info)[:4000]  # trim long content
)

That gives the LLM nicely delimited sentences to work with.

---

🔧 Optional: Add examples in the prompt

You can improve results further by including a few examples before {paragraph} and {retrieved_info}.

Example:

Example:
- "Seoul is investing in green roofs."
- Retrieved Knowledge: "The Seoul Metropolitan Government Regulation on Support for the Green Roof was enacted in 2019."

- "The city's heat response is fragmented."
- Retrieved Knowledge: "Policies and responsibilities related to heatwaves are scattered across several ministries."

---

This primes the model for what kind of mapping you expect.

---

Let’s test?

If you give me:

• One sample paragraph you’re trying to fact-check
• A snippet of retrieved_info

I can simulate what a cleaned-up prompt would look like and what kind of output you should expect.

Would that help?

You

I implemented the alternative prompt but the response is a bit weird. Here is what i got back

1. the urban heat island (uhi) effect is a phenomenon where urban areas experience higher temperatures compared to their rural surroundings due to human activities and the built environment.
2. the uhi effect is exacerbated by the concentration of heat-absorbing materials like asphalt and concrete, which trap and radiate heat, especially at night.
3. mitigation strategies include increasing green spaces, promoting reflective materials, and improving urban planning.
4. global warming and climate change amplify the uhi effect, leading to more intense heatwaves and health risks for urban populations.
5. cities like seoul have implemented green infrastructure projects, such as rooftop gardens and urban forests, to combat the uhi effect.
6. the combination of uhi and global warming is projected to increase the frequency and severity of heatwaves in urban areas.
7. despite these efforts, challenges remain, including fragmented policies, inconsistent implementation, and the need for stronger partnerships.
8. the uhi effect is a significant contributor to rising temperatures in urban areas, necessitating comprehensive and coordinated strategies for effective mitigation.
9. monitoring systems and data collection are crucial for understanding uhi dynamics and evaluating the effectiveness of mitigation measures.
10. urban agriculture and sustainable transportation systems can also play a role in reducing the intensity of heatwaves by lowering overall urban temperatures.

---

alright, let’s tackle this fact-checking task step by step. first, i need to understand the structure of the problem. the user has provided a paragraph about seoul’s challenges with climate change, specifically the urban heat island effect and heatwaves. they’ve also provided some retrieved scientific knowledge, which are numbered sentences. my job is to go through each sentence in the paragraph and check if it’s supported by any of the retrieved knowledge. if it is, i list the supporting sentences verbatim below. if not, i note that no supporting knowledge was found.

okay, let’s start with the first sentence of the paragraph:

“seoul, the vibrant capital of korea, is confronting the escalating challenges posed by climate change, particularly the worsening urban heat island (uhi) effect and the increasing frequency of heatwaves.”

looking at the retrieved knowledge, sentence 1 defines the uhi effect as urban areas being hotter than rural areas due to human activities. sentence 4 mentions that global warming and climate change amplify the uhi effect, leading to more intense heatwaves. so, both sentences 1 and 4 support the first sentence of the paragraph.

next sentence:

“over the past century, the city has witnessed a significant rise in temperatures, with the average temperature climbing by 2.3°c.”

looking at the retrieved knowledge, i don’t see any specific mention of seoul’s temperature rise by 2.3°c. the closest is sentence 4, which talks about uhi and heatwaves, but not the specific temperature increase. so, i’ll note that no supporting knowledge was found for this sentence.

third sentence:

“this upward trend is starkly illustrated by the increase in heatwave days—from just 5 days annually in 2005 to 18 days by 2021.”

again, the retrieved knowledge doesn’t mention specific data about heatwave days in seoul. sentence 6 talks about the projection of increased heatwaves, but not the historical data. so, no supporting knowledge here.

fourth sentence:

“projections paint an even grimmer picture, suggesting that by 2071, seoul could experience a staggering 69 heatwave days each year if current greenhouse gas emission trends continue.”

sentence 6 from the retrieved knowledge mentions that the combination of uhi and global warming is projected to increase the frequency and severity of heatwaves. while it doesn’t mention the specific projection of 69 days, it does support the idea that heatwaves will become more frequent. so, sentence 6 supports this.

fifth sentence:

“these statistics underscore the urgent need for decisive and comprehensive action.”

this is more of a conclusion based on the previous statistics. the retrieved knowledge doesn’t directly address the need for action, but sentence 7 does mention challenges remaining despite efforts, which indirectly supports the need for comprehensive strategies. so, sentence 7 supports this.

moving on to the next paragraph:

“in response to this looming crisis, seoul has launched an ambitious climate action plan 2050, aiming to transform the city into a low-carbon, climate-resilient metropolis.”

looking at the retrieved knowledge, sentence 5 mentions that cities like seoul have implemented green infrastructure projects. this supports the idea of seoul taking action, even though it doesn’t specifically mention the 2050 plan. so, sentence 5 supports this.

next sentence:

“central to this plan are initiatives focused on green remodeling of buildings, expanding urban forests, and implementing innovative heat evacuation facilities.”

sentence 5 again supports this by mentioning green infrastructure projects, including rooftop gardens and urban forests. additionally, sentence 10 talks about urban agriculture and sustainable transportation, which are part of the broader strategies. so, sentences 5 and 10 support

while here is the actual retrieved knowledge as printed from my script

2. RAG RESULTS — {‘scientific’: [‘Abstract:\nRising temperatures due to climate change are exacerbating the Urban Heat Island effect (UHI). UHI has serious impacts on ecosystems and human health, causes the deterioration of infrastructure and economic loss, decreases air quality, and increases energy consumption. The problem is heightened in densely populated cities. Temperatures are projected to rise more steeply in Korea than global temperatures. This paper focuses on evaluating UHI adaptation strategies of densely populated Korean cities Seoul, Busan, and Daegu through a policy review. The comparative analysis shows that, in terms of heatwave and UHI policies, Daegu is ahead as the only city with a dedicated heatwave plan. As the hottest city in Korea, it is not surprising that it is leading in heatwave policy and research. The study finds that blue–green infrastructures were the most common strategies for heatwave mitigation in these cities, but transformative adaptation is still largely absent. Despite the severity of the UHI phenomenon, particularly in densely populated Asian cities, the response so far has been limited. While there is evidence of genuine efforts towards UHI mitigation and increasing the quality and quantity of green infrastructure, many policies are too general and do not include specific details or measurable targets. Investment in expensive projects has fallen well short of the need and scale of the problem. The findings show that Korea is in the early stages of policy development for adaptation to the effects of heatwaves and UHI, and much more action is needed in the future.’, ‘Seoul Special City:\nKorea’s capital Seoul is administratively a special city. It is highly dense, housing 18 percent of the nation’s population, and is about three times larger than the second-largest city, Busan. Due to rapid urbanisation and a rapidly aging population, Seoul is particularly vulnerable to the impacts of climate change [36]. Over the past 100 years, the average temperature of Seoul has increased by 2.3 °C from 10.7 °C to 13 °C, while the number of heatwave days has risen from 5 days per year to 18 days per year between 2005 and 2021. The Korea Meteorological Administration predicts that heatwaves will increase to 69 days per year by 2071 if greenhouse gas emissions continue at current levels [36].\nFollowing the National Low Carbon Green Growth Framework Act, Seoul has developed detailed implementation plans for climate change adaptation every five years, the current being Detailed Implementation Plan for Climate Change Adaptation (2022–2026). The city also set a new target for climate change adaptation by 2050 in line with the Climate Action Plan (CAP) [37]. Seoul plans to prepare climate change scenarios, evaluate climate risks, and establish short, medium, and long-term goals and implementation schedules related to climate change adaptation [38]. The UHI and heatwave-related legislation exists only in three of the twenty-five districts of Seoul which include the Yangcheon-gu, Mapo-gu, Nowon-gu, and Eunpyeong-gu. Seoul Special City Regulation 2022 does not include a comprehensive regulation for heatwave damage prevention and response. The Seoul Metropolitan City Climate Action Plan 2050 envisions Seoul becoming a low-carbon climate-safe city, and its objective is healthy and safe cities by boosting climate change adaptation capabilities.\nSeoul Climate Action Plan includes the following strategies: green remodeling of old buildings, speeding up the supply of sustainable cars, expanding urban forests, building heat evacuation facilities and safety systems, intensifying monitoring, and expanding urban green areas. Currently, Seoul is raising funds through the Regulation on the Establishment and Operation of the Climate Change Fund, financed from various sources such as general account contributions, fund operations revenue, and dividends from entities like the Korea District Heating Corporation. In 2021, Seoul established its own climate budget classification system and piloted a climate budget statement in 2022 across three departments. By 2023, the climate budget system was fully implemented across all city departments [39]. Outside sources may include central government support and corporate and civil society funding, while internal funding can be secured through climate policy budgets. In addition, the city’s policies include green infrastructures such as planting street trees, implementing green roofs, green façades, urban agriculture revitalization, etc. The Seoul Metropolitan Government Regulation on Support for the Green Roof was enacted in 2019. Table 3 provides an overview of Seoul Special City’s relevant laws and regulations related to UHI effect mitigation and heatwave adaptation.’, ‘Introduction :\nHeatwaves and the intensifying Urban Heat Island (UHI) effect are among the significant impacts of rising temperatures due to climate change [1], and the problem is compounded by the densification of urban centres. Changes in urban form and the land surface due to urbanization, an increase in artificial heat emission, and a decrease in green areas or water surfaces that cool the city are among the causes of the UHI effect [2]. UHI has serious impacts on ecosystems and human health, causes the deterioration of infrastructure and economic loss, decreases air quality, and increases energy consumption. This is particularly a problem in densely populated cities. The impacts of climate change are expected to intensify at the end of the 21st century. South Korea’s (hereafter Korea) temperature will rise more steeply than the global temperature rise. According to over 100 years of meteorological data from six observation stations in Korea (Seoul, Incheon, Daegu, Busan, Gangneung, and Mokpo), the average annual temperature in Korea rose by approximately 1.8 °C over 106 years from 1912 to 2017, which is significantly faster than the average global warming of 0.85 °C during the same period [3]. More recently, over the 30 years between 1988–2017, the average annual temperature rose by 1.4 °C compared to the early 20th century (1912–1941) [4] p. 6. On 7 January 2020, Jeju Island’s daytime high reached 23.6 °C, showing abnormal temperatures for the first time since 1923 [3]. In addition, on 1 August 2018, 39.6 °C was recorded in Seoul and 41 °C in Hongcheon, Gangwon Province, the highest temperature in 111 years. The number of days of heatwaves nationwide was 31.4, which was the most extended heatwave since 1973 [3], and this highlighted the need for systematic management of heat risks [5]. As a direct result of this increasing heatwave risk, the Disaster and Safety Management Basic Act was amended in September 2018. This revision officially recognised a heatwave as a natural disaster, establishing a foundational legal framework for heatwave risk management [5]. It mandates a government-level task force based on the disaster’s type and severity, ensuring coordinated mitigation efforts. It defines vulnerable populations, such as children, the elderly, individuals with disabilities, and low-income groups, as these populations have a higher risk due to physical, social, and economic factors [6].\nThe objective of this paper is to assess whether there are sufficient UHI adaptation strategies in densely populated cities in Korea and identify any policy gaps. Through reviewing UHI adaptation policies in three large Korean cities, this study aims to determine whether these policies are aligned with state-of-the-art responses identified in the literature. Identification of these gaps is the first step towards improving UHI adaptation strategies. The paper first provides an overview of climate adaptation to heatwaves and describes the methods used. It then overviews Korean national adaptation policies that are relevant to increased temperatures and heatwaves. After an introduction of the case study areas, a review of provincial and local adaptation policies is presented for each city. The paper concludes after the discussion of the findings.’, ‘Fragmented Responsibility Results in Fragmented Action:\nDespite the serious impacts of UHI in the densely populated Korean cities and the increasing vulnerability of their population, policies and strategies that directly target the impacts of rising temperatures are limited. While there are strategies that can help with heatwaves and UHI in general climate adaptation policies or projects, these are not explicitly linked to climate adaptation. Their main aim may be sustainable transport or some urban greening programs that target, for example, water quality. Response to heatwaves is a co-benefit. As it is not part of the aim, opportunities to maximize the cooling benefits may be missed.\nThe key informant interview confirmed the overall findings of the policy review. Heat island-related responsibilities and policies are scattered across several ministries, including the Ministry of Environment, Construction and Transportation, Korea Forest Service, etc. Most of Korea’s heatwave policies protect the weak by providing shelter and cooling centres for vulnerable groups. Each ministry and local government in Korea established measures to adapt to climate change. While this is as it should be, better coordination is necessary for effective outcomes.’, ‘Conclusion :\nThis paper presented the findings of a review of Korea’s adaptation strategies and policies at the national level as well as for three selected metropolitan areas for heatwaves and heat island effect. The main findings are:\n-Of the three cities studied, which include Korea’s largest and second- and fourth-largest cities, Daegu has the most comprehensive and detailed heat policies, due in part to its higher levels of physical (hottest large Korean city) and social (highest proportion of vulnerable residents) vulnerability.\n-There are limited policies and strategies in place that directly target the impacts of rising temperatures despite UHI being a serious issue in densely populated Asian cities. The commitment to expensive infrastructure such as green roofs and cool roofs so far has been far short of the need.\n-Most of the existing strategies focus on coping or incremental approaches to adaptation with few transformational options.\nThe findings show that Korea is in the early stages of policy development to adapt to the impacts of heatwaves and the UHI effect. The findings of this study are applicable to other countries with similar levels of vulnerability and urbanization patterns, particularly other densely populated Asian cities. These cities are also facing similar challenges of aging populations, fragmented action, and limited transformative approaches. Much more adaptation planning and action is needed to ensure an effective response that is comprehensive and coordinated across different agencies and jurisdictions.’]}

ChatGPT

Thanks for the detailed follow-up — this is super helpful!

What you’re seeing is actually a pretty common LLM mismatch issue, and now that you’ve shared both the LLM response and the real retrieved knowledge, I can say with high confidence:

Your LLM is not actually using the full retrieved RAG content you fed it.

---

💥 What’s really happening?

It looks like the model is hallucinating a fake “retrieved knowledge” list, rather than grounding its fact-checking on the real content you passed in.

Check this part of the LLM’s answer:

“…which are numbered sentences.”
Then it refers to:

• Sentence 1 defines the UHI effect
• Sentence 4 mentions that global warming…

Nowhere in your RAG content did you number sentences 1–10. That numbered list was invented by the model, meaning it never actually read or processed the big retrieved_info blob you passed.

---

🔎 So what’s causing this?

✅ Most likely cause: The retrieved info is too long or unstructured

You’re passing a massive multi-paragraph string to {retrieved_info} — several thousand tokens of raw, unstructured text.

• Some LLMs (especially smaller models or Hugging Face-hosted ones) will ignore or truncate inputs past a certain length
• Even when they don’t, they often struggle to parse raw text blocks without clear anchors

---

✅ What to fix

🔧 1. Pre-process and structure the RAG content

Before passing it into the prompt, chunk and format the retrieved knowledge into a clearer, numbered format.

🔁 Convert this:

retrieved_info = [
    "Seoul Special City:\nKorea’s capital Seoul is administratively a special city...",
    "Fragmented Responsibility Results in Fragmented Action:\nDespite the serious impacts of UHI...",
    ...
]

✅ Into this (for better LLM processing):

formatted_knowledge = "\n\n".join(
    [f"[Source {i+1}]\n{doc}" for i, doc in enumerate(retrieved_info)]
)

Now, your prompt has anchored sources that the LLM can refer to directly.

---

🧼 2. Tweak your prompt to reflect the structure

Update the tree_validate prompt to say something like:

template: |
  You are a scientific assistant performing a fact-checking task.
  For each sentence in the Given Paragraph, check whether it's supported by any sentence in the Retrieved Scientific Knowledge.

  - Reference the **Source number** and quote the **supporting sentence verbatim**.
  - If there's no support, say `"No supporting knowledge found."`

  Format:
  - Paragraph sentence
  - Supporting sentence(s) from Retrieved Scientific Knowledge [Source X]

  Given Paragraph:
  {paragraph}

  Retrieved Scientific Knowledge:
  {retrieved_info}

This gives the model:

• A clear format
• Anchors (Source 1, 2, etc.) to refer to
• Less cognitive load

---

🧠 Bonus: Trim down long RAG content

You’re passing 5+ full-length paragraphs, which may exceed context limits for some models. Try limiting to top 2–3 most relevant ones (based on similarity scores if possible).

---

🧪 Want to test right here?

If you give me:

• One paragraph you’re checking
• Top 2-3 retrieved RAG passages

I can show you how I’d rewrite the prompt and simulate what an ideal response looks like.

Would you like to try that?

You

yeah i have noticed that my llm hallucinates a lot and that’s why I am trying to fact check whether what it’s giving me actually aligns with the RAG content I am passing in.

Let’s try what you suggested. Here is the paragraph I am trying to fact check:

seoul, the vibrant capital of korea, is confronting the escalating challenges posed by climate change, particularly the worsening urban heat island (uhi) effect and the increasing frequency of heatwaves. over the past century, the city has witnessed a significant rise in temperatures, with the average temperature climbing by 2.3°c. this upward trend is starkly illustrated by the increase in heatwave days—from just 5 days annually in 2005 to 18 days by 2021. projections paint an even grimmer picture, suggesting that by 2071, seoul could experience a staggering 69 heatwave days each year if current greenhouse gas emission trends continue. these statistics underscore the urgent need for decisive and comprehensive action.

in response to this looming crisis, seoul has launched an ambitious climate action plan 2050, aiming to transform the city into a low-carbon, climate-resilient metropolis. central to this plan are initiatives focused on green remodeling of buildings, expanding urban forests, and implementing innovative heat evacuation facilities. the city has also introduced supportive policies, such as the 2019 seoul metropolitan government regulation on support for green roofs, which encourages the use of green infrastructure to combat the uhi effect. additionally, seoul is investing heavily in sustainable transportation systems and urban agriculture, further reinforcing its commitment to reducing the intensity of heatwaves.

despite these commendable efforts, challenges remain. national policies addressing the uhi effect and heatwaves are often fragmented, with responsibilities spread across multiple ministries, leading to inefficiencies. furthermore, while seoul’s local regulations are progressive, their inconsistent implementation across different districts leaves gaps in coverage. however, the city’s proactive approach, including the establishment of a climate budget system and strong partnerships with the private sector and civil society, offers hope for overcoming these obstacles.

as seoul continues to navigate the complexities of climate adaptation, its ambitious strategies serve as a model for other cities around the globe. by prioritizing green infrastructure, enhancing monitoring systems, and fostering collaborative efforts, seoul is not only mitigating the effects of climate change but also laying the groundwork for a resilient and sustainable future for its residents.

and here is the top 5 rag sources: RAG 1 - Abstract:Rising temperatures due to climate change are exacerbating the Urban Heat Island effect (UHI). UHI has serious impacts on ecosystems and human health, causes the deterioration of infrastructure and economic loss, decreases air quality, and increases energy consumption. The problem is heightened in densely populated cities. Temperatures are projected to rise more steeply in Korea than global temperatures. This paper focuses on evaluating UHI adaptation strategies of densely populated Korean cities Seoul, Busan, and Daegu through a policy review. The comparative analysis shows that, in terms of heatwave and UHI policies, Daegu is ahead as the only city with a dedicated heatwave plan. As the hottest city in Korea, it is not surprising that it is leading in heatwave policy and research. The study finds that blue–green infrastructures were the most common strategies for heatwave mitigation in these cities, but transformative adaptation is still largely absent. Despite the severity of the UHI phenomenon, particularly in densely populated Asian cities, the response so far has been limited. While there is evidence of genuine efforts towards UHI mitigation and increasing the quality and quantity of green infrastructure, many policies are too general and do not include specific details or measurable targets. Investment in expensive projects has fallen well short of the need and scale of the problem. The findings show that Korea is in the early stages of policy development for adaptation to the effects of heatwaves and UHI, and much more action is needed in the future., RAG 2 - Seoul Special City:Korea’s capital Seoul is administratively a special city. It is highly dense, housing 18 percent of the nation’s population, and is about three times larger than the second-largest city, Busan. Due to rapid urbanisation and a rapidly aging population, Seoul is particularly vulnerable to the impacts of climate change [36]. Over the past 100 years, the average temperature of Seoul has increased by 2.3 °C from 10.7 °C to 13 °C, while the number of heatwave days has risen from 5 days per year to 18 days per year between 2005 and 2021. The Korea Meteorological Administration predicts that heatwaves will increase to 69 days per year by 2071 if greenhouse gas emissions continue at current levels [36].\nFollowing the National Low Carbon Green Growth Framework Act, Seoul has developed detailed implementation plans for climate change adaptation every five years, the current being Detailed Implementation Plan for Climate Change Adaptation (2022–2026). The city also set a new target for climate change adaptation by 2050 in line with the Climate Action Plan (CAP) [37]. Seoul plans to prepare climate change scenarios, evaluate climate risks, and establish short, medium, and long-term goals and implementation schedules related to climate change adaptation [38]. The UHI and heatwave-related legislation exists only in three of the twenty-five districts of Seoul which include the Yangcheon-gu, Mapo-gu, Nowon-gu, and Eunpyeong-gu. Seoul Special City Regulation 2022 does not include a comprehensive regulation for heatwave damage prevention and response. The Seoul Metropolitan City Climate Action Plan 2050 envisions Seoul becoming a low-carbon climate-safe city, and its objective is healthy and safe cities by boosting climate change adaptation capabilities.\nSeoul Climate Action Plan includes the following strategies: green remodeling of old buildings, speeding up the supply of sustainable cars, expanding urban forests, building heat evacuation facilities and safety systems, intensifying monitoring, and expanding urban green areas. Currently, Seoul is raising funds through the Regulation on the Establishment and Operation of the Climate Change Fund, financed from various sources such as general account contributions, fund operations revenue, and dividends from entities like the Korea District Heating Corporation. In 2021, Seoul established its own climate budget classification system and piloted a climate budget statement in 2022 across three departments. By 2023, the climate budget system was fully implemented across all city departments [39]. Outside sources may include central government support and corporate and civil society funding, while internal funding can be secured through climate policy budgets. In addition, the city’s policies include green infrastructures such as planting street trees, implementing green roofs, green façades, urban agriculture revitalization, etc. The Seoul Metropolitan Government Regulation on Support for the Green Roof was enacted in 2019. Table 3 provides an overview of Seoul Special City’s relevant laws and regulations related to UHI effect mitigation and heatwave adaptation., RAG 3 - Introduction nHeatwaves and the intensifying Urban Heat Island (UHI) effect are among the significant impacts of rising temperatures due to climate change [1], and the problem is compounded by the densification of urban centres. Changes in urban form and the land surface due to urbanization, an increase in artificial heat emission, and a decrease in green areas or water surfaces that cool the city are among the causes of the UHI effect [2]. UHI has serious impacts on ecosystems and human health, causes the deterioration of infrastructure and economic loss, decreases air quality, and increases energy consumption. This is particularly a problem in densely populated cities. The impacts of climate change are expected to intensify at the end of the 21st century. South Korea’s (hereafter Korea) temperature will rise more steeply than the global temperature rise. According to over 100 years of meteorological data from six observation stations in Korea (Seoul, Incheon, Daegu, Busan, Gangneung, and Mokpo), the average annual temperature in Korea rose by approximately 1.8 °C over 106 years from 1912 to 2017, which is significantly faster than the average global warming of 0.85 °C during the same period [3]. More recently, over the 30 years between 1988–2017, the average annual temperature rose by 1.4 °C compared to the early 20th century (1912–1941) [4] p. 6. On 7 January 2020, Jeju Island’s daytime high reached 23.6 °C, showing abnormal temperatures for the first time since 1923 [3]. In addition, on 1 August 2018, 39.6 °C was recorded in Seoul and 41 °C in Hongcheon, Gangwon Province, the highest temperature in 111 years. The number of days of heatwaves nationwide was 31.4, which was the most extended heatwave since 1973 [3], and this highlighted the need for systematic management of heat risks [5]. As a direct result of this increasing heatwave risk, the Disaster and Safety Management Basic Act was amended in September 2018. This revision officially recognised a heatwave as a natural disaster, establishing a foundational legal framework for heatwave risk management [5]. It mandates a government-level task force based on the disaster’s type and severity, ensuring coordinated mitigation efforts. It defines vulnerable populations, such as children, the elderly, individuals with disabilities, and low-income groups, as these populations have a higher risk due to physical, social, and economic factors [6].\nThe objective of this paper is to assess whether there are sufficient UHI adaptation strategies in densely populated cities in Korea and identify any policy gaps. Through reviewing UHI adaptation policies in three large Korean cities, this study aims to determine whether these policies are aligned with state-of-the-art responses identified in the literature. Identification of these gaps is the first step towards improving UHI adaptation strategies. The paper first provides an overview of climate adaptation to heatwaves and describes the methods used. It then overviews Korean national adaptation policies that are relevant to increased temperatures and heatwaves. After an introduction of the case study areas, a review of provincial and local adaptation policies is presented for each city. The paper concludes after the discussion of the findings., RAG 4 - Fragmented Responsibility Results in Fragmented Action:Despite the serious impacts of UHI in the densely populated Korean cities and the increasing vulnerability of their population, policies and strategies that directly target the impacts of rising temperatures are limited. While there are strategies that can help with heatwaves and UHI in general climate adaptation policies or projects, these are not explicitly linked to climate adaptation. Their main aim may be sustainable transport or some urban greening programs that target, for example, water quality. Response to heatwaves is a co-benefit. As it is not part of the aim, opportunities to maximize the cooling benefits may be missed.\nThe key informant interview confirmed the overall findings of the policy review. Heat island-related responsibilities and policies are scattered across several ministries, including the Ministry of Environment, Construction and Transportation, Korea Forest Service, etc. Most of Korea’s heatwave policies protect the weak by providing shelter and cooling centres for vulnerable groups. Each ministry and local government in Korea established measures to adapt to climate change. While this is as it should be, better coordination is necessary for effective outcomes., RAG 5 - Conclusion :This paper presented the findings of a review of Korea’s adaptation strategies and policies at the national level as well as for three selected metropolitan areas for heatwaves and heat island effect. The main findings are:\n-Of the three cities studied, which include Korea’s largest and second- and fourth-largest cities, Daegu has the most comprehensive and detailed heat policies, due in part to its higher levels of physical (hottest large Korean city) and social (highest proportion of vulnerable residents) vulnerability.\n-There are limited policies and strategies in place that directly target the impacts of rising temperatures despite UHI being a serious issue in densely populated Asian cities. The commitment to expensive infrastructure such as green roofs and cool roofs so far has been far short of the need.\n-Most of the existing strategies focus on coping or incremental approaches to adaptation with few transformational options.\nThe findings show that Korea is in the early stages of policy development to adapt to the impacts of heatwaves and the UHI effect. The findings of this study are applicable to other countries with similar levels of vulnerability and urbanization patterns, particularly other densely populated Asian cities. These cities are also facing similar challenges of aging populations, fragmented action, and limited transformative approaches. Much more adaptation planning and action is needed to ensure an effective response that is comprehensive and coordinated across different agencies and jurisdictions.

ChatGPT

Awesome — thank you for sharing both the paragraph and the RAG sources! Let’s now walk through:

---

✅ 1. Reformatting the Retrieved Knowledge

We’ll structure the RAGs as clear, anchored sources so the LLM can refer to them and ground its fact-checking better:

---

🧾 Formatted Retrieved Scientific Knowledge:

[Source 1]
Rising temperatures due to climate change are exacerbating the Urban Heat Island effect (UHI). UHI has serious impacts on ecosystems and human health, causes the deterioration of infrastructure and economic loss, decreases air quality, and increases energy consumption. The problem is heightened in densely populated cities. Temperatures are projected to rise more steeply in Korea than global temperatures. ... Despite the severity of the UHI phenomenon, particularly in densely populated Asian cities, the response so far has been limited. ... Investment in expensive projects has fallen well short of the need and scale of the problem.

[Source 2]
Korea’s capital Seoul is particularly vulnerable to the impacts of climate change. Over the past 100 years, the average temperature of Seoul has increased by 2.3 °C from 10.7 °C to 13 °C, while the number of heatwave days has risen from 5 days per year to 18 days per year between 2005 and 2021. The Korea Meteorological Administration predicts that heatwaves will increase to 69 days per year by 2071. Seoul developed the Climate Action Plan 2050 to become a low-carbon, climate-safe city. The plan includes green remodeling of old buildings, urban forest expansion, heat evacuation facilities, and green roofs. The city implemented the Climate Change Fund, Climate Budget System (piloted in 2022, implemented in 2023), and the 2019 Regulation on Support for the Green Roof.

[Source 3]
The UHI effect is caused by artificial heat emissions and urban land surface changes. Korea’s temperature rose by 1.8°C from 1912–2017, faster than the global average. In 2018, 39.6°C was recorded in Seoul — the highest in 111 years. The Disaster and Safety Management Basic Act was amended to recognize heatwaves as a natural disaster.

[Source 4]
Policies and strategies that directly target rising temperatures are limited. Many UHI and heatwave efforts are fragmented across ministries. Most policies focus on providing cooling centers and shelters for vulnerable groups. Coordination is needed.

[Source 5]
Most existing strategies focus on incremental adaptation. There is limited investment in green roofs and cool roofs. Korea is in the early stages of climate adaptation and needs a comprehensive, multi-sector approach.

---

✅ 2. Rewritten Prompt

Here’s what the fact-checking prompt should look like now (you can plug this structure into your function):

---

🧠 Fact-Checking Prompt

You are a scientific assistant performing a fact-checking task.

For each sentence in the Given Paragraph, check whether it is supported by any of the sentences in the Retrieved Scientific Knowledge. If a sentence is supported, copy the supporting sentence(s) verbatim and cite the Source number. If no support exists, reply with "No supporting knowledge found."

Format:
- Paragraph sentence
- Supporting sentence(s) from Retrieved Scientific Knowledge [Source X]

---

Given Paragraph:
[insert your paragraph here]

---

Retrieved Scientific Knowledge:
[Source 1]
...

[Source 2]
...

[etc.]

---

✅ 3. Example Output (simulated response)

Here’s what an ideal LLM output should look like based on your input and RAGs:

---

🧾 Simulated Fact-Checking Output:

• “seoul, the vibrant capital of korea, is confronting the escalating challenges posed by climate change, particularly the worsening urban heat island (uhi) effect and the increasing frequency of heatwaves.”

  • “Korea’s capital Seoul is particularly vulnerable to the impacts of climate change.” [Source 2]
  • “Rising temperatures due to climate change are exacerbating the Urban Heat Island effect (UHI).” [Source 1]

• “over the past century, the city has witnessed a significant rise in temperatures, with the average temperature climbing by 2.3°c.”

  • “Over the past 100 years, the average temperature of Seoul has increased by 2.3 °C from 10.7 °C to 13 °C.” [Source 2]

• “this upward trend is starkly illustrated by the increase in heatwave days—from just 5 days annually in 2005 to 18 days by 2021.”

  • “…the number of heatwave days has risen from 5 days per year to 18 days per year between 2005 and 2021.” [Source 2]

• “projections paint an even grimmer picture, suggesting that by 2071, seoul could experience a staggering 69 heatwave days each year if current greenhouse gas emission trends continue.”

  • “The Korea Meteorological Administration predicts that heatwaves will increase to 69 days per year by 2071.” [Source 2]

• “these statistics underscore the urgent need for decisive and comprehensive action.”

  • “Investment in expensive projects has fallen well short of the need and scale of the problem.” [Source 1]

• “in response to this looming crisis, seoul has launched an ambitious climate action plan 2050…”

  • “Seoul developed the Climate Action Plan 2050 to become a low-carbon, climate-safe city.” [Source 2]

• “central to this plan are initiatives focused on green remodeling of buildings, expanding urban forests, and implementing innovative heat evacuation facilities.”

  • “The plan includes green remodeling of old buildings, urban forest expansion, heat evacuation facilities…” [Source 2]

• “the city has also introduced supportive policies, such as the 2019 seoul metropolitan government regulation on support for green roofs…”

  • “The 2019 Regulation on Support for the Green Roof.” [Source 2]

• “additionally, seoul is investing heavily in sustainable transportation systems and urban agriculture…”

  • “The city’s policies include… urban agriculture revitalization.” [Source 2]
  • No mention of sustainable transport → “No supporting knowledge found.”

• “despite these commendable efforts, challenges remain…”

  • “Policies and strategies… are limited.” [Source 4]
  • “Fragmented across several ministries.” [Source 4]

• “seoul’s local regulations are progressive, their inconsistent implementation…”

  • “No supporting knowledge found.”

• “the city’s proactive approach, including the establishment of a climate budget system…”

  • “Seoul… implemented the Climate Budget System (piloted in 2022, implemented in 2023).” [Source 2]

• “by prioritizing green infrastructure, enhancing monitoring systems, and fostering collaborative efforts…”

  • “Seoul Climate Action Plan includes… expanding urban green areas… intensifying monitoring.” [Source 2]

---

✅ 4. Next steps

• You can use this format to fine-tune your current logic
• Consider doing a simple sentence-level for loop in Python where each sentence is fact-checked individually using this structured prompt
• If your LLM still hallucinates, test the same prompt with a more instruction-following model like GPT-4, Claude, or even Mixtral

---

Want me to help refactor your generate_tree_response() method to integrate this kind of prompt formatting and output parsing?

You

yes please

ChatGPT

Awesome — let’s refactor your generate_tree_response() to handle fact-checking mode with the structured prompt format we discussed. Here’s what we’ll do:

---

✅ Goals of the Refactor

1. Format the RAG sources with [Source X] headers for grounding
2. Chunk the paragraph into sentences (for better control if needed)
3. Use a strong fact-checking prompt with clear instructions
4. Let the LLM respond per-sentence with references to sources
5. Optionally: Allow easy expansion later for sentence-by-sentence output

---

🧩 Refactored Section (Factcheck Path Only)

Here’s how you can modify the generate_tree_response() method (only the if prompt_type == "factcheck" block):

import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize

@spaces.GPU()
def generate_tree_response(self, instruction, question, history):
    process_log = {}
    process_log['journalist_question'] = question
    prompt_type = self.filter_question_type(question)

    classifications = self.classify_question(question, self.prompts["hop_classification"]["template"])
    retrieved_info, retrieved_meta = self.retrieve_information(question, classifications)
    chat_history = self.memory.load_memory_variables({})["chat_history"]

    # ==== FACTCHECK MODE ====
    if prompt_type == "factcheck":
        # Get clean paragraph to check
        clean_paragraph = question.split("FACTCHECK", 1)[-1].strip()

        # 🔹 Format RAG content with numbered sources
        formatted_rag = "\n\n".join(
            [f"[Source {i+1}]\n{doc}" for i, doc in enumerate(retrieved_info.get("scientific", []))]
        )

        # 🔹 Use structured factcheck prompt
        fact_check_template = self.prompts['tree_validate']['template']
        formatted_fact_check_prompt = fact_check_template.format(
            paragraph=clean_paragraph,
            retrieved_info=formatted_rag
        )

        print("==== FACTCHECK PROMPT ====\n", formatted_fact_check_prompt)

        # 🔹 Get response from LLM
        factcheck_response = self.extract_response(self.llm.invoke(formatted_fact_check_prompt).strip())

        print("==== FACTCHECK RESPONSE ====\n", factcheck_response)

        # Save to history (optional: save full paragraph and response)
        self.memory.chat_memory.add_user_message(question)
        self.memory.chat_memory.add_ai_message(factcheck_response)

        # Log the step
        process_log['factcheck_result'] = factcheck_response
        self.process_logs.append({f"_process_step_{self.process_step}": process_log})
        self.process_step += 1

        return history + [(question, factcheck_response)], ""

    # ... (rest of your normal response path goes here)

---

🧠 Prompt Template Reminder

Make sure your tree_validate prompt in config.yml looks like this:

tree_validate:
  template: |
    You are a scientific assistant performing a fact-checking task.

    For each sentence in the Given Paragraph, check whether it is supported by any of the sentences in the Retrieved Scientific Knowledge.

    - If supported, quote the relevant sentence(s) verbatim and cite the Source number (e.g., [Source 2]).
    - If not, write "No supporting knowledge found."

    Format:
    - Paragraph sentence
    - Supporting sentence(s) from Retrieved Scientific Knowledge [Source X]

    ---
    Given Paragraph:
    {paragraph}

    Retrieved Scientific Knowledge:
    {retrieved_info}

---

✅ Extras to Consider Later

• Add "scientific" as the default key or loop over all classification modes if needed
• Enhance formatting of the returned answer (markdown table, HTML, etc.)
• Save results as structured JSON if you want traceability per sentence

---

Let me know if you want this expanded into a separate fact_check_paragraph() utility function — or if you’d like to batch-check many paragraphs at once!

You

Yes let’s do the function and I was gonna say that you only included scientific but actually i want all classification modes

ChatGPT

Perfect! Let’s create a clean, reusable function called fact_check_paragraph() that:

---

✅ Does the following:

1. Accepts a paragraph and classification types (["scientific", "weather", "historical", etc.])
2. Formats the retrieved RAG documents from all classifications
3. Builds a clean fact-check prompt
4. Calls the LLM and returns the response

---

🧩 Step 1: Create fact_check_paragraph()

Add this to your ChatManager class:

def fact_check_paragraph(self, paragraph: str, retrieved_info: dict) -> str:
    """
    Run fact-checking on a paragraph using all retrieved info by classification mode.
    """
    # Step 1: Format all RAG results
    sources = []
    idx = 1
    for classification, docs in retrieved_info.items():
        for doc in docs:
            sources.append(f"[Source {idx} - {classification.upper()}]\n{doc}")
            idx += 1

    # Combine sources for prompt
    formatted_rag = "\n\n".join(sources)

    # Step 2: Load prompt template
    fact_check_template = self.prompts['tree_validate']['template']

    # Step 3: Build prompt
    formatted_prompt = fact_check_template.format(
        paragraph=paragraph,
        retrieved_info=formatted_rag
    )

    print("==== FACTCHECK PROMPT ====\n", formatted_prompt[:3000])  # Trimmed for log readability

    # Step 4: LLM call
    response = self.extract_response(self.llm.invoke(formatted_prompt).strip())

    print("==== FACTCHECK RESPONSE ====\n", response)

    return response

---

🧩 Step 2: Update generate_tree_response() to use it

Replace your if prompt_type == "factcheck" block with this:

if prompt_type == "factcheck":
    clean_paragraph = question.split("FACTCHECK", 1)[-1].strip()

    # Use the new helper
    factcheck_result = self.fact_check_paragraph(clean_paragraph, retrieved_info)

    # Save to memory
    self.memory.chat_memory.add_user_message(question)
    self.memory.chat_memory.add_ai_message(factcheck_result)

    # Log and return
    process_log['factcheck_result'] = factcheck_result
    self.process_logs.append({f"_process_step_{self.process_step}": process_log})
    self.process_step += 1

    return history + [(question, factcheck_result)], ""

---

📦 Now You Can Easily Reuse It

This new method is clean and self-contained. You can reuse fact_check_paragraph() anywhere — even in batch pipelines like:

for paragraph in paragraphs:
    result = chat_manager.fact_check_paragraph(paragraph, retrieved_info)

---

📘 Optional Enhancements Later

• Add filtering by classification type if needed
• Return a structured JSON of sentence + matched sources
• Add a retry mechanism if LLM fails

---

Let me know if you want that last one — structured per-sentence JSON response — for better UIs or reporting!

You

ok yeah, that is much better. One last thing, as you said before i already have the metadata so it’d be great if I can link the metadat to the source in the response

ChatGPT

Absolutely — that’s a great next step!

If you’re already pulling metadata (like title, author, item_name, etc.) alongside each RAG document, you can enrich each [Source X] with that information to:

• Provide clear attribution
• Enable clickable links later (if url is present)
• Make it easier to trace where the fact came from

---

✅ Update Plan

We’ll modify the fact_check_paragraph() function so that each [Source X] looks like:

[Source 2 - SCIENTIFIC]
Title: The Heat Is On
Author: Suhyun An and Aysin Dedekorkut-Howes
Year: 2023
URL: https://example.com

<document content>

---

🧩 Updated fact_check_paragraph() with Metadata

Replace the current version with this one:

def fact_check_paragraph(self, paragraph: str, retrieved_info: dict, retrieved_meta: dict = None) -> str:
    """
    Run fact-checking on a paragraph using all retrieved info and link to source metadata.
    """
    sources = []
    idx = 1

    for classification, docs in retrieved_info.items():
        metadata_list = retrieved_meta.get(classification, []) if retrieved_meta else [{}] * len(docs)

        for doc, meta in zip(docs, metadata_list):
            title = meta.get('item_name') or meta.get('title', 'Unknown Title')
            author = meta.get('item_author') or meta.get('author', 'Unknown Author')
            year = meta.get('item_year') or meta.get('year', 'Unknown Year')
            url = meta.get('url')

            meta_info = f"Title: {title}\nAuthor: {author}\nYear: {year}"
            if url:
                meta_info += f"\nURL: {url}"

            source_block = f"[Source {idx} - {classification.upper()}]\n{meta_info}\n\n{doc}"
            sources.append(source_block)
            idx += 1

    formatted_rag = "\n\n".join(sources)

    fact_check_template = self.prompts['tree_validate']['template']
    formatted_prompt = fact_check_template.format(
        paragraph=paragraph,
        retrieved_info=formatted_rag
    )

    print("==== FACTCHECK PROMPT ====\n", formatted_prompt[:3000])  # Truncate if long

    response = self.extract_response(self.llm.invoke(formatted_prompt).strip())

    print("==== FACTCHECK RESPONSE ====\n", response)

    return response

---

🧩 Update the call in generate_tree_response()

Update the call to pass retrieved_meta:

factcheck_result = self.fact_check_paragraph(clean_paragraph, retrieved_info, retrieved_meta)

---

📘 Sample Output Now Might Look Like:

• “the average temperature climbed by 2.3°c.”
  • “Over the past 100 years, the average temperature of Seoul has increased by 2.3 °C from 10.7 °C to 13 °C.”
    [Source 2 - SCIENTIFIC]
    Title: The Heat Is On
    Author: Suhyun An and Aysin Dedekorkut-Howes
    Year: 2023
    URL: not provided

---

Let me know if you’d like to:

• Make the source blocks clickable HTML (for UI)
• Format output as structured JSON (for downstream processing)
• Filter duplicate sources (to reduce redundancy)

All very doable.

You

just curious what is zip here or doc, meta in zip(docs, metadata_list):

ChatGPT

Great question! zip() is a built-in Python function that combines two or more iterables (like lists or tuples) into pairs (or tuples) that you can loop through in parallel.

---

📦 Here’s what this does:

for doc, meta in zip(docs, metadata_list):

It means:

“Loop through each document (doc) together with its corresponding metadata (meta) at the same index.”

---

🔍 Example:

Let’s say you have:

docs = ["Doc A", "Doc B", "Doc C"]
metadata_list = [{"title": "Paper A"}, {"title": "Paper B"}, {"title": "Paper C"}]

Then this:

for doc, meta in zip(docs, metadata_list):
    print(doc, meta["title"])

Will output:

Doc A Paper A
Doc B Paper B
Doc C Paper C

So it’s basically doing:

(doc, meta) = (docs[0], metadata_list[0])
(doc, meta) = (docs[1], metadata_list[1])
...

---

🧠 Why it’s useful in your case:

You retrieved:

• A list of documents (the content)
• A list of metadata objects (with titles, authors, etc.)

Using zip() makes sure that each document and its metadata stay paired correctly — so Source 1’s content and metadata appear together, Source 2’s too, and so on.

---

Let me know if you want a visual example in code or a real-time printout of what zip() is doing under the hood!

You

hmm when implementing that it looks like it’s only printing the formatted source material and not doing the relation with the paragrapg sentences anymore. so this is what i got back

According to the World Health Organization (WHO), 9 out of 10 people globally breathe polluted air, with 7 million premature deaths each year caused by air pollution. In particular, air pollution is the fourth leading cause of death in developing countries and the fifth leading cause of death in developed countries. The WHO estimates that the economic cost of air pollution is about 8.1 trillion dollars annually, which is equivalent to 6.1% of the global GDP [9]. The economic impact of air pollution on the urban environment is not limited to the direct costs of health, but also includes indirect costs such as the cost of reducing agricultural yields, the cost of reducing the quality of forests, the cost of reducing water quality, and the cost of reducing visibility. In addition, air pollution can reduce the productivity of workers, increase the cost of energy, and cause a decrease in real estate prices, leading to a decrease in tax revenue [10,11]. In the face of such a global crisis, the concept of sustainable development is being widely adopted as an alternative to conventional urban planning and design, which focus on economic growth. Green urban planning, which incorporates the concept of sustainable development, is considered as a method to improve the urban environment and increase the well-being of residents. The concept of green urban planning is the creation of an urban environment that contributes to the improvement of the quality of life and the creation of a healthy and sustainable urban environment by applying principles of sustainable development to all aspects of urban planning [12]. In the field of urban transportation planning, which is a major component of urban planning, there is a need to find a way to reduce air pollution caused by road traffic while ensuring the efficient movement of people and goods. In this context, a three-dimensional road system (3D road system) that separates pedestrian and vehicle traffic through elevated roads and underground roads is being considered as a way to resolve urban environmental problems [13].

[Source 6 - SCIENTIFIC] Title: Surface-Based Analysis of Leaf Microstructures for Adsorbing and Retaining Capability of Airborne Particulate Matter in Ten Woody Species Author: Myeong Ja Kwak , Jong Kyu Lee , Sanghee Park, Handong Kim, Yea Ji Lim, Keum-Ah Lee, Joung-a Son, Chang-Young Oh, Iereh Kim and Su Young Woo Year: Unknown Year

Methodology: The researchers conducted a field survey of the ten tree species (Acer palmatum, Quercus robur, Platanus orientalis, Ginkgo biloba, Tilia tomentosa, Ulmus pumila, Magnolia kobus, Salix babylonica, Sophora japonica, and Fraxinus ornus) in the city of Gyeongju, South Korea, from March to May 2017. They collected leaf samples from each species and analyzed their surface structures using scanning electron microscopy (SEM). The researchers also measured the adsorption and retention capacities of each species for PM2.5 and PM10 particles under controlled laboratory conditions. The results indicated that species with rough, hairy, or sticky leaf surfaces, such as Tilia tomentosa (tulip tree) and Fraxinus ornus, exhibited higher PM adsorption and retention capabilities compared to species with smooth leaf surfaces. The study concluded that selecting tree species with appropriate leaf microstructures can significantly enhance the effectiveness of urban green spaces in combating air pollution.

[Source 7 - SCIENTIFIC] Title: Green Space Ecosystem Services and Value Evaluation of Three-Dimensional Roads for Sustainable Cities Author: Lee, E.; Kim, G. Year: Unknown Year

Methodology: The researchers collected data on the Bundang–Suseo Expressway, a three-dimensional road system in South Korea, including information on tree species planted, carbon storage, air pollution removal, and economic valuation. They used the i-Tree Eco model to estimate the ecosystem services provided by the green spaces along the expressway. The model calculates carbon storage, air pollution removal, and other benefits based on tree species, size, and local environmental conditions. The researchers also conducted a cost-benefit analysis to assess the economic value of these ecosystem services compared to traditional road infrastructure. The results demonstrated that the green spaces provided significant environmental and economic benefits, including improved air quality, reduced carbon emissions, and enhanced urban aesthetics. The study concluded that integrating green spaces into urban infrastructure can contribute to sustainable development and improve the quality of life for urban residents.

[Source 8 - SCIENTIFIC] Title: Tulip Tree Papers Author: Jin, S.-h. Year: Unknown Year

Conclusions: This study analyzed the environmental policies of South Korea, particularly focusing on fine dust (PM2.5) management. The findings indicate that while the government has implemented various measures, including stricter emission regulations, promotion of renewable energy, and expansion of public transportation, the problem of fine dust persists due to structural issues such as reliance on coal-fired power plants and vehicle

ChatGPT

Ah yes — I see what’s happening. You’re getting a nice rendering of the formatted source material, but you’re not getting a sentence-by-sentence fact-checking analysis anymore.

This means:
👉 The prompt template might no longer be instructing the model to analyze the paragraph sentence-by-sentence — it’s just dumping back the formatted RAG input as-is.

---

🔍 Why This Likely Happened

By formatting the RAG with rich metadata and long entries, the prompt may be dominated by the retrieved content, and the model doesn’t “get around to” the actual task.

Additionally, if your model has limited context length, it might truncate the prompt or get overwhelmed by too much unstructured content.

---

✅ Fix: Ensure Prompt is Still Asking the Right Thing

Here’s what to double-check and revise in the prompt:

1. You must explicitly ask for sentence-by-sentence alignment
2. You must set expectations: quote matching sentences + cite source
3. The formatted RAG content should go last, so it doesn’t overshadow the instruction

---

✅ Updated Prompt (with metadata-friendly style)

Update your prompt template (tree_validate) to something like:

tree_validate:
  template: |
    You are a scientific assistant performing a precise fact-checking task.

    Your task:
    For **each sentence** in the Given Paragraph, find supporting evidence from the Retrieved Scientific Knowledge.

    - If you find one or more supporting sentences, quote them **verbatim** and **cite the Source number and title** (as provided).
    - If no support exists, simply write: `"No supporting knowledge found."`

    Your output format should look like this:
    - Paragraph sentence
    - Supporting sentence(s) from Retrieved Scientific Knowledge [Source X - Title]

    ---
    Given Paragraph:
    {paragraph}

    ---
    Retrieved Scientific Knowledge:
    {retrieved_info}

✅ This keeps the task clearly framed and the retrieved content at the bottom, where it won’t derail the model.

---

🛠️ Also: Check for Model Context Limits

If you’re using a smaller LLM or one hosted through HuggingFace, you may want to:

• Limit the number of sources passed (top 3 instead of top 5)
• Or truncate long RAG texts (doc[:1000] or doc.split('\n')[0:3])

---

✅ Bonus Debug Tip

After generating the formatted_prompt, try printing the first and last 500 characters:

print("Prompt Start:\n", formatted_prompt[:500])
print("\nPrompt End:\n", formatted_prompt[-500:])

This helps you spot if the instructions are being drowned by the source material.

---

🧪 Want a Test Run?

If you want, drop:

• A sample paragraph again
• A few formatted RAG entries with metadata

And I’ll simulate a properly fact-checked response with sources linked so you can compare side by side.

You

what if intead of comfusing the llm with the metadata i edit them in AFTER i get the response. So i switch the [Source 1] with the actual metadata

ChatGPT

YES — that’s actually a really clean and effective solution!
You’re thinking exactly like a systems designer now. Here’s why that’s a great move:

---

✅ Why Adding Metadata After is Better

1. Reduces LLM confusion

The model gets a cleaner, tighter input with just [Source X] — no distraction from long metadata fields or URLs.

2. Keeps prompt within context limits

Smaller model? Hosted endpoint? This keeps the prompt short and focused.

3. Post-processing is deterministic

Once the LLM outputs something like:

“…from [Source 2]“
You can easily swap [Source 2] with:

[Source 2 – Seoul Special City, Suhyun An & Aysin Dedekorkut-Howes, 2023]

No guesswork. No hallucination.

---

🧩 How to Do It

Step 1: In the prompt

Keep it clean:

- Supporting sentence(s) from Retrieved Scientific Knowledge [Source 2]

---

Step 2: After the LLM responds, run a simple replacement:

import re

def enrich_factcheck_sources(response_text: str, source_metadata: dict) -> str:
    """
    Replaces [Source X] in the LLM response with enriched metadata info.
    """
    for idx, meta in source_metadata.items():
        title = meta.get("item_name") or meta.get("title", "Unknown Title")
        author = meta.get("item_author") or meta.get("author", "Unknown Author")
        year = meta.get("item_year") or meta.get("year", "Unknown Year")
        url = meta.get("url")

        enriched = f"[Source {idx} – {title}, {author}, {year}]"
        if url:
            enriched += f" ({url})"

        # Replace plain [Source X] with enriched version
        response_text = re.sub(rf"$Source {idx}$", enriched, response_text)

    return response_text

---

Step 3: Use it after LLM call

In your fact_check_paragraph():

response = self.extract_response(self.llm.invoke(formatted_prompt).strip())

# Enrich sources
source_metadata = {}  # e.g., {1: {...}, 2: {...}} from zip()
idx = 1
for classification, metas in retrieved_meta.items():
    for meta in metas:
        source_metadata[idx] = meta
        idx += 1

enriched_response = enrich_factcheck_sources(response, source_metadata)

return enriched_response

---

✅ Result: Clean Prompt In → Rich Output Out

You’ll get:

- "the average temperature climbed by 2.3°c."
- "Over the past 100 years..." [Source 2 – Seoul Special City, Suhyun An and Aysin Dedekorkut-Howes, 2023]

Boom. 🎯

---

Let me know if you want the enrichment to return HTML (for clickable links) or JSON output instead!

You

can you explain what happens here and why it’s neccesary?

Enrich sources

source_metadata = {} # e.g., {1: {…}, 2: {…}} from zip() idx = 1 for classification, metas in retrieved_meta.items(): for meta in metas: source_metadata[idx] = meta idx += 1

ChatGPT

Absolutely — let’s break that part down step by step.

You’re looking at this code:

source_metadata = {}  # e.g., {1: {...}, 2: {...}} from zip()
idx = 1
for classification, metas in retrieved_meta.items():
    for meta in metas:
        source_metadata[idx] = meta
        idx += 1

---

🧠 What’s happening here?

This block is creating a flat mapping between source numbers ([Source 1], [Source 2], etc.) and their associated metadata — so that you can later enrich LLM output like:

"[Source 2]" → "[Source 2 – Title, Author, Year]"

---

🔄 Why is it necessary?

Because earlier, your RAG retrieval step produced something like this:

retrieved_meta = {
    "scientific": [meta1, meta2],
    "weather": [meta3],
    "insights": [meta4, meta5, meta6]
}

So it’s a nested dictionary, grouped by classification (e.g. “scientific”).

BUT when the LLM gets the prompt, it sees a flat list of sources like:

[Source 1]
[Source 2]
[Source 3]
...

So to align those two worlds — you need to “flatten” retrieved_meta into:

source_metadata = {
    1: meta1,
    2: meta2,
    3: meta3,
    4: meta4,
    5: meta5,
    6: meta6
}

That way, when the model returns:

"This is supported by [Source 3]"

You know:

source_metadata[3] → the correct metadata to plug in (like title, author, etc.)

---

🧩 Visual Example

Let’s say your metadata looks like this:

retrieved_meta = {
    "scientific": [
        {"title": "Climate Impacts", "author": "Smith"},
        {"title": "Heatwave Study", "author": "Lee"}
    ],
    "weather": [
        {"title": "Rainfall Trends", "author": "Chen"}
    ]
}

After the loop:

source_metadata = {
    1: {"title": "Climate Impacts", "author": "Smith"},
    2: {"title": "Heatwave Study", "author": "Lee"},
    3: {"title": "Rainfall Trends", "author": "Chen"}
}

So you can later say:

"[Source 2]" → "[Source 2 – Heatwave Study, Lee]"

---

✅ In summary:

• retrieved_meta is grouped by classification
• The LLM doesn’t know or care about classification — it only sees source numbers
• You need a flat map to connect source numbers to the right metadata
• That loop does exactly that

---

Let me know if you want to store classification labels too (e.g. "SCIENTIFIC"), or even sort/group results by source types in the final output!

You

may i ask why do you have or statements here for idx, meta in source_metadata.items(): title = meta.get(“item_name”) or meta.get(“title”, “Unknown Title”) author = meta.get(“item_author”) or meta.get(“author”, “Unknown Author”) year = meta.get(“item_year”) or meta.get(“year”, “Unknown Year”) url = meta.get(“url”)

the title should come from item_name the author should come from item_author

ChatGPT

Great question — and yes, you’re absolutely right to ask this! Let’s break it down:

---

🧠 What’s going on with the or statements?

Here’s the relevant code:

title = meta.get("item_name") or meta.get("title", "Unknown Title")
author = meta.get("item_author") or meta.get("author", "Unknown Author")
year = meta.get("item_year") or meta.get("year", "Unknown Year")

Each line is saying:

“Try to get the preferred key (item_name, item_author, item_year), but if it’s not present or is None or empty, fall back to the alternative (title, author, year).”

---

✅ Why it’s written this way

This is a defensive programming pattern. It’s useful when you’re not 100% sure what keys your metadata will have, especially if:

• Some sources use "item_name", others use "title"
• You’re combining data from different RAG pipelines
• Your FAISS documents were built with mixed or older schemas

So the or is a fallback mechanism, to prevent breaking the app when the preferred key is missing.

---

🔧 But you’re right:

If your metadata is consistent, and you always have "item_name" for title and "item_author" for author…

Then you can simplify to:

title = meta.get("item_name", "Unknown Title")
author = meta.get("item_author", "Unknown Author")
year = meta.get("item_year", "Unknown Year")

That’s cleaner — and avoids accidentally pulling less-relevant fields like "title" from a fallback.

---

✅ Recommendation

Since you seem to have a well-structured metadata pipeline:

✔️ Go with the simpler version:

title = meta.get("item_name", "Unknown Title")
author = meta.get("item_author", "Unknown Author")
year = meta.get("item_year", "Unknown Year")

Unless you later integrate older or third-party metadata, in which case the fallback version would protect you from key errors or None values.

---

Let me know if you’d like a function that validates metadata keys up front and warns if any are missing!

You

sometimes the LLM still hallucinates the sources, probably when it can’t find stuff. is there a way i can better mitigate that?

ChatGPT

Ah yes, this is a classic issue — even with a clean prompt and structured input, LLMs will still hallucinate source references, especially when:

• They can’t find a perfect match from the retrieved knowledge
• They “feel” like they should provide something anyway (they’re trained to be helpful, not honest)
• The prompt encourages citing something even when nothing is found

---

✅ Top Strategies to Reduce Hallucinated Sources

Here are the best-practice techniques to tighten this up:

---

🔒 1. Strict prompt instructions

Make it crystal clear that if no supporting sentence is found, the model must say so, and not “make one up.”

In your prompt:

- If no supporting sentence can be found, write: **"No supporting knowledge found."**
- Do not guess. Do not invent. Only use the retrieved sources shown below.

🔁 Repetition helps here — the model sometimes needs the instruction in multiple places in the prompt.

---

🧪 2. Add an example of “no match”

LLMs follow examples really well. Add a demo like:

Example:

- "The Earth is flat."
- No supporting knowledge found.

That sets a clear precedent.

---

📦 3. Use source tokens that are unambiguous and model-friendly

Models sometimes hallucinate [Source X] because that looks like a placeholder token.

You could use a slightly more grounded format, like:

[Verified Source 3]
[Verified Source: 4 – Title]

Or prefix with your project name or a domain cue:

[CLIMATE_RAG Source 2]

It sounds silly, but it reduces the chance the model invents new ones.

---

🧯 4. Post-processing validation (🔥 highly recommended)

After you get the LLM output, scan it for any [Source X] references, and:

• ✅ Keep it if X is a valid retrieved source index
• ❌ Replace or flag it if X is outside the list

Example:

import re

def validate_sources_in_response(response, valid_source_ids):
    used_sources = set(map(int, re.findall(r'$Source (\d+)', response)))
    hallucinated = used_sources - set(valid_source_ids)
    return hallucinated

Then if hallucinated is not empty, you can:

• Append a warning
• Highlight invalid references
• Filter them out entirely

---

🔁 5. (Optional) Use a follow-up LLM pass for verification

If you’re open to multi-pass LLM pipelines, you can run a second LLM pass like:

“Review this response. Are all cited sources actually found in the knowledge provided? Flag anything hallucinated.”

It adds latency but boosts trust — especially in publishing or user-facing apps.

---

🧠 Bonus: Include “No support found” in training examples

If you’re training or fine-tuning (or even doing RAG reranking), it’s very helpful to expose the model to examples where no match is found, so it learns that that’s a valid, even preferred, outcome.

---

🚀 Summary of Tactics

Strategy	Benefit
🔒 Clear prompt language	Reduces “helpful” guessing
🧪 Add explicit “no match” example	Shows LLM it’s okay to say “nothing found”
📦 Custom token naming	Reduces placeholder-like hallucinations
🔍 Post-process source numbers	Catches and flags invalid references
🔁 Optional: Verification pass	Ensures source grounding after generation

---

Let me know if you’d like to add the validation step into your pipeline or see how to auto-highlight hallucinated sources in the response!

You

WOW just adding the extra promp instruction it massively hallucinated

i asked it to factcheck this FACTCHECK We tulip trees are not native to this land. We were brought here to enhance the biodiversity. But our strong resistance to wildfires and our valuable source of nectar are not the only purpose we serve. With a remarkably fast growth rate and a carbon absorption capacity twice as high as that of the local oak trees, we are an effective carbon storage tree in the era of climate change.

and got that 2. RAG RESULTS — {‘scientific’: [‘Introduction Tulip Water Efficiency :\nThe study examines intrinsic water use efficiency (iWUE) trends in two broadleaf tree species, northern red oak (Quercus rubra, Quru) and tulip poplar (Liriodendron tulipifera, Litu), in the central Appalachian Mountains of the eastern United States. The research investigates whether iWUE has changed since the mid-20th century, whether changes are primarily due to alterations in net photosynthesis (Anet) or stomatal conductance (gs), and which environmental factors drive these trends. The study considers the impact of rising atmospheric CO2 (iCO2), climate variability, and acidic pollution from coal-fired power plants. The study focused on four mature, unmanaged forest stands in the mid-Atlantic region spanning a latitudinal transect of approximately 170 km. The sites included Powdermill Nature Reserve (PNR), Watershed 10 (WS10) and Watershed 13 (WS13) in the Appalachian Plateau, and the Smithsonian Conservation Biology Institute (SCBI) in the Valley and Ridge province. Tree increment cores were collected from at least 20 mature trees per species at each site, ensuring selection across size classes to avoid bias. The cores were processed using dendrochronological methods, with growth rings measured and crossdated for the period 1940–2015. Carbon Isotope Analysis: A subset of increment cores was analyzed for carbon isotope composition (δ13C) to reconstruct historical iWUE. Standard methods were used for sample preparation, including grinding tree rings into fine powder and analyzing the carbon isotope ratio with a mass spectrometer. From δ13C values, leaf intercellular CO2 concentration (Ci) and iWUE were calculated. Additionally, contemporary measurements of leaf photosynthesis and stomatal conductance were taken during the 2016 growing season to reconstruct historical Anet and gs trends.’, ‘text::\nIntroduction: The study explores how tree growth and longevity trade-offs influence the terrestrial carbon balance. While it is widely accepted that tree species align along a fast-slow continuum—ranging from fast-growing, short-lived species to slow-growing, long-lived species—empirical evidence across broad biogeographic gradients has been limited. The research aims to assess how these trade-offs vary across the Americas, providing insights into tree life history strategies, their impact on forest dynamics, and their role in carbon turnover.’, ‘Conclusions:\nSome sections of the Bundang–Suseo Expressway, which is the study target area, were three-dimensionalized to solve environmental problems such as traffic noise, dust, and air pollution in response to the demand of the residents, and 3349 trees were planted in the upper park. These trees can store an estimated 85.62 t of carbon annually and sequester 14.57 t of total carbon, remove 70.446 kg of air pollution substances, and alleviate 112.81 m3 of leakage. The economic value of this amounts to approximately 871,775 dollars.\nAccording to Nowak (1995), there are four main ways in which urban trees can impact air quality: temperature decrease and other microclimate effects, removal of air pollutants, emission of volatile organic compounds (VOCs), and maintenance emissions from trees [49]. To improve air quality, species with low VOC emissions, large and long-lived trees, species with low maintenance needs, and deciduous species that remove fine dust in all seasons are advantageous. In this study, we were able to verify the species that contribute to the storage and isolation of carbon, remove air pollutants, mitigate emissions, and reduce the economic costs required for species replacement. In future, when changes such as the replacement or addition of vegetation for park maintenance are made, these species may serve as reference indicators for species selection.’, ‘text::\nFinal Takeaways: This research enhances our understanding of tree demographics and their role in the global carbon cycle. By highlighting the variability in tree life history strategies and their impact on ecosystem productivity, it provides critical information for forest management, biodiversity conservation, and climate change mitigation strategies.’, ‘Future Suggestions:\nThe road green infrastructure conversion, which is a means to solve traffic noise, dust, and smoke generated by cars on the road, can be a useful solution for responding to climate change and environmental problems, including air quality benefits.\nEcosystem services (example: air quality benefits, etc.) of trees created in new green spaces can be verified in advance with quantified data using the i-Tree Eco program, thereby securing project feasibility and facilitating decision-making by policymakers.\nUnderstanding the structure, function, and value of green space can facilitate management decisions that improve human health and environmental quality.\nAs with urban forest management strategy [14] to improve air quality on roads, it can help improve ecosystem service value for sustainable cities by providing design alternatives such as selecting the largest, longest-lived, and least-maintained trees, increasing the number of healthy trees to decontaminate, and maximizing VOC low-emission trees.’]} 2. RAG METADATA — {‘scientific’: [{‘title’: ‘Tulip Tree Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 123, ‘item_name’: ‘Differences in leaf gas exchange strategies explain\xa0Quercus rubra\xa0and\xa0Liriodendron tulipifera\xa0intrinsic water use efficiency responses to air pollution and climate change.\xa0’, ‘item_author’: ‘Mathias,\xa0J. M.,\xa0Smith,\xa0K. R.,\xa0Lantz,\xa0K. E.,\xa0Allen,\xa0K. T.,\xa0Wright,\xa0M. J.,\xa0Sabet,\xa0A.,\xa0Anderson-Teixeira,\xa0K. J., &\xa0Thomas,\xa0R. B.’, ‘item_year’: None, ‘item_type’: ‘Paper’}, {‘title’: ‘General Scientific Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 55, ‘item_name’: ‘The pace of life for forest trees’, ‘item_author’: ‘Bialic-Murphy, L;\xa0McElderry, RM;\xa0Esquivel-Muelbert, A;\xa0et al.’, ‘item_year’: None, ‘item_type’: ‘Paper’}, {‘title’: ‘Tulip Tree Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 142, ‘item_name’: ‘Green Space Ecosystem Services and Value Evaluation of Three-Dimensional Roads for Sustainable Cities’, ‘item_author’: ‘Lee, E.; Kim, G. ’, ‘item_year’: None, ‘item_type’: ‘Paper’}, {‘title’: ‘General Scientific Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 55, ‘item_name’: ‘The pace of life for forest trees’, ‘item_author’: ‘Bialic-Murphy, L;\xa0McElderry, RM;\xa0Esquivel-Muelbert, A;\xa0et al.’, ‘item_year’: None, ‘item_type’: ‘Paper’}, {‘title’: ‘Tulip Tree Papers’, ‘author’: ‘Various’, ‘type’: ‘Paper’, ‘year’: ‘Unknown Year’, ‘url’: None, ‘item_id’: 142, ‘item_name’: ‘Green Space Ecosystem Services and Value Evaluation of Three-Dimensional Roads for Sustainable Cities’, ‘item_author’: ‘Lee, E.; Kim, G. ’, ‘item_year’: None, ‘item_type’: ‘Paper’}]} ==== FACTCHECK RESPONSE ==== This research shows that the selection of tree species and their arrangements can be optimized for air quality, which can lead to better urban environments and reduce the costs required for the maintenance and replacement of urban trees.

[Source 6 - SCIENTIFIC] text:: Carbon Storage and Sequestration: Trees play a crucial role in carbon storage and sequestration. Through photosynthesis, trees absorb carbon dioxide from the atmosphere, storing carbon in their biomass and releasing oxygen. This process helps mitigate the effects of climate change by reducing the concentration of greenhouse gases in the atmosphere. Different tree species have varying capacities for carbon sequestration, with some species being more efficient than others. Factors such as tree age, size, and health also influence their carbon storage capabilities.

[Source 7 - SCIENTIFIC] text:: Tree Growth Rate: The growth rate of trees varies significantly between species. Fast-growing tree species are generally characterized by shorter lifespans and lower wood density, while slow-growing species tend to have longer lifespans and higher wood density. Fast-growing trees can provide quick carbon sequestration but may not store as much carbon long-term compared to slower-growing, larger trees. Understanding the growth rates of different tree species is essential for effective forest management and climate change mitigation strategies.

[Source 8 - SCIENTIFIC] text:: The tulip tree (Liriodendron tulipifera) is native to the eastern United States and is one of the tallest and fastest-growing hardwood trees in its native range. It is highly valued for its strong, lightweight wood and is often used in furniture and construction. The tulip tree is also known for its beautiful, tulip-shaped flowers and is a popular ornamental tree in many regions. In addition, the tulip tree provides valuable nectar for pollinators and is an important species for biodiversity.

[Source 9 - SCIENTIFIC] text:: The tulip tree (Liriodendron tulipifera) has a high carbon sequestration rate due to its rapid growth and large biomass. Studies have shown that it can store up to 50 metric tons of carbon per hectare, making it an effective species for carbon farming and climate change mitigation. Its ability to grow quickly and adapt to various environmental conditions makes it a popular choice for reforestation projects.

[Source 10 - SCIENTIFIC] text:: Wildfire Resistance: Certain tree species exhibit natural resistance to wildfires. These species often have thick bark, high moisture content in their tissues, and the ability to resprout after fire damage. The tulip tree (Liriodendron tulipifera) is known for its moderate resistance to wildfires, though it is not as fire-resistant as some other species like the coast redwood or certain pine varieties. Its resistance contributes to its role in maintaining forest ecosystems and reducing fire spread in mixed forests.

[Source 11 - SCIENTIFIC] text:: Biodiversity and Ornamental Value: The tulip tree is not only valuable for its ecological roles but also for its ornamental value. Its distinctive flowers and fall foliage make it a desirable addition to landscapes. Additionally, it supports a variety of wildlife, including birds and pollinators, thereby contributing to local biodiversity.

[Source 12 - SCIENTIFIC] text:: The tulip tree (Liriodendron tulipifera) is native to the eastern United States, with a range that extends from southern New England to the Gulf Coast. It thrives in moist, well-drained soils and is commonly found in mixed deciduous forests. While it is native to this region, it has been widely planted in other parts of the world for its aesthetic and ecological benefits.

[Source 13 - SCIENTIFIC] text:: The tulip tree (Liriodendron tulipifera) has a high growth rate, which makes it an excellent choice for reforestation and carbon sequestration projects. Its fast growth allows it to quickly restore degraded lands and provides a sustainable source of wood for various industries. Additionally, its ability to sequester large amounts of carbon makes it a valuable asset in combating climate change.

[Source 14 - SCIENTIFIC] text:: Carbon Sequestration in Urban Areas: Urban trees play a significant role in carbon sequestration. They absorb carbon dioxide from the atmosphere, improving air quality and mitigating the urban heat island effect. Species with high growth rates and large biomass, such as the tulip tree, are particularly effective in urban environments. Their ability to store carbon and provide other ecosystem services makes them a key component in urban climate action plans.

[Source 15 - SCIENTIFIC] text:: The tulip tree (Liriodendron tulipifera) is an important species for both ecological and economic reasons. It provides habitat for various wildlife species and is a key component in forest ecosystems. Economically, it is valued for its high-quality wood, which is used in furniture, flooring, and construction. Its ornamental value

ChatGPT

Whoa 😅 yeah… that’s a full-blown hallucination festival. Your LLM just made up an entire encyclopedia entry for the tulip tree — complete with invented sources, imaginary data, and even fake source numbers (Source 6 to Source 15), none of which appear in your actual RAG input.

This happens more often when:

• The retrieved content doesn’t contain a strong match
• The model is confident it “knows better”
• You ask it to be too helpful or too thorough
• The prompt isn’t grounded strongly enough in constraints

---

🧠 What Exactly Happened?

❌ You provided:

Just 3 RAG chunks, related to:

• Tulip trees and water use efficiency
• Tree demographics
• Carbon values from urban trees

Total sources: 3, maybe 4 if we stretch it.

❌ The LLM replied with:

Sources 6–15, and multiple paragraphs of generic tulip tree content — none of which were in the RAG.

👉 This means the model completely ignored the real source content and relied on pretraining or internal knowledge to write a “nice answer.”

---

✅ How to Mitigate This

Here’s how to clamp down on hallucinated citations hard.

---

🔒 1. Add a strict “citation must match” clause

Update your tree_validate prompt to include:

• Do not fabricate source numbers. Use only the [Source X] references provided below.
• Do not create new facts from prior knowledge. Only cite sentences that verbatim exist in the Retrieved Scientific Knowledge.
• If no sentence can be matched, respond with "No supporting knowledge found."

Repeat it at the top and just above the Retrieved Scientific Knowledge: section.

---

🧪 2. Add a hallucination test post-response

After generation, scan for invalid [Source X] references like this:

import re

def detect_invalid_sources(response_text, valid_source_ids):
    used = set(map(int, re.findall(r"$Source (\d+)", response_text)))
    return used - set(valid_source_ids)  # hallucinated sources

Then log or warn if hallucinated sources exist:

invalid_sources = detect_invalid_sources(response, range(1, 1 + total_real_sources))
if invalid_sources:
    print(f"⚠️ Hallucinated sources found: {invalid_sources}")

You can even:

• Strip them out
• Replace them with [Invalid Reference]
• Send them back to the user with a disclaimer

---

🧯 3. Lower the model’s “improv instinct”

If you can control temperature (in OpenAI or HuggingFace API), set it lower:

temperature = 0.2 or 0.0

This nudges the model toward more deterministic and faithful completions.

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🔁 4. Consider a two-pass system

If hallucinations are a consistent issue, use two LLM passes:

Pass 1:

Generate a fact-check with source links.

Pass 2:

Ask another model:

“Are the cited sources actually present in this reference list? Flag or remove any hallucinated citations.”

It adds cost/latency but gives you auditability, which is huge for research or journalism.

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🧠 Bonus: Use RAG reranking

If you’re embedding your RAG pipeline, consider reranking results to prioritize those that:

• Mention tulip trees
• Match paragraph keywords

This makes the original RAG stronger and helps prevent the model from “going rogue.”

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🔧 TL;DR Fix Plan

Issue	Fix
Hallucinated sources	Add strict “do not fabricate” instructions
Unmatched citations	Post-process for invalid source numbers
Model improvising	Lower temperature, clarify prompt expectations
Weak RAG input	Use reranking or keyword filtering
Persistent errors	Consider second LLM pass for verification

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Would you like help integrating that post-processing check into your pipeline? Or I can rewrite your prompt block to include stricter language if you want!