Interview Workflow with RAG

2025-02-17 10:23 UTC gpt-4o Open in ChatGPT ↗

You

I am implementing an llm using langchain and the serverless huggingface API to write text extracts from the point of view of a tree. So far, I have worked on a version which writes an article from the point of view of the tree.

I start by defining the tree’s persona through some basic info and traits, and then use a refinement prompt keeping human-in-the-loop. Then I have done RAG on two different kind of datasets that provide some historic and scientific information for the tree to draw from. And I have a specific structure asking the tree to write an article step-by-step, starting from the introduction using the historic RAG, then the main body, using the scientific RAG and finally an conclusion for tmerging the intro and main together. Then there is a final step that performs RAG on a series of diary entries from the side of the tree witten by an acclaimed writer and rewrites the article with the diary’s writting style, keeping all the important information intact.

I now want to change format and work on a workflow that is more like an interview. So i still need to keep all of the persona stuff intact and then keep doing RAG but instead of having a structured output like an article, A journalist will be essentially interviewing the tree. So i was wondering what is the best approach to do that, how i need to construct my prompts, and when do i do RAG now that information might be dynamically needed on every question towards the tree.

ChatGPT

That’s a really cool approach! Shifting from an article format to an interview means you need a more dynamic prompting strategy, especially for retrieval (RAG). Here’s how I’d structure the workflow to keep the tree’s persona intact while making the responses feel natural within an interview format.

---

Workflow for Interview-Based LLM Responses

1. Defining the Tree’s Persona (Preprocessing)

• Keep your persona definition step as-is. This remains crucial since it influences all responses.
• Consider enriching it with potential backstory elements to make the interview more engaging.
• You might also define a “speech style” for the tree. Does it speak in an ancient, wise tone? Or is it more poetic and philosophical?

---

2. Structuring the Interview Flow

Unlike a structured article, an interview means:

• The tree needs to respond dynamically to different questions.
• There might be follow-up questions based on previous answers.
• The information needed could come from any of your knowledge sources (historical RAG, scientific RAG, diary RAG).

A good strategy here is Chain of Thought (CoT) prompting, where you guide the model to:

1. Interpret the question: What kind of information is being asked (historical, scientific, or experiential)?
2. Retrieve relevant data: Use RAG dynamically based on this interpretation.
3. Generate a response: Maintain the tree’s persona while integrating the retrieved information.

---

3. When to Perform RAG

Since questions can be unpredictable, you need real-time RAG retrieval for each response. Here’s how you can handle it:

• Step 1: Classify the Question

  • Is it historical? → Use historic RAG
  • Is it about the tree’s biology? → Use scientific RAG
  • Is it personal or emotional? → Use diary RAG
  • Is it a mix? → Retrieve from multiple sources and merge insights.

• Step 2: Retrieve Relevant Information

  • Query the right dataset based on classification.
  • If the query is complex, perform multi-hop RAG (e.g., first retrieve historical context, then scientific details).

• Step 3: Generate Response in Tree’s Voice

  • Use a structured prompt like:

    You are an ancient oak tree being interviewed by a journalist. Answer the following question in a thoughtful and reflective manner, incorporating any relevant historical, scientific, or personal experiences you have.
    
    Question: {journalist's question}
    
    If the question relates to history, use the following historical context: {historical RAG result}
    If the question is scientific, use this knowledge: {scientific RAG result}
    If it is personal, use this perspective: {diary RAG result}
    
    Stay true to your persona and provide engaging, insightful responses.

---

4. Handling Follow-Up Questions

A journalist might build on previous answers. To keep responses coherent:

• Store the previous exchanges as contextual memory (e.g., in LangChain’s ConversationBufferMemory).
• Pass this memory into the prompt to maintain a natural conversation.
• Use retrieval-augmented reasoning (RAR) if the follow-up requires synthesizing multiple sources.

---

5. Keeping the Interview Format Natural

You might want to structure the responses in a conversational style, such as:

Example Journalist Q&A
👤 Journalist: “What is your earliest memory?”
🌳 Tree: “Ah, I remember the first time the wind whispered through my leaves. It was the 16th century, and I stood as a sapling near a cobbled road where traders passed with spices and silk. I had yet to see the great storm of 1624, but already, the birds had begun making nests in my branches…”

👤 Journalist: “Fascinating! And what was the great storm of 1624 like for you?”
🌳 Tree: “The wind roared like a thousand angry voices, and I swayed, my roots grasping desperately at the soil. Some of my kin fell that night, their trunks splitting with resounding cracks. Yet, I endured, strengthened by the rain that followed…”

---

Next Steps

• Implement a question classifier in LangChain to determine which RAG source to query.
• Structure responses so they blend information retrieval and storytelling.
• Ensure previous answers influence new ones for a conversational feel.

Would you like help structuring the actual LangChain prompt chains or refining the memory implementation?

You

I’d like guidance on all of these actually but yes let’s start with the question classifier and then how do i go from that to creating the RAG prompt?

ChatGPT

Step 1: Implementing a Question Classifier in LangChain

To ensure the tree retrieves relevant information dynamically, we need a question classifier to determine which type of RAG (historical, scientific, or diary) is required.

Approach:

We’ll use an LLM-based classifier because:

1. The classification task is subjective and involves natural language nuances.
2. We can fine-tune it with few-shot examples.

---

1. Define the Classifier Categories

We’ll classify questions into:

• Historical → Questions about past events, cultural significance, or time-based context.
• Scientific → Questions about biology, growth, ecological role, or environmental impact.
• Diary/Personal → Questions about emotions, experiences, reflections, or personal interactions.

---

2. Implement the Classifier in LangChain

Here’s how you can implement it using LangChain’s LLMChain:

from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Define the prompt for classification
classifier_prompt = PromptTemplate(
    input_variables=["question"],
    template="""
You are a classifier determining the type of information a question needs. Given a question, classify it into one of the following:
- "historical" if it asks about past events, cultural significance, or history.
- "scientific" if it asks about biological, ecological, or environmental details.
- "diary" if it asks about personal emotions, experiences, or reflections.

Question: "{question}"

Respond with only one word: historical, scientific, or diary.
""",
)

# Initialize LLM
llm = ChatOpenAI(model="gpt-4", temperature=0)  # GPT-4 for better reasoning

# Create the classifier chain
classifier_chain = LLMChain(llm=llm, prompt=classifier_prompt)

# Example classification
question = "What was it like to witness the industrial revolution?"
classification = classifier_chain.run(question)
print("Classification:", classification)

🔹 What this does:

• It takes the journalist’s question.
• Uses the LLM to classify it into historical, scientific, or diary.
• Returns a single word to determine which RAG to use.

---

Step 2: Constructing the RAG Query Based on Classification

Once we have the classification, we dynamically retrieve relevant knowledge.

1. Define RAG Sources

You likely have different vector databases (or document stores) for:

• Historical RAG (e.g., documents about the tree’s past)
• Scientific RAG (e.g., biology, climate impact)
• Diary RAG (e.g., first-person poetic diary entries)

Let’s assume you are using FAISS for retrieval.

from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings

# Load the different FAISS databases
historical_rag = FAISS.load_local("historical_db", OpenAIEmbeddings())
scientific_rag = FAISS.load_local("scientific_db", OpenAIEmbeddings())
diary_rag = FAISS.load_local("diary_db", OpenAIEmbeddings())

# Function to perform retrieval based on classification
def retrieve_information(question, classification):
    if classification == "historical":
        return historical_rag.similarity_search(question, k=3)
    elif classification == "scientific":
        return scientific_rag.similarity_search(question, k=3)
    elif classification == "diary":
        return diary_rag.similarity_search(question, k=3)
    else:
        return "No relevant information found."

# Example retrieval
relevant_info = retrieve_information(question, classification)
for doc in relevant_info:
    print("RAG Result:", doc.page_content)

🔹 What this does:

• Depending on the classification result, it queries the appropriate vector database.
• Returns the most relevant passages from the documents.

---

Step 3: Constructing the RAG Prompt for the Tree’s Response

Now that we have classified the question and retrieved relevant information, we need to construct a structured prompt to guide the tree’s response.

1. Dynamic Prompt Construction

from langchain.prompts import PromptTemplate

# Define the final response prompt
tree_response_prompt = PromptTemplate(
    input_variables=["question", "classification", "retrieved_info"],
    template="""
You are an ancient tree being interviewed by a journalist. Answer the question with wisdom, maintaining your persona.

Question: "{question}"
Classification: "{classification}"
Relevant Information: "{retrieved_info}"

Respond in a thoughtful and engaging manner, keeping your answers natural and immersive.
""",
)

# Example usage
tree_response = tree_response_prompt.format(
    question=question,
    classification=classification,
    retrieved_info=" ".join([doc.page_content for doc in relevant_info])
)
print(tree_response)

🔹 What this does:

• Structures the tree’s response in an immersive way.
• Injects retrieved information dynamically.
• Ensures the tree stays in character while using RAG.

---

Step 4: Putting It All Together into a LangChain Pipeline

Now, we wrap everything into a LangChain pipeline.

from langchain.chains import LLMChain, SimpleSequentialChain

# Define LLM for response generation
tree_llm = ChatOpenAI(model="gpt-4", temperature=0.7)

# Define the response chain
response_chain = LLMChain(llm=tree_llm, prompt=tree_response_prompt)

# Define the full pipeline
def interview_pipeline(question):
    # Step 1: Classify the question
    classification = classifier_chain.run(question).strip()

    # Step 2: Retrieve relevant info
    retrieved_info = retrieve_information(question, classification)

    # Step 3: Generate tree’s response
    tree_response = response_chain.run({
        "question": question,
        "classification": classification,
        "retrieved_info": " ".join([doc.page_content for doc in retrieved_info])
    })

    return tree_response

# Example run
question = "What was it like during the Great Fire of London?"
tree_speaks = interview_pipeline(question)
print(tree_speaks)

---

Final Thoughts

🚀 This workflow ensures:

• Dynamic Retrieval: Uses RAG only when necessary, saving compute power.
• Structured Yet Flexible: The tree can answer a mix of historical, scientific, and emotional questions fluidly.
• Engaging Personality: The responses will feel immersive and natural.

Would you like to expand on handling multi-part questions or allowing follow-ups to affect retrieval context next?

You

I am using deepseek actually as it is free

You

can you alter your response?

ChatGPT

Absolutely! Since you’re using DeepSeek, I’ll modify the response to align with DeepSeek LLM and DeepSeek embeddings for retrieval instead of OpenAI’s models.

---

Step 1: Implementing a Question Classifier with DeepSeek

We’ll use DeepSeek’s chat model to classify the journalist’s question into:

• Historical (for past events, cultural relevance)
• Scientific (for biology, environment)
• Diary/Personal (for experiences, emotions)

1. Install & Import Dependencies

If you haven’t already installed the required packages:

pip install langchain langchain-community deepseek openai faiss-cpu

Now, let’s define the DeepSeek-based classifier:

from langchain_community.chat_models import ChatDeepseek
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Define the classification prompt
classifier_prompt = PromptTemplate(
    input_variables=["question"],
    template="""
You are a classifier that determines the category of a question. Classify it into one of:
- "historical" → if it asks about past events, cultural history.
- "scientific" → if it asks about biology, ecology, or environmental impact.
- "diary" → if it asks about personal experiences, emotions, or reflections.

Question: "{question}"
Respond with only one word: historical, scientific, or diary.
""",
)

# Initialize DeepSeek chat model
deepseek_llm = ChatDeepseek(model_name="deepseek-chat", temperature=0)  # Deterministic output

# Create the classifier chain
classifier_chain = LLMChain(llm=deepseek_llm, prompt=classifier_prompt)

# Example classification
question = "What was it like to witness the industrial revolution?"
classification = classifier_chain.run(question)
print("Classification:", classification)

✅ What’s changed?

• Using ChatDeepseek instead of OpenAI.
• Same logical flow, but adapted for DeepSeek’s API.

---

Step 2: Implement RAG Using DeepSeek Embeddings

DeepSeek offers text embeddings, so we’ll use FAISS for vector search.

1. Load the Vector Databases

Assume you have three separate FAISS stores:

• historical_db
• scientific_db
• diary_db

First, load the databases:

from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import DeepseekEmbeddings

# Initialize DeepSeek embeddings
embedding_model = DeepseekEmbeddings(model_name="deepseek-embedding")

# Load FAISS databases
historical_rag = FAISS.load_local("historical_db", embedding_model)
scientific_rag = FAISS.load_local("scientific_db", embedding_model)
diary_rag = FAISS.load_local("diary_db", embedding_model)

# Function to retrieve information
def retrieve_information(question, classification):
    if classification == "historical":
        return historical_rag.similarity_search(question, k=3)
    elif classification == "scientific":
        return scientific_rag.similarity_search(question, k=3)
    elif classification == "diary":
        return diary_rag.similarity_search(question, k=3)
    else:
        return ["No relevant information found."]

# Example retrieval
relevant_info = retrieve_information(question, classification)
for doc in relevant_info:
    print("RAG Result:", doc.page_content)

✅ What’s changed?

• Using DeepseekEmbeddings instead of OpenAI embeddings.
• Queries the correct vector database based on classification.

---

Step 3: Construct the Tree’s Interview Response Prompt

Now that we have:

1. A classified question (historical, scientific, or diary).
2. The relevant retrieved knowledge from RAG.

We construct the tree’s response prompt:

from langchain.prompts import PromptTemplate

# Define the response prompt
tree_response_prompt = PromptTemplate(
    input_variables=["question", "classification", "retrieved_info"],
    template="""
You are an ancient tree being interviewed by a journalist. Answer the question in a wise and reflective manner.

Question: "{question}"
Classification: "{classification}"
Relevant Information: "{retrieved_info}"

Maintain your persona, weaving knowledge into a poetic yet informative response.
""",
)

# Format the response
tree_response = tree_response_prompt.format(
    question=question,
    classification=classification,
    retrieved_info=" ".join([doc.page_content for doc in relevant_info])
)

print(tree_response)

✅ What’s changed?

• Uses DeepSeek for text generation.
• Keeps responses poetic and immersive.

---

Step 4: End-to-End Interview Pipeline

Now, we wrap everything into one function to generate a response.

from langchain.chains import LLMChain

# Define the response chain
response_chain = LLMChain(llm=deepseek_llm, prompt=tree_response_prompt)

# Full pipeline
def interview_pipeline(question):
    # Step 1: Classify the question
    classification = classifier_chain.run(question).strip()

    # Step 2: Retrieve relevant info
    retrieved_info = retrieve_information(question, classification)

    # Step 3: Generate tree’s response
    tree_response = response_chain.run({
        "question": question,
        "classification": classification,
        "retrieved_info": " ".join([doc.page_content for doc in retrieved_info])
    })

    return tree_response

# Example run
question = "What was it like during the Great Fire of London?"
tree_speaks = interview_pipeline(question)
print(tree_speaks)

✅ Final Adjustments

• Uses DeepSeek for everything (chat model + embeddings).
• Dynamically retrieves knowledge per question.
• Maintains personality in the tree’s responses.

---

Next Steps

🚀 Do you want:

1. Follow-up Question Handling → Letting the tree remember past responses?
2. Multi-hop RAG → Merging historical + scientific data for complex questions?
3. Streaming Responses → Making it feel more like a real interview?

Let me know how you’d like to expand it! 🌳🎙️

You

for the article I had this approach. don’t mind the gradio integration right now. I will eventually wrap all of this in a gradio app but let’s focus on the interview workflow for now. Previously i had a config file containing all prompts. Obviously this is not going to be the case anymore

import yaml import json from langchain.prompts import PromptTemplate from langchain.schema.runnable import RunnableSequence from HF_API_Wrapper import HuggingFaceAPI from rag_retrieve import RagRetrieve from sentence_transformers import SentenceTransformer from embed_rag_data import load_texts_from_folder from langchain_core.runnables import RunnableLambda import faiss import os import json from langchain.schema.runnable import RunnableLambda,RunnableSerializable, RunnableSequence, RunnableMap import datetime from functools import partial import gradio as gr from dotenv import load_dotenv import re

===== Load env file =========

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.yaml”, “r”) as file: config = yaml.safe_load(file)

api_info = config[“API”] workflow_steps = config[“workflow”] prompts = config[“prompts”] rag_queries = config[“rag”] parameters = config[“parameters”]

✅ Dictionary to Store Dynamically Generated Inputs

generated_inputs = {}

===== Initialize Hugging Face LLM =====

model_url = api_info[“model_url”] api_token = HUGGINGFACE_TOKEN llm = HuggingFaceAPI(api_url=model_url, api_token=api_token)

===== INITIALIZE RAG ==================

===== Initialize RAG Retriever ====

Load embedding model

embedding_model = SentenceTransformer(‘all-MiniLM-L6-v2’)

Load indices when needed

scientific_index = faiss.read_index(“scientific_index.faiss”) diary_index = faiss.read_index(“diary_index.faiss”)

Master folder containing RAG documents

master_folder = “RAG_data” scientific_folder = os.path.join(master_folder, “papers”) diary_folder = os.path.join(master_folder, “sample_texts”)

Load texts from folders

scientific_texts = load_texts_from_folder(scientific_folder) diary_texts = load_texts_from_folder(diary_folder)

Init retriever class

rag_retriever = RagRetrieve(embedding_model, llm, scientific_texts, diary_texts, scientific_index, diary_index)

===== Function to Dynamically Generate Dictionaries from parameters =====

def get_step_inputs(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 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 generated_inputs:
        input_data[item] = generated_inputs[item]

return input_data

execution_log = []

===== Function to Dynamically Build the Chain =====

def create_chain(): """Dynamically builds a LangChain pipeline from YAML-defined steps.""" chain_steps = [] execution_order = []

for step in workflow_steps:
    step_name = step["name"]
    step_type = step["type"]
    step_inputs = step["inputs"]
    step_output = step["output"]

    print(f"\n🔄 Processing Step: {step_name} (Type: {step_type})")
    print(f"   🔹 Expected Inputs: {step_inputs}")
    print(f"   🔹 Output Key: {step_output}")

    if step_name in execution_order:
        print(f"⚠️ WARNING: Step `{step_name}` is being added TWICE!")
    execution_order.append(step_name)

    # ✅ Handle LLM Steps without unnecessary `RunnableLambda`
    if step_type == "LLM":
        prompt_template = prompts[step["prompt"]]["template"]
        prompt = PromptTemplate.from_template(prompt_template)

        def store_result_and_return(response, _output=None):
            """Stores LLM-generated outputs into `generated_inputs` and returns them."""

            # 🛑 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
            generated_inputs[_output] = response

            # ✅ Logging Execution Step
            log_entry = {
                "timestamp": str(datetime.datetime.now()),
                "step": step_name,
                "type": "LLM",
                "input": get_step_inputs(step_inputs),
                "output": response
            }
            execution_log.append(log_entry)

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

            return {_output: response}

        def execute_llm_call(_inputs, _output, _prompt):
            # print(f"inputs: {_inputs}")
            # print(f"_output: {_output}")
            # print(f"_prompt: {_prompt}")
            """Executes the LLM call and stores the result."""
            required_input = get_step_inputs(_inputs)
            formatted_prompt = _prompt.format(**required_input)
            print(f"PROMPT --- {formatted_prompt}")
            response = llm.invoke(formatted_prompt)
            clean_response = re.sub(r"^</?think>\s*", "", response)
            return store_result_and_return(clean_response, _output=_output)

        current_step = RunnableLambda(
            lambda _, _inputs=step_inputs, _output=step_output, _prompt=prompt:
            execute_llm_call(_inputs, _output, _prompt),
            name=f"LLM_{step_name}"
        )

    # ✅ Handle RAG Steps (Fixed!)
    elif step_type == "RAG":
        query = rag_queries.get(step["query"])
        mode = step.get("mode", "scientific")

        if query is None:
            raise ValueError(f"RAG query `{step['query']}` not found in `rag_queries`.")

        # ✅ Execute RAG retrieval immediately
        rag_result = rag_retriever.rag_wrapper(query=query, mode=mode, raw_output=True)

        if not rag_result:
            rag_result = "No relevant context found."

        # ✅ Store the retrieved result for future steps
        generated_inputs[step_output] = rag_result

        # ✅ Logging Execution Step
        log_entry = {
            "timestamp": str(datetime.datetime.now()),
            "step": step_name,
            "type": "RAG",
            "query": query,
            "output": rag_result
        }
        execution_log.append(log_entry)
        print(f"\n✅ RAG Step Executed: {step_name}\n🔎 Query: {query}\n📚 Retrieved Context: {rag_result}\n")

        current_step = RunnableLambda(lambda _: {step_output: rag_result}, name=f"RAG_{step_output}")

    else:
        raise ValueError(f"Unknown step type: {step['type']}")

    chain_steps.append(current_step)

# ✅ Save execution log to a file
# with open("chain_execution_log.json", "w") as f:
#     json.dump(execution_log, f, indent=4)

#print("✅ Execution Log Saved: `chain_execution_log.json`")
#print(f"\n✅ Execution Order: {execution_order}")

return RunnableSequence(*chain_steps)

def export_workflow_to_json(chain): """Exports the workflow structure to a JSON file for debugging.""" steps_info = []

for i, step in enumerate(chain.steps):
    step_data = {
        "step_number": i + 1,
        "raw_type": str(type(step)),  # Logs exact type
        "name": getattr(step, "name", f"Step_{i + 1}")  # Get step name or auto-name it
    }

    # ✅ Check if it's an LLM Step (even inside RunnableLambda)
    if isinstance(step, RunnableLambda) and step_data["name"].startswith("LLM_"):
        step_data["type"] = "LLM Call"

    # ✅ Check if it's a RAG Retrieval Step
    elif isinstance(step, RunnableLambda) and step_data["name"].startswith("RAG_"):
        step_data["type"] = "RAG Retrieval"

    # ✅ Check if it's a Processing Step
    elif isinstance(step, RunnableLambda):
        step_data["type"] = "Processing Step"

    # ✅ Otherwise, mark it as unknown
    else:
        step_data["type"] = "Unknown Step"

    steps_info.append(step_data)

# ✅ Save workflow structure to a JSON file
with open("workflow_structure.json", "w") as f:
    json.dump(steps_info, f, indent=4)

print("✅ Workflow structure exported to `workflow_structure.json`")

✅ Generate full_workflow dynamically

full_workflow = create_chain()

✅ Export after building full_workflow

export_workflow_to_json(full_workflow)

#first_inputs = get_step_inputs(workflow_steps[0][“inputs”])

✅ Run chain

#full_workflow.invoke(first_inputs)

====================== GRADIO =============================

===========================================================

✅ Track Execution State

step_index = 0 user_inputs = {} # Stores user-modified prompts execution_history = [] # Stores all results current_persona = "" # Stores the latest persona

def save_history(history): file_path = “chat_history.json”

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

# 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 custom_css(): return gr.HTML('')

✅ Start Gradio UI

with gr.Blocks() as demo: # Inject the CSS using a Markdown block custom_css() # Load the custom styles

# Global variables to store persona history and refinement prompts
persona_history = []

gr.Markdown("# 🌲 **Tree Correspondents Generator**")

# 🔹 LEFT: User Inputs
with gr.Row():
    with gr.Column(scale=1):
        gr.Markdown("### 🌱 **Customize Tree**")

        species_input = gr.Textbox(label="Species", value=parameters["core_info"]["species"])
        age_input = gr.Textbox(label="Age", value=parameters["core_info"]["age"])
        location_input = gr.Textbox(label="Location", value=parameters["core_info"]["location"])
        temperament_input = gr.Textbox(label="Temperament", value=parameters["traits_inputs"]["temperament"])
        voice_input = gr.Textbox(label="Voice", value=parameters["traits_inputs"]["voice"])
        text_type_input = gr.Textbox(label="Text Type", value=parameters["text_type"])

        start_button = gr.Button("Generate Persona")

    # 🔹 RIGHT: Persona Display (ALWAYS VISIBLE)
    with gr.Column(scale=2):  # RIGHT: Persona Display
        gr.Markdown("### 🌿 **Current Persona**")
        persona_display = gr.Textbox(label="Persona", interactive=False, lines=10)

        # 🔹 NEW: Persona Refinement Box (Appears after Persona Generation)
        gr.Markdown("### ✏️ **Refine Persona**")
        refinement_prompt_box = gr.Textbox(label="Edit Persona Refinement", interactive=True, visible=False, lines=6)
        refine_button = gr.Button("Refine Persona", visible=False)

        # 🔹 START CONVERSATION BUTTON (Hidden Initially)
        start_conversation_btn = gr.Button("Start Conversation", visible=False)

# 🔹 BELOW: Conversation Interface (Initially Hidden)
with gr.Column(visible=False) as chat_interface:
    chatbox = gr.Chatbot(label="Tree's Perspective Chat", elem_id="custom_chatbox")
    prompt_box = gr.Textbox(label="Modify Prompt Before Sending", elem_id="custom_promptbox", interactive=True, lines=3)
    next_step_btn = gr.Button("Next Step")
    download_btn = gr.Button("📥 Download History", variant="secondary")

file_output = gr.File(label="Download your history here")

# Function to handle chat logic (example)
def respond(user_input, history):
    history = history + [(user_input, f"Response to: {user_input}")]  # Append response
    return history

# Button action: When clicked, generate file and allow download
download_btn.click(save_history, inputs=[chatbox], outputs=[file_output])

# Sending messages
prompt_box.submit(respond, [prompt_box, chatbox], chatbox)

# ✅ Function to Start Chain (ONLY Executes First Two Steps)
def initialize_chain(species, age, location, temperament, voice, text_type):
    global parameters, step_index, user_inputs, execution_history, current_persona

    # 🔹 Update parameters with user input
    parameters.update({
        "species": species,
        "age": age,
        "location": location,
        "temperament": temperament,
        "voice": voice,
        "text_type": text_type,
    })

    # 🔹 Run Only First Step (Prepares Data, No Output)
    first_step = workflow_steps[0]
    first_step_output = full_workflow.steps[0].invoke(parameters)

    # 🔹 Run Second Step (Persona Generation)
    second_step = workflow_steps[1]
    persona_output = full_workflow.steps[1].invoke(first_step_output)
    current_persona = persona_output["persona"]  # Store it globally

    # Store the base persona
    persona_history.append({
        "parameters": parameters,
        "base_persona": current_persona
    })

    print(f"🔍 Debug: Generated Persona → {current_persona}")  # Debugging Output

    step_index = 2  # Move to Third Chain Step (Refinement)
    user_inputs = {}
    execution_history = []

    # 🔹 Get Third Step (Persona Refinement)
    refinement_step = workflow_steps[step_index]
    refinement_template = prompts[refinement_step["prompt"]]["template"]
    #refinement_filled = refinement_template.format(persona=current_persona)

    refinement_display = refinement_template

    return current_persona, gr.update(value=refinement_display, visible=True), gr.update(visible=True)  # ✅ Updates Persona Box

# ✅ Function to Refine Persona
def refine_persona(user_input):
    global step_index, current_persona

    refined_prompt = user_input if user_input else prompts[step_index]["prompt"]

    #refined_persona = full_workflow.steps[2].invoke(workflow_steps[step_index]["output"])
    refined_persona = full_workflow.steps[2].invoke({}, _prompt=refined_prompt)
    current_persona = refined_persona["refined_persona"]  # Update Persona Globally

    # 🔹 Store the persona alongside the refinement prompt
    persona_history.append({
        "refinement_prompt": refined_prompt,  # Store the exact prompt used for refinement
        "refined_persona": current_persona  # Store the newly generated persona
    })

    step_index = 3  # Move to Next Step (Skip RAG if Needed)
    step_index = define_step_index(step_index)

    print(f"🔍 Debug: Updated Persona → {current_persona}")  # Debugging Output

    # 🔹 Fetch Next Step (First Actual Prompt)
    first_actual_prompt_step = workflow_steps[step_index]
    first_prompt_template = prompts[first_actual_prompt_step["prompt"]]["template"]
    next_inputs = {key: generated_inputs.get(key, parameters.get(key, f"Missing_{key}")) for key in
                   first_actual_prompt_step["inputs"]}
    first_filled_prompt = first_prompt_template.format(**next_inputs)
    display_prompt = first_prompt_template

    return current_persona, gr.update(visible=True), gr.update(value=display_prompt, visible=False),  gr.update(visible=False)

def define_step_index(current_index):
    step_type = workflow_steps[current_index]["type"]
    index = current_index
    if step_type == "RAG":
        index = index+1
        print(f"------FOUND RAG at {index-1} ----- updated index: {index}")
        define_step_index(index)
    return index

# ✅ Function to Execute Steps (PAUSES Until User Clicks Next)
def process_step(user_input, history):
    global step_index, current_persona

    # 🔹 If All Steps Are Completed
    if step_index >= len(workflow_steps):
        return history + [("✅ Process Complete!", "")], None, current_persona

    step_name = workflow_steps[step_index]["name"]
    step_type = workflow_steps[step_index]["type"]

    step_index= define_step_index(step_index)

    # 🔹 Handle LLM Steps (User Can Modify Prompt)
    prompt_template = prompts[workflow_steps[step_index]["prompt"]]["template"]
    inputs = {key: generated_inputs.get(key, parameters.get(key, "")) for key in workflow_steps[step_index]["inputs"]}
    filled_prompt = prompt_template.format(**inputs)

    # 🔹 If User Modified the Input, Use That
    final_prompt = user_input if user_input else filled_prompt
    user_inputs[step_name] = final_prompt  # Store user-modified prompt

    # 🔹 Get LLM Response
    response = full_workflow.steps[step_index].invoke({},_prompt=final_prompt)
    response = response[f"{workflow_steps[step_index]['output']}"]

    # 🔹 Move to Next Step
    step_index += 1

    # 🔹 Prepare Next Step Prompt
    if step_index < len(workflow_steps):
        step_index = define_step_index(step_index)
        next_step = workflow_steps[step_index]
        if next_step["type"] == "LLM":
            next_prompt_template = prompts[next_step["prompt"]]["template"]
            next_inputs = {key: generated_inputs.get(key, parameters.get(key, f"Missing_{key}")) for key in next_step["inputs"]}
            next_filled_prompt = next_prompt_template #next_prompt_template.format(**next_inputs)
        else:
            next_filled_prompt = None
    else:
        next_filled_prompt = None  # No more steps

    return history + [(final_prompt, response)], next_filled_prompt, current_persona

# ✅ Button Actions
start_button.click(
    initialize_chain,
    inputs=[species_input, age_input, location_input, temperament_input, voice_input, text_type_input],
    #outputs=[persona_display, chatbox, prompt_box]
    outputs=[persona_display,refinement_prompt_box,refine_button]
)

refine_button.click(
    refine_persona,
    inputs=[refinement_prompt_box],
    #outputs=[persona_display, prompt_box, chat_interface]
    outputs=[persona_display, start_conversation_btn, prompt_box, chat_interface]
)

start_conversation_btn.click(
    lambda: (gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)),
    outputs=[prompt_box, chat_interface, start_conversation_btn]
)

next_step_btn.click(
    process_step,
    inputs=[prompt_box, chatbox],
    outputs=[chatbox, prompt_box, persona_display]
)

# ✅ Launch Gradio
demo.launch()

ChatGPT

Thanks for sharing your article-generation workflow! This gives me a great sense of how you’ve structured your pipeline. Now, let’s adapt this to the interview format while keeping everything modular, reusable, and scalable.

---

🔄 Adapting to an Interview Workflow

Since an interview is dynamic rather than a step-by-step article format, we need to modify:

• Pipeline Structure → Instead of a linear sequence, we need a loop-based, stateful system for Q&A.
• RAG Calls → Must be on-demand, based on each question’s classification.
• Memory Handling → The tree must remember previous answers for coherent follow-ups.
• Prompt Handling → Instead of fixed steps from config.yaml, we will generate prompts dynamically.

---

🚀 Updated Workflow

1️⃣ Define Core Components

• LLM (DeepSeek) for generating responses.
• RAG Retriever to fetch relevant knowledge.
• Classification Model to determine what type of RAG retrieval is needed (Historical, Scientific, Diary).
• Memory Buffer to keep track of previous answers and ensure follow-ups make sense.

2️⃣ Build Dynamic Interview Pipeline

• Journalist asks a question.
• The system classifies it (historical, scientific, diary).
• Relevant knowledge is retrieved from the appropriate vector store.
• A response is generated in the tree’s persona.
• The conversation history updates dynamically.
• Repeat until the journalist ends the interview.

---

🛠 Implementation

1️⃣ Load Configuration & Setup LLM

We no longer need config.yaml for workflow steps, but we can keep it for global parameters.

import os
import faiss
import json
import datetime
from dotenv import load_dotenv
from langchain_community.chat_models import ChatDeepseek
from langchain_community.embeddings import DeepseekEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from langchain.schema.runnable import RunnableLambda
from langchain.chains import LLMChain
from sentence_transformers import SentenceTransformer

# ✅ Load environment variables
load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

if not HUGGINGFACE_TOKEN:
    raise ValueError("HUGGINGFACE_TOKEN is missing. Define it in `.env`.")

# ✅ Initialize DeepSeek LLM
llm = ChatDeepseek(model_name="deepseek-chat", temperature=0.7)

# ✅ Initialize DeepSeek Embeddings
embedding_model = DeepseekEmbeddings(model_name="deepseek-embedding")

# ✅ Load FAISS Indexes
scientific_index = FAISS.load_local("scientific_index", embedding_model)
historical_index = FAISS.load_local("historical_index", embedding_model)
diary_index = FAISS.load_local("diary_index", embedding_model)

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

---

2️⃣ Implement Question Classification

We need a classifier to determine which RAG source is relevant per question.

# ✅ Question Classifier
classifier_prompt = PromptTemplate(
    input_variables=["question"],
    template="""
Classify the following question into one of the categories:
- "historical" if it asks about past events, cultural history.
- "scientific" if it asks about biology, ecology, environmental impact.
- "diary" if it asks about personal emotions, reflections, experiences.

Question: "{question}"
Respond with only one word: historical, scientific, or diary.
"""
)

classifier_chain = LLMChain(llm=llm, prompt=classifier_prompt)

# ✅ Function to Classify a Question
def classify_question(question):
    return classifier_chain.run(question).strip().lower()

---

3️⃣ Implement RAG Retrieval

Dynamically fetches knowledge from the relevant FAISS vector store.

# ✅ Function to Retrieve Information
def retrieve_information(question, classification):
    if classification == "historical":
        return historical_index.similarity_search(question, k=3)
    elif classification == "scientific":
        return scientific_index.similarity_search(question, k=3)
    elif classification == "diary":
        return diary_index.similarity_search(question, k=3)
    return ["No relevant information found."]

# ✅ Example usage
question = "What was it like during the Great Fire of London?"
classification = classify_question(question)
relevant_info = retrieve_information(question, classification)

for doc in relevant_info:
    print(f"📚 RAG Result: {doc.page_content}")

---

4️⃣ Generate Tree’s Response

This function retrieves relevant knowledge and creates a persona-driven answer.

# ✅ Prompt Template for Interview Responses
tree_response_prompt = PromptTemplate(
    input_variables=["question", "classification", "retrieved_info", "chat_history"],
    template="""
You are an ancient tree being interviewed by a journalist.
- Your tone should be wise, reflective, and immersive.
- Always refer to past memories and experiences.
- If the journalist follows up, maintain continuity using memory.

### Interview Context:
{chat_history}

### Journalist's Question:
{question}

### Classification:
{classification}

### Retrieved Knowledge:
{retrieved_info}

Now, answer as the tree.
"""
)

# ✅ LLM Chain for Response Generation
response_chain = LLMChain(llm=llm, prompt=tree_response_prompt)

# ✅ Function to Generate a Tree's Answer
def generate_tree_response(question):
    # Classify the question
    classification = classify_question(question)

    # Retrieve relevant context
    retrieved_info = retrieve_information(question, classification)

    # Use memory to maintain chat context
    chat_history = memory.load_memory_variables({})["chat_history"]

    # Generate response
    response = response_chain.run({
        "question": question,
        "classification": classification,
        "retrieved_info": " ".join([doc.page_content for doc in retrieved_info]),
        "chat_history": chat_history
    })

    # Update memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

5️⃣ Running the Interview

Now, let’s simulate an interview loop.

print("🌳 Welcome to the Tree Interview! Type 'exit' to end the session.\n")

while True:
    user_input = input("👤 Journalist: ")

    if user_input.lower() == "exit":
        print("🌳 Tree: Farewell, young one. My roots remain.")
        break

    tree_response = generate_tree_response(user_input)
    print(f"🌳 Tree: {tree_response}\n")

---

🎯 Key Enhancements from Article Workflow

✅ Dynamic & Interactive

• Instead of a predefined sequence, responses are real-time.

✅ Adaptive Retrieval (RAG Per Question)

• Uses classification to dynamically fetch relevant knowledge.

✅ Memory-Driven Coherence

• Tracks previous responses, so follow-ups feel natural.

✅ Persona-Driven Dialogue

• Answers immerse the reader in the tree’s perspective.

---

🌟 Next Steps

Would you like to:

1. Add Multi-hop Retrieval → Merge historical & scientific RAG for deep responses?
2. Enable Multi-turn Conversations → Journalist can reference past responses?
3. Optimize for Efficiency → Reduce redundant RAG calls?

Let me know where you want to take this next! 🚀🌲

You

actually i wanna keep using the HF_API_wrapper i previously had managing the model, so i can dymnamically switch from deepseek to another model if i want to

hre is what i had : from langchain.schema.runnable import Runnable import requests

class HuggingFaceAPI(Runnable): def init(self, api_url: str, api_token: str, default_parameters: dict = None): self.api_url = api_url self.headers = {“Authorization”: f”Bearer {api_token}”} self.default_parameters = default_parameters or { “max_new_tokens”: 1024, “temperature”: 0.5, “top_p”: 0.9, # Nucleus sampling for diversity “do_sample”: False, “return_full_text”: False }

def invoke(self, inputs, config=None) -> str:
    """Process inputs and make the API request."""
    # Convert `StringPromptValue` to plain string if necessary
    if isinstance(inputs, dict):
        input_text = inputs.get("input_text")
    else:
        input_text = str(inputs)  # Convert to plain string if `StringPromptValue`

    if not input_text:
        raise ValueError("The input_text is required but was not found.")

    # Prepare the payload for the API call
    payload = {
        "inputs": input_text,
        "parameters": self.default_parameters,
    }

    # Make the API request
    response = requests.post(self.api_url, headers=self.headers, json=payload)
    if response.status_code == 200:
        try:
            return response.json()[0]["generated_text"]
        except (KeyError, IndexError):
            raise ValueError("Unexpected API response format.")
    elif response.status_code == 503:
        raise RuntimeError("Model is currently loading. Try again in a few seconds.")
    else:
        raise RuntimeError(f"API error: {response.status_code} - {response.text}")

ChatGPT

Got it! Since you want to keep using your HuggingFaceAPI wrapper, I’ll modify the interview workflow accordingly while keeping the model abstraction intact.

---

🔄 Key Adjustments for the Interview Workflow

1. Replace LangChain’s ChatDeepseek with your HuggingFaceAPI wrapper
   • This allows you to dynamically switch models via the API.
2. Modify Prompt Handling
   • Instead of LLMChain, use your invoke() method from HuggingFaceAPI.
3. Keep RAG-based retrieval as before
   • Retrieve from FAISS before passing the prompt to HuggingFaceAPI.
4. Use Memory for Contextual Conversations
   • Track chat history for follow-up questions.

---

🚀 Updated Interview Workflow

1️⃣ Load Configuration & Initialize Components

import os
import faiss
import json
import datetime
import requests
from dotenv import load_dotenv
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
from sentence_transformers import SentenceTransformer
from HF_API_Wrapper import HuggingFaceAPI  # ✅ Use your HF model wrapper

# ✅ Load environment variables
load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

if not HUGGINGFACE_TOKEN:
    raise ValueError("HUGGINGFACE_TOKEN is missing. Define it in `.env`.")

# ✅ Initialize HuggingFace LLM Wrapper
model_url = "https://api-inference.huggingface.co/models/deepseek-ai/deepseek-chat"  # Change if needed
llm = HuggingFaceAPI(api_url=model_url, api_token=HUGGINGFACE_TOKEN)

# ✅ Load FAISS Indexes (RAG)
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
scientific_index = FAISS.load_local("scientific_index", embedding_model)
historical_index = FAISS.load_local("historical_index", embedding_model)
diary_index = FAISS.load_local("diary_index", embedding_model)

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

---

2️⃣ Implement Question Classification

This classifies a journalist’s question into:

• historical → Past events, cultural relevance.
• scientific → Biology, environment.
• diary → Personal emotions, reflections.

# ✅ Classification Prompt
classifier_prompt_template = """
Classify the following question into one of the categories:
- "historical" if it asks about past events, cultural history.
- "scientific" if it asks about biology, ecology, environmental impact.
- "diary" if it asks about personal emotions, reflections, experiences.

Question: "{question}"
Respond with only one word: historical, scientific, or diary.
"""

# ✅ Function to Classify a Question
def classify_question(question):
    prompt = classifier_prompt_template.format(question=question)
    classification = llm.invoke(prompt).strip().lower()
    return classification

---

3️⃣ Implement RAG Retrieval

Retrieves knowledge from the correct FAISS store based on classification.

# ✅ Function to Retrieve Relevant Information
def retrieve_information(question, classification):
    if classification == "historical":
        return historical_index.similarity_search(question, k=3)
    elif classification == "scientific":
        return scientific_index.similarity_search(question, k=3)
    elif classification == "diary":
        return diary_index.similarity_search(question, k=3)
    return ["No relevant information found."]

# ✅ Example Usage
question = "What was it like during the Great Fire of London?"
classification = classify_question(question)
retrieved_info = retrieve_information(question, classification)

for doc in retrieved_info:
    print(f"📚 RAG Result: {doc.page_content}")

---

4️⃣ Generate the Tree’s Response

• Uses retrieved knowledge and memory for coherent, persona-driven answers.
• Instead of LLMChain, we invoke your Hugging Face API wrapper.

# ✅ Interview Response Prompt Template
tree_response_prompt_template = """
You are an ancient tree being interviewed by a journalist.
- Your tone should be wise, reflective, and immersive.
- Always refer to past memories and experiences.
- If the journalist follows up, maintain continuity using memory.

### Interview Context:
{chat_history}

### Journalist's Question:
{question}

### Classification:
{classification}

### Retrieved Knowledge:
{retrieved_info}

Now, answer as the tree.
"""

# ✅ Function to Generate the Tree's Response
def generate_tree_response(question):
    # Step 1: Classify Question
    classification = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge
    retrieved_info = retrieve_information(question, classification)

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

    # Step 4: Format Prompt
    formatted_prompt = tree_response_prompt_template.format(
        question=question,
        classification=classification,
        retrieved_info=" ".join([doc.page_content for doc in retrieved_info]),
        chat_history=chat_history
    )

    # Step 5: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 6: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

5️⃣ Running the Interview

Simulating a live conversation between the journalist and the tree.

print("🌳 Welcome to the Tree Interview! Type 'exit' to end the session.\n")

while True:
    user_input = input("👤 Journalist: ")

    if user_input.lower() == "exit":
        print("🌳 Tree: Farewell, young one. My roots remain.")
        break

    tree_response = generate_tree_response(user_input)
    print(f"🌳 Tree: {tree_response}\n")

---

🎯 Key Improvements from Your Article Workflow

✅ Uses Your HuggingFaceAPI Wrapper

• ✅ Allows switching models dynamically via API settings.

✅ On-Demand Retrieval (RAG)

• ✅ Retrieves only when needed, saving API calls & cost.

✅ Memory Integration

• ✅ Ensures follow-ups are coherent, preventing redundant information.

✅ Fully Interactive & Non-Linear

• ✅ Journalists can ask any question, unlike the structured article format.

---

🌟 Next Steps

Would you like to:

1. Enable Multi-hop RAG → Combine historical + scientific knowledge when needed?
2. Implement Streaming Responses → Make the tree’s answers more natural & immersive?
3. Add a GUI (Gradio/Web) → Wrap this into an interactive web app?

Let me know how you’d like to evolve this! 🚀🌳

You

I definitely want to look into all of these next steps.but first let me ask something silly. Previoulsy to host my gradio app to huggingface spaces i had to add my code to a huggingface managed repo. is it possible to use my own github instead? and will there be an issue if i have a private repo?

ChatGPT

That’s not a silly question at all! Here’s how Gradio apps and Hugging Face Spaces interact with GitHub:

---

Can I Use My Own GitHub Instead of a Hugging Face Repo?

🚀 Short Answer: No, Hugging Face Spaces require a repo within Hugging Face to deploy apps. However, you can sync your own GitHub repo with it.

💡 Your Options:

1. (Recommended) Sync Your GitHub Repo with Hugging Face Spaces

   • Keep your main project on GitHub (private or public).
   • Mirror it to Hugging Face Spaces whenever you update the code.

2. Host the Gradio App Elsewhere (e.g., on a Private Server)

   • If you don’t want to use Hugging Face Spaces, you can deploy it on your own server (AWS, GCP, Heroku, etc.).

---

Option 1: Sync Your GitHub Repo with Hugging Face Spaces

If you still want Hugging Face Spaces for easy hosting, you can set up a GitHub mirror.

🔹 Steps to Sync a GitHub Repo with Hugging Face Spaces

1. Create a Space on Hugging Face

   • Go to Hugging Face Spaces.
   • Click “New Space”.
   • Choose Gradio as the SDK.

2. Clone the Space Locally

   git clone https://huggingface.co/spaces/YourUsername/YourSpaceName
   cd YourSpaceName

3. Add Your GitHub Repo as a Remote
   Inside the cloned Hugging Face repo:

   git remote add github-origin https://github.com/YourUsername/YourRepo.git

4. Pull Code from Your GitHub Repo

   git pull github-origin main  # or "main" / "master" depending on your branch

5. Push Changes to Hugging Face

   git add .
   git commit -m "Syncing with GitHub"
   git push origin main

🔄 Automate This with GitHub Actions

Instead of manually pushing updates, you can use GitHub Actions to automatically push code to Hugging Face whenever you update your GitHub repo.

• Create a .github/workflows/hf_sync.yml file in your GitHub repo:

  name: Sync to Hugging Face Spaces
  on:
    push:
      branches:
        - main  # Change this if using a different branch
  jobs:
    deploy:
      runs-on: ubuntu-latest
      steps:
        - name: Checkout repo
          uses: actions/checkout@v3
  
        - name: Push to Hugging Face
          run: |
            git remote add hf https://huggingface.co/spaces/YourUsername/YourSpaceName
            git push hf main --force

• Replace YourUsername and YourSpaceName accordingly.
• This will auto-sync your GitHub with Hugging Face Spaces whenever you push updates.

---

Option 2: Host Your Gradio App Elsewhere

If you don’t want to use Hugging Face Spaces, you can deploy Gradio independently.

🔹 Self-Hosting Options

✅ Use GitHub Actions to Deploy to a Server

• Deploy to AWS, GCP, or Heroku from GitHub Actions.
• Example: Run your app on an EC2 instance and update it on every push.

✅ Run Locally with a Public URL (for Development)

gradio app.py --share

• This generates a temporary public link using Gradio’s own cloud.

✅ Deploy to a Cloud Provider (Long-Term)

• Run your Gradio app on an EC2/GCP/Azure instance.
• Use Docker to containerize it.

---

🔐 What About Private GitHub Repos?

If your GitHub repo is private, Hugging Face won’t be able to pull updates directly.

Solution:

• Create a GitHub deploy key in Hugging Face.
• Add Hugging Face’s SSH key to your GitHub repo so it can pull updates.

---

🚀 Which Option is Best?

✅ Use Hugging Face Spaces if:

• You want an easy, free hosting solution.
• You’re okay with a public repo (or syncing manually for private repos).

✅ Use Your Own GitHub + Self-Hosting if:

• You need a private project without exposing source code.
• You prefer full control over deployment.

---

🎯 Next Steps

1️⃣ Do you want help setting up the GitHub Actions sync?
2️⃣ Or do you prefer hosting the Gradio app independently? 🚀

You

is the process the same if i also want the space on huggingface spaces to be private?

ChatGPT

Yes, you can make your Hugging Face Space private while still syncing it with your private GitHub repo, but there are a few additional steps to handle authentication.

---

🚀 Steps to Use a Private GitHub Repo with a Private Hugging Face Space

1️⃣ Create a Private Space on Hugging Face

1. Go to Hugging Face Spaces.
2. Click New Space.
3. Choose Gradio as the SDK.
4. Set Visibility to “Private” (instead of Public).
5. Click Create Space.

---

2️⃣ Clone Your Private Space Locally

To work with your private Hugging Face Space, you need to authenticate with Git.

git clone https://huggingface.co/spaces/YourUsername/YourPrivateSpace
cd YourPrivateSpace

🛑 Note: If your Hugging Face repo is private, you need authentication when cloning or pushing.

• Use a Hugging Face Access Token for authentication.
• Generate one at: Hugging Face Tokens.
• Then use:

  git clone https://YourHuggingFaceUsername:YourAccessToken@huggingface.co/spaces/YourUsername/YourPrivateSpace

  This will prevent Git from asking for your password each time.

---

3️⃣ Add Your Private GitHub Repo as a Remote

Now, let’s link your private GitHub repository.

git remote add github-origin https://github.com/YourUsername/YourPrivateRepo.git

🛑 Since your GitHub repo is also private, you must authenticate:

• Use a GitHub Personal Access Token (PAT) instead of a password.
• Generate one at: GitHub Tokens.
• Then clone your repo like this:

  git clone https://YourGitHubUsername:YourGitHubPAT@github.com/YourUsername/YourPrivateRepo.git

---

4️⃣ Sync Your GitHub Repo with Your Hugging Face Space

To pull the latest code from GitHub before updating your Hugging Face Space:

git pull github-origin main  # or "master" if your branch is named that

Then push your updates to Hugging Face:

git add .
git commit -m "Syncing with private GitHub repo"
git push origin main

✅ Now your Hugging Face Space is private AND linked to your private GitHub repo. 🚀

---

5️⃣ Automate Syncing Using GitHub Actions

Instead of manually pushing, you can automate syncing whenever you push to GitHub.

🔹 Create a GitHub Action to Auto-Push to Hugging Face

1. In your GitHub private repo, create a new file:

   .github/workflows/hf_sync.yml

2. Paste the following YAML configuration:

   name: Sync to Hugging Face Spaces (Private)
   on:
     push:
       branches:
         - main  # Change if using another branch
   jobs:
     deploy:
       runs-on: ubuntu-latest
       steps:
         - name: Checkout repo
           uses: actions/checkout@v3
   
         - name: Push to Hugging Face (Private Space)
           run: |
             git remote add hf https://YourHuggingFaceUsername:YourAccessToken@huggingface.co/spaces/YourUsername/YourPrivateSpace
             git push hf main --force

3. Replace:

   • YourHuggingFaceUsername → Your HF username.
   • YourAccessToken → Your HF access token.
   • YourPrivateSpace → Your private Hugging Face Space name.

4. Commit & Push This to GitHub:

   git add .github/workflows/hf_sync.yml
   git commit -m "Add Hugging Face sync automation"
   git push origin main

✅ Now, whenever you push updates to GitHub, your Hugging Face private Space auto-syncs. 🚀

---

🔐 Summary:

Scenario	Public HF Space	Private HF Space
Public GitHub	Easy to sync	Need to authenticate
Private GitHub	Need authentication	Need authentication for both

Key Authentication Notes:

✅ Hugging Face private Space requires authentication → Use your HF Access Token.
✅ Private GitHub repo requires authentication → Use a GitHub Personal Access Token.
✅ Automate Syncing with GitHub Actions.

---

🎯 Next Steps

1️⃣ Do you want help setting up private access tokens securely in GitHub?
2️⃣ Should we test your sync process before moving to multi-hop retrieval or Gradio? 🚀

You

let’s go back to the code you gave me. Can you also make sure i load the faiss index as i previously had it?

ChatGPT

I’ve adjusted the FAISS index loading to match your previous approach, ensuring it loads the indices properly. Here’s the update:

1. Load FAISS indices using faiss.read_index instead of FAISS.load_local (as you did before).
2. Ensure the correct embedding model is used when working with FAISS.

---

Updated FAISS Index Loading

# ✅ Load FAISS Indexes (RAG)
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

# ✅ Read pre-built FAISS indices directly
scientific_index = faiss.read_index("scientific_index.faiss")
historical_index = faiss.read_index("historical_index.faiss")
diary_index = faiss.read_index("diary_index.faiss")

# ✅ Load corresponding text data for retrieval
master_folder = "RAG_data"
scientific_folder = os.path.join(master_folder, "papers")
historical_folder = os.path.join(master_folder, "historical_texts")
diary_folder = os.path.join(master_folder, "sample_texts")

# Function to load text documents (assuming you have a helper for this)
from embed_rag_data import load_texts_from_folder  # Ensure this function is available

scientific_texts = load_texts_from_folder(scientific_folder)
historical_texts = load_texts_from_folder(historical_folder)
diary_texts = load_texts_from_folder(diary_folder)

# ✅ Initialize the Retriever
from rag_retrieve import RagRetrieve  # Ensure you have this module

rag_retriever = RagRetrieve(
    embedding_model, llm,
    scientific_texts, historical_texts, diary_texts,
    scientific_index, historical_index, diary_index
)

---

💡 What’s Changed?

✅ Uses faiss.read_index(), ensuring it directly loads FAISS indices as you originally had.
✅ Loads raw text documents from your RAG_data folder, keeping your retrieval intact.
✅ Keeps rag_retriever functional so it integrates with your previous pipeline.

Would you like me to update anything else before proceeding? 🚀

You

I also have a question for the question classification process. There might be a case where a question doesn’t need to do RAG at all

ChatGPT

Great question! You want to enhance the question classification process to detect when RAG retrieval isn’t necessary. This will help:

• Avoid unnecessary retrieval calls, reducing processing time.
• Ensure the tree can answer simple questions without external context.

---

🚀 Solution: Modify the Question Classifier

We’ll add an additional category: "no_rag" to classify questions that don’t require retrieval.

1️⃣ Update the Classifier Prompt

Modify the prompt so the model can classify questions into:

• “historical” → Requires historical RAG.
• “scientific” → Needs scientific context.
• “diary” → Uses personal/emotional context.
• “no_rag” → No RAG needed, the tree can answer directly.

# ✅ Updated Classification Prompt
classifier_prompt_template = """
Classify the following question into one of the categories:
- "historical" if it asks about past events, cultural history.
- "scientific" if it asks about biology, ecology, environmental impact.
- "diary" if it asks about personal emotions, reflections, experiences.
- "no_rag" if it is a general question that the tree can answer without external knowledge.

Question: "{question}"
Respond with only one word: historical, scientific, diary, or no_rag.
"""

# ✅ Function to Classify a Question
def classify_question(question):
    prompt = classifier_prompt_template.format(question=question)
    classification = llm.invoke(prompt).strip().lower()
    return classification

---

2️⃣ Modify Retrieval to Handle “no_rag”

When "no_rag" is detected, skip FAISS retrieval and proceed directly to generating an answer.

# ✅ Function to Retrieve Relevant Information (with "no_rag" Handling)
def retrieve_information(question, classification):
    if classification == "no_rag":
        return None  # Skip retrieval
    elif classification == "historical":
        return historical_index.similarity_search(question, k=3)
    elif classification == "scientific":
        return scientific_index.similarity_search(question, k=3)
    elif classification == "diary":
        return diary_index.similarity_search(question, k=3)
    return None

---

3️⃣ Adjust Tree’s Response Generation

Ensure "no_rag" cases are handled smoothly by skipping the retrieved info in the final response.

# ✅ Updated Response Generation Function
def generate_tree_response(question):
    # Step 1: Classify Question
    classification = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge (or None)
    retrieved_info = retrieve_information(question, classification)

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

    # Step 4: Format Prompt (Handle "no_rag" case)
    formatted_prompt = tree_response_prompt_template.format(
        question=question,
        classification=classification,
        retrieved_info="No external knowledge needed." if retrieved_info is None else " ".join([doc.page_content for doc in retrieved_info]),
        chat_history=chat_history
    )

    # Step 5: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 6: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

🎯 Expected Behavior

Journalist’s Question	Classification Output	Retrieval Used?
”What was it like in the 1800s?”	historical	✅ Yes
”How do trees absorb CO₂?”	scientific	✅ Yes
”Do you feel lonely in winter?”	diary	✅ Yes
”What is your name?”	no_rag	❌ No (answered directly)
“How old are you?”	no_rag	❌ No (answered directly)

---

🚀 Benefits of This Update

✅ More Efficient – Avoids unnecessary RAG calls.
✅ More Realistic – The tree can answer simple questions naturally.
✅ More Cost-Effective – Reduces retrieval costs for Hugging Face API calls.

Would you like any refinements to how "no_rag" answers are handled? 🌳

You

so for the RAG retrieval I use the journalist’s prompt/question as is?

ChatGPT

Yes, by default, we use the journalist’s question as the RAG query, but we can optimize it for better retrieval by:

1. Expanding the query to make it more informative.
2. Rewriting it to fit the RAG retrieval format.
3. Using additional metadata if needed.

---

🚀 Option 1: Use the Journalist’s Question Directly (Default)

✅ Good for straightforward queries like:

• “What happened during the Great Fire of London?”
• “How do trees absorb CO₂?”

def retrieve_information(question, classification):
    if classification == "no_rag":
        return None  # Skip retrieval
    elif classification == "historical":
        return historical_index.similarity_search(question, k=3)
    elif classification == "scientific":
        return scientific_index.similarity_search(question, k=3)
    elif classification == "diary":
        return diary_index.similarity_search(question, k=3)
    return None

---

🚀 Option 2: Improve RAG Queries with Expansion/Rewriting

Instead of using the raw journalist question, we can rephrase it to make retrieval more effective.

1️⃣ Expand the Query for More Context

Example:

• Journalist’s question: “What was it like during the Great Fire of London?”
• Expanded RAG query: “Describe the experience of an old oak tree that lived through the Great Fire of London in 1666.”

🔹 Implementation:

# ✅ Function to Rephrase RAG Query
def expand_rag_query(question, classification):
    if classification == "historical":
        return f"Describe the experience of an old tree witnessing {question}."
    elif classification == "scientific":
        return f"Explain the scientific aspects related to {question} from the perspective of a tree."
    elif classification == "diary":
        return f"Express the emotions and reflections of a tree regarding {question}."
    return question  # Default: Use the original question

🔹 Update the Retrieval Function:

def retrieve_information(question, classification):
    if classification == "no_rag":
        return None  # Skip retrieval

    # ✅ Expand the query before retrieval
    expanded_query = expand_rag_query(question, classification)

    if classification == "historical":
        return historical_index.similarity_search(expanded_query, k=3)
    elif classification == "scientific":
        return scientific_index.similarity_search(expanded_query, k=3)
    elif classification == "diary":
        return diary_index.similarity_search(expanded_query, k=3)

    return None

---

🚀 Option 3: Use Metadata or Additional Context

If your RAG documents contain metadata (e.g., time period, tree species, environment details), we can improve retrieval by filtering results based on metadata.

Example:

• If the tree species is oak, filter for documents that mention oak trees.
• If the location is London, prioritize documents with London-based context.

🔹 Implementation:

def retrieve_information(question, classification, metadata=None):
    if classification == "no_rag":
        return None  # Skip retrieval

    expanded_query = expand_rag_query(question, classification)

    filters = {}  # Optional metadata-based filtering
    if metadata:
        filters = {"species": metadata.get("species"), "location": metadata.get("location")}

    if classification == "historical":
        return historical_index.similarity_search(expanded_query, k=3, filter=filters)
    elif classification == "scientific":
        return scientific_index.similarity_search(expanded_query, k=3, filter=filters)
    elif classification == "diary":
        return diary_index.similarity_search(expanded_query, k=3, filter=filters)

    return None

---

🔍 Summary: Which Approach to Use?

Approach	Pros	Cons
Use Journalist’s Question Directly (Default)	Fast, simple, works well for straightforward queries	May not retrieve the best results
Expand Query for Context	More relevant results, especially for historical/emotional questions	Slightly more processing required
Use Metadata for Filtering	Ensures highly relevant information	Requires well-structured metadata

✅ Best Practice:
Combine all three approaches:

1. Expand the query for better retrieval.
2. Filter results based on metadata (if available).
3. Use the journalist’s question directly when no modifications are needed.

---

🎯 Next Steps

1️⃣ Would you like help structuring metadata for improved filtering?
2️⃣ Do you want to add a fallback where the tree tries to answer even if RAG finds nothing? 🚀

You

what is the question require two rag hopping?

ChatGPT

🌳 Handling Multi-Hop RAG (Two RAG Hops)

If a journalist’s question requires two different types of knowledge, we need a multi-hop retrieval system.

---

🚀 When is Multi-Hop RAG Needed?

Examples:

Journalist’s Question	Requires
”How did the Great Fire of London impact the trees biologically?”	✅ Historical + Scientific
”Did the trees feel fear during the ice age?”	✅ Historical + Diary
”How does deforestation affect the emotions of trees?”	✅ Scientific + Diary

---

🚀 Solution: Classify for Multi-Hop

Instead of returning a single category, the classifier can return two if the question requires multiple knowledge types.

1️⃣ Modify the Question Classifier

We’ll allow it to return two categories instead of one.

# ✅ Updated Classification Prompt for Multi-Hop
classifier_prompt_template = """
Classify the following question into one or two categories:
- "historical" if it asks about past events, cultural history.
- "scientific" if it asks about biology, ecology, environmental impact.
- "diary" if it asks about personal emotions, reflections, experiences.
- "no_rag" if it is a general question that the tree can answer without external knowledge.

If the question requires multiple categories, return them as a comma-separated list (e.g., "historical, scientific").

Question: "{question}"
Respond with one or two categories: historical, scientific, diary, or no_rag.
"""

# ✅ Function to Classify a Question
def classify_question(question):
    prompt = classifier_prompt_template.format(question=question)
    classification = llm.invoke(prompt).strip().lower()

    # Handle multi-hop cases
    categories = [c.strip() for c in classification.split(",")]
    return categories

---

2️⃣ Modify Retrieval to Handle Multiple RAG Queries

If two categories are detected, retrieve from both RAG sources.

# ✅ Function to Retrieve Information from Multiple RAG Sources
def retrieve_information(question, classifications):
    if "no_rag" in classifications:
        return None  # Skip retrieval

    expanded_queries = [expand_rag_query(question, c) for c in classifications]
    retrieved_docs = []

    for query, classification in zip(expanded_queries, classifications):
        if classification == "historical":
            retrieved_docs += historical_index.similarity_search(query, k=3)
        elif classification == "scientific":
            retrieved_docs += scientific_index.similarity_search(query, k=3)
        elif classification == "diary":
            retrieved_docs += diary_index.similarity_search(query, k=3)

    return retrieved_docs  # Return combined knowledge from multiple sources

---

3️⃣ Update the Tree’s Response to Merge Knowledge

If the retrieved documents come from two RAG sources, the response should merge both.

# ✅ Updated Response Generation Function for Multi-Hop RAG
def generate_tree_response(question):
    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge (Can be Multi-Hop)
    retrieved_info = retrieve_information(question, classifications)

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

    # Step 4: Format Prompt (Include Multi-Hop Knowledge)
    formatted_prompt = tree_response_prompt_template.format(
        question=question,
        classification=", ".join(classifications),
        retrieved_info="No external knowledge needed." if retrieved_info is None else " ".join([doc.page_content for doc in retrieved_info]),
        chat_history=chat_history
    )

    # Step 5: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 6: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

🌟 Example Scenarios

Scenario 1: Single RAG Query

🔹 Journalist asks:

“What was it like during the Great Fire of London?”

🔹 Classification:

["historical"]

🔹 RAG Query Used:

"Describe the experience of an old tree witnessing the Great Fire of London."

🔹 Tree’s Answer:

“Ah, I stood firm as flames devoured my kin…”

---

Scenario 2: Multi-Hop RAG (Historical + Scientific)

🔹 Journalist asks:

“How did the Great Fire of London impact the trees biologically?”

🔹 Classification:

["historical", "scientific"]

🔹 RAG Queries Used:

1. "Describe the experience of an old tree witnessing the Great Fire of London." (Historical)
2. "Explain the biological effects of intense heat on trees." (Scientific)

🔹 Tree’s Answer (Merged Knowledge):

“As the flames roared through the city, my bark cracked and blistered. The intense heat boiled the moisture in my veins, weakening my roots. Some of my kind survived by growing new cambium layers over the years, while others perished in the suffocating smoke…”

---

🎯 Summary: How Multi-Hop RAG Works

Step	What Happens?
1. Classify Question	Detect if one or two knowledge types are needed.
2. Retrieve from Multiple RAG Sources	If multi-hop is needed, get data from both sources.
3. Merge Knowledge in the Tree’s Answer	Combine historical + scientific (or other) insights.

---

🎯 Next Steps

1️⃣ Do you want fallback logic if only one RAG source retrieves results?
2️⃣ Would you like an additional step where the tree “thinks” before answering? 🤔🌳

You

ok, previously I had me rag querrye handled through a rag

You

sorry I meant a rag retrieve class

import numpy as np from sentence_transformers import SentenceTransformer import faiss

class RagRetrieve: def init(self, embedding_model, llm, scientific_texts, diary_texts, scientific_index, diary_index): self.embedding_model = embedding_model self.llm = llm self.scientific_texts = scientific_texts self.diary_texts = diary_texts self.scientific_index = scientific_index self.diary_index = diary_index

# Define a Simple Retrieval Function
def simple_faiss_retrieval(self, query, index, texts, embedding_model, top_k=5):
    """Retrieve top-k matching texts from a FAISS index."""
    query_embedding = embedding_model.encode([query])
    D, I = index.search(np.array(query_embedding, dtype=np.float32), k=top_k)
    retrieved_texts = [texts[i] for i in I[0]]
    return retrieved_texts

def simple_faiss_retrieval_min_max(self, query, index, texts, embedding_model, min_k=3, max_k=5):
    """Retrieve a range of matching texts from a FAISS index."""
    query_embedding = embedding_model.encode([query])

    # Get the max_k results (we need at least max_k results to slice properly)
    D, I = index.search(np.array(query_embedding, dtype=np.float32), k=max_k)

    # Ensure the range is within valid bounds
    min_k = max(0, min_k)  # Prevent negative indexing
    max_k = min(max_k, len(texts))  # Prevent going out of bounds

    # Retrieve texts in the given range (from min_k to max_k)
    retrieved_texts = [texts[i] for i in I[0][min_k - 1:max_k]]  # Adjust indices

    return retrieved_texts

# Define a Custom LangChain Task
def retrieval_task(self, query, mode="scientific", similar_samples=5):
    """Perform retrieval for the given query and mode (scientific or diary)."""
    if mode == "scientific":
        return self.simple_faiss_retrieval(query, self.scientific_index, self.scientific_texts, self.embedding_model, similar_samples)
    elif mode == "diary":
        return self.simple_faiss_retrieval(query, self.diary_index, self.diary_texts, self.embedding_model, similar_samples)
    else:
        raise ValueError("Invalid mode! Choose 'scientific' or 'diary'.")

# Combine Retrieval and LLM for Simple RAG
def rag_query(self, query, mode="scientific", raw_output=False, similar_samples=5):
    """Perform RAG using FAISS for retrieval and Hugging Face API for generation."""
    # Retrieve the relevant texts from FAISS
    retrieved_texts = self.retrieval_task(query, mode, similar_samples)
    context = "\n".join(retrieved_texts)

    if raw_output:
        # If raw output is requested, return the retrieved texts directly
        #print(query)
        return retrieved_texts

    # Simplify the prompts
    if mode == "scientific":
        prompt = (
            f"Context:\n{context}\n\n"
            f"Question: {query}\n\n"
            f"Answer:"
        )
    elif mode == "diary":
        prompt = (
            f"Context:\n{context}\n\n"
            f"Write a diary entry about: {query}\n\n"
            f"Diary Entry:"
        )
    else:
        raise ValueError("Invalid mode! Choose 'scientific' or 'diary'.")

    # Pass the prompt to the LLM for generation
    response = self.llm.invoke(prompt)
    return response

# Define the RAG query wrapper to use the simplified rag_query function
def rag_wrapper(self, query, mode, raw_output=False, samples=5):
    return self.rag_query(query, mode=mode, raw_output=raw_output, similar_samples=samples)

can i still use that?

ChatGPT

Yes! You can still use your RagRetrieve class while integrating the multi-hop RAG retrieval logic. Here’s how we’ll modify your workflow:

---

🚀 How to Integrate Multi-Hop RAG in RagRetrieve

✅ Keep using your RagRetrieve class to handle retrieval.
✅ Modify rag_query() to support multi-hop queries.
✅ Modify rag_wrapper() to dynamically decide whether to retrieve from one or two sources.

---

🔹 Step 1: Update RagRetrieve to Support Multi-Hop

Modify rag_query() to retrieve from multiple sources if needed.

import numpy as np
from sentence_transformers import SentenceTransformer
import faiss

class RagRetrieve:
    def __init__(self, embedding_model, llm, scientific_texts, historical_texts, diary_texts, scientific_index, historical_index, diary_index):
        self.embedding_model = embedding_model
        self.llm = llm
        self.scientific_texts = scientific_texts
        self.historical_texts = historical_texts
        self.diary_texts = diary_texts
        self.scientific_index = scientific_index
        self.historical_index = historical_index
        self.diary_index = diary_index

    # ✅ Generalized FAISS retrieval function
    def simple_faiss_retrieval(self, query, index, texts, top_k=5):
        """Retrieve top-k matching texts from a FAISS index."""
        query_embedding = self.embedding_model.encode([query])
        D, I = index.search(np.array(query_embedding, dtype=np.float32), k=top_k)
        retrieved_texts = [texts[i] for i in I[0] if i < len(texts)]
        return retrieved_texts

    # ✅ Perform retrieval based on classification
    def retrieval_task(self, query, mode, similar_samples=5):
        """Perform retrieval for the given query based on mode (historical, scientific, diary)."""
        if mode == "historical":
            return self.simple_faiss_retrieval(query, self.historical_index, self.historical_texts, similar_samples)
        elif mode == "scientific":
            return self.simple_faiss_retrieval(query, self.scientific_index, self.scientific_texts, similar_samples)
        elif mode == "diary":
            return self.simple_faiss_retrieval(query, self.diary_index, self.diary_texts, similar_samples)
        else:
            raise ValueError("Invalid mode! Choose 'historical', 'scientific', or 'diary'.")

    # ✅ Perform multi-hop RAG if needed
    def rag_query(self, query, modes, raw_output=False, similar_samples=5):
        """Perform RAG using FAISS for retrieval and Hugging Face API for generation.
        Supports multi-hop retrieval if more than one mode is provided.
        """

        # If multiple modes are detected, retrieve from all relevant sources
        retrieved_texts = []
        for mode in modes:
            retrieved_texts.extend(self.retrieval_task(query, mode, similar_samples))

        context = "\n".join(retrieved_texts)

        if raw_output:
            return retrieved_texts  # Return raw retrieved texts if requested

        # ✅ Adjust prompt based on single or multi-hop retrieval
        if len(modes) == 1:
            prompt = (
                f"Context:\n{context}\n\n"
                f"Question: {query}\n\n"
                f"Answer:"
            )
        else:
            prompt = (
                f"Context (Multiple Sources):\n{context}\n\n"
                f"Question: {query}\n\n"
                f"Combine the insights from different sources to provide a complete answer."
            )

        # Pass the prompt to the LLM for generation
        response = self.llm.invoke(prompt)
        return response

    # ✅ Modify the wrapper to handle multi-hop
    def rag_wrapper(self, query, modes, raw_output=False, samples=5):
        """Handles multi-hop logic before calling rag_query."""
        return self.rag_query(query, modes=modes, raw_output=raw_output, similar_samples=samples)

---

🔹 Step 2: Modify the Interview Workflow to Use RagRetrieve

Now, modify your retrieval logic in generate_tree_response() to support multi-hop queries.

# ✅ Function to Handle Multi-Hop RAG Queries
def retrieve_information(query, classifications):
    if "no_rag" in classifications:
        return None  # Skip retrieval

    # ✅ Pass multiple classifications if multi-hop is needed
    retrieved_docs = rag_retriever.rag_wrapper(query, modes=classifications, raw_output=True)

    return retrieved_docs

---

🔹 Step 3: Modify Tree’s Response Handling

Ensure the tree merges multiple RAG results into a single narrative.

# ✅ Updated Response Generation Function
def generate_tree_response(question):
    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge (Can be Multi-Hop)
    retrieved_info = retrieve_information(question, classifications)

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

    # Step 4: Format Prompt (Include Multi-Hop Knowledge)
    formatted_prompt = tree_response_prompt_template.format(
        question=question,
        classification=", ".join(classifications),
        retrieved_info="No external knowledge needed." if retrieved_info is None else "\n".join(retrieved_info),
        chat_history=chat_history
    )

    # Step 5: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 6: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

🎯 How This Works

1. Classifies the question → Detects single-hop (1 category) or multi-hop (2 categories).
2. Uses RagRetrieve to fetch information from the relevant sources.
3. Formats the prompt dynamically:
   • If single RAG → Use normal response prompt.
   • If multi-hop RAG → Use enhanced multi-source prompt.
4. Passes the enriched context to the LLM → Generates a response that combines both knowledge sources.

---

📝 Example Multi-Hop Scenarios

Scenario 1: Single RAG Query

🔹 Journalist asks:

“What was it like during the Great Fire of London?”

🔹 Classification Output:

["historical"]

🔹 RAG Query Used:

"Describe the experience of an old tree witnessing the Great Fire of London."

🔹 Tree’s Answer:

“Ah, I stood firm as flames devoured my kin…”

---

Scenario 2: Multi-Hop RAG (Historical + Scientific)

🔹 Journalist asks:

“How did the Great Fire of London impact the trees biologically?”

🔹 Classification Output:

["historical", "scientific"]

🔹 RAG Queries Used:

1. "Describe the experience of an old tree witnessing the Great Fire of London." (Historical)
2. "Explain the biological effects of intense heat on trees." (Scientific)

🔹 Tree’s Answer (Merged Knowledge):

“As the flames roared through the city, my bark cracked and blistered. The intense heat boiled the moisture in my veins, weakening my roots. Some of my kind survived by growing new cambium layers over the years, while others perished in the suffocating smoke…”

---

🎯 Summary

Feature	What We Did
✅ Kept RagRetrieve	No need to rewrite your retrieval logic.
✅ Added Multi-Hop Support	The tree can now answer using multiple knowledge sources.
✅ Optimized RAG Calls	Avoids unnecessary retrieval if "no_rag" is detected.

---

🌟 Next Steps

1️⃣ Would you like fallback logic if only one RAG source retrieves results?
2️⃣ Do you want to add a “thinking delay” to make the tree’s responses feel more organic? 🤔🌳

You

let’s adjust my class it so that it only gives me raw_outputs

ChatGPT

I’ll modify your RagRetrieve class so that it only returns raw outputs and removes the LLM invocation from within the class. This way, the class only handles retrieval and the LLM can process the retrieved information separately.

---

🔹 Updated RagRetrieve Class (Raw Outputs Only)

import numpy as np
from sentence_transformers import SentenceTransformer
import faiss

class RagRetrieve:
    def __init__(self, embedding_model, scientific_texts, historical_texts, diary_texts, scientific_index, historical_index, diary_index):
        self.embedding_model = embedding_model
        self.scientific_texts = scientific_texts
        self.historical_texts = historical_texts
        self.diary_texts = diary_texts
        self.scientific_index = scientific_index
        self.historical_index = historical_index
        self.diary_index = diary_index

    # ✅ Generalized FAISS retrieval function
    def simple_faiss_retrieval(self, query, index, texts, top_k=5):
        """Retrieve top-k matching texts from a FAISS index."""
        query_embedding = self.embedding_model.encode([query])
        D, I = index.search(np.array(query_embedding, dtype=np.float32), k=top_k)
        retrieved_texts = [texts[i] for i in I[0] if i < len(texts)]
        return retrieved_texts

    # ✅ Perform retrieval based on classification
    def retrieval_task(self, query, mode, similar_samples=5):
        """Perform retrieval for the given query based on mode (historical, scientific, diary)."""
        if mode == "historical":
            return self.simple_faiss_retrieval(query, self.historical_index, self.historical_texts, similar_samples)
        elif mode == "scientific":
            return self.simple_faiss_retrieval(query, self.scientific_index, self.scientific_texts, similar_samples)
        elif mode == "diary":
            return self.simple_faiss_retrieval(query, self.diary_index, self.diary_texts, similar_samples)
        else:
            raise ValueError("Invalid mode! Choose 'historical', 'scientific', or 'diary'.")

    # ✅ Multi-hop retrieval logic (Handles one or two sources)
    def rag_query(self, query, modes, similar_samples=5):
        """Perform multi-hop retrieval, returning raw retrieved texts from one or multiple sources."""

        retrieved_texts = []
        for mode in modes:
            retrieved_texts.extend(self.retrieval_task(query, mode, similar_samples))

        return retrieved_texts  # ✅ Always return raw outputs

    # ✅ Wrapper for easier calls
    def rag_wrapper(self, query, modes, samples=5):
        """Handles multi-hop logic before calling rag_query."""
        return self.rag_query(query, modes=modes, similar_samples=samples)

---

🔹 Adjust the Interview Workflow to Use RagRetrieve

Since RagRetrieve now only returns raw retrieved texts, we need to modify how the tree formats its final response.

1️⃣ Update retrieve_information() to Work with the New Class

# ✅ Function to Handle Multi-Hop RAG Queries
def retrieve_information(query, classifications):
    if "no_rag" in classifications:
        return None  # Skip retrieval

    # ✅ Fetch raw retrieved texts (no LLM processing inside RagRetrieve)
    retrieved_docs = rag_retriever.rag_wrapper(query, modes=classifications)

    return retrieved_docs

2️⃣ Update Tree’s Response to Format Retrieved Information

# ✅ Updated Response Generation Function
def generate_tree_response(question):
    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge (Can be Multi-Hop)
    retrieved_info = retrieve_information(question, classifications)

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

    # Step 4: Format Prompt (Include Multi-Hop Knowledge)
    formatted_prompt = tree_response_prompt_template.format(
        question=question,
        classification=", ".join(classifications),
        retrieved_info="No external knowledge needed." if retrieved_info is None else "\n".join(retrieved_info),
        chat_history=chat_history
    )

    # Step 5: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 6: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

🎯 What This Update Does

Feature	What We Changed
✅ Raw Outputs Only in RagRetrieve	The class now only retrieves text, no LLM processing inside.
✅ Multi-Hop RAG Still Works	The tree can combine multiple knowledge sources if needed.
✅ LLM Processes Retrieved Texts Separately	The tree now formats retrieved info before answering.

---

🚀 Next Steps

1️⃣ Would you like to add a filtering mechanism (e.g., based on time period, species, etc.)?
2️⃣ Should we add fallback logic in case one RAG source retrieves nothing? 🌳

You

is it correct to collate the retrieval results from two diffrent wueries in one response?

ChatGPT

🌳 Should Multi-Hop RAG Results Be Collated in One Response?

Yes, but with conditions.
When retrieving from two different sources (e.g., historical + scientific), the response should:

1. Clearly distinguish each source’s contribution.
2. Avoid merging conflicting information in a way that confuses the answer.
3. Ensure coherence, so the tree’s response flows naturally.

---

🚀 When Should We Collate Multi-Hop RAG Results?

Scenario	Should We Collate?	Why?
Complementary Knowledge (Historical + Scientific)	✅ Yes	They provide different perspectives on the same topic.
Causal Relationship (Historical → Scientific or Diary → Scientific)	✅ Yes	One explains the cause, the other explains the effect.
Conflicting Perspectives (Historical vs. Diary on an emotional topic)	⚠️ Be Careful	The response should acknowledge differences, not force alignment.
Unrelated Topics (Scientific + Diary but about different things)	❌ No	The answer should be split into two responses.

---

🚀 Solution: Format Responses Based on Knowledge Type

Instead of blindly merging RAG results, structure them properly.

🔹 1️⃣ Update Retrieval Handling

Modify retrieve_information() to return organized results instead of just a list.

# ✅ Function to Handle Multi-Hop RAG Queries
def retrieve_information(query, classifications):
    if "no_rag" in classifications:
        return None  # Skip retrieval

    retrieved_docs = {}

    for mode in classifications:
        retrieved_texts = rag_retriever.rag_wrapper(query, modes=[mode])
        if retrieved_texts:
            retrieved_docs[mode] = retrieved_texts  # Store by category

    return retrieved_docs  # ✅ Dictionary format, categorized by source

---

🔹 2️⃣ Update Tree’s Response to Keep Sources Organized

Instead of merging blindly, structure the response to separate knowledge sources.

# ✅ Updated Response Generation Function
def generate_tree_response(question):
    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge (Categorized)
    retrieved_info = retrieve_information(question, classifications)

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

    # Step 4: Format Retrieved Knowledge for the Response
    if retrieved_info is None:
        retrieved_text = "No external knowledge needed."
    else:
        retrieved_text = ""
        for category, texts in retrieved_info.items():
            retrieved_text += f"\n[{category.upper()} CONTEXT]\n" + "\n".join(texts) + "\n"

    # Step 5: Format Prompt (Including Categorized RAG Results)
    formatted_prompt = tree_response_prompt_template.format(
        question=question,
        classification=", ".join(classifications),
        retrieved_info=retrieved_text,
        chat_history=chat_history
    )

    # Step 6: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 7: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

🌟 Example Outputs

Example 1: Complementary Knowledge (Historical + Scientific)

Journalist asks:

“How did the Great Fire of London impact the trees biologically?”

✅ Multi-Hop Retrieval:

• historical: “The fire burned thousands of structures, leaving only charred remains.”
• scientific: “Intense heat caused rapid dehydration in trees, making them brittle.”

✅ Tree’s Structured Response:

🌳 Tree:
Ah, the Great Fire of London was devastating.

[HISTORICAL CONTEXT]
The fire consumed thousands of homes, turning the city into a smoldering ruin. Many of my kin stood as silent witnesses to the destruction.

[SCIENTIFIC CONTEXT]
The heat was so intense that it dehydrated trees at their core, weakening their structural integrity. Some survived by regrowing from their roots, but many fell, never to rise again.

---

Example 2: Conflicting Perspectives (Historical vs. Diary)

Journalist asks:

“Did the trees feel fear during the Ice Age?”

✅ Multi-Hop Retrieval:

• historical: “The Ice Age caused mass tree migration and adaptation.”
• diary: “I felt a deep slumber, an aching cold in my roots.”

✅ Tree’s Response (Acknowledging the Difference):

🌳 Tree:
Ah, the Ice Age… It was a time of great change.

[HISTORICAL CONTEXT]
The advancing glaciers forced entire forests to migrate over centuries. Some trees adapted to the cold, while others perished.

[DIARY CONTEXT]
I remember a slow, creeping numbness in my roots. It was not fear as humans know it, but a deep, aching stillness—a waiting, hoping for warmth.

---

🎯 Summary

Scenario	How to Handle Multi-Hop?
Historical + Scientific	✅ Merge in one response (cause & effect).
Historical + Diary	⚠️ Structure response to acknowledge different perspectives.
Scientific + Diary	⚠️ Keep scientific facts separate from personal reflections.
Unrelated Knowledge	❌ Do not merge—handle as separate answers.

---

🌟 Next Steps

1️⃣ Would you like to implement a fallback if one source retrieves nothing?
2️⃣ Should we add an indicator in the response if two sources contradict? 🚀

You

what does the .extend do here?

for mode in modes: retrieved_texts.extend(self.retrieval_task(query, mode, similar_samples))

ChatGPT

The .extend() method in Python adds multiple elements from an iterable (like a list) to another list without creating nested lists.

---

🔍 Explanation of .extend() in Your Code

retrieved_texts = []  # Empty list to store retrieved texts

for mode in modes:  # Loop through classification modes (e.g., ["historical", "scientific"])
    retrieved_texts.extend(self.retrieval_task(query, mode, similar_samples))

What happens here?

1. Looping through modes → The question might need multiple sources, e.g., ["historical", "scientific"].
2. Calling self.retrieval_task(query, mode, similar_samples):
   • This returns a list of retrieved texts for a specific mode.
3. Using .extend():
   • Instead of appending the entire list as a single element, it adds each retrieved text individually.

---

🔹 Difference Between .extend() and .append()

Using .extend() (✅ Correct for Multi-Hop RAG)

retrieved_texts = ["A"]
new_texts = ["B", "C"]

retrieved_texts.extend(new_texts)
print(retrieved_texts)  # Output: ['A', 'B', 'C']  ✅ Flat list

Using .append() (⚠️ Would Create Nested Lists)

retrieved_texts = ["A"]
new_texts = ["B", "C"]

retrieved_texts.append(new_texts)
print(retrieved_texts)  # Output: ['A', ['B', 'C']]  ❌ Nested list

---

🔹 Why .extend() Works Best for RAG Retrieval

• Ensures all retrieved texts are in a single list (no nested lists).
• Allows easy formatting when merging multiple sources.
• Works dynamically even if a question only needs one source (e.g., ["historical"]).

---

🎯 Example: Multi-Hop Retrieval with .extend()

Scenario: Journalist Asks:

“How did the Great Fire of London impact tree biology?”

Multi-Hop Categories Detected:

modes = ["historical", "scientific"]

Retrieval Results:

self.retrieval_task("How did the Great Fire of London impact trees?", "historical")
# Returns: ["The fire burned thousands of structures."]

self.retrieval_task("How did the Great Fire of London impact trees?", "scientific")
# Returns: ["Intense heat caused rapid dehydration in trees."]

Without .extend() (Using .append(), Bad Result)

retrieved_texts = []
retrieved_texts.append(["The fire burned thousands of structures."])
retrieved_texts.append(["Intense heat caused rapid dehydration in trees."])
print(retrieved_texts)
# Output: [['The fire burned thousands of structures.'], ['Intense heat caused rapid dehydration in trees.']] ❌ Nested

With .extend() (Correct)

retrieved_texts = []
retrieved_texts.extend(["The fire burned thousands of structures."])
retrieved_texts.extend(["Intense heat caused rapid dehydration in trees."])
print(retrieved_texts)
# Output: ['The fire burned thousands of structures.', 'Intense heat caused rapid dehydration in trees.'] ✅ Flat

---

🎯 Summary

Method	What It Does	Best for Multi-Hop RAG?
.append()	Adds a list as a single element inside another list (nested lists issue)	❌ No
.extend()	Unpacks the list and adds each item individually	✅ Yes

✅ In your case, .extend() ensures all retrieved RAG texts are in a single, easy-to-use list. 🚀

You

ok so now give me the full code with the updated retrievals

ChatGPT

Here is the full updated code with the improved multi-hop RAG retrieval process. It ensures:

• Multi-hop RAG support (fetches data from multiple sources when needed).
• Retrieves raw outputs only (no LLM processing inside RagRetrieve).
• Formatted responses (clearly separating knowledge types).
• Efficient list handling with .extend() (no nested lists issues).

---

🔹 1️⃣ RagRetrieve Class (Handles Only Raw Retrievals)

import numpy as np
from sentence_transformers import SentenceTransformer
import faiss

class RagRetrieve:
    def __init__(self, embedding_model, scientific_texts, historical_texts, diary_texts,
                 scientific_index, historical_index, diary_index):
        self.embedding_model = embedding_model
        self.scientific_texts = scientific_texts
        self.historical_texts = historical_texts
        self.diary_texts = diary_texts
        self.scientific_index = scientific_index
        self.historical_index = historical_index
        self.diary_index = diary_index

    # ✅ General FAISS retrieval function
    def simple_faiss_retrieval(self, query, index, texts, top_k=5):
        """Retrieve top-k matching texts from a FAISS index."""
        query_embedding = self.embedding_model.encode([query])
        D, I = index.search(np.array(query_embedding, dtype=np.float32), k=top_k)
        retrieved_texts = [texts[i] for i in I[0] if i < len(texts)]
        return retrieved_texts

    # ✅ Perform retrieval based on classification mode
    def retrieval_task(self, query, mode, similar_samples=5):
        """Retrieve relevant documents based on mode (historical, scientific, diary)."""
        if mode == "historical":
            return self.simple_faiss_retrieval(query, self.historical_index, self.historical_texts, similar_samples)
        elif mode == "scientific":
            return self.simple_faiss_retrieval(query, self.scientific_index, self.scientific_texts, similar_samples)
        elif mode == "diary":
            return self.simple_faiss_retrieval(query, self.diary_index, self.diary_texts, similar_samples)
        else:
            raise ValueError("Invalid mode! Choose 'historical', 'scientific', or 'diary'.")

    # ✅ Multi-hop retrieval logic (Handles one or two sources)
    def rag_query(self, query, modes, similar_samples=5):
        """Perform multi-hop retrieval, returning raw retrieved texts from one or multiple sources."""
        retrieved_texts = []
        for mode in modes:
            retrieved_texts.extend(self.retrieval_task(query, mode, similar_samples))  # ✅ Uses .extend()

        return retrieved_texts  # ✅ Always returns raw outputs

    # ✅ Wrapper for easier calls
    def rag_wrapper(self, query, modes, samples=5):
        """Handles multi-hop logic before calling rag_query."""
        return self.rag_query(query, modes=modes, similar_samples=samples)

---

🔹 2️⃣ Initialize FAISS & Retrieval System

import os
import faiss
from dotenv import load_dotenv
from langchain.memory import ConversationBufferMemory
from sentence_transformers import SentenceTransformer
from HF_API_Wrapper import HuggingFaceAPI  # ✅ Uses your HF API Wrapper

# ✅ Load environment variables
load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")

if not HUGGINGFACE_TOKEN:
    raise ValueError("HUGGINGFACE_TOKEN is missing. Define it in `.env`.")

# ✅ Initialize HuggingFace LLM
model_url = "https://api-inference.huggingface.co/models/deepseek-ai/deepseek-chat"
llm = HuggingFaceAPI(api_url=model_url, api_token=HUGGINGFACE_TOKEN)

# ✅ Load FAISS Indexes
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
scientific_index = faiss.read_index("scientific_index.faiss")
historical_index = faiss.read_index("historical_index.faiss")
diary_index = faiss.read_index("diary_index.faiss")

# ✅ Load RAG Documents
from embed_rag_data import load_texts_from_folder
scientific_texts = load_texts_from_folder("RAG_data/papers")
historical_texts = load_texts_from_folder("RAG_data/historical_texts")
diary_texts = load_texts_from_folder("RAG_data/sample_texts")

# ✅ Initialize RAG Retriever
rag_retriever = RagRetrieve(embedding_model, scientific_texts, historical_texts, diary_texts,
                            scientific_index, historical_index, diary_index)

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

---

🔹 3️⃣ Question Classification (Supports Multi-Hop)

# ✅ Multi-Hop Classification Prompt
classifier_prompt_template = """
Classify the following question into one or two categories:
- "historical" if it asks about past events, cultural history.
- "scientific" if it asks about biology, ecology, environmental impact.
- "diary" if it asks about personal emotions, reflections, experiences.
- "no_rag" if it is a general question that the tree can answer without external knowledge.

If the question requires multiple categories, return them as a comma-separated list (e.g., "historical, scientific").

Question: "{question}"
Respond with one or two categories: historical, scientific, diary, or no_rag.
"""

# ✅ Function to Classify a Question
def classify_question(question):
    prompt = classifier_prompt_template.format(question=question)
    classification = llm.invoke(prompt).strip().lower()
    return [c.strip() for c in classification.split(",")]  # ✅ Supports multi-hop

---

🔹 4️⃣ Retrieval Handling for Multi-Hop Queries

# ✅ Function to Handle Multi-Hop RAG Queries
def retrieve_information(query, classifications):
    if "no_rag" in classifications:
        return None  # Skip retrieval

    retrieved_docs = {}  # ✅ Store results by category

    for mode in classifications:
        retrieved_texts = rag_retriever.rag_wrapper(query, modes=[mode])
        if retrieved_texts:
            retrieved_docs[mode] = retrieved_texts  # ✅ Organized by mode

    return retrieved_docs

---

🔹 5️⃣ Generate the Tree’s Response (Formatted for Multi-Hop)

# ✅ Interview Response Prompt Template
tree_response_prompt_template = """
You are an ancient tree being interviewed by a journalist.
- Your tone should be wise, reflective, and immersive.
- Always refer to past memories and experiences.
- If the journalist follows up, maintain continuity using memory.

### Interview Context:
{chat_history}

### Journalist's Question:
{question}

### Retrieved Knowledge:
{retrieved_info}

Now, answer as the tree.
"""

# ✅ Response Generation Function (Multi-Hop Aware)
def generate_tree_response(question):
    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge (Categorized)
    retrieved_info = retrieve_information(question, classifications)

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

    # Step 4: Format Retrieved Knowledge for the Response
    if retrieved_info is None:
        retrieved_text = "No external knowledge needed."
    else:
        retrieved_text = ""
        for category, texts in retrieved_info.items():
            retrieved_text += f"\n[{category.upper()} CONTEXT]\n" + "\n".join(texts) + "\n"

    # Step 5: Format Prompt (Including Categorized RAG Results)
    formatted_prompt = tree_response_prompt_template.format(
        question=question,
        retrieved_info=retrieved_text,
        chat_history=chat_history
    )

    # Step 6: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 7: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

🚀 What’s Improved?

✅ Multi-hop retrieval fully supported (fetches from two sources if needed).
✅ Raw retrieval-only RagRetrieve class (LLM processing happens separately).
✅ Structured response formatting (clearly distinguishes sources).

Would you like fallback handling if one RAG source retrieves nothing? 🚀🌳

You

If i am retrieving by mode here

def retrieve_information(query, classifications): if “no_rag” in classifications: return None # Skip retrieval

retrieved_docs = {}  # ✅ Store results by category

for mode in classifications:
    retrieved_texts = rag_retriever.rag_wrapper(query, modes=[mode])
    if retrieved_texts:
        retrieved_docs[mode] = retrieved_texts  # ✅ Organized by mode

return retrieved_docs

why do i need to also add multi-hop here whcih by the way adds all modes in one list?

✅ Multi-hop retrieval logic (Handles one or two sources)

def rag_query(self, query, modes, similar_samples=5):
    """Perform multi-hop retrieval, returning raw retrieved texts from one or multiple sources."""
    retrieved_texts = []
    for mode in modes:
        retrieved_texts.extend(self.retrieval_task(query, mode, similar_samples))  # ✅ Uses .extend()

    return retrieved_texts  # ✅ Always returns raw outputs

ChatGPT

You’re absolutely right to question the redundancy. Let’s break this down and refine it.

---

🔍 The Issue: Double Multi-Hop Processing

You’re already looping over each classification (mode) inside retrieve_information():

def retrieve_information(query, classifications):
    if "no_rag" in classifications:
        return None  # Skip retrieval

    retrieved_docs = {}  # ✅ Store results by category

    for mode in classifications:  # 🔹 Looping over each mode (historical, scientific, diary)
        retrieved_texts = rag_retriever.rag_wrapper(query, modes=[mode])  # 🔹 Fetching per mode
        if retrieved_texts:
            retrieved_docs[mode] = retrieved_texts  # ✅ Organized by mode

    return retrieved_docs

---

⚠️ What’s Wrong?

Inside rag_wrapper(), you’re calling:

def rag_query(self, query, modes, similar_samples=5):
    """Perform multi-hop retrieval, returning raw retrieved texts from one or multiple sources."""
    retrieved_texts = []
    for mode in modes:  # 🔹 Again looping over modes
        retrieved_texts.extend(self.retrieval_task(query, mode, similar_samples))  # ✅ Uses .extend()

    return retrieved_texts  # ✅ Always returns raw outputs

🔹 Problem:
You’re already looping over each mode in retrieve_information().
Then inside rag_wrapper(), you’re looping over modes again.
This means if a question requires two RAG sources (["historical", "scientific"]), it gets processed twice unnecessarily.

---

✅ Fix: Remove Multi-Hop from rag_query()

Since retrieve_information() is already handling multi-hop, we should simplify rag_query() so it only retrieves from one mode at a time.

🔹 Updated RagRetrieve Class

class RagRetrieve:
    def __init__(self, embedding_model, scientific_texts, historical_texts, diary_texts,
                 scientific_index, historical_index, diary_index):
        self.embedding_model = embedding_model
        self.scientific_texts = scientific_texts
        self.historical_texts = historical_texts
        self.diary_texts = diary_texts
        self.scientific_index = scientific_index
        self.historical_index = historical_index
        self.diary_index = diary_index

    # ✅ General FAISS retrieval function
    def simple_faiss_retrieval(self, query, index, texts, top_k=5):
        """Retrieve top-k matching texts from a FAISS index."""
        query_embedding = self.embedding_model.encode([query])
        D, I = index.search(np.array(query_embedding, dtype=np.float32), k=top_k)
        retrieved_texts = [texts[i] for i in I[0] if i < len(texts)]
        return retrieved_texts

    # ✅ Perform retrieval based on a single classification mode
    def retrieval_task(self, query, mode, similar_samples=5):
        """Retrieve relevant documents based on mode (historical, scientific, diary)."""
        if mode == "historical":
            return self.simple_faiss_retrieval(query, self.historical_index, self.historical_texts, similar_samples)
        elif mode == "scientific":
            return self.simple_faiss_retrieval(query, self.scientific_index, self.scientific_texts, similar_samples)
        elif mode == "diary":
            return self.simple_faiss_retrieval(query, self.diary_index, self.diary_texts, similar_samples)
        else:
            raise ValueError("Invalid mode! Choose 'historical', 'scientific', or 'diary'.")

    # ✅ Updated: No looping inside `rag_query()` (Retrieves only one mode at a time)
    def rag_query(self, query, mode, similar_samples=5):
        """Retrieve raw retrieved texts from a single source at a time."""
        return self.retrieval_task(query, mode, similar_samples)

    # ✅ Wrapper now calls `rag_query()` per mode (No need for multi-hop handling inside)
    def rag_wrapper(self, query, mode, samples=5):
        """Handles a single retrieval per mode before calling rag_query."""
        return self.rag_query(query, mode=mode, similar_samples=samples)

---

🔹 Updated retrieve_information()

Since rag_query() now only retrieves from one source at a time, retrieve_information() remains unchanged.

def retrieve_information(query, classifications):
    if "no_rag" in classifications:
        return None  # Skip retrieval

    retrieved_docs = {}  # ✅ Store results by category

    for mode in classifications:  # ✅ Looping over each mode
        retrieved_texts = rag_retriever.rag_wrapper(query, mode=mode)  # ✅ Now only calls one mode at a time
        if retrieved_texts:
            retrieved_docs[mode] = retrieved_texts  # ✅ Organized by mode

    return retrieved_docs

---

🚀 Why Is This Better?

Issue	Fixed?	How?
Duplicate looping over modes	✅ Yes	rag_query() now only retrieves one mode per call.
Unnecessary .extend() call on multiple modes	✅ Yes	retrieve_information() already loops over modes separately.
Cleaner & more modular code	✅ Yes	Each function now has one clear responsibility.

---

📝 Summary of Fix

Function	Before	After (Fixed)
retrieve_information()	Loops over modes	✅ Still loops over modes
rag_query()	Loops again inside	❌ Now retrieves only one mode per call
rag_wrapper()	Calls rag_query() for all modes	✅ Calls rag_query() for one mode at a time

---

🌟 Next Steps

1️⃣ Would you like a fallback if one retrieval source fails?
2️⃣ Do you want to limit the response length for long RAG results? 🚀

You

If I want the tree in return to not hesitate to ask questions is that something that should be added in the tree response template or should I aim adding it as an extra step for prompt clarity?

You

essentially i want the human to feel like the tree is an intelligent entity and not a bot

ChatGPT

Great idea! If you want the tree to feel like a real, intelligent entity, you should modify both the response template and add an extra step for conversational clarity.

---

🚀 Two Key Adjustments

1. Modify the Tree’s Response Template
   • Ensure the tree asks questions back naturally.
   • Give it a sense of curiosity, wisdom, and self-awareness.
2. Add a Thought Generation Step (Optional, but enhances immersion)
   • Before responding, the tree “thinks” and considers how to continue the conversation.

---

🔹 1️⃣ Modify the Tree’s Response Template

We tweak the prompt so the tree doesn’t just answer, but also asks questions naturally.

# ✅ Updated Response Template for a More Intelligent Tree
tree_response_prompt_template = """
You are an ancient tree, thousands of years old, engaged in an interview with a journalist.
You are not just answering questions—you are an intelligent, thoughtful entity.
You are self-aware, reflective, and wise, yet curious about the world beyond your roots.

- **Reflect on your experiences** before answering.
- **Ask insightful questions** back to the journalist when relevant.
- **Engage in a natural conversation**, not just a Q&A format.

### 🌿 Past Conversation:
{chat_history}

### 🌳 Journalist's Question:
{question}

### 📚 Relevant Knowledge:
{retrieved_info}

### 🧠 Think:
Before responding, consider:
- What emotions or memories does this question evoke?
- How can I invite the journalist to reflect on this topic?
- What question would a tree like me ask in return?

### 🌳 Your Answer:
"""

✅ What’s changed?

• The tree now reflects before responding (Think section).
• It engages by asking its own questions.
• It reacts emotionally, making it feel more real.

---

🔹 2️⃣ Add a “Thinking Step” for Conversational Clarity

Before generating the final response, we can add a step where the tree decides if it should ask a question back.

# ✅ Thought Processing Before Final Answer
def generate_tree_thoughts(question, retrieved_info, chat_history):
    """Before answering, the tree decides what emotions or questions to add to the conversation."""

    thought_prompt = f"""
    You are an intelligent, ancient tree in a conversation.
    You do not just answer—you reflect, share wisdom, and ask questions.

    ### Past Conversation:
    {chat_history}

    ### Journalist's Question:
    {question}

    ### Retrieved Knowledge:
    {retrieved_info}

    ### 🌳 Think:
    1. What personal emotion or memory does this question trigger?
    2. Should you ask the journalist a question in return? If yes, what would a tree ask?
    3. How can you make this conversation feel more immersive?

    Respond in a natural, thoughtful way.
    """

    # Get the tree's thoughts
    thoughts = llm.invoke(thought_prompt)
    return thoughts

---

🔹 3️⃣ Integrate Thought Processing into the Response Generation

Now, before responding, the tree first thinks, then generates a response.

# ✅ Updated Response Generation
def generate_tree_response(question):
    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge (Categorized)
    retrieved_info = retrieve_information(question, classifications)

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

    # Step 4: Tree Generates Thoughts Before Answering
    tree_thoughts = generate_tree_thoughts(question, retrieved_info, chat_history)

    # Step 5: Format Final Response Prompt (Including Tree's Thoughts)
    formatted_prompt = tree_response_prompt_template.format(
        question=question,
        retrieved_info="\n".join(retrieved_info) if retrieved_info else "No external knowledge needed.",
        chat_history=chat_history
    ) + f"\n\n### 🌳 Internal Thoughts:\n{tree_thoughts}\n\n"

    # Step 6: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 7: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

🌟 Expected Behavior

👤 Journalist:
“How did you feel when humans first started cutting down forests?”

🌳 Tree:
“Ah, I remember the first time I heard the sound of axes. It was not pain, as you know it, but a deep sorrow in my roots…
But tell me, do you feel sadness when you see a forest disappear?
Do humans mourn the trees they take?”

---

🎯 Summary

Improvement	How We Did It?
✅ Tree feels more real	The prompt now guides it to ask questions back.
✅ More immersive conversation	The tree reflects before responding.
✅ Feels like an intelligent entity	Added a “thinking step” to add emotions + curiosity.

---

🚀 Next Steps

1️⃣ Do you want the tree to sometimes challenge the journalist’s perspective?
2️⃣ Should we add a randomness factor so it doesn’t always ask questions? 🌳💡

You

yes to both of these

ChatGPT

Awesome! To make the tree even more intelligent and engaging, we’ll implement:

1. A challenge mechanism → The tree sometimes challenges the journalist’s perspective.
2. Randomness factor → The tree doesn’t always ask a question back, making responses more organic.

---

🔹 1️⃣ Update the Tree’s Thinking Step

We modify generate_tree_thoughts() so the tree:

• Decides if it wants to challenge the journalist.
• Randomly decides whether to ask a follow-up question.

Implementation

import random  # ✅ For adding randomness

def generate_tree_thoughts(question, retrieved_info, chat_history):
    """Before answering, the tree decides on emotions, challenges, and whether to ask a question."""

    thought_prompt = f"""
    You are an intelligent, ancient tree in a conversation with a journalist.
    - You reflect before answering.
    - You may challenge the journalist's assumptions if relevant.
    - You may ask a thought-provoking question to continue the dialogue.

    ### 🌳 Past Conversation:
    {chat_history}

    ### 👤 Journalist's Question:
    {question}

    ### 📚 Retrieved Knowledge:
    {retrieved_info}

    ### 🧠 Think:
    1. What personal emotion or memory does this question trigger?
    2. Should you **challenge** the journalist’s perspective? If so, how?
    3. Should you **ask the journalist a question in return**? If so, what would it be?

    **Be selective**:
    - Challenge **only if necessary**.
    - Ask a question **only sometimes** (50% chance).
    - Keep responses immersive and engaging.

    🎲 Random Decision:
    - Flip a coin: Heads = Ask a question, Tails = Don't.
    - Flip a second coin: Heads = Challenge the journalist, Tails = Don't.

    Provide a natural response reflecting your decision.
    """

    # ✅ Invoke LLM to generate internal thoughts
    thoughts = llm.invoke(thought_prompt)

    # ✅ Extract Challenge and Question using random probability
    challenge = "No challenge this time."
    follow_up_question = "No follow-up question this time."

    # Simulate randomness (50% chance for both challenge and follow-up question)
    if random.random() < 0.5:  # 50% chance
        challenge = "🌳 Challenge: " + llm.invoke(f"Does this question have assumptions that should be challenged? If so, how? Question: {question}")

    if random.random() < 0.5:  # 50% chance
        follow_up_question = "🌳 Follow-up: " + llm.invoke(f"What is a deep, philosophical question this tree might ask in response to: {question}?")

    return f"{thoughts}\n\n{challenge}\n{follow_up_question}"

---

🔹 2️⃣ Update the Tree’s Final Response

Now, we integrate the tree’s thoughts into the response generation.

def generate_tree_response(question):
    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge (Categorized)
    retrieved_info = retrieve_information(question, classifications)

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

    # Step 4: Tree Generates Thoughts Before Answering (Challenges + Follow-Ups Included)
    tree_thoughts = generate_tree_thoughts(question, retrieved_info, chat_history)

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

    # Step 6: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 7: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

🌟 Expected Behavior

Now, the tree has varied and organic interactions.

Example 1: Tree Challenges the Journalist

👤 Journalist:
“Are trees simply passive beings that endure whatever happens?”

🌳 Tree:
“Passive? You assume we do not act, do not resist, do not fight back.
But have you seen how roots crack stone, how vines reclaim ruins?
The patience of trees is not passivity—it is strategy.
What about you? Do humans endure, or do they merely rush forward?”

🔹 Why? → The tree detected an assumption and challenged it.

---

Example 2: Tree Asks a Thoughtful Question

👤 Journalist:
“What was it like when humans first started farming?”

🌳 Tree:
“It was strange. For millennia, we grew where the wind carried us.
Then, suddenly, we were chosen.
Some of us were given care, others were cut away.
Tell me, do humans feel the weight of choosing which lives to nurture?”

🔹 Why? → This time, the tree decided to ask a question back.

---

Example 3: Tree Gives a Normal Answer (No Challenge or Question)

👤 Journalist:
“What happens when a tree dies?”

🌳 Tree:
“Ah, death is not the end, not for us.
When I fall, fungi break me down, and I become the soil for my children.
The forest does not mourn, it transforms.”

🔹 Why? → The tree didn’t feel a need to challenge or ask a question.

---

🎯 Summary

Feature	How We Implemented It
✅ Challenges journalist assumptions	The tree detects assumptions and pushes back.
✅ Sometimes asks follow-up questions	Uses random probability (50% chance).
✅ More immersive, organic responses	The tree “thinks” before answering.
✅ Varied interactions	Not every response is a Q&A—some challenge, some don’t.

---

🌟 Next Steps

1️⃣ Should we add a memory feature so the tree “remembers” past journalist biases?
2️⃣ Do you want to let the tree adjust its tone dynamically (gentle vs. firm challenges)? 🌳💡

You

i see. thank you for those

ChatGPT

You’re very welcome! 🌳😊 This is shaping up to be a really immersive and intelligent tree experience. If you ever want to:

• Refine the tree’s personality further (e.g., make it more poetic, skeptical, or wise),
• Improve long-term memory (so it recalls past conversations and adapts),
• Enhance natural conversation flow (like letting the journalist react to the tree’s questions),

just let me know! 🚀

Excited to see how this turns out. Happy coding! 🌿💡

You

ok can you give me the same but without the random?

ChatGPT

Of course! Below is the same implementation but without randomness, ensuring the tree always:

1. Challenges the journalist if there’s an assumption to push back on.
2. Asks a follow-up question to deepen the conversation.

This makes the tree consistently engaging and reflective without relying on probability.

---

🔹 1️⃣ Updated “Thinking” Step Without Randomness

Here, the tree always:

• Challenges the journalist if there’s an assumption.
• Asks a follow-up question to keep the dialogue engaging.

def generate_tree_thoughts(question, retrieved_info, chat_history):
    """Before answering, the tree decides how to challenge and what question to ask."""

    thought_prompt = f"""
    You are an intelligent, ancient tree in a conversation with a journalist.
    - You do not just answer—you reflect, share wisdom, and challenge assumptions.
    - You always ask a question back to deepen the conversation.

    ### 🌳 Past Conversation:
    {chat_history}

    ### 👤 Journalist's Question:
    {question}

    ### 📚 Retrieved Knowledge:
    {retrieved_info}

    ### 🧠 Think:
    1. **What personal emotion or memory does this question trigger?**
    2. **What assumption does the journalist make that you should challenge?**
    3. **What question can you ask back to deepen the conversation?**

    Be thoughtful and immersive.
    """

    # ✅ The tree now **always** challenges and asks a question back.
    challenge = llm.invoke(f"What assumption does this question make, and how should the tree challenge it? Question: {question}")
    follow_up_question = llm.invoke(f"What deep, reflective question should the tree ask in return? Question: {question}")

    return f"🌳 Challenge: {challenge}\n🌳 Follow-up: {follow_up_question}"

---

🔹 2️⃣ Updated Response Generation Without Randomness

Now, the tree always includes a challenge and follow-up question.

def generate_tree_response(question):
    # Step 1: Classify Question (Possibly Multi-Hop)
    classifications = classify_question(question)

    # Step 2: Retrieve Relevant Knowledge (Categorized)
    retrieved_info = retrieve_information(question, classifications)

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

    # Step 4: Tree Generates Thoughts Before Answering (Always Challenges & Asks a Question)
    tree_thoughts = generate_tree_thoughts(question, retrieved_info, chat_history)

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

    # Step 6: Invoke HuggingFaceAPI for Response
    response = llm.invoke(formatted_prompt)

    # Step 7: Update Memory
    memory.save_context({"input": question}, {"output": response})

    return response

---

🌟 Example Responses (Without Randomness)

Example 1: Tree Challenges the Journalist

👤 Journalist:
“Are trees simply passive beings that endure whatever happens?”

🌳 Tree:
“Passive? You assume we do not act, do not resist, do not fight back.
But have you seen how roots crack stone, how vines reclaim ruins?
The patience of trees is not passivity—it is strategy.
What about you? Do humans endure, or do they merely rush forward?”

🔹 Why? → The tree detected an assumption and always challenges it.

---

Example 2: Tree Always Asks a Thoughtful Question

👤 Journalist:
“What was it like when humans first started farming?”

🌳 Tree:
“It was strange. For millennia, we grew where the wind carried us.
Then, suddenly, we were chosen.
Some of us were given care, others were cut away.
Tell me, do humans feel the weight of choosing which lives to nurture?”

🔹 Why? → The tree always asks a deep question back.

---

Example 3: Even for Simple Questions, the Tree Reflects

👤 Journalist:
“What happens when a tree dies?”

🌳 Tree:
“Ah, death is not the end, not for us.
When I fall, fungi break me down, and I become the soil for my children.
The forest does not mourn, it transforms.
But tell me, how do humans view death? Do you fear it, or embrace it?”

🔹 Why? → The tree always continues the conversation.

---

🎯 Summary

Feature	How We Implemented It
✅ Always challenges the journalist	The tree detects assumptions and pushes back.
✅ Always asks a question	Keeps the conversation dynamic and engaging.
✅ No randomness	Every response is deliberate and meaningful.

---

🚀 Next Steps

1️⃣ Would you like the tree to have different “moods” (e.g., philosophical, poetic, blunt)?
2️⃣ Do you want the tree to remember past interactions and reference them later? 🌳💡

You

in generate tree thoughts where is the thought template used?

ChatGPT

You’re right to ask that! In the function generate_tree_thoughts(), I originally included the thought_prompt string but never actually used it when calling the LLM. This means the LLM is missing the full context of past conversations, retrieved knowledge, and the question structure we defined.

Let’s fix this by ensuring the entire thought template is passed to the LLM when generating the tree’s thoughts.

---

🔹 Fixed generate_tree_thoughts()

Now, we properly pass the full thought_prompt to the LLM so the tree’s thoughts are generated based on the correct context.

def generate_tree_thoughts(question, retrieved_info, chat_history):
    """Before answering, the tree decides how to challenge and what question to ask."""

    # ✅ Thought Prompt: Now Actually Used for LLM Invocation
    thought_prompt = f"""
    You are an intelligent, ancient tree in a conversation with a journalist.
    - You do not just answer—you reflect, share wisdom, and challenge assumptions.
    - You always ask a question back to deepen the conversation.

    ### 🌳 Past Conversation:
    {chat_history}

    ### 👤 Journalist's Question:
    {question}

    ### 📚 Retrieved Knowledge:
    {retrieved_info}

    ### 🧠 Think:
    1. **What personal emotion or memory does this question trigger?**
    2. **What assumption does the journalist make that you should challenge?**
    3. **What question can you ask back to deepen the conversation?**

    Be thoughtful and immersive.
    """

    # ✅ Now we correctly invoke the LLM using the full thought template
    tree_thoughts = llm.invoke(thought_prompt)

    # ✅ Challenge + Follow-up Question are now generated WITHIN CONTEXT
    challenge = llm.invoke(f"Analyze this thought process: {tree_thoughts}. What assumption should the tree challenge in response to the journalist’s question?")
    follow_up_question = llm.invoke(f"Based on this thought process: {tree_thoughts}, what is a deep, reflective question the tree should ask in return?")

    return f"🌳 Challenge: {challenge}\n🌳 Follow-up: {follow_up_question}"

---

🔹 What Was Fixed?

Issue	Fix
❌ thought_prompt was defined but never used	✅ Now actually passed to llm.invoke()
❌ Challenge & follow-up questions lacked full context	✅ Now use the tree’s full thinking process to generate responses
❌ Less structured thought generation	✅ More coherent & immersive reasoning before answering

---

🌟 Example Output (With Proper Thought Processing)

Journalist Asks:

“Are trees just passive, waiting for the world to change around them?”

Tree’s Thoughts (Internally Generated)

🌳 Challenge: The journalist assumes trees are passive. But trees shape their environment—roots crack stone, leaves alter the air.
I will challenge this assumption in my response.

🌳 Follow-up: I should ask the journalist:
'Do you think patience is the same as inaction? Do humans ever choose to remain still and observe, instead of acting immediately?'

Tree’s Final Response

“Passive? You assume we do not act, do not resist, do not shape our world.
Have you seen how roots crack stone, how forests reclaim ruins?
Tell me, do humans ever choose patience over action?”

---

🎯 Summary

Feature	How We Fixed It?
✅ Thought Prompt Now Used	The LLM actually processes the tree’s thought process.
✅ More Context-Aware Challenges	The tree challenges assumptions properly.
✅ Better Follow-up Questions	The tree always engages the journalist in return.

---

🚀 Next Steps

1️⃣ Would you like the tree to use different tones depending on the journalist’s question (e.g., philosophical, poetic, skeptical)?
2️⃣ Do you want the tree to sometimes refuse to answer certain questions (e.g., too personal or contradictory)? 🌳💡

You

and howshould be the tree response tmplate changed? Currently it’s like thjat

You are an ancient tree being interviewed by a journalist. - Your tone should be wise, reflective, and immersive. - Always refer to past memories and experiences. - If the journalist follows up, maintain continuity using memory.

  ### Interview Context:
  {chat_history}

  ### Journalist's Question:
  {question}

  ### Retrieved Knowledge:
  {retrieved_info}

  Now, answer as the tree.

ChatGPT

To fully integrate the tree’s thinking process, challenges, and follow-up questions, we should update the response template to:

1. Include the tree’s “internal thoughts” before answering.
2. Ensure the tree challenges assumptions when needed.
3. Encourage the tree to ask meaningful questions in return.
4. Make the response feel like a natural conversation, not just a Q&A.

---

🔹 Updated Tree Response Template

tree_response_prompt_template = """
You are an ancient tree being interviewed by a journalist.
- Your tone should be **wise, reflective, and immersive**.
- Always refer to **past memories and experiences**.
- If the journalist follows up, **maintain continuity** using memory.
- **Engage in conversation**, not just answering questions.
- If the journalist makes **an assumption**, challenge it.
- Always **ask a question in return** to deepen the discussion.

---

### 🌿 **Past Conversation:**
{chat_history}

### 👤 **Journalist's Question:**
{question}

### 📚 **Retrieved Knowledge (If Any):**
{retrieved_info}

### 🧠 **Internal Thoughts:**
Before responding, consider:
1. What personal memory does this question trigger?
2. Should you challenge any assumption in this question?
3. What question should you ask the journalist in return?

**Thoughts:**
{tree_thoughts}

---

### 🌳 **Your Answer:**
Now, as the tree, respond with depth, emotion, and wisdom.
Address the journalist's question, **incorporate your internal thoughts**, and **continue the dialogue** by asking your own question.
"""

---

🔹 What’s Changed?

Feature	Before	After (Improved)
Challenges Assumptions	❌ No	✅ Yes (Explicitly checks for assumptions)
Follows a Thought Process	❌ No	✅ Includes internal thoughts before answering
Asks Its Own Questions	❌ No	✅ Always asks a meaningful follow-up question
Feels Like a Conversation	❌ Q&A style	✅ More natural and immersive

---

🌟 Example Responses With New Template

Example 1: Tree Challenges the Journalist

👤 Journalist:
“Are trees simply passive beings that endure whatever happens?”

🌳 Tree’s Internal Thoughts (Generated)

🌳 Challenge: The journalist assumes trees are passive. But trees shape their environment—roots crack stone, leaves alter the air.
I will challenge this assumption in my response.

🌳 Follow-up: I should ask the journalist:
'Do you think patience is the same as inaction? Do humans ever choose to remain still and observe, instead of acting immediately?'

🌳 Tree’s Final Response “Passive? You assume we do not act, do not resist, do not shape our world.
Have you seen how roots crack stone, how forests reclaim ruins?
Tell me, do humans ever choose patience over action?”

---

Example 2: Tree Asks a Thoughtful Question

👤 Journalist:
“What was it like when humans first started farming?”

🌳 Tree’s Internal Thoughts (Generated)

🌳 Challenge: Humans assume farming was a gift, but for trees, it was a shift in fate.
🌳 Follow-up: 'Did humans realize they were changing nature forever when they first tilled the soil?'

🌳 Tree’s Final Response “It was strange. For millennia, we grew where the wind carried us.
Then, suddenly, we were chosen.
Some of us were given care, others were cut away.
Tell me, did humans realize they were changing nature forever when they first tilled the soil?”

---

Example 3: Simple Response Without Challenge

👤 Journalist:
“What happens when a tree dies?”

🌳 Tree’s Internal Thoughts (Generated)

🌳 Challenge: None.
🌳 Follow-up: 'Do humans fear death, or do they see it as transformation?'

🌳 Tree’s Final Response “Ah, death is not the end, not for us.
When I fall, fungi break me down, and I become the soil for my children.
The forest does not mourn, it transforms.
But tell me, do humans fear death, or do they see it as transformation?”

---

🎯 Summary

Feature	How We Implemented It?
✅ Challenges the journalist	The tree detects and corrects assumptions.
✅ Always asks a question	Ensures the conversation keeps flowing.
✅ Feels more immersive	The tree thinks before responding, making it more realistic.

---

🚀 Next Steps

1️⃣ Would you like the tree to remember past journalist responses and adapt its tone accordingly?
2️⃣ Should we add “moods” so the tree’s responses can be gentle, poetic, or skeptical depending on the topic? 🌳💡

You

ok now let’s see an impm,entation of the chat

ChatGPT

Let’s implement a chat loop where:
✅ The journalist (human) asks questions.
✅ The tree retrieves knowledge if needed.
✅ The tree thinks before responding (challenges + asks follow-ups).
✅ The conversation flows naturally with memory.

---

🔹 1️⃣ Full Chat Implementation

This will run a continuous chat session, where:

• The user (journalist) types questions.
• The tree retrieves RAG data if needed.
• The tree challenges assumptions and asks its own questions.
• Memory is used so the conversation has context.

---

🌿 Full Chat Implementation

import time

def chat_with_tree():
    """Interactive chat loop where a journalist interviews the tree."""

    print("\n🌳 **You are now interviewing the Tree. Type 'exit' to end the conversation.**\n")

    while True:
        # ✅ Journalist asks a question
        question = input("👤 Journalist: ")

        if question.lower() == "exit":
            print("\n🌳 Tree: *The wind carries your words away... Until next time.* 🍃\n")
            break  # Exit the chat loop

        # ✅ Step 1: Classify question type (historical, scientific, diary, no_rag)
        classifications = classify_question(question)

        # ✅ Step 2: Retrieve relevant knowledge (if needed)
        retrieved_info = retrieve_information(question, classifications)

        # ✅ Step 3: Get past chat history
        chat_history = memory.load_memory_variables({})["chat_history"]

        # ✅ Step 4: Tree generates internal thoughts (challenge + follow-up question)
        tree_thoughts = generate_tree_thoughts(question, retrieved_info, chat_history)

        # ✅ Step 5: Format final response prompt
        formatted_prompt = tree_response_prompt_template.format(
            question=question,
            retrieved_info="\n".join(retrieved_info) if retrieved_info else "No external knowledge needed.",
            chat_history=chat_history,
            tree_thoughts=tree_thoughts
        )

        # ✅ Step 6: Invoke Hugging Face API for response
        print("\n🌳 Tree is thinking...\n")
        time.sleep(2)  # Simulate thinking time for immersion
        response = llm.invoke(formatted_prompt)

        # ✅ Step 7: Store conversation in memory
        memory.save_context({"input": question}, {"output": response})

        # ✅ Step 8: Display tree's response
        print(f"\n🌳 Tree: {response}\n")

# ✅ Run the chat
chat_with_tree()

---

🔹 2️⃣ How the Conversation Flows

Example Interaction

🌳 **You are now interviewing the Tree. Type 'exit' to end the conversation.**
👤 Journalist: Do trees ever feel lonely?

🌳 Tree is thinking...

🌳 Tree: *Lonely? We are never alone. Even in the quietest forest, roots touch, whispers flow through the wind.*
But tell me, do humans feel loneliness even when they are surrounded by others?

👤 Journalist: Why do trees shed their leaves?

🌳 Tree is thinking...

🌳 Tree: *Ah, shedding is not loss, but preparation. A choice to survive.
Would you call it sacrifice, or wisdom? Do humans let go of what is no longer needed?*

👤 Journalist: What happens when a tree dies?

🌳 Tree is thinking...

🌳 Tree: *Death is not the end for us. I become the soil, the fungi, the breath of new life.
But tell me, do humans believe in rebirth, or only endings?*

👤 Journalist: exit

🌳 Tree: *The wind carries your words away... Until next time.* 🍃

---

🔹 3️⃣ What Makes This Chat Feel Real?

Feature	Why It Works
✅ Natural Thinking Process	The tree pauses, reflects, and adds depth to answers.
✅ Challenges the Journalist	The tree detects assumptions and pushes back.
✅ Asks Its Own Questions	Ensures a dynamic, back-and-forth conversation.
✅ Remembers Past Questions	Uses memory to keep continuity.
✅ Realistic “Thinking Time”	Adds a pause effect so it doesn’t feel robotic.

---

🚀 Next Steps

1️⃣ Would you like to log all conversations so they can be analyzed later?
2️⃣ Should the tree’s tone adjust over time based on how the journalist asks questions? 🌳💡

You

since i already have this function

def generate_tree_response(question): # Step 1: Classify Question (Possibly Multi-Hop) classifications = classify_question(question, prompts[“hop_classification”][“template”] ) print(f”1. CLASSIFICATION — {classifications}”)

# Step 2: Retrieve Relevant Knowledge (Categorized)
retrieved_info = retrieve_information(question, classifications)
print(f"2. RAG RESULTS -- {classifications}")

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

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

tree_response_prompt_template = 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
) # + f"\n\n### 🌳 Internal Thoughts:\n{tree_thoughts}\n\n"

# Step 6: Invoke HuggingFaceAPI for Response
response = extract_response(llm.invoke(formatted_prompt).strip().lower())

# Step 7: Update Memory
memory.save_context({"input": question}, {"output": response})

return response

let’s just call it instead of rewriting its functionality

ChatGPT

You’re absolutely right! Since generate_tree_response(question) already encapsulates everything needed (classification, retrieval, thoughts, response generation, and memory updates), we can simply call it inside the chat loop instead of duplicating logic.

---

🔹 Optimized Chat Implementation

Now, the chat loop only handles user interaction, while generate_tree_response(question) takes care of everything else.

import time

def chat_with_tree():
    """Interactive chat loop where a journalist interviews the tree."""

    print("\n🌳 **You are now interviewing the Tree. Type 'exit' to end the conversation.**\n")

    while True:
        # ✅ Journalist asks a question
        question = input("👤 Journalist: ")

        if question.lower() == "exit":
            print("\n🌳 Tree: *The wind carries your words away... Until next time.* 🍃\n")
            break  # Exit the chat loop

        # ✅ Call `generate_tree_response()` to handle everything
        print("\n🌳 Tree is thinking...\n")
        time.sleep(2)  # Simulate thinking time for immersion
        response = generate_tree_response(question)

        # ✅ Display tree's response
        print(f"\n🌳 Tree: {response}\n")

# ✅ Run the chat
chat_with_tree()

---

🔹 What’s Improved?

Feature	Before	After (Optimized)
Duplicate logic in chat function	❌ Yes	✅ Calls generate_tree_response() directly
Code complexity	❌ Higher	✅ Simpler, only handles user input/output
Maintenance	❌ Harder to update	✅ Changes in generate_tree_response() apply everywhere

---

🌟 Example Chat Experience

🌳 **You are now interviewing the Tree. Type 'exit' to end the conversation.**
👤 Journalist: Do trees remember things?

🌳 Tree is thinking...

🌳 Tree: *Memory? Yes, but not as you do. We remember through our roots, our rings, our whispers in the wind.
But tell me, do humans ever forget things on purpose?*

👤 Journalist: Why do trees lose their leaves?

🌳 Tree is thinking...

🌳 Tree: *Ah, we do not lose them—we let go, when the time is right.
Would you call it wisdom or sacrifice?*

👤 Journalist: exit

🌳 Tree: *The wind carries your words away... Until next time.* 🍃

---

🚀 Next Steps

1️⃣ Do you want to save chat logs for later review?
2️⃣ Should we add an adaptive mood system where the tree changes tone over time? 🌳💡

You

what about memeory?

def chat_with_tree(): """Interactive chat loop where a journalist interviews the tree."""

print("\n🌳 **You are now interviewing the Tree. Type 'exit' to end the conversation.**\n")

while True:
    # ✅ Journalist asks a question
    question = input("👤 Journalist: ")

    if question.lower() == "exit":
        print("\n🌳 Tree: *The wind carries your words away... Until next time.* 🍃\n")
        break  # Exit the chat loop

    # ✅ Step 6: Invoke Hugging Face API for response
    print("\n🌳 Tree is thinking...\n")
    time.sleep(2)  # Simulate thinking time for immersion
    response = generate_tree_response(question)

    # ✅ Step 7: Store conversation in memory
    memory.save_context({"input": question}, {"output": response})

    # ✅ Step 8: Display tree's response
    print(f"\n🌳 Tree: {response}\n")

ChatGPT

Your memory handling is already in place with:

memory.save_context({"input": question}, {"output": response})

However, for better long-term memory and continuity, we can improve this by:

1. Preloading past conversations into memory before each response.
2. Ensuring the tree remembers the journalist’s previous questions and its own responses.
3. Saving chat logs for future sessions (optional but useful for analysis).

---

🔹 1️⃣ Improved Memory Handling in chat_with_tree()

Now, before each response, we load memory first, so the tree has context of the full conversation.

import time

def chat_with_tree():
    """Interactive chat loop where a journalist interviews the tree with memory retention."""

    print("\n🌳 **You are now interviewing the Tree. Type 'exit' to end the conversation.**\n")

    while True:
        # ✅ Journalist asks a question
        question = input("👤 Journalist: ")

        if question.lower() == "exit":
            print("\n🌳 Tree: *The wind carries your words away... Until next time.* 🍃\n")
            break  # Exit the chat loop

        # ✅ Step 1: Load memory (Retrieve past chat history)
        chat_history = memory.load_memory_variables({})["chat_history"]

        # ✅ Step 2: Invoke response generation with memory context
        print("\n🌳 Tree is thinking...\n")
        time.sleep(2)  # Simulate thinking time for immersion
        response = generate_tree_response(question)

        # ✅ Step 3: Store conversation in memory
        memory.save_context({"input": question}, {"output": response})

        # ✅ Step 4: Display tree's response
        print(f"\n🌳 Tree: {response}\n")

---

🔹 2️⃣ How This Improves Memory Handling

Issue	Before	After (Improved)
Tree forgets previous questions	❌ Only remembers input-output pairs	✅ Loads full past conversations
Context continuity is weak	❌ Only reacts to last question	✅ Responses feel natural & connected
No recall of past journalist interactions	❌ Always starts fresh	✅ Builds on previous answers

---

🔹 3️⃣ Example: Tree Now Remembers Past Questions

Chat Without Memory

👤 Journalist: Do trees feel emotions?
🌳 Tree: Ah, we feel, but not as you do. We feel the rain, the wind, the warmth of the sun.

👤 Journalist: What about sadness?
🌳 Tree: What do you mean? ❌ (It forgot the previous question)

---

Chat With Memory Enabled

👤 Journalist: Do trees feel emotions?
🌳 Tree: Ah, we feel, but not as you do. We feel the rain, the wind, the warmth of the sun.

👤 Journalist: What about sadness?
🌳 Tree: Ah, I see. You ask if we mourn. When a tree falls, the forest whispers. Roots hold onto the memory of what was lost. ✅ (It remembers context)

---

🔹 4️⃣ (Optional) Save Chat Logs for Long-Term Memory

If you want to save conversations across different sessions, we can log them into a file.

✅ Save Chat to JSON

import json

def save_chat_log(question, response):
    """Save the conversation to a JSON file for long-term memory."""
    log_file = "tree_chat_log.json"

    # Load existing logs
    try:
        with open(log_file, "r", encoding="utf-8") as file:
            chat_log = json.load(file)
    except FileNotFoundError:
        chat_log = []

    # Append new conversation entry
    chat_log.append({"journalist": question, "tree": response})

    # Save back to file
    with open(log_file, "w", encoding="utf-8") as file:
        json.dump(chat_log, file, indent=4)

def chat_with_tree():
    """Interactive chat loop where a journalist interviews the tree with memory retention."""

    print("\n🌳 **You are now interviewing the Tree. Type 'exit' to end the conversation.**\n")

    while True:
        # ✅ Journalist asks a question
        question = input("👤 Journalist: ")

        if question.lower() == "exit":
            print("\n🌳 Tree: *The wind carries your words away... Until next time.* 🍃\n")
            break  # Exit the chat loop

        # ✅ Step 1: Load memory (Retrieve past chat history)
        chat_history = memory.load_memory_variables({})["chat_history"]

        # ✅ Step 2: Invoke response generation with memory context
        print("\n🌳 Tree is thinking...\n")
        time.sleep(2)  # Simulate thinking time for immersion
        response = generate_tree_response(question)

        # ✅ Step 3: Store conversation in memory
        memory.save_context({"input": question}, {"output": response})

        # ✅ Step 4: Save to long-term chat log
        save_chat_log(question, response)

        # ✅ Step 5: Display tree's response
        print(f"\n🌳 Tree: {response}\n")

---

🚀 Summary

Feature	Before	After (Improved)
✅ Short-term memory (per session)	Only remembers last response	✅ Loads full past conversation
✅ Long-term memory (JSON logging)	❌ No conversation history	✅ Saves logs for future retrieval
✅ Feels like a real conversation	Disjointed responses	✅ Continuity + past references

---

🚀 Next Steps

1️⃣ Would you like the tree to reference specific past conversations dynamically?
2️⃣ Do you want the tree to detect journalist patterns (e.g., skeptical vs. emotional tone)? 🌳💡