LLM Style Replication System

I want to use smolagents with ollama locally or using litellm and instead I want to be able to have a folder of text or pdfs and to have the LLM be able to read my style and replicate it so that I can input a reddit thread and it will take a new prompt that I enter, which will be the basis for how the response is created, and it will use an LLM to expand on the ideas, to make them more logical and educated, to fact check and make rational, and then to output the final message in my original style, the style from the folder of text and pdfs

You're essentially building a **style-adaptive, logic-enhancing AI responder** that: 1. **Learns your writing style** from a folder of text/PDFs. 2. **Processes a Reddit thread** to understand context. 3. **Expands on ideas** to make them more logical, fact-checked, and rational. 4. **Outputs the response** in your personal style. To do this locally with **SmolAgents** + **Ollama (or LiteLLM)**, you'd need: ### **Key Components** - **Text & PDF Parsing:** Use `PyMuPDF` or `pdfplumber` for PDFs, and `langchain` for text embedding. - **Style Extraction:** Train a simple **embedding model** on your past writing, fine-tune or use similarity matching. - **Thread Processing:** Scrape Reddit threads (`praw` or `Pushshift API`), convert them into structured data. - **Idea Expansion:** Use an LLM (Ollama model like `mistral` or `phi`), and apply reasoning chains via SmolAgents. - **Style Application:** Reconstruct responses using embeddings + LLM style transfer. --- ### **Implementation Plan** 1. **Index your writing style** - Convert your documents into text. - Use **ChromaDB** or `faiss` for vector embeddings. - Extract frequent phrases, syntax patterns, and tone markers. 2. **Scrape & Structure Reddit Threads** - Use `praw` to fetch a thread’s comments. - Summarize key ideas (via LLM). - Parse sentiment and logical structure. 3. **Generate Responses** - Input: **Your prompt + extracted Reddit discussion** - Step 1: Expand logic with **SmolAgents chains**. - Step 2: Fact-check with **retrieval-based grounding** (e.g., Wikipedia API, local knowledge base). - Step 3: Reapply your **writing style** using fine-tuned text embeddings or GPT-style rephrasing. 4. **Output Final Response** - Ensure the response mimics **your word choices, structure, and phrasing.** - Allow for further user refinements. --- ### **Stack Choices** - **LLM Backend:** `Ollama` (`mistral`, `phi-2`, `llama3`) or **LiteLLM** - **Vector DB:** `ChromaDB` (fast local search) - **Agentic Framework:** `SmolAgents` (for orchestration) - **Text Processing:** `LangChain`, `pymupdf`, `pdfplumber` - **Reddit Scraping:** `praw`, `Pushshift API` --- ### **Next Steps** - Do you want the response to **always sound like you**, or should it sometimes provide a neutral tone? - Should fact-checking **alter the style**, or should it maintain the original but highlight inconsistencies? - Do you want **caching** for previously analyzed Reddit discussions? Would you like a basic script to start with? 🚀

yes, let us begin, outline everything with a high level architecture and then proceed with helping me plan out how I will construct this

### **High-Level Architecture for Your Style-Adaptive AI Responder** This system has **four main components**: 1. **Style Learning Module** (Extracts your writing style from stored documents) 2. **Reddit Thread Processing** (Scrapes and structures Reddit data) 3. **Idea Expansion & Fact-Checking** (Uses an LLM to improve logic & correctness) 4. **Response Generation & Style Application** (Ensures final output matches your style) --- ## **1️⃣ High-Level System Architecture** ### **📂 Input Sources** - **Folder of Text/PDFs** (Your past writing) - **Reddit Thread URL** (For context) - **New Prompt** (Your input to guide the response) ### **🏗 System Flow** 1. **Preprocessing & Style Extraction** - Parse **text & PDFs** → Extract embeddings, common phrases, sentence structure. - Store in **ChromaDB (vector database)** for retrieval. 2. **Reddit Thread Processing** - Scrape Reddit thread using `praw` or `Pushshift API`. - Extract **key themes, arguments, and logical structure**. - Summarize with an **LLM call** (Ollama or LiteLLM). 3. **Idea Expansion & Fact-Checking** - Use **SmolAgents** to expand ideas, ensuring **logical flow & fact-checking**. - If necessary, pull data from Wikipedia or a **local knowledge base**. 4. **Response Generation in Your Style** - Retrieve **writing style embeddings** from ChromaDB. - Prompt LLM to **reconstruct output in your tone & phrasing**. - Ensure output maintains your **sentence structure, rhythm, and vocabulary**. 5. **Final Output** - Display response **with a confidence score**. - Optional: Allow **manual refinements before posting**. --- ## **2️⃣ Tech Stack & Libraries** | Component | Tool/Library | Purpose | |-------------------------|----------------------------|---------| | **LLM Backend** | `Ollama` (`mistral`, `phi2`) / LiteLLM | Local model for processing | | **Vector Database** | `ChromaDB` / `FAISS` | Store & retrieve style embeddings | | **Agentic Orchestration** | `SmolAgents` | Multi-step reasoning for fact-checking & expansion | | **PDF/Text Processing** | `pymupdf`, `pdfplumber` | Extract writing data | | **Web Scraping** | `praw`, `Pushshift API` | Fetch Reddit threads | | **Logic & Summarization** | `LangChain`, `nltk`, `spacy` | Extract key themes from Reddit | --- ## **3️⃣ Step-by-Step Development Plan** ### **🟢 Step 1: Extract Your Writing Style** ✅ **Goal**: Convert a folder of text/PDFs into a **retrievable style profile** 1. Load text/PDFs 2. Process text: - Extract **word frequency, sentence length, common phrases** - Generate **embeddings** using `sentence-transformers` or `ChromaDB` 3. Store style embeddings in **ChromaDB** for later retrieval ### **🟢 Step 2: Scrape & Process Reddit Threads** ✅ **Goal**: Extract and summarize key ideas from a Reddit thread 1. Input Reddit URL 2. Scrape thread using `praw` 3. Summarize with `Ollama` (e.g., extract key points) 4. Parse sentiment & logical flow ### **🟢 Step 3: Expand & Fact-Check the Ideas** ✅ **Goal**: Use **SmolAgents** to refine the logic, fact-check, and improve clarity 1. Send extracted ideas to **LLM** to refine arguments 2. Use **SmolAgents** to fact-check against Wikipedia or a knowledge base 3. Generate a structured **expanded outline** ### **🟢 Step 4: Generate a Response in Your Style** ✅ **Goal**: Apply your writing style to the final response 1. Retrieve **style embeddings** from ChromaDB 2. Structure final response using **LLM style transfer** 3. Output response with **editable fields** --- ## **4️⃣ Next Steps** Would you like me to generate: - **A basic prototype script** to get started? - **Specific code snippets** for each step? - **A checklist of tasks** for easier tracking? Let me know how you'd like to proceed! 🚀

**A basic prototype script** to get started?

I'll create a **basic prototype script** that covers the core pipeline: 1. **Extract Writing Style** → Load PDFs/Text files and store embeddings in ChromaDB. 2. **Process Reddit Threads** → Scrape a thread and summarize key points. 3. **Expand & Fact-Check** → Use Ollama to refine ideas. 4. **Generate a Response in Your Style** → Reapply your style using embeddings. The script will be modular, so you can improve each component later. Let me draft the initial version. 🚀

### **How This Works** 1. **Stores Your Writing Style** → Extracts text from `.txt` and `.pdf` files, generates embeddings, and saves them in **ChromaDB**. 2. **Fetches a Reddit Thread** → Uses `praw` to scrape a post and comments. 3. **Expands Ideas Logically** → Calls an **Ollama model** (`mistral`) to refine arguments. 4. **Applies Your Style** → Uses stored style data to generate a response in your voice. ### **Next Steps** - Replace `"YOUR_CLIENT_ID"`, `"YOUR_CLIENT_SECRET"`, and `"YOUR_USER_AGENT"` with real **Reddit API credentials**. - Test with real PDFs and text files to see how well it mimics your style. - Improve **fact-checking** by integrating Wikipedia or another knowledge base. Would you like me to refine any part further? 🚀

write the requirements.txt for it

Here is your `requirements.txt` file, listing all the necessary dependencies: ``` chromadb pymupdf praw sentence-transformers ollama smolagents ``` You can save this as `requirements.txt` in your project directory and install dependencies using: ```sh pip install -r requirements.txt ``` Would you like any additional dependencies or refinements? 🚀

now rewrite the program to use .env for environment variables

I've updated your program to use a `.env` file for storing environment variables. Now, you'll need to create a `.env` file in your project directory with the following contents: ``` REDDIT_CLIENT_ID=your_client_id REDDIT_CLIENT_SECRET=your_client_secret REDDIT_USER_AGENT=your_user_agent ``` Then install `python-dotenv` if you haven't already: ```sh pip install python-dotenv ``` Let me know if you need further refinements! 🚀

(venv) (3.10.0) danielkliewer@Daniels-MacBook-Pro RedComm01 % python3 main.py Add of existing embedding ID: BrothersKaramazov.txt Insert of existing embedding ID: BrothersKaramazov.txt Writing style stored successfully. Traceback (most recent call last): File "/Users/danielkliewer/RedComm01/main.py", line 76, in <module> final_response = generate_response(expanded_comment) File "/Users/danielkliewer/RedComm01/main.py", line 60, in generate_response style_docs = [doc["documents"] for doc in style_collection.get()] # Retrieve stored styles File "/Users/danielkliewer/RedComm01/main.py", line 60, in <listcomp> style_docs = [doc["documents"] for doc in style_collection.get()] # Retrieve stored styles TypeError: string indices must be integers import os import chromadb import fitz # PyMuPDF for PDFs import praw # Reddit API from sentence_transformers import SentenceTransformer from ollama import generate from dotenv import load_dotenv # Load environment variables load_dotenv() # Initialize ChromaDB for style storage chroma_client = chromadb.PersistentClient(path="./style_db") style_collection = chroma_client.get_or_create_collection("writing_style") # Initialize Sentence Transformer for embeddings model = SentenceTransformer("all-MiniLM-L6-v2") # --- Step 1: Extract Writing Style --- def process_text_file(filepath): with open(filepath, "r", encoding="utf-8") as f: return f.read() def process_pdf_file(filepath): doc = fitz.open(filepath) return "\n".join([page.get_text() for page in doc]) def store_writing_style(folder): for file in os.listdir(folder): path = os.path.join(folder, file) if file.endswith(".txt"): text = process_text_file(path) elif file.endswith(".pdf"): text = process_pdf_file(path) else: continue embedding = model.encode(text).tolist() style_collection.add(documents=[text], embeddings=[embedding], ids=[file]) print("Writing style stored successfully.") # --- Step 2: Scrape & Process Reddit Thread --- reddit = praw.Reddit(client_id=os.getenv('REDDIT_CLIENT_ID'), client_secret=os.getenv('REDDIT_CLIENT_SECRET'), user_agent=os.getenv('REDDIT_USER_AGENT')) def fetch_reddit_thread(url): post_id = url.split("/")[-3] submission = reddit.submission(id=post_id) comments = [comment.body for comment in submission.comments if hasattr(comment, "body")] return submission.title + "\n" + submission.selftext, comments # --- Step 3: Expand & Fact-Check --- def expand_ideas(text): response = generate(model="mistral-small:24b-instruct-2501-q8_0", prompt=f"Expand and fact-check this idea logically: {text}") return response # --- Step 4: Generate Response in User Style --- def generate_response(prompt): style_docs = [doc["documents"] for doc in style_collection.get()] # Retrieve stored styles combined_style = "\n".join(style_docs) response = generate(model="mistral-small:24b-instruct-2501-q8_0", prompt=f"Write a response in this style: {combined_style}\n\n{prompt}") return response # --- Main Pipeline --- if __name__ == "__main__": # Load writing samples store_writing_style("./writing_samples") # Fetch Reddit Thread thread_title, thread_comments = fetch_reddit_thread("https://www.reddit.com/r/austincirclejerk/comments/1it3ywa/proxy_protestors_for_hire/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button") # Expand ideas expanded_comment = expand_ideas(thread_comments[0]) # Process first comment # Generate response in user style final_response = generate_response(expanded_comment) print("Generated Response:") print(final_response)

The error: ``` TypeError: string indices must be integers ``` suggests that `style_collection.get()` is returning a different data structure than expected. Instead of a list of dictionaries, it's likely returning a dictionary where `doc["documents"]` is not accessible directly. ### **Fix: Properly Retrieve Data from ChromaDB** Modify this line in `generate_response`: ```python style_docs = [doc["documents"] for doc in style_collection.get()] # Retrieve stored styles ``` to: ```python retrieved_data = style_collection.get() style_docs = retrieved_data["documents"] if "documents" in retrieved_data else [] ``` Then, update the final response generation: ```python combined_style = "\n".join(style_docs) if style_docs else "Default writing style" ``` --- ### **Updated `generate_response` Function** ```python def generate_response(prompt): retrieved_data = style_collection.get() style_docs = retrieved_data["documents"] if "documents" in retrieved_data else [] combined_style = "\n".join(style_docs) if style_docs else "Default writing style" response = generate(model="mistral-small:24b-instruct-2501-q8_0", prompt=f"Write a response in this style: {combined_style}\n\n{prompt}") return response ``` This ensures that: 1. It properly extracts **only documents** from the ChromaDB result. 2. If no writing style data is found, it falls back to `"Default writing style"`. Try this update and let me know if you need further debugging! 🚀