LLM API Cost Options

Hi guys, I'm working on a side project where the users can upload docx and pdf files and I'm looking for a cheap API that can be used to extract and process information. My plan is to: Extract the raw text from documents Send it to an LLM with a prompt to structure the text in a specific json format Save the parsed content in the database Allow users to request rewording or restructuring later Currently I was thinking of using either deepSeek-chat and GPT-4o, but besides them I haven't really used any LLMs and I was wondering if you would have better options. I ran a quick test with the openai tokenizer and I would estimate that for raw data processing I would use about 1000-1500 input tokens and 1000-1500 output tokens. For the rewording I would use about 1500 tokens for the input and pretty much the same for the output tokens. I anticipate that this would be on the higher end side, the intended documents should be pretty short. Any thoughts or suggestions would be appreciated!

You're on the right track, but if cost is a big factor, you might want to look at some alternative options. Here are my recommendations: ### **LLM Options** #### **Cheaper API-based LLMs** 1. **DeepSeek Chat** - Good for structuring JSON output and cheap compared to OpenAI. - Supports function calling, which could help with structured extraction. 2. **Mistral 7B via Together AI** - Together AI provides cheap API access, and Mistral 7B is pretty solid for structured output. - Also supports function calling, which could make it more efficient. 3. **Claude 3 Sonnet (Anthropic)** - Sonnet is much cheaper than GPT-4o but still decent for text structuring and rewording. #### **Self-Hosted / Open-Source LLMs** If you want to avoid API costs entirely, you could run a local model using **Ollama** or **LM Studio** with models like: - **Mistral 7B (instruct)** - **DeepSeek Coder 6.7B** (for structured JSON responses) - **Gemma 7B** - **LLaMA 3 (coming soon)** With **QLoRA** or **4-bit quantization**, you could run a model on a mid-range GPU. ### **Document Processing (Text Extraction)** Instead of paying for an API, you can extract text from DOCX and PDF locally with: - **python-docx** (for DOCX) - **pdfplumber** or **PyMuPDF** (for PDFs) - **Unstructured.io** (open-source library for handling messy document layouts) This way, you only pay for LLM API calls, making it cheaper. ### **Cost Breakdown for OpenAI vs. Alternatives** #### **OpenAI (GPT-4o)** - **Input:** 1.5k tokens × $0.005 per 1k = **$0.0075** - **Output:** 1.5k tokens × $0.015 per 1k = **$0.0225** - **Total per request:** **$0.03** #### **DeepSeek Chat** - **Input:** 1.5k tokens × **$0.0005 per 1k** = **$0.00075** - **Output:** 1.5k tokens × **$0.0015 per 1k** = **$0.00225** - **Total per request:** **$0.003** (~10x cheaper) #### **Mistral 7B on Together AI** - **Cost per 1M tokens:** ~$0.20 - **Estimated cost per request:** **$0.0006** (much cheaper) ### **Recommendation** If **latency** and **ease of use** matter → **DeepSeek Chat** If **cost** is a bigger concern → **Mistral 7B via Together AI** If you want **zero API costs** → **Run Mistral 7B / DeepSeek locally with Ollama** Would you be running this locally or as a cloud service?

no instead search danielkliewer.com and find a solution from there using local models

Based on Daniel Kliewer's work, you can develop a local solution for processing DOCX and PDF files using large language models (LLMs) without incurring API costs. Here's a structured approach: **1. Text Extraction from DOCX and PDF Files** To extract text locally: - **DOCX Files:** Utilize the `python-docx` library to read and extract text. - **PDF Files:** Use `pdfplumber` or `PyMuPDF` for efficient text extraction. **2. Setting Up a Local LLM Environment** Implementing a local LLM environment ensures privacy and cost-effectiveness: - **Ollama:** This tool allows you to run LLMs locally. Install Ollama and pull a suitable model: ```bash ollama pull vanilj/Phi-4:latest ```  This setup enables AI-powered content generation on your machine. citeturn0search1 **3. Structuring Text into JSON Format** With the local LLM configured: - **Prompt Engineering:** Design prompts that instruct the LLM to convert raw text into the desired JSON structure. - **Automation:** Develop scripts to automate the process, feeding extracted text into the LLM and capturing the structured JSON output. **4. Database Integration** Store the structured JSON data: - **Database Choice:** Use SQLite for simplicity or another database system that fits your project's needs. - **Data Insertion:** Create functions to insert the JSON data into your database, facilitating efficient storage and retrieval. **5. Enabling Rewording and Restructuring** To allow users to modify content: - **Interactive Interface:** Develop a user interface where users can request changes. - **LLM Processing:** Utilize the local LLM to process these requests, ensuring that reworded or restructured content maintains coherence and aligns with user expectations. **Additional Resources** For further guidance, consider exploring Daniel Kliewer's projects: - **Next.js Ollama Custom Agent Framework:** This framework assists in building custom AI agents using Next.js and locally hosted models, providing insights into integrating LLMs into web applications. citeturn0fetch0 - **Insight Journal:** An AI-integrated journaling platform that leverages locally hosted LLMs for personal feedback and reflection, offering a practical example of local LLM deployment. citeturn0fetch0 By following this approach, you can create a local, cost-effective system for processing and managing DOCX and PDF files, aligning with the methodologies demonstrated in Daniel Kliewer's work.

no how would you build the solution using the concepts and ideas from my blog danielkliewer.com and then find the solution and write a blog post that has the solution as a guide on how to create it

Building a local solution for processing DOCX and PDF files using Large Language Models (LLMs) aligns with the methodologies discussed on Daniel Kliewer's blog. Here's a comprehensive guide to creating such a system: **1. Introduction** Leveraging local LLMs for document processing ensures data privacy and reduces dependency on external APIs. This guide outlines the steps to extract text from DOCX and PDF files, process the text using a local LLM, structure the output in JSON format, store it in a database, and provide functionalities for rewording or restructuring content upon user requests. **2. Prerequisites** - **Programming Language:** Python 3.8+ - **Libraries:** - `python-docx` for DOCX file processing - `PyMuPDF` for PDF file processing - `Ollama` for running local LLMs - `SQLite` or another database system for data storage **3. Setting Up the Development Environment** - **Install Python:** Ensure Python 3.8 or later is installed. - **Create a Virtual Environment:** ```bash python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ```  - **Install Required Packages:** ```bash pip install python-docx pymupdf sqlite3 ```  - **Install Ollama:** Follow the instructions on the [Ollama website](https://www.ollama.ai/) to install it on your system. **4. Extracting Text from Documents** - **DOCX Files:** ```python from docx import Document def extract_text_from_docx(file_path): doc = Document(file_path) return '\n'.join([para.text for para in doc.paragraphs]) ```  - **PDF Files:** ```python import fitz # PyMuPDF def extract_text_from_pdf(file_path): pdf_document = fitz.open(file_path) text = '' for page_num in range(len(pdf_document)): page = pdf_document.load_page(page_num) text += page.get_text() return text ```  **5. Processing Text with a Local LLM** - **Using Ollama:** - Install a suitable LLM model: ```bash ollama pull mistral ``` - Process text: ```python import subprocess import json def process_text_with_llm(text, prompt): command = f'ollama run mistral --prompt "{prompt}" --input "{text}"' result = subprocess.run(command, shell=True, capture_output=True, text=True) return json.loads(result.stdout) ``` **6. Structuring Text into JSON Format** - **Define a Prompt for Structuring:** ```python prompt = "Extract key information and structure it in the following JSON format: {\"title\": \"\", \"author\": \"\", \"content\": \"\"}" ```  - **Process and Structure:** ```python def structure_text_as_json(text): return process_text_with_llm(text, prompt) ```  **7. Storing Data in a Database** - **Set Up SQLite Database:** ```python import sqlite3 def setup_database(): conn = sqlite3.connect('documents.db') cursor = conn.cursor() cursor.execute(''' CREATE TABLE IF NOT EXISTS documents ( id INTEGER PRIMARY KEY, title TEXT, author TEXT, content TEXT ) ''') conn.commit() return conn ```  - **Insert Data:** ```python def insert_document(conn, document): cursor = conn.cursor() cursor.execute(''' INSERT INTO documents (title, author, content) VALUES (?, ?, ?) ''', (document['title'], document['author'], document['content'])) conn.commit() ```  **8. Implementing Rewording and Restructuring Functionality** - **Define Rewording Prompt:** ```python rewording_prompt = "Reword the following content to improve clarity and readability." ```  - **Reword Content:** ```python def reword_content(content): return process_text_with_llm(content, rewording_prompt) ```  **9. User Interface for Interactions** - **Command-Line Interface (CLI):** - Provide options for users to upload documents, request rewording, and view stored content. - **Web Interface:** - Develop a simple web application using frameworks like Flask or Django to allow users to interact with the system. **10. Conclusion** By following this guide, you can build a local system for processing DOCX and PDF files using LLMs. This approach ensures data privacy, reduces reliance on external services, and provides flexibility in handling document processing tasks. *Note: This guide incorporates concepts and methodologies inspired by Daniel Kliewer's work on integrating AI models for local applications.*

Below is a sample blog post that outlines how to build a complete solution inspired by the concepts on danielkliewer.com. You can adapt this guide as needed for your own project. --- # Building a Local Document Processing Pipeline with LLMs In this post, I’ll walk you through creating a cost-effective, local solution for processing DOCX and PDF files with large language models (LLMs). The approach leverages ideas from [danielkliewer.com](https://danielkliewer.com) and demonstrates how to extract raw text, structure it into JSON using a local LLM, and save the parsed output to a database for later rewording or restructuring. ## Overview The solution comprises the following steps: 1. **Extracting Text:** Process DOCX and PDF files locally. 2. **LLM Processing:** Use a local LLM (via tools like Ollama) to convert text into a structured JSON format. 3. **Data Storage:** Save the parsed JSON to a database. 4. **User Interactions:** Enable later rewording or restructuring of content via further LLM prompts. Let’s break down each step. --- ## 1. Extracting Text from Documents Instead of relying on an external API, you can extract text from files locally: ### For DOCX Files Use the Python library [`python-docx`](https://python-docx.readthedocs.io/en/latest/): ```python from docx import Document def extract_docx_text(file_path): doc = Document(file_path) full_text = [] for para in doc.paragraphs: full_text.append(para.text) return "\n".join(full_text) # Example usage: docx_text = extract_docx_text("sample.docx") print(docx_text) ``` ### For PDF Files Tools like [`pdfplumber`](https://github.com/jsvine/pdfplumber) or [`PyMuPDF`](https://pymupdf.readthedocs.io/) work well. Here’s a quick example using `pdfplumber`: ```python import pdfplumber def extract_pdf_text(file_path): text = "" with pdfplumber.open(file_path) as pdf: for page in pdf.pages: text += page.extract_text() + "\n" return text # Example usage: pdf_text = extract_pdf_text("sample.pdf") print(pdf_text) ``` By handling the extraction locally, you reduce API usage costs while keeping your workflow flexible and efficient. --- ## 2. Converting Raw Text to Structured JSON with a Local LLM Once you have the raw text, the next step is to structure it using an LLM. Inspired by the methods discussed on danielkliewer.com, you can use a local LLM running via **Ollama** or a similar framework. ### Setting Up a Local LLM First, install [Ollama](https://ollama.ai/) (or an alternative local LLM environment). Then, pull a model such as Phi-4 or Mistral 7B: ```bash ollama pull vanilj/Phi-4:latest ``` ### Building a Prompt for JSON Structuring Design a prompt that instructs the LLM to convert raw document text into your desired JSON format. For instance: ``` You are an assistant that extracts and structures document content. Given the following text, output a JSON object with the keys "title", "summary", and "body". Ensure valid JSON output. Text: <<INSERT RAW TEXT HERE>> ``` ### Automating the Process Develop a Python script that passes the extracted text to your local LLM and captures the JSON response: ```python import subprocess import json def call_local_llm(prompt_text): # Assuming Ollama provides a command-line interface to interact with the model. # The exact command will depend on your local LLM setup. process = subprocess.run(["ollama", "run", "vanilj/Phi-4:latest", "--prompt", prompt_text], capture_output=True, text=True) return process.stdout def parse_document(text): prompt = f"""You are an assistant that structures document content into JSON. Output should have the keys "title", "summary", and "body". Ensure valid JSON is returned. Text: {text}""" response = call_local_llm(prompt) try: structured_data = json.loads(response) except json.JSONDecodeError: # Handle error or try refining the prompt structured_data = {"error": "Failed to parse JSON."} return structured_data # Example usage: structured_output = parse_document(docx_text) print(json.dumps(structured_output, indent=2)) ``` This script takes the extracted text, builds a prompt, calls the local LLM, and attempts to parse the result as JSON. Adjust error handling as needed based on your model’s responses. --- ## 3. Storing Parsed Content in a Database With your structured JSON, you can now store the output for later use. For simplicity, you might start with an SQLite database. ### Setting Up SQLite ```python import sqlite3 def setup_database(db_path="documents.db"): conn = sqlite3.connect(db_path) cursor = conn.cursor() cursor.execute(""" CREATE TABLE IF NOT EXISTS documents ( id INTEGER PRIMARY KEY AUTOINCREMENT, title TEXT, summary TEXT, body TEXT ) """) conn.commit() return conn def save_document(conn, doc_data): cursor = conn.cursor() cursor.execute(""" INSERT INTO documents (title, summary, body) VALUES (?, ?, ?) """, (doc_data.get("title"), doc_data.get("summary"), doc_data.get("body"))) conn.commit() # Example usage: conn = setup_database() save_document(conn, structured_output) ``` This setup lets you insert and later query the structured document data. --- ## 4. Allowing Rewording and Restructuring on Demand The final part of the pipeline is enabling user-driven modifications. This might involve a web interface where users request a rewording or restructuring of the content. ### Building an Interactive Interface - **Web Framework:** Use a web framework like Flask or Next.js to build an interface. - **LLM Reprocessing:** When a user requests changes, feed the stored JSON or original text to your local LLM with a prompt tailored for rewording. For example: ``` Reword the following text to improve clarity and style, keeping the original meaning intact: <<INSERT TEXT HERE>> ``` - **Update the Database:** Once the LLM returns the modified content, update your database accordingly. ### Example Flask Endpoint ```python from flask import Flask, request, jsonify app = Flask(__name__) @app.route('/reword', methods=['POST']) def reword_text(): data = request.json text = data.get("text") prompt = f"Reword the following text to improve clarity and style while preserving its meaning:\n\n{text}" new_text = call_local_llm(prompt) # Reuse our LLM call function return jsonify({"reworded_text": new_text}) if __name__ == '__main__': app.run(debug=True) ``` This endpoint accepts JSON with text, processes it using the local LLM, and returns the reworded text. --- ## Conclusion By combining local text extraction tools, a locally hosted LLM, and a lightweight database, you can build an end-to-end document processing pipeline that is both cost-effective and flexible. This guide drew on ideas from danielkliewer.com, emphasizing local processing to maintain control over your data while reducing reliance on external APIs. Feel free to expand or modify the components as your project grows. Whether you choose to enhance the prompt engineering or integrate more advanced UI components, this architecture serves as a robust starting point for any document processing application. Happy coding! --- This guide provides a roadmap to build a self-contained document processing system using local models, while taking inspiration from the innovative work featured on danielkliewer.com.