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https://github.com/bjornmelin/docmind-ai

DocMind AI is a powerful, open-source Streamlit application leveraging LangChain and local Large Language Models (LLMs) via Ollama for advanced document analysis. Analyze, summarize, and extract insights from a wide array of file formats—securely and privately, all offline.
https://github.com/bjornmelin/docmind-ai

ai ai-applications docker document-analysis generative-ai langchain llm local-ai machine-learning natural-language-processing ollama privacy-first pydantic python streamlit text-analysis

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DocMind AI is a powerful, open-source Streamlit application leveraging LangChain and local Large Language Models (LLMs) via Ollama for advanced document analysis. Analyze, summarize, and extract insights from a wide array of file formats—securely and privately, all offline.

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README 

          
# 🧠 DocMind AI: Local LLM for AI-Powered Document Analysis



![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)

![Streamlit](https://img.shields.io/badge/Streamlit-FF4B4B?style=for-the-badge&logo=streamlit&logoColor=white)

![LangChain](https://img.shields.io/badge/🦜_LangChain-2C2C2C?style=for-the-badge)

![Docker](https://img.shields.io/badge/Docker-2496ED?style=for-the-badge&logo=docker&logoColor=white)

![Ollama](https://img.shields.io/badge/🦙_Ollama-000000?style=for-the-badge)



[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)

[![GitHub](https://img.shields.io/badge/GitHub-BjornMelin-181717?logo=github)](https://github.com/BjornMelin)

[![LinkedIn](https://img.shields.io/badge/LinkedIn-Bjorn%20Melin-0077B5?logo=linkedin)](https://www.linkedin.com/in/bjorn-melin/)



**DocMind AI** is a powerful, open-source Streamlit application that leverages the capabilities of Large Language Models (LLMs) running locally on your machine through [Ollama](https://ollama.com/). Analyze a vast array of document types, extract key insights, generate summaries, identify action items, and surface open questions – all without sending your data to the cloud!



## ✨ Features of DocMind AI



- **Privacy-Focused:** Your documents are processed locally, ensuring data privacy and security.

- **Versatile Document Handling:** Supports a wide range of file formats:

  - 📄 PDF

  - 📑 DOCX

  - 📝 TXT

  - 📊 XLSX

  - 🌐 MD (Markdown)

  - 🗃️ JSON

  - 🗂️ XML

  - 🔤 RTF

  - 📇 CSV

  - 📧 MSG (Email)

  - 🖥️ PPTX (PowerPoint)

  - 📘 ODT (OpenDocument Text)

  - 📚 EPUB (E-book)

  - 💻 Code files (PY, JS, JAVA, TS, TSX, C, CPP, H, and more!)

- **Powerful AI Analysis:** Uses the power of LangChain to provide in-depth analysis.

- **Structured Output:** Get results in a well-defined format using Pydantic.

- **Customizable Prompts:** Tailor the analysis to your specific needs with pre-defined or custom prompts.

- **Tone and Instruction Control:** Fine-tune the LLM's responses by selecting the desired tone (e.g., professional, informal, academic) and specific instructions (e.g., act as a researcher, software engineer, business analyst).

- **Length/Detail Selection:** Control the length and level of detail of the generated responses (e.g., concise, detailed, comprehensive).

- **Flexible Analysis Modes:** Choose to analyze each document individually or combine them for a holistic analysis.

- **Interactive Chat:** Continue the conversation with the LLM to explore the documents further.

- **Docker Support:** Easily deploy the application using Docker or Docker Compose.



## 📖 Table of Contents



1. [Features of DocMind AI](#-features-of-docmind-ai)

2. [Getting Started with DocMind AI: Local LLM Analysis](#-getting-started-with-docmind-ai-local-llm-analysis)

   - [Prerequisites](#-prerequisites)

   - [Installation](#️-installation)

   - [Running the App](#️-running-the-app)

3. [Usage](#-usage)

   - [Selecting a Model](#️-selecting-a-model)

   - [Uploading Documents](#-uploading-documents)

   - [Choosing Prompts](#️-choosing-prompts)

   - [Selecting Tone](#-selecting-tone)

   - [Selecting Instructions](#-selecting-instructions)

   - [Setting Length/Detail](#-setting-lengthdetail)

   - [Choosing Analysis Mode](#️-choosing-analysis-mode)

   - [Analyzing Documents](#-analyzing-documents)

   - [Interacting with the LLM](#-interacting-with-the-llm)

4. [Architecture](#️-architecture)

5. [How to Cite](#-how-to-cite)

6. [Contributing](#-contributing)

7. [License](#-license)



## 🚀 Getting Started with DocMind AI: Local LLM Analysis



### 📋 Prerequisites



- [Ollama](https://ollama.com/) installed and running.

- Python 3.8 or higher.

- (Optional) Docker and Docker Compose for containerized deployment.



### ⚙️ Installation



1. **Clone the repository:**



   ```bash

   git clone https://github.com/BjornMelin/docmind-ai.git

   cd docmind-ai

   ```



2. **Install dependencies:**



   ```bash

   pip install -r requirements.txt

   ```



### ▶️ Running the App



**Locally:**



```bash

streamlit run app.py

```



**With Docker:**



```bash

docker-compose up --build

```



The app will be accessible at `http://localhost:8501`.



## 💻 Usage



### 🎛️ Selecting a Model



1. Enter the **Ollama Base URL** (default: `http://localhost:11434`).

2. Choose your desired **Ollama Model Name** (e.g., `llama2`) from the dropdown.



### 📁 Uploading Documents



Click the **"Browse files"** button to upload one or more documents. Supported file types are listed above in the [Features](#-features) section.



### ✍️ Choosing Prompts



Select a pre-defined prompt from the dropdown:



- **Comprehensive Document Analysis:** Get a summary, key insights, action items, and open questions.

- **Extract Key Insights and Action Items:** Focus on extracting these two elements.

- **Summarize and Identify Open Questions:** Generate a summary and a list of open questions.

- **Custom Prompt:** Enter your own prompt to guide the analysis.



### 😃 Selecting Tone



Choose the desired tone for the LLM's response:



- **Professional:** Objective and formal.

- **Academic:** Scholarly and research-oriented.

- **Informal:** Casual and conversational.

- **Creative:** Imaginative and artistic.

- **Neutral:** Unbiased and objective.

- **Direct:** Concise and to-the-point.

- **Empathetic:** Understanding and compassionate.

- **Humorous:** Witty and lighthearted.

- **Authoritative:** Confident and expert.

- **Inquisitive:** Curious and exploratory.



### 🧮 Selecting Instructions



Select the persona or instructions that the LLM should follow:



- **General Assistant:** Act as a helpful assistant.

- **Researcher:** Provide in-depth research and analysis.

- **Software Engineer:** Focus on technical details and code.

- **Product Manager:** Consider product strategy and user experience.

- **Data Scientist:** Emphasize data analysis and modeling.

- **Business Analyst:** Analyze from a business and strategic perspective.

- **Technical Writer:** Create clear and concise documentation.

- **Marketing Specialist:** Focus on branding and customer engagement.

- **HR Manager:** Consider human resources aspects.

- **Legal Advisor:** Provide information from a legal standpoint.

- **Custom Instructions:** Enter your own specific instructions.



### 📏 Setting Length/Detail



Choose the desired length and level of detail for the LLM's response:



- **Concise:** Brief and to-the-point.

- **Detailed:** Thorough and comprehensive.

- **Comprehensive:** Extensive and in-depth.

- **Bullet Points:** Provide response in bullet point format.



### 🗂️ Choosing Analysis Mode



Select the analysis mode:



- **Analyze each document separately:** Process and analyze each document individually.

- **Combine analysis for all documents:** Treat all uploaded documents as a single unit for analysis.



### 🧠 Analyzing Documents



1. Upload your documents.

2. Choose your analysis prompt, tone, instructions, desired length, and analysis mode.

3. Click the **"Extract and Analyze"** button.



The application will display the analysis results, attempting to format them according to the defined output schema. If parsing fails, the raw LLM output will be shown.



### 💬 Interacting with the LLM



Use the chat interface to ask follow-up questions about the analyzed documents. The LLM will use the extracted information as context for its responses.



## 🏗️ Architecture



Here's a Mermaid diagram illustrating the application's architecture:



```mermaid

graph TD

    A[User] -->|Uploads Documents| B(Streamlit App - app.py);

    B -->|Selects Model, Prompt, Tone, Instructions, Length, Mode| C{Ollama API};

    C -->|Processes Documents| D[LangChain];

    D -->|Loads Documents| E{Document Loaders};

    E -->|PDF| F[PyPDFLoader];

    E -->|DOCX| G[Docx2Loader];

    E -->|TXT, Code| H[TextLoader];

    E -->|...| I;

    D -->|Splits Text| J[RecursiveCharacterTextSplitter];

    D -->|Generates Analysis| K[LLM - Ollama Model];

    K -->|Structured Output| L[PydanticOutputParser];

    B -->|Displays Results| A;

    A -->|Asks Follow-up Questions| B;

    B -->|Interacts with LLM| C;



    style A fill:#f9f,stroke:#333,stroke-width:2px

    style B fill:#ccf,stroke:#333,stroke-width:2px

    style C fill:#cfc,stroke:#333,stroke-width:2px

    style D fill:#fcc,stroke:#333,stroke-width:2px

    style K fill:#ccf,stroke:#333,stroke-width:2px

```



## 📖 How to Cite



If you use DocMind AI in your research or work, please cite it as follows:



```bibtex

@software{melin_docmind_ai_2025,

  author = {Melin, Bjorn},

  title = {DocMind AI: Local LLM for AI-Powered Document Analysis},

  url = {https://github.com/BjornMelin/docmind-ai},

  version = {0.1.0},

  year = {2025}

}

```



## 🙌 Contributing



We welcome contributions! Please see the [CONTRIBUTING.md](CONTRIBUTING.md) file for details on how to contribute to this project.



## 📃 License



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.



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Built with ❤️ by [Bjorn Melin](https://bjornmelin.io)