🧠 Local File Intelligence

A powerful, offline RAG (Retrieval-Augmented Generation) system that lets you chat with your local documents. It uses a Hybrid Architecture to handle both small summaries and massive reports with high precision.

Privacy First: 100% Local. No data leaves your machine. Powered by Ollama.

🚀 Key Features

• Hybrid Retrieval Engine:
  • RAM Mode: Small files are kept in memory for 100% accuracy and zero latency.
  • Vector Mode: Large files (>4000 chars) are automatically chunked and indexed in ChromaDB for scalable semantic search.
• Multi-Format Intelligence:
  • 📄 PDFs: Extracts text and parses structure.
  • 📊 CSVs: Auto-calculates row counts and formats data into readable Markdown tables.
  • 🗄️ NoSQL/JSON: Flattens nested JSON data into analyze-able records (great for logs & mongo dumps).
• 🧠 Deep Summarizer (Map-Reduce):
  • Can summarize massive documents (500+ pages) by breaking them down, summarizing chunks, and combining them into a master report.
• Interactive CLI:
  • Focus Mode: Lock the chat to a specific file to eliminate noise.
  • Session Recorder: Save your entire analysis to a .txt report.

🏗️ Architecture

%%{init: {'theme': 'dark'}}%%
graph TD
    %% Styling
    classDef storage fill:#006064,stroke:#4dd0e1,stroke-width:2px,color:#ffffff;
    classDef logic fill:#bf360c,stroke:#ffab91,stroke-width:2px,color:#ffffff;
    classDef newFeat fill:#4a148c,stroke:#e1bee7,stroke-width:2px,color:#ffffff;

    subgraph Startup ["Phase 1: Ingestion"]
        Loader["File Loader"] -->|"PDF/Txt"| TextData["Raw Text"]
        Loader -->|"CSV"| CSVData["Metadata + Tables"]
        Loader -->|"JSON"| JSONData["Flattened JSONL"]:::newFeat
        
        TextData & CSVData & JSONData --> Router{"Size < 4000 chars?"}
        
        Router -->|Yes| RAM["RAM Storage"]:::storage
        Router -->|No| VectorDB[("ChromaDB")]:::storage
    end

    subgraph Loop ["Phase 2: Interaction"]
        User([User]) --> CmdCheck{"Command?"}
        CmdCheck -->|":deep"| MapReduce["Deep Map-Reduce Engine"]:::newFeat
        CmdCheck -->|":focus"| FocusedSearch["Targeted Search"]
        CmdCheck -->|"Query"| HybridSearch["Hybrid Context Builder"]:::logic
        
        MapReduce & FocusedSearch & HybridSearch --> Agent["Ollama Agent"]:::logic
        Agent --> User
    end

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🛠️ Installation

Prerequisites

• Python 3.10+
• Ollama installed and running (if using the included Ollama agent)
• Git

1. Clone the repository

🧠 Local File Intelligence

A powerful, offline RAG (Retrieval-Augmented Generation) system that lets you chat with your local documents. It uses a Hybrid Architecture to handle both small summaries and massive reports with high precision.

Privacy First: 100% Local. No data leaves your machine. Powered by Ollama.

🚀 Key Features

• Hybrid Retrieval Engine:
  • RAM Mode: Small files are kept in memory for 100% accuracy and zero latency.
  • Vector Mode: Large files (>4000 chars) are automatically chunked and indexed in ChromaDB for scalable semantic search.
• Multi-Format Intelligence:
  • 📄 PDFs: Extracts text and parses structure.
  • 📊 CSVs: Auto-calculates row counts and formats data into readable Markdown tables.
  • 🗄️ NoSQL/JSON: Flattens nested JSON data into analyze-able records (great for logs & mongo dumps).
• 🧠 Deep Summarizer (Map-Reduce):
  • Can summarize massive documents (500+ pages) by breaking them down, summarizing chunks, and combining them into a master report.
• Interactive CLI:
  • Focus Mode: Lock the chat to a specific file to eliminate noise.
  • Session Recorder: Save your entire analysis to a .txt report.

4. Pull the model (if using Ollama)

Ensure Ollama is running, then pull the default model (for example Llama 3):

ollama pull llama3

🖥️ Usage

1. Prepare data: place your documents (PDF, CSV, JSON, TXT) under the data/ folder.
2. Run the CLI:

python main.py --files ./data

🎮 CLI Commands

• files : List all loaded files and their storage status (RAM vs Vector).
• :focus [filename] : Lock the chat to a specific file (e.g., :focus sales.csv).
• :deep [filename] : Run a Deep Map-Reduce summary on a large document.
• :save [name] : Export the current chat history to a text file.
• :all : Return to global search mode (search all files).
• exit : Quit the application.

📂 Project Structure

local_file_intelligence/ ├── core/ │ ├── agent.py # LLM Interaction Handler │ ├── hybrid_manager.py # Router for RAM vs Vector DB │ └── summarizer.py # Map-Reduce Engine for large docs ├── utils/ │ ├── file_loader.py # Parsers for PDF, CSV, JSON │ ├── chunker.py # Semantic Text Splitter │ └── vector_store.py # ChromaDB wrapper ├── data/ # Your documents go here ├── main.py # CLI Entry Point ├── requirements.txt # Dependencies ├── LICENSE # MIT License └── README.md # Documentation

🤝 Contributing

Contributions are welcome!

1. Fork the project
2. Create your feature branch: git checkout -b feature/AmazingFeature
3. Commit your changes: git commit -m 'Add some AmazingFeature'
4. Push to the branch: git push origin feature/AmazingFeature
5. Open a Pull Request

📜 License

Distributed under the MIT License. See LICENSE for more information.