unidoc-agent 0.2.3

Universal Document Agent for extracting and analyzing various documents with Ollama support.

UniversalDocAgent (unidoc_agent)

UniversalDocAgent is a Python package designed to intelligently detect document types and automatically extract or summarize their contents using a set of specialized tools. It supports a wide range of file types such as PDFs, Word documents, emails, source code, Excel files, XML, OCR-recognizable images, and plain text.

You can optionally integrate with an LLM backend like Ollama to generate summaries and maintain conversation history across sessions.

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🚧 Installation

pip install .

Run this from the root directory of your cloned project or package.

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📂 Supported Document Types

File Type	Handled By
.pdf	PDFTool
.docx	WordTool
.txt	TextTool
.py, .js, etc.	CodeTool
.eml	EmailTool
.xlsx	ExcelTool
.jpg, .png, etc.	OCRTool
.xml	XMLTool

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🚀 Usage Examples

1. Extracting Content

from unidoc_agent.agent import read_document

file_path = "sample.pdf"
content = read_document(file_path)
print(content)

2. Summarizing Content

summary = read_document("example.docx", summarize=True)
print(summary)

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🧠 Advanced: Use Ollama LLM Backend

Custom LLM model or session

from unidoc_agent.agent import UniversalDocAgent
from unidoc_agent.agent import tools

agent = UniversalDocAgent(tools=tools, llm_backend="ollama")
summary = agent.summarize_content("report.txt")
print(summary)

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💬 Conversation History with OllamaClient

The OllamaClient class is used internally to manage conversation context for summarization.

• Caching: Stores conversation history locally in ~/.unidoc_ollama_cache/{model}_{session_id}.json
• Session Management: Custom session IDs let you manage multiple user contexts

Clearing History

from unidoc_agent.ollama_client import OllamaClient

client = OllamaClient(session_id="user123")
client.clear_history()

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🔧 API Reference

read_document(file_path, summarize=False)

• file_path: Path to the input document
• summarize: If True, returns a summary via LLM; else returns extracted content

UniversalDocAgent

• extract_content(file_path): Extracts raw content
• summarize_content(file_path): Summarizes content using the selected tool + LLM

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🔮 Tests

Make sure you have pytest or unittest installed:

pytest

Or:

python -m unittest discover tests/

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📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

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📊 Use Cases

• ✉️ Email Parsing – Automatically extract the body of .eml files and summarize them.
• 📄 Document Summary – Get concise summaries of long reports, manuals, or meeting notes.
• 📈 Spreadsheet Reader – Read .xlsx Excel files and extract tables or data grids.
• 🔧 OCR Scanning – Use OCRTool to read text from images (e.g., scanned receipts).
• 📁 Source Code Insight – Extract and analyze comments or logic from .py or .js files.
• 📖 Multi-format Aggregation – Use the same interface (read_document) for any supported format.

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🚀 Contributing

Pull requests are welcome. Please open issues for bugs or feature requests.

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✨ Acknowledgements

Thanks to OpenAI, Ollama, and the open-source contributors whose tools helped build this module.