ecdallm 0.1.3

Retrieval-Augmented Generation - (RAG)

ecdallm

ecdallm is a lightweight Retrieval-Augmented Generation (RAG) application that lets you chat with your own documents using a locally running LLM.

It combines:

• FastAPI web interface
• Local embedding pipeline (FastEmbed)
• Persistent vector storage (ChromaDB)
• Document ingestion (PDF, TXT, DOCX)
• CLI launcher
• Local LLM support (e.g., LM Studio)

The goal is to provide a simple, reproducible environment for document-grounded LLM interaction without relying on external APIs.

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Overview

ecdallm allows you to:

1. Upload documents
2. Index them into a vector database
3. Run semantic retrieval
4. Query a local LLM with grounded context

All embeddings and vector storage run locally.

Your LLM also runs locally — for example using LM Studio.

This makes the system suitable for:

• research environments
• private document analysis
• offline experimentation
• RAG prototyping

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Installation

Install from PyPI:

pip install ecdallm

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Running the application

Start the CLI:

ecdallm

The CLI will:

• find a free port (starting from 8000)
• start the FastAPI server
• open the browser automatically

Example output:

ecdallm running at http://127.0.0.1:8000/
INFO: Uvicorn running on http://127.0.0.1:8000

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Using a local LLM

ecdallm expects an OpenAI-compatible endpoint.

For example, with LM Studio:

1. Start LM Studio server
2. Load a chat model
3. Enable the local API server

Typical endpoint:

http://localhost:1234/v1

You will configure this from the web interface.

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Supported document types

• PDF
• TXT
• DOCX

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Workflow

1. Upload documents

Use the Upload page to add files.

2. Index documents

Files are automatically indexed into ChromaDB using FastEmbed.

3. Chat with documents

Open the Chat page and ask questions.

The assistant will:

• retrieve relevant chunks
• build a grounded prompt
• query the local LLM
• return a concise answer

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Project structure

ecdallm/
├── cli.py
└── app/
    ├── main.py
    ├── rag.py
    ├── paths.py
    ├── search_engine.py
    ├── vector.py
    ├── templates/
    ├── static/
    ├── uploads/
    └── rag_store/

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Notes

At this stage, ecdallm is designed to work with locally running LLMs. No external API calls are required for embeddings or retrieval.

This keeps the system:

• private
• reproducible
• offline-friendly

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Erasmus Data Collaboratory

Developed by the Erasmus Data Collaboratory (ECDA).

• Zaman Ziabakhshganji — creator and maintainer
• Farshad Radman — co-author and contributor
• Jos van Dongen — co-author and contributor

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License

MIT License