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EcoToxFred - Dialogues with a Knowledge Keeper

This is EcoToxFred, a Neo4j-backed Chatbot with whom you can discuss environmental monitoring data collected in a large knowledge graph and stored in a Neo4j Graph Database.

EcoToxFred is a prototype that demonstrates how domain-specific knowledge can be combined with LLMs to facilitate the interaction between users and (domain-specific) knowledge.

EcoToxFred can answer questions about chemicals and their measured concentrations in European surface waters like rivers and lakes. EcoToxFred is a friendly and intuitive chatbot designed to help researchers, stakeholders, and users explore and analyze chemical concentrations and toxicity data in European surface waters. By leveraging advanced language models and a graph database, EcoToxFred makes complex environmental toxicology data accessible and understandable for everyone, regardless of their technical background.

In its currently trained state, it can

  • provide general information about rivers, lakes, or substances
  • provide sampling sites where individual (sets of) chemicals have been measured and detected at which concentrations
  • plot measured concentrations across time and/or sampling sites
  • find all substances measured at a certain sampling site (identified by country, water body, or coordinate range)
  • identify the most frequent drivers in regions (countries, water bodies, Europe)
  • extract summarized risk for regions or species

Upcoming skills are

  • plot requested information on a map of the selected region
  • provide subgraphs of graph database results, ready to be used in Cytoscape or else

Running the application

To run the application, you must install the libraries listed in requirements.txt.

pip install -r requirements.txt

Then run the streamlit run command to start the app on localhost:8501.

streamlit run bot.py

Contributing

You may experience that EcoToxFred does not provide appropriate responses. Please provide your query and your expected response, and we will improve LLM prompts, provide more examples for zero-shot and few-shot learning, and if necessary even make improvements or adjustments to our Neo4j database (e.g., by integrating additional knowledge).

Contact

Jana Schor - [email protected]