This is a basic version of a knowledge management system using a locally run LLM as part of a RAG (=Retrieval Augmented Generation) chain. Funtionality includes:

• End point for ingesting text data with source information.
• Frontend app offering a chat interface in the browser.
• Backend process that retrieves relevant text data and uses an LLM to summarize the content.

** Deployment with docker compose is currently under development. Please use dev install in the meantime. **

Production version can be run with docker compose up. This will create all required services and storage volumes. Please note: LLM models, which will be stored in a volume will be several GB in size. Any ingested data is stored inside those volumes as well, so sufficient storage space should be provided.

The app will expose the following services:

• The webservice to access the chat frontend at port :8000
• An endpoint to ingest and process new data at port :8001/inserttext/

myexpertchat requires a running instance of a Weaviate vector database. For local development, you can either run Weaviate from binary (as described here) or use the existing docker-compose.yml and change expose to ports for the service "weaviate".

Python installation assumes that Poetry is available and working. All required libraries can then be installed by running poetry install in the project root directory.

The data ingestion endpoint can be run manually by executing in the project root directory:

flask --app myexpertchat/ingest.py run

By default, this will expose a port on localhost:5000/inserttext/ that can receive POST requests with JSON payload. See docstring in myexpertchat/ingest.py for details.

The frontend service can be run manually by executing in the project root directory:

streamlit run myexpertchat/frontend.py 

This exposes a port at localhost:8501 that can be opened in the browser.

myexpertchat is licensed under the Apache license v2.0, a copy of which is included in LICENSE