README.md

Function Calling demo

What does this application wants to demonstrate

This application is built as an extension to this

1. Data retrieval: With both RAG and DB search (via API created from Flask)
2. Routing: Use Function Call for autonomous tool choice & invocation
3. UI Via Streamlit

Tech stack

• Embedding model: all-MiniLM-L6-v2
• Vector Database: Haystack's InMemoryDocumentStore
• LLM: GPT-4 Turbo accessed via OpenRouter. But the flow can be adapted into using other LLMs
• LLM Framework: Haystack for their great documentations, and transparency in pipeline construction. This tutorial is actually an extension to their fantastic tutorial for the same topic

Running this tool

1. Create and activate a virtual environment, then pip install -r requirements.txt to install the required packages
2. Spin up the API server with python db_api.py
3. If you are seeking for an initial tutorial for the concept behind, run rag_plus_db_search.ipynb. Or proceed to #3 directly
4. Run the streamlit application with the below

export OPENROUTER_API_KEY = '@REPLACE WITH YOUR API KEY'
cd streamlit
streamlit run app.py