llm-chatbot-python

llm-chatbot-python

https://graphacademy.neo4j.com/courses/llm-chatbot-python/

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This repository provides resources for building a chatbot backed by Neo4j using Python. It includes instructions on running the application, setting up tests, and installing necessary libraries. The chatbot is designed to interact with users and provide recommendations based on data stored in a Neo4j database. The repository is part of the Neo4j GraphAcademy course on building chatbots with Python.

README:

= Build an Neo4j-backed Chatbot using Python

This repository accompanies the link:https://graphacademy.neo4j.com/courses/llm-chatbot-python/?ref=github[Build an Neo4j-backed Chatbot using Python^] course on link:https://graphacademy.neo4j.com/?ref=github[Neo4j GraphAcademy^].

For a complete walkthrough of this repository, link:https://graphacademy.neo4j.com/courses/llm-chatbot-python/?ref=github[enrol now^].

== Running the application

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

[source,sh] pip install -r requirements.txt

Then run the streamlit run command to start the app on link:http://localhost:8501/[http://localhost:8501/^].

[source,sh] streamlit run bot.py

== Tests

To run the solution tests:

. Create Neo4j instance with the recommendations dataset . Run the link:https://raw.githubusercontent.com/neo4j-graphacademy/courses/refs/heads/main/asciidoc/courses/llm-chatbot-python/modules/3-tools/lessons/1-vector-tool/reset.cypher[Cypher to add embeddings and create the vector index^]. . Create a virtual environment and install the requirements. + [source,sh] pip install -r requirements.txt . Install pytest + [source,sh] pip install pytest . Create a secrets.toml file in the .streamlit directory. Use secrets.toml.example as a template. . Run the tests + [source,sh] pytest

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