How to Build User Interfaces For AI Applications Using Streamlit And LangChain

This tutorial demonstrates building a Streamlit UI for a LangChain application interacting with a Neo4j graph database. It creates a chatbot answering questions about international football history using Retrieval Augmented Generation (RAG). Let's explore the key steps and concepts.

The tutorial leverages several technologies:

• Streamlit: A Python framework for rapidly building user-friendly web apps. It provides components for displaying chat messages and accepting user input, creating a ChatGPT-like interface.
• LangChain: A framework unifying various LLMs, simplifying the integration of LLMs with other tools. Here, it connects OpenAI's GPT models to the database.
• Neo4j (with AuraDB): A graph database ideal for storing interconnected data like football players, teams, matches, and tournaments. The tutorial uses a cloud-based AuraDB instance.
• Retrieval Augmented Generation (RAG): A technique enhancing LLMs with information retrieved from a database. The chatbot uses RAG to answer specific questions about football history.

The data used is a Kaggle dataset containing over 47,000 matches, including scores, goalscorers, and match details. This data is ingested into the Neo4j database. The chatbot's graph schema includes nodes for players, teams, matches, tournaments, cities, and countries, linked by relationships such as "PLAYED_HOME" and "SCORED_FOR".

The tutorial walks through building the chatbot step-by-step:

1. Environment Setup: Creating a Conda environment and installing necessary libraries (Streamlit, LangChain, Langchain-OpenAI, Langchain-Community, Neo4j). Secrets (Neo4j URI, username, password, and OpenAI API key) are stored in .streamlit/secrets.toml.

2. Library Imports and Secret Loading: Importing necessary modules and loading secrets using st.secrets.

3. Authentication: A sidebar prompts the user for their OpenAI API key.

4. Database Connection and QA Chain Initialization: The init_resources function connects to Neo4j, refreshes the schema, and initializes a GraphCypherQAChain using ChatOpenAI. st.cache_resource caches these resources for efficiency.

5. Message History: Streamlit's session state manages chat history, displaying previous messages using st.chat_message and st.markdown.

6. Chat Components: The query_graph function executes the chain, handling potential errors. st.chat_input accepts user queries, and the response is displayed using st.chat_message.

7. Code Optimization: The code is refactored into modular files (graph_utils.py and chat_utils.py) for better organization.

8. Deployment: The app is deployed to Streamlit Cloud, requiring a requirements.txt file and secrets management.

The final application provides a user-friendly interface for querying the football database. The tutorial also emphasizes that while UI development is relatively straightforward, optimizing the underlying query generation and ensuring accuracy requires significant effort. The provided example, while functional, serves as a starting point and may require further refinement for production use. The tutorial concludes with FAQs addressing common questions about required skills, costs, database alternatives, and the chatbot's differences from ChatGPT.

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