knowledge-based-agent-pgai

This is a submission for the Open Source AI Challenge with pgai and Ollama

What I Built

I developed an AI-powered FAQ chatbot using AWS Lex as the conversational interface and AWS Lambda as the backend fulfillment codehook. The chatbot is designed to handle complex user queries by searching a database of frequently asked questions (FAQs) and returning the most relevant answers. The project integrates the power of advanced vector search capabilities and AI directly within a PostgreSQL database, utilizing the following extensions:

pgvector: Enables efficient vector-based similarity searches using high-dimensional embeddings.

pgvectorscale: Enhances performance with advanced indexing methods like StreamingDiskANN, optimizing large-scale vector searches.

pgai: Integrates AI capabilities directly into the database, generating embeddings using AI models and handling queries seamlessly.

The backend is built with Python, leveraging OpenAI embeddings for transforming user queries into vector representations. These vectors are stored and queried directly in PostgreSQL, making use of pgvector and pgvectorscale for efficient retrieval. The addition of pgai allows for direct embedding generation and AI-based query handling within the database, reducing the need for external processing.

The entire solution is deployed on AWS, utilizing services like:

AWS Lambda for serverless execution and efficient backend processing.
AWS S3 for data storage and management.
AWS Lex for natural language understanding and chatbot capabilities.
This project demonstrates a robust, scalable approach to building an intelligent FAQ chatbot, integrating cutting-edge vector search and AI capabilities directly within the database layer, resulting in faster, more accurate responses to user queries.

Demo

Link: https://github.com/inquisitour/knowledge-based-agent-pgai

Screenshots:

Tools Used

In this project, I utilized the following PostgreSQL extensions and AWS services to build a sophisticated AI-powered FAQ chatbot:

pgvector:
Used to store and manage high-dimensional vector embeddings for the questions in the FAQ database.
Enabled efficient similarity search operations using vector distance calculations (e.g., cosine similarity) within PostgreSQL, allowing for quick retrieval of semantically similar questions.

pgvectorscale:
Enhanced the performance of vector searches with advanced indexing techniques, such as StreamingDiskANN.
Improved scalability by optimizing the search process, particularly for large datasets, making the chatbot faster and more responsive.

pgai:
Integrated AI functionalities directly into the PostgreSQL database.
Leveraged pgai for generating embeddings using AI models without relying on external API calls. This allowed for seamless, in-database embedding generation and efficient handling of user queries.
Enabled the chatbot to dynamically create and update embeddings, reducing latency and dependency on external services.

AWS Lambda:
Used as the backend fulfillment codehook for the AWS Lex chatbot.
Managed serverless execution of the embedding retrieval and similarity search logic, ensuring scalability and efficient processing.

AWS Lex:
Provided the natural language understanding (NLU) capabilities for the chatbot, interpreting user queries and passing them to the Lambda backend for processing.

AWS S3:
Used for storage of the FAQ data files, enabling dynamic updates and easy management of the dataset.

By combining these tools, I built a highly efficient and scalable AI-driven FAQ chatbot that leverages advanced vector search techniques and AI capabilities directly within the database layer, ensuring quick and accurate responses to user queries.

Final Thoughts

Building this AI-powered FAQ chatbot has been an exciting and rewarding journey. The integration of pgvector, pgvectorscale, and pgai within PostgreSQL transformed a traditional relational database into a powerful AI-enhanced engine capable of efficient vector searches and on-demand embedding generation. This approach eliminated the need for extensive external API calls, reducing latency and simplifying the architecture.

Leveraging AWS Lambda for backend processing and AWS Lex for the conversational interface created a seamless user experience. The serverless architecture provided by AWS allowed for effortless scaling, while the tight integration of PostgreSQL extensions enabled advanced vector search capabilities right within the database layer.

A few key takeaways from this project are:

The synergy between pgvector, pgvectorscale, and pgai demonstrates the potential of in-database AI, streamlining the workflow and significantly improving query response times.
The use of pgai for embedding generation was particularly impactful, as it simplified the codebase and reduced the dependency on external services, leading to a more robust and efficient solution.
AWS services like Lambda and Lex provided a powerful and scalable infrastructure, allowing the chatbot to handle varying loads with ease.
Overall, this project highlights how combining cutting-edge database extensions with cloud services can lead to innovative solutions in the AI space. I am proud of the outcome and excited about the possibilities this architecture offers for future AI applications. I hope the judges recognize the effort put into making this chatbot not only functional but also scalable, efficient, and forward-thinking.

Finally, I think I qualify for the category "All the Extensions!".

Thank you!!