This demo features GCP Matching Engine and VertexAI PaLM to combine the functionality of retrieval augmentation and conversational engines to create a question answering system where the user can ask a question and the LLM will use it's given context to answer the question.

The Dataset used is the Stanford Question Answering Dataset (SQuAD) , a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles.

The demo can be accessed here.

• VertexAI Matching Engine: ANN Similarity Seach
• VertexAI PaLM: Conversational Engine
• Cloud Run: Hosting of the API
• Firestore: Document Database
• Firebase: Frontend hosting
• Cloud Build: CI/CD

Frameworks:

• LangChain: Framework for creating conversational agent and retrieval augmentation
• Tensorflow Hub: Embeddings

• Terraform
• A GCP project created

1. Infrastructure and Matching Engine Setup: Setup the required infrastructure using Terraform and create the Matching Engine index
2. Create embeddings: Generate the embeddings for the documents and index them in Matching Engine
3. Firestore: Index the documents in Firestore
4. LangChain Retriever and Agent: Create a LangChain retriever and conversational agent
5. Cloud Run: Grab all the code, package it and deploy the API to Cloud Run
6. Firebase WebUI: Create the Web app