August 19, 2024 • Shreyas Mocherla • 4 min read

Developing an AI Agent for Smart Contextual Q&A

A technical deep-dive into building InSightful, a ReAct AI agent that helps tech communities by intelligently retrieving past conversations, searching Stack Overflow, and browsing the web for relevant information.

This article was originally published on the CNCF Blog.

The Problem

Online tech communities have grown rapidly, especially after the pandemic. With new members joining every day, it’s tough to keep track of past conversations, and newcomers often ask questions that have already been answered. This creates repetitive work for community moderators and experienced members.

To tackle this, we built InSightful - an intelligent assistant that tracks past conversations, searches Stack Overflow for technical help, and browses the web for relevant information.

What is InSightful?

InSightful is a ReAct (Reasoning and Action) Agent with access to multiple tools, such as a web searcher and a context retriever, to achieve the given task. It uses state-of-the-art Generative AI to provide an intelligent assistant for tech communities and enterprises, reducing redundancy and improving the efficiency of information retrieval.

Key Capabilities

  • Conversation Analysis: Identifies topics frequently discussed in tech communities
  • Community Health Evaluation: Assesses engagement, sentiment, and community wellbeing
  • Stack Overflow Integration: Searches for relevant technical questions and answers
  • Web Research: Conducts independent searches for up-to-date information

Architecture Overview

The system uses three main services, all deployable on GPU-enabled Kubernetes clusters:

1. Text Generation Inference (TGI)

Runs the large language model that powers the agent’s reasoning capabilities.

2. Text Embeddings Inference (TEI)

Generates vector embeddings for semantic similarity searches across conversations.

3. Chroma DB

A vector database that stores and retrieves conversation embeddings for the RAG (Retrieval-Augmented Generation) approach.

The ReAct Agent Pattern

Unlike linear reasoning methods like Chain-of-Thought, the ReAct pattern enables the agent to learn from its environment through a cycle of:

  1. Thought: The agent reasons about what to do next
  2. Action: The agent executes a tool or function
  3. Observation: The agent observes the result and incorporates it into its reasoning

This cycle continues until the agent reaches a satisfactory conclusion.

Tools Available to the Agent

Conversation Retriever Tool

Converts the RAG approach into a callable tool. When invoked, it performs vector similarity searches through the Chroma DB to find relevant past conversations.

# Simplified example of the retriever tool
def retrieve_conversations(query: str) -> List[Document]:
    embeddings = generate_embeddings(query)
    results = chroma_db.similarity_search(embeddings, k=5)
    return results

Stack Overflow Search Tool

Leverages the Stack Exchange API to find relevant technical questions and answers from Stack Overflow.

Web Search Tool

Integrates with Tavily Search API, which is purpose-built for LLM applications, to search the broader web for information.

Agent Workflow

When a user sends a query:

  1. The agent analyzes the query and determines which tools might be helpful
  2. It executes the appropriate tools (retriever, Stack Overflow, web search)
  3. Results from each tool are aggregated
  4. The agent generates a contextual response incorporating all gathered information
  5. The reasoning steps are visible for debugging and transparency

Benefits of On-Premise Deployment

Running InSightful on your own infrastructure offers several advantages:

  • Enhanced Data Security: All conversations stay within your organizational infrastructure
  • Customization: Configure hardware and software to your specific needs
  • Lower Latency: Faster processing without external API round-trips
  • Complete Control: Full ownership of security protocols and data handling

Getting Started

Prerequisites

  • GPU-enabled Kubernetes cluster or Docker machine
  • HuggingFace API credentials for model access
  • Python 3.9+ with LangChain framework

Deployment

The system can be deployed using Helm charts on Kubernetes, with separate deployments for TGI, TEI, and Chroma DB services.

Conclusion

InSightful demonstrates how AI agents can transform community management by:

  • Reducing repetitive questions through intelligent context retrieval
  • Providing accurate, sourced answers from multiple knowledge bases
  • Operating securely within enterprise infrastructure

The combination of ReAct reasoning, RAG architecture, and multi-tool access creates a powerful assistant that genuinely helps tech communities become more efficient.

For the complete implementation details and code, check out the InSightful repository on GitHub.

© 2025 Shreyas Mocherla