Building a RAG-based Query Resolution System with LangChain

This article details building a Retrieval-Augmented Generation (RAG) system for efficient query resolution, using LangChain, ChromaDB, and CrewAI. Manually handling the large volume of queries faced by modern businesses is inefficient. This AI-powered solution offers fast, accurate, and scalable responses.

Table of Contents

• Why an AI-Powered Query Resolution System?
• Understanding the RAG Workflow
• Building a RAG-Based Query Resolution System
• Implementation Details
• Future Enhancements
• Conclusion
• Frequently Asked Questions

Why an AI-Powered Query Resolution System?

Manual query responses are slow and inconsistent. Businesses need rapid, accurate information access to meet customer expectations. An AI system automates this process, boosting productivity and decision-making across various sectors (customer support, sales, finance, healthcare, e-commerce).

Understanding the RAG Workflow

The RAG system operates in three phases:

1. Indexing: Documents are processed, chunked, converted into vector embeddings using an AI model, and stored in a vector database (e.g., ChromaDB).
2. Retrieval: A user's query is vectorized, and the system searches the database for the most relevant chunks.
3. Generation: The retrieved chunks are combined with the original query, and a large language model (LLM) generates a response.

Building a RAG-Based Query Resolution System

This article demonstrates a simplified RAG system for answering learner queries using an AI agent. Data selection is crucial; the author experimented with various data types (PowerPoint slides, FAQs, past discussions, course video subtitles) before settling on subtitles as the most effective source for providing relevant, structured content.

The system comprises three components:

1. Subtitle Processing: Extracts and processes text from SRT files, storing embeddings in ChromaDB.
2. Retrieval: Retrieves relevant course materials based on learner queries.
3. Query Answering Agent: Uses CrewAI to generate responses.

Implementation Details

1. Library Imports: pysrt, langchain (text splitting, embeddings, vectorstores), crewai, pandas, ast, os, tqdm are imported.

2. Environment Setup: The OpenAI API key and model name are set as environment variables.

3. Extracting and Storing Subtitle Data: The system iterates through course folders, extracts text from SRT files using pysrt, chunks the text using RecursiveCharacterTextSplitter, generates embeddings with OpenAIEmbeddings, and stores them in ChromaDB. Cost estimation for token usage is included.

4. Querying and Responding to Learner Queries: A retrieve_course_materials function uses similarity search in ChromaDB to retrieve relevant content, filtered by course.

5. Implementing the AI Query Answering Agent: A CrewAI agent ("Learning Support Specialist") is defined with a specific role and backstory. A task is defined to handle queries, incorporating retrieved context and past discussions. The CrewAI instance is initialized, and a function (reply_to_query) iterates through learner queries in a CSV file, generating responses using the agent. Error handling is included.

Future Enhancements

• Incorporate a structured FAQ system.
• Add image processing capabilities.
• Improve image column boolean logic.
• Explore semantic chunking and other chunking techniques.

Conclusion

This RAG system, built with LangChain, ChromaDB, and CrewAI, automates learner support efficiently. It improves scalability, retrieval, and response quality. Future improvements will enhance its functionality and accuracy.

Frequently Asked Questions

The FAQs section answers questions about LangChain, ChromaDB, CrewAI, OpenAI embeddings, subtitle processing, handling multiple queries, and future improvements, mirroring the original content.

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