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AI-Powered Text Generation from Data Prompts

Introduction

Golden Eagle IT Technologies partnered with a client to build a Retrieval-Augmented Generation (RAG) pipeline designed for analysing and summarising data from case studies across multiple file formats. The objective was to help the client access meaningful insights through a seamless process of data loading, embedding, storage, and retrieval, supported by a user-friendly interface.

Problem Statement

The client needed a solution that could:

  • Analyse & Summarise Data : Extract insights from large volumes of case study documents.
  • Handle Multiple File Formats : Support CSV, Excel, PDF, and other formats.
  • Embed Data for Retrieval : Convert processed data into high-quality embeddings.
  • Enable Fast Querying : Retrieve context-aware and relevant responses.
  • Provide User Authentication & Project Management : Allow multiple users to manage their data securely.
  • Deliver a Smooth User Experience : Build an intuitive system for interaction with data.

Solution

Golden Eagle IT Technologies developed a robust RAG pipeline that integrates modern AI, NLP, and database technologies to ensure efficient data summarisation and query response.

  • File Upload & Processing : Implemented FastAPI endpoints for file upload and automated processing.
  • Data Splitting & Chunking : Used recursive chunking for coherent text segmentation.
  • Embedding Creation : Generated semantic embeddings using Hugging Face sentence-transformers.
  • Vector Storage : Stored embeddings in PGVector (PostgreSQL with pgvector extension) for fast similarity searches.
  • Query Execution : Enabled real-time query interpretation and retrieval with OpenAI’s LLM.
  • Prompt Engineering : Applied ChatPromptTemplate for context-aware query results.

This solution ensured that the client could upload documents, query them, and instantly receive accurate, summarised insights.

Feature List

01
Multi-format File Loading

(CSV, Excel, PDF, more).

02
Efficient File Splitting & Chunking

for embedding-ready text.

03
Semantic Embedding Creation

with all-MiniLM-L6-v2.

04
Vector Store Integration

with PGVector.

05
FastAPI Backend

with POST (upload) & GET (query) endpoints.

06
Similarity Search

for relevant result retrieval.

07
Prompt Engineering

with ChatPromptTemplate.

08
LLM Integration

with OpenAI for accurate responses.

Tech and Solution Stack

File Loading

LangChain Community Loaders (CSVLoader, UnstructuredExcelLoader, PyMuPDFLoader).

Data Processing

RecursiveCharacterTextSplitter.

Embedding Model

Hugging Face sentence-transformers (all-MiniLM-L6-v2).

Vector Database

PostgreSQL with pgvector extension.

Backend Framework

FastAPI.

Query Handling

Similarity Search + ChatPromptTemplate.

LLM Integration

OpenAI API

Hosting Platform

Azure App Service + Azure Database for PostgreSQL Flexible Server.

Hosting

The pipeline was deployed on Azure for scalability and reliability:

  • Azure App Service : Hosting the RAG application with autoscaling capabilities.
  • Azure PostgreSQL Flexible Server : Storing embeddings with pgvector extension.
  • Key Advantages : Auto-scaling for traffic spikes, Fully managed infrastructure, High availability & redundancy, Enterprise-grade security (SSL/TLS, private networking), Cost efficiency with pay-as-you-go pricing

Maintenance

Golden Eagle IT Technologies also ensured a long-term maintenance plan for the RAG pipeline:

  • Database Management : Regular monitoring, vacuuming, and backups.
  • Model Updates : Refreshing embeddings with new model versions.
  • Performance Monitoring : Logging, alerts, and Application Insights for API performance.
  • Scalability : Scaling Azure resources as data and traffic grow.
  • Security : Regular patches, encryption, and access controls.
  • User Feedback Loop : Continuous improvements based on real usage.

Team & Support

While the implementation was primarily handled by Golden Eagle IT Technologies, team lead guidance played a pivotal role in overcoming technical blockers and ensuring timely delivery. The collaborative approach ensured:

  • Timely troubleshooting of complex challenges.
  • Mentorship in refining the RAG pipeline’s architecture.
  • Smooth integration with Azure services.

This strong teamwork enabled successful deployment and knowledge transfer for long-term client benefits.

Conclusion

By designing and deploying a custom RAG pipeline, Golden Eagle IT Technologies helped the client seamlessly analyse, summarise, and retrieve insights from multi-format case study data. With Azure-hosted scalability, robust vector search, and OpenAI-powered responses, the client now has a powerful knowledge management tool that ensures efficiency, accuracy, and scalability.