INTRODUCTION

To develop a system that extracts text data from a given website URL and utilizes it to generate answers for user queries using an AI model.

CHALLENGE's

Handling Diverse Website Structures : Labor-intensive & manual categorization of medical reports, prescriptions, and test results.
Data Quality and Consistency : Maintaining high data quality and consistency across the extraction and ingestion processes.
Data Conversion Accuracy : Ensuring the conversion process accurately maps JSON to Vectara’s required format.
Scalability : Ensuring the system can handle large-scale data extraction and processing without performance degradation.
API Integration : Seamlessly integrating OpenAI and Vectara APIs for smooth data processing and storage.

Project Scope

The project included Data Extraction, Data Ingestion, Data Management:

Extract content from websites based on specified criteria such as depth and link inclusion/exclusion.
Convert extracted data into a format compatible with Vectara and enrich it with additional information using OpenAI.
Store, manage, and query the enriched data effectively within Vectara.

Problem Statement

Data Extraction : To create complex Data Extraction module for extracting content from websites with varying structures and depths.
Data Conversion and Formatting : Converting extracted data into a format understandable by Vectara for efficient database creation and querying.
Enhanced Data Utilization : Generating useful insights using LLM model like summaries, facts, Q&A, and categories from the extracted data.

Tech Details

Node.js : For building the extraction module.
Crawler : For web scraping and data extraction.
Python : For the ingestion module to process and enrich data.
OpenAI : For converting data into Vectara format and generating additional insights.
Vectara : For storing, managing, and querying the enriched data.
APIs : APIs for real-time data synchronization

Solutions

Extraction Module : Utilize Crawlee with Node.js to scrape website content according to predefined rules and depth, outputting the data in JSON format.
Ingestion Module : Use Python to process the JSON, convert it into Vectara's format, and further enrich the data using OpenAI for generating summaries, facts, Q&A, and categories.

RESULTS

Data Collection and Scraping : Successfully extracted text data from websites
AI Model Integration : Seamlessly connected multiple data sources, reducing collection time.
User Interaction & Interface Development : Developed a user-friendly interface
Data Processing and Quality Enhancement : Cleaned the data to remove HTML tags, advertisements, and irrelevant content.

CONCLUSION

generation system offers a fast and reliable method for users to access information, thereby reducing the reliance on manual FAQs. The project highlights the potential of AI in automating information retrieval processes, suggesting applicability in various domains such as customer support and knowledge management.

The system successfully extracted and processed large volumes of text data, demonstrating the efficacy of web scraping techniques in gathering information. The integration of the AI model allowed for efficient and relevant responses to user queries, significantly enhancing the user experience. The AI-powered FAQ generation system offers a fast and reliable method for users to access information, thereby reducing the reliance on manual FAQs. The project highlights the potential of AI in automating information retrieval processes, suggesting applicability in various domains such as customer support and knowledge management.

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