E-Prescription on Chat – Smart Prescription Analyser
A document-based chatbot enables users to upload their medical files, extract content, and chat with the AI-enabled bot for instant prescription insights and data clarity.
Team
2–3 Members
Duration
2 Months
Industry
Healthcare Technology
Product Overview
Prescription Chatbot is integrated into the backend healthcare software utility, which transforms the complicated medical document into an easy, searchable, and chat-enabled format. It supports multi-format uploads and allows interaction directly with prescription data through a smart interface. The vector-based document embedding and LLM integration are integrated in the solution, making it capable of delivering quick, context-aware insights. However, enabling streamlined informed decision making while saving time and manual efforts.
How It Works
The Prescription chatbot that understands and translates uploaded media files via API and Longflow Pipeline. When the documents are uploaded, these are parsed, chunked, and converted into vector embeddings stored in AstraDB. Later, chatbot uses OpenAI’s LLM to respond to documents by translating complex language in real-time. Also, delivering context-rich and prescription-specific insights.
User Cases
- EHR & Medical Portals – Integrates with hospital systems to enable smart prescription queries.
- Telemedicine Platforms – Offers real-time insights during virtual consultations.
- Pharmacy Software – Assists pharmacists in validating prescriptions quickly.
- Patient Self-Service – Allows users to understand and verify their prescriptions independently.
- Medical Document Analytics – Enables data extraction from clinical files for research and review.
Benefits
- 99% API Upload Reliability – Ensures uninterrupted ingestion of medical documents.
- 95% Contextual Response Accuracy – Accurate, AI-driven answers to prescription-related queries.
- 92% Manual Effort Reduction – Cuts down the need for manual parsing and review.
- 100% File Compatibility – Supports .pdf, .docx, .csv, .txt formats natively.
- Time-Efficient Interaction – Enables doctors and patients to make quick, informed decisions.
- Flask API Gateway – Facilitates secure document upload and processing initiation.
- Custom Langflow Node – Parses and chunks documents before embedding.
- OpenAI Integration – Generates responses based on stored vectors and live prompts.
- AstraDB Vector Store – Powers document search and retrieval via embeddings.
- Chat Interface – Provides a conversational UI for document-based queries.
Challenges
Multi-Format File Handling
The Langflow’s default settings weren’t supporting .pdf, .docx, .txt, and .csv files appropriately.
Unstable API File Uploads
The file uploads through APIs were failing or requiring manual intervention.
Unstructured Text Extraction
Different content file formats needed manual cleaning before processing.
Data Flow to Vector DB
The integration of Langflow with AstraDB for structured embeddings did not provide direct support.
Solutions
Flask-Based File Upload System
Seamless multi-file uploads with automatic routing to Langflow via API.
Custom Langflow Node Creation
Developed a node to extract, chunk, and prepare file data in Langflow flows.
Dual Flow Architecture
Build separate flows for storage and chat to ensure a streamlined process and retrieval.
Optimized AstraDB Integration
Used OpenAI embeddings with chunking logic to ensure accurate vector storage and quick retrieval.
Outcome
Technology Stack
Features
The solution is embedded with automated document processing and real-time chat capabilities, while including some other key features:
Multi-format file upload support (.pdf, .docx, .csv, .txt)
API-based document ingestion via Flask
Custom Langflow node for file reading and chunking
OpenAI embedding integration for vector storage
AstraDB-powered document search and retrieval
Real-time chat responses generated using LLMs
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