InterLang: Technical Implementation of Streamlit Chatbot and LangChain FHIR Tool

Overview

In our previous post, we discussed the motivation for developing a chatbot agent with access to FHIR resources. In this post, we will dive into the high-level design aspects of integrating a Streamlit-based chat interface with a Java SpringBoot backend, and enabling a LangChain agent with access to FHIR (Fast Healthcare Interoperability Resources) via APIs.

Connecting Streamlit to Java SpringBoot

The Streamlit application acts as the frontend, providing an interactive chat interface. It communicates with the Java SpringBoot backend, which handles processing and responding to user inputs. Here's a high-level overview of this integration:

1. User Input Handling: The Streamlit app captures user input through a chat interface. This input is then sent to the Java SpringBoot backend via an HTTP POST request.

response = requests.post(BASE_URL, json={"user_input": prompt})

1. Backend Processing: The Java SpringBoot application receives this input, processes it, and generates a response. This process may involve accessing and manipulating FHIR resources.
2. Response Delivery: The response from the backend is sent back to the Streamlit app. Streamlit then displays this response in the chat interface, providing an interactive experience for the user.
3. State Management: Streamlit maintains a session state to keep track of the chat history. This allows for a persistent conversation flow, which is crucial for a chatbot experience.

LangChain Agent with FHIR Resources via API

The LangChain agent, integrated into the Java SpringBoot backend, leverages FHIR resources to provide healthcare-related information and services. The key components of this setup are:

FHIR Tools Service: This Java-based service is responsible for interacting with FHIR resources. It includes functionalities like fetching patient data, creating observations, and managing goals.

    @Tool("Fetches a FHIR Patient resource using a GET request by ID")
    Patient getPatientById(String patientId) {
        return client.read()
                .resource(Patient.class)
                .withId(patientId)
                .execute();
    }

Integration with LangChain: The LangChain agent, utilizing GPT-4, is integrated into the SpringBoot application. This agent has access to the FHIR Tools service, allowing it to perform actions like retrieving patient information or updating records based on user input.

this.assistant = AiServices.builder(Assistant.class)
                .chatLanguageModel(chatModel)
                .tools(this.fhirTools)
                .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
                .build();

Handling Conversational Context: The agent uses a MessageWindowChatMemory to maintain the context of the conversation. This is crucial for understanding the flow of the conversation and providing relevant responses.

API Key Management: Both the FHIR service and the OpenAI GPT-4 model require API keys for authentication. These keys are managed securely within the SpringBoot application.

Video Demo

Demo

Authors

*   Zacchaeus Chok
*   Varun Swaminathan
*   Gabriel Yang