Chatbot

Our app's Chatbot feature serves as a convenient and accessible resource for users to inquire about health-related queries or obtain information about their recent scans. With natural language processing capabilities, users can seamlessly interact with the Chatbot by typing or speaking their questions, receiving prompt and accurate responses.

Whether seeking advice on symptoms, medication, or general health concerns, or wanting to understand the results of recent scans or medical tests, users can rely on the Chatbot to provide relevant information and guidance.

The Chatbot enhances user experience by offering personalized assistance, facilitating informed decision-making, and empowering users to take proactive steps towards managing their health effectively within the app's ecosystem.

Base Model

For our Chatbot feature, we have integrated the Mistral 7B LLM from Amazon Bedrock as the base model. The Mistral 7B LLM is a state-of-the-art language model specifically designed for natural language processing tasks. It leverages advanced machine learning techniques to understand and respond to user queries accurately and efficiently.

With the Mistral 7B LLM, our Chatbot is equipped with the ability to comprehend both typed and spoken questions, ensuring a seamless user experience. It has been trained on a vast amount of health-related data, enabling it to provide prompt and accurate responses to a wide range of health-related queries.

By utilizing the Mistral 7B LLM, our Chatbot enhances user experience by offering personalized assistance tailored to individual needs. It empowers users to make informed decisions about their health by providing relevant information and guidance on symptoms, medication, general health concerns, as well as explaining the results of recent scans or medical tests.

The integration of the Mistral 7B LLM into our Chatbot feature demonstrates our commitment to leveraging cutting-edge technologies to provide users with a convenient and accessible resource for managing their health effectively within our app's ecosystem.

Implementing RAG

To interface the Mistral 7B LLM with our Chatbot feature, we have utilized the LangChain library. LangChain provides a seamless integration between the language model and our application, allowing us to leverage its natural language processing capabilities effectively.

For efficient storage and retrieval of vectors, we have employed the Chroma DB database. With a chunk size of 300, Chroma DB ensures optimal performance and scalability for our vector store.

To enhance the user experience and provide personalized responses, we have implemented prompt templating. This allows us to dynamically generate prompts based on user input, ensuring accurate and context-aware responses from the Chatbot.

Additionally, we have utilized LLMChains from LangChain to create a chain of language models. This enables us to handle complex queries and generate more comprehensive and accurate responses.

By combining these technologies, we have created a powerful and efficient Chatbot feature that offers prompt and accurate responses to a wide range of health-related queries, empowering users to manage their health effectively within our app's ecosystem.

Third-party Integration

To enable seamless integration with third-party language models, our Chatbot feature supports the use of API keys from various LLM vendors such as Gemini, OpenAI, and Anthropic. This allows users to leverage their preferred language model while maintaining the same context within our application.

By providing the flexibility to choose from different LLM vendors, we ensure that users can access a wide range of language models and take advantage of their unique capabilities. Whether it's Gemini's advanced conversational abilities, OpenAI's powerful text generation, or Anthropic's contextual understanding, users can select the LLM that best suits their needs.

To use a specific LLM with our Chatbot feature, users can simply provide their API key from the desired vendor. This API key acts as the authentication mechanism, allowing our application to communicate with the chosen LLM and retrieve responses based on user queries.

By supporting third-party LLM integration, we empower users to harness the power of different language models and enhance their experience with our Chatbot feature. This flexibility ensures that users can access the most relevant and accurate information while interacting with our application.

Deployment and Scalability

For deployment, we have chosen to use FastAPI. FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use and efficient, making it a great choice for deploying our Chatbot feature.

With FastAPI, we can easily create and deploy our Chatbot API, allowing users to interact with the Chatbot through HTTP requests. FastAPI's asynchronous capabilities enable efficient handling of multiple requests, ensuring scalability and responsiveness.

Additionally, FastAPI provides automatic interactive documentation, making it easier for developers to understand and test the API endpoints. This feature-rich framework also supports features like request validation, authentication, and authorization, ensuring the security and reliability of our Chatbot deployment.

By leveraging FastAPI for deployment, we can ensure a robust and scalable infrastructure for our Chatbot feature, providing users with a seamless and efficient experience within our app's ecosystem.