AI powered Ayurvedic App.

Conducts health assessment, provides diagnosis along with treatment planning and medication recommendation.

1. Overall Architecture:
    - RAG (Retrieval-Augmented Generation) architecture
    - Built with Langchain, Langgraph, and GoogleGenerativeAI (Gemini API)
    - Developed in Google Colab notebook for prototyping
    - Uses Qdrant vector database (pre-populated with Ayurvedic knowledge)
2. Agent System:
    - Two-agent workflow
    
    A. Agent 1: Triage and Interview
    
    - Conducts dynamic, realistic interview
    - Creates detailed report on current scenario
    - Does not provide diagnosis
    - Interview process:
        1. Asks user what brought them to the app
        2. For each user response:
            - Checks vector database for context
            - Considers entire chat history
            - Formulates next question based on retrieved information
        3. Repeats until a comprehensive, relevant report is generated
        4. Sends report to Agent 2
    
    B. Agent 2: Diagnostics and Treatment Planning
    
    - Receives report from Agent 1
    - Provides diagnosis
    - Develops treatment plan
    - Recommends medications
    - Uses vector database for Ayurvedic knowledge retrieval
3. Key Components:
    - GoogleGenerativeAI with Gemini API for natural language processing
    - Qdrant vector database for storing and retrieving Ayurvedic knowledge
    - Langchain for building the language model pipeline
    - Langgraph for managing the agent workflow
4. Workflow:
    1. User initiates interaction
    2. Agent 1 conducts interview and generates report
    3. Report is sent to Agent 2
    4. Agent 2 analyzes report and provides diagnosis, treatment plan, and medication recommendations
    5. Results are presented to the user
5. Development Environment:
    - Google Colab notebook for prototyping and development

To achieve a realistic interview, Agent 1 employs a dynamic and context-aware approach. Here's a detailed explanation of how this process works:

1. Initialization:
    - The agent starts with a broad, open-ended question like "What brings you here today?" or "How can I help you?"
    - This allows the user to express their concerns in their own words.
2. Active Listening and Context Building:
    - As the user responds, the agent processes the input and begins building a context model.
    - It identifies key terms, symptoms, or concerns mentioned by the user.
3. Vector Database Querying:
    - The agent queries the Qdrant vector database using the identified key terms and concepts.
    - This retrieves relevant Ayurvedic knowledge and potential follow-up questions.
4. Natural Language Understanding:
    - Using the Gemini API, the agent analyzes the user's response for sentiment, urgency, and implicit information.
5. Dynamic Question Formulation:
    - Based on the retrieved information and NLU analysis, the agent formulates the next question.
    - Questions are tailored to probe deeper into the user's specific concerns.
    - The agent avoids redundant questions by keeping track of information already provided.
6. Contextual Memory:
    - The agent maintains a running summary of the conversation, updating it with each exchange.
    - This allows for reference to previously mentioned information, making the conversation feel more natural and connected.
7. Empathetic Responses:
    - The agent is programmed to recognize emotional cues and respond with appropriate empathy.
    - It may use phrases like "I understand that must be difficult" or "It's good that you're seeking help for this."
8. Clarification and Expansion:
    - If the user's responses are vague or incomplete, the agent asks for clarification or more details.
    - It might say, "Could you tell me more about when you first noticed this symptom?"
9. Holistic Approach:
    - In line with Ayurvedic principles, the agent asks about various aspects of the user's life, including diet, sleep patterns, stress levels, and daily routines.
10. Adaptive Questioning:
    - The agent adjusts the complexity and depth of questions based on the user's responses and perceived health literacy.
11. Non-Linear Conversation Flow:
    - While the agent has certain key areas it needs to cover, it allows the conversation to flow naturally, following the user's lead when appropriate.
12. Time and Relevance Management:
    - The agent is programmed to keep the interview focused and relevant, gently steering the conversation back on track if it diverges too far.
13. Comprehensive Coverage:
    - The agent ensures all relevant areas are covered before concluding the interview.
    - It may use a checklist approach internally but presents questions in a conversational manner.
14. Conclusion and Handover:
    - Once sufficient information is gathered, the agent summarizes the key points of the interview.
    - It informs the user that it will now compile a report for further analysis.

By combining these elements, Agent 1 can conduct an interview that feels natural, responsive, and thorough, mimicking the approach of a skilled Ayurvedic practitioner. The use of AI and the vector database allows for a vast knowledge base to inform the questioning, while the natural language processing capabilities ensure a human-like interaction.

Let me elaborate on how the agent utilizes the entire chat context and continuously refers to the vector database to guide the interview process:

1. Continuous Context Update:
    - After each user response, the agent updates its understanding of the entire conversation context.
    - This context includes not just the latest response, but the cumulative information from the entire chat history.
2. Vector Database Querying:
    - With each context update, the agent formulates a query based on the entire conversation context.
    - This query is sent to the Qdrant vector database.
3. Relevant Information Retrieval:
    - The vector database returns the most relevant Ayurvedic knowledge, potential questions, and interview strategies based on the current context.
4. Dynamic Interview Path:
    - Using the retrieved information, the agent determines the most appropriate direction for the interview.
    - This could involve exploring a new area of concern, diving deeper into a previously mentioned issue, or shifting focus based on newly understood connections.
5. Adaptive Questioning Strategy:
    - The agent uses the vector database's suggestions to formulate questions that are most relevant to the user's specific situation.
    - This ensures that the interview doesn't follow a pre-determined script, but rather adapts in real-time to the user's unique health profile.
6. Contextual Understanding:
    - As the interview progresses, the agent builds a more comprehensive understanding of the user's health situation.
    - This understanding is continually refined with each database query and user response.
7. Holistic Approach Maintenance:
    - The vector database helps the agent maintain a holistic Ayurvedic approach by suggesting questions about different aspects of the user's life that may not be immediately obvious but are relevant in Ayurvedic practice.
8. Knowledge-Guided Probing:
    - When the user mentions symptoms or conditions, the agent queries the database for related Ayurvedic concepts.
    - This allows the agent to ask informed follow-up questions that a knowledgeable Ayurvedic practitioner would ask.
9. Conversation Flow Management:
    - The agent uses the database's input to manage the flow of the conversation, ensuring all relevant areas are covered while maintaining a natural dialogue.
10. Relevance Checking:
    - With each potential question, the agent cross-references with the vector database to ensure its relevance to the current context of the conversation.
11. Insight Generation:
    - As the interview progresses, the agent uses the database to generate insights about potential connections between different pieces of information provided by the user.
12. Interview Progression Tracking:
    - The agent tracks the progress of the interview against Ayurvedic diagnostic criteria stored in the vector database, ensuring comprehensive coverage.

By constantly referring to the vector database and considering the entire chat context, the agent can conduct a highly personalized, knowledge-driven interview that feels natural and adapts in real-time to the user's responses. This approach allows for a dynamic interview process that can uncover relevant health information even if the user doesn't initially present it, mimicking the intuitive and holistic approach of an experienced Ayurvedic practitioner.

Now i want you to design a system/algorithm to make the Agent -1 super efficient in conducting the interview.