MiniatureAgent_DSPy

** Open the code in Google Colab or Jupyter Notebook in case the preview flickers. **

Colab Link: https://colab.research.google.com/drive/1h75aBfqKs04HpBLzqKS3JrKG05zBt0bT?usp=sharing

Health Report Analysis DSPy Agent

An intelligent health report analysis agent built with DSPy that provides personalized insights, risk assessments, and recommendations based on microbiome health data.

Overview

This DSPy-powered agent analyzes health reports containing microbiome data to provide comprehensive insights about similarity scores, health scores, underrepresented species, and personalized health recommendations. The agent uses context-aware analysis with training data to deliver accurate, report-specific responses while maintaining strict validation to ensure queries are relevant to the available health data.

Key Features

Comprehensive Health Analysis: Analyzes microbiome data including similarity scores, health scores, and species composition
Context-Aware Responses: Uses training data examples to provide consistent, high-quality answers
Multi-Modal Analysis: Supports summary generation, species analysis, risk assessment, and personalized recommendations
Intelligent Question Classification: Automatically categorizes user questions into relevant health domains
Strict Validation: Ensures queries are relevant to available health report data
Modular Architecture: Built with DSPy's signature-based approach for maintainability and extensibility

Supported Analysis Types

The agent provides specialized analysis across multiple health domains:

Report Summarization: Comprehensive summaries of health report findings
Species Analysis: Detailed analysis of underrepresented bacterial species
Risk Assessment: Health implications and risk profiling based on microbiome data
Personalized Recommendations: Dietary, supplement, and lifestyle suggestions
Score Interpretation: Health score and similarity score explanations
Insights Generation: Key takeaways and actionable insights

Installation

Prerequisites

Python 3.8 or higher
DSPy framework
API access to a supported language model (OpenAI, Anthropic, etc.)

Setup Instructions

Clone the repository:

git clone https://github.com/yourusername/health-report-dspy-agent.git
cd health-report-dspy-agent

Install required dependencies:

pip install dspy
pip install numpy

Configure your language model:

import dspy

lm = dspy.LM('model_name', 
             api_key="your_api_key",
             api_base="",
             custom_llm_provider="")
dspy.configure(lm=lm, temperature=0.7)

Data Structure

The agent operates on structured health report data defined as follows:

@dataclass
class HealthReport:
    report_id: int
    similarity_score: int
    similarity_label: str
    underrepresented_species: List[str]
    health_score: float
    interpretation: str

Quick Start Guide

import dspy
from health_agent import HealthAgent, HealthReport

# Initialize the health analysis agent
agent = HealthAgent()

# Create a sample health report
report = HealthReport(
    report_id=12345,
    similarity_score=75,
    similarity_label="Good",
    underrepresented_species=["Bifidobacterium", "Lactobacillus"],
    health_score=8.2,
    interpretation="Your microbiome shows good diversity..."
)

# Generate analysis and recommendations
summary = agent.forward(report, "Can you summarize my health report?")
recommendations = agent.forward(report, "What dietary recommendations do you have?")
risk_analysis = agent.forward(report, "What are my health risks?")

Supported Query Types

The agent intelligently classifies and responds to various categories of health-related questions:

Summary and Overview Queries

"Summarize my health report"
"What are the key takeaways?"
"Provide an overview of my results"
"What are the top three insights?"

Microbiome Species Analysis

"Which bacteria are underrepresented?"
"What specific microbes should I boost?"
"Analyze my bacterial composition"
"Tell me about missing bacteria"

Risk Assessment Queries

"What are my health risks?"
"What health implications should I be aware of?"
"Are there concerning patterns in my data?"
"What does this mean for my health?"

Recommendation Requests

"What dietary changes should I make?"
"Do you recommend any supplements?"
"How can I improve my microbiome health?"
"What lifestyle modifications are suggested?"

Score Interpretation

"Explain my health score"
"What does my similarity score mean?"
"Interpret these numerical values"
"What is my similarity label?"

System Architecture

The agent employs a modular DSPy architecture with specialized components for different analysis tasks:

HealthAgent/
├── signatures/
│   ├── Report_Summarizer         # Generates comprehensive summaries
│   ├── Species_Analyzer          # Analyzes bacterial species data
│   ├── Risk_Analyzer            # Assesses health risks
│   ├── Recommendation_Generator  # Creates personalized recommendations
│   ├── Question_Valid           # Validates query relevance
│   ├── Health_Score             # Interprets health scores
│   └── Similarity_Score_Info    # Analyzes similarity metrics
├── validation/                  # Query validation logic
├── classification/              # Question categorization system
└── training_data/              # Contextual examples and patterns

Validation Framework

The agent implements a comprehensive validation system to ensure query relevance and maintain response quality:

Accepted Query Categories

Similarity scores and label interpretation
Health score analysis and implications
Underrepresented species identification and analysis
Risk assessment based on microbiome data
Evidence-based dietary and lifestyle recommendations
Supplement suggestions related to microbiome health
Retesting frequency and monitoring advice

Rejected Query Categories

General health advice unrelated to microbiome reports
Medical diagnosis or treatment recommendations
Personal or family medical history inquiries
Unrelated laboratory tests or medication advice
General wellness topics outside microbiome scope

Usage Examples

Comprehensive Report Analysis

question = "What are the most important findings from my microbiome analysis?"
response = agent.forward(report, question)
# Returns detailed summary focusing on key health indicators

Targeted Species Recommendations

question = "Which specific bacteria should I focus on improving?"
response = agent.forward(report, question)
# Provides targeted recommendations for underrepresented species

Risk Profile Assessment

question = "Based on my results, what health risks should I monitor?"
response = agent.forward(report, question)
# Delivers evidence-based risk assessment with actionable insights

Configuration and Customization

Training Data Requirements

The agent requires properly structured training datasets:

reports.json: Sample health reports for contextual reference
questioner.json: Question-answer pairs for response training

Question Pattern Configuration

question_patterns = {
    'summary': ['summary', 'summarize', 'brief', 'overview', 'takeaway'],
    'species': ['microbe', 'species', 'bacteria', 'underrepresented', 'boost'],
    'risk': ['risk', 'implications', 'diseases', 'condition', 'health implications'],
    'recommendations': ['recommendations', 'diet', 'supplement', 'lifestyle', 'advice']
}

Testing and Validation

Comprehensive testing capabilities are built into the system:

# Validate query relevance
is_valid, validation_message = agent.validate("What supplements should I consider?", report)

# Test question classification
question_category = agent.classify("Tell me about my bacterial diversity")

# Execute complete analysis pipeline
response = agent.forward(report, "How frequently should I retest my microbiome?")

Extending the Agent

Adding New Analysis Modules

Define a new DSPy signature:

class CustomAnalysis(dspy.Signature):
    """Custom analysis signature for specialized health insights"""
    input_data = dspy.InputField(desc="Input data description")
    analysis_result = dspy.OutputField(desc="Analysis output description")

Integrate into the HealthAgent architecture:

self.custom_analyzer = dspy.ChainOfThought(CustomAnalysis)

Update classification patterns and validation rules accordingly

Custom Validation Implementation

def implement_custom_validation(self, question: str, report: HealthReport) -> tuple[bool, str]:
    """
    Implement domain-specific validation logic
    Returns validation status and explanatory message
    """
    # Custom validation implementation
    return validation_result, explanation

Performance Considerations

Response Time: Optimized for real-time health report analysis
Accuracy: Leverages context-aware training data for consistent responses
Scalability: Modular architecture supports easy feature expansion
Reliability: Comprehensive validation ensures relevant, accurate responses

Contributing Guidelines

We welcome contributions to enhance the health analysis capabilities:

Fork the repository and create a feature branch
Implement enhancements following the established architecture patterns
Add comprehensive tests for new functionality
Ensure all validation rules are properly updated
Submit a pull request with detailed documentation

Documentation and Resources

Technical Notes

The agent is specifically designed for microbiome health report analysis
Structured health report data is required in the predefined format
Training data files must be properly configured for optimal performance
All analytical responses are generated based exclusively on provided health report data
The system maintains strict boundaries around medical advice and diagnosis

Future Development Roadmap

Support for additional microbiome health metrics and biomarkers
Integration capabilities with popular health tracking platforms
Enhanced data visualization and reporting features
Multi-language support for international deployment
RESTful API development for web application integration
Batch processing capabilities for multiple report analysis

Powered by DSPy Framework for Advanced Health Report Analysis

Note: This is just a miniature version of a multi-purpose agentic AI system, and is still under progress.

FilesExpand file tree

README.md

Latest commit

History