RFP Analyzer Architecture

This document provides a comprehensive overview of the RFP Analyzer application architecture, including component diagrams, data flow, and Azure resource topology.

System Overview
Application Architecture
Component Architecture
Multi-Agent System
Azure Infrastructure
Data Flow
Security Architecture

System Overview

RFP Analyzer is a cloud-native application that leverages Azure AI services to automate the evaluation of vendor proposals against RFP requirements. The system uses a multi-agent architecture powered by Azure OpenAI to provide intelligent, consistent, and scalable proposal evaluation.

Architecture Principles

Cloud-Native: Designed for Azure Container Apps with managed scaling
Serverless AI: Leverages Azure AI services without managing infrastructure
Secure by Default: Uses managed identities and RBAC for authentication
Observable: Integrated monitoring with Application Insights and Log Analytics

Application Architecture

flowchart TB
    subgraph UI["🖥️ User Interface"]
        direction LR
        ST["Streamlit Web Application"]
        subgraph Panels["Application Panels"]
            UP["📤 Upload Panel"]
            EP["⚙️ Extract Panel"]
            EV["🤖 Evaluate Panel"]
            EX["📊 Export Panel"]
        end
        ST --> Panels
    end

    subgraph APP["⚡ Application Layer"]
        direction TB
        subgraph DP["Document Processor"]
            CU["Content Understanding Client"]
            DI["Document Intelligence Client"]
        end
        
        subgraph MAS["Multi-Agent Scoring System"]
            CEA["Criteria Extraction Agent"]
            PSA["Proposal Scoring Agent"]
            CA["Comparison Agent"]
            CEA --> PSA --> CA
        end
        
        subgraph SS["Support Services"]
            PQ["Processing Queue"]
            LC["Logging Config"]
            RG["Report Generator"]
        end
    end

    subgraph AZURE["☁️ Azure AI Services"]
        direction LR
        AOA["Azure OpenAI<br>(GPT-4.1+)"]
        ADI["Azure Document<br>Intelligence"]
        ACU["Azure AI Content<br>Understanding"]
    end

    UI --> APP
    APP --> AZURE
    
    style UI fill:#e1f5fe,stroke:#01579b
    style APP fill:#f3e5f5,stroke:#4a148c
    style AZURE fill:#e8f5e9,stroke:#1b5e20

Component Architecture

Core Components

1. Streamlit Web Application (`main.py`)

The main entry point providing an interactive web interface:

flowchart TB
    subgraph StreamlitApp["Streamlit Application"]
        subgraph SSM["Session State Manager"]
            DS["Document Storage"]
            ER["Extraction Results"]
            EVR["Evaluation Results"]
            UIS["UI State"]
        end
        
        subgraph Steps["Workflow Steps"]
            S1["Step 1: Upload<br>• RFP file<br>• Proposals<br>• Preview"]
            S2["Step 2: Extract<br>• Service selection<br>• Progress<br>• Results"]
            S3["Step 3: Evaluate<br>• Criteria extraction<br>• Proposal scoring<br>• Vendor comparison<br>• Export options"]
        end
        
        SSM --> Steps
        S1 --> S2 --> S3
    end
    
    style StreamlitApp fill:#fff3e0,stroke:#e65100

2. Document Processor (`document_processor.py`)

Orchestrates document extraction across multiple Azure AI services:

flowchart TB
    subgraph DocProcessor["Document Processor"]
        subgraph Selector["Service Selector"]
            CUS["ExtractionService.CONTENT_UNDERSTANDING"]
            DIS["ExtractionService.DOCUMENT_INTELLIGENCE"]
        end
        
        subgraph Clients["Client Layer"]
            CUC["Content Understanding Client<br>• Analyzer API<br>• Multi-modal<br>• Markdown output"]
            DIC["Document Intelligence Client<br>• Layout model<br>• Pre-built models<br>• Markdown output"]
        end
        
        CUS --> CUC
        DIS --> DIC
        
        Formats["📄 Supported Formats:<br>PDF, DOCX, PNG, JPG, JPEG, BMP, TIFF"]
    end
    
    style DocProcessor fill:#e3f2fd,stroke:#1565c0

3. Multi-Agent Scoring System (`scoring_agent_v2.py`)

Implements the AI-powered evaluation using specialized agents:

flowchart TB
    subgraph ScoringSystem["Scoring Agent V2 System"]
        subgraph Agent1["Criteria Extraction Agent"]
            I1["Input: RFP Document (Markdown)"]
            O1["Output: ExtractedCriteria<br>• rfp_title<br>• rfp_summary<br>• criteria[] with weights<br>• evaluation_guidance"]
            I1 --> O1
        end
        
        subgraph Agent2["Proposal Scoring Agent"]
            I2["Input: Proposal + ExtractedCriteria"]
            O2["Output: ProposalEvaluationV2<br>• total_score<br>• criterion_scores[]<br>• strengths / weaknesses<br>• recommendation"]
            I2 --> O2
        end
        
        Agent1 --> Agent2
        
        subgraph Models["Pydantic Models"]
            M1["ScoringCriterion"]
            M2["ExtractedCriteria"]
            M3["CriterionScore"]
            M4["ProposalEvaluationV2"]
        end
    end
    
    style ScoringSystem fill:#fce4ec,stroke:#880e4f

4. Comparison Agent (`comparison_agent.py`)

Compares multiple vendor evaluations and generates comparative analysis:

flowchart TB
    subgraph CompAgent["Comparison Agent"]
        Input["Input: List[ProposalEvaluationV2]"]
        
        subgraph Engine["Analysis Engine"]
            E1["1. Rank vendors by total score"]
            E2["2. Compare performance by criterion"]
            E3["3. Identify patterns across vendors"]
            E4["4. Generate recommendations"]
            E1 --> E2 --> E3 --> E4
        end
        
        subgraph Output["Output: ComparisonResult"]
            VR["vendor_rankings: List[VendorRanking]"]
            CC["criterion_comparisons: List[CriterionComparison]"]
            WS["winner_summary"]
            CI["comparison_insights"]
            SR["selection_recommendation"]
            RC["risk_comparison"]
        end
        
        subgraph Reports["Report Generation"]
            R1["📄 generate_word_report()"]
            R2["📊 generate_full_analysis_report()"]
            R3["📋 CSV export"]
            R4["🔧 JSON export"]
        end
        
        Input --> Engine --> Output --> Reports
    end
    
    style CompAgent fill:#e8eaf6,stroke:#283593

Multi-Agent System

The evaluation pipeline uses a sequential multi-agent architecture:

flowchart TB
    subgraph Inputs["📥 Inputs"]
        RFP["RFP Document"]
        VP["Vendor Proposals"]
    end
    
    subgraph Agent1["🔍 AGENT 1: Criteria Extraction"]
        A1D["• Analyzes RFP requirements<br>• Identifies evaluation criteria<br>• Assigns weights total = 100%<br>• Provides scoring guidance"]
        A1M["Model: Azure OpenAI GPT-4.1+"]
        A1O["Output: ExtractedCriteria"]
    end
    
    EC["📋 Extracted Criteria<br>JSON/Pydantic"]
    
    subgraph Agent2["📊 AGENT 2: Proposal Scoring - Parallel"]
        direction LR
        A2A["Scoring Agent<br>Vendor A<br>• Evaluates proposal<br>• Scores per criterion<br>• Provides evidence"]
        A2B["Scoring Agent<br>Vendor B<br>• Evaluates proposal<br>• Scores per criterion<br>• Provides evidence"]
        A2N["Scoring Agent<br>Vendor N<br>• Evaluates proposal<br>• Scores per criterion<br>• Provides evidence"]
    end
    
    subgraph Agent3["🏆 AGENT 3: Comparison"]
        A3D["• Ranks all vendors by score<br>• Compares criterion performance<br>• Identifies best/worst performers<br>• Generates recommendations<br>• Assesses comparative risks"]
        A3M["Model: Azure OpenAI GPT-4.1+"]
        A3O["Output: ComparisonResult"]
    end
    
    subgraph Outputs["📤 Final Reports"]
        FR1["📄 Word documents"]
        FR2["📊 CSV exports"]
        FR3["🔧 JSON data"]
    end
    
    RFP --> Agent1
    VP --> EC
    Agent1 --> EC
    EC --> A2A & A2B & A2N
    A2A & A2B & A2N --> Agent3
    Agent3 --> Outputs
    
    style Agent1 fill:#bbdefb,stroke:#1976d2
    style Agent2 fill:#c8e6c9,stroke:#388e3c
    style Agent3 fill:#fff9c4,stroke:#f9a825

Agent Communication Pattern

flowchart TB
    subgraph Framework["Agent Framework Integration"]
        subgraph Client["AzureOpenAIResponsesClient"]
            C1["• Structured output generation"]
            C2["• Pydantic model integration"]
            C3["• Automatic JSON schema generation"]
            C4["• Response validation"]
        end
        
        subgraph Auth["DefaultAzureCredential"]
            A1["1. Managed Identity (in Azure)"]
            A2["2. Azure CLI (local development)"]
            A3["3. Environment variables"]
        end
        
        Client --> Auth
    end
    
    style Framework fill:#f5f5f5,stroke:#616161

Azure Infrastructure

Resource Topology

flowchart TB
    subgraph Subscription["☁️ Azure Subscription"]
        subgraph RG["📁 Resource Group: rg-{env-name}"]
            subgraph AIFoundry["🧠 Azure AI Foundry Account - AIServices"]
                direction TB
                Capabilities["Capabilities:<br>• OpenAI Language Model API<br>• Form Recognizer<br>• Content Understanding"]
                
                subgraph Models["Model Deployments"]
                    GPT52["gpt-5.2<br>GlobalStd<br>100K TPM"]
                    GPT41["gpt-4.1<br>GlobalStd<br>100K TPM"]
                    GPT41M["gpt-4.1-mini<br>GlobalStd<br>100K TPM"]
                    EMB["text-embedding-3-large<br>GlobalStd<br>300K TPM"]
                end
                
                subgraph Project["AI Foundry Project"]
                    AppInsConn["App Insights Connection<br>Telemetry integration"]
                end
            end
            
            subgraph Monitoring["📊 Monitoring Stack"]
                LAW["Log Analytics<br>Workspace<br>• Container logs<br>• AI service logs<br>• Custom metrics"]
                AI["Application Insights<br>• Performance metrics<br>• Request tracing<br>• Exception tracking<br>• Custom events"]
                Dashboard["Application Insights Dashboard"]
                AI --> LAW
            end
            
            subgraph Container["🐳 Container Platform"]
                ACR["Container Registry<br>• rfp-analyzer image<br>• SKU: Standard"]
                
                subgraph CAE["Container Apps Environment"]
                    CA["rfp-analyzer<br>Container App<br>• Port: 8501<br>• CPU: 2 cores<br>• Memory: 4Gi<br>• Scale: 1-10"]
                end
                
                ACR --> CA
            end
            
            MI["🔐 User-Assigned Managed Identity<br>• Azure AI Developer<br>• Cognitive Services User<br>• AcrPull"]
        end
    end
    
    MI -.-> AIFoundry
    MI -.-> ACR
    CA -.-> AIFoundry
    
    style Subscription fill:#e3f2fd,stroke:#1565c0
    style AIFoundry fill:#fff3e0,stroke:#ef6c00
    style Monitoring fill:#e8f5e9,stroke:#2e7d32
    style Container fill:#fce4ec,stroke:#c2185b

Infrastructure as Code (Bicep)

flowchart TB
    subgraph Infra["📁 infra/"]
        Main["main.bicep<br>subscription scope"]
        Params["main.parameters.json"]
        Resources["resources.bicep"]
        Abbr["abbreviations.json"]
        
        subgraph Modules["modules/"]
            Fetch["fetch-container-image.bicep"]
        end
        
        subgraph Hooks["hooks/"]
            PostSh["postprovision.sh"]
            PostPs["postprovision.ps1"]
        end
    end
    
    style Infra fill:#f5f5f5,stroke:#424242

Bicep Module Dependencies

flowchart TB
    Main["main.bicep<br>subscription scope"]
    
    Main --> RG["rfpResourceGroup<br>AVM module"]
    
    RG --> Resources["resources.bicep<br>resource group scope"]
    
    Resources --> Monitoring["monitoring<br>AVM pattern"]
    Resources --> Identity["rfpAnalyzerIdentity<br>AVM module"]
    Resources --> Foundry["foundryAccount<br>AVM module"]
    Resources --> Project["foundryProject<br>native resource"]
    Resources --> Registry["containerRegistry<br>AVM module"]
    Resources --> CAEnv["containerAppsEnvironment<br>AVM module"]
    Resources --> FetchImg["rfpAnalyzerFetchLatestImage<br>custom module"]
    Resources --> ContainerApp["rfpAnalyzer<br>AVM module"]
    Resources --> Roles["Role Assignments<br>AVM modules"]
    
    Monitoring --> LAW["Log Analytics Workspace"]
    Monitoring --> AppIns["Application Insights"]
    Monitoring --> Dash["Dashboard"]
    
    Identity --> UAMI["User-Assigned Managed Identity"]
    
    Foundry --> AIS["AIServices Account"]
    Foundry --> Deploy["Model Deployments"]
    
    Project --> AIC["App Insights Connection"]
    
    ContainerApp --> CA["Container App"]
    
    Roles --> R1["Azure AI Developer"]
    Roles --> R2["Cognitive Services User"]
    
    Main --> Outputs["Outputs"]
    Outputs --> O1["AZURE_CONTAINER_REGISTRY_ENDPOINT"]
    Outputs --> O2["AZURE_RESOURCE_RFP_ANALYZER_ID"]
    Outputs --> O3["AZURE_OPENAI_ENDPOINT"]
    Outputs --> O4["AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT"]
    Outputs --> O5["AZURE_CONTENT_UNDERSTANDING_ENDPOINT"]
    
    style Main fill:#bbdefb,stroke:#1976d2
    style Resources fill:#c8e6c9,stroke:#388e3c

Data Flow

Document Processing Flow

flowchart TB
    Upload["👤 User uploads document(s)"]
    
    subgraph DocProc["Document Processor"]
        V["1. Validate file type<br>PDF, DOCX, PNG, JPG, etc."]
        R["2. Read file bytes into memory"]
        S["3. Select extraction service<br>based on user choice"]
    end
    
    Upload --> DocProc
    
    DocProc --> CU & DI
    
    subgraph CU["Content Understanding"]
        CU1["POST /contentunderstanding/analyzer"]
        CU2["• Create analyzer"]
        CU3["• Upload document"]
        CU4["• Poll for results"]
        CU5["• Get markdown output"]
    end
    
    subgraph DI["Document Intelligence"]
        DI1["POST /documentModels/prebuilt-layout:analyze"]
        DI2["• Submit for analysis"]
        DI3["• Poll for results"]
        DI4["• Extract markdown"]
    end
    
    CU & DI --> Output
    
    subgraph Output["📄 Extracted Markdown"]
        O1["Stored in session:"]
        O2["• RFP content"]
        O3["• Proposal contents[]"]
    end
    
    style DocProc fill:#e3f2fd,stroke:#1565c0
    style CU fill:#fff3e0,stroke:#ef6c00
    style DI fill:#e8f5e9,stroke:#2e7d32

Evaluation Pipeline Flow

flowchart TB
    subgraph Session["Session State"]
        RFP["RFP Markdown"]
        P1["Proposal 1 Markdown"]
        PN["Proposal N Markdown"]
    end
    
    RFP --> CEA
    
    subgraph CEA["Criteria Extraction Agent"]
        CEA1["Azure OpenAI Call<br>structured output"]
    end
    
    CEA --> EC["ExtractedCriteria<br>Pydantic model"]
    
    EC --> SA1 & SAN
    P1 --> SA1
    PN --> SAN
    
    subgraph Scoring["Parallel Scoring"]
        SA1["Scoring Agent<br>Proposal 1<br>Azure OpenAI"]
        SAN["Scoring Agent<br>Proposal N<br>Azure OpenAI"]
    end
    
    SA1 --> EV1["Evaluation V2<br>Vendor 1"]
    SAN --> EVN["Evaluation V2<br>Vendor N"]
    
    EV1 & EVN --> CA
    
    subgraph CA["Comparison Agent"]
        CA1["• Rank vendors"]
        CA2["• Compare criteria"]
        CA3["• Recommend winner"]
    end
    
    CA --> CR
    
    subgraph CR["ComparisonResult"]
        CR1["📄 Word documents"]
        CR2["📊 CSV comparison"]
        CR3["🔧 JSON data"]
    end
    
    style Session fill:#f3e5f5,stroke:#7b1fa2
    style Scoring fill:#e8f5e9,stroke:#388e3c
    style CA fill:#fff9c4,stroke:#f9a825

Security Architecture

Authentication Flow

flowchart TB
    subgraph DAC["DefaultAzureCredential"]
        direction TB
        Chain["Credential Chain (tried in order):"]
        
        C1["1. EnvironmentCredential<br>AZURE_CLIENT_ID, AZURE_TENANT_ID,<br>AZURE_CLIENT_SECRET"]
        C2["2. ManagedIdentityCredential ◀️ Used in Azure<br>User-Assigned Managed Identity<br>AZURE_CLIENT_ID env var"]
        C3["3. AzureCliCredential ◀️ Used in local dev<br>az login session"]
        C4["4. AzurePowerShellCredential"]
        C5["5. VisualStudioCodeCredential"]
        
        Chain --> C1 --> C2 --> C3 --> C4 --> C5
    end
    
    DAC --> Token
    
    subgraph Token["Token Acquisition"]
        T1["Scope: https://cognitiveservices.azure.com/.default"]
        T2["Token used for:<br>• Azure OpenAI API calls<br>• Content Understanding API calls<br>• Document Intelligence API calls"]
    end
    
    style DAC fill:#e3f2fd,stroke:#1565c0
    style Token fill:#e8f5e9,stroke:#2e7d32

RBAC Configuration

flowchart TB
    MI["🔐 User-Assigned Managed Identity<br>rfp-analyzer-identity"]
    
    MI --> R1 & R2 & R3
    
    subgraph Roles["Role Assignments"]
        R1["Azure AI Developer<br><br>Scope: Resource Group<br><br>Allows:<br>• Create/manage AI resources<br>• Deploy models"]
        R2["Cognitive Services User<br><br>Scope: Resource Group<br><br>Allows:<br>• Use AI services<br>• Make API calls"]
        R3["AcrPull<br><br>Scope: Container Registry<br><br>Allows:<br>• Pull images"]
    end
    
    subgraph Security["🛡️ Security Features"]
        S1["• No stored credentials in application code"]
        S2["• Keys rotation handled by Azure"]
        S3["• Audit logging via Azure Activity Log"]
        S4["• Network isolation available (private endpoints)"]
        S5["• disableLocalAuth: true on AI Services account"]
    end
    
    style MI fill:#fff3e0,stroke:#ef6c00
    style Roles fill:#e3f2fd,stroke:#1565c0
    style Security fill:#e8f5e9,stroke:#2e7d32

Deployment Architecture

Azure Developer CLI (azd) Workflow

flowchart TB
    subgraph AZD["azd up Workflow"]
        S1["Step 1: Initialize"]
        S1D["azd init<br>• Creates .azure/ directory<br>• Selects subscription and location<br>• Creates environment configuration"]
        
        S2["Step 2: Provision Infrastructure"]
        S2D["azd provision<br>• Deploys main.bicep at subscription scope<br>• Creates resource group<br>• Provisions all Azure resources<br>• Configures role assignments<br>• Runs postprovision hooks"]
        
        S3["Step 3: Build & Deploy Application"]
        S3D["azd deploy<br>• Builds Docker image remote build<br>• Pushes to Azure Container Registry<br>• Updates Container App with new image"]
        
        S4["Step 4: Output"]
        S4D["Environment variables and URLs<br>• Application URL<br>• Azure OpenAI endpoint<br>• Content Understanding endpoint<br>• Document Intelligence endpoint"]
        
        S1 --> S1D --> S2 --> S2D --> S3 --> S3D --> S4 --> S4D
    end
    
    style AZD fill:#e3f2fd,stroke:#1565c0

Container Deployment

flowchart TB
    Source["📁 Source Code<br>app/"]
    
    subgraph Docker["Dockerfile"]
        D1["FROM python:3.13-slim"]
        D2["• Install UV package manager"]
        D3["• Copy pyproject.toml, uv.lock"]
        D4["• Install dependencies"]
        D5["• Copy application code"]
        D6["• EXPOSE 8501"]
        D7["• CMD: streamlit run main.py"]
    end
    
    subgraph ACR["Azure Container Registry"]
        ACR1["• Remote build (Cloud Build)"]
        ACR2["• Image: {acr}.azurecr.io/rfp-analyzer:latest"]
    end
    
    subgraph ACA["Azure Container Apps"]
        ACA1["Configuration:"]
        ACA2["• Ingress: External, port 8501"]
        ACA3["• Scale: 1-10 replicas"]
        ACA4["• Resources: 2 CPU, 4GB memory"]
        ACA5["• Identity: User-assigned managed identity"]
        
        ACA6["Environment Variables:"]
        ACA7["• AZURE_OPENAI_ENDPOINT"]
        ACA8["• AZURE_OPENAI_DEPLOYMENT_NAME"]
        ACA9["• AZURE_CONTENT_UNDERSTANDING_ENDPOINT"]
        ACA10["• AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT"]
        ACA11["• AZURE_CLIENT_ID (managed identity)"]
        ACA12["• APPLICATIONINSIGHTS_CONNECTION_STRING"]
    end
    
    Source --> Docker --> ACR --> ACA
    
    style Docker fill:#e3f2fd,stroke:#1565c0
    style ACR fill:#fff3e0,stroke:#ef6c00
    style ACA fill:#e8f5e9,stroke:#2e7d32

Monitoring & Observability

flowchart TB
    subgraph AppLayer["Application Layer"]
        L["📝 Logging<br>Python"]
        M["📊 Metrics<br>Custom"]
        T["🔍 Tracing<br>OpenTelemetry"]
    end
    
    AppLayer --> SDK
    
    subgraph SDK["Application Insights SDK"]
        SDK1["azure-monitor-opentelemetry"]
        SDK2["Automatic instrumentation for HTTP, Azure SDK"]
        SDK3["Custom spans for agent operations"]
    end
    
    SDK --> AI
    
    subgraph AI["Application Insights"]
        AI1["• Live Metrics Stream"]
        AI2["• Transaction Search"]
        AI3["• Application Map"]
        AI4["• Failures and Performance"]
    end
    
    AI --> LAW
    
    subgraph LAW["Log Analytics Workspace"]
        LAW1["Tables:"]
        LAW2["• AppTraces - Application logs"]
        LAW3["• AppRequests - HTTP requests"]
        LAW4["• AppDependencies - External calls"]
        LAW5["• AppExceptions - Errors"]
        LAW6["• ContainerAppConsoleLogs - Container logs"]
        LAW7["• ContainerAppSystemLogs - Platform logs"]
    end
    
    style AppLayer fill:#f3e5f5,stroke:#7b1fa2
    style SDK fill:#e3f2fd,stroke:#1565c0
    style AI fill:#fff3e0,stroke:#ef6c00
    style LAW fill:#e8f5e9,stroke:#2e7d32

Class Diagrams

Pydantic Models

classDiagram
    class ScoringCriterion {
        +str criterion_id
        +str name
        +str description
        +str category
        +float weight
        +int max_score
        +str evaluation_guidance
    }
    
    class ExtractedCriteria {
        +str rfp_title
        +str rfp_summary
        +float total_weight
        +List~ScoringCriterion~ criteria
        +str extraction_notes
    }
    
    class CriterionScore {
        +str criterion_id
        +str criterion_name
        +float weight
        +float raw_score
        +float weighted_score
        +str evidence
        +str justification
        +List~str~ strengths
        +List~str~ gaps
    }
    
    class ProposalEvaluationV2 {
        +str rfp_title
        +str supplier_name
        +str supplier_site
        +str response_id
        +str evaluation_date
        +float total_score
        +float score_percentage
        +str grade
        +str recommendation
        +List~CriterionScore~ criterion_scores
        +str executive_summary
        +List~str~ overall_strengths
        +List~str~ overall_weaknesses
        +List~str~ recommendations
        +str risk_assessment
    }
    
    class VendorRanking {
        +int rank
        +str vendor_name
        +float total_score
        +str grade
        +List~str~ key_strengths
        +List~str~ key_concerns
        +str recommendation
    }
    
    class ComparisonResult {
        +str rfp_title
        +str comparison_date
        +int total_vendors
        +List~VendorRanking~ vendor_rankings
        +List~CriterionComparison~ criterion_comparisons
        +str winner_summary
        +List~str~ comparison_insights
        +str selection_recommendation
        +str risk_comparison
    }
    
    ExtractedCriteria "1" *-- "*" ScoringCriterion
    ProposalEvaluationV2 "1" *-- "*" CriterionScore
    ComparisonResult "1" *-- "*" VendorRanking

Service Classes

classDiagram
    class DocumentProcessor {
        +ExtractionService service
        +AzureContentUnderstandingClient cu_client
        +AzureDocumentIntelligenceClient di_client
        +__init__(service: ExtractionService)
        +process_document(file_bytes, filename) str
    }
    
    class CriteriaExtractionAgent {
        +AzureOpenAIResponsesClient client
        +str model
        +extract_criteria(rfp_content, scoring_guide) ExtractedCriteria
    }
    
    class ProposalScoringAgent {
        +AzureOpenAIResponsesClient client
        +str model
        +score_proposal(proposal, criteria) ProposalEvaluationV2
    }
    
    class ComparisonAgent {
        +AzureOpenAIResponsesClient client
        +str model
        +compare_vendors(evaluations) ComparisonResult
        +generate_word_report(evaluation) bytes
        +generate_full_analysis_report(comparison) bytes
    }
    
    class ExtractionService {
        <<enumeration>>
        CONTENT_UNDERSTANDING
        DOCUMENT_INTELLIGENCE
    }
    
    DocumentProcessor --> ExtractionService
    CriteriaExtractionAgent --> ExtractedCriteria
    ProposalScoringAgent --> ProposalEvaluationV2
    ComparisonAgent --> ComparisonResult

Sequence Diagrams

Full Evaluation Flow

sequenceDiagram
    actor User
    participant UI as Streamlit UI
    participant DP as Document Processor
    participant CU as Content Understanding
    participant CEA as Criteria Agent
    participant PSA as Scoring Agent
    participant CA as Comparison Agent
    participant AOAI as Azure OpenAI
    
    User->>UI: Upload RFP & Proposals
    UI->>UI: Store in session state
    
    User->>UI: Click "Extract"
    UI->>DP: Process documents
    DP->>CU: Extract content
    CU-->>DP: Markdown content
    DP-->>UI: Store extracted content
    
    User->>UI: Click "Evaluate"
    UI->>CEA: Extract criteria from RFP
    CEA->>AOAI: Structured output request
    AOAI-->>CEA: ExtractedCriteria
    CEA-->>UI: Criteria extracted
    
    loop For each proposal
        UI->>PSA: Score proposal
        PSA->>AOAI: Structured output request
        AOAI-->>PSA: ProposalEvaluationV2
        PSA-->>UI: Evaluation complete
    end
    
    UI->>CA: Compare all vendors
    CA->>AOAI: Structured output request
    AOAI-->>CA: ComparisonResult
    CA-->>UI: Comparison complete
    
    UI-->>User: Display results & export options

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RFP Analyzer Architecture

Table of Contents

System Overview

Architecture Principles

Application Architecture

Component Architecture

Core Components

1. Streamlit Web Application (`main.py`)

2. Document Processor (`document_processor.py`)

3. Multi-Agent Scoring System (`scoring_agent_v2.py`)

4. Comparison Agent (`comparison_agent.py`)

Multi-Agent System

Agent Communication Pattern

Azure Infrastructure

Resource Topology

Infrastructure as Code (Bicep)

Bicep Module Dependencies

Data Flow

Document Processing Flow

Evaluation Pipeline Flow

Security Architecture

Authentication Flow

RBAC Configuration

Deployment Architecture

Azure Developer CLI (azd) Workflow

Container Deployment

Monitoring & Observability

Class Diagrams

Pydantic Models

Service Classes

Sequence Diagrams

Full Evaluation Flow

Additional Resources

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RFP Analyzer Architecture

Table of Contents

System Overview

Architecture Principles

Application Architecture

Component Architecture

Core Components

1. Streamlit Web Application (main.py)

2. Document Processor (document_processor.py)

3. Multi-Agent Scoring System (scoring_agent_v2.py)

4. Comparison Agent (comparison_agent.py)

Multi-Agent System

Agent Communication Pattern

Azure Infrastructure

Resource Topology

Infrastructure as Code (Bicep)

Bicep Module Dependencies

Data Flow

Document Processing Flow

Evaluation Pipeline Flow

Security Architecture

Authentication Flow

RBAC Configuration

Deployment Architecture

Azure Developer CLI (azd) Workflow

Container Deployment

Monitoring & Observability

Class Diagrams

Pydantic Models

Service Classes

Sequence Diagrams

Full Evaluation Flow

Additional Resources

1. Streamlit Web Application (`main.py`)

2. Document Processor (`document_processor.py`)

3. Multi-Agent Scoring System (`scoring_agent_v2.py`)

4. Comparison Agent (`comparison_agent.py`)