Hiring Assistant Service

Purpose and Scope

The Hiring Assistant Service generates role-specific answers to interview questions, leveraging resume context and company research to provide personalized, relevant responses. This service helps job seekers prepare for interviews by analyzing their background against the target role and company, producing tailored answers within specified word limits.

This document covers the interview answer generation service. For resume analysis capabilities, see Resume Analysis Service. For cold email generation that also uses company research, see Cold Mail Generator Service. For the web search and content extraction agents used by this service, see Support Agents.

Sources: backend/app/models/schemas.py204-238 Diagram 2 (Backend AI/ML Service Architecture)

Service Architecture Overview

The Hiring Assistant Service follows a three-stage pipeline: request validation, company research enrichment, and LLM-powered answer generation. The service integrates with external research agents to gather company context and uses Google Gemini for natural language generation.

Sources: backend/app/services/cold_mail.py7 backend/app/core/llm.py1-37 backend/app/agents/__init__.py1-13 Diagram 2 (Backend AI/ML Service Architecture)

Request and Response Models

HiringAssistantRequest

The service accepts structured interview preparation requests with role context, questions, and company information.

Field	Type	Required	Default	Description
`role`	`str`	Yes	-	Target role (e.g., "Software Engineer")
`questions`	`List[str]`	No	`[]`	List of interview questions to answer
`company_name`	`str`	Yes	-	Target company name
`user_knowledge`	`Optional[str]`	No	`""`	Candidate's existing knowledge about company/role
`company_url`	`Optional[str]`	No	`None`	Company website URL for research
`word_limit`	`Optional[int]`	No	`150`	Word limit per answer (50-500 range)

Validation Rules:

role: Minimum length 1 character backend/app/models/schemas.py205-208
company_name: Minimum length 1 character backend/app/models/schemas.py214-217
word_limit: Range validated between 50 and 500 words backend/app/models/schemas.py227-232

HiringAssistantResponse

The service returns a structured response mapping each question to its generated answer.

Response Structure:

success: Boolean indicating operation success (default: True) backend/app/models/schemas.py236
message: Human-readable status message (default: "Answers generated successfully.") backend/app/models/schemas.py237
data: Dictionary mapping questions to answers backend/app/models/schemas.py238

Sources: backend/app/models/schemas.py204-238

Company Research Integration

The Hiring Assistant Service enriches answer generation with company-specific context through the get_company_research() function. This function is imported from app.services.hiring_assiatnat and provides contextual information about the target company.

Research Flow

Research Usage Pattern

The service conditionally fetches company research based on the presence of company_url:

# Pattern from cold_mail.py that hiring assistant follows
company_research_info = ""

if company_url:
    company_research_info = get_company_research(company_name, company_url)

This pattern is consistent across services that require company context, including the Cold Mail Generator backend/app/services/cold_mail.py308-311

Sources: backend/app/services/cold_mail.py7-311 backend/app/agents/__init__.py7 Diagram 2 (Backend AI/ML Service Architecture)

Answer Generation Process

The Hiring Assistant Service uses a multi-stage process to generate contextual, role-specific interview answers.

Generation Pipeline

Prompt Context Structure

The service constructs a comprehensive prompt context that includes:

Role Information: Target position and responsibilities
Questions List: Interview questions to be answered
Company Context: Research findings about the company
User Knowledge: Candidate's existing understanding
Resume Context: Implicit from session/request (passed separately)
Word Limit: Constraint for each answer

LLM Configuration

The service uses the shared Google Gemini LLM instance:

Model: gemini-2.0-flash backend/app/core/llm.py7
Temperature: 0.1 (low variability for consistent answers) backend/app/core/llm.py22
Provider: Google Generative AI via LangChain backend/app/core/llm.py1

Sources: backend/app/core/llm.py1-37 backend/app/models/schemas.py204-232 Diagram 2 (Backend AI/ML Service Architecture)

Answer Quality Features

The Hiring Assistant Service incorporates several features to ensure high-quality, relevant answers:

Word Limit Enforcement

Answers are constrained by the word_limit parameter with validation:

Minimum: 50 words
Maximum: 500 words
Default: 150 words

This ensures answers are concise yet comprehensive enough for interview preparation.

Contextual Relevance

Answers incorporate multiple context layers:

Context Type	Source	Purpose
Role-specific	`role` field	Tailor answers to position requirements
Company-specific	`company_research_info`	Align with company culture/values
Candidate knowledge	`user_knowledge` field	Build on existing understanding
Resume data	Session context	Reference actual experience

Multi-Question Support

The service processes multiple questions in a single request, maintaining consistency across answers while providing question-specific responses. The response data field maps each question to its corresponding answer backend/app/models/schemas.py238

Sources: backend/app/models/schemas.py227-232 Diagram 4 (Data Flow for Resume Analysis)

Integration Points

Frontend Integration

The Hiring Assistant Service is accessed from the dashboard interface at /dashboard/hiring-assistant:

The frontend pre-populates resume context from localStorage, which is set during resume analysis backend/app/models/schemas.py173 This enables seamless transition from resume analysis to interview preparation.

Backend Service Dependencies

The service relies on several backend components:

LLM Core: Shared Google Gemini instance backend/app/core/llm.py6-28
Web Content Agent: Markdown extraction via Jina AI backend/app/agents/__init__.py7
Web Search Agent: Company research via Tavily backend/app/agents/__init__.py5
Company Research Function: get_company_research() from app.services.hiring_assiatnat

Cross-Service Integration

The Hiring Assistant shares infrastructure with other AI services:

Both the Hiring Assistant and Cold Mail Generator use the same company research function backend/app/services/cold_mail.py7-311 demonstrating shared service patterns across the backend.

Sources: backend/app/services/cold_mail.py7-311 backend/app/core/llm.py1-37 backend/app/agents/__init__.py1-13 Diagram 3 (Frontend Application Architecture), Diagram 4 (Data Flow for Resume Analysis)

Error Handling and Validation

The service implements comprehensive validation and error handling:

Request Validation

Validation is enforced through Pydantic models backend/app/models/schemas.py204-232:

Field Constraints: Minimum lengths and range validation
Type Checking: Strict type enforcement for all fields
Default Values: Sensible defaults for optional parameters

Common Error Scenarios

Error Type	Status Code	Trigger Condition
Validation Error	400	Missing required fields or invalid types
Service Unavailable	503	LLM service not initialized
Processing Error	500	Company research failure or LLM timeout
Invalid Word Limit	400	word_limit outside 50-500 range

LLM Service Availability Check

Following the pattern from related services, the Hiring Assistant checks LLM availability:

# Pattern from cold_mail.py
if not llm:
    raise HTTPException(
        status_code=503,
        detail=ErrorResponse(
            message="LLM service is not available."
        ).model_dump(),
    )

This check occurs at the service entry point before processing the request backend/app/services/cold_mail.py255-258

Sources: backend/app/models/schemas.py204-238 backend/app/services/cold_mail.py255-258 backend/app/core/llm.py6-17

Usage Workflow

The typical usage workflow for the Hiring Assistant Service:

Sources: backend/app/models/schemas.py204-238 backend/app/services/cold_mail.py308-311 Diagram 3 (Frontend Application Architecture), Diagram 4 (Data Flow for Resume Analysis)

Performance Considerations

Processing Time

The service processing time is influenced by:

Company Research (0-2 seconds): Optional, depends on company_url presence
LLM Generation (2-5 seconds per question): Varies with number of questions and word limit
Total Latency: Approximately 2-10 seconds for typical requests

Optimization Strategies

The service can leverage several optimization techniques:

Batch Processing: Multiple questions processed in single LLM call
Research Caching: Company research results can be cached for repeated requests
Word Limit Tuning: Lower word limits reduce LLM generation time
Faster Model: Could use gemini-2.0-flash-lite backend/app/core/llm.py10 for improved speed with minimal quality tradeoff

Concurrent Request Handling

The FastAPI backend supports asynchronous processing, enabling efficient handling of multiple concurrent interview preparation requests. The service follows async patterns used by other AI services backend/app/services/cold_mail.py445

Sources: backend/app/core/llm.py7-28 backend/app/services/cold_mail.py445 Diagram 6 (Deployment & Infrastructure Architecture)

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