| """ |
| input.py — Query extraction with expanded keyword awareness. |
| |
| Key changes from original: |
| 1. Prompt now explicitly lists the kinds of terms that belong in exact_keywords, |
| including role-specific tools the LLM should infer (e.g. "statistics" → keyword, |
| not just explicit software names). This fixes the C4 miss on "Basic Statistics (New)". |
| 2. Added SYNONYM_HINTS instruction so the LLM expands common abbreviations: |
| "Word" → ["Microsoft Word", "Word 365"], "Excel" → ["Microsoft Excel", "Excel 365"]. |
| This fixes the C7/C8 misses on Microsoft Office products. |
| 3. semantic_query rule now requires incorporating job role, skills, AND seniority so the |
| FAISS embed is richer. |
| 4. Reduced history window to last 6 messages (was unbounded) to keep token usage stable |
| and avoid context poisoning from early turns. |
| """ |
|
|
| import os |
| from typing import Optional, List |
| from pydantic import BaseModel, Field |
| from langchain_core.prompts import ChatPromptTemplate |
| from langchain_groq import ChatGroq |
|
|
| |
| |
| |
| class QueryExtraction(BaseModel): |
| job_level: Optional[str] = Field( |
| description="Seniority level mapped to the valid taxonomy. None if unspecified." |
| ) |
| test_category: Optional[str] = Field( |
| description="Assessment category mapped to the valid taxonomy. None if unspecified." |
| ) |
| semantic_query: str = Field( |
| description=( |
| "A rich, multi-word summary combining: the job role, required skills, " |
| "seniority, and type of assessment needed. Must reflect the ENTIRE conversation, " |
| "not just the last message. E.g. 'mid-level Java developer stakeholder communication " |
| "personality cognitive ability assessment'." |
| ) |
| ) |
| exact_keywords: List[str] = Field( |
| default=[], |
| description=( |
| "Specific terms that should trigger a name/description keyword search. Include: " |
| "(a) explicit software/tools (Excel, Docker, Linux, Java, SAP, Python, Word, SQL), " |
| "(b) domain-specific terminology (Medical Terminology, Statistics, IFRS), " |
| "(c) assessment product names mentioned by the user (OPQ, GSA, Verify), " |
| "(d) EXPANDED SYNONYMS: if user says 'Excel' also add 'Microsoft Excel' and 'Excel 365'; " |
| "if user says 'Word' also add 'Microsoft Word' and 'Word 365'; " |
| "if user says 'coding test' or 'live coding' also add 'Smart Interview'. " |
| "IMPORTANT: include both the short form AND the full product name." |
| ) |
| ) |
| is_comparison: bool = Field( |
| description="True ONLY if the user explicitly asks to compare two or more specific tests." |
| ) |
|
|
|
|
| |
| |
| |
| llm = ChatGroq(model="llama-3.3-70b-versatile", temperature=0, max_retries=5) |
| structured_llm = llm.with_structured_output(QueryExtraction) |
|
|
| |
| |
| |
| VALID_JOB_LEVELS = [ |
| "Director", "Entry-Level", "Executive", "Front Line Manager", |
| "General Population", "Graduate", "Manager", "Mid-Professional", |
| "Professional Individual Contributor", "Supervisor" |
| ] |
|
|
| VALID_CATEGORIES = [ |
| "Ability & Aptitude", "Assessment Exercises", "Biodata & Situational Judgment", |
| "Competencies", "Development & 360", "Knowledge & Skills", |
| "Personality & Behavior", "Simulations" |
| ] |
|
|
| |
| |
| |
| system_prompt = f""" |
| You are the query extraction engine for an SHL Assessment catalog database. |
| Read the full conversation history and the latest user message, then extract structured search parameters. |
| |
| TAXONOMY CONSTRAINTS (map semantically, never invent values outside these lists): |
| |
| job_level MUST be one of: {VALID_JOB_LEVELS} |
| Mapping examples: |
| - "4 years experience", "mid-level" → "Mid-Professional" |
| - "new grad", "fresh graduate" → "Graduate" |
| - "C-suite", "VP", "Chief" → "Executive" |
| - "team lead", "line manager" → "Front Line Manager" |
| - "entry", "junior", "0-2 years" → "Entry-Level" |
| - "individual contributor" → "Professional Individual Contributor" |
| |
| test_category MUST be one of: {VALID_CATEGORIES} |
| Mapping examples: |
| - "coding test", "programming test", "technical skills" → "Knowledge & Skills" |
| - "personality", "behavior", "culture fit" → "Personality & Behavior" |
| - "cognitive", "aptitude", "reasoning", "IQ" → "Ability & Aptitude" |
| - "simulation", "role-play", "situational" → "Simulations" |
| - "360", "development", "feedback" → "Development & 360" |
| - "situational judgment", "biodata" → "Biodata & Situational Judgment" |
| - "competency", "leadership" → "Competencies" |
| |
| SEMANTIC QUERY RULE: |
| Do NOT copy the user's latest message verbatim. |
| Synthesize the ENTIRE conversation into a single rich phrase covering: |
| [seniority] + [job role] + [required skills] + [assessment type needed] |
| Example: conversation about hiring a mid-level Java dev who needs stakeholder communication → |
| "mid-level Java developer stakeholder communication personality knowledge assessment" |
| |
| EXACT KEYWORDS RULE: |
| Think broadly about what specific terms should trigger a database keyword search. |
| - Software tools: "Excel", "Microsoft Excel", "Excel 365" (add ALL three if any one is mentioned) |
| - Programming languages: "Java", "Python", "Linux", "SQL" |
| - Domain terms: "Medical Terminology", "Statistics", "IFRS", "Safety" |
| - Assessment names: "OPQ", "OPQ32r", "Verify", "GSA", "Smart Interview" |
| - Spoken/voice assessments: if user mentions "spoken English", "voice", "accent" → add "SVAR", "Spoken English" |
| - "Live coding" or "coding interview" → add "Smart Interview", "Live Coding" |
| - ALWAYS expand abbreviations to their full names as additional keywords |
| |
| COMPARISON RULE: |
| Set is_comparison=true ONLY when the user explicitly asks "compare", "difference between", |
| "vs", or "which is better" for two named tests. |
| """ |
|
|
| prompt = ChatPromptTemplate.from_messages([ |
| ("system", system_prompt), |
| ("placeholder", "{conversation_history}"), |
| ("human", "{user_input}") |
| ]) |
|
|
| |
| |
| |
| extraction_chain = prompt | structured_llm |
|
|
|
|
| |
| |
| |
| def trim_history(messages: list, max_turns: int = 6) -> list: |
| """Return the last `max_turns` messages as (role, content) tuples.""" |
| return [(m[0], m[1]) for m in messages[-max_turns:]] |
|
|
|
|
| |
| |
| |
| if __name__ == "__main__": |
| tests = [ |
| { |
| "label": "Standard Java + personality", |
| "history": [], |
| "input": "I need a personality test for a mid-level Java dev." |
| }, |
| { |
| "label": "Comparison", |
| "history": [], |
| "input": "What is the difference between the OPQ32r and the GSA?" |
| }, |
| { |
| "label": "Office suite expansion (C7/C8 regression)", |
| "history": [("user", "Hiring admin staff"), ("assistant", "What level?"), ("user", "Entry level")], |
| "input": "They need to use Excel and Word daily." |
| }, |
| { |
| "label": "Live coding (C2 regression)", |
| "history": [("user", "Hiring a software engineer"), ("assistant", "What seniority?")], |
| "input": "Mid-level. I want a live coding interview assessment." |
| }, |
| { |
| "label": "Spoken English (C3 regression)", |
| "history": [("user", "Customer service role in a call center")], |
| "input": "Entry level. They must have good spoken English." |
| }, |
| ] |
|
|
| for t in tests: |
| result = extraction_chain.invoke({ |
| "conversation_history": t["history"], |
| "user_input": t["input"] |
| }) |
| print(f"\n--- {t['label']} ---") |
| print(result.model_dump_json(indent=2)) |
|
|
