From 7586725f11616722fd16e3281c5e597653480b41 Mon Sep 17 00:00:00 2001 From: James Ketrenos Date: Tue, 3 Jun 2025 21:28:55 -0700 Subject: [PATCH] Skill tracking almost working --- frontend/src/components/JobMatchAnalysis.tsx | 167 ++++++--- frontend/src/pages/DemoComponent.tsx | 153 -------- frontend/src/pages/GenerateCandidate.tsx | 4 +- frontend/src/pages/JobAnalysisPage.tsx | 5 +- frontend/src/services/api-client.ts | 18 +- frontend/src/types/types.ts | 274 +++------------ src/backend/agents/base.py | 139 +++++++- src/backend/agents/generate_persona.py | 67 +--- src/backend/generate_types.py | 348 +++---------------- src/backend/main.py | 70 +++- src/backend/models.py | 60 +++- src/backend/rag/rag.py | 3 +- 12 files changed, 480 insertions(+), 828 deletions(-) delete mode 100644 frontend/src/pages/DemoComponent.tsx diff --git a/frontend/src/components/JobMatchAnalysis.tsx b/frontend/src/components/JobMatchAnalysis.tsx index 6bcf9d4..5c8b6f0 100644 --- a/frontend/src/components/JobMatchAnalysis.tsx +++ b/frontend/src/components/JobMatchAnalysis.tsx @@ -20,75 +20,144 @@ import CheckCircleIcon from '@mui/icons-material/CheckCircle'; import ErrorIcon from '@mui/icons-material/Error'; import PendingIcon from '@mui/icons-material/Pending'; import WarningIcon from '@mui/icons-material/Warning'; +import { Candidate, ChatMessage, ChatMessageBase, ChatMessageUser, ChatSession, JobRequirements, SkillMatch } from 'types/types'; +import { useAuth } from 'hooks/AuthContext'; +import { BackstoryPageProps } from './BackstoryTab'; +import { toCamelCase } from 'types/conversion'; -// Define TypeScript interfaces for our data structures -interface Citation { - text: string; - source: string; - relevance: number; // 0-100 scale + +interface Job { + title: string; + description: string; } -interface SkillMatch { - requirement: string; - status: 'pending' | 'complete' | 'error'; - matchScore: number; // 0-100 scale - assessment: string; - citations: Citation[]; +interface JobAnalysisProps extends BackstoryPageProps { + job: Job; + candidate: Candidate; } -interface JobAnalysisProps { - jobTitle: string; - candidateName: string; - // This function would connect to your backend and return updates - fetchRequirements: () => Promise; - // This function would fetch match data for a specific requirement - fetchMatchForRequirement: (requirement: string) => Promise; -} +const defaultMessage: ChatMessageUser = { + type: "preparing", status: "done", sender: "user", sessionId: "", timestamp: new Date(), content: "" +}; -const JobMatchAnalysis: React.FC = ({ - jobTitle, - candidateName, - fetchRequirements, - fetchMatchForRequirement -}) => { +const JobMatchAnalysis: React.FC = (props: JobAnalysisProps) => { + const { + job, + candidate, + setSnack, + } = props + const { apiClient } = useAuth(); const theme = useTheme(); + const [jobRequirements, setJobRequirements] = useState(null); const [requirements, setRequirements] = useState([]); const [skillMatches, setSkillMatches] = useState([]); - const [loadingRequirements, setLoadingRequirements] = useState(true); + const [creatingSession, setCreatingSession] = useState(false); + const [loadingRequirements, setLoadingRequirements] = useState(false); const [expanded, setExpanded] = useState(false); const [overallScore, setOverallScore] = useState(0); + const [requirementsSession, setRequirementsSession] = useState(null); + const [statusMessage, setStatusMessage] = useState(null); // Handle accordion expansion const handleAccordionChange = (panel: string) => (event: React.SyntheticEvent, isExpanded: boolean) => { setExpanded(isExpanded ? panel : false); }; + useEffect(() => { + if (requirementsSession || creatingSession) { + return; + } + + const createSession = async () => { + try { + const session: ChatSession = await apiClient.createCandidateChatSession( + candidate.username, + 'job_requirements', + `Generate requirements for ${job.title}` + ); + setSnack("Job analysis session started"); + setRequirementsSession(session); + } catch (error) { + console.log(error); + setSnack("Unable to create requirements session", "error"); + } + setCreatingSession(false); + }; + setCreatingSession(true); + createSession(); + }, [requirementsSession, apiClient, candidate]); + // Fetch initial requirements useEffect(() => { + if (!job.description || !requirementsSession || loadingRequirements || jobRequirements) { + return; + } + const getRequirements = async () => { + setLoadingRequirements(true); try { - const fetchedRequirements = await fetchRequirements(); - setRequirements(fetchedRequirements); - + const chatMessage: ChatMessageUser = { ...defaultMessage, sessionId: requirementsSession.id || '', content: job.description }; + apiClient.sendMessageStream(chatMessage, { + onMessage: (msg: ChatMessage) => { + console.log(`onMessage: ${msg.type}`, msg); + if (msg.type === "response") { + const incoming: any = toCamelCase(JSON.parse(msg.content || '')); + const requirements: string[] = ['technicalSkills', 'experienceRequirements'].flatMap((type) => { + return ['required', 'preferred'].flatMap((level) => { + return incoming[type][level].map((s: string) => s); + }) + }); + ['softSkills', 'experience', 'education', 'certifications', 'preferredAttributes'].forEach(l => { + if (incoming[l]) { + incoming[l].forEach((s: string) => requirements.push(s)); + } + }); + // Initialize skill matches with pending status - const initialSkillMatches = fetchedRequirements.map(req => ({ - requirement: req, - status: 'pending' as const, - matchScore: 0, - assessment: '', - citations: [] - })); - - setSkillMatches(initialSkillMatches); - setLoadingRequirements(false); + const initialSkillMatches = requirements.map(req => ({ + requirement: req, + status: 'pending' as const, + matchScore: 0, + assessment: '', + citations: [] + })); + + setRequirements(requirements); + setSkillMatches(initialSkillMatches); + setStatusMessage(null); + setLoadingRequirements(false); + } + }, + onError: (error: string | ChatMessageBase) => { + console.log("onError:", error); + // Type-guard to determine if this is a ChatMessageBase or a string + if (typeof error === "object" && error !== null && "content" in error) { + setSnack(error.content || 'Error obtaining requirements from job description.', "error"); + } else { + setSnack(error as string, "error"); + } + setLoadingRequirements(false); + }, + onStreaming: (chunk: ChatMessageBase) => { + // console.log("onStreaming:", chunk); + }, + onStatusChange: (status: string) => { + console.log(`onStatusChange: ${status}`); + }, + onComplete: () => { + console.log("onComplete"); + setStatusMessage(null); + setLoadingRequirements(false); + } + }); } catch (error) { - console.error("Error fetching requirements:", error); + console.error('Failed to send message:', error); setLoadingRequirements(false); } }; - + getRequirements(); - }, [fetchRequirements]); + }, [job, requirementsSession]); // Fetch match data for each requirement useEffect(() => { @@ -98,14 +167,14 @@ const JobMatchAnalysis: React.FC = ({ // Process requirements one by one for (let i = 0; i < requirements.length; i++) { try { - const match = await fetchMatchForRequirement(requirements[i]); - + const match: SkillMatch = await apiClient.candidateMatchForRequirement(candidate.id || '', requirements[i]); + console.log(match); setSkillMatches(prev => { const updated = [...prev]; updated[i] = match; return updated; }); - + // Update overall score setSkillMatches(current => { const completedMatches = current.filter(match => match.status === 'complete'); @@ -133,7 +202,7 @@ const JobMatchAnalysis: React.FC = ({ if (!loadingRequirements) { fetchMatchData(); } - }, [requirements, loadingRequirements, fetchMatchForRequirement]); + }, [requirements, loadingRequirements]); // Get color based on match score const getMatchColor = (score: number): string => { @@ -165,13 +234,13 @@ const JobMatchAnalysis: React.FC = ({ - Job: {jobTitle} + Job: {job.title} - Candidate: {candidateName} + Candidate: {candidate.fullName} @@ -329,7 +398,7 @@ const JobMatchAnalysis: React.FC = ({ Supporting Evidence: - {match.citations.length > 0 ? ( + {match.citations && match.citations.length > 0 ? ( match.citations.map((citation, citIndex) => ( => { - return new Promise((resolve) => { - setTimeout(() => { - resolve(mockRequirements); - }, 1500); // Simulate network delay - }); -}; - -// Simulates fetching match data for a requirement with varying delays -const mockFetchMatchForRequirement = async (requirement: string): Promise => { - // Create different mock responses based on the requirement - const mockResponses: Record = { - "5+ years of React development experience": { - requirement: "5+ years of React development experience", - status: "complete", - matchScore: 85, - assessment: "The candidate demonstrates extensive React experience spanning over 6 years, with a strong portfolio of complex applications and deep understanding of React's component lifecycle and hooks.", - citations: [ - { - text: "Led frontend development team of 5 engineers to rebuild our customer portal using React and TypeScript, resulting in 40% improved performance and 30% reduction in bugs.", - source: "Resume, Work Experience", - relevance: 95 - }, - { - text: "Developed and maintained reusable React component library used across 12 different products within the organization.", - source: "Resume, Work Experience", - relevance: 90 - }, - { - text: "I've been working with React since 2017, building everything from small widgets to enterprise applications.", - source: "Cover Letter", - relevance: 85 - } - ] - }, - "Strong TypeScript skills": { - requirement: "Strong TypeScript skills", - status: "complete", - matchScore: 90, - assessment: "The candidate shows excellent TypeScript proficiency through their work history and personal projects. They have implemented complex type systems and demonstrate an understanding of advanced TypeScript features.", - citations: [ - { - text: "Converted a legacy JavaScript codebase of 100,000+ lines to TypeScript, implementing strict type checking and reducing runtime errors by 70%.", - source: "Resume, Projects", - relevance: 98 - }, - { - text: "Created comprehensive TypeScript interfaces for our GraphQL API, ensuring type safety across the entire application stack.", - source: "Resume, Technical Skills", - relevance: 95 - } - ] - }, - "Experience with RESTful APIs": { - requirement: "Experience with RESTful APIs", - status: "complete", - matchScore: 75, - assessment: "The candidate has good experience with RESTful APIs, having both consumed and designed them. They understand REST principles but have less documented experience with API versioning and caching strategies.", - citations: [ - { - text: "Designed and implemented a RESTful API serving over 1M requests daily with a focus on performance and scalability.", - source: "Resume, Technical Projects", - relevance: 85 - }, - { - text: "Worked extensively with third-party APIs including Stripe, Twilio, and Salesforce to integrate payment processing and communication features.", - source: "Resume, Work Experience", - relevance: 70 - } - ] - }, - "Knowledge of state management solutions (Redux, Context API)": { - requirement: "Knowledge of state management solutions (Redux, Context API)", - status: "complete", - matchScore: 65, - assessment: "The candidate has moderate experience with state management, primarily using Redux. There is less evidence of Context API usage, which could indicate a knowledge gap in more modern React state management approaches.", - citations: [ - { - text: "Implemented Redux for global state management in an e-commerce application, handling complex state logic for cart, user preferences, and product filtering.", - source: "Resume, Skills", - relevance: 80 - }, - { - text: "My experience includes working with state management libraries like Redux and MobX.", - source: "Cover Letter", - relevance: 60 - } - ] - }, - "Experience with CI/CD pipelines": { - requirement: "Experience with CI/CD pipelines", - status: "complete", - matchScore: 40, - assessment: "The candidate shows limited experience with CI/CD pipelines. While they mention some exposure to Jenkins and GitLab CI, there is insufficient evidence of setting up or maintaining comprehensive CI/CD workflows.", - citations: [ - { - text: "Familiar with CI/CD tools including Jenkins and GitLab CI.", - source: "Resume, Skills", - relevance: 40 - } - ] - }, - "Cloud platform experience (AWS, Azure, GCP)": { - requirement: "Cloud platform experience (AWS, Azure, GCP)", - status: "complete", - matchScore: 30, - assessment: "The candidate demonstrates minimal experience with cloud platforms. There is a brief mention of AWS S3 and Lambda, but no substantial evidence of deeper cloud architecture knowledge or experience with Azure or GCP.", - citations: [ - { - text: "Used AWS S3 for file storage and Lambda for image processing in a photo sharing application.", - source: "Resume, Projects", - relevance: 35 - } - ] - } - }; - - // Return a promise that resolves with the mock data after a delay - return new Promise((resolve) => { - // Different requirements resolve at different speeds to simulate real-world analysis - const delay = Math.random() * 5000 + 2000; // 2-7 seconds - setTimeout(() => { - resolve(mockResponses[requirement]); - }, delay); - }); -}; - -const DemoComponent: React.FC = () => { - return ( - - ); -}; - -export { DemoComponent }; \ No newline at end of file diff --git a/frontend/src/pages/GenerateCandidate.tsx b/frontend/src/pages/GenerateCandidate.tsx index 4c73dbb..a20eb36 100644 --- a/frontend/src/pages/GenerateCandidate.tsx +++ b/frontend/src/pages/GenerateCandidate.tsx @@ -187,11 +187,11 @@ const GenerateCandidate = (props: BackstoryElementProps) => { controllerRef.current = apiClient.sendMessageStream(chatMessage, { onMessage: async (msg: ChatMessage) => { console.log(`onMessage: ${msg.type} ${msg.content}`, msg); - if (msg.type === "heartbeat") { + if (msg.type === "heartbeat" && msg.content) { const heartbeat = JSON.parse(msg.content); setTimestamp(heartbeat.timestamp); } - if (msg.type === "thinking") { + if (msg.type === "thinking" && msg.content) { const status = JSON.parse(msg.content); setProcessingMessage({ ...defaultMessage, content: status.message }); } diff --git a/frontend/src/pages/JobAnalysisPage.tsx b/frontend/src/pages/JobAnalysisPage.tsx index d8e5c6d..55bd5ee 100644 --- a/frontend/src/pages/JobAnalysisPage.tsx +++ b/frontend/src/pages/JobAnalysisPage.tsx @@ -47,7 +47,8 @@ const JobAnalysisPage: React.FC = (props: BackstoryPageProps const theme = useTheme(); const { user } = useAuth(); const { selectedCandidate, setSelectedCandidate } = useSelectedCandidate() - + const { setSnack, submitQuery } = props; + const backstoryProps = { setSnack, submitQuery }; // State management const [activeStep, setActiveStep] = useState(0); const [jobDescription, setJobDescription] = useState(''); @@ -57,7 +58,6 @@ const JobAnalysisPage: React.FC = (props: BackstoryPageProps const [error, setError] = useState(null); const [openUploadDialog, setOpenUploadDialog] = useState(false); const { apiClient } = useAuth(); - const { setSnack } = props; const [candidates, setCandidates] = useState(null); useEffect(() => { @@ -421,6 +421,7 @@ const JobAnalysisPage: React.FC = (props: BackstoryPageProps )} diff --git a/frontend/src/services/api-client.ts b/frontend/src/services/api-client.ts index 039a085..e63b831 100644 --- a/frontend/src/services/api-client.ts +++ b/frontend/src/services/api-client.ts @@ -824,6 +824,18 @@ class ApiClient { return result; } + async candidateMatchForRequirement(candidate_id: string, requirement: string) : Promise { + const response = await fetch(`${this.baseUrl}/candidates/${candidate_id}/skill-match`, { + method: 'POST', + headers: this.defaultHeaders, + body: JSON.stringify(requirement) + }); + + const result = await handleApiResponse(response); + + return result; + } + async updateCandidateDocument(document: Types.Document) : Promise { const request : Types.DocumentUpdateRequest = { filename: document.filename, @@ -1040,7 +1052,11 @@ class ApiClient { default: incomingMessageList.push(convertedIncoming); - options.onMessage?.(convertedIncoming); + try { + options.onMessage?.(convertedIncoming); + } catch (error) { + console.error('onMessage handler failed: ', error); + } break; } } diff --git a/frontend/src/types/types.ts b/frontend/src/types/types.ts index 091f21d..0915ac9 100644 --- a/frontend/src/types/types.ts +++ b/frontend/src/types/types.ts @@ -1,6 +1,6 @@ // Generated TypeScript types from Pydantic models // Source: src/backend/models.py -// Generated on: 2025-06-03T23:59:28.355326 +// Generated on: 2025-06-04T03:59:11.250216 // DO NOT EDIT MANUALLY - This file is auto-generated // ============================ @@ -13,11 +13,11 @@ export type ActivityType = "login" | "search" | "view_job" | "apply_job" | "mess export type ApplicationStatus = "applied" | "reviewing" | "interview" | "offer" | "rejected" | "accepted" | "withdrawn"; -export type ChatContextType = "job_search" | "job_requirements" | "candidate_chat" | "interview_prep" | "resume_review" | "general" | "generate_persona" | "generate_profile" | "generate_image" | "rag_search"; +export type ChatContextType = "job_search" | "job_requirements" | "candidate_chat" | "interview_prep" | "resume_review" | "general" | "generate_persona" | "generate_profile" | "generate_image" | "rag_search" | "skill_match"; export type ChatMessageType = "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user"; -export type ChatSenderType = "user" | "assistant" | "system"; +export type ChatSenderType = "user" | "assistant" | "agent" | "system"; export type ChatStatusType = "initializing" | "streaming" | "status" | "done" | "error"; @@ -49,6 +49,8 @@ export type SearchType = "similarity" | "mmr" | "hybrid" | "keyword"; export type SkillLevel = "beginner" | "intermediate" | "advanced" | "expert"; +export type SkillStatus = "pending" | "complete" | "error"; + export type SocialPlatform = "linkedin" | "twitter" | "github" | "dribbble" | "behance" | "website" | "other"; export type SortOrder = "asc" | "desc"; @@ -272,7 +274,7 @@ export interface Certification { } export interface ChatContext { - type: "job_search" | "job_requirements" | "candidate_chat" | "interview_prep" | "resume_review" | "general" | "generate_persona" | "generate_profile" | "generate_image" | "rag_search"; + type: "job_search" | "job_requirements" | "candidate_chat" | "interview_prep" | "resume_review" | "general" | "generate_persona" | "generate_profile" | "generate_image" | "rag_search" | "skill_match"; relatedEntityId?: string; relatedEntityType?: "job" | "candidate" | "employer"; additionalContext?: Record; @@ -284,7 +286,7 @@ export interface ChatMessage { senderId?: string; status: "initializing" | "streaming" | "status" | "done" | "error"; type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user"; - sender: "user" | "assistant" | "system"; + sender: "user" | "assistant" | "agent" | "system"; timestamp?: Date; tunables?: Tunables; content: string; @@ -297,7 +299,7 @@ export interface ChatMessageBase { senderId?: string; status: "initializing" | "streaming" | "status" | "done" | "error"; type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user"; - sender: "user" | "assistant" | "system"; + sender: "user" | "assistant" | "agent" | "system"; timestamp?: Date; tunables?: Tunables; content: string; @@ -328,7 +330,7 @@ export interface ChatMessageRagSearch { senderId?: string; status: "initializing" | "streaming" | "status" | "done" | "error"; type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user"; - sender: "user" | "assistant" | "system"; + sender: "user" | "assistant" | "agent" | "system"; timestamp?: Date; tunables?: Tunables; content: string; @@ -341,7 +343,7 @@ export interface ChatMessageUser { senderId?: string; status: "initializing" | "streaming" | "status" | "done" | "error"; type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user"; - sender: "user" | "assistant" | "system"; + sender: "user" | "assistant" | "agent" | "system"; timestamp?: Date; tunables?: Tunables; content: string; @@ -386,6 +388,12 @@ export interface ChromaDBGetResponse { umapEmbedding3D?: Array; } +export interface Citation { + text: string; + source: string; + relevance: number; +} + export interface CreateCandidateRequest { email: string; username: string; @@ -613,6 +621,16 @@ export interface JobListResponse { meta?: Record; } +export interface JobRequirements { + technicalSkills: Requirements; + experienceRequirements: Requirements; + softSkills?: Array; + experience?: Array; + education?: Array; + certifications?: Array; + preferredAttributes?: Array; +} + export interface JobResponse { success: boolean; data?: Job; @@ -765,6 +783,11 @@ export interface RefreshToken { revokedReason?: string; } +export interface Requirements { + required?: Array; + preferred?: Array; +} + export interface ResendVerificationRequest { email: string; } @@ -810,6 +833,14 @@ export interface SkillAssessment { comments?: string; } +export interface SkillMatch { + requirement: string; + status: "pending" | "complete" | "error"; + matchScore: number; + assessment: string; + citations?: Array; +} + export interface SocialLink { platform: "linkedin" | "twitter" | "github" | "dribbble" | "behance" | "website" | "other"; url: string; @@ -857,233 +888,6 @@ export interface WorkExperience { achievements?: Array; } -// ============================ -// Default Objects -// ============================ - -// These objects contain the default values from your Pydantic models -// Use them to initialize objects with sensible defaults: -// const message: ChatMessage = { ...DefaultChatMessage, sessionId: '123', content: 'Hello' }; - -/** - * Default values for BaseUser - * Fields with defaults: isAdmin - */ -export const DefaultBaseUser: Partial = { - isAdmin: False -}; - -/** - * Default values for BaseUserWithType - * Fields with defaults: isAdmin - */ -export const DefaultBaseUserWithType: Partial = { - isAdmin: False -}; - -/** - * Default values for Candidate - * Fields with defaults: isAdmin, userType, ragContentSize - */ -export const DefaultCandidate: Partial = { - isAdmin: False, - userType: "candidate", - ragContentSize: 0 -}; - -/** - * Default values for CandidateAI - * Fields with defaults: isAdmin, userType, ragContentSize, isAI - */ -export const DefaultCandidateAI: Partial = { - isAdmin: False, - userType: "candidate", - ragContentSize: 0, - isAI: True -}; - -/** - * Default values for ChatContext - * Fields with defaults: additionalContext - */ -export const DefaultChatContext: Partial = { - additionalContext: {} -}; - -/** - * Default values for ChatMessage - * Fields with defaults: status, type, sender, content - */ -export const DefaultChatMessage: Partial = { - status: "initializing", - type: "preparing", - sender: "system", - content: "" -}; - -/** - * Default values for ChatMessageBase - * Fields with defaults: status, type, sender, content - */ -export const DefaultChatMessageBase: Partial = { - status: "initializing", - type: "preparing", - sender: "system", - content: "" -}; - -/** - * Default values for ChatMessageMetaData - * Fields with defaults: model, temperature, maxTokens, topP, evalCount, evalDuration, promptEvalCount, promptEvalDuration - */ -export const DefaultChatMessageMetaData: Partial = { - model: "qwen2.5", - temperature: 0.7, - maxTokens: 8092, - topP: 1, - evalCount: 0, - evalDuration: 0, - promptEvalCount: 0, - promptEvalDuration: 0 -}; - -/** - * Default values for ChatMessageRagSearch - * Fields with defaults: status, type, sender, content, dimensions - */ -export const DefaultChatMessageRagSearch: Partial = { - status: "done", - type: "rag_result", - sender: "user", - content: "", - dimensions: 3 -}; - -/** - * Default values for ChatMessageUser - * Fields with defaults: status, type, sender, content - */ -export const DefaultChatMessageUser: Partial = { - status: "done", - type: "user", - sender: "user", - content: "" -}; - -/** - * Default values for ChatOptions - * Fields with defaults: seed, temperature - */ -export const DefaultChatOptions: Partial = { - seed: 8911, - temperature: 0.7 -}; - -/** - * Default values for ChatSession - * Fields with defaults: isArchived - */ -export const DefaultChatSession: Partial = { - isArchived: False -}; - -/** - * Default values for ChromaDBGetResponse - * Fields with defaults: ids, embeddings, documents, metadatas, distances, name, size, dimensions, query - */ -export const DefaultChromaDBGetResponse: Partial = { - ids: [], - embeddings: [], - documents: [], - metadatas: [], - distances: [], - name: "", - size: 0, - dimensions: 3, - query: "" -}; - -/** - * Default values for Document - * Fields with defaults: includeInRAG, ragChunks - */ -export const DefaultDocument: Partial = { - includeInRAG: True, - ragChunks: 0 -}; - -/** - * Default values for Employer - * Fields with defaults: isAdmin, userType - */ -export const DefaultEmployer: Partial = { - isAdmin: False, - userType: "employer" -}; - -/** - * Default values for Job - * Fields with defaults: views, applicationCount - */ -export const DefaultJob: Partial = { - views: 0, - applicationCount: 0 -}; - -/** - * Default values for LLMMessage - * Fields with defaults: role, content, toolCalls - */ -export const DefaultLLMMessage: Partial = { - role: "", - content: "", - toolCalls: {} -}; - -/** - * Default values for MFAVerifyRequest - * Fields with defaults: rememberDevice - */ -export const DefaultMFAVerifyRequest: Partial = { - rememberDevice: False -}; - -/** - * Default values for PaginatedRequest - * Fields with defaults: page, limit - */ -export const DefaultPaginatedRequest: Partial = { - page: 1, - limit: 20 -}; - -/** - * Default values for RagEntry - * Fields with defaults: description, enabled - */ -export const DefaultRagEntry: Partial = { - description: "", - enabled: True -}; - -/** - * Default values for SearchQuery - * Fields with defaults: page, limit - */ -export const DefaultSearchQuery: Partial = { - page: 1, - limit: 20 -}; - -/** - * Default values for Tunables - * Fields with defaults: enableRAG, enableTools, enableContext - */ -export const DefaultTunables: Partial = { - enableRAG: True, - enableTools: True, - enableContext: True -}; // ============================ // Date Conversion Functions // ============================ diff --git a/src/backend/agents/base.py b/src/backend/agents/base.py index a3bc797..0ea0917 100644 --- a/src/backend/agents/base.py +++ b/src/backend/agents/base.py @@ -17,6 +17,7 @@ from typing import ( import json import time import inspect +import re from abc import ABC import asyncio from datetime import datetime, UTC @@ -79,6 +80,12 @@ class Agent(BaseModel, ABC): def context_size(self, value: int): Agent._context_size = value + async def get_last_item(self, generator): + last_item = None + async for item in generator: + last_item = item + return last_item + def set_optimal_context_size( self, llm: Any, model: str, prompt: str, ctx_buffer=2048 ) -> int: @@ -297,7 +304,7 @@ class Agent(BaseModel, ABC): self, chat_message: ChatMessage, top_k: int=defines.default_rag_top_k, - threshold: float=defines.default_rag_threshold + threshold: float=defines.default_rag_threshold, ) -> AsyncGenerator[ChatMessage, None]: """ Generate RAG results for the given query. @@ -320,24 +327,29 @@ class Agent(BaseModel, ABC): ) if not self.user: + logger.error("No user set for RAG generation") rag_message.status = ChatStatusType.DONE - rag_message.content = "No user connected to this chat, so no RAG content." + rag_message.content = "" yield rag_message return try: entries: int = 0 user: Candidate = self.user + rag_content: str = "" for rag in user.rags: if not rag.enabled: continue - rag_message.type = ChatMessageType.SEARCHING - rag_message.status = ChatStatusType.INITIALIZING - rag_message.content = f"Checking RAG context {rag.name}..." - yield rag_message + status_message = ChatMessage( + session_id=chat_message.session_id, + sender=ChatSenderType.AGENT, + status = ChatStatusType.INITIALIZING, + type = ChatMessageType.SEARCHING, + content = f"Checking RAG context {rag.name}...") + yield status_message chroma_results = user.file_watcher.find_similar( - query=rag_message.content, top_k=top_k, threshold=threshold + query=chat_message.content, top_k=top_k, threshold=threshold ) if chroma_results: query_embedding = np.array(chroma_results["query_embedding"]).flatten() @@ -360,15 +372,26 @@ class Agent(BaseModel, ABC): entries += len(rag_metadata.documents) rag_message.metadata.rag_results.append(rag_metadata) - rag_message.content = f"Results from {rag.name} RAG: {len(rag_metadata.documents)} results." - yield rag_message - rag_message.content = ( - f"RAG context gathered from results from {entries} documents." - ) + for index, metadata in enumerate(chroma_results["metadatas"]): + content = "\n".join( + [ + line.strip() + for line in chroma_results["documents"][index].split("\n") + if line + ] + ).strip() + rag_content += f""" +Source: {metadata.get("doc_type", "unknown")}: {metadata.get("path", "")} +Document reference: {chroma_results["ids"][index]} +Content: { content } +""" + rag_message.content = rag_content.strip() + rag_message.type = ChatMessageType.RAG_RESULT rag_message.status = ChatStatusType.DONE yield rag_message return + except Exception as e: rag_message.status = ChatStatusType.ERROR rag_message.content = f"Error generating RAG results: {str(e)}" @@ -377,6 +400,80 @@ class Agent(BaseModel, ABC): yield rag_message return + async def llm_one_shot(self, llm: Any, model: str, user_message: ChatMessageUser, system_prompt: str, temperature=0.7): + chat_message = ChatMessage( + session_id=user_message.session_id, + tunables=user_message.tunables, + status=ChatStatusType.INITIALIZING, + type=ChatMessageType.PREPARING, + sender=ChatSenderType.AGENT, + content="", + timestamp=datetime.now(UTC) + ) + + self.set_optimal_context_size( + llm, model, prompt=chat_message.content + ) + + chat_message.metadata = ChatMessageMetaData() + chat_message.metadata.options = ChatOptions( + seed=8911, + num_ctx=self.context_size, + temperature=temperature, # Higher temperature to encourage tool usage + ) + + messages: List[LLMMessage] = [ + LLMMessage(role="system", content=system_prompt), + LLMMessage(role="user", content=user_message.content), + ] + + # Reset the response for streaming + chat_message.content = "" + chat_message.type = ChatMessageType.GENERATING + chat_message.status = ChatStatusType.STREAMING + + logger.info(f"Message options: {chat_message.metadata.options.model_dump(exclude_unset=True)}") + response = None + for response in llm.chat( + model=model, + messages=messages, + options={ + **chat_message.metadata.options.model_dump(exclude_unset=True), + }, + stream=True, + ): + if not response: + chat_message.status = ChatStatusType.ERROR + chat_message.content = "No response from LLM." + yield chat_message + return + + chat_message.content += response.message.content + + if not response.done: + chat_chunk = model_cast.cast_to_model(ChatMessageBase, chat_message) + chat_chunk.content = response.message.content + yield chat_message + continue + + if not response: + chat_message.status = ChatStatusType.ERROR + chat_message.content = "No response from LLM." + yield chat_message + return + + self.collect_metrics(response) + chat_message.metadata.eval_count += response.eval_count + chat_message.metadata.eval_duration += response.eval_duration + chat_message.metadata.prompt_eval_count += response.prompt_eval_count + chat_message.metadata.prompt_eval_duration += response.prompt_eval_duration + self.context_tokens = ( + response.prompt_eval_count + response.eval_count + ) + chat_message.type = ChatMessageType.RESPONSE + chat_message.status = ChatStatusType.DONE + yield chat_message + async def generate( self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate | None, temperature=0.7 ) -> AsyncGenerator[ChatMessage | ChatMessageBase, None]: @@ -392,6 +489,10 @@ class Agent(BaseModel, ABC): timestamp=datetime.now(UTC) ) + self.set_optimal_context_size( + llm, model, prompt=chat_message.content + ) + chat_message.metadata = ChatMessageMetaData() chat_message.metadata.options = ChatOptions( seed=8911, @@ -679,6 +780,20 @@ Content: { content } # return + def extract_json_from_text(self, text: str) -> str: + """Extract JSON string from text that may contain other content.""" + json_pattern = r"```json\s*([\s\S]*?)\s*```" + match = re.search(json_pattern, text) + if match: + return match.group(1).strip() + + # Try to find JSON without the markdown code block + json_pattern = r"({[\s\S]*})" + match = re.search(json_pattern, text) + if match: + return match.group(1).strip() + + raise ValueError("No JSON found in the response") # Register the base agent agent_registry.register(Agent._agent_type, Agent) diff --git a/src/backend/agents/generate_persona.py b/src/backend/agents/generate_persona.py index 8f43a5f..fdef77a 100644 --- a/src/backend/agents/generate_persona.py +++ b/src/backend/agents/generate_persona.py @@ -315,69 +315,6 @@ class GeneratePersona(Agent): self.first_name, self.last_name, self.ethnicity, self.gender = self.generator.generate_random_name() self.full_name = f"{self.first_name} {self.last_name}" - async def call_llm(self, llm: Any, model: str, user_message: ChatMessageUser, system_prompt: str, temperature=0.7): - chat_message = ChatMessage( - session_id=user_message.session_id, - tunables=user_message.tunables, - status=ChatStatusType.INITIALIZING, - type=ChatMessageType.PREPARING, - sender=ChatSenderType.ASSISTANT, - content="", - timestamp=datetime.now(UTC) - ) - - chat_message.metadata = ChatMessageMetaData() - chat_message.metadata.options = ChatOptions( - seed=8911, - num_ctx=self.context_size, - temperature=temperature, # Higher temperature to encourage tool usage - ) - - messages: List[LLMMessage] = [ - LLMMessage(role="system", content=system_prompt), - LLMMessage(role="user", content=user_message.content), - ] - - # Reset the response for streaming - chat_message.content = "" - chat_message.type = ChatMessageType.GENERATING - chat_message.status = ChatStatusType.STREAMING - - for response in llm.chat( - model=model, - messages=messages, - options={ - **chat_message.metadata.options.model_dump(exclude_unset=True), - }, - stream=True, - ): - if not response: - chat_message.status = ChatStatusType.ERROR - chat_message.content = "No response from LLM." - yield chat_message - return - - chat_message.content += response.message.content - - if not response.done: - chat_chunk = model_cast.cast_to_model(ChatMessageBase, chat_message) - chat_chunk.content = response.message.content - yield chat_message - continue - - if response.done: - self.collect_metrics(response) - chat_message.metadata.eval_count += response.eval_count - chat_message.metadata.eval_duration += response.eval_duration - chat_message.metadata.prompt_eval_count += response.prompt_eval_count - chat_message.metadata.prompt_eval_duration += response.prompt_eval_duration - self.context_tokens = ( - response.prompt_eval_count + response.eval_count - ) - chat_message.type = ChatMessageType.RESPONSE - chat_message.status = ChatStatusType.DONE - yield chat_message - async def generate( self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate, temperature=0.7 ): @@ -409,7 +346,7 @@ Incorporate the following into the job description: {original_prompt} # logger.info(f"šŸ¤– Generating persona for {self.full_name}") generating_message = None - async for generating_message in self.call_llm( + async for generating_message in self.llm_one_shot( llm=llm, model=model, user_message=user_message, system_prompt=generate_persona_system_prompt, @@ -515,7 +452,7 @@ Incorporate the following into the job description: {original_prompt} user_message.content += f""" Make sure at least one of the candidate's job descriptions take into account the following: {original_prompt}.""" - async for generating_message in self.call_llm( + async for generating_message in self.llm_one_shot( llm=llm, model=model, user_message=user_message, system_prompt=generate_resume_system_prompt, diff --git a/src/backend/generate_types.py b/src/backend/generate_types.py index 2e7745c..e69dcb2 100644 --- a/src/backend/generate_types.py +++ b/src/backend/generate_types.py @@ -138,14 +138,10 @@ def is_date_type(python_type: Any) -> bool: return False -def get_field_default_value(field_info: Any, debug: bool = False) -> tuple[bool, Any]: - """Extract the default value from a field, if it exists - - Returns: - tuple: (has_default, default_value) - """ +def get_default_enum_value(field_info: Any, debug: bool = False) -> Optional[Any]: + """Extract the specific enum value from a field's default, if it exists""" if not hasattr(field_info, 'default'): - return False, None + return None default_val = field_info.default @@ -156,7 +152,7 @@ def get_field_default_value(field_info: Any, debug: bool = False) -> tuple[bool, if default_val is ... or default_val is None: if debug: print(f" ā””ā”€ Default is undefined marker") - return False, None + return None # Check for Pydantic's internal "PydanticUndefined" or similar markers default_str = str(default_val) @@ -177,72 +173,17 @@ def get_field_default_value(field_info: Any, debug: bool = False) -> tuple[bool, if is_undefined_marker: if debug: print(f" ā””ā”€ Default is undefined marker pattern") - return False, None + return None - # We have a real default value - if debug: - print(f" ā””ā”€ Has real default value: {repr(default_val)}") - return True, default_val - -def convert_default_to_typescript(default_val: Any, debug: bool = False) -> str: - """Convert a Python default value to TypeScript literal""" - if debug: - print(f" šŸ”„ Converting default: {repr(default_val)} (type: {type(default_val)})") - - # Handle None - if default_val is None: - return "undefined" - - # Handle Enum instances + # Check if it's an enum instance if isinstance(default_val, Enum): - return f'"{default_val.value}"' + if debug: + print(f" ā””ā”€ Default is enum instance: {default_val.value}") + return default_val - # Handle basic types - if isinstance(default_val, str): - # Escape quotes and special characters - escaped = default_val.replace('\\', '\\\\').replace('"', '\\"') - return f'"{escaped}"' - elif isinstance(default_val, (int, float)): - return str(default_val) - elif isinstance(default_val, bool): - return "true" if default_val else "false" - elif isinstance(default_val, list): - if not default_val: # Empty list - return "[]" - # For non-empty lists, convert each item - items = [convert_default_to_typescript(item, debug) for item in default_val] - return f"[{', '.join(items)}]" - elif isinstance(default_val, dict): - if not default_val: # Empty dict - return "{}" - # For non-empty dicts, convert each key-value pair - items = [] - for key, value in default_val.items(): - key_str = f'"{key}"' if isinstance(key, str) else str(key) - value_str = convert_default_to_typescript(value, debug) - items.append(f"{key_str}: {value_str}") - return f"{{{', '.join(items)}}}" - elif isinstance(default_val, datetime): - # Convert datetime to ISO string, then wrap in new Date() - iso_string = default_val.isoformat() - return f'new Date("{iso_string}")' - - # For other types, try to convert to string if debug: - print(f" āš ļø Unknown default type, converting to string: {type(default_val)}") - - # Try to convert to a reasonable TypeScript representation - try: - if hasattr(default_val, '__dict__'): - # It's an object, try to serialize its properties - return "{}" # Fallback to empty object for complex types - else: - # Try string conversion - str_val = str(default_val) - escaped = str_val.replace('\\', '\\\\').replace('"', '\\"') - return f'"{escaped}"' - except: - return "undefined" + print(f" ā””ā”€ Default is not an enum instance") + return None def python_type_to_typescript(python_type: Any, field_info: Any = None, debug: bool = False) -> str: """Convert a Python type to TypeScript type string, considering field defaults""" @@ -257,9 +198,14 @@ def python_type_to_typescript(python_type: Any, field_info: Any = None, debug: b if debug and original_type != python_type: print(f" šŸ”„ Unwrapped: {original_type} -> {python_type}") - # REMOVED: The problematic enum default checking that returns only the default value - # This was causing the issue where enum fields would only show the default value - # instead of all possible enum values + # FIXED: Don't lock enum types to their default values + # Instead, always return the full enum type + if field_info: + default_enum = get_default_enum_value(field_info, debug) + if default_enum is not None: + if debug: + print(f" šŸŽÆ Field has specific enum default: {default_enum.value}, but returning full enum type") + # Don't return just the default value - continue to process the full enum type # Handle None/null if python_type is type(None): @@ -323,12 +269,9 @@ def python_type_to_typescript(python_type: Any, field_info: Any = None, debug: b literal_values.append(str(arg)) return " | ".join(literal_values) - # Handle Enum types - THIS IS THE CORRECT BEHAVIOR - # Return all possible enum values, not just the default + # Handle Enum types if isinstance(python_type, type) and issubclass(python_type, Enum): enum_values = [f'"{v.value}"' for v in python_type] - if debug: - print(f" šŸŽÆ Enum type detected: {python_type.__name__} with values: {enum_values}") return " | ".join(enum_values) # Handle individual enum instances @@ -433,12 +376,18 @@ def is_field_optional(field_info: Any, field_type: Any, debug: bool = False) -> print(f" ā””ā”€ RESULT: Required (default is undefined marker)") return False - # FIXED: Fields with actual default values (including enums) should be REQUIRED + # Special case: if field has a specific default value (like enum), it's required + # because it will always have a value, just not optional for the consumer + if isinstance(default_val, Enum): + if debug: + print(f" ā””ā”€ RESULT: Required (has specific enum default: {default_val.value})") + return False + + # FIXED: Fields with actual default values (like [], "", 0) should be REQUIRED # because they will always have a value (either provided or the default) - # This applies to enum fields with defaults as well if debug: print(f" ā””ā”€ RESULT: Required (has actual default value - field will always have a value)") - return False + return False # Changed from True to False else: if debug: print(f" ā””ā”€ No default attribute found") @@ -472,7 +421,6 @@ def process_pydantic_model(model_class, debug: bool = False) -> Dict[str, Any]: interface_name = model_class.__name__ properties = [] date_fields = [] # Track date fields for conversion functions - default_fields = [] # Track fields with default values for default object generation if debug: print(f" šŸ” Processing model: {interface_name}") @@ -498,17 +446,6 @@ def process_pydantic_model(model_class, debug: bool = False) -> Dict[str, Any]: if debug: print(f" Raw type: {field_type}") - # Check for default values - has_default, default_value = get_field_default_value(field_info, debug) - if has_default: - ts_default = convert_default_to_typescript(default_value, debug) - default_fields.append({ - 'name': ts_name, - 'value': ts_default - }) - if debug: - print(f" šŸŽÆ Default value: {repr(default_value)} -> {ts_default}") - # Check if this is a date field is_date = is_date_type(field_type) if debug: @@ -525,7 +462,7 @@ def process_pydantic_model(model_class, debug: bool = False) -> Dict[str, Any]: elif debug and ('date' in str(field_type).lower() or 'time' in str(field_type).lower()): print(f" āš ļø Field {ts_name} contains 'date'/'time' but not detected as date type: {field_type}") - # Pass field_info to the type converter (but now it won't override enum types) + # Pass field_info to the type converter for default enum handling ts_type = python_type_to_typescript(field_type, field_info, debug) # Check if optional @@ -561,17 +498,6 @@ def process_pydantic_model(model_class, debug: bool = False) -> Dict[str, Any]: if debug: print(f" Raw type: {field_type}") - # Check for default values - has_default, default_value = get_field_default_value(field_info, debug) - if has_default: - ts_default = convert_default_to_typescript(default_value, debug) - default_fields.append({ - 'name': ts_name, - 'value': ts_default - }) - if debug: - print(f" šŸŽÆ Default value: {repr(default_value)} -> {ts_default}") - # Check if this is a date field is_date = is_date_type(field_type) if debug: @@ -588,7 +514,7 @@ def process_pydantic_model(model_class, debug: bool = False) -> Dict[str, Any]: elif debug and ('date' in str(field_type).lower() or 'time' in str(field_type).lower()): print(f" āš ļø Field {ts_name} contains 'date'/'time' but not detected as date type: {field_type}") - # Pass field_info to the type converter (but now it won't override enum types) + # Pass field_info to the type converter for default enum handling ts_type = python_type_to_typescript(field_type, field_info, debug) # For Pydantic v1, check required and default @@ -609,8 +535,7 @@ def process_pydantic_model(model_class, debug: bool = False) -> Dict[str, Any]: return { 'name': interface_name, 'properties': properties, - 'date_fields': date_fields, - 'default_fields': default_fields + 'date_fields': date_fields } def process_enum(enum_class) -> Dict[str, Any]: @@ -624,159 +549,6 @@ def process_enum(enum_class) -> Dict[str, Any]: 'values': " | ".join(values) } -def generate_default_objects(interfaces: List[Dict[str, Any]]) -> str: - """Generate TypeScript default objects for models with default values""" - default_objects = [] - - for interface in interfaces: - interface_name = interface['name'] - default_fields = interface.get('default_fields', []) - - if not default_fields: - continue # Skip interfaces without default values - - object_name = f"Default{interface_name}" - - # Generate default object - obj_lines = [ - f"/**", - f" * Default values for {interface_name}", - f" * Fields with defaults: {', '.join([f['name'] for f in default_fields])}", - f" */", - f"export const {object_name}: Partial<{interface_name}> = {{" - ] - - # Add default field values - for i, default_field in enumerate(default_fields): - field_name = default_field['name'] - field_value = default_field['value'] - - # Add comma for all but the last field - comma = "," if i < len(default_fields) - 1 else "" - obj_lines.append(f" {field_name}: {field_value}{comma}") - - obj_lines.append("};") - obj_lines.append("") # Empty line after each object - - default_objects.append('\n'.join(obj_lines)) - - if not default_objects: - return "" - - # Generate the default objects section - result = [ - "// ============================", - "// Default Objects", - "// ============================", - "", - "// These objects contain the default values from your Pydantic models", - "// Use them to initialize objects with sensible defaults:", - "// const message: ChatMessage = { ...DefaultChatMessage, sessionId: '123', content: 'Hello' };", - "", - ] - - result.extend(default_objects) - - return '\n'.join(result) - """Generate TypeScript conversion functions for models with date fields""" - conversion_functions = [] - - for interface in interfaces: - interface_name = interface['name'] - date_fields = interface.get('date_fields', []) - - if not date_fields: - continue # Skip interfaces without date fields - - function_name = f"convert{interface_name}FromApi" - - # Generate function - func_lines = [ - f"/**", - f" * Convert {interface_name} from API response, parsing date fields", - f" * Date fields: {', '.join([f['name'] for f in date_fields])}", - f" */", - f"export function {function_name}(data: any): {interface_name} {{", - f" if (!data) return data;", - f" ", - f" return {{", - f" ...data," - ] - - # Add date field conversions with validation - for date_field in date_fields: - field_name = date_field['name'] - is_optional = date_field['optional'] - - # Add a comment for clarity - func_lines.append(f" // Convert {field_name} from ISO string to Date") - - if is_optional: - func_lines.append(f" {field_name}: data.{field_name} ? new Date(data.{field_name}) : undefined,") - else: - func_lines.append(f" {field_name}: new Date(data.{field_name}),") - - func_lines.extend([ - f" }};", - f"}}" - ]) - - conversion_functions.append('\n'.join(func_lines)) - - if not conversion_functions: - return "" - - # Generate the conversion functions section - result = [ - "// ============================", - "// Date Conversion Functions", - "// ============================", - "", - "// These functions convert API responses to properly typed objects", - "// with Date objects instead of ISO date strings", - "", - ] - - result.extend(conversion_functions) - result.append("") - - # Generate a generic converter function - models_with_dates = [interface['name'] for interface in interfaces if interface.get('date_fields')] - - if models_with_dates: - result.extend([ - "/**", - " * Generic converter that automatically selects the right conversion function", - " * based on the model type", - " */", - "export function convertFromApi(data: any, modelType: string): T {", - " if (!data) return data;", - " ", - " switch (modelType) {" - ]) - - for model_name in models_with_dates: - result.append(f" case '{model_name}':") - result.append(f" return convert{model_name}FromApi(data) as T;") - - result.extend([ - " default:", - " return data as T;", - " }", - "}", - "", - "/**", - " * Convert array of items using the appropriate converter", - " */", - "export function convertArrayFromApi(data: any[], modelType: string): T[] {", - " if (!data || !Array.isArray(data)) return data;", - " return data.map(item => convertFromApi(item, modelType));", - "}", - "" - ]) - - return '\n'.join(result) - def generate_conversion_functions(interfaces: List[Dict[str, Any]]) -> str: """Generate TypeScript conversion functions for models with date fields""" conversion_functions = [] @@ -935,10 +707,8 @@ def generate_typescript_interfaces(source_file: str, debug: bool = False): continue total_date_fields = sum(len(interface.get('date_fields', [])) for interface in interfaces) - total_default_fields = sum(len(interface.get('default_fields', [])) for interface in interfaces) print(f"\nšŸ“Š Found {len(interfaces)} interfaces and {len(enums)} enums") print(f"šŸ—“ļø Found {total_date_fields} date fields across all models") - print(f"šŸŽÆ Found {total_default_fields} fields with default values across all models") # Generate TypeScript content ts_content = f"""// Generated TypeScript types from Pydantic models @@ -972,11 +742,6 @@ def generate_typescript_interfaces(source_file: str, debug: bool = False): ts_content += "}\n\n" - # Add default objects - default_objects = generate_default_objects(interfaces) - if default_objects: - ts_content += default_objects - # Add conversion functions conversion_functions = generate_conversion_functions(interfaces) if conversion_functions: @@ -1016,7 +781,7 @@ def compile_typescript(ts_file: str) -> bool: def main(): """Main function with command line argument parsing""" parser = argparse.ArgumentParser( - description='Generate TypeScript types from Pydantic models with date conversion functions, default objects, and proper enum handling', + description='Generate TypeScript types from Pydantic models with date conversion functions and proper enum handling', formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Examples: @@ -1031,12 +796,8 @@ Generated conversion functions can be used like: const candidate = convertCandidateFromApi(apiResponse); const jobs = convertArrayFromApi(apiResponse, 'Job'); -Generated default objects can be used like: - const message: ChatMessage = { ...DefaultChatMessage, sessionId: '123', content: 'Hello' }; - const overrideMessage: ChatMessage = { ...DefaultChatMessage, status: 'error' }; - -Enum fields now properly support all enum values: - status: ChatStatusType = ChatStatusType.DONE -> status: "pending" | "processing" | "done" | "error" +Enum types are now properly handled: + status: ChatStatusType = ChatStatusType.DONE -> status: ChatStatusType (not locked to "done") """ ) @@ -1073,12 +834,12 @@ Enum fields now properly support all enum values: parser.add_argument( '--version', '-v', action='version', - version='TypeScript Generator 3.3 (with Default Objects and Fixed Enum Handling)' + version='TypeScript Generator 3.2 (Fixed Enum Default Handling)' ) args = parser.parse_args() - print("šŸš€ Enhanced TypeScript Type Generator with Default Objects and Fixed Enum Handling") + print("šŸš€ Enhanced TypeScript Type Generator with Fixed Enum Handling") print("=" * 60) print(f"šŸ“ Source file: {args.source}") print(f"šŸ“ Output file: {args.output}") @@ -1123,37 +884,27 @@ Enum fields now properly support all enum values: # Count conversion functions and provide detailed feedback conversion_count = ts_content.count('export function convert') - ts_content.count('convertFromApi') - ts_content.count('convertArrayFromApi') - default_objects_count = ts_content.count('export const Default') - enum_union_count = ts_content.count(' | ') + enum_type_count = ts_content.count('export type') if conversion_count > 0: print(f"šŸ—“ļø Generated {conversion_count} date conversion functions") - if default_objects_count > 0: - print(f"šŸŽÆ Generated {default_objects_count} default objects") - if enum_union_count > 0: - print(f"šŸ”— Generated {enum_union_count} union types (including proper enum types)") + if enum_type_count > 0: + print(f"šŸŽÆ Generated {enum_type_count} enum types (properly allowing all values)") if args.debug: # Show which models have date conversion models_with_dates = [] - models_with_defaults = [] for line in ts_content.split('\n'): if line.startswith('export function convert') and 'FromApi' in line and 'convertFromApi' not in line: model_name = line.split('convert')[1].split('FromApi')[0] models_with_dates.append(model_name) - elif line.startswith('export const Default'): - model_name = line.split('export const Default')[1].split(':')[0] - models_with_defaults.append(model_name) - if models_with_dates: print(f" Models with date conversion: {', '.join(models_with_dates)}") - if models_with_defaults: - print(f" Models with default objects: {', '.join(models_with_defaults)}") # Provide troubleshooting info if debug mode if args.debug: print(f"\nšŸ› Debug mode was enabled. If you see incorrect type conversions:") - print(f" 1. Look for 'šŸŽÆ Enum type detected' lines to verify enum handling") + print(f" 1. Check the debug output above for enum default handling") print(f" 2. Look for 'šŸ“… Date type check' lines for date handling") print(f" 3. Look for 'āš ļø' warnings about fallback types") print(f" 4. Verify your Pydantic model field types and defaults are correct") @@ -1174,24 +925,19 @@ Enum fields now properly support all enum values: print(f"āœ… File size: {file_size} characters") if conversion_count > 0: print(f"āœ… Date conversion functions: {conversion_count}") - if default_objects_count > 0: - print(f"āœ… Default objects: {default_objects_count}") - if enum_union_count > 0: - print(f"āœ… Union types (proper enum support): {enum_union_count}") + if enum_type_count > 0: + print(f"āœ… Enum types (with full value range): {enum_type_count}") if not args.skip_test: print("āœ… Model validation passed") if not args.skip_compile: print("āœ… TypeScript syntax validated") print(f"\nšŸ’” Usage in your TypeScript project:") - print(f" import {{ ChatMessage, ChatStatusType, DefaultChatMessage, convertChatMessageFromApi }} from './{Path(args.output).stem}';") - print(f" const message: ChatMessage = {{ ...DefaultChatMessage, sessionId: '123', content: 'Hello' }};") + print(f" import {{ Candidate, Employer, Job, convertCandidateFromApi }} from './{Path(args.output).stem}';") if conversion_count > 0: - print(f" const message = convertChatMessageFromApi(apiResponse);") - print(f" const messages = convertArrayFromApi(apiResponse, 'ChatMessage');") - if default_objects_count > 0: - print(f" const overrideMessage: ChatMessage = {{ ...DefaultChatMessage, status: 'error' }};") - + print(f" const candidate = convertCandidateFromApi(apiResponse);") + print(f" const jobs = convertArrayFromApi(apiResponse, 'Job');") + return True except KeyboardInterrupt: diff --git a/src/backend/main.py b/src/backend/main.py index db2dd4f..87d1be7 100644 --- a/src/backend/main.py +++ b/src/backend/main.py @@ -279,6 +279,12 @@ async def get_database() -> RedisDatabase: """ return db_manager.get_database() +async def get_last_item(generator): + last_item = None + async for item in generator: + last_item = item + return last_item + def create_success_response(data: Any, meta: Optional[Dict] = None) -> Dict: return { "success": True, @@ -3050,7 +3056,7 @@ async def post_chat_session_message_stream( status_code=404, content=create_error_response("CANDIDATE_NOT_FOUND", "Candidate not found for this chat session") ) - logger.info(f"šŸ”— User {current_user.id} posting message to chat session {user_message.session_id} with query: {user_message.content}") + logger.info(f"šŸ”— User {current_user.id} posting message to chat session {user_message.session_id} with query length: {len(user_message.content)}") async with entities.get_candidate_entity(candidate=candidate) as candidate_entity: # Entity automatically released when done @@ -3343,6 +3349,68 @@ async def reset_chat_session( content=create_error_response("RESET_ERROR", str(e)) ) +@api_router.post("/candidates/{candidate_id}/skill-match") +async def get_candidate_skill_match( + candidate_id: str = Path(...), + requirement: str = Body(...), + current_user = Depends(get_current_user), + database: RedisDatabase = Depends(get_database) +): + """Get skill match for a candidate against a requirement""" + try: + # Find candidate by ID + candidate_data = await database.get_candidate(candidate_id) + if not candidate_data: + return JSONResponse( + status_code=404, + content=create_error_response("CANDIDATE_NOT_FOUND", f"Candidate with ID '{candidate_id}' not found") + ) + + candidate = Candidate.model_validate(candidate_data) + + logger.info(f"šŸ” Running skill match for candidate {candidate.id} against requirement: {requirement}") + async with entities.get_candidate_entity(candidate=candidate) as candidate_entity: + agent = candidate_entity.get_or_create_agent(agent_type=ChatContextType.SKILL_MATCH) + if not agent: + return JSONResponse( + status_code=400, + content=create_error_response("AGENT_NOT_FOUND", "No skill match agent found for this candidate") + ) + # Entity automatically released when done + skill_match = await get_last_item( + agent.generate( + llm=llm_manager.get_llm(), + model=defines.model, + user_message=ChatMessageUser( + sender_id=candidate.id, + session_id="", + content=requirement, + timestamp=datetime.now(UTC) + ), + user=candidate, + ) + ) + if skill_match is None: + return JSONResponse( + status_code=500, + content=create_error_response("NO_MATCH", "No skill match found for the given requirement") + ) + skill_match = skill_match.content.strip() + logger.info(f"āœ… Skill match found for candidate {candidate.id}: {skill_match}") + + return create_success_response({ + "candidateId": candidate.id, + "skillMatch": skill_match + }) + + except Exception as e: + logger.error(traceback.format_exc()) + logger.error(f"āŒ Get candidate skill match error: {e}") + return JSONResponse( + status_code=500, + content=create_error_response("SKILL_MATCH_ERROR", str(e)) + ) + @api_router.get("/candidates/{username}/chat-sessions") async def get_candidate_chat_sessions( username: str = Path(...), diff --git a/src/backend/models.py b/src/backend/models.py index dc1013d..90aeaba 100644 --- a/src/backend/models.py +++ b/src/backend/models.py @@ -71,8 +71,51 @@ class InterviewRecommendation(str, Enum): class ChatSenderType(str, Enum): USER = "user" ASSISTANT = "assistant" + AGENT = "agent" SYSTEM = "system" +class Requirements(BaseModel): + required: List[str] = Field(default_factory=list) + preferred: List[str] = Field(default_factory=list) + + @model_validator(mode='before') + def validate_requirements(cls, values): + if not isinstance(values, dict): + raise ValueError("Requirements must be a dictionary with 'required' and 'preferred' keys.") + return values + +class Citation(BaseModel): + text: str + source: str + relevance: int # 0-100 scale + +class SkillStatus(str, Enum): + PENDING = "pending" + COMPLETE = "complete" + ERROR = "error" + +class SkillMatch(BaseModel): + requirement: str + status: SkillStatus + match_score: int = Field(..., alias='matchScore') + assessment: str + citations: List[Citation] = Field(default_factory=list) + model_config = { + "populate_by_name": True # Allow both field names and aliases + } + +class JobRequirements(BaseModel): + technical_skills: Requirements = Field(..., alias="technicalSkills") + experience_requirements: Requirements = Field(..., alias="experienceRequirements") + soft_skills: Optional[List[str]] = Field(default_factory=list, alias="softSkills") + experience: Optional[List[str]] = [] + education: Optional[List[str]] = [] + certifications: Optional[List[str]] = [] + preferred_attributes: Optional[List[str]] = Field(None, alias="preferredAttributes") + model_config = { + "populate_by_name": True # Allow both field names and aliases + } + class ChatMessageType(str, Enum): ERROR = "error" GENERATING = "generating" @@ -106,6 +149,7 @@ class ChatContextType(str, Enum): GENERATE_PROFILE = "generate_profile" GENERATE_IMAGE = "generate_image" RAG_SEARCH = "rag_search" + SKILL_MATCH = "skill_match" class AIModelType(str, Enum): QWEN2_5 = "qwen2.5" @@ -710,20 +754,24 @@ class ChatOptions(BaseModel): seed: Optional[int] = 8911 num_ctx: Optional[int] = Field(default=None, alias="numCtx") # Number of context tokens temperature: Optional[float] = Field(default=0.7) # Higher temperature to encourage tool usage + model_config = { + "populate_by_name": True # Allow both field names and aliases + } + class LLMMessage(BaseModel): role: str = Field(default="") content: str = Field(default="") - tool_calls: Optional[List[Dict]] = Field(default={}, exclude=True) + tool_calls: Optional[List[Dict]] = Field(default=[], exclude=True) class ChatMessageBase(BaseModel): id: str = Field(default_factory=lambda: str(uuid.uuid4())) session_id: str = Field(..., alias="sessionId") sender_id: Optional[str] = Field(None, alias="senderId") - status: ChatStatusType = ChatStatusType.INITIALIZING - type: ChatMessageType = ChatMessageType.PREPARING - sender: ChatSenderType = ChatSenderType.SYSTEM + status: ChatStatusType #= ChatStatusType.INITIALIZING + type: ChatMessageType #= ChatMessageType.PREPARING + sender: ChatSenderType #= ChatSenderType.SYSTEM timestamp: datetime = Field(default_factory=lambda: datetime.now(UTC), alias="timestamp") tunables: Optional[Tunables] = None content: str = "" @@ -759,8 +807,8 @@ class ChatMessageMetaData(BaseModel): } class ChatMessageUser(ChatMessageBase): - status: ChatStatusType = ChatStatusType.DONE - type: ChatMessageType = ChatMessageType.USER + status: ChatStatusType = ChatStatusType.INITIALIZING + type: ChatMessageType = ChatMessageType.GENERATING sender: ChatSenderType = ChatSenderType.USER class ChatMessage(ChatMessageBase): diff --git a/src/backend/rag/rag.py b/src/backend/rag/rag.py index 965832d..5ce21c3 100644 --- a/src/backend/rag/rag.py +++ b/src/backend/rag/rag.py @@ -473,6 +473,7 @@ class ChromaDBFileWatcher(FileSystemEventHandler): logging.error(chunk) def prepare_metadata(self, meta: Dict[str, Any], buffer=defines.chunk_buffer)-> str | None: + source_file = meta.get("source_file") try: source_file = meta["source_file"] path_parts = source_file.split(os.sep) @@ -487,7 +488,7 @@ class ChromaDBFileWatcher(FileSystemEventHandler): meta["chunk_end"] = end return "".join(lines[start:end]) except: - logging.warning(f"Unable to open {meta["source_file"]}") + logging.warning(f"Unable to open {source_file}") return None # Cosine Distance Equivalent Similarity Retrieval Characteristics