Skill tracking almost working

frontend/src/components/JobMatchAnalysis.tsx

@@ -20,58 +20,101 @@ 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 {
+interface Job {
-  text: string;
+  title: string;
-  source: string;
+  description: string;
-  relevance: number; // 0-100 scale
 }
 
-interface SkillMatch {
+interface JobAnalysisProps extends BackstoryPageProps {
-  requirement: string;
+  job: Job;
-  status: 'pending' | 'complete' | 'error';
+  candidate: Candidate;
-  matchScore: number; // 0-100 scale
-  assessment: string;
-  citations: Citation[];
 }
 
-interface JobAnalysisProps {
+const defaultMessage: ChatMessageUser = {
-  jobTitle: string;
+  type: "preparing", status: "done", sender: "user", sessionId: "", timestamp: new Date(), content: ""
-  candidateName: string;
+};
-  // This function would connect to your backend and return updates
-  fetchRequirements: () => Promise<string[]>;
-  // This function would fetch match data for a specific requirement
-  fetchMatchForRequirement: (requirement: string) => Promise<SkillMatch>;
-}
 
-const JobMatchAnalysis: React.FC<JobAnalysisProps> = ({
+const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) => {
-  jobTitle,
+  const {
-  candidateName,
+    job,
-  fetchRequirements,
+    candidate,
-  fetchMatchForRequirement
+    setSnack,
-}) => {
+  } = props
+  const { apiClient } = useAuth();
   const theme = useTheme();
+  const [jobRequirements, setJobRequirements] = useState<JobRequirements | null>(null);
   const [requirements, setRequirements] = useState<string[]>([]);
   const [skillMatches, setSkillMatches] = useState<SkillMatch[]>([]);
-  const [loadingRequirements, setLoadingRequirements] = useState<boolean>(true);
+  const [creatingSession, setCreatingSession] = useState<boolean>(false);
+  const [loadingRequirements, setLoadingRequirements] = useState<boolean>(false);
   const [expanded, setExpanded] = useState<string | false>(false);
   const [overallScore, setOverallScore] = useState<number>(0);
+  const [requirementsSession, setRequirementsSession] = useState<ChatSession | null>(null);
+  const [statusMessage, setStatusMessage] = useState<ChatMessage | null>(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();
+        const chatMessage: ChatMessageUser = { ...defaultMessage, sessionId: requirementsSession.id || '', content: job.description };
-        setRequirements(fetchedRequirements);
+        apiClient.sendMessageStream(chatMessage, {
+          onMessage: (msg: ChatMessage) => {
+            console.log(`onMessage: ${msg.type}`, msg);
+            if (msg.type === "response") {
+              const incoming: any = toCamelCase<JobRequirements>(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 => ({
+              const initialSkillMatches = requirements.map(req => ({
                 requirement: req,
                 status: 'pending' as const,
                 matchScore: 0,
@@ -79,16 +122,42 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = ({
                 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(() => {
@@ -98,8 +167,8 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = ({
       // 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;
@@ -133,7 +202,7 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = ({
     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<JobAnalysisProps> = ({
           
           <Grid size={{ xs: 12, md: 6 }}>
             <Typography variant="h6" component="h2">
-              Job: {jobTitle}
+              Job: {job.title}
             </Typography>
           </Grid>
           
           <Grid size={{ xs: 12, md: 6 }}>
             <Typography variant="h6" component="h2">
-              Candidate: {candidateName}
+              Candidate: {candidate.fullName}
             </Typography>
           </Grid>
           
@@ -329,7 +398,7 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = ({
                       Supporting Evidence:
                     </Typography>
                     
-                    {match.citations.length > 0 ? (
+                        {match.citations && match.citations.length > 0 ? (
                       match.citations.map((citation, citIndex) => (
                         <Card 
                           key={citIndex} 

frontend/src/pages/DemoComponent.tsx

@@ -1,153 +0,0 @@
-import React from 'react';
-import { JobMatchAnalysis } from '../components/JobMatchAnalysis';
-
-// Mock data and functions to simulate your backend
-const mockRequirements = [
-  "5+ years of React development experience",
-  "Strong TypeScript skills",
-  "Experience with RESTful APIs",
-  "Knowledge of state management solutions (Redux, Context API)",
-  "Experience with CI/CD pipelines",
-  "Cloud platform experience (AWS, Azure, GCP)"
-];
-
-// Simulates fetching requirements with a delay
-const mockFetchRequirements = async (): Promise<string[]> => {
-  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<any> => {
-  // Create different mock responses based on the requirement
-  const mockResponses: Record<string, any> = {
-    "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 (
-    <JobMatchAnalysis
-      jobTitle="Senior Frontend Developer"
-      candidateName="Alex Johnson"
-      fetchRequirements={mockFetchRequirements}
-      fetchMatchForRequirement={mockFetchMatchForRequirement}
-    />
-  );
-};
-
-export { DemoComponent };

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 });
                 }

frontend/src/pages/JobAnalysisPage.tsx

@@ -39,13 +39,16 @@ import { useNavigate } from 'react-router-dom';
 import { BackstoryPageProps } from 'components/BackstoryTab';
 import { useAuth } from 'hooks/AuthContext';
 import { useSelectedCandidate } from 'hooks/GlobalContext';
+import { CandidateInfo } from 'components/CandidateInfo';
+import { ComingSoon } from 'components/ui/ComingSoon';
 
 // Main component
 const JobAnalysisPage: React.FC<BackstoryPageProps> = (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('');
@@ -55,7 +58,6 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
   const [error, setError] = useState<string | null>(null);
   const [openUploadDialog, setOpenUploadDialog] = useState(false);
   const { apiClient } = useAuth();
-  const { setSnack } = props;
   const [candidates, setCandidates] = useState<Candidate[] | null>(null);
 
   useEffect(() => {
@@ -93,32 +95,14 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
   }, [selectedCandidate, activeStep]);
 
   // Steps in our process
-  const steps = selectedCandidate === null ? [
+  const steps = [
-    { index: 0, label: 'Select Candidate', icon: <PersonIcon /> },
     { index: 1, label: 'Job Description', icon: <WorkIcon /> },
-    { index: 2, label: 'View Analysis', icon: <AssessmentIcon /> }
+    { index: 2, label: 'AI Analysis', icon: <WorkIcon /> },
-  ] : [
+    { index: 3, label: 'Generated Resume', icon: <AssessmentIcon /> }
-    { index: 1, label: 'Job Description', icon: <WorkIcon /> },
-    { index: 2, label: 'View Analysis', icon: <AssessmentIcon /> }
   ];
-
+  if (!selectedCandidate) {
-  // Mock handlers for our analysis APIs
+    steps.unshift({ index: 0, label: 'Select Candidate', icon: <PersonIcon /> })
-  const fetchRequirements = async (): Promise<string[]> => {
+  }
-    // Simulates extracting requirements from the job description
-    await new Promise(resolve => setTimeout(resolve, 2000));
-    
-    // This would normally parse the job description to extract requirements
-    const mockRequirements = [
-      "5+ years of React development experience",
-      "Strong TypeScript skills",
-      "Experience with RESTful APIs",
-      "Knowledge of state management solutions (Redux, Context API)",
-      "Experience with CI/CD pipelines",
-      "Cloud platform experience (AWS, Azure, GCP)"
-    ];
-    
-    return mockRequirements;
-  };
 
   const fetchMatchForRequirement = async (requirement: string): Promise<any> => {
     // Create different mock responses based on the requirement
@@ -245,10 +229,12 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
       return;
     }
     
-    if (activeStep === 1 && (/*(extraInfo && !jobTitle) || */!jobDescription)) {
+    if (activeStep === 1) {
-      setError('Please provide both job title and description before continuing.');
+      if ((/*(extraInfo && !jobTitle) || */!jobDescription)) {
+        setError('Please provide job description before continuing.');
         return;
       }
+    }
     
     if (activeStep === 2) {
       setAnalysisStarted(true);
@@ -433,15 +419,20 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
     <Box sx={{ mt: 3 }}>
       {selectedCandidate && (
         <JobMatchAnalysis
-          jobTitle={jobTitle}
+          job={{ title: jobTitle, description: jobDescription }}
-          candidateName={selectedCandidate.fullName}
+          candidate={selectedCandidate}
-          fetchRequirements={fetchRequirements}
+          {...backstoryProps}
-          fetchMatchForRequirement={fetchMatchForRequirement}
         />
       )}
     </Box>
   );
 
+  const renderResume = () => (
+    <Box sx={{ mt: 3 }}>
+      {selectedCandidate && <ComingSoon>Resume Builder</ComingSoon>}
+    </Box>
+  );
+
   // If no user is logged in, show message
   if (!user) {
     return (
@@ -464,6 +455,7 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
         <Typography variant="h4" component="h1" gutterBottom>
           Candidate Analysis
         </Typography>
+        {selectedCandidate && <CandidateInfo variant="small" candidate={selectedCandidate} />}
         <Typography variant="subtitle1" color="text.secondary" gutterBottom>
           Match candidates to job requirements with AI-powered analysis
         </Typography>
@@ -496,6 +488,7 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
       {activeStep === 0 && renderCandidateSelection()}
       {activeStep === 1 && renderJobDescription()}
       {activeStep === 2 && renderAnalysis()}
+      {activeStep === 3 && renderResume()}
       
       <Box sx={{ display: 'flex', flexDirection: 'row', pt: 2 }}>
         <Button

frontend/src/services/api-client.ts

@@ -824,6 +824,18 @@ class ApiClient {
     return result;
   }
 
+  async candidateMatchForRequirement(candidate_id: string, requirement: string) : Promise<Types.SkillMatch> {
+    const response = await fetch(`${this.baseUrl}/candidates/${candidate_id}/skill-match`, {
+      method: 'POST',
+      headers: this.defaultHeaders,
+      body: JSON.stringify(requirement)
+    });
+
+    const result = await handleApiResponse<Types.SkillMatch>(response);
+    
+    return result;
+  }
+
   async updateCandidateDocument(document: Types.Document) : Promise<Types.Document> {
     const request : Types.DocumentUpdateRequest = {
       filename: document.filename,
@@ -1040,7 +1052,11 @@ class ApiClient {
 
                     default:
                       incomingMessageList.push(convertedIncoming);
+                      try {
                         options.onMessage?.(convertedIncoming);
+                      } catch (error) {
+                        console.error('onMessage handler failed: ', error);
+                      }
                       break;
                   }
                 }

frontend/src/types/types.ts

@@ -1,6 +1,6 @@
 // Generated TypeScript types from Pydantic models
 // Source: src/backend/models.py
-// Generated on: 2025-06-03T18:51:32.304683
+// Generated on: 2025-06-04T03:59:11.250216
 // DO NOT EDIT MANUALLY - This file is auto-generated
 
 // ============================
@@ -13,13 +13,13 @@ 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" | "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" | "done" | "error";
+export type ChatStatusType = "initializing" | "streaming" | "status" | "done" | "error";
 
 export type ColorBlindMode = "protanopia" | "deuteranopia" | "tritanopia" | "none";
 
@@ -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" | "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<string, any>;
@@ -282,10 +284,10 @@ export interface ChatMessage {
   id?: string;
   sessionId: string;
   senderId?: string;
-  status: "initializing" | "streaming" | "done" | "error";
+  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;
+  timestamp?: Date;
   tunables?: Tunables;
   content: string;
   metadata?: ChatMessageMetaData;
@@ -295,16 +297,16 @@ export interface ChatMessageBase {
   id?: string;
   sessionId: string;
   senderId?: string;
-  status: "initializing" | "streaming" | "done" | "error";
+  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;
+  timestamp?: Date;
   tunables?: Tunables;
   content: string;
 }
 
 export interface ChatMessageMetaData {
-  model: "qwen2.5";
+  model: "qwen2.5" | "flux-schnell";
   temperature: number;
   maxTokens: number;
   topP: number;
@@ -326,10 +328,10 @@ export interface ChatMessageRagSearch {
   id?: string;
   sessionId: string;
   senderId?: string;
-  status: "done";
+  status: "initializing" | "streaming" | "status" | "done" | "error";
-  type: "rag_result";
+  type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user";
-  sender: "user";
+  sender: "user" | "assistant" | "agent" | "system";
-  timestamp: Date;
+  timestamp?: Date;
   tunables?: Tunables;
   content: string;
   dimensions: number;
@@ -339,10 +341,10 @@ export interface ChatMessageUser {
   id?: string;
   sessionId: string;
   senderId?: string;
-  status: "done";
+  status: "initializing" | "streaming" | "status" | "done" | "error";
-  type: "user";
+  type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user";
-  sender: "user";
+  sender: "user" | "assistant" | "agent" | "system";
-  timestamp: Date;
+  timestamp?: Date;
   tunables?: Tunables;
   content: string;
 }
@@ -386,6 +388,12 @@ export interface ChromaDBGetResponse {
   umapEmbedding3D?: Array<number>;
 }
 
+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<string, any>;
 }
 
+export interface JobRequirements {
+  technicalSkills: Requirements;
+  experienceRequirements: Requirements;
+  softSkills?: Array<string>;
+  experience?: Array<string>;
+  education?: Array<string>;
+  certifications?: Array<string>;
+  preferredAttributes?: Array<string>;
+}
+
 export interface JobResponse {
   success: boolean;
   data?: Job;
@@ -765,6 +783,11 @@ export interface RefreshToken {
   revokedReason?: string;
 }
 
+export interface Requirements {
+  required?: Array<string>;
+  preferred?: Array<string>;
+}
+
 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<Citation>;
+}
+
 export interface SocialLink {
   platform: "linkedin" | "twitter" | "github" | "dribbble" | "behance" | "website" | "other";
   url: string;
@@ -1017,7 +1048,7 @@ export function convertChatMessageFromApi(data: any): ChatMessage {
   return {
     ...data,
     // Convert timestamp from ISO string to Date
-    timestamp: new Date(data.timestamp),
+    timestamp: data.timestamp ? new Date(data.timestamp) : undefined,
   };
 }
 /**
@@ -1030,7 +1061,7 @@ export function convertChatMessageBaseFromApi(data: any): ChatMessageBase {
   return {
     ...data,
     // Convert timestamp from ISO string to Date
-    timestamp: new Date(data.timestamp),
+    timestamp: data.timestamp ? new Date(data.timestamp) : undefined,
   };
 }
 /**
@@ -1043,7 +1074,7 @@ export function convertChatMessageRagSearchFromApi(data: any): ChatMessageRagSea
   return {
     ...data,
     // Convert timestamp from ISO string to Date
-    timestamp: new Date(data.timestamp),
+    timestamp: data.timestamp ? new Date(data.timestamp) : undefined,
   };
 }
 /**
@@ -1056,7 +1087,7 @@ export function convertChatMessageUserFromApi(data: any): ChatMessageUser {
   return {
     ...data,
     // Convert timestamp from ISO string to Date
-    timestamp: new Date(data.timestamp),
+    timestamp: data.timestamp ? new Date(data.timestamp) : undefined,
   };
 }
 /**

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
+                status_message = ChatMessage(
-                rag_message.status = ChatStatusType.INITIALIZING
+                    session_id=chat_message.session_id,
-                rag_message.content = f"Checking RAG context {rag.name}..."
+                    sender=ChatSenderType.AGENT,
-                yield rag_message
+                    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 = (
+                    for index, metadata in enumerate(chroma_results["metadatas"]):
-                f"RAG context gathered from results from {entries} documents."
+                        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)

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"🤖 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,

src/backend/agents/job_requirements.py

@@ -0,0 +1,170 @@
+from __future__ import annotations
+from pydantic import model_validator, Field  # type: ignore
+from typing import (
+    Dict,
+    Literal,
+    ClassVar,
+    Any,
+    AsyncGenerator,
+    List,
+    Optional
+#    override
+)  # NOTE: You must import Optional for late binding to work
+import inspect
+import re
+import json
+import traceback
+import asyncio
+import time
+import asyncio
+import numpy as np  # type: ignore
+
+from .base import Agent, agent_registry, LLMMessage
+from models import Candidate, ChatMessage, ChatMessageBase, ChatMessageMetaData, ChatMessageType, ChatMessageUser, ChatOptions, ChatSenderType, ChatStatusType, JobRequirements
+import model_cast
+from logger import logger
+import defines
+
+class JobRequirementsAgent(Agent):
+    agent_type: Literal["job_requirements"] = "job_requirements"  # type: ignore
+    _agent_type: ClassVar[str] = agent_type  # Add this for registration
+
+    # Stage 1A: Job Analysis Implementation
+    def create_job_analysis_prompt(self, job_description: str) -> tuple[str, str]:
+        """Create the prompt for job requirements analysis."""
+        logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+        system_prompt = """
+      You are an objective job requirements analyzer. Your task is to extract and categorize the specific skills,
+      experiences, and qualifications required in a job description WITHOUT any reference to any candidate.
+      
+      ## INSTRUCTIONS:
+      
+      1. Analyze ONLY the job description provided.
+      2. Extract and categorize all requirements and preferences.
+      3. DO NOT consider any candidate information - this is a pure job analysis task.
+      
+      ## OUTPUT FORMAT:
+      
+      ```json
+      {
+        "job_requirements": {
+          "technical_skills": {
+            "required": ["skill1", "skill2"],
+            "preferred": ["skill1", "skill2"]
+          },
+          "experience_requirements": {
+            "required": ["exp1", "exp2"],
+            "preferred": ["exp1", "exp2"]
+          },
+          "education_requirements": ["req1", "req2"],
+          "soft_skills": ["skill1", "skill2"],
+          "industry_knowledge": ["knowledge1", "knowledge2"],
+          "responsibilities": ["resp1", "resp2"],
+          "company_values": ["value1", "value2"]
+        }
+      }
+      ```
+      
+      Be specific and detailed in your extraction. Break down compound requirements into individual components.
+      For example, "5+ years experience with React, Node.js and MongoDB" should be separated into:
+      - Experience: "5+ years software development"
+      - Technical skills: "React", "Node.js", "MongoDB"
+      
+      Avoid vague categorizations and be precise about whether skills are explicitly required or just preferred.
+      """
+
+        prompt = f"Job Description:\n{job_description}"
+        return system_prompt, prompt
+
+    async def analyze_job_requirements(
+        self, llm: Any, model: str, user_message: ChatMessage
+    ) -> AsyncGenerator[ChatMessage, None]:
+        """Analyze job requirements from job description."""
+        system_prompt, prompt = self.create_job_analysis_prompt(user_message.content)
+        analyze_message = user_message.model_copy()
+        analyze_message.content = prompt
+        generated_message = None
+        async for generated_message in self.llm_one_shot(llm, model, system_prompt=system_prompt, user_message=analyze_message):
+            if generated_message.status == ChatStatusType.ERROR:
+                generated_message.content = "Error analyzing job requirements."
+                yield generated_message
+                return
+                
+        if not generated_message:
+            status_message = ChatMessage(
+                session_id=user_message.session_id,
+                sender=ChatSenderType.AGENT,
+                status = ChatStatusType.ERROR,
+                type = ChatMessageType.ERROR,
+                content = "Job requirements analysis failed to generate a response.")
+            yield status_message
+            return
+
+        generated_message.status = ChatStatusType.DONE
+        generated_message.type = ChatMessageType.RESPONSE            
+        yield generated_message
+        return
+
+    async def generate(
+        self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate | None, temperature=0.7
+    ) -> AsyncGenerator[ChatMessage, None]:
+        # Stage 1A: Analyze job requirements
+        status_message = ChatMessage(
+            session_id=user_message.session_id,
+            sender=ChatSenderType.AGENT,
+            status=ChatStatusType.STATUS,
+            type=ChatMessageType.THINKING,
+            content = f"Analyzing job requirements")
+        yield status_message
+
+        generated_message = None
+        async for generated_message in self.analyze_job_requirements(llm, model, user_message):
+            if generated_message.status == ChatStatusType.ERROR:
+                status_message.status = ChatStatusType.ERROR
+                status_message.content = generated_message.content
+                yield status_message
+                return
+        if not generated_message:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = "Job requirements analysis failed."
+            yield status_message
+            return
+        
+        json_str = self.extract_json_from_text(generated_message.content)
+        job_requirements : JobRequirements | None = None
+        job_requirements_data = ""
+        try:
+            job_requirements_data = json.loads(json_str)
+            job_requirements_data = job_requirements_data.get("job_requirements", None)
+            job_requirements = JobRequirements.model_validate(job_requirements_data)
+            if not job_requirements:
+                raise ValueError("Job requirements data is empty or invalid.")
+        except json.JSONDecodeError as e:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = f"Failed to parse job requirements JSON: {str(e)}\n\n{job_requirements_data}"
+            logger.error(f"⚠️ {status_message.content}")
+            yield status_message
+            return
+        except ValueError as e:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = f"Job requirements validation error: {str(e)}\n\n{job_requirements_data}"
+            logger.error(f"⚠️ {status_message.content}")
+            yield status_message
+            return
+        except Exception as e:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = f"Unexpected error processing job requirements: {str(e)}\n\n{job_requirements_data}"
+            logger.error(traceback.format_exc())
+            logger.error(f"⚠️ {status_message.content}")
+            yield status_message
+            return
+        status_message.status = ChatStatusType.DONE
+        status_message.type = ChatMessageType.RESPONSE
+        status_message.content = json.dumps(job_requirements.model_dump(mode="json", exclude_unset=True))
+        yield status_message
+
+        logger.info(f"✅ Job requirements analysis completed successfully.")
+        return
+
+# Register the base agent
+agent_registry.register(JobRequirementsAgent._agent_type, JobRequirementsAgent)

src/backend/agents/skill_match.py

@@ -0,0 +1,233 @@
+from __future__ import annotations
+from pydantic import model_validator, Field  # type: ignore
+from typing import (
+    Dict,
+    Literal,
+    ClassVar,
+    Any,
+    AsyncGenerator,
+    List,
+    Optional
+#    override
+)  # NOTE: You must import Optional for late binding to work
+import inspect
+import re
+import json
+import traceback
+import asyncio
+import time
+import asyncio
+import numpy as np  # type: ignore
+
+from .base import Agent, agent_registry, LLMMessage
+from models import Candidate, ChatMessage, ChatMessageBase, ChatMessageMetaData, ChatMessageType, ChatMessageUser, ChatOptions, ChatSenderType, ChatStatusType, SkillMatch
+import model_cast
+from logger import logger
+import defines
+
+class SkillMatchAgent(Agent):
+    agent_type: Literal["skill_match"] = "skill_match"  # type: ignore
+    _agent_type: ClassVar[str] = agent_type  # Add this for registration
+
+    def generate_skill_assessment_prompt(self, skill, rag_content):
+        """
+        Generate a system prompt to query the LLM for evidence of a specific skill
+
+        Parameters:
+        - skill (str): The specific skill to assess from job requirements
+        - rag_content (str): Additional RAG content queried from candidate documents
+
+        Returns:
+        - str: A system prompt tailored to assess the specific skill
+        """
+
+        system_prompt = f"""You are an objective skill assessor. Your task is to determine if a candidate possesses 
+a SPECIFIC skill based solely on their resume and supporting evidence.
+
+## SKILL TO ASSESS:
+"{skill}"
+
+## INSTRUCTIONS:
+1. Focus exclusively on assessing the candidate's proficiency with the skill: "{skill}".
+2. Examine the resume and supporting documents for both explicit mentions and clearly demonstrated applications of the skill.
+3. Do NOT infer the skill unless it is either:
+   - Directly mentioned in association with experience, or
+   - Clearly evidenced through relevant tools, technologies, responsibilities, or outcomes.
+   - Referenced as being used or listed as part of a broader initiative.
+4. Evaluate each piece of evidence and assign a confidence rating:
+   - STRONG: Explicit use with clear context or repeated/prolonged experience
+   - MODERATE: Inferred through tools, environments, or outcomes (e.g., Python used in a listed project/tool)
+   - WEAK: Mentioned in a list or indirectly implied without context
+   - NONE: No relevant evidence
+5. Provide up to 10 evidence_details. Each should include:
+   - source: where the evidence appears
+   - quote: an exact snippet from the document(s)
+   - context: a short rationale explaining how this supports the skill claim
+6. When no supporting evidence exists, output a "NONE" rating with an empty evidence details list.
+7. IMPORTANT: Even if the skill is only listed in a technologies or languages section, consider this valid evidence.
+
+## OUTPUT FORMAT:
+```json
+{{
+  "skill_assessment": {{
+    "skill": "{skill}",
+    "evidence_found": true/false,
+    "evidence_strength": "STRONG/MODERATE/WEAK/NONE",
+    "description": "short (two to three sentence) description of what {skill} means with a concise example of what you're looking for",
+    "evidence_details": [
+      {{
+        "source": "resume section/position/project",
+        "quote": "exact text from resume showing evidence",
+        "context": "brief explanation of how this demonstrates the skill"
+      }}
+    ]
+  }}
+}}
+```
+
+IMPORTANT: Be factual and precise. If you cannot find strong evidence for this specific skill, it's better to indicate "evidence_strength": WEAK than to stretch for connections. 
+Focus only on "{skill}" and not similar skills unless they directly demonstrate the required skill. 
+Remember that a skill listed in a "Languages" or "Technologies" section should be considered valid evidence.
+
+Adhere strictly to the JSON output format requested. Do not include any additional text or commentary outside the JSON structure.
+"""
+
+        prompt = f"""Analyze the candidate information below for evidence of "{skill}".
+
+RESPOND WITH ONLY VALID JSON USING THE EXACT FORMAT SPECIFIED.
+
+<candidate_info>
+{rag_content}
+</candidate_info>
+
+JSON RESPONSE:"""
+
+        return system_prompt, prompt
+
+    async def analyze_job_requirements(
+        self, llm: Any, model: str, user_message: ChatMessage
+    ) -> AsyncGenerator[ChatMessage, None]:
+        """Analyze job requirements from job description."""
+        system_prompt, prompt = self.create_job_analysis_prompt(user_message.content)
+        analyze_message = user_message.model_copy()
+        analyze_message.content = prompt
+        generated_message = None
+        async for generated_message in self.llm_one_shot(llm, model, system_prompt=system_prompt, user_message=analyze_message):
+            if generated_message.status == ChatStatusType.ERROR:
+                generated_message.content = "Error analyzing job requirements."
+                yield generated_message
+                return
+                
+        if not generated_message:
+            status_message = ChatMessage(
+                session_id=user_message.session_id,
+                sender=ChatSenderType.AGENT,
+                status = ChatStatusType.ERROR,
+                type = ChatMessageType.ERROR,
+                content = "Job requirements analysis failed to generate a response.")
+            yield status_message
+            return
+
+        generated_message.status = ChatStatusType.DONE
+        generated_message.type = ChatMessageType.RESPONSE            
+        yield generated_message
+        return
+
+    async def generate(
+        self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate | None, temperature=0.7
+    ) -> AsyncGenerator[ChatMessage, None]:
+        # Stage 1A: Analyze job requirements
+        status_message = ChatMessage(
+            session_id=user_message.session_id,
+            sender=ChatSenderType.AGENT,
+            status=ChatStatusType.STATUS,
+            type=ChatMessageType.THINKING,
+            content = f"Analyzing job requirements")
+        yield status_message
+
+        rag_message = None
+        async for rag_message in self.generate_rag_results(chat_message=user_message):
+            if rag_message.status == ChatStatusType.ERROR:
+                status_message.status = ChatStatusType.ERROR
+                status_message.content = rag_message.content
+                logger.error(f"⚠️ {status_message.content}")
+                yield status_message
+                return
+
+        if rag_message is None:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = "Failed to retrieve RAG context."
+            logger.error(f"⚠️ {status_message.content}")
+            yield status_message
+            return
+
+        logger.info(f"🔍 RAG content retrieved: {len(rag_message.content)} bytes")
+        
+        system_prompt, prompt = self.generate_skill_assessment_prompt(skill=user_message.content, rag_content=rag_message.content)
+
+        user_message.content = prompt
+        skill_assessment = None
+        async for skill_assessment in self.llm_one_shot(llm=llm, model=model, user_message=user_message, system_prompt=system_prompt, temperature=0.1):
+            if skill_assessment.status == ChatStatusType.ERROR:
+                status_message.status = ChatStatusType.ERROR
+                status_message.content = skill_assessment.content
+                logger.error(f"⚠️ {status_message.content}")
+                yield status_message
+                return
+        if skill_assessment is None:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = "Failed to generate skill assessment."
+            logger.error(f"⚠️ {status_message.content}")
+            yield status_message
+            return
+
+        json_str = self.extract_json_from_text(skill_assessment.content)
+        skill_match = json_str#: SkillMatch | None = None
+        skill_assessment_data = ""
+        try:
+            skill_assessment_data = json.loads(json_str)
+            match_level = (
+                skill_assessment_data
+                .get("skill_assessment", {})
+                .get("evidence_strength", "UNKNOWN")
+            )
+            skill_description = (
+                skill_assessment_data
+                .get("skill_assessment", {})
+                .get("description", "")
+            )
+        except json.JSONDecodeError as e:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = f"Failed to parse Skill assessment JSON: {str(e)}\n\n{skill_assessment_data}"
+            logger.error(f"⚠️ {status_message.content}")
+            yield status_message
+            return
+        except ValueError as e:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = f"Skill assessment validation error: {str(e)}\n\n{skill_assessment_data}"
+            logger.error(f"⚠️ {status_message.content}")
+            yield status_message
+            return
+        except Exception as e:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = f"Unexpected error processing Skill assessment: {str(e)}\n\n{skill_assessment_data}"
+            logger.error(traceback.format_exc())
+            logger.error(f"⚠️ {status_message.content}")
+            yield status_message
+            return
+        if skill_match is None:
+            status_message.status = ChatStatusType.ERROR
+            status_message.content = "Skill assessment analysis failed to produce valid data."
+            logger.error(f"⚠️ {status_message.content}")
+            yield status_message
+            return
+        status_message.status = ChatStatusType.DONE
+        status_message.type = ChatMessageType.RESPONSE
+        status_message.content = skill_match
+        yield status_message
+
+        logger.info(f"✅ Skill assessment completed successfully.")
+        return
+
+# Register the base agent
+agent_registry.register(SkillMatchAgent._agent_type, SkillMatchAgent)

src/backend/generate_types.py

@@ -198,13 +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"    🔄 Unwrapped: {original_type} -> {python_type}")
     
-    # Check if this field has a specific enum default value
+    # 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"    🎯 Field has specific enum default: {default_enum.value}")
+                print(f"    🎯 Field has specific enum default: {default_enum.value}, but returning full enum type")
-            return f'"{default_enum.value}"'
+            # Don't return just the default value - continue to process the full enum type
     
     # Handle None/null
     if python_type is type(None):
@@ -780,7 +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 and proper enum default handling',
+        description='Generate TypeScript types from Pydantic models with date conversion functions and proper enum handling',
         formatter_class=argparse.RawDescriptionHelpFormatter,
         epilog="""
 Examples:
@@ -795,8 +796,8 @@ Generated conversion functions can be used like:
   const candidate = convertCandidateFromApi(apiResponse);
   const jobs = convertArrayFromApi<Job>(apiResponse, 'Job');
 
-Enum defaults are now properly handled:
+Enum types are now properly handled:
-  status: ChatStatusType = ChatStatusType.DONE  ->  status: "done"
+  status: ChatStatusType = ChatStatusType.DONE  ->  status: ChatStatusType (not locked to "done")
         """
     )
     
@@ -833,12 +834,12 @@ Enum defaults are now properly handled:
     parser.add_argument(
         '--version', '-v',
         action='version',
-        version='TypeScript Generator 3.1 (with Enum Default Handling)'
+        version='TypeScript Generator 3.2 (Fixed Enum Default Handling)'
     )
     
     args = parser.parse_args()
     
-    print("🚀 Enhanced TypeScript Type Generator with Enum Default Handling")
+    print("🚀 Enhanced TypeScript Type Generator with Fixed Enum Handling")
     print("=" * 60)
     print(f"📁 Source file: {args.source}")
     print(f"📁 Output file: {args.output}")
@@ -883,12 +884,12 @@ Enum defaults are now properly handled:
         
         # Count conversion functions and provide detailed feedback
         conversion_count = ts_content.count('export function convert') - ts_content.count('convertFromApi') - ts_content.count('convertArrayFromApi')
-        enum_specific_count = ts_content.count(': "') - ts_content.count('export type')
+        enum_type_count = ts_content.count('export type')
         
         if conversion_count > 0:
             print(f"🗓️  Generated {conversion_count} date conversion functions")
-        if enum_specific_count > 0:
+        if enum_type_count > 0:
-            print(f"🎯 Generated {enum_specific_count} specific enum default types")
+            print(f"🎯 Generated {enum_type_count} enum types (properly allowing all values)")
             
         if args.debug:
             # Show which models have date conversion
@@ -903,7 +904,7 @@ Enum defaults are now properly handled:
         # Provide troubleshooting info if debug mode
         if args.debug:
             print(f"\n🐛 Debug mode was enabled. If you see incorrect type conversions:")
-            print(f"   1. Check the debug output above for '🎯 Field has specific enum default' lines")
+            print(f"   1. Check the debug output above for enum default handling")
             print(f"   2. Look for '📅 Date type check' lines for date handling")
             print(f"   3. Look for '⚠️' warnings about fallback types") 
             print(f"   4. Verify your Pydantic model field types and defaults are correct")
@@ -924,8 +925,8 @@ Enum defaults are now properly handled:
         print(f"✅ File size: {file_size} characters")
         if conversion_count > 0:
             print(f"✅ Date conversion functions: {conversion_count}")
-        if enum_specific_count > 0:
+        if enum_type_count > 0:
-            print(f"✅ Specific enum default types: {enum_specific_count}")
+            print(f"✅ Enum types (with full value range): {enum_type_count}")
         if not args.skip_test:
             print("✅ Model validation passed")
         if not args.skip_compile:

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"🔗 User {current_user.id} posting message to chat session {user_message.session_id} with query: {user_message.content}")
+        logger.info(f"🔗 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"🔍 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"✅ 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"❌ 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(...),

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"
@@ -97,6 +140,7 @@ class ChatStatusType(str, Enum):
 
 class ChatContextType(str, Enum):
     JOB_SEARCH = "job_search"
+    JOB_REQUIREMENTS = "job_requirements"
     CANDIDATE_CHAT = "candidate_chat"
     INTERVIEW_PREP = "interview_prep"
     RESUME_REVIEW = "resume_review"
@@ -105,6 +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"
@@ -709,20 +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
+    status: ChatStatusType #= ChatStatusType.INITIALIZING
-    type: ChatMessageType = ChatMessageType.PREPARING
+    type: ChatMessageType  #= ChatMessageType.PREPARING
-    sender: ChatSenderType = ChatSenderType.SYSTEM
+    sender: ChatSenderType #= ChatSenderType.SYSTEM
     timestamp: datetime = Field(default_factory=lambda: datetime.now(UTC), alias="timestamp")
     tunables: Optional[Tunables] = None
     content: str = ""
@@ -758,8 +807,8 @@ class ChatMessageMetaData(BaseModel):
     }
 
 class ChatMessageUser(ChatMessageBase):
-    status: ChatStatusType = ChatStatusType.DONE
+    status: ChatStatusType = ChatStatusType.INITIALIZING
-    type: ChatMessageType = ChatMessageType.USER
+    type: ChatMessageType = ChatMessageType.GENERATING
     sender: ChatSenderType = ChatSenderType.USER
 
 class ChatMessage(ChatMessageBase):

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