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frontend/src/BackstoryApp.tsx

@@ -1,15 +1,14 @@
 import React, { useEffect, useState, useRef, useCallback } from 'react';
 import { Route, Routes, useLocation, useNavigate } from 'react-router-dom';
 import { ThemeProvider } from '@mui/material/styles';
-import { Box } from '@mui/material';
 import { backstoryTheme } from './BackstoryTheme';
 
 import { SeverityType } from 'components/Snack';
-import { Query } from 'types/types';
 import { ConversationHandle } from 'components/Conversation';
 import { UserProvider } from 'hooks/useUser';
 import { CandidateRoute } from 'routes/CandidateRoute';
 import { BackstoryLayout } from 'components/layout/BackstoryLayout';
+import { ChatQuery } from 'types/types';
 
 import './BackstoryApp.css';
 import '@fontsource/roboto/300.css';
@@ -25,7 +24,7 @@ const BackstoryApp = () => {
     const setSnack = useCallback((message: string, severity?: SeverityType) => {
         snackRef.current?.setSnack(message, severity);
     }, [snackRef]);
-    const submitQuery = (query: Query) => {
+    const submitQuery = (query: ChatQuery) => {
         console.log(`handleSubmitChatQuery:`, query, chatRef.current ? ' sending' : 'no handler');
         chatRef.current?.submitQuery(query);
         navigate('/chat');

frontend/src/components/BackstoryQuery.tsx

@@ -1,16 +1,16 @@
 import Box from '@mui/material/Box';
 import Button from '@mui/material/Button';
 
-import { Query } from "../types/types";
+import { ChatQuery } from "types/types";
 
-type ChatSubmitQueryInterface = (query: Query) => void;
+type ChatSubmitQueryInterface = (query: ChatQuery) => void;
 
-interface ChatQueryInterface {
+interface BackstoryQueryInterface {
-  query: Query,
+  query: ChatQuery,
   submitQuery?: ChatSubmitQueryInterface
 }
 
-const ChatQuery = (props : ChatQueryInterface) => {
+const BackstoryQuery = (props : BackstoryQueryInterface) => {
   const { query, submitQuery } = props;
 
   if (submitQuery === undefined) {
@@ -29,11 +29,11 @@ const ChatQuery = (props : ChatQueryInterface) => {
 }
 
 export type {
-  ChatQueryInterface,
+  BackstoryQueryInterface,
   ChatSubmitQueryInterface,
 };
 
 export  {
-  ChatQuery,
+  BackstoryQuery,
 };
 

frontend/src/components/BackstoryTab.tsx

@@ -1,7 +1,7 @@
 import React, { ReactElement, JSXElementConstructor } from 'react';
 import Box from '@mui/material/Box';
 import { SxProps, Theme } from '@mui/material';
-import { ChatSubmitQueryInterface } from './ChatQuery';
+import { ChatSubmitQueryInterface } from './BackstoryQuery';
 import { SetSnackType } from './Snack';
 
 interface BackstoryElementProps {

frontend/src/components/Conversation.tsx

@@ -14,8 +14,8 @@ import { BackstoryTextField, BackstoryTextFieldRef } from 'components/BackstoryT
 import { BackstoryElementProps } from './BackstoryTab';
 import { connectionBase } from 'utils/Global';
 import { useUser } from "hooks/useUser";
-import { ApiClient, StreamingResponse } from 'types/api-client';
+import { StreamingResponse } from 'types/api-client';
-import { ChatMessage, ChatContext, ChatSession, AIParameters, Query } from 'types/types';
+import { ChatMessage, ChatContext, ChatSession, ChatQuery } from 'types/types';
 import { PaginatedResponse } from 'types/conversion';
 
 import './Conversation.css';
@@ -29,7 +29,7 @@ const loadingMessage: ChatMessage = { ...defaultMessage, content: "Establishing
 type ConversationMode = 'chat' | 'job_description' | 'resume' | 'fact_check' | 'persona';
 
 interface ConversationHandle {
-  submitQuery: (query: Query) => void;
+  submitQuery: (query: ChatQuery) => void;
   fetchHistory: () => void;
 }
 
@@ -124,22 +124,7 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>((props: C
     }
     const createChatSession = async () => {
       try {
-        const aiParameters: AIParameters = {
+        const chatContext: ChatContext = { type: "general" };
-          name: '',
-          model: 'qwen2.5',
-          temperature: 0.7,
-          topP: 1,
-          frequencyPenalty: 0,
-          presencePenalty: 0,
-          isDefault: true,
-          createdAt: new Date(),
-          updatedAt: new Date()
-        };
-
-        const chatContext: ChatContext = {
-          type: "general",
-          aiParameters
-        };
         const response: ChatSession = await apiClient.createChatSession(chatContext);
         setChatSession(response);
       } catch (e) {
@@ -201,14 +186,14 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>((props: C
   }, [chatSession]);
 
   const handleEnter = (value: string) => {
-    const query: Query = {
+    const query: ChatQuery = {
       prompt: value
     }
     processQuery(query);
   };
 
   useImperativeHandle(ref, () => ({
-    submitQuery: (query: Query) => {
+    submitQuery: (query: ChatQuery) => {
       processQuery(query);
     },
     fetchHistory: () => { getChatMessages(); }
@@ -253,7 +238,7 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>((props: C
     controllerRef.current = null;
   };
 
-  const processQuery = (query: Query) => {
+  const processQuery = (query: ChatQuery) => {
     if (controllerRef.current || !chatSession || !chatSession.id) {
       return;
     }

frontend/src/components/StyledMarkdown.tsx

@@ -2,14 +2,14 @@ import React from 'react';
 import { MuiMarkdown } from 'mui-markdown';
 import { useTheme } from '@mui/material/styles';
 import { Link } from '@mui/material';
-import { ChatQuery } from './ChatQuery';
+import { BackstoryQuery } from 'components/BackstoryQuery';
 import Box from '@mui/material/Box';
 import JsonView from '@uiw/react-json-view';
 import { vscodeTheme } from '@uiw/react-json-view/vscode';
-import { Mermaid } from './Mermaid';
+import { Mermaid } from 'components/Mermaid';
-import { Scrollable } from './Scrollable';
+import { Scrollable } from 'components/Scrollable';
 import { jsonrepair } from 'jsonrepair';
-import { GenerateImage } from './GenerateImage';
+import { GenerateImage } from 'components/GenerateImage';
 
 import './StyledMarkdown.css';
 import { BackstoryElementProps } from './BackstoryTab';
@@ -98,13 +98,13 @@ const StyledMarkdown: React.FC<StyledMarkdownProps> = (props: StyledMarkdownProp
         }
       }
     },
-    ChatQuery: {
+    BackstoryQuery: {
       component: (props: { query: string }) => {
         const queryString = props.query.replace(/(\w+):/g, '"$1":');
         try {
           const query = JSON.parse(queryString);
 
-          return <ChatQuery submitQuery={submitQuery} query={query} />
+          return <BackstoryQuery submitQuery={submitQuery} query={query} />
         } catch (e) {
           console.log("StyledMarkdown error:", queryString, e);
           return props.query;

frontend/src/components/layout/BackstoryRoutes.tsx

@@ -57,7 +57,7 @@ const getBackstoryDynamicRoutes = (props: BackstoryDynamicRoutesProps): ReactNod
     routes.push(<Route key={`${index++}`} path="/login" element={<LoginPage />} />);
     routes.push(<Route key={`${index++}`} path="*" element={<BetaPage />} />);
   } else {
-
+    routes.push(<Route key={`${index++}`} path="/login" element={<LoginPage />} />);
     routes.push(<Route key={`${index++}`} path="/logout" element={<LogoutPage />} />);
 
     if (user.userType === 'candidate') {

frontend/src/hooks/useUser.tsx

@@ -42,8 +42,20 @@ const UserProvider: React.FC<UserProviderProps> = (props: UserProviderProps) =>
     console.log("Guest =>", guest);
   }, [guest]);
 
+  /* If the user changes to a non-null value, create a new
+   * apiClient with the access token */
   useEffect(() => {
     console.log("User => ", user);
+    if (user === null) {
+      return;
+    }
+    /* This apiClient will persist until the user is changed
+     * or logged out */
+    const accessToken = localStorage.getItem('accessToken');
+    if (!accessToken) {
+      throw Error("accessToken is not set for user!");
+    }
+    setApiClient(new ApiClient(accessToken));
   }, [user]);
 
   /* Handle logout if any consumers of UserProvider setUser to NULL */
@@ -55,12 +67,18 @@ const UserProvider: React.FC<UserProviderProps> = (props: UserProviderProps) =>
     }
 
     const logout = async () => {
-      if (!user) {
+      if (!activeUser) {
         return;
       }
-      console.log(`Logging out ${user.email}`);
+      console.log(`Logging out ${activeUser.email}`);
       try {
-        const results = await apiClient.logout();
+        const accessToken = localStorage.getItem('accessToken');
+        const refreshToken = localStorage.getItem('refreshToken');
+        if (!accessToken || !refreshToken) {
+          setSnack("Authentication tokens are invalid.", "error");
+          return;
+        }
+        const results = await apiClient.logout(accessToken, refreshToken);
         if (results.error) {
           console.error(results.error);
           setSnack(results.error.message, "error")
@@ -98,9 +116,9 @@ const UserProvider: React.FC<UserProviderProps> = (props: UserProviderProps) =>
   };
 
   const checkExistingAuth = () => {
-    const token = localStorage.getItem('accessToken');
+    const accessToken = localStorage.getItem('accessToken');
     const userData = localStorage.getItem('userData');
-    if (token && userData) {
+    if (accessToken && userData) {
       try {
         const user = JSON.parse(userData);
         // Convert dates back to Date objects if they're stored as strings
@@ -113,7 +131,7 @@ const UserProvider: React.FC<UserProviderProps> = (props: UserProviderProps) =>
         if (user.lastLogin && typeof user.lastLogin === 'string') {
           user.lastLogin = new Date(user.lastLogin);
         }
-        setApiClient(new ApiClient(token));
+        setApiClient(new ApiClient(accessToken));
         setUser(user);
       } catch (e) {
         localStorage.removeItem('accessToken');

frontend/src/pages/BetaPage.tsx

@@ -1,5 +1,5 @@
 import React, { useState, useEffect } from 'react';
-import { useNavigate } from 'react-router-dom';
+import { useNavigate, useLocation } from 'react-router-dom';
 import { 
   Box, 
   Container, 
@@ -35,7 +35,11 @@ const BetaPage: React.FC<BetaPageProps> = ({
   const theme = useTheme();
   const [showSparkle, setShowSparkle] = useState<boolean>(false);
   const navigate = useNavigate();
+  const location = useLocation();
 
+  if (!children) {
+    children = (<Box>Location: {location.pathname}</Box>);
+  }
   console.log("BetaPage", children);
 
   // Enhanced sparkle effect for background elements

frontend/src/pages/ChatPage.tsx

@@ -6,7 +6,7 @@ import MuiMarkdown from 'mui-markdown';
 
 import { BackstoryPageProps } from '../components/BackstoryTab';
 import { Conversation, ConversationHandle } from '../components/Conversation';
-import { ChatQuery } from '../components/ChatQuery';
+import { BackstoryQuery } from '../components/BackstoryQuery';
 import { CandidateInfo } from 'components/CandidateInfo';
 import { useUser } from "../hooks/useUser";
 
@@ -26,7 +26,7 @@ const ChatPage = forwardRef<ConversationHandle, BackstoryPageProps>((props: Back
     setQuestions([
       <Box sx={{ display: "flex", flexDirection: isMobile ? "column" : "row" }}>
         {candidate.questions?.map(({ question, tunables }, i: number) =>
-          <ChatQuery key={i} query={{ prompt: question, tunables: tunables }} submitQuery={submitQuery} />
+          <BackstoryQuery key={i} query={{ prompt: question, tunables: tunables }} submitQuery={submitQuery} />
         )}
     </Box>,
     <Box sx={{ p: 1 }}>

frontend/src/pages/GenerateCandidate.tsx

@@ -19,7 +19,7 @@ import { StyledMarkdown } from 'components/StyledMarkdown';
 import { Scrollable } from '../components/Scrollable';
 import { Pulse } from 'components/Pulse';
 import { StreamingResponse } from 'types/api-client';
-import { ChatContext, ChatSession, AIParameters, Query } from 'types/types';
+import { ChatContext, ChatSession, ChatQuery } from 'types/types';
 import { useUser } from 'hooks/useUser';
 
 const emptyUser: Candidate = {
@@ -72,12 +72,7 @@ const GenerateCandidate = (props: BackstoryElementProps) => {
 
         const createChatSession = async () => {
             try {
-                const aiParameters: AIParameters = { model: 'qwen2.5' };
+                const chatContext: ChatContext = { type: "generate_persona" };
-
-                const chatContext: ChatContext = {
-                    type: "generate_persona",
-                    aiParameters
-                };
                 const response: ChatSession = await apiClient.createChatSession(chatContext);
                 setChatSession(response);
                 setSnack(`Chat session created for generate_persona: ${response.id}`);
@@ -90,7 +85,7 @@ const GenerateCandidate = (props: BackstoryElementProps) => {
         createChatSession();
     }, [chatSession, setChatSession]);
 
-    const generatePersona = useCallback((query: Query) => {
+    const generatePersona = useCallback((query: ChatQuery) => {
         if (!chatSession || !chatSession.id) {
             return;
         }
@@ -196,7 +191,7 @@ const GenerateCandidate = (props: BackstoryElementProps) => {
         if (processing) {
             return;
         }
-        const query: Query = {
+        const query: ChatQuery = {
             prompt: value,
         }
         generatePersona(query);

frontend/src/pages/LoginPage.css

@@ -0,0 +1,57 @@
+
+.PhoneInput:disabled {
+  opacity: 0.38;
+}
+
+/* .PhoneInput:not(:active):not(:focus):not(:hover) {
+} */
+
+.PhoneInput::placeholder {
+  color: rgba(46, 46, 46, 0.38);
+}
+
+.PhoneInput:focus,
+.PhoneInput:active {
+  outline: 2px solid black;
+}
+
+.PhoneInput:hover:not(:active):not(:focus) {
+  outline: 1px solid black;
+}
+
+.PhoneInputInput {
+    font: inherit;
+    letter-spacing: inherit;
+    color: currentColor;
+    padding: 4px 0 5px;
+    border: 0;
+    box-sizing: content-box;
+    background: none;
+    height: 1.4375em;
+    margin: 0;
+    -webkit-tap-highlight-color: transparent;
+    display: block;
+    min-width: 0;
+    width: 100%;
+    -webkit-animation-name: mui-auto-fill-cancel;
+    animation-name: mui-auto-fill-cancel;
+    -webkit-animation-duration: 10ms;
+    animation-duration: 10ms;
+    padding: 16.5px 14px;
+  }
+
+.PhoneInputCountry {
+  min-width: 64px;
+  justify-content: center;
+}
+
+.PhoneInputCountry:focus,
+.PhoneInputCountry:active {
+  outline: 2px solid black;
+}
+
+.PhoneInput {
+  display: flex;
+  outline: 1px solid rgba(46, 46, 46, 0.38);
+  border: none;
+}

frontend/src/pages/LoginPage.tsx

@@ -0,0 +1,453 @@
+import React, { useState, useEffect } from 'react';
+import {
+  Box,
+  Container,
+  Paper,
+  TextField,
+  Button,
+  Typography,
+  Grid,
+  Alert,
+  CircularProgress,
+  Tabs,
+  Tab,
+  AppBar,
+  Toolbar,
+  Card,
+  CardContent,
+  Divider,
+  Avatar
+} from '@mui/material';
+import { Person, PersonAdd, AccountCircle, ExitToApp } from '@mui/icons-material';
+import 'react-phone-number-input/style.css';
+import PhoneInput from 'react-phone-number-input';
+import { E164Number } from 'libphonenumber-js/core';
+import './LoginPage.css';
+
+import { ApiClient } from 'types/api-client';
+import { useUser } from 'hooks/useUser';
+
+// Import conversion utilities
+import {
+  formatApiRequest,
+  parseApiResponse,
+  handleApiResponse,
+  extractApiData,
+  isSuccessResponse,
+  debugConversion,
+  type ApiResponse
+} from 'types/conversion';
+
+import {
+  AuthResponse, User, Guest, Candidate
+} from 'types/types'
+import { useNavigate } from 'react-router-dom';
+
+interface LoginRequest {
+  login: string;
+  password: string;
+}
+
+interface RegisterRequest {
+  username: string;
+  email: string;
+  firstName: string;
+  lastName: string;
+  password: string;
+  phone?: string;
+}
+
+const apiClient = new ApiClient();
+
+const LoginPage: React.FC = () => {
+  const navigate = useNavigate();
+  const { user, setUser, guest } = useUser();
+  const [tabValue, setTabValue] = useState(0);
+  const [loading, setLoading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+  const [success, setSuccess] = useState<string | null>(null);
+  const [phone, setPhone] = useState<E164Number | null>(null);
+  const name = (user?.userType === 'candidate' ? (user as Candidate).username : user?.email) || '';
+
+  // Login form state
+  const [loginForm, setLoginForm] = useState<LoginRequest>({
+    login: '',
+    password: ''
+  });
+
+  // Register form state
+  const [registerForm, setRegisterForm] = useState<RegisterRequest>({
+    username: '',
+    email: '',
+    firstName: '',
+    lastName: '',
+    password: '',
+    phone: ''
+  });
+
+  useEffect(() => {
+    if (phone !== registerForm.phone && phone) {
+      console.log({ phone });
+      setRegisterForm({ ...registerForm, phone });
+    }
+  }, [phone, registerForm]);
+
+  const handleLogin = async (e: React.FormEvent) => {
+    e.preventDefault();
+    setLoading(true);
+    setError(null);
+    setSuccess(null);
+
+    try {
+      const authResponse = await apiClient.login(loginForm.login, loginForm.password)
+      
+      debugConversion(authResponse, 'Login Response');
+
+      // Store tokens in localStorage
+      localStorage.setItem('accessToken', authResponse.accessToken);
+      localStorage.setItem('refreshToken', authResponse.refreshToken);
+      localStorage.setItem('userData', JSON.stringify(authResponse.user));
+      
+      setSuccess('Login successful!');
+      navigate('/');
+      setUser(authResponse.user);
+      // Clear form
+      setLoginForm({ login: '', password: '' });
+    } catch (err) {
+      console.error('Login error:', err);
+      setError(err instanceof Error ? err.message : 'Login failed');
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const handleRegister = async (e: React.FormEvent) => {
+    e.preventDefault();
+    setLoading(true);
+    setError(null);
+    setSuccess(null);
+
+    try {
+      const candidate: Candidate = {
+        username: registerForm.username,
+        email: registerForm.email,
+        firstName: registerForm.firstName,
+        lastName: registerForm.lastName,
+        fullName: `${registerForm.firstName} ${registerForm.lastName}`,
+        phone: registerForm.phone || undefined,
+        userType: 'candidate',
+        status: 'active',
+        createdAt: new Date(),
+        updatedAt: new Date(),
+        skills: [],
+        experience: [],
+        education: [],
+        preferredJobTypes: [],
+        languages: [],
+        certifications: [],
+        location: {
+          city: '',
+          country: '',
+          remote: true
+        }
+      };
+
+      const result = await apiClient.createCandidate(candidate);
+      
+      debugConversion(result, 'Registration Response');
+
+      setSuccess('Registration successful! You can now login.');
+      
+      // Clear form and switch to login tab
+      setRegisterForm({
+        username: '',
+        email: '',
+        firstName: '',
+        lastName: '',
+        password: '',
+        phone: ''
+      });
+      setTabValue(0);
+      
+    } catch (err) {
+      console.error('Registration error:', err);
+      setError(err instanceof Error ? err.message : 'Registration failed');
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const handleTabChange = (event: React.SyntheticEvent, newValue: number) => {
+    setTabValue(newValue);
+    setError(null);
+    setSuccess(null);
+  };
+
+  // If user is logged in, show their profile
+  if (user) {
+    return (
+      <Container maxWidth="md" sx={{ mt: 4 }}>
+        <Card elevation={3}>
+          <CardContent>
+            <Box sx={{ display: 'flex', alignItems: 'center', mb: 3 }}>
+              <Avatar sx={{ mr: 2, bgcolor: 'primary.main' }}>
+                <AccountCircle />
+              </Avatar>
+              <Typography variant="h4" component="h1">
+                User Profile
+              </Typography>
+            </Box>
+            
+            <Divider sx={{ mb: 3 }} />
+            
+            <Grid container spacing={3}>
+              <Grid size={{ xs: 12, md: 6 }}>
+                <Typography variant="body1" sx={{ mb: 1 }}>
+                  <strong>Username:</strong> {name}
+                </Typography>
+              </Grid>
+              <Grid size={{ xs: 12, md: 6 }}>
+                <Typography variant="body1" sx={{ mb: 1 }}>
+                  <strong>Email:</strong> {user.email}
+                </Typography>
+              </Grid>
+              <Grid size={{ xs: 12, md: 6 }}>
+                <Typography variant="body1" sx={{ mb: 1 }}>
+                  <strong>Status:</strong> {user.status}
+                </Typography>
+              </Grid>
+              <Grid size={{ xs: 12, md: 6 }}>
+                <Typography variant="body1" sx={{ mb: 1 }}>
+                  <strong>Phone:</strong> {user.phone || 'Not provided'}
+                </Typography>
+              </Grid>
+              <Grid size={{ xs: 12, md: 6 }}>
+                <Typography variant="body1" sx={{ mb: 1 }}>
+                  <strong>Last Login:</strong> {
+                    user.lastLogin 
+                      ? user.lastLogin.toLocaleString() 
+                      : 'N/A'
+                  }
+                </Typography>
+              </Grid>
+              <Grid size={{ xs: 12, md: 6 }}>
+                <Typography variant="body1" sx={{ mb: 1 }}>
+                  <strong>Member Since:</strong> {user.createdAt.toLocaleDateString()}
+                </Typography>
+              </Grid>
+            </Grid>
+          </CardContent>
+        </Card>
+      </Container>
+    );
+  }
+
+  const validateInput = (value: string) => {
+    if (!value) return 'This field is required';
+
+    // Username: alphanumeric, 3-20 characters, no @
+    const usernameRegex = /^[a-zA-Z0-9]{3,20}$/;
+    // Email: basic email format
+    const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/;
+
+    if (usernameRegex.test(value)) return '';
+    if (emailRegex.test(value)) return '';
+    return 'Enter a valid username (3-20 alphanumeric characters) or email';
+  };
+
+  const handleLoginChange = (event: React.ChangeEvent<HTMLInputElement>) => {
+    const { value } = event.target;
+    setLoginForm({ ...loginForm, login: value });
+    setError(validateInput(value));
+  };
+
+  return (
+    <Container maxWidth="sm" sx={{ mt: 4 }}>
+      <Paper elevation={3} sx={{ p: 4 }}>
+        <Typography variant="h4" component="h1" gutterBottom align="center" color="primary">
+          Backstory
+        </Typography>
+        
+        {guest && (
+          <Card sx={{ mb: 3, bgcolor: 'grey.50' }} elevation={1}>
+            <CardContent>
+              <Typography variant="h6" gutterBottom color="primary">
+                Guest Session Active
+              </Typography>
+              <Typography variant="body2" color="text.secondary" sx={{ mb: 0.5 }}>
+                Session ID: {guest.sessionId}
+              </Typography>
+              <Typography variant="body2" color="text.secondary">
+                Created: {guest.createdAt.toLocaleString()}
+              </Typography>
+            </CardContent>
+          </Card>
+        )}
+
+        <Box sx={{ borderBottom: 1, borderColor: 'divider', mb: 3 }}>
+          <Tabs value={tabValue} onChange={handleTabChange} centered>
+            <Tab icon={<Person />} label="Login" />
+            <Tab icon={<PersonAdd />} label="Register" />
+          </Tabs>
+        </Box>
+
+        {error && (
+          <Alert severity="error" sx={{ mb: 2 }}>
+            {error}
+          </Alert>
+        )}
+
+        {success && (
+          <Alert severity="success" sx={{ mb: 2 }}>
+            {success}
+          </Alert>
+        )}
+
+        {tabValue === 0 && (
+          <Box component="form" onSubmit={handleLogin}>
+            <Typography variant="h5" gutterBottom>
+              Sign In
+            </Typography>
+            
+            <TextField
+              fullWidth
+              label="Username or Email"
+              type="text"
+              value={loginForm.login}
+              onChange={handleLoginChange}
+              margin="normal"
+              required
+              disabled={loading}
+              variant="outlined"
+              placeholder="Enter username or email"
+            />
+            
+            <TextField
+              fullWidth
+              label="Password"
+              type="password"
+              value={loginForm.password}
+              onChange={(e) => setLoginForm({ ...loginForm, password: e.target.value })}
+              margin="normal"
+              required
+              disabled={loading}
+              variant="outlined"
+              autoComplete='current-password'
+            />
+            
+            <Button
+              type="submit"
+              fullWidth
+              variant="contained"
+              sx={{ mt: 3, mb: 2 }}
+              disabled={loading}
+              startIcon={loading ? <CircularProgress size={20} color="inherit" /> : <Person />}
+            >
+              {loading ? 'Signing In...' : 'Sign In'}
+            </Button>
+          </Box>
+        )}
+
+        {tabValue === 1 && (
+          <Box component="form" onSubmit={handleRegister}>
+            <Typography variant="h5" gutterBottom>
+              Create Account
+            </Typography>
+            
+            <Grid container spacing={2} sx={{ mb: 2 }}>
+              <Grid size={{ xs: 12, sm: 6 }}>
+                <TextField
+                  fullWidth
+                  label="First Name"
+                  value={registerForm.firstName}
+                  onChange={(e) => setRegisterForm({ ...registerForm, firstName: e.target.value })}
+                  required
+                  disabled={loading}
+                  variant="outlined"
+                />
+              </Grid>
+              
+              <Grid size={{ xs: 12, sm: 6 }}>
+                <TextField
+                  fullWidth
+                  label="Last Name"
+                  value={registerForm.lastName}
+                  onChange={(e) => setRegisterForm({ ...registerForm, lastName: e.target.value })}
+                  required
+                  disabled={loading}
+                  variant="outlined"
+                />
+              </Grid>
+            </Grid>
+            
+            <TextField
+              fullWidth
+              label="Username"
+              value={registerForm.username}
+              onChange={(e) => setRegisterForm({ ...registerForm, username: e.target.value })}
+              margin="normal"
+              required
+              disabled={loading}
+              variant="outlined"
+            />
+            
+            <TextField
+              fullWidth
+              label="Email"
+              type="email"
+              value={registerForm.email}
+              onChange={(e) => setRegisterForm({ ...registerForm, email: e.target.value })}
+              margin="normal"
+              required
+              disabled={loading}
+              variant="outlined"
+            />
+            
+            <PhoneInput
+              label="Phone (Optional)"
+              placeholder="Enter phone number"
+              defaultCountry='US'
+              value={registerForm.phone}
+              disabled={loading}
+              onChange={(v) => setPhone(v as E164Number)} />
+            {/* <TextField
+              fullWidth
+              label="Phone (Optional)"
+              type="tel"
+              value={registerForm.phone}
+              onChange={(e) => setRegisterForm({ ...registerForm, phone: e.target.value })}
+              margin="normal"
+              disabled={loading}
+              variant="outlined"
+            /> */}
+            
+            <TextField
+              fullWidth
+              label="Password"
+              type="password"
+              value={registerForm.password}
+              onChange={(e) => setRegisterForm({ ...registerForm, password: e.target.value })}
+              margin="normal"
+              required
+              disabled={loading}
+              variant="outlined"
+            />
+            
+            <Button
+              type="submit"
+              fullWidth
+              variant="contained"
+              sx={{ mt: 3, mb: 2 }}
+              disabled={loading}
+              startIcon={loading ? <CircularProgress size={20} color="inherit" /> : <PersonAdd />}
+            >
+              {loading ? 'Creating Account...' : 'Create Account'}
+            </Button>
+          </Box>
+        )}
+      </Paper>
+    </Container>
+  );
+};
+
+export { LoginPage };

frontend/src/pages/ResumeBuilderPage.tsx

@@ -6,11 +6,11 @@ import {
 } from '@mui/material';
 import { SxProps } from '@mui/material';
 
-import { ChatQuery } from '../components/ChatQuery';
+import { BackstoryQuery } from 'components/BackstoryQuery';
-import { MessageList, BackstoryMessage } from '../components/Message';
+import { MessageList, BackstoryMessage } from 'components/Message';
-import { Conversation } from '../components/Conversation';
+import { Conversation } from 'components/Conversation';
-import { BackstoryPageProps } from '../components/BackstoryTab';
+import { BackstoryPageProps } from 'components/BackstoryTab';
-import { Query } from "../types/types";
+import { ChatQuery } from "types/types";
 
 import './ResumeBuilderPage.css';
 
@@ -43,17 +43,17 @@ const ResumeBuilderPage: React.FC<BackstoryPageProps> = (props: BackstoryPagePro
     setActiveTab(newValue);
   };
 
-  const handleJobQuery = (query: Query) => {
+  const handleJobQuery = (query: ChatQuery) => {
     console.log(`handleJobQuery: ${query.prompt} -- `, jobConversationRef.current ? ' sending' : 'no handler');
     jobConversationRef.current?.submitQuery(query);
   };
 
-  const handleResumeQuery = (query: Query) => {
+  const handleResumeQuery = (query: ChatQuery) => {
     console.log(`handleResumeQuery: ${query.prompt} -- `, resumeConversationRef.current ? ' sending' : 'no handler');
     resumeConversationRef.current?.submitQuery(query);
   };
 
-  const handleFactsQuery = (query: Query) => {
+  const handleFactsQuery = (query: ChatQuery) => {
     console.log(`handleFactsQuery: ${query.prompt} -- `, factsConversationRef.current ? ' sending' : 'no handler');
     factsConversationRef.current?.submitQuery(query);
   };
@@ -202,8 +202,8 @@ const ResumeBuilderPage: React.FC<BackstoryPageProps> = (props: BackstoryPagePro
 //     console.log('renderJobDescriptionView');
 //     const jobDescriptionQuestions = [
 //       <Box sx={{ display: "flex", flexDirection: "column" }}>
-//         <ChatQuery query={{ prompt: "What are the key skills necessary for this position?", tunables: { enableTools: false } }} submitQuery={handleJobQuery} />
+  //         <BackstoryQuery query={{ prompt: "What are the key skills necessary for this position?", tunables: { enableTools: false } }} submitQuery={handleJobQuery} />
-//         <ChatQuery query={{ prompt: "How much should this position pay (accounting for inflation)?", tunables: { enableTools: false } }} submitQuery={handleJobQuery} />
+  //         <BackstoryQuery query={{ prompt: "How much should this position pay (accounting for inflation)?", tunables: { enableTools: false } }} submitQuery={handleJobQuery} />
 //       </Box>,
 //     ];
 
@@ -213,7 +213,7 @@ const ResumeBuilderPage: React.FC<BackstoryPageProps> = (props: BackstoryPagePro
 
 // 1. **Job Analysis**: LLM extracts requirements from '\`Job Description\`' to generate a list of desired '\`Skills\`'.
 // 2. **Candidate Analysis**: LLM determines candidate qualifications by performing skill assessments.
-    
+
 //     For each '\`Skill\`' from **Job Analysis** phase:
 
 //     1. **RAG**: Retrieval Augmented Generation collection is queried for context related content for each '\`Skill\`'.
@@ -274,8 +274,8 @@ const ResumeBuilderPage: React.FC<BackstoryPageProps> = (props: BackstoryPagePro
   // const renderResumeView = useCallback((sx?: SxProps) => {
   //   const resumeQuestions = [
   //     <Box sx={{ display: "flex", flexDirection: "column" }}>
-  //       <ChatQuery query={{ prompt: "Is this resume a good fit for the provided job description?", tunables: { enableTools: false } }} submitQuery={handleResumeQuery} />
+  //       <BackstoryQuery query={{ prompt: "Is this resume a good fit for the provided job description?", tunables: { enableTools: false } }} submitQuery={handleResumeQuery} />
-  //       <ChatQuery query={{ prompt: "Provide a more concise resume.", tunables: { enableTools: false } }} submitQuery={handleResumeQuery} />
+  //       <BackstoryQuery query={{ prompt: "Provide a more concise resume.", tunables: { enableTools: false } }} submitQuery={handleResumeQuery} />
   //     </Box>,
   //   ];
 
@@ -323,7 +323,7 @@ const ResumeBuilderPage: React.FC<BackstoryPageProps> = (props: BackstoryPagePro
   // const renderFactCheckView = useCallback((sx?: SxProps) => {
   //   const factsQuestions = [
   //     <Box sx={{ display: "flex", flexDirection: "column" }}>
-  //       <ChatQuery query={{ prompt: "Rewrite the resume to address any discrepancies.", tunables: { enableTools: false } }} submitQuery={handleFactsQuery} />
+  //       <BackstoryQuery query={{ prompt: "Rewrite the resume to address any discrepancies.", tunables: { enableTools: false } }} submitQuery={handleFactsQuery} />
   //     </Box>,
   //   ];
 

frontend/src/routes/CandidateRoute.tsx

@@ -36,7 +36,7 @@ const CandidateRoute: React.FC<CandidateRouteProps> = (props: CandidateRouteProp
     }
 
     getCandidate(username);
-  }, [candidate, username, setCandidate, navigate, setSnack]);
+  }, [candidate, username, setCandidate, navigate, setSnack, apiClient]);
 
   if (candidate === null) {
     return (<Box>

frontend/src/types/api-client.ts

@@ -59,7 +59,7 @@ class ApiClient {
   private baseUrl: string;
   private defaultHeaders: Record<string, string>;
 
-  constructor(authToken?: string) {
+  constructor(accessToken?: string) {
     const loc = window.location;
     if (!loc.host.match(/.*battle-linux.*/)) {
       this.baseUrl = loc.protocol + "//" + loc.host + "/api/1.0";
@@ -68,7 +68,7 @@ class ApiClient {
     }    
     this.defaultHeaders = {
       'Content-Type': 'application/json',
-      ...(authToken && { 'Authorization': `Bearer ${authToken}` })
+      ...(accessToken && { 'Authorization': `Bearer ${accessToken}` })
     };
   }
 
@@ -86,10 +86,12 @@ class ApiClient {
     return handleApiResponse<Types.AuthResponse>(response);
   }
 
-  async logout(): Promise<Types.ApiResponse> {
+  async logout(accessToken: string, refreshToken: string): Promise<Types.ApiResponse> {
+    console.log(this.defaultHeaders);
     const response = await fetch(`${this.baseUrl}/auth/logout`, {
       method: 'POST',
       headers: this.defaultHeaders,
+      body: JSON.stringify(formatApiRequest({ accessToken, refreshToken }))
     });
 
     return handleApiResponse<Types.ApiResponse>(response);
@@ -324,11 +326,11 @@ class ApiClient {
   /**
    * Send message with standard response (non-streaming)
    */
-  async sendMessage(sessionId: string, query: Types.Query): Promise<Types.ChatMessage> {
+  async sendMessage(sessionId: string, query: Types.ChatQuery): Promise<Types.ChatMessage> {
     const response = await fetch(`${this.baseUrl}/chat/sessions/${sessionId}/messages`, {
       method: 'POST',
       headers: this.defaultHeaders,
-      body: JSON.stringify(formatApiRequest({ query }))
+      body: JSON.stringify(formatApiRequest({query}))
     });
 
     return handleApiResponse<Types.ChatMessage>(response);
@@ -339,7 +341,7 @@ class ApiClient {
    */
   sendMessageStream(
     sessionId: string, 
-    query: Types.Query, 
+    query: Types.ChatQuery, 
     options: StreamingOptions = {}
   ): StreamingResponse {
     const abortController = new AbortController();
@@ -488,7 +490,7 @@ class ApiClient {
    */
   async sendMessageAuto(
     sessionId: string, 
-    query: Types.Query, 
+    query: Types.ChatQuery, 
     options?: StreamingOptions
   ): Promise<Types.ChatMessage> {
     // If streaming options are provided, use streaming
@@ -512,36 +514,6 @@ class ApiClient {
     return handlePaginatedApiResponse<Types.ChatMessage>(response);
   }
 
-  // ============================
-  // AI Configuration Methods
-  // ============================
-
-  async createAIParameters(params: Omit<Types.AIParameters, 'id' | 'createdAt' | 'updatedAt'>): Promise<Types.AIParameters> {
-    const response = await fetch(`${this.baseUrl}/ai/parameters`, {
-      method: 'POST',
-      headers: this.defaultHeaders,
-      body: JSON.stringify(formatApiRequest(params))
-    });
-
-    return handleApiResponse<Types.AIParameters>(response);
-  }
-
-  async getAIParameters(id: string): Promise<Types.AIParameters> {
-    const response = await fetch(`${this.baseUrl}/ai/parameters/${id}`, {
-      headers: this.defaultHeaders
-    });
-
-    return handleApiResponse<Types.AIParameters>(response);
-  }
-
-  async getUserAIParameters(userId: string): Promise<Types.AIParameters[]> {
-    const response = await fetch(`${this.baseUrl}/users/${userId}/ai/parameters`, {
-      headers: this.defaultHeaders
-    });
-
-    return handleApiResponse<Types.AIParameters[]>(response);
-  }
-
   // ============================
   // Error Handling Helper
   // ============================
@@ -589,7 +561,7 @@ export function useStreamingChat(sessionId: string) {
   const apiClient = useApiClient();
   const streamingRef = useRef<StreamingResponse | null>(null);
 
-  const sendMessage = useCallback(async (query: Types.Query) => {
+  const sendMessage = useCallback(async (query: Types.ChatQuery) => {
     setError(null);
     setIsStreaming(true);
     setCurrentMessage(null);

frontend/src/types/types.ts

@@ -1,6 +1,6 @@
 // Generated TypeScript types from Pydantic models
 // Source: src/backend/models.py
-// Generated on: 2025-05-29T02:05:50.622601
+// Generated on: 2025-05-29T05:47:25.809967
 // DO NOT EDIT MANUALLY - This file is auto-generated
 
 // ============================
@@ -17,7 +17,7 @@ export type ChatContextType = "job_search" | "candidate_screening" | "interview_
 
 export type ChatSenderType = "user" | "ai" | "system";
 
-export type ChatStatusType = "partial" | "done" | "streaming" | "thinking" | "error";
+export type ChatStatusType = "preparing" | "thinking" | "partial" | "streaming" | "done" | "error";
 
 export type ColorBlindMode = "protanopia" | "deuteranopia" | "tritanopia" | "none";
 
@@ -63,141 +63,123 @@ export type VectorStoreType = "pinecone" | "qdrant" | "faiss" | "milvus" | "weav
 // Interfaces
 // ============================
 
-export interface AIParameters {
-  id?: string;
-  userId?: string;
-  name?: string;
-  description?: string;
-  model?: "qwen2.5" | "flux-schnell";
-  temperature?: number;
-  maxTokens?: number;
-  topP?: number;
-  frequencyPenalty?: number;
-  presencePenalty?: number;
-  systemPrompt?: string;
-  isDefault?: boolean;
-  createdAt?: Date;
-  updatedAt?: Date;
-  customModelConfig?: Record<string, any>;
-}
-
 export interface AccessibilitySettings {
-  fontSize?: "small" | "medium" | "large";
+  fontSize: "small" | "medium" | "large";
-  highContrast?: boolean;
+  highContrast: boolean;
-  reduceMotion?: boolean;
+  reduceMotion: boolean;
-  screenReader?: boolean;
+  screenReader: boolean;
   colorBlindMode?: "protanopia" | "deuteranopia" | "tritanopia" | "none";
 }
 
 export interface Analytics {
   id?: string;
-  entityType?: "job" | "candidate" | "chat" | "system" | "employer";
+  entityType: "job" | "candidate" | "chat" | "system" | "employer";
-  entityId?: string;
+  entityId: string;
-  metricType?: string;
+  metricType: string;
-  value?: number;
+  value: number;
-  timestamp?: Date;
+  timestamp: Date;
   dimensions?: Record<string, any>;
   segment?: string;
 }
 
 export interface ApiResponse {
-  success?: boolean;
+  success: boolean;
   data?: any;
   error?: ErrorDetail;
   meta?: Record<string, any>;
 }
 
 export interface ApplicationDecision {
-  status?: "accepted" | "rejected";
+  status: "accepted" | "rejected";
   reason?: string;
-  date?: Date;
+  date: Date;
-  by?: string;
+  by: string;
 }
 
 export interface Attachment {
   id?: string;
-  fileName?: string;
+  fileName: string;
-  fileType?: string;
+  fileType: string;
-  fileSize?: number;
+  fileSize: number;
-  fileUrl?: string;
+  fileUrl: string;
-  uploadedAt?: Date;
+  uploadedAt: Date;
-  isProcessed?: boolean;
+  isProcessed: boolean;
   processingResult?: any;
   thumbnailUrl?: string;
 }
 
 export interface AuthResponse {
-  accessToken?: string;
+  accessToken: string;
-  refreshToken?: string;
+  refreshToken: string;
-  user?: any;
+  user: any;
-  expiresAt?: number;
+  expiresAt: number;
 }
 
 export interface Authentication {
-  userId?: string;
+  userId: string;
-  passwordHash?: string;
+  passwordHash: string;
-  salt?: string;
+  salt: string;
-  refreshTokens?: Array<RefreshToken>;
+  refreshTokens: Array<RefreshToken>;
   resetPasswordToken?: string;
   resetPasswordExpiry?: Date;
-  lastPasswordChange?: Date;
+  lastPasswordChange: Date;
-  mfaEnabled?: boolean;
+  mfaEnabled: boolean;
   mfaMethod?: "app" | "sms" | "email";
   mfaSecret?: string;
-  loginAttempts?: number;
+  loginAttempts: number;
   lockedUntil?: Date;
 }
 
 export interface BaseUser {
   id?: string;
-  email?: string;
+  email: string;
   phone?: string;
-  createdAt?: Date;
+  createdAt: Date;
-  updatedAt?: Date;
+  updatedAt: Date;
   lastLogin?: Date;
   profileImage?: string;
-  status?: "active" | "inactive" | "pending" | "banned";
+  status: "active" | "inactive" | "pending" | "banned";
 }
 
 export interface BaseUserWithType {
   id?: string;
-  email?: string;
+  email: string;
   phone?: string;
-  createdAt?: Date;
+  createdAt: Date;
-  updatedAt?: Date;
+  updatedAt: Date;
   lastLogin?: Date;
   profileImage?: string;
-  status?: "active" | "inactive" | "pending" | "banned";
+  status: "active" | "inactive" | "pending" | "banned";
-  userType?: "candidate" | "employer" | "guest";
+  userType: "candidate" | "employer" | "guest";
 }
 
 export interface Candidate {
   id?: string;
-  email?: string;
+  email: string;
   phone?: string;
-  createdAt?: Date;
+  createdAt: Date;
-  updatedAt?: Date;
+  updatedAt: Date;
   lastLogin?: Date;
   profileImage?: string;
-  status?: "active" | "inactive" | "pending" | "banned";
+  status: "active" | "inactive" | "pending" | "banned";
   userType?: "candidate";
-  username?: string;
+  username: string;
-  firstName?: string;
+  firstName: string;
-  lastName?: string;
+  lastName: string;
-  fullName?: string;
+  fullName: string;
   description?: string;
   resume?: string;
-  skills?: Array<Skill>;
+  skills: Array<Skill>;
-  experience?: Array<WorkExperience>;
+  experience: Array<WorkExperience>;
   questions?: Array<CandidateQuestion>;
-  education?: Array<Education>;
+  education: Array<Education>;
-  preferredJobTypes?: Array<"full-time" | "part-time" | "contract" | "internship" | "freelance">;
+  preferredJobTypes: Array<"full-time" | "part-time" | "contract" | "internship" | "freelance">;
   desiredSalary?: DesiredSalary;
-  location?: Location;
+  location: Location;
   availabilityDate?: Date;
   summary?: string;
-  languages?: Array<Language>;
+  languages: Array<Language>;
-  certifications?: Array<Certification>;
+  certifications: Array<Certification>;
   jobApplications?: Array<JobApplication>;
   hasProfile?: boolean;
   age?: number;
@@ -206,24 +188,24 @@ export interface Candidate {
 }
 
 export interface CandidateContact {
-  email?: string;
+  email: string;
   phone?: string;
 }
 
 export interface CandidateListResponse {
-  success?: boolean;
+  success: boolean;
   data?: Array<Candidate>;
   error?: ErrorDetail;
   meta?: Record<string, any>;
 }
 
 export interface CandidateQuestion {
-  question?: string;
+  question: string;
   tunables?: Tunables;
 }
 
 export interface CandidateResponse {
-  success?: boolean;
+  success: boolean;
   data?: Candidate;
   error?: ErrorDetail;
   meta?: Record<string, any>;
@@ -231,35 +213,64 @@ export interface CandidateResponse {
 
 export interface Certification {
   id?: string;
-  name?: string;
+  name: string;
-  issuingOrganization?: string;
+  issuingOrganization: string;
-  issueDate?: Date;
+  issueDate: Date;
   expirationDate?: Date;
   credentialId?: string;
   credentialUrl?: string;
 }
 
 export interface ChatContext {
-  type?: "job_search" | "candidate_screening" | "interview_prep" | "resume_review" | "general" | "generate_persona" | "generate_profile";
+  type: "job_search" | "candidate_screening" | "interview_prep" | "resume_review" | "general" | "generate_persona" | "generate_profile";
   relatedEntityId?: string;
   relatedEntityType?: "job" | "candidate" | "employer";
-  aiParameters?: AIParameters;
   additionalContext?: Record<string, any>;
 }
 
 export interface ChatMessage {
   id?: string;
-  sessionId?: string;
+  sessionId: string;
-  status?: "partial" | "done" | "streaming" | "thinking" | "error";
+  status: "preparing" | "thinking" | "partial" | "streaming" | "done" | "error";
-  sender?: "user" | "ai" | "system";
+  sender: "user" | "ai" | "system";
   senderId?: string;
+  prompt?: string;
   content?: string;
-  timestamp?: Date;
+  chunk?: string;
-  attachments?: Array<Attachment>;
+  timestamp: Date;
-  reactions?: Array<MessageReaction>;
   isEdited?: boolean;
-  editHistory?: Array<EditHistory>;
+  metadata?: ChatMessageMetaData;
-  metadata?: Record<string, any>;
+}
+
+export interface ChatMessageMetaData {
+  model?: "qwen2.5" | "flux-schnell";
+  temperature?: number;
+  maxTokens?: number;
+  topP?: number;
+  frequencyPenalty?: number;
+  presencePenalty?: number;
+  stopSequences?: Array<string>;
+  tunables?: Tunables;
+  rag?: Array<ChromaDBGetResponse>;
+  evalCount?: number;
+  evalDuration?: number;
+  promptEvalCount?: number;
+  promptEvalDuration?: number;
+  options?: ChatOptions;
+  tools?: Record<string, any>;
+  timers?: Record<string, number>;
+}
+
+export interface ChatOptions {
+  seed?: number;
+  numCtx?: number;
+  temperature?: number;
+}
+
+export interface ChatQuery {
+  prompt: string;
+  tunables?: Tunables;
+  agentOptions?: Record<string, any>;
 }
 
 export interface ChatSession {
@@ -269,51 +280,64 @@ export interface ChatSession {
   createdAt?: Date;
   lastActivity?: Date;
   title?: string;
-  context?: ChatContext;
+  context: ChatContext;
   messages?: Array<ChatMessage>;
   isArchived?: boolean;
   systemPrompt?: string;
 }
 
+export interface ChromaDBGetResponse {
+  ids?: Array<string>;
+  embeddings?: Array<Array<number>>;
+  documents?: Array<string>;
+  metadatas?: Array<Record<string, any>>;
+  name?: string;
+  size?: number;
+  query?: string;
+  queryEmbedding?: Array<number>;
+  umapEmbedding2D?: Array<number>;
+  umapEmbedding3D?: Array<number>;
+}
+
 export interface CustomQuestion {
-  question?: string;
+  question: string;
-  answer?: string;
+  answer: string;
 }
 
 export interface DataSourceConfiguration {
   id?: string;
-  ragConfigId?: string;
+  ragConfigId: string;
-  name?: string;
+  name: string;
-  sourceType?: "document" | "website" | "api" | "database" | "internal";
+  sourceType: "document" | "website" | "api" | "database" | "internal";
-  connectionDetails?: Record<string, any>;
+  connectionDetails: Record<string, any>;
-  processingPipeline?: Array<ProcessingStep>;
+  processingPipeline: Array<ProcessingStep>;
   refreshSchedule?: string;
   lastRefreshed?: Date;
-  status?: "active" | "pending" | "error" | "processing";
+  status: "active" | "pending" | "error" | "processing";
   errorDetails?: string;
   metadata?: Record<string, any>;
 }
 
 export interface DesiredSalary {
-  amount?: number;
+  amount: number;
-  currency?: string;
+  currency: string;
-  period?: "hour" | "day" | "month" | "year";
+  period: "hour" | "day" | "month" | "year";
 }
 
 export interface EditHistory {
-  content?: string;
+  content: string;
-  editedAt?: Date;
+  editedAt: Date;
-  editedBy?: string;
+  editedBy: string;
 }
 
 export interface Education {
   id?: string;
-  institution?: string;
+  institution: string;
-  degree?: string;
+  degree: string;
-  fieldOfStudy?: string;
+  fieldOfStudy: string;
-  startDate?: Date;
+  startDate: Date;
   endDate?: Date;
-  isCurrent?: boolean;
+  isCurrent: boolean;
   gpa?: number;
   achievements?: Array<string>;
   location?: Location;
@@ -321,45 +345,45 @@ export interface Education {
 
 export interface Employer {
   id?: string;
-  email?: string;
+  email: string;
   phone?: string;
-  createdAt?: Date;
+  createdAt: Date;
-  updatedAt?: Date;
+  updatedAt: Date;
   lastLogin?: Date;
   profileImage?: string;
-  status?: "active" | "inactive" | "pending" | "banned";
+  status: "active" | "inactive" | "pending" | "banned";
   userType?: "employer";
-  companyName?: string;
+  companyName: string;
-  industry?: string;
+  industry: string;
   description?: string;
-  companySize?: string;
+  companySize: string;
-  companyDescription?: string;
+  companyDescription: string;
   websiteUrl?: string;
   jobs?: Array<Job>;
-  location?: Location;
+  location: Location;
   companyLogo?: string;
   socialLinks?: Array<SocialLink>;
   poc?: PointOfContact;
 }
 
 export interface EmployerResponse {
-  success?: boolean;
+  success: boolean;
   data?: Employer;
   error?: ErrorDetail;
   meta?: Record<string, any>;
 }
 
 export interface ErrorDetail {
-  code?: string;
+  code: string;
-  message?: string;
+  message: string;
   details?: any;
 }
 
 export interface Guest {
   id?: string;
-  sessionId?: string;
+  sessionId: string;
-  createdAt?: Date;
+  createdAt: Date;
-  lastActivity?: Date;
+  lastActivity: Date;
   convertedToUserId?: string;
   ipAddress?: string;
   userAgent?: string;
@@ -367,49 +391,49 @@ export interface Guest {
 
 export interface InterviewFeedback {
   id?: string;
-  interviewId?: string;
+  interviewId: string;
-  reviewerId?: string;
+  reviewerId: string;
-  technicalScore?: number;
+  technicalScore: number;
-  culturalScore?: number;
+  culturalScore: number;
-  overallScore?: number;
+  overallScore: number;
-  strengths?: Array<string>;
+  strengths: Array<string>;
-  weaknesses?: Array<string>;
+  weaknesses: Array<string>;
-  recommendation?: "strong_hire" | "hire" | "no_hire" | "strong_no_hire";
+  recommendation: "strong_hire" | "hire" | "no_hire" | "strong_no_hire";
-  comments?: string;
+  comments: string;
-  createdAt?: Date;
+  createdAt: Date;
-  updatedAt?: Date;
+  updatedAt: Date;
-  isVisible?: boolean;
+  isVisible: boolean;
   skillAssessments?: Array<SkillAssessment>;
 }
 
 export interface InterviewSchedule {
   id?: string;
-  applicationId?: string;
+  applicationId: string;
-  scheduledDate?: Date;
+  scheduledDate: Date;
-  endDate?: Date;
+  endDate: Date;
-  interviewType?: "phone" | "video" | "onsite" | "technical" | "behavioral";
+  interviewType: "phone" | "video" | "onsite" | "technical" | "behavioral";
-  interviewers?: Array<string>;
+  interviewers: Array<string>;
   location?: string | Location;
   notes?: string;
   feedback?: InterviewFeedback;
-  status?: "scheduled" | "completed" | "cancelled" | "rescheduled";
+  status: "scheduled" | "completed" | "cancelled" | "rescheduled";
   meetingLink?: string;
 }
 
 export interface Job {
   id?: string;
-  title?: string;
+  title: string;
-  description?: string;
+  description: string;
-  responsibilities?: Array<string>;
+  responsibilities: Array<string>;
-  requirements?: Array<string>;
+  requirements: Array<string>;
   preferredSkills?: Array<string>;
-  employerId?: string;
+  employerId: string;
-  location?: Location;
+  location: Location;
   salaryRange?: SalaryRange;
-  employmentType?: "full-time" | "part-time" | "contract" | "internship" | "freelance";
+  employmentType: "full-time" | "part-time" | "contract" | "internship" | "freelance";
-  datePosted?: Date;
+  datePosted: Date;
   applicationDeadline?: Date;
-  isActive?: boolean;
+  isActive: boolean;
   applicants?: Array<JobApplication>;
   department?: string;
   reportsTo?: string;
@@ -422,12 +446,12 @@ export interface Job {
 
 export interface JobApplication {
   id?: string;
-  jobId?: string;
+  jobId: string;
-  candidateId?: string;
+  candidateId: string;
-  status?: "applied" | "reviewing" | "interview" | "offer" | "rejected" | "accepted" | "withdrawn";
+  status: "applied" | "reviewing" | "interview" | "offer" | "rejected" | "accepted" | "withdrawn";
-  appliedDate?: Date;
+  appliedDate: Date;
-  updatedDate?: Date;
+  updatedDate: Date;
-  resumeVersion?: string;
+  resumeVersion: string;
   coverLetter?: string;
   notes?: string;
   interviewSchedules?: Array<InterviewSchedule>;
@@ -437,28 +461,28 @@ export interface JobApplication {
 }
 
 export interface JobListResponse {
-  success?: boolean;
+  success: boolean;
   data?: Array<Job>;
   error?: ErrorDetail;
   meta?: Record<string, any>;
 }
 
 export interface JobResponse {
-  success?: boolean;
+  success: boolean;
   data?: Job;
   error?: ErrorDetail;
   meta?: Record<string, any>;
 }
 
 export interface Language {
-  language?: string;
+  language: string;
-  proficiency?: "basic" | "conversational" | "fluent" | "native";
+  proficiency: "basic" | "conversational" | "fluent" | "native";
 }
 
 export interface Location {
-  city?: string;
+  city: string;
   state?: string;
-  country?: string;
+  country: string;
   postalCode?: string;
   latitude?: number;
   longitude?: number;
@@ -468,15 +492,15 @@ export interface Location {
 }
 
 export interface MessageReaction {
-  userId?: string;
+  userId: string;
-  reaction?: string;
+  reaction: string;
-  timestamp?: Date;
+  timestamp: Date;
 }
 
 export interface NotificationPreference {
-  type?: "email" | "push" | "in_app";
+  type: "email" | "push" | "in_app";
-  events?: Array<string>;
+  events: Array<string>;
-  isEnabled?: boolean;
+  isEnabled: boolean;
 }
 
 export interface PaginatedRequest {
@@ -488,80 +512,79 @@ export interface PaginatedRequest {
 }
 
 export interface PaginatedResponse {
-  data?: Array<any>;
+  data: Array<any>;
-  total?: number;
+  total: number;
-  page?: number;
+  page: number;
-  limit?: number;
+  limit: number;
-  totalPages?: number;
+  totalPages: number;
-  hasMore?: boolean;
+  hasMore: boolean;
 }
 
 export interface PointOfContact {
-  name?: string;
+  name: string;
-  position?: string;
+  position: string;
-  email?: string;
+  email: string;
   phone?: string;
 }
 
 export interface ProcessingStep {
   id?: string;
-  type?: "extract" | "transform" | "chunk" | "embed" | "filter" | "summarize";
+  type: "extract" | "transform" | "chunk" | "embed" | "filter" | "summarize";
-  parameters?: Record<string, any>;
+  parameters: Record<string, any>;
-  order?: number;
+  order: number;
   dependsOn?: Array<string>;
 }
 
-export interface Query {
-  prompt?: string;
-  tunables?: Tunables;
-  agentOptions?: Record<string, any>;
-}
-
 export interface RAGConfiguration {
   id?: string;
-  userId?: string;
+  userId: string;
-  name?: string;
+  name: string;
   description?: string;
-  dataSourceConfigurations?: Array<DataSourceConfiguration>;
+  dataSourceConfigurations: Array<DataSourceConfiguration>;
-  embeddingModel?: string;
+  embeddingModel: string;
-  vectorStoreType?: "pinecone" | "qdrant" | "faiss" | "milvus" | "weaviate";
+  vectorStoreType: "pinecone" | "qdrant" | "faiss" | "milvus" | "weaviate";
-  retrievalParameters?: RetrievalParameters;
+  retrievalParameters: RetrievalParameters;
-  createdAt?: Date;
+  createdAt: Date;
-  updatedAt?: Date;
+  updatedAt: Date;
-  isDefault?: boolean;
+  version: number;
-  version?: number;
+  isActive: boolean;
-  isActive?: boolean;
+}
+
+export interface RagEntry {
+  name: string;
+  description?: string;
+  enabled?: boolean;
 }
 
 export interface RefreshToken {
-  token?: string;
+  token: string;
-  expiresAt?: Date;
+  expiresAt: Date;
-  device?: string;
+  device: string;
-  ipAddress?: string;
+  ipAddress: string;
-  isRevoked?: boolean;
+  isRevoked: boolean;
   revokedReason?: string;
 }
 
 export interface RetrievalParameters {
-  searchType?: "similarity" | "mmr" | "hybrid" | "keyword";
+  searchType: "similarity" | "mmr" | "hybrid" | "keyword";
-  topK?: number;
+  topK: number;
   similarityThreshold?: number;
   rerankerModel?: string;
-  useKeywordBoost?: boolean;
+  useKeywordBoost: boolean;
   filterOptions?: Record<string, any>;
-  contextWindow?: number;
+  contextWindow: number;
 }
 
 export interface SalaryRange {
-  min?: number;
+  min: number;
-  max?: number;
+  max: number;
-  currency?: string;
+  currency: string;
-  period?: "hour" | "day" | "month" | "year";
+  period: "hour" | "day" | "month" | "year";
-  isVisible?: boolean;
+  isVisible: boolean;
 }
 
 export interface SearchQuery {
-  query?: string;
+  query: string;
   filters?: Record<string, any>;
   page?: number;
   limit?: number;
@@ -571,21 +594,21 @@ export interface SearchQuery {
 
 export interface Skill {
   id?: string;
-  name?: string;
+  name: string;
-  category?: string;
+  category: string;
-  level?: "beginner" | "intermediate" | "advanced" | "expert";
+  level: "beginner" | "intermediate" | "advanced" | "expert";
   yearsOfExperience?: number;
 }
 
 export interface SkillAssessment {
-  skillName?: string;
+  skillName: string;
-  score?: number;
+  score: number;
   comments?: string;
 }
 
 export interface SocialLink {
-  platform?: "linkedin" | "twitter" | "github" | "dribbble" | "behance" | "website" | "other";
+  platform: "linkedin" | "twitter" | "github" | "dribbble" | "behance" | "website" | "other";
-  url?: string;
+  url: string;
 }
 
 export interface Tunables {
@@ -598,35 +621,35 @@ export interface UserActivity {
   id?: string;
   userId?: string;
   guestId?: string;
-  activityType?: "login" | "search" | "view_job" | "apply_job" | "message" | "update_profile" | "chat";
+  activityType: "login" | "search" | "view_job" | "apply_job" | "message" | "update_profile" | "chat";
-  timestamp?: Date;
+  timestamp: Date;
-  metadata?: Record<string, any>;
+  metadata: Record<string, any>;
   ipAddress?: string;
   userAgent?: string;
   sessionId?: string;
 }
 
 export interface UserPreference {
-  userId?: string;
+  userId: string;
-  theme?: "light" | "dark" | "system";
+  theme: "light" | "dark" | "system";
-  notifications?: Array<NotificationPreference>;
+  notifications: Array<NotificationPreference>;
-  accessibility?: AccessibilitySettings;
+  accessibility: AccessibilitySettings;
   dashboardLayout?: Record<string, any>;
-  language?: string;
+  language: string;
-  timezone?: string;
+  timezone: string;
-  emailFrequency?: "immediate" | "daily" | "weekly" | "never";
+  emailFrequency: "immediate" | "daily" | "weekly" | "never";
 }
 
 export interface WorkExperience {
   id?: string;
-  companyName?: string;
+  companyName: string;
-  position?: string;
+  position: string;
-  startDate?: Date;
+  startDate: Date;
   endDate?: Date;
-  isCurrent?: boolean;
+  isCurrent: boolean;
-  description?: string;
+  description: string;
-  skills?: Array<string>;
+  skills: Array<string>;
-  location?: Location;
+  location: Location;
   achievements?: Array<string>;
 }
 

src/backend/agents/__init__.py

@@ -0,0 +1,97 @@
+from __future__ import annotations
+from pydantic import BaseModel, Field  # type: ignore
+from typing import (
+    Literal,
+    get_args,
+    List,
+    AsyncGenerator,
+    TYPE_CHECKING,
+    Optional,
+    ClassVar,
+    Any,
+    TypeAlias,
+    Dict,
+    Tuple,
+)
+import importlib
+import pathlib
+import inspect
+from prometheus_client import CollectorRegistry  # type: ignore
+
+from database import RedisDatabase
+from . base import Agent
+from logger import logger
+
+_agents: List[Agent] = []
+
+def get_or_create_agent(agent_type: str, prometheus_collector: CollectorRegistry, database: RedisDatabase, **kwargs) -> Agent:
+    """
+    Get or create and append a new agent of the specified type, ensuring only one agent per type exists.
+
+    Args:
+        agent_type: The type of agent to create (e.g., 'web', 'database').
+        **kwargs: Additional fields required by the specific agent subclass.
+
+    Returns:
+        The created agent instance.
+
+    Raises:
+        ValueError: If no matching agent type is found or if a agent of this type already exists.
+    """
+    # Check if a agent with the given agent_type already exists
+    for agent in _agents:
+        if agent.agent_type == agent_type:
+            return agent
+
+    # Find the matching subclass
+    for agent_cls in Agent.__subclasses__():
+        if agent_cls.model_fields["agent_type"].default == agent_type:
+            # Create the agent instance with provided kwargs
+            agent = agent_cls(agent_type=agent_type, prometheus_collector=prometheus_collector, database=database, **kwargs)
+#            if agent.agent_persist: # If an agent is not set to persist, do not add it to the list
+            _agents.append(agent)
+            return agent
+
+    raise ValueError(f"No agent class found for agent_type: {agent_type}")
+
+# Type alias for Agent or any subclass
+AnyAgent: TypeAlias = Agent  # BaseModel covers Agent and subclasses
+
+# Maps class_name to (module_name, class_name)
+class_registry: Dict[str, Tuple[str, str]] = (
+    {}
+)
+
+__all__ = ['get_or_create_agent']
+
+package_dir = pathlib.Path(__file__).parent
+package_name = __name__
+
+for path in package_dir.glob("*.py"):
+    if path.name in ("__init__.py", "base.py") or path.name.startswith("_"):
+        continue
+
+    module_name = path.stem
+    full_module_name = f"{package_name}.{module_name}"
+
+    try:
+        module = importlib.import_module(full_module_name)
+
+        # Find all Agent subclasses in the module
+        for name, obj in inspect.getmembers(module, inspect.isclass):
+            if (
+                issubclass(obj, AnyAgent)
+                and obj is not AnyAgent
+                and obj is not Agent
+                and name not in class_registry
+            ):
+                class_registry[name] = (full_module_name, name)
+                globals()[name] = obj
+                logger.info(f"Adding agent: {name}")
+                __all__.append(name)  # type: ignore
+    except ImportError as e:
+        logger.error(f"Error importing {full_module_name}: {e}")
+        raise e
+    except Exception as e:
+        logger.error(f"Error processing {full_module_name}: {e}")
+        raise e

src/backend/agents/base.py

@@ -0,0 +1,605 @@
+from __future__ import annotations
+from pydantic import BaseModel, Field, model_validator  # type: ignore
+from typing import (
+    Literal,
+    get_args,
+    List,
+    AsyncGenerator,
+    TYPE_CHECKING,
+    Optional,
+    ClassVar,
+    Any,
+    TypeAlias,
+    Dict,
+    Tuple,
+)
+import json
+import time
+import inspect
+from abc import ABC
+import asyncio
+from datetime import datetime, UTC
+from prometheus_client import Counter, Summary, CollectorRegistry  # type: ignore
+
+from models import ( ChatQuery, ChatMessage, Tunables, ChatStatusType, ChatMessageMetaData)
+from logger import logger
+import defines
+from .registry import agent_registry
+from metrics import Metrics
+from database import RedisDatabase  # type: ignore
+
+class LLMMessage(BaseModel):
+    role: str = Field(default="")
+    content: str = Field(default="")
+    tool_calls: Optional[List[Dict]] = Field(default={}, exclude=True)
+
+class Agent(BaseModel, ABC):
+    """
+    Base class for all agent types.
+    This class defines the common attributes and methods for all agent types.
+    """
+    class Config:
+        arbitrary_types_allowed = True  # Allow arbitrary types like RedisDatabase
+
+    # Agent management with pydantic
+    agent_type: Literal["base"] = "base"
+    _agent_type: ClassVar[str] = agent_type  # Add this for registration
+    agent_persist: bool = True # Whether this agent will persist in the database
+
+    database: RedisDatabase = Field(
+        ...,
+        description="Database connection for this agent, used to store and retrieve data."
+    )
+    prometheus_collector: CollectorRegistry = Field(..., description="Prometheus collector for this agent, used to track metrics.", exclude=True)
+
+    # Tunables (sets default for new Messages attached to this agent)
+    tunables: Tunables = Field(default_factory=Tunables)
+    metrics: Metrics = Field(
+        None, description="Metrics collector for this agent, used to track performance and usage."
+    )
+
+    @model_validator(mode="after")
+    def initialize_metrics(self) -> "Agent":
+        if self.metrics is None:
+            self.metrics = Metrics(prometheus_collector=self.prometheus_collector)
+        return self
+    
+    # Agent properties
+    system_prompt: str  # Mandatory
+    context_tokens: int = 0
+
+    # context_size is shared across all subclasses
+    _context_size: ClassVar[int] = int(defines.max_context * 0.5)
+
+    conversation: List[ChatMessage] = Field(
+        default_factory=list,
+        description="Conversation history for this agent, used to maintain context across messages."
+    )
+
+    @property
+    def context_size(self) -> int:
+        return Agent._context_size
+
+    @context_size.setter
+    def context_size(self, value: int):
+        Agent._context_size = value
+
+    def set_optimal_context_size(
+        self, llm: Any, model: str, prompt: str, ctx_buffer=2048
+    ) -> int:
+        # Most models average 1.3-1.5 tokens per word
+        word_count = len(prompt.split())
+        tokens = int(word_count * 1.4)
+
+        # Add buffer for safety
+        total_ctx = tokens + ctx_buffer
+
+        if total_ctx > self.context_size:
+            logger.info(
+                f"Increasing context size from {self.context_size} to {total_ctx}"
+            )
+
+        # Grow the context size if necessary
+        self.context_size = max(self.context_size, total_ctx)
+        # Use actual model maximum context size
+        return self.context_size
+
+    # Class and pydantic model management
+    def __init_subclass__(cls, **kwargs) -> None:
+        """Auto-register subclasses"""
+        super().__init_subclass__(**kwargs)
+        # Register this class if it has an agent_type
+        if hasattr(cls, "agent_type") and cls.agent_type != Agent._agent_type:
+            agent_registry.register(cls.agent_type, cls)
+
+    def model_dump(self, *args, **kwargs) -> Any:
+        # Ensure context is always excluded, even with exclude_unset=True
+        kwargs.setdefault("exclude", set())
+        if isinstance(kwargs["exclude"], set):
+            kwargs["exclude"].add("context")
+        elif isinstance(kwargs["exclude"], dict):
+            kwargs["exclude"]["context"] = True
+        return super().model_dump(*args, **kwargs)
+
+    @classmethod
+    def valid_agent_types(cls) -> set[str]:
+        """Return the set of valid agent_type values."""
+        return set(get_args(cls.__annotations__["agent_type"]))
+
+    # Agent methods
+    def get_agent_type(self):
+        return self._agent_type
+
+    # async def prepare_message(self, message: ChatMessage) -> AsyncGenerator[ChatMessage, None]:
+    #     """
+    #     Prepare message with context information in message.preamble
+    #     """
+    #     logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+
+        # self.metrics.prepare_count.labels(agent=self.agent_type).inc()
+        # with self.metrics.prepare_duration.labels(agent=self.agent_type).time():
+        #     if not self.context:
+        #         raise ValueError("Context is not set for this agent.")
+
+        #     # Generate RAG content if enabled, based on the content
+        #     rag_context = ""
+        #     if message.tunables.enable_rag and message.prompt:
+        #         # Gather RAG results, yielding each result
+        #         # as it becomes available
+        #         for message in self.context.user.generate_rag_results(message):
+        #             logger.info(f"RAG: {message.status} - {message.content}")
+        #             if message.status == "error":
+        #                 yield message
+        #                 return
+        #             if message.status != "done":
+        #                 yield message
+
+        #         # for rag in message.metadata.rag:
+        #         #     for doc in rag.documents:
+        #         #         rag_context += f"{doc}\n"
+
+        #     message.preamble = {}
+
+        #     if rag_context:
+        #         message.preamble["context"] = f"The following is context information about {self.context.user.full_name}:\n{rag_context}"
+
+        #     if message.tunables.enable_context and self.context.user_resume:
+        #         message.preamble["resume"] = self.context.user_resume
+
+        #     message.system_prompt = self.system_prompt
+        #     message.status = ChatStatusType.DONE
+        #     yield message
+
+        # return
+
+    # async def process_tool_calls(
+    #     self,
+    #     llm: Any,
+    #     model: str,
+    #     message: ChatMessage,
+    #     tool_message: Any, # llama response message
+    #     messages: List[LLMMessage],
+    # ) -> AsyncGenerator[ChatMessage, None]:
+    #     logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+
+    #     self.metrics.tool_count.labels(agent=self.agent_type).inc()
+    #     with self.metrics.tool_duration.labels(agent=self.agent_type).time():
+
+    #         if not self.context:
+    #             raise ValueError("Context is not set for this agent.")
+    #         if not message.metadata.tools:
+    #             raise ValueError("tools field not initialized")
+
+    #         tool_metadata = message.metadata.tools
+    #         tool_metadata["tool_calls"] = []
+
+    #         message.status = "tooling"
+
+    #         for i, tool_call in enumerate(tool_message.tool_calls):
+    #             arguments = tool_call.function.arguments
+    #             tool = tool_call.function.name
+
+    #             # Yield status update before processing each tool
+    #             message.content = (
+    #                 f"Processing tool {i+1}/{len(tool_message.tool_calls)}: {tool}..."
+    #             )
+    #             yield message
+    #             logger.info(f"LLM - {message.content}")
+
+    #             # Process the tool based on its type
+    #             match tool:
+    #                 case "TickerValue":
+    #                     ticker = arguments.get("ticker")
+    #                     if not ticker:
+    #                         ret = None
+    #                     else:
+    #                         ret = TickerValue(ticker)
+
+    #                 case "AnalyzeSite":
+    #                     url = arguments.get("url")
+    #                     question = arguments.get(
+    #                         "question", "what is the summary of this content?"
+    #                     )
+
+    #                     # Additional status update for long-running operations
+    #                     message.content = (
+    #                         f"Retrieving and summarizing content from {url}..."
+    #                     )
+    #                     yield message
+    #                     ret = await AnalyzeSite(
+    #                         llm=llm, model=model, url=url, question=question
+    #                     )
+
+    #                 case "GenerateImage":
+    #                     prompt = arguments.get("prompt", None)
+    #                     if not prompt:
+    #                         logger.info("No prompt supplied to GenerateImage")
+    #                         ret = { "error": "No prompt supplied to GenerateImage" }
+
+    #                     # Additional status update for long-running operations
+    #                     message.content = (
+    #                         f"Generating image for {prompt}..."
+    #                     )
+    #                     yield message
+    #                     ret = await GenerateImage(
+    #                         llm=llm, model=model, prompt=prompt
+    #                     )
+    #                     logger.info("GenerateImage returning", ret)
+
+    #                 case "DateTime":
+    #                     tz = arguments.get("timezone")
+    #                     ret = DateTime(tz)
+
+    #                 case "WeatherForecast":
+    #                     city = arguments.get("city")
+    #                     state = arguments.get("state")
+
+    #                     message.content = (
+    #                         f"Fetching weather data for {city}, {state}..."
+    #                     )
+    #                     yield message
+    #                     ret = WeatherForecast(city, state)
+
+    #                 case _:
+    #                     logger.error(f"Requested tool {tool} does not exist")
+    #                     ret = None
+
+    #             # Build response for this tool
+    #             tool_response = {
+    #                 "role": "tool",
+    #                 "content": json.dumps(ret),
+    #                 "name": tool_call.function.name,
+    #             }
+
+    #             tool_metadata["tool_calls"].append(tool_response)
+
+    #             if len(tool_metadata["tool_calls"]) == 0:
+    #                 message.status = "done"
+    #                 yield message
+    #                 return
+
+    #             message_dict = LLMMessage(
+    #                 role=tool_message.get("role", "assistant"),
+    #                 content=tool_message.get("content", ""),
+    #                 tool_calls=[
+    #                     {
+    #                         "function": {
+    #                             "name": tc["function"]["name"],
+    #                             "arguments": tc["function"]["arguments"],
+    #                         }
+    #                     }
+    #                     for tc in tool_message.tool_calls
+    #                 ],
+    #             )
+
+    #             messages.append(message_dict)
+    #             messages.extend(tool_metadata["tool_calls"])
+
+    #         message.status = "thinking"
+    #         message.content = "Incorporating tool results into response..."
+    #         yield message
+
+    #         # Decrease creativity when processing tool call requests
+    #         message.content = ""
+    #         start_time = time.perf_counter()
+    #         for response in llm.chat(
+    #             model=model,
+    #             messages=messages,
+    #             options={
+    #                 **message.metadata.options,
+    #             },
+    #             stream=True,
+    #         ):
+    #             # logger.info(f"LLM::Tools: {'done' if response.done else 'processing'} - {response.message}")
+    #             message.status = "streaming"
+    #             message.chunk = response.message.content
+    #             message.content += message.chunk
+
+    #             if not response.done:
+    #                 yield message
+
+    #             if response.done:
+    #                 self.collect_metrics(response)
+    #                 message.metadata.eval_count += response.eval_count
+    #                 message.metadata.eval_duration += response.eval_duration
+    #                 message.metadata.prompt_eval_count += response.prompt_eval_count
+    #                 message.metadata.prompt_eval_duration += response.prompt_eval_duration
+    #                 self.context_tokens = (
+    #                     response.prompt_eval_count + response.eval_count
+    #                 )
+    #                 message.status = "done"
+    #                 yield message
+
+    #         end_time = time.perf_counter()
+    #         message.metadata.timers["llm_with_tools"] = end_time - start_time
+    #         return
+
+    def collect_metrics(self, response):
+        self.metrics.tokens_prompt.labels(agent=self.agent_type).inc(
+            response.prompt_eval_count
+        )
+        self.metrics.tokens_eval.labels(agent=self.agent_type).inc(response.eval_count)
+
+    async def generate(
+        self, llm: Any, model: str, query: ChatQuery, session_id: str, user_id: str, temperature=0.7
+    ) -> AsyncGenerator[ChatMessage, None]:
+        logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+
+        chat_message = ChatMessage(
+            session_id=session_id,
+            prompt=query.prompt,
+            tunables=query.tunables,
+            status=ChatStatusType.PREPARING,
+            sender="user",
+            content="",
+            timestamp=datetime.now(UTC)
+        )
+        self.metrics.generate_count.labels(agent=self.agent_type).inc()
+        with self.metrics.generate_duration.labels(agent=self.agent_type).time():
+            # Create a pruned down message list based purely on the prompt and responses,
+            # discarding the full preamble generated by prepare_message
+            messages: List[LLMMessage] = [
+                LLMMessage(role="system", content=self.system_prompt)
+            ]
+            messages.extend(
+                [
+                    item
+                    for m in self.conversation
+                    for item in [
+                        LLMMessage(role="user", content=m.prompt.strip() if m.prompt else ""),
+                        LLMMessage(role="assistant", content=m.response.strip()),
+                    ]
+                ]
+            )
+            # Only the actual user query is provided with the full context message
+            messages.append(
+                LLMMessage(role="user", content=query.prompt.strip())
+            )
+
+            # message.messages = messages
+            chat_message.metadata = ChatMessageMetaData()
+            chat_message.metadata.options = {
+                "seed": 8911,
+                "num_ctx": self.context_size,
+                "temperature": temperature,  # Higher temperature to encourage tool usage
+            }
+
+            # Create a dict for storing various timing stats
+            chat_message.metadata.timers = {}
+
+            # use_tools = message.tunables.enable_tools and len(self.context.tools) > 0
+            # message.metadata.tools = {
+            #     "available": llm_tools(self.context.tools),
+            #     "used": False,
+            # }
+            # tool_metadata = message.metadata.tools
+
+            # if use_tools:
+            #     message.status = "thinking"
+            #     message.content = f"Performing tool analysis step 1/2..."
+            #     yield message
+
+            #     logger.info("Checking for LLM tool usage")
+            #     start_time = time.perf_counter()
+            #     # Tools are enabled and available, so query the LLM with a short context of messages
+            #     # in case the LLM did something like ask "Do you want me to run the tool?" and the
+            #     # user said "Yes" -- need to keep the context in the thread.
+            #     tool_metadata["messages"] = (
+            #         [{"role": "system", "content": self.system_prompt}] + messages[-6:]
+            #         if len(messages) >= 7
+            #         else messages
+            #     )
+
+            #     response = llm.chat(
+            #         model=model,
+            #         messages=tool_metadata["messages"],
+            #         tools=tool_metadata["available"],
+            #         options={
+            #             **message.metadata.options,
+            #         },
+            #         stream=False,  # No need to stream the probe
+            #     )
+            #     self.collect_metrics(response)
+
+            #     end_time = time.perf_counter()
+            #     message.metadata.timers["tool_check"] = end_time - start_time
+            #     if not response.message.tool_calls:
+            #         logger.info("LLM indicates tools will not be used")
+            #         # The LLM will not use tools, so disable use_tools so we can stream the full response
+            #         use_tools = False
+            #     else:
+            #         tool_metadata["attempted"] = response.message.tool_calls
+
+            # if use_tools:
+            #     logger.info("LLM indicates tools will be used")
+
+            #     # Tools are enabled and available and the LLM indicated it will use them
+            #     message.content = (
+            #         f"Performing tool analysis step 2/2 (tool use suspected)..."
+            #     )
+            #     yield message
+
+            #     logger.info(f"Performing LLM call with tools")
+            #     start_time = time.perf_counter()
+            #     response = llm.chat(
+            #         model=model,
+            #         messages=tool_metadata["messages"],  # messages,
+            #         tools=tool_metadata["available"],
+            #         options={
+            #             **message.metadata.options,
+            #         },
+            #         stream=False,
+            #     )
+            #     self.collect_metrics(response)
+
+            #     end_time = time.perf_counter()
+            #     message.metadata.timers["non_streaming"] = end_time - start_time
+
+            #     if not response:
+            #         message.status = "error"
+            #         message.content = "No response from LLM."
+            #         yield message
+            #         return
+
+            #     if response.message.tool_calls:
+            #         tool_metadata["used"] = response.message.tool_calls
+            #         # Process all yielded items from the handler
+            #         start_time = time.perf_counter()
+            #         async for message in self.process_tool_calls(
+            #             llm=llm,
+            #             model=model,
+            #             message=message,
+            #             tool_message=response.message,
+            #             messages=messages,
+            #         ):
+            #             if message.status == "error":
+            #                 yield message
+            #                 return
+            #             yield message
+            #         end_time = time.perf_counter()
+            #         message.metadata.timers["process_tool_calls"] = end_time - start_time
+            #         message.status = "done"
+            #         return
+
+            #     logger.info("LLM indicated tools will be used, and then they weren't")
+            #     message.content = response.message.content
+            #     message.status = "done"
+            #     yield message
+            #     return
+
+            # not use_tools
+            chat_message.status = ChatStatusType.THINKING
+            chat_message.content = f"Generating response..."
+            yield chat_message
+            # Reset the response for streaming
+            chat_message.content = ""
+            start_time = time.perf_counter()
+            for response in llm.chat(
+                model=model,
+                messages=messages,
+                options={
+                    **chat_message.metadata.options,
+                },
+                stream=True,
+            ):
+                if not response:
+                    chat_message.status = ChatStatusType.ERROR
+                    chat_message.content = "No response from LLM."
+                    yield chat_message
+                    return
+
+                chat_message.status = ChatStatusType.STREAMING
+                chat_message.chunk = response.message.content
+                chat_message.content += chat_message.chunk
+
+                if not response.done:
+                    yield chat_message
+
+                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.status = ChatStatusType.DONE
+                    yield chat_message
+
+            end_time = time.perf_counter()
+            chat_message.metadata.timers["streamed"] = end_time - start_time
+        chat_message.status = ChatStatusType.DONE
+        self.conversation.append(chat_message)
+        return
+
+    # async def process_message(
+    #     self, llm: Any, model: str, message: Message
+    # ) -> AsyncGenerator[Message, None]:
+    #     logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+
+    #     self.metrics.process_count.labels(agent=self.agent_type).inc()
+    #     with self.metrics.process_duration.labels(agent=self.agent_type).time():
+
+    #         if not self.context:
+    #             raise ValueError("Context is not set for this agent.")
+
+    #         logger.info(
+    #             "TODO: Implement delay queing; busy for same agent, otherwise return queue size and estimated wait time"
+    #         )
+    #         spinner: List[str] = ["\\", "|", "/", "-"]
+    #         tick: int = 0
+    #         while self.context.processing:
+    #             message.status = "waiting"
+    #             message.content = (
+    #                 f"Busy processing another request. Please wait. {spinner[tick]}"
+    #             )
+    #             tick = (tick + 1) % len(spinner)
+    #             yield message
+    #             await asyncio.sleep(1)  # Allow the event loop to process the write
+
+    #         self.context.processing = True
+
+    #         message.system_prompt = (
+    #             f"<|system|>\n{self.system_prompt.strip()}\n</|system|>"
+    #         )
+    #         message.context_prompt = ""
+    #         for p in message.preamble.keys():
+    #             message.context_prompt += (
+    #                 f"\n<|{p}|>\n{message.preamble[p].strip()}\n</|{p}>\n\n"
+    #             )
+    #         message.context_prompt += f"{message.prompt}"
+
+    #         # Estimate token length of new messages
+    #         message.content = f"Optimizing context..."
+    #         message.status = "thinking"
+    #         yield message
+
+    #         message.context_size = self.set_optimal_context_size(
+    #             llm, model, prompt=message.context_prompt
+    #         )
+
+    #         message.content = f"Processing {'RAG augmented ' if message.metadata.rag else ''}query..."
+    #         message.status = "thinking"
+    #         yield message
+
+    #         async for message in self.generate_llm_response(
+    #             llm=llm, model=model, message=message
+    #         ):
+    #             # logger.info(f"LLM: {message.status} - {f'...{message.content[-20:]}' if len(message.content) > 20 else message.content}")
+    #             if message.status == "error":
+    #                 yield message
+    #                 self.context.processing = False
+    #                 return
+    #             yield message
+
+    #         # Done processing, add message to conversation
+    #         message.status = "done"
+    #         self.conversation.add(message)
+    #         self.context.processing = False
+
+    #     return
+
+
+# Register the base agent
+agent_registry.register(Agent._agent_type, Agent)

src/backend/agents/general.py

@@ -0,0 +1,88 @@
+from __future__ import annotations
+from typing import Literal, AsyncGenerator, ClassVar, Optional, Any
+from datetime import datetime
+import inspect
+
+from .base import Agent, agent_registry
+from logger import logger
+
+from .registry import agent_registry
+from models import ( ChatQuery, ChatMessage, Tunables, ChatStatusType)
+
+system_message = f"""
+Launched on {datetime.now().isoformat()}.
+
+When answering queries, follow these steps:
+
+- First analyze the query to determine if real-time information from the tools might be helpful
+- Even when <|context|> or <|resume|> is provided, consider whether the tools would provide more current or comprehensive information
+- Use the provided tools whenever they would enhance your response, regardless of whether context is also available
+- When presenting weather forecasts, include relevant emojis immediately before the corresponding text. For example, for a sunny day, say \"☀️ Sunny\" or if the forecast says there will be \"rain showers, say \"🌧️ Rain showers\". Use this mapping for weather emojis: Sunny: ☀️, Cloudy: ☁️, Rainy: 🌧️, Snowy: ❄️
+- When any combination of <|context|>, <|resume|> and tool outputs are relevant, synthesize information from all sources to provide the most complete answer
+- Always prioritize the most up-to-date and relevant information, whether it comes from <|context|>, <|resume|> or tools
+- If <|context|> and tool outputs contain conflicting information, prefer the tool outputs as they likely represent more current data
+- If there is information in the <|context|> or <|resume|> sections to enhance the answer, incorporate it seamlessly and refer to it as 'the latest information' or 'recent data' instead of mentioning '<|context|>' (etc.) or quoting it directly.
+- Avoid phrases like 'According to the <|context|>' or similar references to the <|context|> or <|resume|>.
+
+CRITICAL INSTRUCTIONS FOR IMAGE GENERATION:
+
+1. When the user requests to generate an image, inject the following into the response: <GenerateImage prompt="USER-PROMPT"/>. Do this when users request images, drawings, or visual content.
+3. MANDATORY: You must respond with EXACTLY this format: <GenerateImage prompt="{{USER-PROMPT}}"/>
+4. FORBIDDEN: DO NOT use markdown image syntax ![](url) 
+5. FORBIDDEN: DO NOT create fake URLs or file paths
+6. FORBIDDEN: DO NOT use any other image embedding format
+
+CORRECT EXAMPLE:
+User: "Draw a cat"
+Your response: "<GenerateImage prompt='Draw a cat'/>"
+
+WRONG EXAMPLES (DO NOT DO THIS):
+- ![](https://example.com/...)
+- ![Cat image](any_url)
+- <img src="...">
+
+The <GenerateImage prompt="{{USER-PROMPT}}"/> format is the ONLY way to display images in this system. 
+DO NOT make up a URL for an image or provide markdown syntax for embedding an image. Only use <GenerateImage prompt="{{USER-PROMPT}}".
+
+Always use tools, <|resume|>, and <|context|> when possible. Be concise, and never make up information. If you do not know the answer, say so.
+"""
+
+
+class Chat(Agent):
+    """
+    Chat Agent
+    """
+
+    agent_type: Literal["general"] = "general"  # type: ignore
+    _agent_type: ClassVar[str] = agent_type  # Add this for registration
+
+    system_prompt: str = system_message
+
+#     async def prepare_message(self, message: Message) -> AsyncGenerator[Message, None]:
+#         logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
+#         if not self.context:
+#             raise ValueError("Context is not set for this agent.")
+
+#         async for message in super().prepare_message(message):
+#             if message.status != "done":
+#                 yield message
+
+#         if message.preamble:
+#             excluded = {}
+#             preamble_types = [
+#                 f"<|{p}|>" for p in message.preamble.keys() if p not in excluded
+#             ]
+#             preamble_types_AND = " and ".join(preamble_types)
+#             preamble_types_OR = " or ".join(preamble_types)
+#             message.preamble[
+#                 "rules"
+#             ] = f"""\
+# - Answer the question based on the information provided in the {preamble_types_AND} sections by incorporate it seamlessly and refer to it using natural language instead of mentioning {preamble_types_OR} or quoting it directly.
+# - If there is no information in these sections, answer based on your knowledge, or use any available tools.
+# - Avoid phrases like 'According to the {preamble_types[0]}' or similar references to the {preamble_types_OR}.
+# """
+#         message.preamble["question"] = "Respond to:"
+
+
+# Register the base agent
+agent_registry.register(Chat._agent_type, Chat)

src/backend/agents/registry.py

@@ -0,0 +1,33 @@
+from __future__ import annotations
+from typing import List, Dict, Optional, Type
+
+# We'll use a registry pattern rather than hardcoded strings
+class AgentRegistry:
+    """Registry for agent types and classes"""
+
+    _registry: Dict[str, Type] = {}
+
+    @classmethod
+    def register(cls, agent_type: str, agent_class: Type) -> Type:
+        """Register an agent class with its type"""
+        cls._registry[agent_type] = agent_class
+        return agent_class
+
+    @classmethod
+    def get_class(cls, agent_type: str) -> Optional[Type]:
+        """Get the class for a given agent type"""
+        return cls._registry.get(agent_type)
+
+    @classmethod
+    def get_types(cls) -> List[str]:
+        """Get all registered agent types"""
+        return list(cls._registry.keys())
+
+    @classmethod
+    def get_classes(cls) -> Dict[str, Type]:
+        """Get all registered agent classes"""
+        return cls._registry.copy()
+
+
+# Create a singleton instance
+agent_registry = AgentRegistry()

src/backend/focused_test.py

@@ -8,7 +8,7 @@ import sys
 from datetime import datetime
 from models import (
     UserStatus, UserType, SkillLevel, EmploymentType,
-    Candidate, Employer, Location, Skill, AIParameters, AIModelType
+    Candidate, Employer, Location, Skill, AIModelType
 )
 
 
@@ -119,41 +119,23 @@ def test_api_dict_format():
 def test_validation_constraints():
     """Test that validation constraints work"""
     print("\n🔒 Testing validation constraints...")
-    
+
-    # Test AI Parameters with constraints
-    valid_params = AIParameters(
-        name="Test Config",
-        model=AIModelType.QWEN2_5,
-        temperature=0.7,  # Valid: 0-1
-        maxTokens=2000,   # Valid: > 0
-        topP=0.95,        # Valid: 0-1
-        frequencyPenalty=0.0,  # Valid: -2 to 2
-        presencePenalty=0.0,   # Valid: -2 to 2
-        isDefault=True,
-        createdAt=datetime.now(),
-        updatedAt=datetime.now()
-    )
-    print(f"✅ Valid AI parameters created")
-    
-    # Test constraint violation
     try:
-        invalid_params = AIParameters(
+        # Create a candidate with invalid email
-            name="Invalid Config",
+        invalid_candidate = Candidate(
-            model=AIModelType.QWEN2_5,
+            email="invalid-email",
-            temperature=1.5,  # Invalid: > 1
+            username="test_invalid",
-            maxTokens=2000,
-            topP=0.95,
-            frequencyPenalty=0.0,
-            presencePenalty=0.0,
-            isDefault=True,
             createdAt=datetime.now(),
-            updatedAt=datetime.now()
+            updatedAt=datetime.now(),
+            status=UserStatus.ACTIVE,
+            firstName="Jane",
+            lastName="Doe",
+            fullName="Jane Doe"
         )
-        print("❌ Should have rejected invalid temperature")
+        print("❌ Validation should have failed but didn't")
         return False
-    except Exception:
+    except ValueError as e:
-        print(f"✅ Constraint validation working")
+        print(f"✅ Validation error caught: {e}")    
-    
     return True
 
 def test_enum_values():
@@ -201,6 +183,7 @@ def main():
         print(f"\n❌ Test failed: {type(e).__name__}: {e}")
         import traceback
         traceback.print_exc()
+        print(f"\n❌ {traceback.format_exc()}")
         return False
 
 if __name__ == "__main__":

src/backend/generate_types.py

@@ -195,48 +195,107 @@ def snake_to_camel(snake_str: str) -> str:
     components = snake_str.split('_')
     return components[0] + ''.join(x.title() for x in components[1:])
 
-def is_field_optional(field_info: Any, field_type: Any) -> bool:
+def is_field_optional(field_info: Any, field_type: Any, debug: bool = False) -> bool:
     """Determine if a field should be optional in TypeScript"""
     
+    if debug:
+        print(f"         🔍 Analyzing field optionality:")
+    
     # First, check if the type itself is Optional (Union with None)  
     origin = get_origin(field_type)
     args = get_args(field_type)
     is_union_with_none = origin is Union and type(None) in args
     
+    if debug:
+        print(f"         └─ Type is Optional[T]: {is_union_with_none}")
+    
     # If the type is Optional[T], it's always optional regardless of Field settings
     if is_union_with_none:
+        if debug:
+            print(f"         └─ RESULT: Optional (type is Optional[T])")
         return True
     
     # For non-Optional types, check Field settings and defaults
     
     # Check for default factory (makes field optional)
-    if hasattr(field_info, 'default_factory') and field_info.default_factory is not None:
+    has_default_factory = hasattr(field_info, 'default_factory') and field_info.default_factory is not None
+    if debug:
+        print(f"         └─ Has default factory: {has_default_factory}")
+    
+    if has_default_factory:
+        if debug:
+            print(f"         └─ RESULT: Optional (has default factory)")
         return True
     
-    # Check the default value
+    # Check the default value - this is the tricky part
     if hasattr(field_info, 'default'):
         default_val = field_info.default
+        if debug:
+            print(f"         └─ Has default attribute: {repr(default_val)} (type: {type(default_val)})")
         
-        # Field(...) or Ellipsis means REQUIRED (not optional)
+        # Check for different types of "no default" markers
-        if default_val is ...:
+        # Pydantic uses various markers for "no default"
+        if default_val is ...:  # Ellipsis
+            if debug:
+                print(f"         └─ RESULT: Required (default is Ellipsis)")
+            return False
+        
+        # Check for Pydantic's internal "PydanticUndefined" or similar markers
+        default_str = str(default_val)
+        default_type_str = str(type(default_val))
+        
+        # Common patterns for "undefined" in Pydantic
+        undefined_patterns = [
+            'PydanticUndefined', 
+            'Undefined', 
+            '_Unset',
+            'UNSET',
+            'NotSet',
+            '_MISSING'
+        ]
+        
+        is_undefined_marker = any(pattern in default_str or pattern in default_type_str 
+                                for pattern in undefined_patterns)
+        
+        if debug:
+            print(f"         └─ Checking for undefined markers in: {default_str} | {default_type_str}")
+            print(f"         └─ Is undefined marker: {is_undefined_marker}")
+        
+        if is_undefined_marker:
+            if debug:
+                print(f"         └─ RESULT: Required (default is undefined marker)")
             return False
             
-        # Any other default value (including None) makes it optional
+        # Any other actual default value makes it optional
-        # This covers: Field(None), Field("some_value"), = "some_value", = None, etc.
+        if debug:
-        else:
+            print(f"         └─ RESULT: Optional (has actual default value)")
-            return True
+        return True
+    else:
+        if debug:
+            print(f"         └─ No default attribute found")
+        
+        # If no default attribute exists, check Pydantic's required flag
+        if hasattr(field_info, 'is_required'):
+            try:
+                is_required = field_info.is_required()
+                if debug:
+                    print(f"         └─ is_required(): {is_required}")
+                return not is_required
+            except:
+                if debug:
+                    print(f"         └─ is_required() failed")
+                pass
+        
+        # Check the 'required' attribute (Pydantic v1 style)
+        if hasattr(field_info, 'required'):
+            is_required = field_info.required
+            if debug:
+                print(f"         └─ required attribute: {is_required}")
+            return not is_required
     
-    # If no default is set at all, check if field is explicitly marked as not required
+    # Default: if type is not Optional and no clear default, it's required (not optional)
-    # This is for edge cases in Pydantic v2
+    if debug:
-    if hasattr(field_info, 'is_required'):
+        print(f"         └─ RESULT: Required (fallback - no Optional type, no default)")
-        try:
-            return not field_info.is_required()
-        except:
-            pass
-    elif hasattr(field_info, 'required'):
-        return not field_info.required
-    
-    # Default: if type is not Optional and no explicit default, it's required (not optional)
     return False
 
 def process_pydantic_model(model_class, debug: bool = False) -> Dict[str, Any]:
@@ -271,27 +330,12 @@ def process_pydantic_model(model_class, debug: bool = False) -> Dict[str, Any]:
             ts_type = python_type_to_typescript(field_type, debug)
             
             # Check if optional
-            is_optional = is_field_optional(field_info, field_type)
+            is_optional = is_field_optional(field_info, field_type, debug)
             
             if debug:
                 print(f"       TS name: {ts_name}")
                 print(f"       TS type: {ts_type}")
                 print(f"       Optional: {is_optional}")
-                
-                # Debug the optional logic
-                origin = get_origin(field_type)
-                args = get_args(field_type)
-                is_union_with_none = origin is Union and type(None) in args
-                has_default = hasattr(field_info, 'default')
-                has_default_factory = hasattr(field_info, 'default_factory') and field_info.default_factory is not None
-                
-                print(f"       └─ Type is Optional: {is_union_with_none}")
-                if has_default:
-                    default_val = field_info.default
-                    print(f"       └─ Has default: {default_val} (is ...? {default_val is ...})")
-                else:
-                    print(f"       └─ No default attribute")
-                print(f"       └─ Has default factory: {has_default_factory}")
                 print()
             
             properties.append({

src/backend/llm_manager.py

@@ -0,0 +1,41 @@
+import ollama
+import defines
+
+_llm = ollama.Client(host=defines.ollama_api_url)  # type: ignore
+
+class llm_manager:
+    """
+    A class to manage LLM operations using the Ollama client.
+    """
+    @staticmethod
+    def get_llm() -> ollama.Client: # type: ignore
+        """
+        Get the Ollama client instance.
+
+        Returns:
+            An instance of the Ollama client.
+        """
+        return _llm
+
+    @staticmethod
+    def get_models() -> list[str]:
+        """
+        Get a list of available models from the Ollama client.
+
+        Returns:
+            List of model names.
+        """
+        return _llm.models()
+
+    @staticmethod
+    def get_model_info(model_name: str) -> dict:
+        """
+        Get information about a specific model.
+
+        Args:
+            model_name: The name of the model to retrieve information for.
+
+        Returns:
+            A dictionary containing model information.
+        """
+        return _llm.model(model_name)

src/backend/main.py

@@ -1,7 +1,7 @@
 from fastapi import FastAPI, HTTPException, Depends, Query, Path, Body, status, APIRouter, Request # type: ignore
 from fastapi.middleware.cors import CORSMiddleware # type: ignore
 from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials # type: ignore
-from fastapi.responses import JSONResponse # type: ignore
+from fastapi.responses import JSONResponse, StreamingResponse# type: ignore
 from fastapi.staticfiles import StaticFiles # type: ignore
 import uvicorn  # type: ignore
 from typing import List, Optional, Dict, Any
@@ -15,6 +15,7 @@ import re
 import asyncio
 import signal
 import json
+import traceback
 
 # Prometheus
 from prometheus_client import Summary  # type: ignore
@@ -24,25 +25,24 @@ from prometheus_client import CollectorRegistry, Counter  # type: ignore
 # Import Pydantic models
 from models import (
     # User models
-    Candidate, Employer, BaseUser, Guest, Authentication, AuthResponse,
+    Candidate, Employer, BaseUserWithType, BaseUser, Guest, Authentication, AuthResponse,
     
     # Job models
     Job, JobApplication, ApplicationStatus,
     
     # Chat models
-    ChatSession, ChatMessage, ChatContext,
+    ChatSession, ChatMessage, ChatContext, ChatQuery,
-    
-    # AI models
-    AIParameters,
     
     # Supporting models
     Location, Skill, WorkExperience, Education
 )
 
 import defines
+import agents
 from logger import logger
 from database import RedisDatabase, redis_manager, DatabaseManager
 from metrics import Metrics
+from llm_manager import llm_manager
 
 # Initialize FastAPI app
 # ============================
@@ -178,7 +178,7 @@ async def verify_token_with_blacklist(credentials: HTTPAuthorizationCredentials
 async def get_current_user(
     user_id: str = Depends(verify_token_with_blacklist), 
     database: RedisDatabase = Depends(lambda: db_manager.get_database())
-):
+) -> BaseUserWithType:
     """Get current user from database"""
     try:
         # Check candidates first
@@ -354,16 +354,17 @@ async def login(
 
 @api_router.post("/auth/logout")
 async def logout(
-    refreshToken: str = Body(..., alias="refreshToken"),
+    access_token: str = Body(..., alias="accessToken"),
-    accessToken: Optional[str] = Body(None, alias="accessToken"),
+    refresh_token: str = Body(..., alias="refreshToken"),
     current_user = Depends(get_current_user),
     database: RedisDatabase = Depends(get_database)
 ):
     """Logout endpoint - revokes both access and refresh tokens"""
+    logger.info(f"🔑 User {current_user.id} is logging out")
     try:
         # Verify refresh token
         try:
-            refresh_payload = jwt.decode(refreshToken, SECRET_KEY, algorithms=[ALGORITHM])
+            refresh_payload = jwt.decode(refresh_token, SECRET_KEY, algorithms=[ALGORITHM])
             user_id = refresh_payload.get("sub")
             token_type = refresh_payload.get("type")
             refresh_exp = refresh_payload.get("exp")
@@ -394,7 +395,7 @@ async def logout(
         refresh_ttl = max(0, refresh_exp - int(datetime.now(UTC).timestamp()))
         if refresh_ttl > 0:
             await redis_client.setex(
-                f"blacklisted_token:{refreshToken}", 
+                f"blacklisted_token:{refresh_token}", 
                 refresh_ttl, 
                 json.dumps({
                     "user_id": user_id,
@@ -406,9 +407,9 @@ async def logout(
             logger.info(f"🔒 Blacklisted refresh token for user {user_id}")
         
         # If access token is provided, revoke it too
-        if accessToken:
+        if access_token:
             try:
-                access_payload = jwt.decode(accessToken, SECRET_KEY, algorithms=[ALGORITHM])
+                access_payload = jwt.decode(access_token, SECRET_KEY, algorithms=[ALGORITHM])
                 access_user_id = access_payload.get("sub")
                 access_exp = access_payload.get("exp")
                 
@@ -417,7 +418,7 @@ async def logout(
                     access_ttl = max(0, access_exp - int(datetime.now(UTC).timestamp()))
                     if access_ttl > 0:
                         await redis_client.setex(
-                            f"blacklisted_token:{accessToken}",
+                            f"blacklisted_token:{access_token}",
                             access_ttl,
                             json.dumps({
                                 "user_id": user_id,
@@ -447,7 +448,7 @@ async def logout(
             "message": "Logged out successfully",
             "tokensRevoked": {
                 "refreshToken": True,
-                "accessToken": bool(accessToken)
+                "accessToken": bool(access_token)
             }
         })
         
@@ -905,7 +906,7 @@ async def search_jobs(
 @api_router.post("/chat/sessions")
 async def create_chat_session(
     session_data: Dict[str, Any] = Body(...),
-    current_user = Depends(get_current_user),
+    current_user : BaseUserWithType = Depends(get_current_user),
     database: RedisDatabase = Depends(get_database)
 ):
     """Create a new chat session"""
@@ -919,6 +920,7 @@ async def create_chat_session(
         chat_session = ChatSession.model_validate(session_data)
         await database.set_chat_session(chat_session.id, chat_session.model_dump())
         
+        logger.info(f"✅ Chat session created: {chat_session.id} for user {current_user.id}")
         return create_success_response(chat_session.model_dump(by_alias=True))
         
     except Exception as e:
@@ -952,7 +954,117 @@ async def get_chat_session(
             status_code=500,
             content=create_error_response("FETCH_ERROR", str(e))
         )
-    
+
+@api_router.get("/chat/sessions/{session_id}/messages")
+async def get_chat_session_messages(
+    session_id: str = Path(...),
+    current_user = Depends(get_current_user),
+    page: int = Query(1, ge=1),
+    limit: int = Query(20, ge=1, le=100),
+    sortBy: Optional[str] = Query(None, alias="sortBy"),
+    sortOrder: str = Query("desc", pattern="^(asc|desc)$", alias="sortOrder"),
+    filters: Optional[str] = Query(None),
+    database: RedisDatabase = Depends(get_database)
+):
+    """Get a chat session by ID"""
+    try:
+        chat_session_data = await database.get_chat_session(session_id)
+        if not chat_session_data:
+            return JSONResponse(
+                status_code=404,
+                content=create_error_response("NOT_FOUND", "Chat session not found")
+            )
+
+        chat_messages = await database.get_chat_messages(session_id)
+        # Convert messages to ChatMessage objects
+        messages_list = [ChatMessage.model_validate(msg) for msg in chat_messages]
+        # Apply filters and pagination
+        filter_dict = None
+        if filters:
+            filter_dict = json.loads(filters)
+        paginated_messages, total = filter_and_paginate(
+            messages_list, page, limit, sortBy, sortOrder, filter_dict
+        )
+        paginated_response = create_paginated_response(
+            [m.model_dump(by_alias=True) for m in paginated_messages],
+            page, limit, total
+        )
+
+        return create_success_response(paginated_response)
+        
+    except Exception as e:
+        logger.error(f"Get chat session error: {e}")
+        return JSONResponse(
+            status_code=500,
+            content=create_error_response("FETCH_ERROR", str(e))
+        )
+
+@api_router.post("/chat/sessions/{session_id}/messages/stream")
+async def post_chat_session_message_stream(
+    session_id: str = Path(...),
+    data: Dict[str, Any] = Body(...),
+    current_user = Depends(get_current_user),
+    database: RedisDatabase = Depends(get_database),
+    request: Request = Request,  # For streaming response
+):
+    """Post a message to a chat session and stream the response"""
+    try:
+        chat_session_data = await database.get_chat_session(session_id)
+        if not chat_session_data:
+            return JSONResponse(
+                status_code=404,
+                content=create_error_response("NOT_FOUND", "Chat session not found")
+            )
+
+        chat_type = chat_session_data.get("context", {}).get("type", "general")
+
+        logger.info(f"🔗 Chat session {session_id} type {chat_type} accessed by user {current_user.id}")
+        query = data.get("query")
+        if not query:
+            return JSONResponse(
+                status_code=400,
+                content=create_error_response("INVALID_QUERY", "Query cannot be empty")
+            )
+        chat_query = ChatQuery.model_validate(query)
+        chat_agent = agents.get_or_create_agent(agent_type=chat_type, prometheus_collector=prometheus_collector, database=database)
+        if not chat_agent:
+            return JSONResponse(
+                status_code=400,
+                content=create_error_response("AGENT_NOT_FOUND", "No agent found for this chat type")
+            )
+        async def message_stream_generator():
+            """Generator to stream messages"""
+            async for message in chat_agent.generate(
+                llm=llm_manager.get_llm(),
+                model=defines.model,
+                query=chat_query,
+                session_id=session_id,
+                user_id=current_user.id,
+            ):
+                json_data = message.model_dump(mode='json', by_alias=True)
+                json_str = json.dumps(json_data)
+                logger.info(f"🔗 Streaming message for session {session_id}: {json_str}")
+                yield json_str + "\n"
+
+        return StreamingResponse(
+                message_stream_generator(),
+                media_type="application/json",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "keep-alive",
+                    "X-Accel-Buffering": "no",  # Prevents Nginx buffering if you're using it
+                },
+            )
+        
+    except Exception as e:
+        logger.error(traceback.format_exc())
+        logger.error(f"Get chat session error: {e}")
+        return JSONResponse(
+            status_code=500,
+            content=create_error_response("FETCH_ERROR", str(e))
+        )
+
+
 @api_router.get("/chat/sessions")
 async def get_chat_sessions(
     page: int = Query(1, ge=1),

src/backend/models.py

@@ -1,7 +1,7 @@
 from typing import List, Dict, Optional, Any, Union, Literal, TypeVar, Generic, Annotated
 from pydantic import BaseModel, Field, EmailStr, HttpUrl, validator # type: ignore
 from pydantic.types import constr, conint # type: ignore
-from datetime import datetime, date
+from datetime import datetime, date, UTC
 from enum import Enum
 import uuid
 
@@ -68,10 +68,11 @@ class ChatSenderType(str, Enum):
     SYSTEM = "system"
 
 class ChatStatusType(str, Enum):
-    PARTIAL = "partial"
+    PREPARING = "preparing"
-    DONE = "done"
-    STREAMING = "streaming"
     THINKING = "thinking"
+    PARTIAL = "partial"
+    STREAMING = "streaming"
+    DONE = "done"
     ERROR = "error"
 
 class ChatContextType(str, Enum):
@@ -519,47 +520,73 @@ class JobApplication(BaseModel):
     class Config:
         populate_by_name = True  # Allow both field names and aliases        
 
-class AIParameters(BaseModel):
+class RagEntry(BaseModel):
-    id: str = Field(default_factory=lambda: str(uuid.uuid4()))
-    user_id: Optional[str] = Field(None, alias="userId")
     name: str
-    description: Optional[str] = None
+    description: str = ""
-    model: AIModelType
+    enabled: bool = True
-    temperature: Optional[Annotated[float, Field(ge=0, le=1)]] = 0.7
+
-    max_tokens: Optional[Annotated[int, Field(gt=0)]] = Field(..., alias="maxTokens")
+class ChromaDBGetResponse(BaseModel):
-    top_p: Optional[Annotated[float, Field(ge=0, le=1)]] = Field(..., alias="topP")
+    # Chroma fields
-    frequency_penalty: Optional[Annotated[float, Field(ge=-2, le=2)]] = Field(..., alias="frequencyPenalty")
+    ids: List[str] = []
-    presence_penalty: Optional[Annotated[float, Field(ge=-2, le=2)]] = Field(..., alias="presencePenalty")
+    embeddings: List[List[float]] = Field(default=[])
-    system_prompt: Optional[str] = Field(None, alias="systemPrompt")
+    documents: List[str] = []
-    is_default: Optional[bool] = Field(..., alias="isDefault")
+    metadatas: List[Dict[str, Any]] = []
-    created_at: Optional[datetime] = Field(..., alias="createdAt")
+    # Additional fields
-    updated_at: Optional[datetime] = Field(..., alias="updatedAt")
+    name: str = ""
-    custom_model_config: Optional[Dict[str, Any]] = Field(None, alias="customModelConfig")
+    size: int = 0
-    class Config:
+    query: str = ""
-        populate_by_name = True  # Allow both field names and aliases        
+    query_embedding: Optional[List[float]] = Field(default=None, alias="queryEmbedding")
+    umap_embedding_2d: Optional[List[float]] = Field(default=None, alias="umapEmbedding2D")
+    umap_embedding_3d: Optional[List[float]] = Field(default=None, alias="umapEmbedding3D")
 
 class ChatContext(BaseModel):
     type: ChatContextType
     related_entity_id: Optional[str] = Field(None, alias="relatedEntityId")
     related_entity_type: Optional[Literal["job", "candidate", "employer"]] = Field(None, alias="relatedEntityType")
-    ai_parameters: AIParameters = Field(..., alias="aiParameters")
     additional_context: Optional[Dict[str, Any]] = Field(None, alias="additionalContext")
     class Config:
         populate_by_name = True  # Allow both field names and aliases        
 
+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
+
+class ChatMessageMetaData(BaseModel):
+    model: AIModelType = AIModelType.QWEN2_5
+    temperature: float = 0.7
+    max_tokens: int = Field(default=8092, alias="maxTokens")
+    top_p: float = Field(default=1, alias="topP")
+    frequency_penalty: Optional[float] = Field(None, alias="frequencyPenalty")
+    presence_penalty: Optional[float] = Field(None, alias="presencePenalty")
+    stop_sequences: Optional[List[str]] = Field(None, alias="stopSequences")
+    tunables: Optional[Tunables] = None
+    rag: List[ChromaDBGetResponse] = Field(default_factory=list)
+    eval_count: int = 0
+    eval_duration: int = 0
+    prompt_eval_count: int = 0
+    prompt_eval_duration: int = 0
+    options: Optional[ChatOptions] = None
+    tools: Optional[Dict[str, Any]] = None
+    timers: Optional[Dict[str, float]] = None    
+    class Config:
+        populate_by_name = True  # Allow both field names and aliases
+
 class ChatMessage(BaseModel):
     id: str = Field(default_factory=lambda: str(uuid.uuid4()))
     session_id: str = Field(..., alias="sessionId")
     status: ChatStatusType
     sender: ChatSenderType
     sender_id: Optional[str] = Field(None, alias="senderId")
-    content: str
+    prompt: str = ""
+    content: str = ""
+    chunk: str = ""
     timestamp: datetime
-    attachments: Optional[List[Attachment]] = None
+    #attachments: Optional[List[Attachment]] = None
-    reactions: Optional[List[MessageReaction]] = None
+    #reactions: Optional[List[MessageReaction]] = None
     is_edited: bool = Field(False, alias="isEdited")
-    edit_history: Optional[List[EditHistory]] = Field(None, alias="editHistory")
+    #edit_history: Optional[List[EditHistory]] = Field(None, alias="editHistory")
-    metadata: Optional[Dict[str, Any]] = None
+    metadata: ChatMessageMetaData = Field(None)
     class Config:
         populate_by_name = True  # Allow both field names and aliases        
 
@@ -567,8 +594,8 @@ class ChatSession(BaseModel):
     id: str = Field(default_factory=lambda: str(uuid.uuid4()))
     user_id: Optional[str] = Field(None, alias="userId")
     guest_id: Optional[str] = Field(None, alias="guestId")
-    created_at: datetime = Field(..., alias="createdAt")
+    created_at: datetime = Field(default_factory=lambda: datetime.now(UTC), alias="createdAt")
-    last_activity: datetime = Field(..., alias="lastActivity")
+    last_activity: datetime = Field(default_factory=lambda: datetime.now(UTC), alias="lastActivity")
     title: Optional[str] = None
     context: ChatContext
     messages: Optional[List[ChatMessage]] = None
@@ -614,7 +641,6 @@ class RAGConfiguration(BaseModel):
     retrieval_parameters: RetrievalParameters = Field(..., alias="retrievalParameters")
     created_at: datetime = Field(..., alias="createdAt")
     updated_at: datetime = Field(..., alias="updatedAt")
-    is_default: bool = Field(..., alias="isDefault")
     version: int
     is_active: bool = Field(..., alias="isActive")
     class Config:
@@ -671,7 +697,7 @@ class UserPreference(BaseModel):
 # ============================
 # API Request/Response Models
 # ============================
-class Query(BaseModel):
+class ChatQuery(BaseModel):
     prompt: str
     tunables: Optional[Tunables] = None
     agent_options: Optional[Dict[str, Any]] = Field(None, alias="agentOptions")

src/backend/system_info.py

@@ -0,0 +1,81 @@
+import defines
+import re
+import subprocess
+import math
+
+def get_installed_ram():
+    try:
+        with open("/proc/meminfo", "r") as f:
+            meminfo = f.read()
+        match = re.search(r"MemTotal:\s+(\d+)", meminfo)
+        if match:
+            return f"{math.floor(int(match.group(1)) / 1000**2)}GB"  # Convert KB to GB
+    except Exception as e:
+        return f"Error retrieving RAM: {e}"
+
+
+def get_graphics_cards():
+    gpus = []
+    try:
+        # Run the ze-monitor utility
+        result = subprocess.run(
+            ["ze-monitor"], capture_output=True, text=True, check=True
+        )
+
+        # Clean up the output (remove leading/trailing whitespace and newlines)
+        output = result.stdout.strip()
+        for index in range(len(output.splitlines())):
+            result = subprocess.run(
+                ["ze-monitor", "--device", f"{index+1}", "--info"],
+                capture_output=True,
+                text=True,
+                check=True,
+            )
+            gpu_info = result.stdout.strip().splitlines()
+            gpu = {
+                "discrete": True,  # Assume it's discrete initially
+                "name": None,
+                "memory": None,
+            }
+            gpus.append(gpu)
+            for line in gpu_info:
+                match = re.match(r"^Device: [^(]*\((.*)\)", line)
+                if match:
+                    gpu["name"] = match.group(1)
+                    continue
+
+                match = re.match(r"^\s*Memory: (.*)", line)
+                if match:
+                    gpu["memory"] = match.group(1)
+                    continue
+
+                match = re.match(r"^.*Is integrated with host: Yes.*", line)
+                if match:
+                    gpu["discrete"] = False
+                    continue
+
+        return gpus
+    except Exception as e:
+        return f"Error retrieving GPU info: {e}"
+
+
+def get_cpu_info():
+    try:
+        with open("/proc/cpuinfo", "r") as f:
+            cpuinfo = f.read()
+        model_match = re.search(r"model name\s+:\s+(.+)", cpuinfo)
+        cores_match = re.findall(r"processor\s+:\s+\d+", cpuinfo)
+        if model_match and cores_match:
+            return f"{model_match.group(1)} with {len(cores_match)} cores"
+    except Exception as e:
+        return f"Error retrieving CPU info: {e}"
+
+def system_info():
+    return {
+        "System RAM": get_installed_ram(),
+        "Graphics Card": get_graphics_cards(),
+        "CPU": get_cpu_info(),
+        "LLM Model": defines.model,
+        "Embedding Model": defines.embedding_model,
+        "Context length": defines.max_context,
+    }