Multi-LLM backend working with Ollama

frontend/src/components/JobCreator.tsx

@@ -0,0 +1,541 @@
+import React, { useState, useEffect, useRef, JSX } from 'react';
+import { 
+  Box, 
+  Button, 
+  Typography, 
+  Paper, 
+  TextField, 
+  Grid,
+  Dialog,
+  DialogTitle,
+  DialogContent,
+  DialogContentText,
+  DialogActions,
+  IconButton,
+  useTheme,
+  useMediaQuery,
+  Chip,
+  Divider,
+  Card,
+  CardContent,
+  CardHeader,
+  LinearProgress,
+  Stack,
+  Alert
+} from '@mui/material';
+import {
+  SyncAlt,
+  Favorite,
+  Settings,
+  Info,
+  Search,
+  AutoFixHigh,
+  Image,
+  Psychology,
+  Build,
+  CloudUpload,
+  Description,
+  Business,
+  LocationOn,
+  Work,
+  CheckCircle,
+  Star
+} from '@mui/icons-material';
+import { styled } from '@mui/material/styles';
+import DescriptionIcon from '@mui/icons-material/Description';
+import FileUploadIcon from '@mui/icons-material/FileUpload';
+
+import { useAuth } from 'hooks/AuthContext';
+import { useSelectedCandidate, useSelectedJob } from 'hooks/GlobalContext';
+import { BackstoryElementProps } from './BackstoryTab';
+import { LoginRequired } from 'components/ui/LoginRequired';
+
+import * as Types from 'types/types';
+
+const VisuallyHiddenInput = styled('input')({
+  clip: 'rect(0 0 0 0)',
+  clipPath: 'inset(50%)',
+  height: 1,
+  overflow: 'hidden',
+  position: 'absolute',
+  bottom: 0,
+  left: 0,
+  whiteSpace: 'nowrap',
+  width: 1,
+});
+
+const UploadBox = styled(Box)(({ theme }) => ({
+  border: `2px dashed ${theme.palette.primary.main}`,
+  borderRadius: theme.shape.borderRadius * 2,
+  padding: theme.spacing(4),
+  textAlign: 'center',
+  backgroundColor: theme.palette.action.hover,
+  transition: 'all 0.3s ease',
+  cursor: 'pointer',
+  '&:hover': {
+    backgroundColor: theme.palette.action.selected,
+    borderColor: theme.palette.primary.dark,
+  },
+}));
+
+const StatusBox = styled(Box)(({ theme }) => ({
+  display: 'flex',
+  alignItems: 'center',
+  gap: theme.spacing(1),
+  padding: theme.spacing(1, 2),
+  backgroundColor: theme.palette.background.paper,
+  borderRadius: theme.shape.borderRadius,
+  border: `1px solid ${theme.palette.divider}`,
+  minHeight: 48,
+}));
+
+const getIcon = (type: Types.ApiActivityType) => {
+  switch (type) {
+    case 'converting': 
+      return <SyncAlt color="primary" />;
+    case 'heartbeat': 
+      return <Favorite color="error" />;
+    case 'system': 
+      return <Settings color="action" />;
+    case 'info': 
+      return <Info color="info" />;
+    case 'searching': 
+      return <Search color="primary" />;
+    case 'generating': 
+      return <AutoFixHigh color="secondary" />;
+    case 'generating_image': 
+      return <Image color="primary" />;
+    case 'thinking': 
+      return <Psychology color="secondary" />;
+    case 'tooling': 
+      return <Build color="action" />;
+    default:
+      return <Info color="action" />;
+  }
+};
+
+interface JobCreator extends BackstoryElementProps {
+    onSave?: (job: Types.Job) => void;
+}
+const JobCreator = (props: JobCreator) => {
+    const { user, apiClient } = useAuth();
+    const { onSave } = props;
+  const { selectedCandidate } = useSelectedCandidate();
+  const { selectedJob, setSelectedJob } = useSelectedJob();
+    const { setSnack, submitQuery } = props;
+    const backstoryProps = { setSnack, submitQuery };
+    const theme = useTheme();
+    const isMobile = useMediaQuery(theme.breakpoints.down('sm'));
+  const isTablet = useMediaQuery(theme.breakpoints.down('md'));
+
+    const [openUploadDialog, setOpenUploadDialog] = useState<boolean>(false);
+    const [jobDescription, setJobDescription] = useState<string>('');
+  const [jobRequirements, setJobRequirements] = useState<Types.JobRequirements | null>(null);
+    const [jobTitle, setJobTitle] = useState<string>('');
+    const [company, setCompany] = useState<string>('');
+  const [summary, setSummary] = useState<string>('');
+    const [jobLocation, setJobLocation] = useState<string>('');
+    const [jobId, setJobId] = useState<string>('');
+    const [jobStatus, setJobStatus] = useState<string>('');
+    const [jobStatusIcon, setJobStatusIcon] = useState<JSX.Element>(<></>);
+  const [isProcessing, setIsProcessing] = useState<boolean>(false);
+
+    useEffect(() => {
+
+    }, [jobTitle, jobDescription, company]);
+
+  const fileInputRef = useRef<HTMLInputElement>(null);
+
+
+    if (!user?.id) {
+      return (
+        <LoginRequired asset="job creation" />
+      );
+    }
+
+  const jobStatusHandlers = {
+    onStatus: (status: Types.ChatMessageStatus) => {
+      console.log('status:', status.content);
+      setJobStatusIcon(getIcon(status.activity));
+      setJobStatus(status.content);
+    },
+    onMessage: (job: Types.Job) => {
+      console.log('onMessage - job', job);
+      setCompany(job.company || '');
+      setJobDescription(job.description);
+      setSummary(job.summary || '');
+      setJobTitle(job.title || '');
+      setJobRequirements(job.requirements || null);
+      setJobStatusIcon(<></>);
+      setJobStatus('');
+    },
+    onError: (error: Types.ChatMessageError) => {
+      console.log('onError', error);
+      setSnack(error.content, "error");
+      setIsProcessing(false);
+    },
+    onComplete: () => {
+      setJobStatusIcon(<></>);
+      setJobStatus('');
+      setIsProcessing(false);
+    }
+  };
+
+  const handleJobUpload = async (e: React.ChangeEvent<HTMLInputElement>) => {
+    if (e.target.files && e.target.files[0]) {
+      const file = e.target.files[0];
+      const fileExtension = '.' + file.name.split('.').pop()?.toLowerCase();
+      let docType: Types.DocumentType | null = null;
+      switch (fileExtension.substring(1)) {
+        case "pdf":
+          docType = "pdf";
+          break;
+        case "docx":
+          docType = "docx";
+          break;
+        case "md":
+          docType = "markdown";
+          break;
+        case "txt":
+          docType = "txt";
+          break;
+      }
+
+      if (!docType) {
+            setSnack('Invalid file type. Please upload .txt, .md, .docx, or .pdf files only.', 'error');
+            return;
+      }
+
+      try {
+        setIsProcessing(true);
+        setJobDescription('');
+        setJobTitle('');
+        setJobRequirements(null);
+        setSummary('');
+        const controller = apiClient.createJobFromFile(file, jobStatusHandlers);
+        const job = await controller.promise;
+        if (!job) {
+          return;
+        }
+        console.log(`Job id: ${job.id}`);
+        e.target.value = '';
+      } catch (error) {
+        console.error(error);
+        setSnack('Failed to upload document', 'error');
+        setIsProcessing(false);
+      }
+    }
+  };
+
+  const handleUploadClick = () => {
+    fileInputRef.current?.click();
+  };
+
+  const renderRequirementSection = (title: string, items: string[] | undefined, icon: JSX.Element, required = false) => {
+    if (!items || items.length === 0) return null;
+
+    return (
+      <Box sx={{ mb: 3 }}>
+        <Box sx={{ display: 'flex', alignItems: 'center', mb: 1.5 }}>
+          {icon}
+          <Typography variant="subtitle1" sx={{ ml: 1, fontWeight: 600 }}>
+            {title}
+          </Typography>
+          {required && <Chip label="Required" size="small" color="error" sx={{ ml: 1 }} />}
+        </Box>
+        <Stack direction="row" spacing={1} flexWrap="wrap" useFlexGap>
+          {items.map((item, index) => (
+            <Chip
+              key={index}
+              label={item}
+              variant="outlined"
+              size="small"
+              sx={{ mb: 1 }}
+            />
+          ))}
+        </Stack>
+      </Box>
+    );
+  };
+
+  const renderJobRequirements = () => {
+    if (!jobRequirements) return null;
+
+    return (
+      <Card elevation={2} sx={{ mt: 3 }}>
+        <CardHeader
+          title="Job Requirements Analysis"
+          avatar={<CheckCircle color="success" />}
+          sx={{ pb: 1 }}
+        />
+        <CardContent sx={{ pt: 0 }}>
+          {renderRequirementSection(
+            "Technical Skills (Required)",
+            jobRequirements.technicalSkills.required,
+            <Build color="primary" />,
+            true
+          )}
+          {renderRequirementSection(
+            "Technical Skills (Preferred)",
+            jobRequirements.technicalSkills.preferred,
+            <Build color="action" />
+          )}
+          {renderRequirementSection(
+            "Experience Requirements (Required)",
+            jobRequirements.experienceRequirements.required,
+            <Work color="primary" />,
+            true
+          )}
+          {renderRequirementSection(
+            "Experience Requirements (Preferred)",
+            jobRequirements.experienceRequirements.preferred,
+            <Work color="action" />
+          )}
+          {renderRequirementSection(
+            "Soft Skills",
+            jobRequirements.softSkills,
+            <Psychology color="secondary" />
+          )}
+          {renderRequirementSection(
+            "Experience",
+            jobRequirements.experience,
+            <Star color="warning" />
+          )}
+          {renderRequirementSection(
+            "Education",
+            jobRequirements.education,
+            <Description color="info" />
+          )}
+          {renderRequirementSection(
+            "Certifications",
+            jobRequirements.certifications,
+            <CheckCircle color="success" />
+          )}
+          {renderRequirementSection(
+            "Preferred Attributes",
+            jobRequirements.preferredAttributes,
+            <Star color="secondary" />
+          )}
+        </CardContent>
+      </Card>
+    );
+  };
+
+  const handleSave = async () => {
+    const newJob: Types.Job = {
+      ownerId: user?.id || '',
+      ownerType: 'candidate',
+      description: jobDescription,
+      company: company,
+      summary: summary,
+      title: jobTitle,
+      requirements: jobRequirements || undefined
+    };
+    setIsProcessing(true);
+    const job = await apiClient.createJob(newJob);
+    setIsProcessing(false);
+    onSave ? onSave(job) : setSelectedJob(job);
+  };
+
+  const handleExtractRequirements = () => {
+    // Implement requirements extraction logic here
+    setIsProcessing(true);
+    // This would call your API to extract requirements from the job description
+  };
+
+  const renderJobCreation = () => {
+    if (!user) {
+      return <Box>You must be logged in</Box>;
+    }
+
+      return (
+        <Box sx={{
+          mx: 'auto', p: { xs: 2, sm: 3 },
+        }}>
+          {/* Upload Section */}
+          <Card elevation={3} sx={{ mb: 4 }}>
+            <CardHeader
+              title="Job Information"
+              subheader="Upload a job description or enter details manually"
+              avatar={<Work color="primary" />}
+            />
+            <CardContent>
+              <Grid container spacing={3}>
+                <Grid size={{ xs: 12, md: 6 }}>
+                  <Typography variant="h6" gutterBottom sx={{ display: 'flex', alignItems: 'center' }}>
+                    <CloudUpload sx={{ mr: 1 }} />
+                    Upload Job Description
+                  </Typography>
+                  <UploadBox onClick={handleUploadClick}>
+                    <CloudUpload sx={{ fontSize: 48, color: 'primary.main', mb: 2 }} />
+                    <Typography variant="h6" gutterBottom>
+                      Drop your job description here
+                    </Typography>
+                    <Typography variant="body2" color="text.secondary" sx={{ mb: 2 }}>
+                      Supported formats: PDF, DOCX, TXT, MD
+                    </Typography>
+                    <Button 
+                      variant="contained"
+                      startIcon={<FileUploadIcon />}
+                      disabled={isProcessing}
+                    // onClick={handleUploadClick}
+                    >
+                      Choose File
+                    </Button>
+                  </UploadBox>
+                  <VisuallyHiddenInput 
+                    ref={fileInputRef}
+                    type="file"
+                    accept=".txt,.md,.docx,.pdf"
+                    onChange={handleJobUpload}
+                  />
+                </Grid>
+
+                <Grid size={{ xs: 12, md: 6 }}>
+                  <Typography variant="h6" gutterBottom sx={{ display: 'flex', alignItems: 'center' }}>
+                    <Description sx={{ mr: 1 }} />
+                    Or Enter Manually
+                  </Typography>
+                  <TextField
+                    fullWidth
+                    multiline
+                    rows={isMobile ? 8 : 12}
+                    placeholder="Paste or type the job description here..."
+                    variant="outlined"
+                    value={jobDescription}
+                    onChange={(e) => setJobDescription(e.target.value)}
+                    disabled={isProcessing}
+                    sx={{ mb: 2 }}
+                  />
+                  {jobRequirements === null && jobDescription && (
+                    <Button
+                      variant="outlined"
+                      onClick={handleExtractRequirements}
+                      startIcon={<AutoFixHigh />}
+                      disabled={isProcessing}
+                      fullWidth={isMobile}
+                    >
+                      Extract Requirements
+                    </Button>
+                  )}
+                </Grid>
+              </Grid>
+
+              {(jobStatus || isProcessing) && (
+                <Box sx={{ mt: 3 }}>
+                  <StatusBox>
+                    {jobStatusIcon}
+                    <Typography variant="body2" sx={{ ml: 1 }}>
+                      {jobStatus || 'Processing...'}
+                    </Typography>
+                  </StatusBox>
+                  {isProcessing && <LinearProgress sx={{ mt: 1 }} />}
+                </Box>
+              )}
+            </CardContent>
+          </Card>
+
+          {/* Job Details Section */}
+          <Card elevation={3} sx={{ mb: 4 }}>
+            <CardHeader
+              title="Job Details"
+              subheader="Enter specific information about the position"
+              avatar={<Business color="primary" />}
+            />
+            <CardContent>
+              <Grid container spacing={3}>
+                <Grid size={{ xs: 12, md: 6 }}>
+                  <TextField
+                    fullWidth
+                    label="Job Title"
+                    variant="outlined"
+                    value={jobTitle}
+                    onChange={(e) => setJobTitle(e.target.value)}
+                    required
+                    disabled={isProcessing}
+                    InputProps={{
+                      startAdornment: <Work sx={{ mr: 1, color: 'text.secondary' }} />
+                    }}
+                  />
+                </Grid>
+
+                <Grid size={{ xs: 12, md: 6 }}>
+                  <TextField
+                    fullWidth
+                    label="Company"
+                    variant="outlined"
+                    value={company}
+                    onChange={(e) => setCompany(e.target.value)}
+                    required
+                    disabled={isProcessing}
+                    InputProps={{
+                      startAdornment: <Business sx={{ mr: 1, color: 'text.secondary' }} />
+                    }}
+                  />
+                </Grid>
+
+                {/* <Grid size={{ xs: 12, md: 6 }}>
+                  <TextField
+                    fullWidth
+                    label="Job Location"
+                    variant="outlined"
+                    value={jobLocation}
+                    onChange={(e) => setJobLocation(e.target.value)}
+                    disabled={isProcessing}
+                    InputProps={{
+                      startAdornment: <LocationOn sx={{ mr: 1, color: 'text.secondary' }} />
+                    }}
+                  />
+                </Grid> */}
+
+                <Grid size={{ xs: 12, md: 6 }}>
+                  <Box sx={{ display: 'flex', gap: 2, alignItems: 'flex-end', height: '100%' }}>
+                    <Button
+                      variant="contained"
+                      onClick={handleSave}
+                      disabled={!jobTitle || !company || !jobDescription || isProcessing}
+                      fullWidth={isMobile}
+                      size="large"
+                      startIcon={<CheckCircle />}
+                    >
+                      Save Job
+                    </Button>
+                  </Box>
+                </Grid>
+              </Grid>
+            </CardContent>
+          </Card>
+
+          {/* Job Summary */}
+          {summary !== '' &&
+            <Card elevation={2} sx={{ mt: 3 }}>
+              <CardHeader
+                title="Job Summary"
+                avatar={<CheckCircle color="success" />}
+                sx={{ pb: 1 }}
+              />
+              <CardContent sx={{ pt: 0 }}>
+                {summary}
+              </CardContent>
+            </Card>
+          }
+
+          {/* Requirements Display */}
+          {renderJobRequirements()}
+
+        </Box>
+      );
+    };
+
+    return (
+      <Box className="JobManagement"
+        sx={{
+          background: "white",
+          p: 0,
+      }}>
+        {selectedJob === null && renderJobCreation()}
+      </Box>
+    );
+};
+
+export { JobCreator };

frontend/src/components/JobManagement.tsx

@@ -115,34 +115,23 @@ const getIcon = (type: Types.ApiActivityType) => {
 };
 
 const JobManagement = (props: BackstoryElementProps) => {
-    const { user, apiClient } = useAuth();
+  const { user, apiClient } = useAuth();
-  const { selectedCandidate } = useSelectedCandidate();
   const { selectedJob, setSelectedJob } = useSelectedJob();
-    const { setSnack, submitQuery } = props;
+  const { setSnack, submitQuery } = props;
-    const backstoryProps = { setSnack, submitQuery };
     const theme = useTheme();
-    const isMobile = useMediaQuery(theme.breakpoints.down('sm'));
+  const isMobile = useMediaQuery(theme.breakpoints.down('sm'));
-  const isTablet = useMediaQuery(theme.breakpoints.down('md'));
 
-    const [openUploadDialog, setOpenUploadDialog] = useState<boolean>(false);
     const [jobDescription, setJobDescription] = useState<string>('');
   const [jobRequirements, setJobRequirements] = useState<Types.JobRequirements | null>(null);
     const [jobTitle, setJobTitle] = useState<string>('');
     const [company, setCompany] = useState<string>('');
   const [summary, setSummary] = useState<string>('');
-    const [jobLocation, setJobLocation] = useState<string>('');
-    const [jobId, setJobId] = useState<string>('');
     const [jobStatus, setJobStatus] = useState<string>('');
     const [jobStatusIcon, setJobStatusIcon] = useState<JSX.Element>(<></>);
   const [isProcessing, setIsProcessing] = useState<boolean>(false);
 
-    useEffect(() => {
-
-    }, [jobTitle, jobDescription, company]);
-
   const fileInputRef = useRef<HTMLInputElement>(null);
 
-
     if (!user?.id) {
       return (
         <LoginRequired asset="candidate analysis" />
@@ -223,12 +212,12 @@ const JobManagement = (props: BackstoryElementProps) => {
         setJobTitle('');
         setJobRequirements(null);
         setSummary('');
-        const controller = apiClient.uploadCandidateDocument(file, { isJobDocument: true, overwrite: true }, documentStatusHandlers);
+        const controller = apiClient.createJobFromFile(file, jobStatusHandlers);
-        const document = await controller.promise;
+        const job = await controller.promise;
-        if (!document) {
+        if (!job) {
           return;
         }
-        console.log(`Document id: ${document.id}`);
+        console.log(`Job id: ${job.id}`);
         e.target.value = '';
       } catch (error) {
         console.error(error);
@@ -354,10 +343,6 @@ const JobManagement = (props: BackstoryElementProps) => {
     // This would call your API to extract requirements from the job description
   };
 
-  const loadJob = async () => {
-    const job = await apiClient.getJob("7594e989-a926-45a2-9b07-ae553d2e0d0d");
-    setSelectedJob(job);
-  }
   const renderJobCreation = () => {
     if (!user) {
       return <Box>You must be logged in</Box>;
@@ -367,7 +352,6 @@ const JobManagement = (props: BackstoryElementProps) => {
         <Box sx={{
           mx: 'auto', p: { xs: 2, sm: 3 },
         }}>
-          <Button onClick={loadJob} variant="contained">Load Job</Button>
           {/* Upload Section */}
           <Card elevation={3} sx={{ mb: 4 }}>
             <CardHeader

frontend/src/components/JobMatchAnalysis.tsx

@@ -14,7 +14,8 @@ import {
   CardContent,
   useTheme,
   LinearProgress,
-  useMediaQuery
+  useMediaQuery,
+  Button
 } from '@mui/material';
 import ExpandMoreIcon from '@mui/icons-material/ExpandMore';
 import CheckCircleIcon from '@mui/icons-material/CheckCircle';
@@ -28,6 +29,8 @@ import { toCamelCase } from 'types/conversion';
 import { Job } from 'types/types';
 import { StyledMarkdown } from './StyledMarkdown';
 import { Scrollable } from './Scrollable';
+import { start } from 'repl';
+import { TypesElement } from '@uiw/react-json-view';
 
 interface JobAnalysisProps extends BackstoryPageProps {
   job: Job;
@@ -56,6 +59,9 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
   const [overallScore, setOverallScore] = useState<number>(0);
   const [requirementsSession, setRequirementsSession] = useState<ChatSession | null>(null);
   const [statusMessage, setStatusMessage] = useState<ChatMessage | null>(null);
+  const [startAnalysis, setStartAnalysis] = useState<boolean>(false);
+  const [analyzing, setAnalyzing] = useState<boolean>(false);
+
   const isMobile = useMediaQuery(theme.breakpoints.down('sm'));
 
   // Handle accordion expansion
@@ -63,7 +69,7 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
     setExpanded(isExpanded ? panel : false);
   };
 
-  useEffect(() => {
+  const initializeRequirements = (job: Job) => {
     if (!job || !job.requirements) {
       return;
     }
@@ -106,116 +112,19 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
     setSkillMatches(initialSkillMatches);
     setStatusMessage(null);
     setLoadingRequirements(false);
-
+    setOverallScore(0);
-  }, [job, setRequirements]);
+  }
 
   useEffect(() => {
-    if (requirementsSession || creatingSession) {
+    initializeRequirements(job);
-      return;
+  }, [job]);
-    }
-
-    try {
-      setCreatingSession(true);
-      apiClient.getOrCreateChatSession(candidate, `Generate requirements for ${candidate.fullName}`, 'job_requirements')
-        .then(session => {
-        setRequirementsSession(session);
-          setCreatingSession(false);
-        });
-    } catch (error) {
-      setSnack('Unable to load chat session', 'error');
-    } finally {
-      setCreatingSession(false);
-    }
-
-  }, [requirementsSession, apiClient, candidate]);
-
-  // Fetch initial requirements
-  useEffect(() => {
-    if (!job.description || !requirementsSession || loadingRequirements) {
-      return;
-    }
-
-    const getRequirements = async () => {
-      setLoadingRequirements(true);
-      try {
-        const chatMessage: ChatMessageUser = { ...defaultMessage, sessionId: requirementsSession.id || '', content: job.description };
-        apiClient.sendMessageStream(chatMessage, {
-          onMessage: (msg: ChatMessage) => {
-            console.log(`onMessage: ${msg.type}`, msg);
-            const job: Job = toCamelCase<Job>(JSON.parse(msg.content || ''));
-            const requirements: { requirement: string, domain: string }[] = [];
-            if (job.requirements?.technicalSkills) {
-              job.requirements.technicalSkills.required?.forEach(req => requirements.push({ requirement: req, domain: 'Technical Skills (required)' }));
-              job.requirements.technicalSkills.preferred?.forEach(req => requirements.push({ requirement: req, domain: 'Technical Skills (preferred)' }));
-            }
-            if (job.requirements?.experienceRequirements) {
-              job.requirements.experienceRequirements.required?.forEach(req => requirements.push({ requirement: req, domain: 'Experience (required)' }));
-              job.requirements.experienceRequirements.preferred?.forEach(req => requirements.push({ requirement: req, domain: 'Experience (preferred)' }));
-            }
-            if (job.requirements?.softSkills) {
-              job.requirements.softSkills.forEach(req => requirements.push({ requirement: req, domain: 'Soft Skills' }));
-            }
-            if (job.requirements?.experience) {
-              job.requirements.experience.forEach(req => requirements.push({ requirement: req, domain: 'Experience' }));
-            }
-            if (job.requirements?.education) {
-              job.requirements.education.forEach(req => requirements.push({ requirement: req, domain: 'Education' }));
-            }
-            if (job.requirements?.certifications) {
-              job.requirements.certifications.forEach(req => requirements.push({ requirement: req, domain: 'Certifications' }));
-            }
-            if (job.requirements?.preferredAttributes) {
-              job.requirements.preferredAttributes.forEach(req => requirements.push({ requirement: req, domain: 'Preferred Attributes' }));
-            }
-
-            const initialSkillMatches = requirements.map(req => ({
-              requirement: req.requirement,
-              domain: req.domain,
-              status: 'waiting' as const,
-              matchScore: 0,
-              assessment: '',
-              description: '',
-              citations: []
-            }));
-
-            setRequirements(requirements);
-            setSkillMatches(initialSkillMatches);
-            setStatusMessage(null);
-            setLoadingRequirements(false);
-          },
-          onError: (error: string | ChatMessageError) => {
-            console.log("onError:", error);
-            // Type-guard to determine if this is a ChatMessageBase or a string
-            if (typeof error === "object" && error !== null && "content" in error) {
-              setSnack(error.content || 'Error obtaining requirements from job description.', "error");
-            } else {
-              setSnack(error as string, "error");
-            }
-            setLoadingRequirements(false);
-          },
-          onStreaming: (chunk: ChatMessageStreaming) => {
-            // console.log("onStreaming:", chunk);
-          },
-          onStatus: (status: ChatMessageStatus) => {
-            console.log(`onStatus: ${status}`);
-          },
-          onComplete: () => {
-            console.log("onComplete");
-            setStatusMessage(null);
-            setLoadingRequirements(false);
-          }
-        });
-      } catch (error) {
-        console.error('Failed to send message:', error);
-        setLoadingRequirements(false);
-      }
-    };
-
-    getRequirements();
-  }, [job, requirementsSession]);
 
   // Fetch match data for each requirement
   useEffect(() => {
+    if (!startAnalysis || analyzing || !job.requirements) {
+      return;
+    }
+
     const fetchMatchData = async () => {
       if (requirements.length === 0) return;
       
@@ -279,10 +188,9 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
       }
     };
     
-    if (!loadingRequirements) {
+    setAnalyzing(true);
-      fetchMatchData();
+    fetchMatchData().then(() => { setAnalyzing(false); setStartAnalysis(false) });
-    }
+  }, [job, startAnalysis, analyzing, requirements, loadingRequirements]);
-  }, [requirements, loadingRequirements]);
 
   // Get color based on match score
   const getMatchColor = (score: number): string => {
@@ -301,6 +209,11 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
     return <ErrorIcon color="error" />;
   };
 
+  const beginAnalysis = () => {
+    initializeRequirements(job);
+    setStartAnalysis(true);
+  };
+
   return (
     <Box>
       <Paper elevation={3} sx={{ p: 3, mb: 4 }}>
@@ -344,7 +257,9 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
           </Box>
           
           <Grid size={{ xs: 12 }} sx={{ mt: 2 }}>
-            <Box sx={{ display: 'flex', alignItems: 'center', mb: 2 }}>
+            <Box sx={{ display: 'flex', alignItems: 'center', mb: 2, gap: 1 }}>
+              {<Button disabled={analyzing || startAnalysis} onClick={beginAnalysis} variant="contained">Start Analysis</Button>}
+              {overallScore !== 0 && <>
               <Typography variant="h5" component="h2" sx={{ mr: 2 }}>
                 Overall Match:
               </Typography>
@@ -391,6 +306,7 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
                   fontWeight: 'bold'
                 }} 
               />
+              </>}
             </Box>
           </Grid>
         </Grid>

frontend/src/components/ui/CandidateInfo.tsx

@@ -13,7 +13,7 @@ import { CopyBubble } from "components/CopyBubble";
 import { rest } from 'lodash';
 import { AIBanner } from 'components/ui/AIBanner';
 import { useAuth } from 'hooks/AuthContext';
-import { DeleteConfirmation } from './DeleteConfirmation';
+import { DeleteConfirmation } from '../DeleteConfirmation';
 
 interface CandidateInfoProps {
   candidate: Candidate;

frontend/src/components/ui/CandidatePicker.tsx

@@ -4,7 +4,7 @@ import Button from '@mui/material/Button';
 import Box from '@mui/material/Box';
 
 import { BackstoryElementProps } from 'components/BackstoryTab';
-import { CandidateInfo } from 'components/CandidateInfo';
+import { CandidateInfo } from 'components/ui/CandidateInfo';
 import { Candidate } from "types/types";
 import { useAuth } from 'hooks/AuthContext';
 import { useSelectedCandidate } from 'hooks/GlobalContext';

frontend/src/components/ui/JobInfo.tsx

@@ -0,0 +1,97 @@
+import React from 'react';
+import { Box, Link, Typography, Avatar, Grid, SxProps, CardActions } from '@mui/material';
+import { 
+  Card,
+  CardContent,
+  Divider,
+  useTheme,
+} from '@mui/material';
+import DeleteIcon from '@mui/icons-material/Delete';
+import { useMediaQuery } from '@mui/material';
+import { JobFull } from 'types/types';
+import { CopyBubble } from "components/CopyBubble";
+import { rest } from 'lodash';
+import { AIBanner } from 'components/ui/AIBanner';
+import { useAuth } from 'hooks/AuthContext';
+import { DeleteConfirmation } from '../DeleteConfirmation';
+
+interface JobInfoProps {
+  job: JobFull;
+  sx?: SxProps;
+  action?: string;
+  elevation?: number;
+  variant?: "small" | "normal" | null
+};
+
+const JobInfo: React.FC<JobInfoProps> = (props: JobInfoProps) => {
+  const { job } = props;
+  const { user, apiClient } = useAuth();
+    const {
+      sx,
+      action = '',
+      elevation = 1,
+      variant = "normal"
+    } = props;
+  const theme = useTheme();
+  const isMobile = useMediaQuery(theme.breakpoints.down('md'));
+  const isAdmin = user?.isAdmin;
+
+  const deleteJob = async (jobId: string | undefined) => {
+    if (jobId) {
+      await apiClient.deleteJob(jobId);
+    }
+  }
+
+  if (!job) {
+    return <Box>No user loaded.</Box>;
+  }
+
+  return (
+    <Card 
+      elevation={elevation}
+      sx={{ 
+        display: "flex",
+        borderColor: 'transparent',
+        borderWidth: 2,
+        borderStyle: 'solid',
+        transition: 'all 0.3s ease',
+        flexDirection: "column",
+        ...sx
+      }}
+      {...rest}
+    >
+      <CardContent sx={{ display: "flex", flexGrow: 1, p: 3, height: '100%', flexDirection: 'column', alignItems: 'stretch', position: "relative" }}>
+        {variant !== "small" && <>
+            {job.location &&
+            <Typography variant="body2" sx={{ mb: 1 }}>
+                <strong>Location:</strong> {job.location.city}, {job.location.state || job.location.country}
+            </Typography>
+            }
+            {job.company &&
+            <Typography variant="body2" sx={{ mb: 1 }}>
+                <strong>Company:</strong> {job.company}
+            </Typography>
+            }
+            {job.summary && <Typography variant="body2">
+                    <strong>Summary:</strong> {job.summary}
+            </Typography>
+            }
+        </>}
+      </CardContent>
+        <CardActions>
+          {isAdmin &&
+            <DeleteConfirmation
+              onDelete={() => { deleteJob(job.id); }}
+              sx={{ minWidth: 'auto', px: 2, maxHeight: "min-content", color: "red" }}
+              action="delete"
+              label="job"
+              title="Delete job"
+              icon=<DeleteIcon />
+              message={`Are you sure you want to delete ${job.id}? This action cannot be undone.`}
+            />}
+        </CardActions>
+    </Card>
+  );
+};
+
+export { JobInfo };

frontend/src/components/ui/JobPicker.tsx

@@ -0,0 +1,69 @@
+import React, { useEffect, useState } from 'react';
+import { useNavigate } from "react-router-dom";
+import Button from '@mui/material/Button';
+import Box from '@mui/material/Box';
+
+import { BackstoryElementProps } from 'components/BackstoryTab';
+import { JobInfo } from 'components/ui/JobInfo';
+import { Job, JobFull } from "types/types";
+import { useAuth } from 'hooks/AuthContext';
+import { useSelectedJob } from 'hooks/GlobalContext';
+
+interface JobPickerProps extends BackstoryElementProps {
+    onSelect?: (job: JobFull) => void
+};
+
+const JobPicker = (props: JobPickerProps) => {
+    const { onSelect } = props;
+    const { apiClient } = useAuth();
+    const { selectedJob, setSelectedJob } = useSelectedJob();
+    const { setSnack } = props;
+    const [jobs, setJobs] = useState<JobFull[] | null>(null);
+
+    useEffect(() => {
+        if (jobs !== null) {
+            return;
+        }
+        const getJobs = async () => {
+            try {
+                const results = await apiClient.getJobs();
+                const jobs: JobFull[] = results.data;
+                jobs.sort((a, b) => {
+                    let result = a.company?.localeCompare(b.company || '');
+                    if (result === 0) {
+                        result = a.title?.localeCompare(b.title || '');
+                    }
+                    return result || 0;
+                });
+                setJobs(jobs);
+            } catch (err) {
+                setSnack("" + err);
+            }
+        };
+
+        getJobs();
+    }, [jobs, setSnack]);
+
+    return (
+        <Box sx={{display: "flex", flexDirection: "column", mb: 1}}>
+            <Box sx={{ display: "flex", gap: 1, flexWrap: "wrap", justifyContent: "center" }}>
+                {jobs?.map((j, i) => 
+                <Box key={`${j.id}`} 
+                    onClick={() => { onSelect ? onSelect(j) : setSelectedJob(j); }}
+                    sx={{ cursor: "pointer" }}>
+                    {selectedJob?.id === j.id &&
+                        <JobInfo sx={{ maxWidth: "320px", "cursor": "pointer", backgroundColor: "#f0f0f0", "&:hover": { border: "2px solid orange" } }} job={j} />
+                    }
+                    {selectedJob?.id !== j.id &&
+                        <JobInfo sx={{ maxWidth: "320px", "cursor": "pointer", border: "2px solid transparent", "&:hover": { border: "2px solid orange" } }} job={j} />
+                    }
+                </Box>
+                )}
+            </Box>
+        </Box>
+    );
+};
+
+export {
+  JobPicker
+};

frontend/src/pages/CandidateChatPage.tsx

@@ -17,7 +17,7 @@ import { ConversationHandle } from 'components/Conversation';
 import { BackstoryPageProps } from 'components/BackstoryTab';
 import { Message } from 'components/Message';
 import { DeleteConfirmation } from 'components/DeleteConfirmation';
-import { CandidateInfo } from 'components/CandidateInfo';
+import { CandidateInfo } from 'components/ui/CandidateInfo';
 import { useNavigate } from 'react-router-dom';
 import { useSelectedCandidate } from 'hooks/GlobalContext';
 import PropagateLoader from 'react-spinners/PropagateLoader';

frontend/src/pages/GenerateCandidate.tsx

@@ -9,7 +9,7 @@ import CancelIcon from '@mui/icons-material/Cancel';
 import SendIcon from '@mui/icons-material/Send';
 import PropagateLoader from 'react-spinners/PropagateLoader';
 
-import { CandidateInfo } from '../components/CandidateInfo';
+import { CandidateInfo } from '../components/ui/CandidateInfo';
 import { Quote } from 'components/Quote';
 import { BackstoryElementProps } from 'components/BackstoryTab';
 import { BackstoryTextField, BackstoryTextFieldRef } from 'components/BackstoryTextField';

frontend/src/pages/JobAnalysisPage.tsx

@@ -7,31 +7,74 @@ import {
   Button, 
   Typography, 
   Paper, 
-  Avatar,
   useTheme,
   Snackbar,
+  Container,
+  Grid,
   Alert,
+  Tabs,
+  Tab,
+  Card,
+  CardContent,
+  Divider,
+  Avatar,
+  Badge,
 } from '@mui/material';
+import {
+  Person,
+  PersonAdd,
+  AccountCircle,
+  Add,
+  WorkOutline,
+  AddCircle,
+} from '@mui/icons-material';
 import PersonIcon from '@mui/icons-material/Person';
 import WorkIcon from '@mui/icons-material/Work';
 import AssessmentIcon from '@mui/icons-material/Assessment';
 import { JobMatchAnalysis } from 'components/JobMatchAnalysis';
-import { Candidate } from "types/types";
+import { Candidate, Job, JobFull } from "types/types";
 import { useNavigate } from 'react-router-dom';
 import { BackstoryPageProps } from 'components/BackstoryTab';
 import { useAuth } from 'hooks/AuthContext';
 import { useSelectedCandidate, useSelectedJob } from 'hooks/GlobalContext';
-import { CandidateInfo } from 'components/CandidateInfo';
+import { CandidateInfo } from 'components/ui/CandidateInfo';
 import { ComingSoon } from 'components/ui/ComingSoon';
 import { JobManagement } from 'components/JobManagement';
 import { LoginRequired } from 'components/ui/LoginRequired';
 import { Scrollable } from 'components/Scrollable';
 import { CandidatePicker } from 'components/ui/CandidatePicker';
+import { JobPicker } from 'components/ui/JobPicker';
+import { JobCreator } from 'components/JobCreator';
+
+function WorkAddIcon() {
+  return (
+    <Box position="relative" display="inline-flex"
+      sx={{
+        lineHeight: "30px",
+        mb: "6px",
+      }}
+    >
+      <WorkOutline sx={{ fontSize: 24 }} />
+      <Add
+        sx={{
+          position: 'absolute',
+          bottom: -2,
+          right: -2,
+          fontSize: 14,
+          bgcolor: 'background.paper',
+          borderRadius: '50%',
+          boxShadow: 1,
+        }}
+        color="primary"
+      />
+    </Box>
+  );
+}
 
 // Main component
 const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps) => {
   const theme = useTheme();
-  const { user } = useAuth();
+  const { user, apiClient } = useAuth();
   const navigate = useNavigate();
   const { selectedCandidate, setSelectedCandidate } = useSelectedCandidate()
   const { selectedJob, setSelectedJob } = useSelectedJob()
@@ -41,12 +84,21 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
   const [activeStep, setActiveStep] = useState(0);
   const [analysisStarted, setAnalysisStarted] = useState(false);
   const [error, setError] = useState<string | null>(null);
+  const [jobTab, setJobTab] = useState<string>('load');
 
   useEffect(() => {
-    if (selectedJob && activeStep === 1) {
+    console.log({ activeStep, selectedCandidate, selectedJob });
-      setActiveStep(2);
+
+    if (!selectedCandidate) {
+      if (activeStep !== 0) {
+        setActiveStep(0);
+      }
+    } else if (!selectedJob) {
+      if (activeStep !== 1) {
+        setActiveStep(1);
+      }
     }
-  }, [selectedJob, activeStep]);
+  }, [selectedCandidate, selectedJob, activeStep])
 
   // Steps in our process
   const steps = [
@@ -93,7 +145,6 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
         setSelectedJob(null);
         break;
       case 1: /* Select Job */
-        setSelectedCandidate(null);
         setSelectedJob(null);
         break;
       case 2: /* Job Analysis */
@@ -109,6 +160,11 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
     setActiveStep(1);
   }
 
+  const onJobSelect = (job: Job) => {
+    setSelectedJob(job)
+    setActiveStep(2);
+  }
+
   // Render function for the candidate selection step
   const renderCandidateSelection = () => (
     <Paper elevation={3} sx={{ p: 3, mt: 3, mb: 4, borderRadius: 2 }}>
@@ -120,16 +176,35 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
     </Paper>
   );
 
+  const handleTabChange = (event: React.SyntheticEvent, value: string) => {
+    setJobTab(value);
+  };
+
   // Render function for the job description step
-  const renderJobDescription = () => (
+  const renderJobDescription = () => {
-    <Box sx={{ mt: 3 }}>
+    if (!selectedCandidate) {
-      {selectedCandidate && (
+      return;
-        <JobManagement
+    }
+
+    return (<Box sx={{ mt: 3 }}>
+      <Box sx={{ borderBottom: 1, borderColor: 'divider', mb: 3 }}>
+        <Tabs value={jobTab} onChange={handleTabChange} centered>
+          <Tab value='load' icon={<WorkOutline />} label="Load" />
+          <Tab value='create' icon={<WorkAddIcon />} label="Create" />
+        </Tabs>
+      </Box>
+
+      {jobTab === 'load' &&
+        <JobPicker {...backstoryProps} onSelect={onJobSelect} />
+      }
+      {jobTab === 'create' &&
+        <JobCreator
           {...backstoryProps}
-        />
+          onSave={onJobSelect}
-      )}
+        />}
     </Box>
-  );
+    );
+  }
 
   // Render function for the analysis step
   const renderAnalysis = () => (
@@ -232,7 +307,7 @@ const JobAnalysisPage: React.FC<BackstoryPageProps> = (props: BackstoryPageProps
             </Button>
           ) : (
             <Button onClick={handleNext} variant="contained">
-              {activeStep === steps[steps.length - 1].index - 1 ? 'Start Analysis' : 'Next'}
+              {activeStep === steps[steps.length - 1].index - 1 ? 'Done' : 'Next'}
             </Button>
           )}
         </Box>

frontend/src/pages/OldChatPage.tsx

@@ -7,7 +7,7 @@ import MuiMarkdown from 'mui-markdown';
 import { BackstoryPageProps } from '../components/BackstoryTab';
 import { Conversation, ConversationHandle } from '../components/Conversation';
 import { BackstoryQuery } from '../components/BackstoryQuery';
-import { CandidateInfo } from 'components/CandidateInfo';
+import { CandidateInfo } from 'components/ui/CandidateInfo';
 import { useAuth } from 'hooks/AuthContext';
 import { Candidate } from 'types/types';
 

frontend/src/services/api-client.ts

@@ -52,7 +52,7 @@ interface StreamingOptions<T = Types.ChatMessage> {
   signal?: AbortSignal;
 }
 
-interface DeleteCandidateResponse {
+interface DeleteResponse {
   success: boolean;
   message: string;
 }
@@ -530,14 +530,24 @@ class ApiClient {
     return this.handleApiResponseWithConversion<Types.Candidate>(response, 'Candidate');
   }
 
-  async deleteCandidate(id: string): Promise<DeleteCandidateResponse> {
+  async deleteCandidate(id: string): Promise<DeleteResponse> {
     const response = await fetch(`${this.baseUrl}/candidates/${id}`, {
       method: 'DELETE',
       headers: this.defaultHeaders,
       body: JSON.stringify({ id })
     });
 
-    return handleApiResponse<DeleteCandidateResponse>(response);
+    return handleApiResponse<DeleteResponse>(response);
+  }
+
+  async deleteJob(id: string): Promise<DeleteResponse> {
+    const response = await fetch(`${this.baseUrl}/jobs/${id}`, {
+      method: 'DELETE',
+      headers: this.defaultHeaders,
+      body: JSON.stringify({ id })
+    });
+
+    return handleApiResponse<DeleteResponse>(response);
   }
 
   async uploadCandidateProfile(file: File): Promise<boolean> {
@@ -641,7 +651,7 @@ class ApiClient {
     return this.handleApiResponseWithConversion<Types.Job>(response, 'Job');
   }
 
-  async getJobs(request: Partial<PaginatedRequest> = {}): Promise<PaginatedResponse<Types.Job>> {
+  async getJobs(request: Partial<PaginatedRequest> = {}): Promise<PaginatedResponse<Types.JobFull>> {
     const paginatedRequest = createPaginatedRequest(request);
     const params = toUrlParams(formatApiRequest(paginatedRequest));
     
@@ -649,7 +659,7 @@ class ApiClient {
       headers: this.defaultHeaders
     });
 
-    return this.handlePaginatedApiResponseWithConversion<Types.Job>(response, 'Job');
+    return this.handlePaginatedApiResponseWithConversion<Types.JobFull>(response, 'JobFull');
   }
 
   async getJobsByEmployer(employerId: string, request: Partial<PaginatedRequest> = {}): Promise<PaginatedResponse<Types.Job>> {
@@ -844,18 +854,28 @@ class ApiClient {
       }
     };
     return this.streamify<Types.DocumentMessage>('/candidates/documents/upload', formData, streamingOptions);
-    // {
+  }
-    //   method: 'POST',
-    //   headers: {
-    //     // Don't set Content-Type - browser will set it automatically with boundary
-    //     'Authorization': this.defaultHeaders['Authorization']
-    //   },
-    //   body: formData
-    // });
 
-    // const result = await handleApiResponse<Types.Document>(response);
+  createJobFromFile(file: File, streamingOptions?: StreamingOptions<Types.Job>): StreamingResponse<Types.Job> {
-    
+    const formData = new FormData()
-    // return result;
+    formData.append('file', file);
+    formData.append('filename', file.name);
+    streamingOptions = {
+      ...streamingOptions,
+      headers: {
+        // Don't set Content-Type - browser will set it automatically with boundary
+        'Authorization': this.defaultHeaders['Authorization']
+      }
+    };
+    return this.streamify<Types.Job>('/jobs/upload', formData, streamingOptions);
+  }
+
+  getJobRequirements(jobId: string, streamingOptions?: StreamingOptions<Types.DocumentMessage>): StreamingResponse<Types.DocumentMessage> {
+    streamingOptions = {
+      ...streamingOptions,
+      headers: this.defaultHeaders,
+    };
+    return this.streamify<Types.DocumentMessage>(`/jobs/requirements/${jobId}`, null, streamingOptions);
   }
 
   async candidateMatchForRequirement(candidate_id: string, requirement: string) : Promise<Types.SkillMatch> {
@@ -1001,7 +1021,7 @@ class ApiClient {
    * @param options callbacks, headers, and method
    * @returns 
    */
-  streamify<T = Types.ChatMessage[]>(api: string, data: BodyInit, options: StreamingOptions<T> = {}) : StreamingResponse<T> {
+  streamify<T = Types.ChatMessage[]>(api: string, data: BodyInit | null, options: StreamingOptions<T> = {}) : StreamingResponse<T> {
     const abortController = new AbortController();
     const signal = options.signal || abortController.signal;
     const headers = options.headers || null;

frontend/src/types/types.ts

@@ -1,6 +1,6 @@
 // Generated TypeScript types from Pydantic models
 // Source: src/backend/models.py
-// Generated on: 2025-06-05T22:02:22.004513
+// Generated on: 2025-06-07T20:43:58.855207
 // DO NOT EDIT MANUALLY - This file is auto-generated
 
 // ============================
@@ -323,7 +323,7 @@ export interface ChatMessageMetaData {
   presencePenalty?: number;
   stopSequences?: Array<string>;
   ragResults?: Array<ChromaDBGetResponse>;
-  llmHistory?: Array<LLMMessage>;
+  llmHistory?: Array<any>;
   evalCount: number;
   evalDuration: number;
   promptEvalCount: number;
@@ -711,12 +711,6 @@ export interface JobResponse {
   meta?: Record<string, any>;
 }
 
-export interface LLMMessage {
-  role: string;
-  content: string;
-  toolCalls?: Array<Record<string, any>>;
-}
-
 export interface Language {
   language: string;
   proficiency: "basic" | "conversational" | "fluent" | "native";

src/backend/agents/base.py

@@ -137,7 +137,7 @@ class Agent(BaseModel, ABC):
     #     llm: Any,
     #     model: str,
     #     message: ChatMessage,
-    #     tool_message: Any, # llama response message
+    #     tool_message: Any, # llama response
     #     messages: List[LLMMessage],
     # ) -> AsyncGenerator[ChatMessage, None]:
     #     logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
@@ -270,15 +270,15 @@ class Agent(BaseModel, ABC):
     #             },
     #             stream=True,
     #         ):
-    #             # logger.info(f"LLM::Tools: {'done' if response.done else 'processing'} - {response.message}")
+    #             # logger.info(f"LLM::Tools: {'done' if response.finish_reason else 'processing'} - {response}")
     #             message.status = "streaming"
-    #             message.chunk = response.message.content
+    #             message.chunk = response.content
     #             message.content += message.chunk
 
-    #             if not response.done:
+    #             if not response.finish_reason:
     #                 yield message
 
-    #             if response.done:
+    #             if response.finish_reason:
     #                 self.collect_metrics(response)
     #                 message.metadata.eval_count += response.eval_count
     #                 message.metadata.eval_duration += response.eval_duration
@@ -296,9 +296,9 @@ class Agent(BaseModel, ABC):
 
     def collect_metrics(self, response):
         self.metrics.tokens_prompt.labels(agent=self.agent_type).inc(
-            response.prompt_eval_count
+            response.usage.prompt_eval_count
         )
-        self.metrics.tokens_eval.labels(agent=self.agent_type).inc(response.eval_count)
+        self.metrics.tokens_eval.labels(agent=self.agent_type).inc(response.usage.eval_count)
 
     def get_rag_context(self, rag_message: ChatMessageRagSearch) -> str:
         """
@@ -361,7 +361,7 @@ Content: {content}
             yield status_message
 
             try:
-                chroma_results = user.file_watcher.find_similar(
+                chroma_results = await user.file_watcher.find_similar(
                     query=prompt, top_k=top_k, threshold=threshold
                 )
                 if not chroma_results:
@@ -430,7 +430,7 @@ Content: {content}
         logger.info(f"Message options: {options.model_dump(exclude_unset=True)}")
         response = None
         content = ""
-        for response in llm.chat(
+        async for response in llm.chat_stream(
             model=model,
             messages=messages,
             options={
@@ -446,12 +446,12 @@ Content: {content}
                 yield error_message
                 return
 
-            content += response.message.content
+            content += response.content
 
-            if not response.done:
+            if not response.finish_reason:
                 streaming_message = ChatMessageStreaming(
                     session_id=session_id,
-                    content=response.message.content,
+                    content=response.content,
                     status=ApiStatusType.STREAMING,
                 )
                 yield streaming_message
@@ -466,7 +466,7 @@ Content: {content}
         
         self.collect_metrics(response)
         self.context_tokens = (
-            response.prompt_eval_count + response.eval_count
+            response.usage.prompt_eval_count + response.usage.eval_count
         )
 
         chat_message = ChatMessage(
@@ -476,10 +476,10 @@ Content: {content}
             content=content,
             metadata = ChatMessageMetaData(
                 options=options,
-                eval_count=response.eval_count,
+                eval_count=response.usage.eval_count,
-                eval_duration=response.eval_duration,
+                eval_duration=response.usage.eval_duration,
-                prompt_eval_count=response.prompt_eval_count,
+                prompt_eval_count=response.usage.prompt_eval_count,
-                prompt_eval_duration=response.prompt_eval_duration,
+                prompt_eval_duration=response.usage.prompt_eval_duration,
 
             )
         )
@@ -588,12 +588,12 @@ Content: {content}
 
             #     end_time = time.perf_counter()
             #     message.metadata.timers["tool_check"] = end_time - start_time
-            #     if not response.message.tool_calls:
+            #     if not response.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
+            #         tool_metadata["attempted"] = response.tool_calls
 
             # if use_tools:
             #     logger.info("LLM indicates tools will be used")
@@ -626,15 +626,15 @@ Content: {content}
             #         yield message
             #         return
 
-            #     if response.message.tool_calls:
+            #     if response.tool_calls:
-            #         tool_metadata["used"] = response.message.tool_calls
+            #         tool_metadata["used"] = response.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,
+            #             tool_message=response,
             #             messages=messages,
             #         ):
             #             if message.status == "error":
@@ -647,7 +647,7 @@ Content: {content}
             #         return
 
             #     logger.info("LLM indicated tools will be used, and then they weren't")
-            #     message.content = response.message.content
+            #     message.content = response.content
             #     message.status = "done"
             #     yield message
             #     return
@@ -674,7 +674,7 @@ Content: {content}
             content = ""
             start_time = time.perf_counter()
             response = None
-            for response in llm.chat(
+            async for response in llm.chat_stream(
                 model=model,
                 messages=messages,
                 options={
@@ -690,12 +690,12 @@ Content: {content}
                     yield error_message
                     return
 
-                content += response.message.content
+                content += response.content
 
-                if not response.done:
+                if not response.finish_reason:
                     streaming_message = ChatMessageStreaming(
                         session_id=session_id,
-                        content=response.message.content,
+                        content=response.content,
                     )
                     yield streaming_message
 
@@ -709,7 +709,7 @@ Content: {content}
             
             self.collect_metrics(response)
             self.context_tokens = (
-                response.prompt_eval_count + response.eval_count
+                response.usage.prompt_eval_count + response.usage.eval_count
             )
             end_time = time.perf_counter()
 
@@ -720,10 +720,10 @@ Content: {content}
                 content=content,
                 metadata = ChatMessageMetaData(
                     options=options,
-                    eval_count=response.eval_count,
+                    eval_count=response.usage.eval_count,
-                    eval_duration=response.eval_duration,
+                    eval_duration=response.usage.eval_duration,
-                    prompt_eval_count=response.prompt_eval_count,
+                    prompt_eval_count=response.usage.prompt_eval_count,
-                    prompt_eval_duration=response.prompt_eval_duration,
+                    prompt_eval_duration=response.usage.prompt_eval_duration,
                     timers={
                         "llm_streamed": end_time - start_time,
                         "llm_with_tools": 0,  # Placeholder for tool processing time

src/backend/database.py

@@ -178,12 +178,13 @@ class RedisDatabase:
             'jobs': 'job:',
             'job_applications': 'job_application:',
             'chat_sessions': 'chat_session:',
-            'chat_messages': 'chat_messages:',  # This will store lists
+            'chat_messages': 'chat_messages:',
             'ai_parameters': 'ai_parameters:',
             'users': 'user:',
-            'candidate_documents': 'candidate_documents:',            
+            'candidate_documents': 'candidate_documents:',
+            'job_requirements': 'job_requirements:',  # Add this line
         }
-    
+
     def _serialize(self, data: Any) -> str:
         """Serialize data to JSON string for Redis storage"""
         if data is None:
@@ -236,8 +237,9 @@ class RedisDatabase:
             # Delete each document's metadata
             for doc_id in document_ids:
                 pipe.delete(f"document:{doc_id}")
+                pipe.delete(f"{self.KEY_PREFIXES['job_requirements']}{doc_id}")
                 deleted_count += 1
-            
+
             # Delete the candidate's document list
             pipe.delete(key)
             
@@ -250,7 +252,110 @@ class RedisDatabase:
         except Exception as e:
             logger.error(f"Error deleting all documents for candidate {candidate_id}: {e}")
             raise
-        
+
+    async def get_cached_skill_match(self, cache_key: str) -> Optional[Dict[str, Any]]:
+        """Retrieve cached skill match assessment"""
+        try:
+            cached_data = await self.redis.get(cache_key)
+            if cached_data:
+                return json.loads(cached_data)
+            return None
+        except Exception as e:
+            logger.error(f"Error retrieving cached skill match: {e}")
+            return None
+    
+    async def cache_skill_match(self, cache_key: str, assessment_data: Dict[str, Any], ttl: int = 86400 * 30) -> bool:
+        """Cache skill match assessment with TTL (default 30 days)"""
+        try:
+            await self.redis.setex(
+                cache_key, 
+                ttl, 
+                json.dumps(assessment_data, default=str)
+            )
+            return True
+        except Exception as e:
+            logger.error(f"Error caching skill match: {e}")
+            return False
+    
+    async def get_candidate_skill_update_time(self, candidate_id: str) -> Optional[datetime]:
+        """Get the last time candidate's skill information was updated"""
+        try:
+            # This assumes you track skill update timestamps in your candidate data
+            candidate_data = await self.get_candidate(candidate_id)
+            if candidate_data and 'skills_updated_at' in candidate_data:
+                return datetime.fromisoformat(candidate_data['skills_updated_at'])
+            return None
+        except Exception as e:
+            logger.error(f"Error getting candidate skill update time: {e}")
+            return None
+    
+    async def get_user_rag_update_time(self, user_id: str) -> Optional[datetime]:
+        """Get the timestamp of the latest RAG data update for a specific user"""
+        try:
+            rag_update_key = f"user:{user_id}:rag_last_update"
+            timestamp_str = await self.redis.get(rag_update_key)
+            if timestamp_str:
+                return datetime.fromisoformat(timestamp_str.decode('utf-8'))
+            return None
+        except Exception as e:
+            logger.error(f"Error getting user RAG update time for user {user_id}: {e}")
+            return None
+    
+    async def update_user_rag_timestamp(self, user_id: str) -> bool:
+        """Update the RAG data timestamp for a specific user (call this when user's RAG data is updated)"""
+        try:
+            rag_update_key = f"user:{user_id}:rag_last_update"
+            current_time = datetime.utcnow().isoformat()
+            await self.redis.set(rag_update_key, current_time)
+            return True
+        except Exception as e:
+            logger.error(f"Error updating RAG timestamp for user {user_id}: {e}")
+            return False
+    
+    async def invalidate_candidate_skill_cache(self, candidate_id: str) -> int:
+        """Invalidate all cached skill matches for a specific candidate"""
+        try:
+            pattern = f"skill_match:{candidate_id}:*"
+            keys = await self.redis.keys(pattern)
+            if keys:
+                return await self.redis.delete(*keys)
+            return 0
+        except Exception as e:
+            logger.error(f"Error invalidating candidate skill cache: {e}")
+            return 0
+    
+    async def clear_all_skill_match_cache(self) -> int:
+        """Clear all skill match cache (useful after major system updates)"""
+        try:
+            pattern = "skill_match:*"
+            keys = await self.redis.keys(pattern)
+            if keys:
+                return await self.redis.delete(*keys)
+            return 0
+        except Exception as e:
+            logger.error(f"Error clearing skill match cache: {e}")
+            return 0
+    
+    async def invalidate_user_skill_cache(self, user_id: str) -> int:
+        """Invalidate all cached skill matches when a user's RAG data is updated"""
+        try:
+            # This assumes all candidates belonging to this user need cache invalidation
+            # You might need to adjust the pattern based on how you associate candidates with users
+            pattern = f"skill_match:*"
+            keys = await self.redis.keys(pattern)
+            
+            # Filter keys that belong to candidates owned by this user
+            # This would require additional logic to determine candidate ownership
+            # For now, you might want to clear all cache when any user's RAG data updates
+            # or implement a more sophisticated mapping
+            
+            if keys:
+                return await self.redis.delete(*keys)
+            return 0
+        except Exception as e:
+            logger.error(f"Error invalidating user skill cache for user {user_id}: {e}")
+            return 0
+
     async def get_candidate_documents(self, candidate_id: str) -> List[Dict]:
         """Get all documents for a specific candidate"""
         key = f"{self.KEY_PREFIXES['candidate_documents']}{candidate_id}"
@@ -330,7 +435,134 @@ class RedisDatabase:
             if (query_lower in doc.get("filename", "").lower() or 
                 query_lower in doc.get("originalName", "").lower())
         ]
-        
+
+    async def get_job_requirements(self, document_id: str) -> Optional[Dict]:
+        """Get cached job requirements analysis for a document"""
+        try:
+            key = f"{self.KEY_PREFIXES['job_requirements']}{document_id}"
+            data = await self.redis.get(key)
+            if data:
+                requirements_data = self._deserialize(data)
+                logger.debug(f"📋 Retrieved cached job requirements for document {document_id}")
+                return requirements_data
+            logger.debug(f"📋 No cached job requirements found for document {document_id}")
+            return None
+        except Exception as e:
+            logger.error(f"❌ Error retrieving job requirements for document {document_id}: {e}")
+            return None
+
+    async def save_job_requirements(self, document_id: str, requirements: Dict) -> bool:
+        """Save job requirements analysis results for a document"""
+        try:
+            key = f"{self.KEY_PREFIXES['job_requirements']}{document_id}"
+            
+            # Add metadata to the requirements
+            requirements_with_meta = {
+                **requirements,
+                "cached_at": datetime.now(UTC).isoformat(),
+                "document_id": document_id
+            }
+            
+            await self.redis.set(key, self._serialize(requirements_with_meta))
+            
+            # Optional: Set expiration (e.g., 30 days) to prevent indefinite storage
+            # await self.redis.expire(key, 30 * 24 * 60 * 60)  # 30 days
+            
+            logger.debug(f"📋 Saved job requirements for document {document_id}")
+            return True
+        except Exception as e:
+            logger.error(f"❌ Error saving job requirements for document {document_id}: {e}")
+            return False
+
+    async def delete_job_requirements(self, document_id: str) -> bool:
+        """Delete cached job requirements for a document"""
+        try:
+            key = f"{self.KEY_PREFIXES['job_requirements']}{document_id}"
+            result = await self.redis.delete(key)
+            if result > 0:
+                logger.debug(f"📋 Deleted job requirements for document {document_id}")
+                return True
+            return False
+        except Exception as e:
+            logger.error(f"❌ Error deleting job requirements for document {document_id}: {e}")
+            return False
+
+    async def get_all_job_requirements(self) -> Dict[str, Any]:
+        """Get all cached job requirements"""
+        try:
+            pattern = f"{self.KEY_PREFIXES['job_requirements']}*"
+            keys = await self.redis.keys(pattern)
+            
+            if not keys:
+                return {}
+            
+            pipe = self.redis.pipeline()
+            for key in keys:
+                pipe.get(key)
+            values = await pipe.execute()
+            
+            result = {}
+            for key, value in zip(keys, values):
+                document_id = key.replace(self.KEY_PREFIXES['job_requirements'], '')
+                if value:
+                    result[document_id] = self._deserialize(value)
+            
+            return result
+        except Exception as e:
+            logger.error(f"❌ Error retrieving all job requirements: {e}")
+            return {}
+
+    async def get_job_requirements_by_candidate(self, candidate_id: str) -> List[Dict]:
+        """Get all job requirements analysis for documents belonging to a candidate"""
+        try:
+            # Get all documents for the candidate
+            candidate_documents = await self.get_candidate_documents(candidate_id)
+            
+            if not candidate_documents:
+                return []
+            
+            # Get job requirements for each document
+            job_requirements = []
+            for doc in candidate_documents:
+                doc_id = doc.get("id")
+                if doc_id:
+                    requirements = await self.get_job_requirements(doc_id)
+                    if requirements:
+                        # Add document metadata to requirements
+                        requirements["document_filename"] = doc.get("filename")
+                        requirements["document_original_name"] = doc.get("originalName")
+                        job_requirements.append(requirements)
+            
+            return job_requirements
+        except Exception as e:
+            logger.error(f"❌ Error retrieving job requirements for candidate {candidate_id}: {e}")
+            return []
+
+    async def invalidate_job_requirements_cache(self, document_id: str) -> bool:
+        """Invalidate (delete) cached job requirements for a document"""
+        # This is an alias for delete_job_requirements for semantic clarity
+        return await self.delete_job_requirements(document_id)
+
+    async def bulk_delete_job_requirements(self, document_ids: List[str]) -> int:
+        """Delete job requirements for multiple documents and return count of deleted items"""
+        try:
+            deleted_count = 0
+            pipe = self.redis.pipeline()
+            
+            for doc_id in document_ids:
+                key = f"{self.KEY_PREFIXES['job_requirements']}{doc_id}"
+                pipe.delete(key)
+                deleted_count += 1
+            
+            results = await pipe.execute()
+            actual_deleted = sum(1 for result in results if result > 0)
+            
+            logger.info(f"📋 Bulk deleted job requirements for {actual_deleted}/{len(document_ids)} documents")
+            return actual_deleted
+        except Exception as e:
+            logger.error(f"❌ Error bulk deleting job requirements: {e}")
+            return 0
+
     # Viewer operations
     async def get_viewer(self, viewer_id: str) -> Optional[Dict]:
         """Get viewer by ID"""
@@ -1484,6 +1716,74 @@ class RedisDatabase:
         key = f"{self.KEY_PREFIXES['users']}{email}"
         await self.redis.delete(key)
     
+    async def get_job_requirements_stats(self) -> Dict[str, Any]:
+        """Get statistics about cached job requirements"""
+        try:
+            pattern = f"{self.KEY_PREFIXES['job_requirements']}*"
+            keys = await self.redis.keys(pattern)
+            
+            stats = {
+                "total_cached_requirements": len(keys),
+                "cache_dates": {},
+                "documents_with_requirements": []
+            }
+            
+            if keys:
+                # Get cache dates for analysis
+                pipe = self.redis.pipeline()
+                for key in keys:
+                    pipe.get(key)
+                values = await pipe.execute()
+                
+                for key, value in zip(keys, values):
+                    if value:
+                        requirements_data = self._deserialize(value)
+                        if requirements_data:
+                            document_id = key.replace(self.KEY_PREFIXES['job_requirements'], '')
+                            stats["documents_with_requirements"].append(document_id)
+                            
+                            # Track cache dates
+                            cached_at = requirements_data.get("cached_at")
+                            if cached_at:
+                                cache_date = cached_at[:10]  # Extract date part
+                                stats["cache_dates"][cache_date] = stats["cache_dates"].get(cache_date, 0) + 1
+            
+            return stats
+        except Exception as e:
+            logger.error(f"❌ Error getting job requirements stats: {e}")
+            return {"total_cached_requirements": 0, "cache_dates": {}, "documents_with_requirements": []}
+
+    async def cleanup_orphaned_job_requirements(self) -> int:
+        """Clean up job requirements for documents that no longer exist"""
+        try:
+            # Get all job requirements
+            all_requirements = await self.get_all_job_requirements()
+            
+            if not all_requirements:
+                return 0
+            
+            orphaned_count = 0
+            pipe = self.redis.pipeline()
+            
+            for document_id in all_requirements.keys():
+                # Check if the document still exists
+                document_exists = await self.get_document(document_id)
+                if not document_exists:
+                    # Document no longer exists, delete its job requirements
+                    key = f"{self.KEY_PREFIXES['job_requirements']}{document_id}"
+                    pipe.delete(key)
+                    orphaned_count += 1
+                    logger.debug(f"📋 Queued orphaned job requirements for deletion: {document_id}")
+            
+            if orphaned_count > 0:
+                await pipe.execute()
+                logger.info(f"🧹 Cleaned up {orphaned_count} orphaned job requirements")
+            
+            return orphaned_count
+        except Exception as e:
+            logger.error(f"❌ Error cleaning up orphaned job requirements: {e}")
+            return 0    
+    
     # Utility methods
     async def clear_all_data(self):
         """Clear all data from Redis (use with caution!)"""

src/backend/entities/candidate_entity.py

@@ -19,8 +19,9 @@ import defines
 from logger import logger
 import agents as agents
 from models import (Tunables, CandidateQuestion, ChatMessageUser, ChatMessage, RagEntry, ChatMessageMetaData, ApiStatusType, Candidate, ChatContextType)
-from llm_manager import llm_manager
+import llm_proxy as llm_manager
 from agents.base import Agent
+from database import RedisDatabase
 
 class CandidateEntity(Candidate):
     model_config = {"arbitrary_types_allowed": True}  # Allow ChromaDBFileWatcher, etc
@@ -115,7 +116,7 @@ class CandidateEntity(Candidate):
             raise ValueError("initialize() has not been called.")
         return self.CandidateEntity__observer    
 
-    async def initialize(self, prometheus_collector: CollectorRegistry):
+    async def initialize(self, prometheus_collector: CollectorRegistry, database: RedisDatabase):
         if self.CandidateEntity__initialized:
             # Initialization can only be attempted once; if there are multiple attempts, it means
             # a subsystem is failing or there is a logic bug in the code.
@@ -140,9 +141,11 @@ class CandidateEntity(Candidate):
 
         self.CandidateEntity__observer, self.CandidateEntity__file_watcher = start_file_watcher(
             llm=llm_manager.get_llm(),
+            user_id=self.id,
             collection_name=self.username,
             persist_directory=vector_db_dir,
             watch_directory=rag_content_dir,
+            database=database,
             recreate=False,  # Don't recreate if exists
         )
         has_username_rag = any(item["name"] == self.username for item in self.rags)

src/backend/entities/entity_manager.py

@@ -7,6 +7,7 @@ from pydantic import BaseModel, Field # type: ignore
 
 from models import ( Candidate )
 from .candidate_entity import CandidateEntity
+from database import RedisDatabase
 from prometheus_client import CollectorRegistry # type: ignore
 
 class EntityManager:
@@ -34,9 +35,10 @@ class EntityManager:
                 pass
             self._cleanup_task = None
 
-    def initialize(self, prometheus_collector: CollectorRegistry):
+    def initialize(self, prometheus_collector: CollectorRegistry, database: RedisDatabase):
         """Initialize the EntityManager with Prometheus collector"""
         self._prometheus_collector = prometheus_collector
+        self._database = database
 
     async def get_entity(self, candidate: Candidate) -> CandidateEntity:
         """Get or create CandidateEntity with proper reference tracking"""
@@ -49,7 +51,7 @@ class EntityManager:
             return entity
         
         entity = CandidateEntity(candidate=candidate)
-        await entity.initialize(prometheus_collector=self._prometheus_collector)
+        await entity.initialize(prometheus_collector=self._prometheus_collector, database=self._database)
         
         # Store with reference tracking
         self._entities[candidate.id] = entity

src/backend/llm_manager.py

@@ -1,41 +0,0 @@
-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

@@ -13,6 +13,9 @@ import uuid
 import defines
 import pathlib
 
+from markitdown import MarkItDown, StreamInfo       # type: ignore
+import io                
+
 import uvicorn  # type: ignore
 from typing import List, Optional, Dict, Any
 from datetime import datetime, timedelta, UTC
@@ -53,7 +56,7 @@ import defines
 from logger import logger
 from database import RedisDatabase, redis_manager, DatabaseManager
 from metrics import Metrics
-from llm_manager import llm_manager
+import llm_proxy as llm_manager
 import entities
 from email_service import VerificationEmailRateLimiter, email_service
 from device_manager import DeviceManager
@@ -116,7 +119,8 @@ async def lifespan(app: FastAPI):
     try:
         # Initialize database
         await db_manager.initialize()
-        
+        entities.entity_manager.initialize(prometheus_collector, database=db_manager.get_database())
+
         signal.signal(signal.SIGTERM, signal_handler)
         signal.signal(signal.SIGINT, signal_handler)
         
@@ -1827,7 +1831,7 @@ async def upload_candidate_document(
             yield error_message
             return
 
-        converted = False;
+        converted = False
         if document_type != DocumentType.MARKDOWN and document_type != DocumentType.TXT:
             p = pathlib.Path(file_path)
             p_as_md = p.with_suffix(".md")
@@ -1873,53 +1877,6 @@ async def upload_candidate_document(
             content=file_content,
         )
         yield chat_message
-
-        # If this is a job description, process it with the job requirements agent        
-        if not options.is_job_document:
-            return
-        
-        status_message = ChatMessageStatus(
-            session_id=MOCK_UUID,  # No session ID for document uploads
-            content=f"Initiating connection with {candidate.first_name}'s AI agent...",
-            activity=ApiActivityType.INFO
-        )
-        yield status_message
-        await asyncio.sleep(0)
-        
-        async with entities.get_candidate_entity(candidate=candidate) as candidate_entity:
-            chat_agent = candidate_entity.get_or_create_agent(agent_type=ChatContextType.JOB_REQUIREMENTS)
-            if not chat_agent:
-                error_message = ChatMessageError(
-                    session_id=MOCK_UUID,  # No session ID for document uploads
-                    content="No agent found for job requirements chat type"
-                )
-                yield error_message
-                return
-            message = None
-            status_message = ChatMessageStatus(
-                session_id=MOCK_UUID,  # No session ID for document uploads
-                content=f"Analyzing document for company and requirement details...",
-                activity=ApiActivityType.SEARCHING
-            )
-            yield status_message
-            await asyncio.sleep(0)
-
-            async for message in chat_agent.generate(
-                llm=llm_manager.get_llm(),
-                model=defines.model,
-                session_id=MOCK_UUID,
-                prompt=file_content
-            ):
-                pass
-            if not message or not isinstance(message, JobRequirementsMessage):
-                error_message = ChatMessageError(
-                    session_id=MOCK_UUID,  # No session ID for document uploads
-                    content="Failed to process job description file"
-                )
-                yield error_message
-                return
-            yield message
-
     try:
         async def to_json(method):
             try:
@@ -1932,7 +1889,6 @@ async def upload_candidate_document(
                 logger.error(f"Error in to_json conversion: {e}")
                 return
 
-#        return DebugStreamingResponse(
         return StreamingResponse(
             to_json(upload_stream_generator(file_content)),
             media_type="text/event-stream",
@@ -1944,15 +1900,64 @@ async def upload_candidate_document(
                 "Access-Control-Allow-Origin": "*",  # Adjust for your CORS needs
                 "Transfer-Encoding": "chunked",
             },
-        )    
+        )
     except Exception as e:
         logger.error(backstory_traceback.format_exc())
         logger.error(f"❌ Document upload error: {e}")
-        return JSONResponse(
+        return StreamingResponse(
-            status_code=500,
+            iter([ChatMessageError(
-            content=create_error_response("UPLOAD_ERROR", "Failed to upload document")
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content="Failed to upload document"
+            )]),
+            media_type="text/event-stream"
         )
 
+async def create_job_from_content(database: RedisDatabase, current_user: Candidate, content: str):
+    status_message = ChatMessageStatus(
+        session_id=MOCK_UUID,  # No session ID for document uploads
+        content=f"Initiating connection with {current_user.first_name}'s AI agent...",
+        activity=ApiActivityType.INFO
+    )
+    yield status_message
+    await asyncio.sleep(0) # Let the status message propagate
+        
+    async with entities.get_candidate_entity(candidate=current_user) as candidate_entity:
+        chat_agent = candidate_entity.get_or_create_agent(agent_type=ChatContextType.JOB_REQUIREMENTS)
+        if not chat_agent:
+            error_message = ChatMessageError(
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content="No agent found for job requirements chat type"
+            )
+            yield error_message
+            return
+        message = None
+        status_message = ChatMessageStatus(
+            session_id=MOCK_UUID,  # No session ID for document uploads
+            content=f"Analyzing document for company and requirement details...",
+            activity=ApiActivityType.SEARCHING
+        )
+        yield status_message
+        await asyncio.sleep(0)
+
+        async for message in chat_agent.generate(
+            llm=llm_manager.get_llm(),
+            model=defines.model,
+            session_id=MOCK_UUID,
+            prompt=content
+        ):
+            pass
+        if not message or not isinstance(message, JobRequirementsMessage):
+            error_message = ChatMessageError(
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content="Failed to process job description file"
+            )
+            yield error_message
+            return
+        
+        logger.info(f"✅ Successfully saved job requirements job {message.id}")
+        yield message
+        return
+        
 @api_router.post("/candidates/profile/upload")
 async def upload_candidate_profile(
     file: UploadFile = File(...),
@@ -2573,6 +2578,7 @@ async def delete_candidate(
             status_code=500,
             content=create_error_response("DELETE_ERROR", "Failed to delete candidate")
         )
+
 @api_router.patch("/candidates/{candidate_id}")
 async def update_candidate(
     candidate_id: str = Path(...),
@@ -2816,6 +2822,139 @@ async def create_candidate_job(
             content=create_error_response("CREATION_FAILED", str(e))
         )
 
+
+@api_router.post("/jobs/upload")
+async def create_job_from_file(
+    file: UploadFile = File(...),
+    current_user = Depends(get_current_user),
+    database: RedisDatabase = Depends(get_database)
+):
+    """Upload a job document for the current candidate and create a Job"""
+    # Check file size (limit to 10MB)
+    max_size = 10 * 1024 * 1024  # 10MB
+    file_content = await file.read()
+    if len(file_content) > max_size:
+        logger.info(f"⚠️ File too large: {file.filename} ({len(file_content)} bytes)")
+        return StreamingResponse(
+            iter([ChatMessageError(
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content="File size exceeds 10MB limit"
+            )]),
+            media_type="text/event-stream"
+        )
+    if len(file_content) == 0:
+        logger.info(f"⚠️ File is empty: {file.filename}")
+        return StreamingResponse(
+            iter([ChatMessageError(
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content="File is empty"
+            )]),
+            media_type="text/event-stream"
+        )
+
+    """Upload a document for the current candidate"""
+    async def upload_stream_generator(file_content):
+        # Verify user is a candidate
+        if current_user.user_type != "candidate":
+            logger.warning(f"⚠️ Unauthorized upload attempt by user type: {current_user.user_type}")
+            error_message = ChatMessageError(
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content="Only candidates can upload documents"
+            )
+            yield error_message
+            return
+        
+        file.filename = re.sub(r'^.*/', '', file.filename) if file.filename else '' # Sanitize filename
+        if not file.filename or file.filename.strip() == "":
+            logger.warning("⚠️ File upload attempt with missing filename")
+            error_message = ChatMessageError(
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content="File must have a valid filename"
+            )
+            yield error_message
+            return
+        
+        logger.info(f"📁 Received file upload: filename='{file.filename}', content_type='{file.content_type}', size='{len(file_content)} bytes'")
+        
+        # Validate file type
+        allowed_types = ['.txt', '.md', '.docx', '.pdf', '.png', '.jpg', '.jpeg', '.gif']
+        file_extension = pathlib.Path(file.filename).suffix.lower() if file.filename else ""
+        
+        if file_extension not in allowed_types:
+            logger.warning(f"⚠️ Invalid file type: {file_extension} for file {file.filename}")
+            error_message = ChatMessageError(
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content=f"File type {file_extension} not supported. Allowed types: {', '.join(allowed_types)}"
+            )
+            yield error_message
+            return
+                
+        document_type = get_document_type_from_filename(file.filename or "unknown.txt")
+        
+        if document_type != DocumentType.MARKDOWN and document_type != DocumentType.TXT:
+            status_message = ChatMessageStatus(
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content=f"Converting content from {document_type}...",
+                activity=ApiActivityType.CONVERTING
+            )
+            yield status_message
+            try:
+                md = MarkItDown(enable_plugins=False)  # Set to True to enable plugins
+                stream = io.BytesIO(file_content)
+                stream_info = StreamInfo(
+                    extension=file_extension,  # e.g., ".pdf"
+                    url=file.filename          # optional, helps with logging and guessing
+                )                
+                result = md.convert_stream(stream, stream_info=stream_info, output_format="markdown")
+                file_content = result.text_content
+                logger.info(f"✅ Converted {file.filename} to Markdown format")
+            except Exception as e:
+                error_message = ChatMessageError(
+                    session_id=MOCK_UUID,  # No session ID for document uploads
+                    content=f"Failed to convert {file.filename} to Markdown.",
+                )
+                yield error_message
+                logger.error(f"❌ Error converting {file.filename} to Markdown: {e}")
+                return
+            async for message in create_job_from_content(database=database, current_user=current_user, content=file_content):
+                yield message
+            return
+
+    try:
+        async def to_json(method):
+            try:
+                async for message in method:
+                    json_data = message.model_dump(mode='json', by_alias=True)
+                    json_str = json.dumps(json_data)
+                    yield f"data: {json_str}\n\n".encode("utf-8")
+            except Exception as e:
+                logger.error(backstory_traceback.format_exc())
+                logger.error(f"Error in to_json conversion: {e}")
+                return
+
+        return StreamingResponse(
+            to_json(upload_stream_generator(file_content)),
+            media_type="text/event-stream",
+            headers={
+                "Cache-Control": "no-cache, no-store, must-revalidate",
+                "Connection": "keep-alive",
+                "X-Accel-Buffering": "no",  # Nginx
+                "X-Content-Type-Options": "nosniff",
+                "Access-Control-Allow-Origin": "*",  # Adjust for your CORS needs
+                "Transfer-Encoding": "chunked",
+            },
+        )
+    except Exception as e:
+        logger.error(backstory_traceback.format_exc())
+        logger.error(f"❌ Document upload error: {e}")
+        return StreamingResponse(
+            iter([ChatMessageError(
+                session_id=MOCK_UUID,  # No session ID for document uploads
+                content="Failed to upload document"
+            )]),
+            media_type="text/event-stream"
+        )
+
 @api_router.get("/jobs/{job_id}")
 async def get_job(
     job_id: str = Path(...),
@@ -2931,6 +3070,49 @@ async def search_jobs(
             content=create_error_response("SEARCH_FAILED", str(e))
         )
 
+
+@api_router.delete("/jobs/{job_id}")
+async def delete_job(
+    job_id: str = Path(...),
+    admin_user = Depends(get_current_admin),
+    database: RedisDatabase = Depends(get_database)
+):
+    """Delete a Job"""
+    try:
+        # Check if admin user
+        if not admin_user.is_admin:
+            logger.warning(f"⚠️ Unauthorized delete attempt by user {admin_user.id}")
+            return JSONResponse(
+                status_code=403,
+                content=create_error_response("FORBIDDEN", "Only admins can delete")
+            )
+        
+        # Get candidate data
+        job_data = await database.get_job(job_id)
+        if not job_data:
+            logger.warning(f"⚠️ Candidate not found for deletion: {job_id}")
+            return JSONResponse(
+                status_code=404,
+                content=create_error_response("NOT_FOUND", "Job not found")
+            )
+
+        # Delete job from database
+        await database.delete_job(job_id)
+        
+        logger.info(f"🗑️ Job deleted: {job_id} by admin {admin_user.id}")
+        
+        return create_success_response({
+            "message": "Job deleted successfully",
+            "jobId": job_id
+        })
+        
+    except Exception as e:
+        logger.error(f"❌ Delete job error: {e}")
+        return JSONResponse(
+            status_code=500,
+            content=create_error_response("DELETE_ERROR", "Failed to delete job")
+        )
+
 # ============================
 # Chat Endpoints
 # ============================
@@ -3541,7 +3723,7 @@ async def get_candidate_skill_match(
     current_user = Depends(get_current_user),
     database: RedisDatabase = Depends(get_database)
 ):
-    """Get skill match for a candidate against a requirement"""
+    """Get skill match for a candidate against a requirement with caching"""
     try:
         # Find candidate by ID
         candidate_data = await database.get_candidate(candidate_id)
@@ -3553,34 +3735,89 @@ async def get_candidate_skill_match(
         
         candidate = Candidate.model_validate(candidate_data)
         
-        async with entities.get_candidate_entity(candidate=candidate) as candidate_entity:
+        # Create cache key for this specific candidate + requirement combination
-            logger.info(f"🔍 Running skill match for candidate {candidate_entity.username} against requirement: {requirement}")
+        cache_key = f"skill_match:{candidate_id}:{hash(requirement)}"
-            agent = candidate_entity.get_or_create_agent(agent_type=ChatContextType.SKILL_MATCH)
+        
-            if not agent:
+        # Get cached assessment if it exists
-                return JSONResponse(
+        cached_assessment = await database.get_cached_skill_match(cache_key)
-                    status_code=400,
+        
-                    content=create_error_response("AGENT_NOT_FOUND", "No skill match agent found for this candidate")
+        # Get the last update time for the candidate's skill information
-                )
+        candidate_skill_update_time = await database.get_candidate_skill_update_time(candidate_id)
-            # Entity automatically released when done
+        
-            skill_match = await get_last_item(
+        # Get the latest RAG data update time for the current user
-                agent.generate(
+        user_rag_update_time = await database.get_user_rag_update_time(current_user.id)
-                    llm=llm_manager.get_llm(),
+        
-                    model=defines.model,
+        # Determine if we need to regenerate the assessment
-                    session_id=MOCK_UUID,
+        should_regenerate = True
-                    prompt=requirement,
+        cached_date = None
+        
+        if cached_assessment:
+            cached_date = cached_assessment.get('cached_at')
+            if cached_date:
+                # Check if cached result is still valid
+                # Regenerate if:
+                # 1. Candidate skills were updated after cache date
+                # 2. User's RAG data was updated after cache date
+                if (not candidate_skill_update_time or cached_date >= candidate_skill_update_time) and \
+                   (not user_rag_update_time or cached_date >= user_rag_update_time):
+                    should_regenerate = False
+                    logger.info(f"🔄 Using cached skill match for candidate {candidate.id}")
+        
+        if should_regenerate:
+            logger.info(f"🔍 Generating new skill match for candidate {candidate.id} against requirement: {requirement}")
+            
+            async with entities.get_candidate_entity(candidate=candidate) as candidate_entity:
+                agent = candidate_entity.get_or_create_agent(agent_type=ChatContextType.SKILL_MATCH)
+                if not agent:
+                    return JSONResponse(
+                        status_code=400,
+                        content=create_error_response("AGENT_NOT_FOUND", "No skill match agent found for this candidate")
+                    )
+                
+                # Generate new skill match
+                skill_match = await get_last_item(
+                    agent.generate(
+                        llm=llm_manager.get_llm(),
+                        model=defines.model,
+                        session_id=MOCK_UUID,
+                        prompt=requirement,
                     ),
                 )
-            if skill_match is None:
+                
+                if skill_match is None:
+                    return JSONResponse(
+                        status_code=500,
+                        content=create_error_response("NO_MATCH", "No skill match found for the given requirement")
+                    )
+                
+                skill_match_data = json.loads(skill_match.content)
+                
+                # Cache the new assessment with current timestamp
+                cached_assessment = {
+                    "skill_match": skill_match_data,
+                    "cached_at": datetime.utcnow().isoformat(),
+                    "candidate_id": candidate_id,
+                    "requirement": requirement
+                }
+                
+                await database.cache_skill_match(cache_key, cached_assessment)
+                logger.info(f"💾 Cached new skill match for candidate {candidate.id}")
+                logger.info(f"✅ Skill match found for candidate {candidate.id}: {skill_match_data['evidence_strength']}")
+        else:
+            # Use cached result - we know cached_assessment is not None here
+            if cached_assessment is None:
                 return JSONResponse(
                     status_code=500,
-                    content=create_error_response("NO_MATCH", "No skill match found for the given requirement")
+                    content=create_error_response("CACHE_ERROR", "Unexpected cache state")
                 )
-            skill_match = json.loads(skill_match.content)
+            skill_match_data = cached_assessment["skill_match"]
-            logger.info(f"✅ Skill match found for candidate {candidate.id}: {skill_match['evidence_strength']}")
+            logger.info(f"✅ Retrieved cached skill match for candidate {candidate.id}: {skill_match_data['evidence_strength']}")
 
         return create_success_response({
             "candidateId": candidate.id,
-            "skillMatch": skill_match
+            "skillMatch": skill_match_data,
+            "cached": not should_regenerate,
+            "cacheTimestamp": cached_date
         })
         
     except Exception as e:
@@ -3589,8 +3826,8 @@ async def get_candidate_skill_match(
         return JSONResponse(
             status_code=500,
             content=create_error_response("SKILL_MATCH_ERROR", str(e))
-        )   
+        )
-
+            
 @api_router.get("/candidates/{username}/chat-sessions")
 async def get_candidate_chat_sessions(
     username: str = Path(...),
@@ -3911,7 +4148,6 @@ async def track_requests(request, call_next):
 # FastAPI Metrics
 # ============================
 prometheus_collector = CollectorRegistry()
-entities.entity_manager.initialize(prometheus_collector)
 
 # Keep the Instrumentator instance alive
 instrumentator = Instrumentator(

src/backend/models.py

@@ -768,11 +768,7 @@ class ChatOptions(BaseModel):
         "populate_by_name": True   # Allow both field names and aliases        
     }
 
-
+from llm_proxy import (LLMMessage)
-class LLMMessage(BaseModel):
-    role: str = Field(default="")
-    content: str = Field(default="")
-    tool_calls: Optional[List[Dict]] = Field(default=[], exclude=True)
 
 class ApiMessage(BaseModel):
     id: str = Field(default_factory=lambda: str(uuid.uuid4()))

src/backend/rag/rag.py

@@ -13,7 +13,6 @@ import numpy as np  # type: ignore
 import traceback
 
 import chromadb # type: ignore
-import ollama
 from watchdog.observers import Observer  # type: ignore
 from watchdog.events import FileSystemEventHandler  # type: ignore
 import umap # type: ignore
@@ -27,6 +26,7 @@ from .markdown_chunker import (
 
 # When imported as a module, use relative imports
 import defines
+from database import RedisDatabase
 from models import ChromaDBGetResponse
 
 __all__ = ["ChromaDBFileWatcher", "start_file_watcher"]
@@ -47,11 +47,16 @@ class ChromaDBFileWatcher(FileSystemEventHandler):
         loop,
         persist_directory,
         collection_name,
+        database: RedisDatabase,
+        user_id: str,
         chunk_size=DEFAULT_CHUNK_SIZE,
         chunk_overlap=DEFAULT_CHUNK_OVERLAP,
         recreate=False,
     ):
         self.llm = llm
+        self.database = database
+        self.user_id = user_id
+        self.database = database
         self.watch_directory = watch_directory
         self.persist_directory = persist_directory or defines.persist_directory
         self.collection_name = collection_name
@@ -284,6 +289,7 @@ class ChromaDBFileWatcher(FileSystemEventHandler):
 
                 if results and "ids" in results and results["ids"]:
                     self.collection.delete(ids=results["ids"])
+                    await self.database.update_user_rag_timestamp(self.user_id)
                     logging.info(
                         f"Removed {len(results['ids'])} chunks for deleted file: {file_path}"
                     )
@@ -372,14 +378,15 @@ class ChromaDBFileWatcher(FileSystemEventHandler):
             name=self.collection_name, metadata={"hnsw:space": "cosine"}
         )
 
-    def get_embedding(self, text: str) -> np.ndarray:
+    async def get_embedding(self, text: str) -> np.ndarray:
         """Generate and normalize an embedding for the given text."""
         
         # Get embedding
         try:
-            response = self.llm.embeddings(model=defines.embedding_model, prompt=text)
+            response = await self.llm.embeddings(model=defines.embedding_model, input_texts=text)
-            embedding = np.array(response["embedding"])
+            embedding = np.array(response.get_single_embedding())
         except Exception as e:
+            logging.error(traceback.format_exc())
             logging.error(f"Failed to get embedding: {e}")
             raise
 
@@ -404,7 +411,7 @@ class ChromaDBFileWatcher(FileSystemEventHandler):
 
         return embedding
 
-    def add_embeddings_to_collection(self, chunks: List[Chunk]):
+    async def _add_embeddings_to_collection(self, chunks: List[Chunk]):
         """Add embeddings for chunks to the collection."""
 
         for i, chunk in enumerate(chunks):
@@ -420,7 +427,7 @@ class ChromaDBFileWatcher(FileSystemEventHandler):
             content_hash = hashlib.md5(text.encode()).hexdigest()[:8]
             chunk_id = f"{path_hash}_{i}_{content_hash}"
 
-            embedding = self.get_embedding(text)
+            embedding = await self.get_embedding(text)
             try:
                 self.collection.add(
                     ids=[chunk_id],
@@ -458,11 +465,11 @@ class ChromaDBFileWatcher(FileSystemEventHandler):
     # 0.5 - 0.7        0.65 - 0.75            Balanced precision/recall
     # 0.7 - 0.9        0.55 - 0.65            Higher recall, more inclusive
     # 0.9 - 1.2        0.40 - 0.55            Very inclusive, may include tangential content
-    def find_similar(self, query, top_k=defines.default_rag_top_k, threshold=defines.default_rag_threshold):
+    async def find_similar(self, query, top_k=defines.default_rag_top_k, threshold=defines.default_rag_threshold):
         """Find similar documents to the query."""
 
         # collection is configured with hnsw:space cosine
-        query_embedding = self.get_embedding(query)
+        query_embedding = await self.get_embedding(query)
         results = self.collection.query(
             query_embeddings=[query_embedding],
             n_results=top_k,
@@ -572,6 +579,7 @@ class ChromaDBFileWatcher(FileSystemEventHandler):
                 and existing_results["ids"]
             ):
                 self.collection.delete(ids=existing_results["ids"])
+                await self.database.update_user_rag_timestamp(self.user_id)
 
             extensions = (".docx", ".xlsx", ".xls", ".pdf")
             if file_path.endswith(extensions):
@@ -606,7 +614,8 @@ class ChromaDBFileWatcher(FileSystemEventHandler):
                 #     f.write(json.dumps(chunk, indent=2))
 
             # Add chunks to collection
-            self.add_embeddings_to_collection(chunks)
+            await self._add_embeddings_to_collection(chunks)
+            await self.database.update_user_rag_timestamp(self.user_id)
 
             logging.info(f"Updated {len(chunks)} chunks for file: {file_path}")
 
@@ -640,9 +649,11 @@ class ChromaDBFileWatcher(FileSystemEventHandler):
 # Function to start the file watcher
 def start_file_watcher(
     llm,
+    user_id,
     watch_directory,
     persist_directory,
     collection_name,
+    database: RedisDatabase,
     initialize=False,
     recreate=False,
 ):
@@ -663,9 +674,11 @@ def start_file_watcher(
         llm,
         watch_directory=watch_directory,
         loop=loop,
+        user_id=user_id,
         persist_directory=persist_directory,
         collection_name=collection_name,
         recreate=recreate,
+        database=database
     )
 
     # Process all files if:

src/multi-llm/config.md

@@ -0,0 +1,193 @@
+# Environment Configuration Examples
+
+# ==== Development (Ollama only) ====
+export OLLAMA_HOST="http://localhost:11434"
+export DEFAULT_LLM_PROVIDER="ollama"
+
+# ==== Production with OpenAI ====
+export OPENAI_API_KEY="sk-your-openai-key-here"
+export DEFAULT_LLM_PROVIDER="openai"
+
+# ==== Production with Anthropic ====
+export ANTHROPIC_API_KEY="sk-ant-your-anthropic-key-here"
+export DEFAULT_LLM_PROVIDER="anthropic"
+
+# ==== Production with multiple providers ====
+export OPENAI_API_KEY="sk-your-openai-key-here"
+export ANTHROPIC_API_KEY="sk-ant-your-anthropic-key-here"
+export GEMINI_API_KEY="your-gemini-key-here"
+export OLLAMA_HOST="http://ollama-server:11434"
+export DEFAULT_LLM_PROVIDER="openai"
+
+# ==== Docker Compose Example ====
+# docker-compose.yml
+version: '3.8'
+services:
+  api:
+    build: .
+    ports:
+      - "8000:8000"
+    environment:
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
+      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
+      - DEFAULT_LLM_PROVIDER=openai
+    depends_on:
+      - ollama
+
+  ollama:
+    image: ollama/ollama
+    ports:
+      - "11434:11434"
+    volumes:
+      - ollama_data:/root/.ollama
+    environment:
+      - OLLAMA_HOST=0.0.0.0
+
+volumes:
+  ollama_data:
+
+# ==== Kubernetes ConfigMap ====
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: llm-config
+data:
+  DEFAULT_LLM_PROVIDER: "openai"
+  OLLAMA_HOST: "http://ollama-service:11434"
+
+---
+apiVersion: v1
+kind: Secret
+metadata:
+  name: llm-secrets
+type: Opaque
+stringData:
+  OPENAI_API_KEY: "sk-your-key-here"
+  ANTHROPIC_API_KEY: "sk-ant-your-key-here"
+
+# ==== Python configuration example ====
+# config.py
+import os
+from llm_proxy import get_llm, LLMProvider
+
+def configure_llm_for_environment():
+    """Configure LLM based on deployment environment"""
+    llm = get_llm()
+    
+    # Development: Use Ollama
+    if os.getenv('ENVIRONMENT') == 'development':
+        llm.configure_provider(LLMProvider.OLLAMA, host='http://localhost:11434')
+        llm.set_default_provider(LLMProvider.OLLAMA)
+    
+    # Staging: Use OpenAI with rate limiting
+    elif os.getenv('ENVIRONMENT') == 'staging':
+        llm.configure_provider(LLMProvider.OPENAI, 
+                              api_key=os.getenv('OPENAI_API_KEY'),
+                              max_retries=3,
+                              timeout=30)
+        llm.set_default_provider(LLMProvider.OPENAI)
+    
+    # Production: Use multiple providers with fallback
+    elif os.getenv('ENVIRONMENT') == 'production':
+        # Primary: Anthropic
+        llm.configure_provider(LLMProvider.ANTHROPIC,
+                              api_key=os.getenv('ANTHROPIC_API_KEY'))
+        
+        # Fallback: OpenAI
+        llm.configure_provider(LLMProvider.OPENAI,
+                              api_key=os.getenv('OPENAI_API_KEY'))
+        
+        # Set primary provider
+        llm.set_default_provider(LLMProvider.ANTHROPIC)
+
+# ==== Usage Examples ====
+
+# Example 1: Basic usage with default provider
+async def basic_example():
+    llm = get_llm()
+    
+    response = await llm.chat(
+        model="gpt-4",
+        messages=[{"role": "user", "content": "Hello!"}]
+    )
+    print(response.content)
+
+# Example 2: Specify provider explicitly
+async def provider_specific_example():
+    llm = get_llm()
+    
+    # Use OpenAI specifically
+    response = await llm.chat(
+        model="gpt-4",
+        messages=[{"role": "user", "content": "Hello!"}],
+        provider=LLMProvider.OPENAI
+    )
+    
+    # Use Anthropic specifically
+    response2 = await llm.chat(
+        model="claude-3-sonnet-20240229",
+        messages=[{"role": "user", "content": "Hello!"}],
+        provider=LLMProvider.ANTHROPIC
+    )
+
+# Example 3: Streaming with provider fallback
+async def streaming_with_fallback():
+    llm = get_llm()
+    
+    try:
+        async for chunk in llm.chat(
+            model="claude-3-sonnet-20240229",
+            messages=[{"role": "user", "content": "Write a story"}],
+            provider=LLMProvider.ANTHROPIC,
+            stream=True
+        ):
+            print(chunk.content, end='', flush=True)
+    except Exception as e:
+        print(f"Primary provider failed: {e}")
+        # Fallback to OpenAI
+        async for chunk in llm.chat(
+            model="gpt-4",
+            messages=[{"role": "user", "content": "Write a story"}],
+            provider=LLMProvider.OPENAI,
+            stream=True
+        ):
+            print(chunk.content, end='', flush=True)
+
+# Example 4: Load balancing between providers
+import random
+
+async def load_balanced_request():
+    llm = get_llm()
+    available_providers = [LLMProvider.OPENAI, LLMProvider.ANTHROPIC]
+    
+    # Simple random load balancing
+    provider = random.choice(available_providers)
+    
+    # Adjust model based on provider
+    model_mapping = {
+        LLMProvider.OPENAI: "gpt-4",
+        LLMProvider.ANTHROPIC: "claude-3-sonnet-20240229"
+    }
+    
+    response = await llm.chat(
+        model=model_mapping[provider],
+        messages=[{"role": "user", "content": "Hello!"}],
+        provider=provider
+    )
+    
+    print(f"Response from {provider.value}: {response.content}")
+
+# ==== FastAPI Startup Configuration ====
+from fastapi import FastAPI
+
+app = FastAPI()
+
+@app.on_event("startup")
+async def startup_event():
+    """Configure LLM providers on application startup"""
+    configure_llm_for_environment()
+    
+    # Verify configuration
+    llm = get_llm()
+    print(f"Configured providers: {[p.value for p in llm._initialized_providers]}")
+    print(f"Default provider: {llm.default_provider.value}")

src/multi-llm/example.py

@@ -0,0 +1,238 @@
+from fastapi import FastAPI, HTTPException # type: ignore
+from fastapi.responses import StreamingResponse # type: ignore
+from pydantic import BaseModel # type: ignore
+from typing import List, Optional, Dict, Any
+import json
+import asyncio
+from llm_proxy import get_llm, LLMProvider, ChatMessage
+
+app = FastAPI(title="Unified LLM API")
+
+# Pydantic models for request/response
+class ChatRequest(BaseModel):
+    model: str
+    messages: List[Dict[str, str]]
+    provider: Optional[str] = None
+    stream: bool = False
+    max_tokens: Optional[int] = None
+    temperature: Optional[float] = None
+
+class GenerateRequest(BaseModel):
+    model: str
+    prompt: str
+    provider: Optional[str] = None
+    stream: bool = False
+    max_tokens: Optional[int] = None
+    temperature: Optional[float] = None
+
+class ChatResponseModel(BaseModel):
+    content: str
+    model: str
+    provider: str
+    finish_reason: Optional[str] = None
+    usage: Optional[Dict[str, int]] = None
+
+@app.post("/chat")
+async def chat_endpoint(request: ChatRequest):
+    """Chat endpoint that works with any configured LLM provider"""
+    try:
+        llm = get_llm()
+        
+        # Convert provider string to enum if provided
+        provider = None
+        if request.provider:
+            try:
+                provider = LLMProvider(request.provider.lower())
+            except ValueError:
+                raise HTTPException(
+                    status_code=400, 
+                    detail=f"Unsupported provider: {request.provider}"
+                )
+        
+        # Prepare kwargs
+        kwargs = {}
+        if request.max_tokens:
+            kwargs['max_tokens'] = request.max_tokens
+        if request.temperature:
+            kwargs['temperature'] = request.temperature
+        
+        if request.stream:
+            async def generate_response():
+                try:
+                    # Use the type-safe streaming method
+                    async for chunk in llm.chat_stream(
+                        model=request.model,
+                        messages=request.messages,
+                        provider=provider,
+                        **kwargs
+                    ):
+                        # Format as Server-Sent Events
+                        data = {
+                            "content": chunk.content,
+                            "model": chunk.model,
+                            "provider": provider.value if provider else llm.default_provider.value,
+                            "finish_reason": chunk.finish_reason
+                        }
+                        yield f"data: {json.dumps(data)}\n\n"
+                except Exception as e:
+                    error_data = {"error": str(e)}
+                    yield f"data: {json.dumps(error_data)}\n\n"
+                finally:
+                    yield "data: [DONE]\n\n"
+            
+            return StreamingResponse(
+                generate_response(),
+                media_type="text/event-stream",
+                headers={
+                    "Cache-Control": "no-cache",
+                    "Connection": "keep-alive"
+                }
+            )
+        else:
+            # Use the type-safe single response method
+            response = await llm.chat_single(
+                model=request.model,
+                messages=request.messages,
+                provider=provider,
+                **kwargs
+            )
+            
+            return ChatResponseModel(
+                content=response.content,
+                model=response.model,
+                provider=provider.value if provider else llm.default_provider.value,
+                finish_reason=response.finish_reason,
+                usage=response.usage
+            )
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/generate")
+async def generate_endpoint(request: GenerateRequest):
+    """Generate endpoint for simple text generation"""
+    try:
+        llm = get_llm()
+        
+        provider = None
+        if request.provider:
+            try:
+                provider = LLMProvider(request.provider.lower())
+            except ValueError:
+                raise HTTPException(
+                    status_code=400, 
+                    detail=f"Unsupported provider: {request.provider}"
+                )
+        
+        kwargs = {}
+        if request.max_tokens:
+            kwargs['max_tokens'] = request.max_tokens
+        if request.temperature:
+            kwargs['temperature'] = request.temperature
+        
+        if request.stream:
+            async def generate_response():
+                try:
+                    # Use the type-safe streaming method
+                    async for chunk in llm.generate_stream(
+                        model=request.model,
+                        prompt=request.prompt,
+                        provider=provider,
+                        **kwargs
+                    ):
+                        data = {
+                            "content": chunk.content,
+                            "model": chunk.model,
+                            "provider": provider.value if provider else llm.default_provider.value,
+                            "finish_reason": chunk.finish_reason
+                        }
+                        yield f"data: {json.dumps(data)}\n\n"
+                except Exception as e:
+                    error_data = {"error": str(e)}
+                    yield f"data: {json.dumps(error_data)}\n\n"
+                finally:
+                    yield "data: [DONE]\n\n"
+            
+            return StreamingResponse(
+                generate_response(),
+                media_type="text/event-stream"
+            )
+        else:
+            # Use the type-safe single response method
+            response = await llm.generate_single(
+                model=request.model,
+                prompt=request.prompt,
+                provider=provider,
+                **kwargs
+            )
+            
+            return ChatResponseModel(
+                content=response.content,
+                model=response.model,
+                provider=provider.value if provider else llm.default_provider.value,
+                finish_reason=response.finish_reason,
+                usage=response.usage
+            )
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/models")
+async def list_models(provider: Optional[str] = None):
+    """List available models for a provider"""
+    try:
+        llm = get_llm()
+        
+        provider_enum = None
+        if provider:
+            try:
+                provider_enum = LLMProvider(provider.lower())
+            except ValueError:
+                raise HTTPException(
+                    status_code=400, 
+                    detail=f"Unsupported provider: {provider}"
+                )
+        
+        models = await llm.list_models(provider_enum)
+        return {
+            "provider": provider_enum.value if provider_enum else llm.default_provider.value,
+            "models": models
+        }
+    
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/providers")
+async def list_providers():
+    """List all configured providers"""
+    llm = get_llm()
+    return {
+        "providers": [provider.value for provider in llm._initialized_providers],
+        "default": llm.default_provider.value
+    }
+
+@app.post("/providers/{provider}/set-default")
+async def set_default_provider(provider: str):
+    """Set the default provider"""
+    try:
+        llm = get_llm()
+        provider_enum = LLMProvider(provider.lower())
+        llm.set_default_provider(provider_enum)
+        return {"message": f"Default provider set to {provider}", "default": provider}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+# Health check endpoint
+@app.get("/health")
+async def health_check():
+    """Health check endpoint"""
+    llm = get_llm()
+    return {
+        "status": "healthy",
+        "providers_configured": len(llm._initialized_providers),
+        "default_provider": llm.default_provider.value
+    }
+
+if __name__ == "__main__":
+    import uvicorn # type: ignore
+    uvicorn.run(app, host="0.0.0.0", port=8000)

src/multi-llm/llm_proxy.py

@@ -0,0 +1,606 @@
+from abc import ABC, abstractmethod
+from typing import Dict, List, Any, AsyncGenerator, Optional, Union
+from enum import Enum
+import asyncio
+import json
+from dataclasses import dataclass
+import os
+
+# Standard message format for all providers
+@dataclass
+class ChatMessage:
+    role: str  # "user", "assistant", "system"
+    content: str
+    
+    def __getitem__(self, key: str):
+        """Allow dictionary-style access for backward compatibility"""
+        if key == 'role':
+            return self.role
+        elif key == 'content':
+            return self.content
+        else:
+            raise KeyError(f"'{key}' not found in ChatMessage")
+    
+    def __setitem__(self, key: str, value: str):
+        """Allow dictionary-style assignment"""
+        if key == 'role':
+            self.role = value
+        elif key == 'content':
+            self.content = value
+        else:
+            raise KeyError(f"'{key}' not found in ChatMessage")
+    
+    @classmethod
+    def from_dict(cls, data: Dict[str, str]) -> 'ChatMessage':
+        """Create ChatMessage from dictionary"""
+        return cls(role=data['role'], content=data['content'])
+    
+    def to_dict(self) -> Dict[str, str]:
+        """Convert ChatMessage to dictionary"""
+        return {'role': self.role, 'content': self.content}
+
+@dataclass
+class ChatResponse:
+    content: str
+    model: str
+    finish_reason: Optional[str] = None
+    usage: Optional[Dict[str, int]] = None
+
+class LLMProvider(Enum):
+    OLLAMA = "ollama"
+    OPENAI = "openai"
+    ANTHROPIC = "anthropic"
+    GEMINI = "gemini"
+    GROK = "grok"
+
+class BaseLLMAdapter(ABC):
+    """Abstract base class for all LLM adapters"""
+    
+    def __init__(self, **config):
+        self.config = config
+    
+    @abstractmethod
+    async def chat(
+        self, 
+        model: str, 
+        messages: List[ChatMessage], 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        """Send chat messages and get response"""
+        pass
+    
+    @abstractmethod
+    async def generate(
+        self, 
+        model: str, 
+        prompt: str, 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        """Generate text from prompt"""
+        pass
+    
+    @abstractmethod
+    async def list_models(self) -> List[str]:
+        """List available models"""
+        pass
+
+class OllamaAdapter(BaseLLMAdapter):
+    """Adapter for Ollama"""
+    
+    def __init__(self, **config):
+        super().__init__(**config)
+        import ollama
+        self.client = ollama.AsyncClient( # type: ignore
+            host=config.get('host', 'http://localhost:11434')
+        )
+    
+    async def chat(
+        self, 
+        model: str, 
+        messages: List[ChatMessage], 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        
+        # Convert ChatMessage objects to Ollama format
+        ollama_messages = []
+        for msg in messages:
+            ollama_messages.append({
+                "role": msg.role, 
+                "content": msg.content
+            })
+        
+        if stream:
+            return self._stream_chat(model, ollama_messages, **kwargs)
+        else:
+            response = await self.client.chat(
+                model=model, 
+                messages=ollama_messages, 
+                stream=False,
+                **kwargs
+            )
+            return ChatResponse(
+                content=response['message']['content'],
+                model=model,
+                finish_reason=response.get('done_reason')
+            )
+    
+    async def _stream_chat(self, model: str, messages: List[Dict], **kwargs):
+        async for chunk in self.client.chat(
+            model=model, 
+            messages=messages, 
+            stream=True,
+            **kwargs
+        ):
+            if chunk.get('message', {}).get('content'):
+                yield ChatResponse(
+                    content=chunk['message']['content'],
+                    model=model,
+                    finish_reason=chunk.get('done_reason')
+                )
+    
+    async def generate(
+        self, 
+        model: str, 
+        prompt: str, 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        
+        if stream:
+            return self._stream_generate(model, prompt, **kwargs)
+        else:
+            response = await self.client.generate(
+                model=model, 
+                prompt=prompt, 
+                stream=False,
+                **kwargs
+            )
+            return ChatResponse(
+                content=response['response'],
+                model=model,
+                finish_reason=response.get('done_reason')
+            )
+    
+    async def _stream_generate(self, model: str, prompt: str, **kwargs):
+        async for chunk in self.client.generate(
+            model=model, 
+            prompt=prompt, 
+            stream=True,
+            **kwargs
+        ):
+            if chunk.get('response'):
+                yield ChatResponse(
+                    content=chunk['response'],
+                    model=model,
+                    finish_reason=chunk.get('done_reason')
+                )
+    
+    async def list_models(self) -> List[str]:
+        models = await self.client.list()
+        return [model['name'] for model in models['models']]
+
+class OpenAIAdapter(BaseLLMAdapter):
+    """Adapter for OpenAI"""
+    
+    def __init__(self, **config):
+        super().__init__(**config)
+        import openai # type: ignore
+        self.client = openai.AsyncOpenAI(
+            api_key=config.get('api_key', os.getenv('OPENAI_API_KEY'))
+        )
+    
+    async def chat(
+        self, 
+        model: str, 
+        messages: List[ChatMessage], 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        
+        # Convert ChatMessage objects to OpenAI format
+        openai_messages = []
+        for msg in messages:
+            openai_messages.append({
+                "role": msg.role, 
+                "content": msg.content
+            })
+        
+        if stream:
+            return self._stream_chat(model, openai_messages, **kwargs)
+        else:
+            response = await self.client.chat.completions.create(
+                model=model,
+                messages=openai_messages,
+                stream=False,
+                **kwargs
+            )
+            return ChatResponse(
+                content=response.choices[0].message.content,
+                model=model,
+                finish_reason=response.choices[0].finish_reason,
+                usage=response.usage.model_dump() if response.usage else None
+            )
+    
+    async def _stream_chat(self, model: str, messages: List[Dict], **kwargs):
+        async for chunk in await self.client.chat.completions.create(
+            model=model,
+            messages=messages,
+            stream=True,
+            **kwargs
+        ):
+            if chunk.choices[0].delta.content:
+                yield ChatResponse(
+                    content=chunk.choices[0].delta.content,
+                    model=model,
+                    finish_reason=chunk.choices[0].finish_reason
+                )
+    
+    async def generate(
+        self, 
+        model: str, 
+        prompt: str, 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        
+        # Convert to chat format for OpenAI
+        messages = [ChatMessage(role="user", content=prompt)]
+        return await self.chat(model, messages, stream, **kwargs)
+    
+    async def list_models(self) -> List[str]:
+        models = await self.client.models.list()
+        return [model.id for model in models.data]
+
+class AnthropicAdapter(BaseLLMAdapter):
+    """Adapter for Anthropic Claude"""
+    
+    def __init__(self, **config):
+        super().__init__(**config)
+        import anthropic # type: ignore
+        self.client = anthropic.AsyncAnthropic(
+            api_key=config.get('api_key', os.getenv('ANTHROPIC_API_KEY'))
+        )
+    
+    async def chat(
+        self, 
+        model: str, 
+        messages: List[ChatMessage], 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        
+        # Anthropic requires system message to be separate
+        system_message = None
+        anthropic_messages = []
+        
+        for msg in messages:
+            if msg.role == "system":
+                system_message = msg.content
+            else:
+                anthropic_messages.append({
+                    "role": msg.role, 
+                    "content": msg.content
+                })
+        
+        request_kwargs = {
+            "model": model,
+            "messages": anthropic_messages,
+            "max_tokens": kwargs.pop('max_tokens', 1000),
+            **kwargs
+        }
+        
+        if system_message:
+            request_kwargs["system"] = system_message
+        
+        if stream:
+            return self._stream_chat(**request_kwargs)
+        else:
+            response = await self.client.messages.create(
+                stream=False,
+                **request_kwargs
+            )
+            return ChatResponse(
+                content=response.content[0].text,
+                model=model,
+                finish_reason=response.stop_reason,
+                usage={
+                    "input_tokens": response.usage.input_tokens,
+                    "output_tokens": response.usage.output_tokens
+                }
+            )
+    
+    async def _stream_chat(self, **kwargs):
+        async with self.client.messages.stream(**kwargs) as stream:
+            async for text in stream.text_stream:
+                yield ChatResponse(
+                    content=text,
+                    model=kwargs['model']
+                )
+    
+    async def generate(
+        self, 
+        model: str, 
+        prompt: str, 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        
+        messages = [ChatMessage(role="user", content=prompt)]
+        return await self.chat(model, messages, stream, **kwargs)
+    
+    async def list_models(self) -> List[str]:
+        # Anthropic doesn't have a list models endpoint, return known models
+        return [
+            "claude-3-5-sonnet-20241022",
+            "claude-3-5-haiku-20241022", 
+            "claude-3-opus-20240229"
+        ]
+
+class GeminiAdapter(BaseLLMAdapter):
+    """Adapter for Google Gemini"""
+    
+    def __init__(self, **config):
+        super().__init__(**config)
+        import google.generativeai as genai # type: ignore
+        genai.configure(api_key=config.get('api_key', os.getenv('GEMINI_API_KEY')))
+        self.genai = genai
+    
+    async def chat(
+        self, 
+        model: str, 
+        messages: List[ChatMessage], 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        
+        model_instance = self.genai.GenerativeModel(model)
+        
+        # Convert messages to Gemini format
+        chat_history = []
+        current_message = None
+        
+        for i, msg in enumerate(messages[:-1]):  # All but last message go to history
+            if msg.role == "user":
+                chat_history.append({"role": "user", "parts": [msg.content]})
+            elif msg.role == "assistant":
+                chat_history.append({"role": "model", "parts": [msg.content]})
+        
+        # Last message is the current prompt
+        if messages:
+            current_message = messages[-1].content
+        
+        chat = model_instance.start_chat(history=chat_history)
+        
+        if not current_message:
+            raise ValueError("No current message provided for chat")
+        
+        if stream:
+            return self._stream_chat(chat, current_message, **kwargs)
+        else:
+            response = await chat.send_message_async(current_message, **kwargs)
+            return ChatResponse(
+                content=response.text,
+                model=model,
+                finish_reason=response.candidates[0].finish_reason.name if response.candidates else None
+            )
+    
+    async def _stream_chat(self, chat, message: str, **kwargs):
+        async for chunk in chat.send_message_async(message, stream=True, **kwargs):
+            if chunk.text:
+                yield ChatResponse(
+                    content=chunk.text,
+                    model=chat.model.model_name
+                )
+    
+    async def generate(
+        self, 
+        model: str, 
+        prompt: str, 
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        
+        messages = [ChatMessage(role="user", content=prompt)]
+        return await self.chat(model, messages, stream, **kwargs)
+    
+    async def list_models(self) -> List[str]:
+        models = self.genai.list_models()
+        return [model.name for model in models if 'generateContent' in model.supported_generation_methods]
+
+class UnifiedLLMProxy:
+    """Main proxy class that provides unified interface to all LLM providers"""
+    
+    def __init__(self, default_provider: LLMProvider = LLMProvider.OLLAMA):
+        self.adapters: Dict[LLMProvider, BaseLLMAdapter] = {}
+        self.default_provider = default_provider
+        self._initialized_providers = set()
+    
+    def configure_provider(self, provider: LLMProvider, **config):
+        """Configure a specific provider with its settings"""
+        adapter_classes = {
+            LLMProvider.OLLAMA: OllamaAdapter,
+            LLMProvider.OPENAI: OpenAIAdapter,
+            LLMProvider.ANTHROPIC: AnthropicAdapter,
+            LLMProvider.GEMINI: GeminiAdapter,
+            # Add other providers as needed
+        }
+        
+        if provider in adapter_classes:
+            self.adapters[provider] = adapter_classes[provider](**config)
+            self._initialized_providers.add(provider)
+        else:
+            raise ValueError(f"Unsupported provider: {provider}")
+    
+    def set_default_provider(self, provider: LLMProvider):
+        """Set the default provider for requests"""
+        if provider not in self._initialized_providers:
+            raise ValueError(f"Provider {provider} not configured")
+        self.default_provider = provider
+    
+    async def chat(
+        self, 
+        model: str, 
+        messages: Union[List[ChatMessage], List[Dict[str, str]]], 
+        provider: Optional[LLMProvider] = None,
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        """Send chat messages using specified or default provider"""
+        
+        provider = provider or self.default_provider
+        adapter = self._get_adapter(provider)
+        
+        # Normalize messages to ChatMessage objects
+        normalized_messages = []
+        for msg in messages:
+            if isinstance(msg, ChatMessage):
+                normalized_messages.append(msg)
+            elif isinstance(msg, dict):
+                normalized_messages.append(ChatMessage.from_dict(msg))
+            else:
+                raise ValueError(f"Invalid message type: {type(msg)}")
+        
+        return await adapter.chat(model, normalized_messages, stream, **kwargs)
+    
+    async def chat_stream(
+        self, 
+        model: str, 
+        messages: Union[List[ChatMessage], List[Dict[str, str]]], 
+        provider: Optional[LLMProvider] = None,
+        **kwargs
+    ) -> AsyncGenerator[ChatResponse, None]:
+        """Stream chat messages using specified or default provider"""
+        
+        result = await self.chat(model, messages, provider, stream=True, **kwargs)
+        # Type checker now knows this is an AsyncGenerator due to stream=True
+        async for chunk in result:  # type: ignore
+            yield chunk
+    
+    async def chat_single(
+        self, 
+        model: str, 
+        messages: Union[List[ChatMessage], List[Dict[str, str]]], 
+        provider: Optional[LLMProvider] = None,
+        **kwargs
+    ) -> ChatResponse:
+        """Get single chat response using specified or default provider"""
+        
+        result = await self.chat(model, messages, provider, stream=False, **kwargs)
+        # Type checker now knows this is a ChatResponse due to stream=False
+        return result  # type: ignore
+    
+    async def generate(
+        self, 
+        model: str, 
+        prompt: str,
+        provider: Optional[LLMProvider] = None,
+        stream: bool = False,
+        **kwargs
+    ) -> Union[ChatResponse, AsyncGenerator[ChatResponse, None]]:
+        """Generate text using specified or default provider"""
+        
+        provider = provider or self.default_provider
+        adapter = self._get_adapter(provider)
+        
+        return await adapter.generate(model, prompt, stream, **kwargs)
+    
+    async def generate_stream(
+        self, 
+        model: str, 
+        prompt: str,
+        provider: Optional[LLMProvider] = None,
+        **kwargs
+    ) -> AsyncGenerator[ChatResponse, None]:
+        """Stream text generation using specified or default provider"""
+        
+        result = await self.generate(model, prompt, provider, stream=True, **kwargs)
+        async for chunk in result:  # type: ignore
+            yield chunk
+    
+    async def generate_single(
+        self, 
+        model: str, 
+        prompt: str,
+        provider: Optional[LLMProvider] = None,
+        **kwargs
+    ) -> ChatResponse:
+        """Get single generation response using specified or default provider"""
+        
+        result = await self.generate(model, prompt, provider, stream=False, **kwargs)
+        return result  # type: ignore
+    
+    async def list_models(self, provider: Optional[LLMProvider] = None) -> List[str]:
+        """List available models for specified or default provider"""
+        
+        provider = provider or self.default_provider
+        adapter = self._get_adapter(provider)
+        
+        return await adapter.list_models()
+    
+    def _get_adapter(self, provider: LLMProvider) -> BaseLLMAdapter:
+        """Get adapter for specified provider"""
+        if provider not in self.adapters:
+            raise ValueError(f"Provider {provider} not configured")
+        return self.adapters[provider]
+
+# Example usage and configuration
+class LLMManager:
+    """Singleton manager for the unified LLM proxy"""
+    
+    _instance = None
+    _proxy = None
+    
+    @classmethod
+    def get_instance(cls):
+        if cls._instance is None:
+            cls._instance = cls()
+        return cls._instance
+    
+    def __init__(self):
+        if LLMManager._proxy is None:
+            LLMManager._proxy = UnifiedLLMProxy()
+            self._configure_from_environment()
+    
+    def _configure_from_environment(self):
+        """Configure providers based on environment variables"""
+        
+        if not self._proxy:
+            raise RuntimeError("LLM proxy not initialized")
+
+        # Configure Ollama if available
+        ollama_host = os.getenv('OLLAMA_HOST', 'http://localhost:11434')
+        self._proxy.configure_provider(LLMProvider.OLLAMA, host=ollama_host)
+        
+        # Configure OpenAI if API key is available
+        if os.getenv('OPENAI_API_KEY'):
+            self._proxy.configure_provider(LLMProvider.OPENAI)
+        
+        # Configure Anthropic if API key is available
+        if os.getenv('ANTHROPIC_API_KEY'):
+            self._proxy.configure_provider(LLMProvider.ANTHROPIC)
+        
+        # Configure Gemini if API key is available
+        if os.getenv('GEMINI_API_KEY'):
+            self._proxy.configure_provider(LLMProvider.GEMINI)
+        
+        # Set default provider from environment or use Ollama
+        default_provider = os.getenv('DEFAULT_LLM_PROVIDER', 'ollama')
+        try:
+            self._proxy.set_default_provider(LLMProvider(default_provider))
+        except ValueError:
+            # Fallback to Ollama if specified provider not available
+            self._proxy.set_default_provider(LLMProvider.OLLAMA)
+    
+    def get_proxy(self) -> UnifiedLLMProxy:
+        """Get the unified LLM proxy instance"""
+        if not self._proxy:
+            raise RuntimeError("LLM proxy not initialized")
+        return self._proxy
+
+# Convenience function for easy access
+def get_llm() -> UnifiedLLMProxy:
+    """Get the configured LLM proxy"""
+    return LLMManager.get_instance().get_proxy()