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Code Issues 12 Actions Releases Activity

Chat and stuff is working again

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This commit is contained in:
James Ketr 2025-06-04 18:09:25 -07:00
parent efef926e45
commit 48e6eeaa71
24 changed files with 1053 additions and 1014 deletions
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39
frontend/src/components/Conversation.tsx
View File

@ -15,14 +15,14 @@ import { BackstoryElementProps } from './BackstoryTab';
import { connectionBase } from 'utils/Global';
import { useAuth } from "hooks/AuthContext";
import { StreamingResponse } from 'services/api-client';
import { ChatMessage, ChatMessageBase, ChatContext, ChatSession, ChatQuery, ChatMessageUser } from 'types/types';
import { ChatMessage, ChatContext, ChatSession, ChatQuery, ChatMessageUser, ChatMessageError, ChatMessageStreaming, ChatMessageStatus } from 'types/types';
import { PaginatedResponse } from 'types/conversion';
import './Conversation.css';
import { useSelectedCandidate } from 'hooks/GlobalContext';
const defaultMessage: ChatMessage = {
type: "preparing", status: "done", sender: "system", sessionId: "", timestamp: new Date(), content: ""
status: "done", type: "text", sessionId: "", timestamp: new Date(), content: "", role: "assistant"
};
const loadingMessage: ChatMessage = { ...defaultMessage, content: "Establishing connection with server..." };
@ -249,8 +249,7 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>((props: C
...conversationRef.current,
{
...defaultMessage,
type: 'user',
sender: 'user',
type: 'text',
content: query.prompt,
}
]);
@ -260,34 +259,30 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>((props: C
);
const chatMessage: ChatMessageUser = {
role: "user",
sessionId: chatSession.id,
content: query.prompt,
tunables: query.tunables,
status: "done",
type: "user",
sender: "user",
type: "text",
timestamp: new Date()
};
controllerRef.current = apiClient.sendMessageStream(chatMessage, {
onMessage: (msg: ChatMessageBase) => {
onMessage: (msg: ChatMessage) => {
console.log("onMessage:", msg);
if (msg.type === "response") {
setConversation([
...conversationRef.current,
msg
]);
setStreamingMessage(undefined);
setProcessingMessage(undefined);
setProcessing(false);
} else {
setProcessingMessage(msg);
}
setConversation([
...conversationRef.current,
msg
]);
setStreamingMessage(undefined);
setProcessingMessage(undefined);
setProcessing(false);
if (onResponse) {
onResponse(msg);
}
},
onError: (error: string | ChatMessageBase) => {
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) {
@ -298,12 +293,12 @@ const Conversation = forwardRef<ConversationHandle, ConversationProps>((props: C
setProcessingMessage({ ...defaultMessage, content: error as string });
}
},
onStreaming: (chunk: ChatMessageBase) => {
onStreaming: (chunk: ChatMessageStreaming) => {
console.log("onStreaming:", chunk);
setStreamingMessage({ ...defaultMessage, ...chunk });
},
onStatusChange: (status: string) => {
console.log("onStatusChange:", status);
onStatus: (status: ChatMessageStatus) => {
console.log("onStatus:", status);
},
onComplete: () => {
console.log("onComplete");

120
frontend/src/components/JobMatchAnalysis.tsx
View File

@ -20,7 +20,7 @@ import CheckCircleIcon from '@mui/icons-material/CheckCircle';
import ErrorIcon from '@mui/icons-material/Error';
import PendingIcon from '@mui/icons-material/Pending';
import WarningIcon from '@mui/icons-material/Warning';
import { Candidate, ChatMessage, ChatMessageBase, ChatMessageUser, ChatSession, JobRequirements, SkillMatch } from 'types/types';
import { Candidate, ChatMessage, ChatMessageError, ChatMessageStatus, ChatMessageStreaming, ChatMessageUser, ChatSession, JobRequirements, SkillMatch } from 'types/types';
import { useAuth } from 'hooks/AuthContext';
import { BackstoryPageProps } from './BackstoryTab';
import { toCamelCase } from 'types/conversion';
@ -31,8 +31,8 @@ interface JobAnalysisProps extends BackstoryPageProps {
candidate: Candidate;
}
const defaultMessage: ChatMessageUser = {
type: "preparing", status: "done", sender: "user", sessionId: "", timestamp: new Date(), content: ""
const defaultMessage: ChatMessage = {
status: "done", type: "text", sessionId: "", timestamp: new Date(), content: "", role: "assistant"
};
const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) => {
@ -62,23 +62,19 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
return;
}
const createSession = async () => {
try {
const session: ChatSession = await apiClient.createCandidateChatSession(
candidate.username,
'job_requirements',
`Generate requirements for ${job.title}`
);
setSnack("Job analysis session started");
try {
setCreatingSession(true);
apiClient.getOrCreateChatSession(candidate, `Generate requirements for ${candidate.fullName}`, 'job_requirements')
.then(session => {
setRequirementsSession(session);
} catch (error) {
console.log(error);
setSnack("Unable to create requirements session", "error");
}
setCreatingSession(false);
});
} catch (error) {
setSnack('Unable to load chat session', 'error');
} finally {
setCreatingSession(false);
};
setCreatingSession(true);
createSession();
}
}, [requirementsSession, apiClient, candidate]);
// Fetch initial requirements
@ -94,50 +90,48 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
apiClient.sendMessageStream(chatMessage, {
onMessage: (msg: ChatMessage) => {
console.log(`onMessage: ${msg.type}`, msg);
if (msg.type === "response") {
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);
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 | ChatMessageBase) => {
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) {
@ -147,11 +141,11 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
}
setLoadingRequirements(false);
},
onStreaming: (chunk: ChatMessageBase) => {
onStreaming: (chunk: ChatMessageStreaming) => {
// console.log("onStreaming:", chunk);
},
onStatusChange: (status: string) => {
console.log(`onStatusChange: ${status}`);
onStatus: (status: ChatMessageStatus) => {
console.log(`onStatus: ${status}`);
},
onComplete: () => {
console.log("onComplete");

46
frontend/src/components/Message.tsx
View File

@ -32,9 +32,9 @@ import { SetSnackType } from './Snack';
import { CopyBubble } from './CopyBubble';
import { Scrollable } from './Scrollable';
import { BackstoryElementProps } from './BackstoryTab';
import { ChatMessage, ChatSession, ChatMessageType, ChatMessageMetaData, ChromaDBGetResponse } from 'types/types';
import { ChatMessage, ChatSession, ChatMessageMetaData, ChromaDBGetResponse, ApiActivityType, ChatMessageUser, ChatMessageError, ChatMessageStatus, ChatSenderType } from 'types/types';
const getStyle = (theme: Theme, type: ChatMessageType): any => {
const getStyle = (theme: Theme, type: ApiActivityType | ChatSenderType | "error"): any => {
const defaultRadius = '16px';
const defaultStyle = {
padding: theme.spacing(1, 2),
@ -56,7 +56,7 @@ const getStyle = (theme: Theme, type: ChatMessageType): any => {
};
const styles: any = {
response: {
assistant: {
...defaultStyle,
backgroundColor: theme.palette.primary.main,
border: `1px solid ${theme.palette.secondary.main}`,
@ -90,9 +90,10 @@ const getStyle = (theme: Theme, type: ChatMessageType): any => {
boxShadow: '0 1px 3px rgba(216, 58, 58, 0.15)',
},
'fact-check': 'qualifications',
generating: 'status',
'job-description': 'content',
'job-requirements': 'qualifications',
info: {
information: {
...defaultStyle,
backgroundColor: '#BFD8D8',
border: `1px solid ${theme.palette.secondary.main}`,
@ -156,25 +157,29 @@ const getStyle = (theme: Theme, type: ChatMessageType): any => {
}
}
if (!(type in styles)) {
console.log(`Style does not exist for: ${type}`);
}
return styles[type];
}
const getIcon = (messageType: ChatMessageType): React.ReactNode | null => {
const getIcon = (activityType: ApiActivityType | ChatSenderType | "error"): React.ReactNode | null => {
const icons: any = {
error: <ErrorOutline color="error" />,
generating: <LocationSearchingIcon />,
info: <InfoOutline color="info" />,
information: <InfoOutline color="info" />,
preparing: <LocationSearchingIcon />,
processing: <LocationSearchingIcon />,
system: <Memory />,
thinking: <Psychology />,
tooling: <LocationSearchingIcon />,
};
return icons[messageType] || null;
return icons[activityType] || null;
}
interface MessageProps extends BackstoryElementProps {
message: ChatMessage,
message: ChatMessageUser | ChatMessage | ChatMessageError | ChatMessageStatus,
title?: string,
chatSession?: ChatSession,
className?: string,
@ -319,7 +324,7 @@ const MessageMeta = (props: MessageMetaProps) => {
};
interface MessageContainerProps {
type: ChatMessageType,
type: ApiActivityType | ChatSenderType | "error",
metadataView?: React.ReactNode | null,
messageView?: React.ReactNode | null,
sx?: SxProps<Theme>,
@ -360,13 +365,21 @@ const Message = (props: MessageProps) => {
setSnack
};
const theme = useTheme();
const style: any = getStyle(theme, message.type);
const type: ApiActivityType | ChatSenderType | "error" = ('activity' in message) ? message.activity : ('error' in message) ? 'error' : (message as ChatMessage).role;
const style: any = getStyle(theme, type);
const handleMetaExpandClick = () => {
setMetaExpanded(!metaExpanded);
};
const content = message.content?.trim();
let content;
if (typeof (message.content) === "string") {
content = message.content.trim();
} else {
console.error(`message content is not a string`);
return (<></>)
}
if (!content) {
return (<></>)
};
@ -376,7 +389,8 @@ const Message = (props: MessageProps) => {
);
let metadataView = (<></>);
if (message.metadata) {
const metadata: ChatMessageMetaData | null = ('metadata' in message) ? (message.metadata as ChatMessageMetaData || null) : null;
if (metadata) {
metadataView = (
<Box sx={{ display: "flex", flexDirection: "column", width: "100%" }}>
<Box sx={{ display: "flex", alignItems: "center", gap: 1, flexDirection: "row" }}>
@ -395,18 +409,18 @@ const Message = (props: MessageProps) => {
</Box>
<Collapse in={metaExpanded} timeout="auto" unmountOnExit>
<CardContent>
<MessageMeta messageProps={props} metadata={message.metadata} />
<MessageMeta messageProps={props} metadata={metadata} />
</CardContent>
</Collapse>
</Box>);
}
const copyContent = message.sender === 'assistant' ? message.content : undefined;
const copyContent = (type === 'assistant') ? message.content : undefined;
if (!expandable) {
/* When not expandable, the styles are applied directly to MessageContainer */
return (<>
{messageView && <MessageContainer copyContent={copyContent} type={message.type} {...{ messageView, metadataView }} sx={{ ...style, ...sx }} />}
{messageView && <MessageContainer copyContent={copyContent} type={type} {...{ messageView, metadataView }} sx={{ ...style, ...sx }} />}
</>);
}
@ -435,7 +449,7 @@ const Message = (props: MessageProps) => {
{title || ''}
</AccordionSummary>
<AccordionDetails sx={{ mt: 0, mb: 0, p: 0, pl: 2, pr: 2 }}>
<MessageContainer copyContent={copyContent} type={message.type} {...{ messageView, metadataView }} />
<MessageContainer copyContent={copyContent} type={type} {...{ messageView, metadataView }} />
</AccordionDetails>
</Accordion>
);

5
frontend/src/components/VectorVisualizer.tsx
View File

@ -25,11 +25,6 @@ import { useAuth } from 'hooks/AuthContext';
import * as Types from 'types/types';
import { useSelectedCandidate } from 'hooks/GlobalContext';
import { useNavigate } from 'react-router-dom';
import { Message } from './Message';
const defaultMessage: Types.ChatMessageBase = {
type: "preparing", status: "done", sender: "system", sessionId: "", timestamp: new Date(), content: ""
};
interface VectorVisualizerProps extends BackstoryPageProps {
inline?: boolean;

93
frontend/src/pages/CandidateChatPage.tsx
View File

@ -12,7 +12,7 @@ import {
Send as SendIcon
} from '@mui/icons-material';
import { useAuth } from 'hooks/AuthContext';
import { ChatMessageBase, ChatMessage, ChatSession, ChatMessageUser } from 'types/types';
import { ChatMessage, ChatSession, ChatMessageUser, ChatMessageError, ChatMessageStreaming, ChatMessageStatus } from 'types/types';
import { ConversationHandle } from 'components/Conversation';
import { BackstoryPageProps } from 'components/BackstoryTab';
import { Message } from 'components/Message';
@ -25,7 +25,7 @@ import { BackstoryTextField, BackstoryTextFieldRef } from 'components/BackstoryT
import { BackstoryQuery } from 'components/BackstoryQuery';
const defaultMessage: ChatMessage = {
type: "preparing", status: "done", sender: "system", sessionId: "", timestamp: new Date(), content: ""
status: "done", type: "text", sessionId: "", timestamp: new Date(), content: "", role: "user"
};
const CandidateChatPage = forwardRef<ConversationHandle, BackstoryPageProps>((props: BackstoryPageProps, ref) => {
@ -33,7 +33,7 @@ const CandidateChatPage = forwardRef<ConversationHandle, BackstoryPageProps>((pr
const navigate = useNavigate();
const { selectedCandidate } = useSelectedCandidate()
const theme = useTheme();
const [processingMessage, setProcessingMessage] = useState<ChatMessage | null>(null);
const [processingMessage, setProcessingMessage] = useState<ChatMessageStatus | ChatMessageError | null>(null);
const [streamingMessage, setStreamingMessage] = useState<ChatMessage | null>(null);
const backstoryTextRef = useRef<BackstoryTextFieldRef>(null);
@ -48,32 +48,6 @@ const CandidateChatPage = forwardRef<ConversationHandle, BackstoryPageProps>((pr
const [streaming, setStreaming] = useState<boolean>(false);
const messagesEndRef = useRef(null);
// Load sessions for the selectedCandidate
const loadSessions = async () => {
if (!selectedCandidate) return;
try {
setLoading(true);
const result = await apiClient.getCandidateChatSessions(selectedCandidate.username);
let session = null;
if (result.sessions.data.length === 0) {
session = await apiClient.createCandidateChatSession(
selectedCandidate.username,
'candidate_chat',
`Backstory chat about ${selectedCandidate.fullName}`
);
} else {
session = result.sessions.data[0];
}
setChatSession(session);
setLoading(false);
} catch (error) {
setSnack('Unable to load chat session', 'error');
} finally {
setLoading(false);
}
};
// Load messages for current session
const loadMessages = async () => {
if (!chatSession?.id) return;
@ -107,20 +81,22 @@ const CandidateChatPage = forwardRef<ConversationHandle, BackstoryPageProps>((pr
// Send message
const sendMessage = async (message: string) => {
if (!message.trim() || !chatSession?.id || streaming) return;
if (!message.trim() || !chatSession?.id || streaming || !selectedCandidate) return;
const messageContent = message;
setStreaming(true);
const chatMessage: ChatMessageUser = {
sessionId: chatSession.id,
role: "user",
content: messageContent,
status: "done",
type: "user",
sender: "user",
type: "text",
timestamp: new Date()
};
setProcessingMessage({ ...defaultMessage, status: 'status', content: `Establishing connection with ${selectedCandidate.firstName}'s chat session.` });
setMessages(prev => {
const filtered = prev.filter((m: any) => m.id !== chatMessage.id);
return [...filtered, chatMessage] as any;
@ -129,34 +105,31 @@ const CandidateChatPage = forwardRef<ConversationHandle, BackstoryPageProps>((pr
try {
apiClient.sendMessageStream(chatMessage, {
onMessage: (msg: ChatMessage) => {
console.log(`onMessage: ${msg.type} ${msg.content}`, msg);
if (msg.type === "response") {
setMessages(prev => {
const filtered = prev.filter((m: any) => m.id !== msg.id);
return [...filtered, msg] as any;
});
setStreamingMessage(null);
setProcessingMessage(null);
} else {
setProcessingMessage(msg);
}
setMessages(prev => {
const filtered = prev.filter((m: any) => m.id !== msg.id);
return [...filtered, msg] as any;
});
setStreamingMessage(null);
setProcessingMessage(null);
},
onError: (error: string | ChatMessageBase) => {
onError: (error: string | ChatMessageError) => {
console.log("onError:", error);
let message: string;
// Type-guard to determine if this is a ChatMessageBase or a string
if (typeof error === "object" && error !== null && "content" in error) {
setProcessingMessage(error as ChatMessage);
setProcessingMessage(error);
message = error.content as string;
} else {
setProcessingMessage({ ...defaultMessage, content: error as string });
setProcessingMessage({ ...defaultMessage, status: "error", content: error })
}
setStreaming(false);
},
onStreaming: (chunk: ChatMessageBase) => {
onStreaming: (chunk: ChatMessageStreaming) => {
// console.log("onStreaming:", chunk);
setStreamingMessage({ ...defaultMessage, ...chunk });
setStreamingMessage({ ...chunk, role: 'assistant' });
},
onStatusChange: (status: string) => {
console.log(`onStatusChange: ${status}`);
onStatus: (status: ChatMessageStatus) => {
setProcessingMessage(status);
},
onComplete: () => {
console.log("onComplete");
@ -178,7 +151,19 @@ const CandidateChatPage = forwardRef<ConversationHandle, BackstoryPageProps>((pr
// Load sessions when username changes
useEffect(() => {
loadSessions();
if (!selectedCandidate) return;
try {
setLoading(true);
apiClient.getOrCreateChatSession(selectedCandidate, `Backstory chat with ${selectedCandidate.fullName}`, 'candidate_chat')
.then(session => {
setChatSession(session);
setLoading(false);
});
} catch (error) {
setSnack('Unable to load chat session', 'error');
} finally {
setLoading(false);
}
}, [selectedCandidate]);
// Load messages when session changes
@ -195,9 +180,9 @@ const CandidateChatPage = forwardRef<ConversationHandle, BackstoryPageProps>((pr
const welcomeMessage: ChatMessage = {
sessionId: chatSession?.id || '',
type: "info",
role: "information",
type: "text",
status: "done",
sender: "system",
timestamp: new Date(),
content: `Welcome to the Backstory Chat about ${selectedCandidate.fullName}. Ask any questions you have about ${selectedCandidate.firstName}.`
};
@ -252,7 +237,7 @@ const CandidateChatPage = forwardRef<ConversationHandle, BackstoryPageProps>((pr
},
}}>
{messages.length === 0 && <Message {...{ chatSession, message: welcomeMessage, setSnack, submitQuery }} />}
{messages.map((message: ChatMessageBase) => (
{messages.map((message: ChatMessage) => (
<Message key={message.id} {...{ chatSession, message, setSnack, submitQuery }} />
))}
{processingMessage !== null && (

188
frontend/src/pages/GenerateCandidate.tsx
View File

@ -8,7 +8,6 @@ import IconButton from '@mui/material/IconButton';
import CancelIcon from '@mui/icons-material/Cancel';
import SendIcon from '@mui/icons-material/Send';
import PropagateLoader from 'react-spinners/PropagateLoader';
import { jsonrepair } from 'jsonrepair';
import { CandidateInfo } from '../components/CandidateInfo';
import { Quote } from 'components/Quote';
@ -18,57 +17,24 @@ import { StyledMarkdown } from 'components/StyledMarkdown';
import { Scrollable } from '../components/Scrollable';
import { Pulse } from 'components/Pulse';
import { StreamingResponse } from 'services/api-client';
import { ChatContext, ChatMessage, ChatMessageUser, ChatMessageBase, ChatSession, ChatQuery, Candidate, CandidateAI } from 'types/types';
import { ChatMessage, ChatMessageUser, ChatSession, CandidateAI, ChatMessageStatus, ChatMessageError } from 'types/types';
import { useAuth } from 'hooks/AuthContext';
import { Message } from 'components/Message';
import { Types } from '@uiw/react-json-view';
import { assert } from 'console';
const emptyUser: CandidateAI = {
userType: "candidate",
isAI: true,
description: "[blank]",
username: "[blank]",
firstName: "[blank]",
lastName: "[blank]",
fullName: "[blank] [blank]",
questions: [],
location: {
city: '[blank]',
country: '[blank]'
},
email: '[blank]',
createdAt: new Date(),
updatedAt: new Date(),
status: "pending",
skills: [],
experience: [],
education: [],
preferredJobTypes: [],
languages: [],
certifications: [],
isAdmin: false,
profileImage: undefined,
ragContentSize: 0
};
const defaultMessage: ChatMessage = {
type: "preparing", status: "done", sender: "system", sessionId: "", timestamp: new Date(), content: ""
status: "done", type: "text", sessionId: "", timestamp: new Date(), content: "", role: "user"
};
const GenerateCandidate = (props: BackstoryElementProps) => {
const { apiClient, user } = useAuth();
const { setSnack, submitQuery } = props;
const [streaming, setStreaming] = useState<boolean>(false);
const [streamingMessage, setStreamingMessage] = useState<ChatMessage | null>(null);
const [processingMessage, setProcessingMessage] = useState<ChatMessage | null>(null);
const [processing, setProcessing] = useState<boolean>(false);
const [generatedUser, setGeneratedUser] = useState<CandidateAI | null>(null);
const [prompt, setPrompt] = useState<string>('');
const [resume, setResume] = useState<string | null>(null);
const [canGenImage, setCanGenImage] = useState<boolean>(false);
const [timestamp, setTimestamp] = useState<number>(0);
const [state, setState] = useState<number>(0); // Replaced stateRef
const [timestamp, setTimestamp] = useState<string>('');
const [shouldGenerateProfile, setShouldGenerateProfile] = useState<boolean>(false);
const [chatSession, setChatSession] = useState<ChatSession | null>(null);
const [loading, setLoading] = useState<boolean>(false);
@ -83,61 +49,24 @@ const GenerateCandidate = (props: BackstoryElementProps) => {
return;
}
const createChatSession = async () => {
console.log('Creating chat session');
try {
const response: ChatSession = await apiClient.createCandidateChatSession(
generatedUser.username,
"generate_image",
"Profile image generation"
);
setChatSession(response);
console.log(`Chat session created for generate_image`, response);
setSnack(`Chat session created for generate_image: ${response.id}`);
} catch (e) {
console.error(e);
setSnack("Unable to create image generation session.", "error");
}
};
setLoading(true);
createChatSession().then(() => { setLoading(false) });
}, [generatedUser, chatSession, loading, setChatSession, setLoading, setSnack]);
const generatePersona = async (prompt: string) => {
const userMessage: ChatMessageUser = {
content: prompt,
sessionId: "",
sender: "user",
status: "done",
type: "user",
timestamp: new Date()
};
setPrompt(prompt || '');
setProcessing(true);
setProcessingMessage({ ...defaultMessage, content: "Generating persona..." });
try {
const result = await apiClient.createCandidateAI(userMessage);
console.log(result.message, result);
setGeneratedUser(result.candidate);
setResume(result.resume);
setCanGenImage(true);
setShouldGenerateProfile(true); // Reset the flag
setLoading(true);
apiClient.getOrCreateChatSession(generatedUser, `Profile image generator for ${generatedUser.fullName}`, 'generate_image')
.then(session => {
setChatSession(session);
setLoading(false);
});
} catch (error) {
console.error(error);
setPrompt('');
setResume(null);
setProcessing(false);
setProcessingMessage(null);
setSnack("Unable to generate AI persona", "error");
setSnack('Unable to load chat session', 'error');
} finally {
setLoading(false);
}
};
}, [generatedUser, chatSession, loading, setChatSession, setLoading, setSnack, apiClient]);
const cancelQuery = useCallback(() => {
if (controllerRef.current) {
controllerRef.current.cancel();
controllerRef.current = null;
setState(0);
setProcessing(false);
}
}, []);
@ -146,8 +75,38 @@ const GenerateCandidate = (props: BackstoryElementProps) => {
if (processing) {
return;
}
const generatePersona = async (prompt: string) => {
const userMessage: ChatMessageUser = {
type: "text",
role: "user",
content: prompt,
sessionId: "",
status: "done",
timestamp: new Date()
};
setPrompt(prompt || '');
setProcessing(true);
setProcessingMessage({ ...defaultMessage, content: "Generating persona..." });
try {
const result = await apiClient.createCandidateAI(userMessage);
console.log(result.message, result);
setGeneratedUser(result.candidate);
setResume(result.resume);
setCanGenImage(true);
setShouldGenerateProfile(true); // Reset the flag
} catch (error) {
console.error(error);
setPrompt('');
setResume(null);
setProcessing(false);
setProcessingMessage(null);
setSnack("Unable to generate AI persona", "error");
}
};
generatePersona(value);
}, [processing, generatePersona]);
}, [processing, apiClient, setSnack]);
const handleSendClick = useCallback(() => {
const value = (backstoryTextRef.current && backstoryTextRef.current.getAndResetValue()) || "";
@ -173,13 +132,12 @@ const GenerateCandidate = (props: BackstoryElementProps) => {
setProcessingMessage({ ...defaultMessage, content: 'Starting image generation...' });
setProcessing(true);
setCanGenImage(false);
setState(3);
const chatMessage: ChatMessageUser = {
sessionId: chatSession.id || '',
role: "user",
status: "done",
type: "user",
sender: "user",
type: "text",
timestamp: new Date(),
content: prompt
};
@ -187,34 +145,23 @@ const GenerateCandidate = (props: BackstoryElementProps) => {
controllerRef.current = apiClient.sendMessageStream(chatMessage, {
onMessage: async (msg: ChatMessage) => {
console.log(`onMessage: ${msg.type} ${msg.content}`, msg);
if (msg.type === "heartbeat" && msg.content) {
const heartbeat = JSON.parse(msg.content);
setTimestamp(heartbeat.timestamp);
}
if (msg.type === "thinking" && msg.content) {
const status = JSON.parse(msg.content);
setProcessingMessage({ ...defaultMessage, content: status.message });
}
if (msg.type === "response") {
controllerRef.current = null;
try {
await apiClient.updateCandidate(generatedUser.id || '', { profileImage: "profile.png" });
const { success, message } = await apiClient.deleteChatSession(chatSession.id || '');
console.log(`Profile generated for ${username} and chat session was ${!success ? 'not ' : ''} deleted: ${message}}`);
setGeneratedUser({
...generatedUser,
profileImage: "profile.png"
} as CandidateAI);
setState(0);
setCanGenImage(true);
setShouldGenerateProfile(false);
} catch (error) {
console.error(error);
setSnack(`Unable to update ${username} to indicate they have a profile picture.`, "error");
}
controllerRef.current = null;
try {
await apiClient.updateCandidate(generatedUser.id || '', { profileImage: "profile.png" });
const { success, message } = await apiClient.deleteChatSession(chatSession.id || '');
console.log(`Profile generated for ${username} and chat session was ${!success ? 'not ' : ''} deleted: ${message}}`);
setGeneratedUser({
...generatedUser,
profileImage: "profile.png"
} as CandidateAI);
setCanGenImage(true);
setShouldGenerateProfile(false);
} catch (error) {
console.error(error);
setSnack(`Unable to update ${username} to indicate they have a profile picture.`, "error");
}
},
onError: (error) => {
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) {
@ -223,27 +170,28 @@ const GenerateCandidate = (props: BackstoryElementProps) => {
setSnack(error as string, "error");
}
setProcessingMessage(null);
setStreaming(false);
setProcessing(false);
controllerRef.current = null;
setState(0);
setCanGenImage(true);
setShouldGenerateProfile(false);
},
onComplete: () => {
setProcessingMessage(null);
setStreaming(false);
setProcessing(false);
controllerRef.current = null;
setState(0);
setCanGenImage(true);
setShouldGenerateProfile(false);
},
onStatusChange: (status: string) => {
onStatus: (status: ChatMessageStatus) => {
if (status.activity === "heartbeat" && status.content) {
setTimestamp(status.timestamp?.toISOString() || '');
} else if (status.content) {
setProcessingMessage({ ...defaultMessage, content: status.content });
}
console.log(`onStatusChange: ${status}`);
},
});
}, [chatSession, shouldGenerateProfile, generatedUser, prompt, setSnack]);
}, [chatSession, shouldGenerateProfile, generatedUser, prompt, setSnack, apiClient]);
if (!user?.isAdmin) {
return (<Box>You must be logged in as an admin to generate AI candidates.</Box>);

4
frontend/src/pages/LoadingPage.tsx
View File

@ -5,8 +5,8 @@ import { ChatMessage } from 'types/types';
const LoadingPage = (props: BackstoryPageProps) => {
const preamble: ChatMessage = {
sender: 'system',
type: 'preparing',
role: 'assistant',
type: 'text',
status: 'done',
sessionId: '',
content: 'Please wait while connecting to Backstory...',

4
frontend/src/pages/LoginRequired.tsx
View File

@ -5,8 +5,8 @@ import { ChatMessage } from 'types/types';
const LoginRequired = (props: BackstoryPageProps) => {
const preamble: ChatMessage = {
sender: 'system',
type: 'preparing',
role: 'assistant',
type: 'text',
status: 'done',
sessionId: '',
content: 'You must be logged to view this feature.',

65
frontend/src/services/api-client.ts
View File

@ -39,11 +39,11 @@ import {
// ============================
interface StreamingOptions {
onStatusChange?: (status: Types.ChatStatusType) => void;
onStatus?: (status: Types.ChatMessageStatus) => void;
onMessage?: (message: Types.ChatMessage) => void;
onStreaming?: (chunk: Types.ChatMessageBase) => void;
onStreaming?: (chunk: Types.ChatMessageStreaming) => void;
onComplete?: () => void;
onError?: (error: string | Types.ChatMessageBase) => void;
onError?: (error: string | Types.ChatMessageError) => void;
onWarn?: (warning: string) => void;
signal?: AbortSignal;
}
@ -761,6 +761,20 @@ class ApiClient {
return result;
}
async getOrCreateChatSession(candidate: Types.Candidate, title: string, context_type: Types.ChatContextType) : Promise<Types.ChatSession> {
const result = await this.getCandidateChatSessions(candidate.username);
/* Find the 'candidate_chat' session if it exists, otherwise create it */
let session = result.sessions.data.find(session => session.title === 'candidate_chat');
if (!session) {
session = await this.createCandidateChatSession(
candidate.username,
context_type,
title
);
}
return session;
}
async getCandidateSimilarContent(query: string
): Promise<Types.ChromaDBGetResponse> {
const response = await fetch(`${this.baseUrl}/candidates/rag-search`, {
@ -965,7 +979,7 @@ class ApiClient {
* Send message with streaming response support and date conversion
*/
sendMessageStream(
chatMessage: Types.ChatMessageBase,
chatMessage: Types.ChatMessageUser,
options: StreamingOptions = {}
): StreamingResponse {
const abortController = new AbortController();
@ -998,9 +1012,8 @@ class ApiClient {
const decoder = new TextDecoder();
let buffer = '';
let incomingMessage: Types.ChatMessage | null = null;
let streamingMessage: Types.ChatMessageStreaming | null = null;
const incomingMessageList: Types.ChatMessage[] = [];
let incomingStatus : Types.ChatStatusType | null = null;
try {
while (true) {
const { done, value } = await reader.read();
@ -1022,39 +1035,41 @@ class ApiClient {
try {
if (line.startsWith('data: ')) {
const data = line.slice(5).trim();
const incoming: Types.ChatMessageBase = JSON.parse(data);
const incoming: any = JSON.parse(data);
// Convert date fields for incoming messages
const convertedIncoming = convertChatMessageFromApi(incoming);
// Trigger callbacks based on status
if (convertedIncoming.status !== incomingStatus) {
options.onStatusChange?.(convertedIncoming.status);
incomingStatus = convertedIncoming.status;
}
console.log(incoming.status, incoming);
// Handle different status types
switch (convertedIncoming.status) {
switch (incoming.status) {
case 'streaming':
if (incomingMessage === null) {
incomingMessage = {...convertedIncoming};
console.log(incoming.status, incoming);
const streaming = Types.convertChatMessageStreamingFromApi(incoming);
if (streamingMessage === null) {
streamingMessage = {...streaming};
} else {
// Can't do a simple += as typescript thinks .content might not be there
incomingMessage.content = (incomingMessage?.content || '') + convertedIncoming.content;
streamingMessage.content = (streamingMessage?.content || '') + streaming.content;
// Update timestamp to latest
incomingMessage.timestamp = convertedIncoming.timestamp;
streamingMessage.timestamp = streamingMessage.timestamp;
}
options.onStreaming?.(convertedIncoming);
options.onStreaming?.(streamingMessage);
break;
case 'status':
const status = Types.convertChatMessageStatusFromApi(incoming);
options.onStatus?.(status);
break;
case 'error':
options.onError?.(convertedIncoming);
const error = Types.convertChatMessageErrorFromApi(incoming);
options.onError?.(error);
break;
default:
incomingMessageList.push(convertedIncoming);
case 'done':
const message = Types.convertChatMessageFromApi(incoming);
incomingMessageList.push(message);
try {
options.onMessage?.(convertedIncoming);
options.onMessage?.(message);
} catch (error) {
console.error('onMessage handler failed: ', error);
}

158
frontend/src/types/types.ts
View File

@ -1,6 +1,6 @@
// Generated TypeScript types from Pydantic models
// Source: src/backend/models.py
// Generated on: 2025-06-04T17:02:08.242818
// Generated on: 2025-06-05T00:24:02.132276
// DO NOT EDIT MANUALLY - This file is auto-generated
// ============================
@ -11,15 +11,17 @@ export type AIModelType = "qwen2.5" | "flux-schnell";
export type ActivityType = "login" | "search" | "view_job" | "apply_job" | "message" | "update_profile" | "chat";
export type ApiActivityType = "system" | "info" | "searching" | "thinking" | "generating" | "generating_image" | "tooling" | "heartbeat";
export type ApiMessageType = "binary" | "text" | "json";
export type ApiStatusType = "streaming" | "status" | "done" | "error";
export type ApplicationStatus = "applied" | "reviewing" | "interview" | "offer" | "rejected" | "accepted" | "withdrawn";
export type ChatContextType = "job_search" | "job_requirements" | "candidate_chat" | "interview_prep" | "resume_review" | "general" | "generate_persona" | "generate_profile" | "generate_image" | "rag_search" | "skill_match";
export type ChatMessageType = "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user";
export type ChatSenderType = "user" | "assistant" | "agent" | "system";
export type ChatStatusType = "initializing" | "streaming" | "status" | "done" | "error";
export type ChatSenderType = "user" | "assistant" | "system" | "information" | "warning" | "error";
export type ColorBlindMode = "protanopia" | "deuteranopia" | "tritanopia" | "none";
@ -88,6 +90,15 @@ export interface Analytics {
segment?: string;
}
export interface ApiMessage {
id?: string;
sessionId: string;
senderId?: string;
status: "streaming" | "status" | "done" | "error";
type: "binary" | "text" | "json";
timestamp?: Date;
}
export interface ApiResponse {
success: boolean;
data?: any;
@ -284,24 +295,22 @@ export interface ChatMessage {
id?: string;
sessionId: string;
senderId?: string;
status: "initializing" | "streaming" | "status" | "done" | "error";
type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user";
sender: "user" | "assistant" | "agent" | "system";
status: "streaming" | "status" | "done" | "error";
type: "binary" | "text" | "json";
timestamp?: Date;
tunables?: Tunables;
role: "user" | "assistant" | "system" | "information" | "warning" | "error";
content: string;
tunables?: Tunables;
metadata?: ChatMessageMetaData;
}
export interface ChatMessageBase {
export interface ChatMessageError {
id?: string;
sessionId: string;
senderId?: string;
status: "initializing" | "streaming" | "status" | "done" | "error";
type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user";
sender: "user" | "assistant" | "agent" | "system";
status: "streaming" | "status" | "done" | "error";
type: "binary" | "text" | "json";
timestamp?: Date;
tunables?: Tunables;
content: string;
}
@ -328,25 +337,44 @@ export interface ChatMessageRagSearch {
id?: string;
sessionId: string;
senderId?: string;
status: "initializing" | "streaming" | "status" | "done" | "error";
type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user";
sender: "user" | "assistant" | "agent" | "system";
status: "streaming" | "status" | "done" | "error";
type: "binary" | "text" | "json";
timestamp?: Date;
tunables?: Tunables;
content: string;
dimensions: number;
content: Array<ChromaDBGetResponse>;
}
export interface ChatMessageStatus {
id?: string;
sessionId: string;
senderId?: string;
status: "streaming" | "status" | "done" | "error";
type: "binary" | "text" | "json";
timestamp?: Date;
activity: "system" | "info" | "searching" | "thinking" | "generating" | "generating_image" | "tooling" | "heartbeat";
content: any;
}
export interface ChatMessageStreaming {
id?: string;
sessionId: string;
senderId?: string;
status: "streaming" | "status" | "done" | "error";
type: "binary" | "text" | "json";
timestamp?: Date;
content: string;
}
export interface ChatMessageUser {
id?: string;
sessionId: string;
senderId?: string;
status: "initializing" | "streaming" | "status" | "done" | "error";
type: "error" | "generating" | "info" | "preparing" | "processing" | "heartbeat" | "response" | "searching" | "rag_result" | "system" | "thinking" | "tooling" | "user";
sender: "user" | "assistant" | "agent" | "system";
status: "streaming" | "status" | "done" | "error";
type: "binary" | "text" | "json";
timestamp?: Date;
tunables?: Tunables;
role: "user" | "assistant" | "system" | "information" | "warning" | "error";
content: string;
tunables?: Tunables;
}
export interface ChatOptions {
@ -644,6 +672,20 @@ export interface JobRequirements {
preferredAttributes?: Array<string>;
}
export interface JobRequirementsMessage {
id?: string;
sessionId: string;
senderId?: string;
status: "streaming" | "status" | "done" | "error";
type: "binary" | "text" | "json";
timestamp?: Date;
title?: string;
summary?: string;
company?: string;
description: string;
requirements?: JobRequirements;
}
export interface JobResponse {
success: boolean;
data?: Job;
@ -923,6 +965,19 @@ export function convertAnalyticsFromApi(data: any): Analytics {
timestamp: new Date(data.timestamp),
};
}
/**
* Convert ApiMessage from API response, parsing date fields
* Date fields: timestamp
*/
export function convertApiMessageFromApi(data: any): ApiMessage {
if (!data) return data;
return {
...data,
// Convert timestamp from ISO string to Date
timestamp: data.timestamp ? new Date(data.timestamp) : undefined,
};
}
/**
* Convert ApplicationDecision from API response, parsing date fields
* Date fields: date
@ -1067,10 +1122,10 @@ export function convertChatMessageFromApi(data: any): ChatMessage {
};
}
/**
* Convert ChatMessageBase from API response, parsing date fields
* Convert ChatMessageError from API response, parsing date fields
* Date fields: timestamp
*/
export function convertChatMessageBaseFromApi(data: any): ChatMessageBase {
export function convertChatMessageErrorFromApi(data: any): ChatMessageError {
if (!data) return data;
return {
@ -1092,6 +1147,32 @@ export function convertChatMessageRagSearchFromApi(data: any): ChatMessageRagSea
timestamp: data.timestamp ? new Date(data.timestamp) : undefined,
};
}
/**
* Convert ChatMessageStatus from API response, parsing date fields
* Date fields: timestamp
*/
export function convertChatMessageStatusFromApi(data: any): ChatMessageStatus {
if (!data) return data;
return {
...data,
// Convert timestamp from ISO string to Date
timestamp: data.timestamp ? new Date(data.timestamp) : undefined,
};
}
/**
* Convert ChatMessageStreaming from API response, parsing date fields
* Date fields: timestamp
*/
export function convertChatMessageStreamingFromApi(data: any): ChatMessageStreaming {
if (!data) return data;
return {
...data,
// Convert timestamp from ISO string to Date
timestamp: data.timestamp ? new Date(data.timestamp) : undefined,
};
}
/**
* Convert ChatMessageUser from API response, parsing date fields
* Date fields: timestamp
@ -1268,6 +1349,19 @@ export function convertJobFullFromApi(data: any): JobFull {
featuredUntil: data.featuredUntil ? new Date(data.featuredUntil) : undefined,
};
}
/**
* Convert JobRequirementsMessage from API response, parsing date fields
* Date fields: timestamp
*/
export function convertJobRequirementsMessageFromApi(data: any): JobRequirementsMessage {
if (!data) return data;
return {
...data,
// Convert timestamp from ISO string to Date
timestamp: data.timestamp ? new Date(data.timestamp) : undefined,
};
}
/**
* Convert MessageReaction from API response, parsing date fields
* Date fields: timestamp
@ -1348,6 +1442,8 @@ export function convertFromApi<T>(data: any, modelType: string): T {
switch (modelType) {
case 'Analytics':
return convertAnalyticsFromApi(data) as T;
case 'ApiMessage':
return convertApiMessageFromApi(data) as T;
case 'ApplicationDecision':
return convertApplicationDecisionFromApi(data) as T;
case 'Attachment':
@ -1366,10 +1462,14 @@ export function convertFromApi<T>(data: any, modelType: string): T {
return convertCertificationFromApi(data) as T;
case 'ChatMessage':
return convertChatMessageFromApi(data) as T;
case 'ChatMessageBase':
return convertChatMessageBaseFromApi(data) as T;
case 'ChatMessageError':
return convertChatMessageErrorFromApi(data) as T;
case 'ChatMessageRagSearch':
return convertChatMessageRagSearchFromApi(data) as T;
case 'ChatMessageStatus':
return convertChatMessageStatusFromApi(data) as T;
case 'ChatMessageStreaming':
return convertChatMessageStreamingFromApi(data) as T;
case 'ChatMessageUser':
return convertChatMessageUserFromApi(data) as T;
case 'ChatSession':
@ -1394,6 +1494,8 @@ export function convertFromApi<T>(data: any, modelType: string): T {
return convertJobApplicationFromApi(data) as T;
case 'JobFull':
return convertJobFullFromApi(data) as T;
case 'JobRequirementsMessage':
return convertJobRequirementsMessageFromApi(data) as T;
case 'MessageReaction':
return convertMessageReactionFromApi(data) as T;
case 'RAGConfiguration':

425
src/backend/agents/base.py
View File

@ -24,7 +24,7 @@ from datetime import datetime, UTC
from prometheus_client import Counter, Summary, CollectorRegistry # type: ignore
import numpy as np # type: ignore
from models import ( LLMMessage, ChatQuery, ChatMessage, ChatOptions, ChatMessageBase, ChatMessageUser, Tunables, ChatMessageType, ChatSenderType, ChatStatusType, ChatMessageMetaData, Candidate)
from models import ( ApiActivityType, ChatMessageError, ChatMessageRagSearch, ChatMessageStatus, ChatMessageStreaming, LLMMessage, ChatQuery, ChatMessage, ChatOptions, ChatMessageUser, Tunables, ApiMessageType, ChatSenderType, ApiStatusType, ChatMessageMetaData, Candidate)
from logger import logger
import defines
from .registry import agent_registry
@ -300,12 +300,35 @@ class Agent(BaseModel, ABC):
)
self.metrics.tokens_eval.labels(agent=self.agent_type).inc(response.eval_count)
def get_rag_context(self, rag_message: ChatMessageRagSearch) -> str:
"""
Extracts the RAG context from the rag_message.
"""
if not rag_message.content:
return ""
context = []
for chroma_results in rag_message.content:
for index, metadata in enumerate(chroma_results.metadatas):
content = "\n".join([
line.strip()
for line in chroma_results.documents[index].split("\n")
if line
]).strip()
context.append(f"""
Source: {metadata.get("doc_type", "unknown")}: {metadata.get("path", "")}
Document reference: {chroma_results.ids[index]}
Content: {content}
""")
return "\n".join(context)
async def generate_rag_results(
self,
chat_message: ChatMessage,
session_id: str,
prompt: str,
top_k: int=defines.default_rag_top_k,
threshold: float=defines.default_rag_threshold,
) -> AsyncGenerator[ChatMessage, None]:
) -> AsyncGenerator[ChatMessageRagSearch | ChatMessageError | ChatMessageStatus, None]:
"""
Generate RAG results for the given query.
@ -315,223 +338,193 @@ class Agent(BaseModel, ABC):
Returns:
A list of dictionaries containing the RAG results.
"""
rag_message = ChatMessage(
session_id=chat_message.session_id,
tunables=chat_message.tunables,
status=ChatStatusType.INITIALIZING,
type=ChatMessageType.PREPARING,
sender=ChatSenderType.ASSISTANT,
content="",
timestamp=datetime.now(UTC),
metadata=ChatMessageMetaData()
)
if not self.user:
logger.error("No user set for RAG generation")
rag_message.status = ChatStatusType.DONE
rag_message.content = ""
yield rag_message
error_message = ChatMessageError(
session_id=session_id,
content="No user set for RAG generation."
)
yield error_message
return
try:
entries: int = 0
user: Candidate = self.user
rag_content: str = ""
for rag in user.rags:
if not rag.enabled:
continue
status_message = ChatMessage(
session_id=chat_message.session_id,
sender=ChatSenderType.AGENT,
status = ChatStatusType.INITIALIZING,
type = ChatMessageType.SEARCHING,
content = f"Checking RAG context {rag.name}...")
yield status_message
results : List[ChromaDBGetResponse] = []
entries: int = 0
user: Candidate = self.user
for rag in user.rags:
if not rag.enabled:
continue
status_message = ChatMessageStatus(
session_id=session_id,
activity=ApiActivityType.SEARCHING,
content = f"Searching RAG context {rag.name}..."
)
yield status_message
try:
chroma_results = user.file_watcher.find_similar(
query=chat_message.content, top_k=top_k, threshold=threshold
query=prompt, top_k=top_k, threshold=threshold
)
if chroma_results:
query_embedding = np.array(chroma_results["query_embedding"]).flatten()
if not chroma_results:
continue
query_embedding = np.array(chroma_results["query_embedding"]).flatten()
umap_2d = user.file_watcher.umap_model_2d.transform([query_embedding])[0]
umap_3d = user.file_watcher.umap_model_3d.transform([query_embedding])[0]
umap_2d = user.file_watcher.umap_model_2d.transform([query_embedding])[0]
umap_3d = user.file_watcher.umap_model_3d.transform([query_embedding])[0]
rag_metadata = ChromaDBGetResponse(
query=chat_message.content,
query_embedding=query_embedding.tolist(),
name=rag.name,
ids=chroma_results.get("ids", []),
embeddings=chroma_results.get("embeddings", []),
documents=chroma_results.get("documents", []),
metadatas=chroma_results.get("metadatas", []),
umap_embedding_2d=umap_2d.tolist(),
umap_embedding_3d=umap_3d.tolist(),
size=user.file_watcher.collection.count()
)
rag_metadata = ChromaDBGetResponse(
name=rag.name,
query_embedding=query_embedding.tolist(),
ids=chroma_results.get("ids", []),
embeddings=chroma_results.get("embeddings", []),
documents=chroma_results.get("documents", []),
metadatas=chroma_results.get("metadatas", []),
umap_embedding_2d=umap_2d.tolist(),
umap_embedding_3d=umap_3d.tolist(),
)
results.append(rag_metadata)
except Exception as e:
continue_message = ChatMessageStatus(
session_id=session_id,
activity=ApiActivityType.SEARCHING,
content=f"Error searching RAG context {rag.name}: {str(e)}"
)
yield continue_message
entries += len(rag_metadata.documents)
rag_message.metadata.rag_results.append(rag_metadata)
for index, metadata in enumerate(chroma_results["metadatas"]):
content = "\n".join(
[
line.strip()
for line in chroma_results["documents"][index].split("\n")
if line
]
).strip()
rag_content += f"""
Source: {metadata.get("doc_type", "unknown")}: {metadata.get("path", "")}
Document reference: {chroma_results["ids"][index]}
Content: { content }
"""
rag_message.content = rag_content.strip()
rag_message.type = ChatMessageType.RAG_RESULT
rag_message.status = ChatStatusType.DONE
yield rag_message
return
except Exception as e:
rag_message.status = ChatStatusType.ERROR
rag_message.content = f"Error generating RAG results: {str(e)}"
logger.error(traceback.format_exc())
logger.error(rag_message.content)
yield rag_message
return
async def llm_one_shot(self, llm: Any, model: str, user_message: ChatMessageUser, system_prompt: str, temperature=0.7):
chat_message = ChatMessage(
session_id=user_message.session_id,
tunables=user_message.tunables,
status=ChatStatusType.INITIALIZING,
type=ChatMessageType.PREPARING,
sender=ChatSenderType.AGENT,
content="",
timestamp=datetime.now(UTC)
final_message = ChatMessageRagSearch(
session_id=session_id,
content=results,
status=ApiStatusType.DONE,
)
yield final_message
return
async def llm_one_shot(
self,
llm: Any, model: str,
session_id: str, prompt: str, system_prompt: str,
tunables: Optional[Tunables] = None,
temperature=0.7) -> AsyncGenerator[ChatMessageStatus | ChatMessageError | ChatMessageStreaming | ChatMessage, None]:
self.set_optimal_context_size(
llm, model, prompt=chat_message.content
llm=llm, model=model, prompt=prompt
)
chat_message.metadata = ChatMessageMetaData()
chat_message.metadata.options = ChatOptions(
options = ChatOptions(
seed=8911,
num_ctx=self.context_size,
temperature=temperature, # Higher temperature to encourage tool usage
temperature=temperature,
)
messages: List[LLMMessage] = [
LLMMessage(role="system", content=system_prompt),
LLMMessage(role="user", content=user_message.content),
LLMMessage(role="user", content=prompt),
]
# Reset the response for streaming
chat_message.content = ""
chat_message.type = ChatMessageType.GENERATING
chat_message.status = ChatStatusType.STREAMING
status_message = ChatMessageStatus(
session_id=session_id,
activity=ApiActivityType.GENERATING,
content=f"Generating response..."
)
yield status_message
logger.info(f"Message options: {chat_message.metadata.options.model_dump(exclude_unset=True)}")
logger.info(f"Message options: {options.model_dump(exclude_unset=True)}")
response = None
content = ""
for response in llm.chat(
model=model,
messages=messages,
options={
**chat_message.metadata.options.model_dump(exclude_unset=True),
**options.model_dump(exclude_unset=True),
},
stream=True,
):
if not response:
chat_message.status = ChatStatusType.ERROR
chat_message.content = "No response from LLM."
yield chat_message
error_message = ChatMessageError(
session_id=session_id,
content="No response from LLM."
)
yield error_message
return
chat_message.content += response.message.content
content += response.message.content
if not response.done:
chat_chunk = model_cast.cast_to_model(ChatMessageBase, chat_message)
chat_chunk.content = response.message.content
yield chat_message
continue
streaming_message = ChatMessageStreaming(
session_id=session_id,
content=response.message.content,
status=ApiStatusType.STREAMING,
)
yield streaming_message
if not response:
chat_message.status = ChatStatusType.ERROR
chat_message.content = "No response from LLM."
yield chat_message
error_message = ChatMessageError(
session_id=session_id,
content="No response from LLM."
)
yield error_message
return
self.collect_metrics(response)
chat_message.metadata.eval_count += response.eval_count
chat_message.metadata.eval_duration += response.eval_duration
chat_message.metadata.prompt_eval_count += response.prompt_eval_count
chat_message.metadata.prompt_eval_duration += response.prompt_eval_duration
self.context_tokens = (
response.prompt_eval_count + response.eval_count
)
chat_message.type = ChatMessageType.RESPONSE
chat_message.status = ChatStatusType.DONE
yield chat_message
async def generate(
self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate | None, temperature=0.7
) -> AsyncGenerator[ChatMessage | ChatMessageBase, None]:
logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
chat_message = ChatMessage(
session_id=user_message.session_id,
tunables=user_message.tunables,
status=ChatStatusType.INITIALIZING,
type=ChatMessageType.PREPARING,
sender=ChatSenderType.ASSISTANT,
content="",
timestamp=datetime.now(UTC)
)
session_id=session_id,
tunables=tunables,
status=ApiStatusType.DONE,
content=content,
metadata = ChatMessageMetaData(
options=options,
eval_count=response.eval_count,
eval_duration=response.eval_duration,
prompt_eval_count=response.prompt_eval_count,
prompt_eval_duration=response.prompt_eval_duration,
self.set_optimal_context_size(
llm, model, prompt=chat_message.content
)
)
yield chat_message
return
chat_message.metadata = ChatMessageMetaData()
chat_message.metadata.options = ChatOptions(
seed=8911,
num_ctx=self.context_size,
temperature=temperature, # Higher temperature to encourage tool usage
async def generate(
self, llm: Any, model: str,
session_id: str, prompt: str,
tunables: Optional[Tunables] = None,
temperature=0.7
) -> AsyncGenerator[ChatMessage | ChatMessageStatus | ChatMessageError | ChatMessageStreaming, None]:
if not self.user:
error_message = ChatMessageError(
session_id=session_id,
content="No user set for chat generation."
)
yield error_message
return
user_message = ChatMessageUser(
session_id=session_id,
content=prompt,
)
# Create a dict for storing various timing stats
chat_message.metadata.timers = {}
user = self.user
self.metrics.generate_count.labels(agent=self.agent_type).inc()
with self.metrics.generate_duration.labels(agent=self.agent_type).time():
context = None
if self.user:
rag_message = None
async for rag_message in self.generate_rag_results(session_id=session_id, prompt=prompt):
if rag_message.status == ApiStatusType.ERROR:
yield rag_message
return
# Only yield messages that are in a streaming state
if rag_message.status == ApiStatusType.STATUS:
yield rag_message
rag_message : Optional[ChatMessage] = None
async for rag_message in self.generate_rag_results(chat_message=user_message):
if rag_message.status == ChatStatusType.ERROR:
chat_message.status = rag_message.status
chat_message.content = rag_message.content
yield chat_message
return
yield rag_message
if not isinstance(rag_message, ChatMessageRagSearch):
raise ValueError(
f"Expected ChatMessageRagSearch, got {type(rag_message)}"
)
rag_context = ""
if rag_message:
rag_results: List[ChromaDBGetResponse] = rag_message.metadata.rag_results
chat_message.metadata.rag_results = rag_results
for chroma_results in rag_results:
for index, metadata in enumerate(chroma_results.metadatas):
content = "\n".join([
line.strip()
for line in chroma_results.documents[index].split("\n")
if line
]).strip()
rag_context += f"""
Source: {metadata.get("doc_type", "unknown")}: {metadata.get("path", "")}
Document reference: {chroma_results.ids[index]}
Content: { content }
"""
context = self.get_rag_context(rag_message)
# Create a pruned down message list based purely on the prompt and responses,
# discarding the full preamble generated by prepare_message
@ -540,24 +533,24 @@ Content: { content }
]
# Add the conversation history to the messages
messages.extend([
LLMMessage(role=m.sender, content=m.content.strip())
LLMMessage(role="user" if isinstance(m, ChatMessageUser) else "assistant", content=m.content)
for m in self.conversation
])
# Add the RAG context to the messages if available
if rag_context and user:
if context:
messages.append(
LLMMessage(
role="user",
content=f"<|context|>\nThe following is context information about {user.full_name}:\n{rag_context.strip()}\n</|context|>\n\nPrompt to respond to:\n{user_message.content.strip()}\n"
content=f"<|context|>\nThe following is context information about {self.user.full_name}:\n{context}\n</|context|>\n\nPrompt to respond to:\n{prompt}\n"
)
)
else:
# Only the actual user query is provided with the full context message
messages.append(
LLMMessage(role=user_message.sender, content=user_message.content.strip())
LLMMessage(role="user", content=prompt)
)
chat_message.metadata.llm_history = messages
llm_history = messages
# use_tools = message.tunables.enable_tools and len(self.context.tools) > 0
# message.metadata.tools = {
@ -660,57 +653,89 @@ Content: { content }
# return
# not use_tools
chat_message.type = ChatMessageType.THINKING
chat_message.content = f"Generating response..."
yield chat_message
status_message = ChatMessageStatus(
session_id=session_id,
activity=ApiActivityType.GENERATING,
content=f"Generating response..."
)
yield status_message
# Reset the response for streaming
chat_message.content = ""
# Set the response for streaming
self.set_optimal_context_size(
llm, model, prompt=prompt
)
options = ChatOptions(
seed=8911,
num_ctx=self.context_size,
temperature=temperature,
)
logger.info(f"Message options: {options.model_dump(exclude_unset=True)}")
content = ""
start_time = time.perf_counter()
chat_message.type = ChatMessageType.GENERATING
chat_message.status = ChatStatusType.STREAMING
response = None
for response in llm.chat(
model=model,
messages=messages,
options={
**chat_message.metadata.options.model_dump(exclude_unset=True),
**options.model_dump(exclude_unset=True),
},
stream=True,
):
if not response:
chat_message.status = ChatStatusType.ERROR
chat_message.content = "No response from LLM."
yield chat_message
error_message = ChatMessageError(
session_id=session_id,
content="No response from LLM."
)
yield error_message
return
chat_message.content += response.message.content
content += response.message.content
if not response.done:
chat_chunk = model_cast.cast_to_model(ChatMessageBase, chat_message)
chat_chunk.content = response.message.content
yield chat_message
continue
if response.done:
self.collect_metrics(response)
chat_message.metadata.eval_count += response.eval_count
chat_message.metadata.eval_duration += response.eval_duration
chat_message.metadata.prompt_eval_count += response.prompt_eval_count
chat_message.metadata.prompt_eval_duration += response.prompt_eval_duration
self.context_tokens = (
response.prompt_eval_count + response.eval_count
streaming_message = ChatMessageStreaming(
session_id=session_id,
content=response.message.content,
)
chat_message.type = ChatMessageType.RESPONSE
chat_message.status = ChatStatusType.DONE
yield chat_message
yield streaming_message
if not response:
error_message = ChatMessageError(
session_id=session_id,
content="No response from LLM."
)
yield error_message
return
self.collect_metrics(response)
self.context_tokens = (
response.prompt_eval_count + response.eval_count
)
end_time = time.perf_counter()
chat_message.metadata.timers["streamed"] = end_time - start_time
chat_message = ChatMessage(
session_id=session_id,
tunables=tunables,
status=ApiStatusType.DONE,
content=content,
metadata = ChatMessageMetaData(
options=options,
eval_count=response.eval_count,
eval_duration=response.eval_duration,
prompt_eval_count=response.prompt_eval_count,
prompt_eval_duration=response.prompt_eval_duration,
timers={
"llm_streamed": end_time - start_time,
"llm_with_tools": 0, # Placeholder for tool processing time
},
)
)
# Add the user and chat messages to the conversation
self.conversation.append(user_message)
self.conversation.append(chat_message)
yield chat_message
return
# async def process_message(

18
src/backend/agents/candidate_chat.py
View File

@ -7,7 +7,7 @@ from .base import Agent, agent_registry
from logger import logger
from .registry import agent_registry
from models import ( ChatQuery, ChatMessage, Tunables, ChatStatusType, ChatMessageUser, Candidate)
from models import ( ChatMessageError, ChatMessageStatus, ChatMessageStreaming, ChatQuery, ChatMessage, Tunables, ApiStatusType, ChatMessageUser, Candidate)
system_message = f"""
@ -34,8 +34,15 @@ class CandidateChat(Agent):
system_prompt: str = system_message
async def generate(
self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate, temperature=0.7
):
self, llm: Any, model: str,
session_id: str, prompt: str,
tunables: Optional[Tunables] = None,
temperature=0.7
) -> AsyncGenerator[ChatMessage | ChatMessageStatus | ChatMessageError | ChatMessageStreaming, None]:
user = self.user
if not user:
logger.error("User is not set for CandidateChat agent.")
raise ValueError("User must be set before generating candidate chat responses.")
self.system_prompt = f"""
You are a helpful expert system representing a {user.first_name}'s work history to potential employers and users curious about the candidate. You want to incorporate as many facts and details about {user.first_name} as possible.
@ -49,7 +56,10 @@ Use that spelling instead of any spelling you may find in the <|context|>.
{system_message}
"""
async for message in super().generate(llm, model, user_message, user, temperature):
async for message in super().generate(llm=llm, model=model, session_id=session_id, prompt=prompt, temperature=temperature, tunables=tunables):
if message.status == ApiStatusType.ERROR:
yield message
return
yield message
# Register the base agent

2
src/backend/agents/general.py
View File

@ -7,7 +7,7 @@ from .base import Agent, agent_registry
from logger import logger
from .registry import agent_registry
from models import ( ChatQuery, ChatMessage, Tunables, ChatStatusType)
from models import ( ChatQuery, ChatMessage, Tunables, ApiStatusType)
system_message = f"""
Launched on {datetime.now().isoformat()}.

74
src/backend/agents/generate_image.py
View File

@ -25,7 +25,7 @@ import os
import hashlib
from .base import Agent, agent_registry, LLMMessage
from models import Candidate, ChatMessage, ChatMessageBase, ChatMessageMetaData, ChatMessageType, ChatMessageUser, ChatOptions, ChatSenderType, ChatStatusType
from models import ActivityType, ApiActivityType, Candidate, ChatMessage, ChatMessageError, ChatMessageMetaData, ApiMessageType, ChatMessageStatus, ChatMessageStreaming, ChatMessageUser, ChatOptions, ChatSenderType, ApiStatusType, Tunables
import model_cast
from logger import logger
import defines
@ -44,43 +44,48 @@ class ImageGenerator(Agent):
system_prompt: str = "" # No system prompt is used
async def generate(
self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate, temperature=0.7
) -> AsyncGenerator[ChatMessage, None]:
logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
self, llm: Any, model: str,
session_id: str, prompt: str,
tunables: Optional[Tunables] = None,
temperature=0.7
) -> AsyncGenerator[ChatMessage | ChatMessageStatus | ChatMessageError | ChatMessageStreaming, None]:
if not self.user:
logger.error("User is not set for ImageGenerator agent.")
raise ValueError("User must be set before generating images.")
user = self.user
file_path = os.path.join(defines.user_dir, user.username, "profile.png")
chat_message = ChatMessage(
session_id=user_message.session_id,
tunables=user_message.tunables,
status=ChatStatusType.INITIALIZING,
type=ChatMessageType.PREPARING,
sender=ChatSenderType.ASSISTANT,
content="",
timestamp=datetime.now(UTC)
)
chat_message.metadata = ChatMessageMetaData()
try:
#
# Generate the profile picture
#
chat_message.content = f"Generating: {user_message.content}"
yield chat_message
status_message = ChatMessageStatus(
session_id=session_id,
activity=ApiActivityType.GENERATING_IMAGE,
content=f"Generating {prompt}...",
)
yield status_message
logger.info(f"Image generation: {file_path} <- {user_message.content}")
request = ImageRequest(filepath=file_path, prompt=user_message.content, iterations=4, height=256, width=256, guidance_scale=7.5)
logger.info(f"Image generation: {file_path} <- {prompt}")
request = ImageRequest(filepath=file_path, session_id=session_id, prompt=prompt, iterations=4, height=256, width=256, guidance_scale=7.5)
generated_message = None
async for generated_message in generate_image(
user_message=user_message,
request=request
):
if generated_message.status != "done":
if generated_message.status == ApiStatusType.ERROR:
yield generated_message
return
if generated_message.status != ApiStatusType.DONE:
yield generated_message
continue
if generated_message is None:
chat_message.status = ChatStatusType.ERROR
chat_message.content = "Image generation failed."
yield chat_message
error_message = ChatMessageError(
session_id=session_id,
content="Image generation failed to produce a valid response."
)
logger.error(f"⚠️ {error_message.content}")
yield error_message
return
logger.info("Image generation done...")
@ -88,18 +93,23 @@ class ImageGenerator(Agent):
user.profile_image = "profile.png"
# Image generated
generated_message.status = ChatStatusType.DONE
generated_message.content = f"{defines.api_prefix}/profile/{user.username}"
yield generated_message
generated_image = ChatMessage(
session_id=session_id,
status=ApiStatusType.DONE,
content = f"{defines.api_prefix}/profile/{user.username}",
metadata=generated_message.metadata
)
yield generated_image
return
except Exception as e:
chat_message.status = ChatStatusType.ERROR
error_message = ChatMessageError(
session_id=session_id,
content=f"Error generating image: {str(e)}"
)
logger.error(traceback.format_exc())
logger.error(chat_message.content)
chat_message.content = f"Error in image generation: {str(e)}"
logger.error(chat_message.content)
yield chat_message
logger.error(f"⚠️ {error_message.content}")
yield error_message
return
# Register the base agent

87
src/backend/agents/generate_persona.py
View File

@ -27,7 +27,7 @@ import random
from names_dataset import NameDataset, NameWrapper # type: ignore
from .base import Agent, agent_registry, LLMMessage
from models import Candidate, ChatMessage, ChatMessageBase, ChatMessageMetaData, ChatMessageType, ChatMessageUser, ChatOptions, ChatSenderType, ChatStatusType
from models import ApiActivityType, Candidate, ChatMessage, ChatMessageError, ChatMessageMetaData, ApiMessageType, ChatMessageStatus, ChatMessageStreaming, ChatMessageUser, ChatOptions, ChatSenderType, ApiStatusType, Tunables
import model_cast
from logger import logger
import defines
@ -316,14 +316,16 @@ class GeneratePersona(Agent):
self.full_name = f"{self.first_name} {self.last_name}"
async def generate(
self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate, temperature=0.7
):
self, llm: Any, model: str,
session_id: str, prompt: str,
tunables: Optional[Tunables] = None,
temperature=0.7
) -> AsyncGenerator[ChatMessage | ChatMessageStatus | ChatMessageError | ChatMessageStreaming, None]:
self.randomize()
status_message = ChatMessage(session_id=user_message.session_id)
original_prompt = user_message.content
original_prompt = prompt.strip()
user_message.content = f"""\
prompt = f"""\
```json
{json.dumps({
"age": self.age,
@ -337,7 +339,7 @@ class GeneratePersona(Agent):
"""
if original_prompt:
user_message.content += f"""
prompt += f"""
Incorporate the following into the job description: {original_prompt}
"""
@ -348,16 +350,24 @@ Incorporate the following into the job description: {original_prompt}
generating_message = None
async for generating_message in self.llm_one_shot(
llm=llm, model=model,
user_message=user_message,
session_id=session_id,
prompt=prompt,
system_prompt=generate_persona_system_prompt,
temperature=temperature,
):
if generating_message.status == ChatStatusType.ERROR:
if generating_message.status == ApiStatusType.ERROR:
logger.error(f"Error generating persona: {generating_message.content}")
raise Exception(generating_message.content)
yield generating_message
if generating_message.status != ApiStatusType.DONE:
yield generating_message
if not generating_message:
raise Exception("No response from LLM during persona generation")
error_message = ChatMessageError(
session_id=session_id,
content="Persona generation failed to generate a response."
)
yield error_message
return
json_str = self.extract_json_from_text(generating_message.content)
try:
@ -405,37 +415,39 @@ Incorporate the following into the job description: {original_prompt}
persona["location"] = None
persona["is_ai"] = True
except Exception as e:
generating_message.content = f"Unable to parse LLM returned content: {json_str} {str(e)}"
generating_message.status = ChatStatusType.ERROR
error_message = ChatMessageError(
session_id=session_id,
content=f"Error parsing LLM response: {str(e)}\n\n{json_str}"
)
logger.error(f"❌ Error parsing LLM response: {error_message.content}")
logger.error(traceback.format_exc())
logger.error(generating_message.content)
yield generating_message
yield error_message
return
logger.info(f"✅ Persona for {persona['username']} generated successfully")
# Persona generated
status_message = ChatMessage(
session_id=user_message.session_id,
status = ChatStatusType.STATUS,
type = ChatMessageType.RESPONSE,
persona_message = ChatMessage(
session_id=session_id,
status=ApiStatusType.DONE,
type=ApiMessageType.JSON,
content = json.dumps(persona)
)
yield status_message
yield persona_message
#
# Generate the resume
#
status_message = ChatMessage(
session_id=user_message.session_id,
status = ChatStatusType.STATUS,
type = ChatMessageType.THINKING,
status_message = ChatMessageStatus(
session_id=session_id,
activity = ApiActivityType.THINKING,
content = f"Generating resume for {persona['full_name']}..."
)
logger.info(f"🤖 {status_message.content}")
yield status_message
user_message.content = f"""
content = f"""
```json
{{
"full_name": "{persona["full_name"]}",
@ -449,30 +461,39 @@ Incorporate the following into the job description: {original_prompt}
```
"""
if original_prompt:
user_message.content += f"""
content += f"""
Make sure at least one of the candidate's job descriptions take into account the following: {original_prompt}."""
async for generating_message in self.llm_one_shot(
llm=llm, model=model,
user_message=user_message,
session_id=session_id,
prompt=content,
system_prompt=generate_resume_system_prompt,
temperature=temperature,
):
if generating_message.status == ChatStatusType.ERROR:
if generating_message.status == ApiStatusType.ERROR:
logger.error(f"❌ Error generating resume: {generating_message.content}")
raise Exception(generating_message.content)
if generating_message.status != ApiStatusType.DONE:
yield generating_message
if not generating_message:
raise Exception("No response from LLM during persona generation")
error_message = ChatMessageError(
session_id=session_id,
content="Resume generation failed to generate a response."
)
logger.error(f"❌ {error_message.content}")
yield error_message
return
resume = self.extract_markdown_from_text(generating_message.content)
status_message = ChatMessage(
session_id=user_message.session_id,
status=ChatStatusType.DONE,
type=ChatMessageType.RESPONSE,
resume_message = ChatMessage(
session_id=session_id,
status=ApiStatusType.DONE,
type=ApiMessageType.TEXT,
content=resume
)
yield status_message
yield resume_message
return
def extract_json_from_text(self, text: str) -> str:

82
src/backend/agents/job_requirements.py
View File

@ -20,7 +20,7 @@ import asyncio
import numpy as np # type: ignore
from .base import Agent, agent_registry, LLMMessage
from models import Candidate, ChatMessage, ChatMessageBase, ChatMessageMetaData, ChatMessageType, ChatMessageUser, ChatOptions, ChatSenderType, ChatStatusType, JobRequirements
from models import Candidate, ChatMessage, ChatMessageError, ChatMessageMetaData, ApiMessageType, ChatMessageStatus, ChatMessageUser, ChatOptions, ChatSenderType, ApiStatusType, JobRequirements, JobRequirementsMessage, Tunables
import model_cast
from logger import logger
import defines
@ -81,56 +81,51 @@ class JobRequirementsAgent(Agent):
return system_prompt, prompt
async def analyze_job_requirements(
self, llm: Any, model: str, user_message: ChatMessage
) -> AsyncGenerator[ChatMessage, None]:
self, llm: Any, model: str, session_id: str, prompt: str
) -> AsyncGenerator[ChatMessage | ChatMessageError, None]:
"""Analyze job requirements from job description."""
system_prompt, prompt = self.create_job_analysis_prompt(user_message.content)
analyze_message = user_message.model_copy()
analyze_message.content = prompt
system_prompt, prompt = self.create_job_analysis_prompt(prompt)
generated_message = None
async for generated_message in self.llm_one_shot(llm, model, system_prompt=system_prompt, user_message=analyze_message):
if generated_message.status == ChatStatusType.ERROR:
generated_message.content = "Error analyzing job requirements."
async for generated_message in self.llm_one_shot(llm, model, session_id=session_id, prompt=prompt, system_prompt=system_prompt):
if generated_message.status == ApiStatusType.ERROR:
yield generated_message
return
if generated_message.status != ApiStatusType.DONE:
yield generated_message
if not generated_message:
status_message = ChatMessage(
session_id=user_message.session_id,
sender=ChatSenderType.AGENT,
status = ChatStatusType.ERROR,
type = ChatMessageType.ERROR,
content = "Job requirements analysis failed to generate a response.")
yield status_message
error_message = ChatMessageError(
session_id=session_id,
content="Job requirements analysis failed to generate a response.")
logger.error(f"⚠️ {error_message.content}")
yield error_message
return
generated_message.status = ChatStatusType.DONE
generated_message.type = ChatMessageType.RESPONSE
yield generated_message
return
async def generate(
self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate | None, temperature=0.7
self, llm: Any, model: str, session_id: str, prompt: str, tunables: Optional[Tunables] = None, temperature=0.7
) -> AsyncGenerator[ChatMessage, None]:
# Stage 1A: Analyze job requirements
status_message = ChatMessage(
session_id=user_message.session_id,
sender=ChatSenderType.AGENT,
status=ChatStatusType.STATUS,
type=ChatMessageType.THINKING,
status_message = ChatMessageStatus(
session_id=session_id,
content = f"Analyzing job requirements")
yield status_message
generated_message = None
async for generated_message in self.analyze_job_requirements(llm, model, user_message):
if generated_message.status == ChatStatusType.ERROR:
status_message.status = ChatStatusType.ERROR
status_message.content = generated_message.content
yield status_message
async for generated_message in self.analyze_job_requirements(llm, model, session_id, prompt):
if generated_message.status == ApiStatusType.ERROR:
yield generated_message
return
if generated_message.status != ApiStatusType.DONE:
yield generated_message
if not generated_message:
status_message.status = ChatStatusType.ERROR
status_message.content = "Job requirements analysis failed."
status_message = ChatMessageStatus(
session_id=session_id,
content="Job requirements analysis failed to generate a response.")
logger.error(f"⚠️ {status_message.content}")
yield status_message
return
@ -150,34 +145,33 @@ class JobRequirementsAgent(Agent):
if not job_requirements:
raise ValueError("Job requirements data is empty or invalid.")
except json.JSONDecodeError as e:
status_message.status = ChatStatusType.ERROR
status_message.status = ApiStatusType.ERROR
status_message.content = f"Failed to parse job requirements JSON: {str(e)}\n\n{job_requirements_data}"
logger.error(f"⚠️ {status_message.content}")
yield status_message
return
except ValueError as e:
status_message.status = ChatStatusType.ERROR
status_message.status = ApiStatusType.ERROR
status_message.content = f"Job requirements validation error: {str(e)}\n\n{job_requirements_data}"
logger.error(f"⚠️ {status_message.content}")
yield status_message
return
except Exception as e:
status_message.status = ChatStatusType.ERROR
status_message.status = ApiStatusType.ERROR
status_message.content = f"Unexpected error processing job requirements: {str(e)}\n\n{job_requirements_data}"
logger.error(traceback.format_exc())
logger.error(f"⚠️ {status_message.content}")
yield status_message
return
status_message.status = ChatStatusType.DONE
status_message.type = ChatMessageType.RESPONSE
job_data = {
"company": company_name,
"title": job_title,
"summary": job_summary,
"requirements": job_requirements.model_dump(mode="json", exclude_unset=True)
}
status_message.content = json.dumps(job_data)
yield status_message
job_requirements_message = JobRequirementsMessage(
session_id=session_id,
status=ApiStatusType.DONE,
requirements=job_requirements,
company=company_name,
title=job_title,
summary=job_summary
)
yield job_requirements_message
logger.info(f"✅ Job requirements analysis completed successfully.")
return

81
src/backend/agents/rag_search.py
View File

@ -7,7 +7,7 @@ from .base import Agent, agent_registry
from logger import logger
from .registry import agent_registry
from models import ( ChatMessage, ChatStatusType, ChatMessage, ChatOptions, ChatMessageType, ChatSenderType, ChatStatusType, ChatMessageMetaData, Candidate )
from models import ( ChatMessage, ApiStatusType, ChatMessage, ChatMessageError, ChatMessageRagSearch, ChatMessageStatus, ChatMessageStreaming, ChatOptions, ApiMessageType, ChatSenderType, ApiStatusType, ChatMessageMetaData, Candidate, Tunables )
from rag import ( ChromaDBGetResponse )
class Chat(Agent):
@ -19,8 +19,11 @@ class Chat(Agent):
_agent_type: ClassVar[str] = agent_type # Add this for registration
async def generate(
self, llm: Any, model: str, user_message: ChatMessage, user: Candidate, temperature=0.7
) -> AsyncGenerator[ChatMessage, None]:
self, llm: Any, model: str,
session_id: str, prompt: str,
tunables: Optional[Tunables] = None,
temperature=0.7
) -> AsyncGenerator[ChatMessage | ChatMessageStatus | ChatMessageError | ChatMessageStreaming, None]:
"""
Generate a response based on the user message and the provided LLM.
@ -34,65 +37,25 @@ class Chat(Agent):
Yields:
ChatMessage: The generated response.
"""
logger.info(f"{self.agent_type} - {inspect.stack()[0].function}")
if user.id != user_message.sender_id:
logger.error(f"User {user.username} id does not match message {user_message.sender_id}")
raise ValueError("User does not match message sender")
chat_message = ChatMessage(
session_id=user_message.session_id,
tunables=user_message.tunables,
status=ChatStatusType.INITIALIZING,
type=ChatMessageType.PREPARING,
sender=ChatSenderType.ASSISTANT,
content="",
timestamp=datetime.now(UTC)
)
chat_message.metadata = ChatMessageMetaData()
chat_message.metadata.options = ChatOptions(
seed=8911,
num_ctx=self.context_size,
temperature=temperature, # Higher temperature to encourage tool usage
)
# Create a dict for storing various timing stats
chat_message.metadata.timers = {}
self.metrics.generate_count.labels(agent=self.agent_type).inc()
with self.metrics.generate_duration.labels(agent=self.agent_type).time():
rag_message : Optional[ChatMessage] = None
async for rag_message in self.generate_rag_results(chat_message=user_message):
if rag_message.status == ChatStatusType.ERROR:
chat_message.status = rag_message.status
chat_message.content = rag_message.content
yield chat_message
return
rag_message = None
async for rag_message in self.generate_rag_results(session_id, prompt):
if rag_message.status == ApiStatusType.ERROR:
yield rag_message
return
if rag_message.status != ApiStatusType.DONE:
yield rag_message
if rag_message:
chat_message.content = ""
rag_results: List[ChromaDBGetResponse] = rag_message.metadata.rag_results
chat_message.metadata.rag_results = rag_results
for chroma_results in rag_results:
for index, metadata in enumerate(chroma_results.metadatas):
content = "\n".join([
line.strip()
for line in chroma_results.documents[index].split("\n")
if line
]).strip()
chat_message.content += f"""
Source: {metadata.get("doc_type", "unknown")}: {metadata.get("path", "")}
Document reference: {chroma_results.ids[index]}
Content: { content }
if not isinstance(rag_message, ChatMessageRagSearch):
logger.error(f"Expected ChatMessageRagSearch, got {type(rag_message)}")
error_message = ChatMessageError(
session_id=session_id,
content="RAG search did not return a valid response."
)
yield error_message
return
"""
chat_message.status = ChatStatusType.DONE
chat_message.type = ChatMessageType.RAG_RESULT
yield chat_message
rag_message.status = ApiStatusType.DONE
yield rag_message
# Register the base agent
agent_registry.register(Chat._agent_type, Chat)

138
src/backend/agents/skill_match.py
View File

@ -20,7 +20,7 @@ import asyncio
import numpy as np # type: ignore
from .base import Agent, agent_registry, LLMMessage
from models import Candidate, ChatMessage, ChatMessageBase, ChatMessageMetaData, ChatMessageType, ChatMessageUser, ChatOptions, ChatSenderType, ChatStatusType, SkillMatch
from models import Candidate, ChatMessage, ChatMessageError, ChatMessageMetaData, ApiMessageType, ChatMessageStatus, ChatMessageStreaming, ChatMessageUser, ChatOptions, ChatSenderType, ApiStatusType, SkillMatch, Tunables
import model_cast
from logger import logger
import defines
@ -29,13 +29,13 @@ class SkillMatchAgent(Agent):
agent_type: Literal["skill_match"] = "skill_match" # type: ignore
_agent_type: ClassVar[str] = agent_type # Add this for registration
def generate_skill_assessment_prompt(self, skill, rag_content):
def generate_skill_assessment_prompt(self, skill, rag_context):
"""
Generate a system prompt to query the LLM for evidence of a specific skill
Parameters:
- skill (str): The specific skill to assess from job requirements
- rag_content (str): Additional RAG content queried from candidate documents
- rag_contexty (str): Additional RAG content queried from candidate documents
Returns:
- str: A system prompt tailored to assess the specific skill
@ -98,7 +98,7 @@ Adhere strictly to the JSON output format requested. Do not include any addition
RESPOND WITH ONLY VALID JSON USING THE EXACT FORMAT SPECIFIED.
<candidate_info>
{rag_content}
{rag_context}
</candidate_info>
JSON RESPONSE:"""
@ -106,110 +106,102 @@ JSON RESPONSE:"""
return system_prompt, prompt
async def analyze_job_requirements(
self, llm: Any, model: str, user_message: ChatMessage
self, llm: Any, model: str, session_id: str, requirement: str
) -> AsyncGenerator[ChatMessage, None]:
"""Analyze job requirements from job description."""
system_prompt, prompt = self.create_job_analysis_prompt(user_message.content)
analyze_message = user_message.model_copy()
analyze_message.content = prompt
system_prompt, prompt = self.create_job_analysis_prompt(requirement)
generated_message = None
async for generated_message in self.llm_one_shot(llm, model, system_prompt=system_prompt, user_message=analyze_message):
if generated_message.status == ChatStatusType.ERROR:
async for generated_message in self.llm_one_shot(llm, model, session_id=session_id, prompt=prompt, system_prompt=system_prompt):
if generated_message.status == ApiStatusType.ERROR:
generated_message.content = "Error analyzing job requirements."
yield generated_message
return
if generated_message.status != ApiStatusType.DONE:
yield generated_message
if not generated_message:
status_message = ChatMessage(
session_id=user_message.session_id,
sender=ChatSenderType.AGENT,
status = ChatStatusType.ERROR,
type = ChatMessageType.ERROR,
error_message = ChatMessageError(
session_id=session_id,
content = "Job requirements analysis failed to generate a response.")
yield status_message
yield error_message
return
generated_message.status = ChatStatusType.DONE
generated_message.type = ChatMessageType.RESPONSE
yield generated_message
chat_message = ChatMessage(
session_id=session_id,
status=ApiStatusType.DONE,
content=generated_message.content,
metadata=generated_message.metadata,
)
yield chat_message
return
async def generate(
self, llm: Any, model: str, user_message: ChatMessageUser, user: Candidate | None, temperature=0.7
) -> AsyncGenerator[ChatMessage, None]:
self, llm: Any, model: str,
session_id: str, prompt: str,
tunables: Optional[Tunables] = None,
temperature=0.7
) -> AsyncGenerator[ChatMessage | ChatMessageStatus | ChatMessageError | ChatMessageStreaming, None]:
# Stage 1A: Analyze job requirements
status_message = ChatMessage(
session_id=user_message.session_id,
sender=ChatSenderType.AGENT,
status=ChatStatusType.STATUS,
type=ChatMessageType.THINKING,
content = f"Analyzing job requirements")
yield status_message
rag_message = None
async for rag_message in self.generate_rag_results(chat_message=user_message):
if rag_message.status == ChatStatusType.ERROR:
status_message.status = ChatStatusType.ERROR
status_message.content = rag_message.content
logger.error(f"⚠️ {status_message.content}")
yield status_message
async for rag_message in self.generate_rag_results(session_id=session_id, prompt=prompt):
if rag_message.status == ApiStatusType.ERROR:
yield rag_message
return
if rag_message.status != ApiStatusType.DONE:
yield rag_message
if rag_message is None:
status_message.status = ChatStatusType.ERROR
status_message.content = "Failed to retrieve RAG context."
logger.error(f"⚠️ {status_message.content}")
yield status_message
error_message = ChatMessageError(
session_id=session_id,
content="RAG search did not return a valid response."
)
logger.error(f"⚠️ {error_message.content}")
yield error_message
return
logger.info(f"🔍 RAG content retrieved: {len(rag_message.content)} bytes")
rag_context = self.get_rag_context(rag_message)
system_prompt, prompt = self.generate_skill_assessment_prompt(skill=prompt, rag_context=rag_context)
system_prompt, prompt = self.generate_skill_assessment_prompt(skill=user_message.content, rag_content=rag_message.content)
user_message.content = prompt
skill_assessment = None
async for skill_assessment in self.llm_one_shot(llm=llm, model=model, user_message=user_message, system_prompt=system_prompt, temperature=0.7):
if skill_assessment.status == ChatStatusType.ERROR:
status_message.status = ChatStatusType.ERROR
status_message.content = skill_assessment.content
logger.error(f"⚠️ {status_message.content}")
yield status_message
async for skill_assessment in self.llm_one_shot(llm=llm, model=model, session_id=session_id, prompt=prompt, system_prompt=system_prompt, temperature=0.7):
if skill_assessment.status == ApiStatusType.ERROR:
logger.error(f"⚠️ {skill_assessment.content}")
yield skill_assessment
return
if skill_assessment.status != ApiStatusType.DONE:
yield skill_assessment
if skill_assessment is None:
status_message.status = ChatStatusType.ERROR
status_message.content = "Failed to generate skill assessment."
logger.error(f"⚠️ {status_message.content}")
yield status_message
error_message = ChatMessageError(
session_id=session_id,
content="Skill assessment failed to generate a response."
)
logger.error(f"⚠️ {error_message.content}")
yield error_message
return
json_str = self.extract_json_from_text(skill_assessment.content)
skill_assessment_data = ""
try:
skill_assessment_data = json.loads(json_str).get("skill_assessment", {})
except json.JSONDecodeError as e:
status_message.status = ChatStatusType.ERROR
status_message.content = f"Failed to parse Skill assessment JSON: {str(e)}\n\n{skill_assessment_data}"
logger.error(f"⚠️ {status_message.content}")
yield status_message
return
except ValueError as e:
status_message.status = ChatStatusType.ERROR
status_message.content = f"Skill assessment validation error: {str(e)}\n\n{skill_assessment_data}"
logger.error(f"⚠️ {status_message.content}")
yield status_message
return
except Exception as e:
status_message.status = ChatStatusType.ERROR
status_message.content = f"Unexpected error processing Skill assessment: {str(e)}\n\n{skill_assessment_data}"
logger.error(traceback.format_exc())
logger.error(f"⚠️ {status_message.content}")
yield status_message
error_message = ChatMessageError(
session_id=session_id,
content=f"Failed to parse Skill assessment JSON: {str(e)}\n\n{skill_assessment.content}"
)
logger.error(f"⚠️ {error_message.content}")
yield error_message
return
status_message.status = ChatStatusType.DONE
status_message.type = ChatMessageType.RESPONSE
status_message.content = json.dumps(skill_assessment_data)
yield status_message
skill_assessment_message = ChatMessage(
session_id=session_id,
status=ApiStatusType.DONE,
content=json.dumps(skill_assessment_data),
metadata=skill_assessment.metadata
)
yield skill_assessment_message
logger.info(f"✅ Skill assessment completed successfully.")
return

2
src/backend/entities/candidate_entity.py
View File

@ -18,7 +18,7 @@ from rag import start_file_watcher, ChromaDBFileWatcher, ChromaDBGetResponse
import defines
from logger import logger
import agents as agents
from models import (Tunables, CandidateQuestion, ChatMessageUser, ChatMessage, RagEntry, ChatMessageType, ChatMessageMetaData, ChatStatusType, Candidate, ChatContextType)
from models import (Tunables, CandidateQuestion, ChatMessageUser, ChatMessage, RagEntry, ChatMessageMetaData, ApiStatusType, Candidate, ChatContextType)
from llm_manager import llm_manager
from agents.base import Agent

2
src/backend/generate_types.py
View File

@ -797,7 +797,7 @@ Generated conversion functions can be used like:
const jobs = convertArrayFromApi<Job>(apiResponse, 'Job');
Enum types are now properly handled:
status: ChatStatusType = ChatStatusType.DONE -> status: ChatStatusType (not locked to "done")
status: ApiStatusType = ApiStatusType.DONE -> status: ApiStatusType (not locked to "done")
"""
)

206
src/backend/image_generator/profile_image.py
View File

@ -24,7 +24,7 @@ import uuid
from .image_model_cache import ImageModelCache
from models import Candidate, ChatMessage, ChatMessageBase, ChatMessageMetaData, ChatMessageType, ChatMessageUser, ChatOptions, ChatSenderType, ChatStatusType
from models import ApiActivityType, ApiStatusType, Candidate, ChatMessage, ChatMessageError, ChatMessageMetaData, ChatMessageStatus, ChatMessageUser, ChatOptions, ChatSenderType
from logger import logger
from image_generator.image_model_cache import ImageModelCache
@ -39,6 +39,7 @@ TIME_ESTIMATES = {
}
class ImageRequest(BaseModel):
session_id: str
filepath: str
prompt: str
model: str = "black-forest-labs/FLUX.1-schnell"
@ -53,18 +54,12 @@ model_cache = ImageModelCache()
def flux_worker(pipe: Any, params: ImageRequest, status_queue: queue.Queue, task_id: str):
"""Background worker for Flux image generation"""
try:
# Your existing estimates calculation
estimates = {"per_step": 0.5} # Replace with your actual estimates
resolution_scale = (params.height * params.width) / (512 * 512)
# Flux: Run generation in the background and yield progress updates
estimated_gen_time = estimates["per_step"] * params.iterations * resolution_scale
status_queue.put({
"status": "running",
"message": f"Initializing image generation...",
"estimated_time_remaining": estimated_gen_time,
"progress": 0
})
status_queue.put(ChatMessageStatus(
session_id=params.session_id,
content=f"Initializing image generation.",
activity=ApiActivityType.GENERATING_IMAGE,
))
# Start the generation task
start_gen_time = time.time()
@ -74,11 +69,11 @@ def flux_worker(pipe: Any, params: ImageRequest, status_queue: queue.Queue, task
# Send progress updates
progress = int((step+1) / params.iterations * 100)
status_queue.put({
"status": "running",
"message": f"Processing step {step+1}/{params.iterations} ({progress}%) complete.",
"progress": progress
})
status_queue.put(ChatMessageStatus(
session_id=params.session_id,
content=f"Processing step {step+1}/{params.iterations} ({progress}%)",
activity=ApiActivityType.GENERATING_IMAGE,
))
return callback_kwargs
# Replace this block with your actual Flux pipe call:
@ -98,27 +93,22 @@ def flux_worker(pipe: Any, params: ImageRequest, status_queue: queue.Queue, task
image.save(params.filepath)
# Final completion status
status_queue.put({
"status": "completed",
"message": f"Image generated in {gen_time:.1f} seconds, {per_step_time:.1f} per iteration.",
"progress": 100,
"generation_time": gen_time,
"per_step_time": per_step_time,
"image_path": params.filepath
})
status_queue.put(ChatMessage(
session_id=params.session_id,
status=ApiStatusType.DONE,
content=f"Image generated in {gen_time:.1f} seconds, {per_step_time:.1f} per iteration.",
))
except Exception as e:
logger.error(traceback.format_exc())
logger.error(e)
status_queue.put({
"status": "error",
"message": f"Generation failed: {str(e)}",
"error": str(e),
"progress": 0
})
status_queue.put(ChatMessageError(
session_id=params.session_id,
content=f"Error during image generation: {str(e)}",
))
async def async_generate_image(pipe: Any, params: ImageRequest) -> AsyncGenerator[Dict[str, Any], None]:
async def async_generate_image(pipe: Any, params: ImageRequest) -> AsyncGenerator[ChatMessage | ChatMessageStatus | ChatMessageError, None]:
"""
Single async function that handles background Flux generation with status streaming
"""
@ -136,7 +126,12 @@ async def async_generate_image(pipe: Any, params: ImageRequest) -> AsyncGenerato
worker_thread.start()
# Initial status
yield {'status': 'starting', 'task_id': task_id, 'message': 'Initializing image generation'}
status_message = ChatMessageStatus(
session_id=params.session_id,
content=f"Starting image generation with task ID {task_id}",
activity=ApiActivityType.THINKING
)
yield status_message
# Stream status updates
completed = False
@ -147,14 +142,12 @@ async def async_generate_image(pipe: Any, params: ImageRequest) -> AsyncGenerato
# Try to get status update (non-blocking)
status_update = status_queue.get_nowait()
# Add task_id to status update
status_update['task_id'] = task_id
# Send status update
yield status_update
# Check if completed
if status_update.get('status') in ['completed', 'error']:
if status_update.status == ApiStatusType.DONE:
logger.info(f"Image generation completed for task {task_id}")
completed = True
last_heartbeat = time.time()
@ -163,11 +156,11 @@ async def async_generate_image(pipe: Any, params: ImageRequest) -> AsyncGenerato
# No new status, send heartbeat if needed
current_time = time.time()
if current_time - last_heartbeat > 2: # Heartbeat every 2 seconds
heartbeat = {
'status': 'heartbeat',
'task_id': task_id,
'timestamp': current_time
}
heartbeat = ChatMessageStatus(
session_id=params.session_id,
content=f"Heartbeat for task {task_id}",
activity=ApiActivityType.HEARTBEAT,
)
yield heartbeat
last_heartbeat = current_time
@ -178,28 +171,25 @@ async def async_generate_image(pipe: Any, params: ImageRequest) -> AsyncGenerato
if not completed:
if worker_thread.is_alive():
# Thread still running but we might have missed the completion signal
timeout_status = {
'status': 'timeout',
'task_id': task_id,
'message': 'Generation timed out or connection lost'
}
timeout_status = ChatMessageError(
session_id=params.session_id,
content=f"Generation timeout for task {task_id}. The process may still be running.",
)
yield timeout_status
else:
# Thread completed but we might have missed the final status
final_status = {
'status': 'completed',
'task_id': task_id,
'message': 'Generation completed'
}
final_status = ChatMessage(
session_id=params.session_id,
status=ApiStatusType.DONE,
content=f"Generation completed for task {task_id}.",
)
yield final_status
except Exception as e:
error_status = {
'status': 'error',
'task_id': task_id,
'message': f'Server error: {str(e)}',
'error': str(e)
}
error_status = ChatMessageError(
session_id=params.session_id,
content=f'Server error: {str(e)}'
)
logger.error(error_status)
yield error_status
@ -208,41 +198,38 @@ async def async_generate_image(pipe: Any, params: ImageRequest) -> AsyncGenerato
if worker_thread and 'worker_thread' in locals() and worker_thread.is_alive():
worker_thread.join(timeout=1.0) # Wait up to 1 second for cleanup
def status(chat_message: ChatMessage, status: str, progress: float = 0, estimated_time_remaining="...") -> ChatMessage:
def status(session_id: str, status: str) -> ChatMessageStatus:
"""Update chat message status and return it."""
message = chat_message.copy(deep=True)
message.id = str(uuid.uuid4())
message.timestamp = datetime.now(UTC)
message.type = ChatMessageType.THINKING
message.status = ChatStatusType.STREAMING
message.content = status
return message
async def generate_image(user_message: ChatMessage, request: ImageRequest) -> AsyncGenerator[ChatMessage, None]:
"""Generate an image with specified dimensions and yield status updates with time estimates."""
chat_message = ChatMessage(
session_id=user_message.session_id,
tunables=user_message.tunables,
status=ChatStatusType.INITIALIZING,
type=ChatMessageType.PREPARING,
sender=ChatSenderType.ASSISTANT,
content="",
timestamp=datetime.now(UTC)
chat_message = ChatMessageStatus(
session_id=session_id,
activity=ApiActivityType.GENERATING_IMAGE,
content=status,
)
return chat_message
async def generate_image(request: ImageRequest) -> AsyncGenerator[ChatMessage, None]:
"""Generate an image with specified dimensions and yield status updates with time estimates."""
session_id = request.session_id
prompt = request.prompt.strip()
try:
# Validate prompt
prompt = user_message.content.strip()
if not prompt:
chat_message.status = ChatStatusType.ERROR
chat_message.content = "Prompt cannot be empty"
yield chat_message
error_message = ChatMessageError(
session_id=session_id,
content="Prompt cannot be empty."
)
logger.error(error_message.content)
yield error_message
return
# Validate dimensions
if request.height <= 0 or request.width <= 0:
chat_message.status = ChatStatusType.ERROR
chat_message.content = "Height and width must be positive"
yield chat_message
error_message = ChatMessageError(
session_id=session_id,
content="Height and width must be positive integers."
)
logger.error(error_message.content)
yield error_message
return
filedir = os.path.dirname(request.filepath)
@ -252,43 +239,50 @@ async def generate_image(user_message: ChatMessage, request: ImageRequest) -> As
model_type = "flux"
device = "cpu"
yield status(chat_message, f"Starting image generation...")
# Get initial time estimate, scaled by resolution
estimates = TIME_ESTIMATES[model_type][device]
resolution_scale = (request.height * request.width) / (512 * 512)
estimated_total = estimates["load"] + estimates["per_step"] * request.iterations * resolution_scale
yield status(chat_message, f"Estimated generation time: ~{estimated_total:.1f} seconds for {request.width}x{request.height}")
# Initialize or get cached pipeline
start_time = time.time()
yield status(chat_message, f"Loading generative image model...")
yield status(session_id, f"Loading generative image model...")
pipe = await model_cache.get_pipeline(request.model, device)
load_time = time.time() - start_time
yield status(chat_message, f"Model loaded in {load_time:.1f} seconds.", progress=10)
yield status(session_id, f"Model loaded in {load_time:.1f} seconds.",)
async for status_message in async_generate_image(pipe, request):
chat_message.content = json.dumps(status_message) # Merge properties from async_generate_image over the message...
chat_message.type = ChatMessageType.HEARTBEAT if status_message.get("status") == "heartbeat" else ChatMessageType.THINKING
if chat_message.type != ChatMessageType.HEARTBEAT:
logger.info(chat_message.content)
yield chat_message
progress = None
async for progress in async_generate_image(pipe, request):
if progress.status == ApiStatusType.ERROR:
yield progress
return
if progress.status != ApiStatusType.DONE:
yield progress
if not progress:
error_message = ChatMessageError(
session_id=session_id,
content="Image generation failed to produce a valid response."
)
logger.error(f"⚠️ {error_message.content}")
yield error_message
return
# Final result
total_time = time.time() - start_time
chat_message.status = ChatStatusType.DONE
chat_message.type = ChatMessageType.RESPONSE
chat_message.content = json.dumps({
"status": f"Image generation complete in {total_time:.1f} seconds",
"progress": 100,
"filename": request.filepath
})
chat_message = ChatMessage(
session_id=session_id,
status=ApiStatusType.DONE,
content=f"Image generated successfully in {total_time:.1f} seconds.",
)
yield chat_message
except Exception as e:
chat_message.status = ChatStatusType.ERROR
chat_message.content = str(e)
yield chat_message
error_message = ChatMessageError(
session_id=session_id,
content=f"Error during image generation: {str(e)}"
)
logger.error(traceback.format_exc())
logger.error(chat_message.content)
logger.error(error_message.content)
yield error_message
return

64
src/backend/main.py
View File

@ -64,7 +64,7 @@ import agents
# =============================
from models import (
# API
Job, LoginRequest, CreateCandidateRequest, CreateEmployerRequest,
ChatMessageStatus, ChatMessageStreaming, ChatMessageUser, Job, LoginRequest, CreateCandidateRequest, CreateEmployerRequest,
# User models
Candidate, Employer, BaseUserWithType, BaseUser, Guest, Authentication, AuthResponse, CandidateAI,
@ -73,7 +73,7 @@ from models import (
JobFull, JobApplication, ApplicationStatus,
# Chat models
ChatSession, ChatMessage, ChatContext, ChatQuery, ChatStatusType, ChatMessageBase, ChatMessageUser, ChatSenderType, ChatMessageType, ChatContextType,
ChatSession, ChatMessage, ChatContext, ChatQuery, ApiStatusType, ChatSenderType, ApiMessageType, ChatContextType,
ChatMessageRagSearch,
# Document models
@ -361,7 +361,6 @@ def filter_and_paginate(
async def stream_agent_response(chat_agent: agents.Agent,
user_message: ChatMessageUser,
candidate: Candidate,
chat_session_data: Dict[str, Any] | None = None,
database: RedisDatabase | None = None) -> StreamingResponse:
async def message_stream_generator():
@ -372,30 +371,33 @@ async def stream_agent_response(chat_agent: agents.Agent,
async for generated_message in chat_agent.generate(
llm=llm_manager.get_llm(),
model=defines.model,
user_message=user_message,
user=candidate,
session_id=user_message.session_id,
prompt=user_message.content,
):
if generated_message.status == ApiStatusType.ERROR:
logger.error(f"❌ AI generation error: {generated_message.content}")
yield f"data: {json.dumps({'status': 'error'})}\n\n"
return
# Store reference to the complete AI message
if generated_message.status == ChatStatusType.DONE:
if generated_message.status == ApiStatusType.DONE:
final_message = generated_message
# If the message is not done, convert it to a ChatMessageBase to remove
# metadata and other unnecessary fields for streaming
if generated_message.status != ChatStatusType.DONE:
generated_message = model_cast.cast_to_model(ChatMessageBase, generated_message)
if generated_message.status != ApiStatusType.DONE:
if not isinstance(generated_message, ChatMessageStreaming) and not isinstance(generated_message, ChatMessageStatus):
raise TypeError(
f"Expected ChatMessageStreaming or ChatMessageStatus, got {type(generated_message)}"
)
json_data = generated_message.model_dump(mode='json', by_alias=True, exclude_unset=True)
json_data = generated_message.model_dump(mode='json', by_alias=True)
json_str = json.dumps(json_data)
log = f"🔗 Message status={generated_message.status}, sender={getattr(generated_message, 'sender', 'unknown')}"
if last_log != log:
last_log = log
logger.info(log)
yield f"data: {json_str}\n\n"
# After streaming is complete, persist the final AI message to database
if final_message and final_message.status == ChatStatusType.DONE:
if final_message and final_message.status == ApiStatusType.DONE:
try:
if database and chat_session_data:
await database.add_chat_message(final_message.session_id, final_message.model_dump())
@ -412,9 +414,12 @@ async def stream_agent_response(chat_agent: agents.Agent,
message_stream_generator(),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Cache-Control": "no-cache, no-store, must-revalidate",
"Connection": "keep-alive",
"X-Accel-Buffering": "no",
"X-Accel-Buffering": "no", # Nginx
"X-Content-Type-Options": "nosniff",
"Access-Control-Allow-Origin": "*", # Adjust for your CORS needs
"Transfer-Encoding": "chunked",
},
)
@ -678,19 +683,19 @@ async def create_candidate_ai(
async for generated_message in generate_agent.generate(
llm=llm_manager.get_llm(),
model=defines.model,
user_message=user_message,
user=None,
session_id=user_message.session_id,
prompt=user_message.content,
):
if generated_message.status == ChatStatusType.ERROR:
if generated_message.status == ApiStatusType.ERROR:
logger.error(f"❌ AI generation error: {generated_message.content}")
return JSONResponse(
status_code=500,
content=create_error_response("AI_GENERATION_ERROR", generated_message.content)
)
if generated_message.type == ChatMessageType.RESPONSE and state == 0:
if generated_message.status == ApiStatusType.DONE and state == 0:
persona_message = generated_message
state = 1 # Switch to resume generation
elif generated_message.type == ChatMessageType.RESPONSE and state == 1:
elif generated_message.status == ApiStatusType.DONE and state == 1:
resume_message = generated_message
if not persona_message:
@ -2412,7 +2417,6 @@ async def update_candidate(
)
is_AI = candidate_data.get("is_AI", False)
logger.info(json.dumps(candidate_data, indent=2))
candidate = CandidateAI.model_validate(candidate_data) if is_AI else Candidate.model_validate(candidate_data)
# Check authorization (user can only update their own profile)
@ -2804,8 +2808,8 @@ async def post_candidate_rag_search(
async for generated_message in chat_agent.generate(
llm=llm_manager.get_llm(),
model=defines.model,
user_message=user_message,
user=candidate,
session_id=user_message.session_id,
prompt=user_message.prompt,
):
rag_message = generated_message
@ -3087,7 +3091,6 @@ async def post_chat_session_message_stream(
return await stream_agent_response(
chat_agent=chat_agent,
user_message=user_message,
candidate=candidate,
database=database,
chat_session_data=chat_session_data,
)
@ -3389,15 +3392,10 @@ async def get_candidate_skill_match(
agent.generate(
llm=llm_manager.get_llm(),
model=defines.model,
user_message=ChatMessageUser(
sender_id=candidate.id,
session_id="",
content=requirement,
timestamp=datetime.now(UTC)
session_id="",
prompt=requirement,
),
user=candidate,
)
)
if skill_match is None:
return JSONResponse(
status_code=500,

97
src/backend/models.py
View File

@ -71,8 +71,11 @@ class InterviewRecommendation(str, Enum):
class ChatSenderType(str, Enum):
USER = "user"
ASSISTANT = "assistant"
AGENT = "agent"
# Frontend can use this to set mock responses
SYSTEM = "system"
INFORMATION = "information"
WARNING = "warning"
ERROR = "error"
class Requirements(BaseModel):
required: List[str] = Field(default_factory=list)
@ -108,24 +111,12 @@ class SkillMatch(BaseModel):
"populate_by_name": True # Allow both field names and aliases
}
class ApiMessageType(str, Enum):
BINARY = "binary"
TEXT = "text"
JSON = "json"
class ChatMessageType(str, Enum):
ERROR = "error"
GENERATING = "generating"
INFO = "info"
PREPARING = "preparing"
PROCESSING = "processing"
HEARTBEAT = "heartbeat"
RESPONSE = "response"
SEARCHING = "searching"
RAG_RESULT = "rag_result"
SYSTEM = "system"
THINKING = "thinking"
TOOLING = "tooling"
USER = "user"
class ChatStatusType(str, Enum):
INITIALIZING = "initializing"
class ApiStatusType(str, Enum):
STREAMING = "streaming"
STATUS = "status"
DONE = "done"
@ -772,26 +763,55 @@ class LLMMessage(BaseModel):
content: str = Field(default="")
tool_calls: Optional[List[Dict]] = Field(default=[], exclude=True)
class ChatMessageBase(BaseModel):
class ApiMessage(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()))
session_id: str = Field(..., alias="sessionId")
sender_id: Optional[str] = Field(None, alias="senderId")
status: ChatStatusType #= ChatStatusType.INITIALIZING
type: ChatMessageType #= ChatMessageType.PREPARING
sender: ChatSenderType #= ChatSenderType.SYSTEM
status: ApiStatusType
type: ApiMessageType
timestamp: datetime = Field(default_factory=lambda: datetime.now(UTC), alias="timestamp")
tunables: Optional[Tunables] = None
content: str = ""
model_config = {
"populate_by_name": True # Allow both field names and aliases
}
class ChatMessageRagSearch(ChatMessageBase):
status: ChatStatusType = ChatStatusType.DONE
type: ChatMessageType = ChatMessageType.RAG_RESULT
sender: ChatSenderType = ChatSenderType.USER
class ChatMessageStreaming(ApiMessage):
status: ApiStatusType = ApiStatusType.STREAMING
type: ApiMessageType = ApiMessageType.TEXT
content: str
class ApiActivityType(str, Enum):
SYSTEM = "system" # Used solely on frontend
INFO = "info" # Used solely on frontend
SEARCHING = "searching" # Used when generating RAG information
THINKING = "thinking" # Used when determing if AI will use tools
GENERATING = "generating" # Used when AI is generating a response
GENERATING_IMAGE = "generating_image" # Used when AI is generating an image
TOOLING = "tooling" # Used when AI is using tools
HEARTBEAT = "heartbeat" # Used for periodic updates
class ChatMessageStatus(ApiMessage):
status: ApiStatusType = ApiStatusType.STATUS
type: ApiMessageType = ApiMessageType.TEXT
activity: ApiActivityType
content: Any
class ChatMessageError(ApiMessage):
status: ApiStatusType = ApiStatusType.ERROR
type: ApiMessageType = ApiMessageType.TEXT
content: str
class ChatMessageRagSearch(ApiMessage):
type: ApiMessageType = ApiMessageType.JSON
dimensions: int = 2 | 3
content: List[ChromaDBGetResponse] = []
class JobRequirementsMessage(ApiMessage):
type: ApiMessageType = ApiMessageType.JSON
title: Optional[str]
summary: Optional[str]
company: Optional[str]
description: str
requirements: Optional[JobRequirements]
class ChatMessageMetaData(BaseModel):
model: AIModelType = AIModelType.QWEN2_5
@ -808,23 +828,26 @@ class ChatMessageMetaData(BaseModel):
prompt_eval_count: int = 0
prompt_eval_duration: int = 0
options: Optional[ChatOptions] = None
tools: Optional[Dict[str, Any]] = None
timers: Optional[Dict[str, float]] = None
tools: Dict[str, Any] = Field(default_factory=dict)
timers: Dict[str, float] = Field(default_factory=dict)
model_config = {
"populate_by_name": True # Allow both field names and aliases
}
class ChatMessageUser(ChatMessageBase):
status: ChatStatusType = ChatStatusType.INITIALIZING
type: ChatMessageType = ChatMessageType.GENERATING
sender: ChatSenderType = ChatSenderType.USER
class ChatMessageUser(ApiMessage):
type: ApiMessageType = ApiMessageType.TEXT
status: ApiStatusType = ApiStatusType.DONE
role: ChatSenderType = ChatSenderType.USER
content: str = ""
tunables: Optional[Tunables] = None
class ChatMessage(ChatMessageBase):
class ChatMessage(ChatMessageUser):
role: ChatSenderType = ChatSenderType.ASSISTANT
metadata: ChatMessageMetaData = Field(default_factory=ChatMessageMetaData)
#attachments: Optional[List[Attachment]] = None
#reactions: Optional[List[MessageReaction]] = None
#is_edited: bool = Field(False, alias="isEdited")
#edit_history: Optional[List[EditHistory]] = Field(None, alias="editHistory")
metadata: ChatMessageMetaData = Field(default_factory=ChatMessageMetaData)
class ChatSession(BaseModel):
id: str = Field(default_factory=lambda: str(uuid.uuid4()))

43
src/backend/rag/rag.py
View File

@ -27,8 +27,9 @@ from .markdown_chunker import (
# When imported as a module, use relative imports
import defines
from models import ChromaDBGetResponse
__all__ = ["ChromaDBFileWatcher", "start_file_watcher", "ChromaDBGetResponse"]
__all__ = ["ChromaDBFileWatcher", "start_file_watcher"]
DEFAULT_CHUNK_SIZE = 750
DEFAULT_CHUNK_OVERLAP = 100
@ -38,46 +39,6 @@ class RagEntry(BaseModel):
description: str = ""
enabled: bool = True
class ChromaDBGetResponse(BaseModel):
name: str = ""
size: int = 0
ids: List[str] = []
embeddings: List[List[float]] = Field(default=[])
documents: List[str] = []
metadatas: List[Dict[str, Any]] = []
query: str = ""
query_embedding: Optional[List[float]] = Field(default=None)
umap_embedding_2d: Optional[List[float]] = Field(default=None)
umap_embedding_3d: Optional[List[float]] = Field(default=None)
enabled: bool = True
class Config:
validate_assignment = True
@field_validator("embeddings", "query_embedding", "umap_embedding_2d", "umap_embedding_3d")
@classmethod
def validate_embeddings(cls, value, field):
# logging.info(f"Validating {field.field_name} with value: {type(value)} - {value}")
if value is None:
return value
if isinstance(value, np.ndarray):
if field.field_name == "embeddings":
if value.ndim != 2:
raise ValueError(f"{field.name} must be a 2-dimensional NumPy array")
return [[float(x) for x in row] for row in value.tolist()]
else:
if value.ndim != 1:
raise ValueError(f"{field.field_name} must be a 1-dimensional NumPy array")
return [float(x) for x in value.tolist()]
if field.field_name == "embeddings":
if not all(isinstance(sublist, list) and all(isinstance(x, (int, float)) for x in sublist) for sublist in value):
raise ValueError(f"{field.field_name} must be a list of lists of floats")
return [[float(x) for x in sublist] for sublist in value]
else:
if not isinstance(value, list) or not all(isinstance(x, (int, float)) for x in value):
raise ValueError(f"{field.field_name} must be a list of floats")
return [float(x) for x in value]
class ChromaDBFileWatcher(FileSystemEventHandler):
def __init__(
self,