mirror of
https://github.com/csunny/DB-GPT.git
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feat: handle conflict
This commit is contained in:
shiweisong.ssw
commit
16198d626f
@ -1,14 +1,50 @@
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"use client"
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import dynamic from 'next/dynamic'
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import { useRequest } from 'ahooks';
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import { sendGetRequest, sendPostRequest } from '@/utils/request';
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import useAgentChat from '@/hooks/useAgentChat';
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import ChatBoxComp from '@/components/chatBoxTemp';
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import { useDialogueContext } from '@/app/context/dialogue';
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import { useSearchParams } from 'next/navigation';
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const DynamicWrapper = dynamic(() => import ('@/components/agentPage'), {
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loading: () => <p>Loading...</p>,
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ssr: false,
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});
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const AgentPage = () => {
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const searchParams = useSearchParams();
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const { refreshDialogList } = useDialogueContext();
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const id = searchParams.get('id');
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const scene = searchParams.get('scene');
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const { data: historyList } = useRequest(async () => await sendGetRequest('/v1/chat/dialogue/messages/history', {
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con_uid: id
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}), {
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ready: !!id,
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refreshDeps: [id]
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});
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const { data: paramsList } = useRequest(async () => await sendPostRequest(`/v1/chat/mode/params/list?chat_mode=${scene}`), {
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ready: !!scene,
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refreshDeps: [id, scene]
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});
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const { history, handleChatSubmit } = useAgentChat({
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queryAgentURL: `/v1/chat/completions`,
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queryBody: {
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conv_uid: id,
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chat_mode: scene || 'chat_normal',
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},
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initHistory: historyList?.data
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});
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const DynamicAgentPage = (props: any) => {
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return (
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<DynamicWrapper {...props} />
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<>
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<ChatBoxComp
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clearIntialMessage={async () => {
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await refreshDialogList();
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}}
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messages={history || []}
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onSubmit={handleChatSubmit}
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paramsList={paramsList?.data}
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/>
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</>
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)
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}
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export default DynamicAgentPage;
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export default AgentPage;
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@ -18,7 +18,7 @@ export default function Home() {
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resolver: zodResolver(Schema),
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defaultValues: {},
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});
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const { data: scenesList } = useRequest(async () => await sendPostRequest('v1/chat/dialogue/scenes'));
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const { data: scenesList } = useRequest(async () => await sendPostRequest('/v1/chat/dialogue/scenes'));
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const submit = async ({ query }: z.infer<typeof Schema>) => {
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try {
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@ -61,6 +61,9 @@ export default function Home() {
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size="md"
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variant="solid"
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className='text-base rounded-none '
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style={{
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boxShadow: '0px 8px 10px 0px rgb(31 31 31 / 50%)'
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}}
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onClick={async () => {
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const res = await sendPostRequest('/v1/chat/dialogue/new', {
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chat_mode: scene['chat_scene']
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@ -1,53 +0,0 @@
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"use client"
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import { useRequest } from 'ahooks';
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import { sendGetRequest, sendPostRequest } from '@/utils/request';
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import useAgentChat from '@/hooks/useAgentChat';
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import ChatBoxComp from '@/components/chatBoxTemp';
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import { useDialogueContext } from '@/app/context/dialogue';
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const AgentPage = (props: {
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searchParams: {
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id?: string;
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scene?: string;
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initMessage?: string;
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}
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}) => {
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const { refreshDialogList } = useDialogueContext();
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const { data: historyList } = useRequest(async () => await sendGetRequest('/v1/chat/dialogue/messages/history', {
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con_uid: props.searchParams?.id
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}), {
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ready: !!props.searchParams?.id,
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refreshDeps: [props.searchParams?.id]
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});
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const { data: paramsList } = useRequest(async () => await sendPostRequest(`/v1/chat/mode/params/list?chat_mode=${props.searchParams?.scene}`), {
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ready: !!props.searchParams?.scene,
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refreshDeps: [props.searchParams?.scene]
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});
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const { history, handleChatSubmit } = useAgentChat({
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queryAgentURL: `/v1/chat/completions`,
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queryBody: {
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conv_uid: props.searchParams?.id,
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chat_mode: props.searchParams?.scene || 'chat_normal',
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},
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initHistory: historyList?.data
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});
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return (
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<>
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<ChatBoxComp
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initialMessage={historyList?.data ? (historyList?.data?.length <= 0 ? props.searchParams?.initMessage : undefined) : undefined}
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clearIntialMessage={async () => {
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await refreshDialogList();
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}}
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messages={history || []}
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onSubmit={handleChatSubmit}
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paramsList={paramsList?.data}
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/>
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</>
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)
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}
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export default AgentPage;
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@ -10,11 +10,11 @@ import SmartToyOutlinedIcon from '@mui/icons-material/SmartToyOutlined';
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import Markdown from 'markdown-to-jsx';
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import { Prism as SyntaxHighlighter } from 'react-syntax-highlighter';
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import { okaidia } from 'react-syntax-highlighter/dist/esm/styles/prism';
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import { useSearchParams } from 'next/navigation';
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type Props = {
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messages: Message[];
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onSubmit: (message: string, otherQueryBody?: any) => Promise<any>;
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initialMessage?: string;
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readOnly?: boolean;
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paramsList?: { [key: string]: string };
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clearIntialMessage?: () => void;
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@ -25,12 +25,13 @@ const Schema = z.object({ query: z.string().min(1) });
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const ChatBoxComp = ({
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messages,
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onSubmit,
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initialMessage,
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readOnly,
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paramsList,
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clearIntialMessage
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}: Props) => {
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const { mode } = useColorScheme();
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const searchParams = useSearchParams();
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const initMessage = searchParams.get('initMessage');
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const scrollableRef = React.useRef<HTMLDivElement>(null);
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const [isLoading, setIsLoading] = useState(false);
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const [currentParam, setCurrentParam] = useState<string | undefined | null>();
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@ -42,6 +43,7 @@ const ChatBoxComp = ({
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const submit = async ({ query }: z.infer<typeof Schema>) => {
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try {
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console.log('submit');
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setIsLoading(true);
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methods.reset();
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await onSubmit(query, {
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@ -55,10 +57,11 @@ const ChatBoxComp = ({
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const handleInitMessage = async () => {
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try {
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const searchParams = new URLSearchParams(window.location.search);
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searchParams.delete('initMessage');
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window.history.replaceState(null, null, `?${searchParams.toString()}`);
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await submit({ query: (initialMessage as string) });
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const searchParamsTemp = new URLSearchParams(window.location.search);
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const initMessage = searchParamsTemp.get('initMessage');
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searchParamsTemp.delete('initMessage');
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window.history.replaceState(null, null, `?${searchParamsTemp.toString()}`);
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await submit({ query: (initMessage as string) });
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} catch (err) {
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console.log(err);
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} finally {
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@ -83,12 +86,12 @@ const ChatBoxComp = ({
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scrollableRef.current.scrollTo(0, scrollableRef.current.scrollHeight);
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}, [messages?.length]);
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React.useEffect(() => {
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if (initialMessage && messages.length <= 0) {
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if (initMessage && messages.length <= 0) {
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handleInitMessage();
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}
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}, [initialMessage]);
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}, [initMessage, messages.length]);
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React.useEffect(() => {
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if (paramsList && Object.keys(paramsList || {})?.length > 0) {
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@ -130,7 +130,7 @@ const LeftSider = () => {
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<ListItemContent>
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<Link href={`/chat?id=${each.conv_uid}&scene=${each?.chat_mode}`} className="flex items-center justify-between">
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<Typography fontSize={14} noWrap={true}>
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<SmsOutlinedIcon className='mr-2' />
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<SmsOutlinedIcon style={{ marginRight: '0.5rem' }} />
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{each?.user_name || each?.user_input || 'undefined'}
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</Typography>
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<IconButton
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@ -146,7 +146,7 @@ const LeftSider = () => {
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width: '276px',
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centered: true,
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async onOk() {
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await sendPostRequest(`v1/chat/dialogue/delete?con_uid=${each.conv_uid}`);
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await sendPostRequest(`/v1/chat/dialogue/delete?con_uid=${each.conv_uid}`);
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await refreshDialogList();
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if (pathname === `/chat` && searchParams.get('id') === each.conv_uid) {
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router.push('/');
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@ -8,7 +8,7 @@ export const joyTheme = extendTheme({
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mode: 'dark',
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primary: {
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...colors.grey,
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solidBg: '#dfdfdf91',
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solidBg: '#9a9a9a91',
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solidColor: '#4e4e4e',
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solidHoverBg: '#d5d5d5',
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outlinedColor: '#4e4e59'
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@ -36,7 +36,9 @@ export const joyTheme = extendTheme({
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...colors.grey,
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softBg: '#353539',
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softHoverBg: '#35353978',
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softDisabledBg: '#353539'
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softDisabledBg: '#353539',
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solidBg: '#434356',
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solidHoverBg: '#5a5a72',
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},
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neutral: {
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plainColor: '#D8D8DF',
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@ -49,7 +49,7 @@ import { useDialogueContext } from '@/app/context/dialogue';
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const ctrl = new AbortController();
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let buffer = '';
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await fetchEventSource(`${process.env.API_BASE_URL + queryAgentURL}`, {
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await fetchEventSource(`${process.env.API_BASE_URL + "/api" + queryAgentURL}`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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@ -8,7 +8,7 @@ const nextConfig = {
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ignoreBuildErrors: true
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},
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env: {
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API_BASE_URL: process.env.API_BASE_URL || 'http://localhost:5000'
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API_BASE_URL: process.env.API_BASE_URL || 'https://u158074-879a-d00019a9.westa.seetacloud.com:8443'
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}
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}
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@ -30,7 +30,7 @@ export const sendGetRequest = (url: string, qs?: { [key: string]: any }) => {
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url += `?${str}`;
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}
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}
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return axios.get(url, {
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return axios.get("/api" + url, {
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headers: DEFAULT_HEADERS
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}).then(res => res).catch(err => {
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message.error(err);
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@ -40,7 +40,7 @@ export const sendGetRequest = (url: string, qs?: { [key: string]: any }) => {
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export const sendPostRequest = (url: string, body?: any) => {
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const reqBody = sanitizeBody(body);
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return axios.post(url, {
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return axios.post("/api" + url, {
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body: reqBody,
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headers: DEFAULT_HEADERS
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}).then(res => res).catch(err => {
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@ -33,9 +33,6 @@ class SourceEmbedding(ABC):
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self.vector_store_config = vector_store_config
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self.embedding_args = embedding_args
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self.embeddings = vector_store_config["embeddings"]
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self.vector_client = VectorStoreConnector(
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CFG.VECTOR_STORE_TYPE, vector_store_config
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)
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@abstractmethod
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@register
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@ -59,11 +56,17 @@ class SourceEmbedding(ABC):
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@register
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def index_to_store(self, docs):
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"""index to vector store"""
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return self.vector_client.load_document(docs)
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self.vector_client = VectorStoreConnector(
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CFG.VECTOR_STORE_TYPE, self.vector_store_config
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)
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self.vector_client.load_document(docs)
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@register
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def similar_search(self, doc, topk):
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"""vector store similarity_search"""
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self.vector_client = VectorStoreConnector(
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CFG.VECTOR_STORE_TYPE, self.vector_store_config
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)
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try:
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ans = self.vector_client.similar_search(doc, topk)
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except NotEnoughElementsException:
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@ -71,6 +74,9 @@ class SourceEmbedding(ABC):
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return ans
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def vector_name_exist(self):
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self.vector_client = VectorStoreConnector(
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CFG.VECTOR_STORE_TYPE, self.vector_store_config
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)
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return self.vector_client.vector_name_exists()
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def source_embedding(self):
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@ -53,7 +53,7 @@ async def validation_exception_handler(request: Request, exc: RequestValidationE
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message = ""
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for error in exc.errors():
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message += ".".join(error.get("loc")) + ":" + error.get("msg") + ";"
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return Result.faild(msg=message)
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return Result.faild(code= "E0001", msg=message)
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def __get_conv_user_message(conversations: dict):
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@ -96,9 +96,9 @@ def knowledge_list():
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return params
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@router.get("/")
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async def read_main():
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return FileResponse(f"{static_file_path}/index.html")
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# @router.get("/")
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# async def read_main():
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# return FileResponse(f"{static_file_path}/index.html")
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@router.get("/v1/chat/dialogue/list", response_model=Result[ConversationVo])
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@ -34,7 +34,7 @@ class ConversationVo(BaseModel):
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dialogue_uid
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"""
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conv_uid: str = Field(..., description="dialogue uid")
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conv_uid: str = ""
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"""
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user input
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"""
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@ -46,7 +46,7 @@ class ConversationVo(BaseModel):
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"""
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the scene of chat
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"""
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chat_mode: str = Field(..., description="the scene of chat ")
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chat_mode: str = ""
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"""
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chat scene select param
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@ -39,7 +39,6 @@ from pilot.scene.base_message import (
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ViewMessage,
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)
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from pilot.configs.config import Config
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from pilot.server.llmserver import worker
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logger = build_logger("BaseChat", LOGDIR + "BaseChat.log")
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headers = {"User-Agent": "dbgpt Client"}
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@ -143,6 +142,7 @@ class BaseChat(ABC):
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)
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return response
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else:
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from pilot.server.llmserver import worker
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return worker.generate_stream_gate(payload)
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except Exception as e:
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print(traceback.format_exc())
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@ -168,6 +168,7 @@ class BaseChat(ABC):
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)
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else:
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###TODO no stream mode need independent
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from pilot.server.llmserver import worker
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output = worker.generate_stream_gate(payload)
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for rsp in output:
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rsp_str = str(rsp, "utf-8")
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@ -65,11 +65,19 @@ app.add_middleware(
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allow_headers=["*"],
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)
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app.mount("/_next/static", StaticFiles(directory=static_file_path + "/_next/static", html=True))
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app.mount("/static", StaticFiles(directory=static_file_path), name="static2")
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app.include_router(knowledge_router)
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app.include_router(api_v1, prefix="/api")
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app.include_router(knowledge_router, prefix="/api")
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app.include_router(api_v1)
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app.include_router(knowledge_router)
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app.mount("/static", StaticFiles(directory=static_file_path), name="static")
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app.mount("/_next/static", StaticFiles(directory=static_file_path + "/_next/static"))
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app.mount("/", StaticFiles(directory=static_file_path, html=True), name="static")
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app.add_exception_handler(RequestValidationError, validation_exception_handler)
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if __name__ == "__main__":
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@ -87,6 +95,9 @@ if __name__ == "__main__":
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# init server config
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args = parser.parse_args()
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from pilot.server.llmserver import worker
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worker.start_check()
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server_init(args)
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CFG.NEW_SERVER_MODE = True
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import uvicorn
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@ -1 +1 @@
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<!DOCTYPE html><html><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><title>404: This page could not be found</title><meta name="next-head-count" content="3"/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-24270a861878a52b.js" defer=""></script><script src="/_next/static/chunks/framework-43665103d101a22d.js" defer=""></script><script src="/_next/static/chunks/main-4d516eb54537b10d.js" defer=""></script><script src="/_next/static/chunks/pages/_app-1f2755172264764d.js" defer=""></script><script src="/_next/static/chunks/pages/_error-f5357f382422dd96.js" defer=""></script><script src="/_next/static/88tBPZRhns7FgumS5knsE/_buildManifest.js" defer=""></script><script src="/_next/static/88tBPZRhns7FgumS5knsE/_ssgManifest.js" defer=""></script></head><body><div id="__next"><div style="font-family:system-ui,"Segoe UI",Roboto,Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji";height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div style="line-height:48px"><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding-right:23px;font-size:24px;font-weight:500;vertical-align:top">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:28px">This page could not be found<!-- -->.</h2></div></div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"88tBPZRhns7FgumS5knsE","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>
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|
||||
|
||||
@ -1,19 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>Streaming Demo</title>
|
||||
<script src="https://code.jquery.com/jquery-3.6.0.min.js"></script>
|
||||
</head>
|
||||
<body>
|
||||
<div id="output">Hello World! I'm DB-GPT!</div>
|
||||
<script>
|
||||
$(document).ready(function() {
|
||||
var source = new EventSource("/v1/chat/completions");
|
||||
source.onmessage = function(event) {
|
||||
$("#output").append(event.data);
|
||||
}
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@ -37,7 +37,7 @@ from pilot.conversation import (
|
||||
|
||||
from pilot.server.gradio_css import code_highlight_css
|
||||
from pilot.server.gradio_patch import Chatbot as grChatbot
|
||||
from pilot.source_embedding.knowledge_embedding import KnowledgeEmbedding
|
||||
from pilot.embedding_engine.knowledge_embedding import KnowledgeEmbedding
|
||||
from pilot.utils import build_logger
|
||||
from pilot.vector_store.extract_tovec import (
|
||||
get_vector_storelist,
|
||||
@ -297,54 +297,40 @@ def http_bot(
|
||||
|
||||
if ChatScene.ChatWithDbExecute == scene:
|
||||
chat_param = {
|
||||
"temperature": temperature,
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"chat_session_id": state.conv_id,
|
||||
"db_name": db_selector,
|
||||
"user_input": state.last_user_input,
|
||||
}
|
||||
elif ChatScene.ChatWithDbQA == scene:
|
||||
chat_param = {
|
||||
"temperature": temperature,
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"chat_session_id": state.conv_id,
|
||||
"db_name": db_selector,
|
||||
"user_input": state.last_user_input,
|
||||
}
|
||||
elif ChatScene.ChatExecution == scene:
|
||||
chat_param = {
|
||||
"temperature": temperature,
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"chat_session_id": state.conv_id,
|
||||
"plugin_selector": plugin_selector,
|
||||
"user_input": state.last_user_input,
|
||||
}
|
||||
elif ChatScene.ChatNormal == scene:
|
||||
chat_param = {
|
||||
"temperature": temperature,
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"chat_session_id": state.conv_id,
|
||||
"user_input": state.last_user_input,
|
||||
}
|
||||
elif ChatScene.ChatDefaultKnowledge == scene:
|
||||
chat_param = {
|
||||
"temperature": temperature,
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"chat_session_id": state.conv_id,
|
||||
"user_input": state.last_user_input,
|
||||
}
|
||||
elif ChatScene.ChatNewKnowledge == scene:
|
||||
chat_param = {
|
||||
"temperature": temperature,
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"chat_session_id": state.conv_id,
|
||||
"user_input": state.last_user_input,
|
||||
"knowledge_name": knowledge_name,
|
||||
}
|
||||
elif ChatScene.ChatUrlKnowledge == scene:
|
||||
chat_param = {
|
||||
"temperature": temperature,
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"chat_session_id": state.conv_id,
|
||||
"user_input": state.last_user_input,
|
||||
"url": url_input,
|
||||
|
||||
@ -38,10 +38,6 @@ def server_init(args):
|
||||
|
||||
# init config
|
||||
cfg = Config()
|
||||
|
||||
from pilot.server.llmserver import worker
|
||||
|
||||
worker.start_check()
|
||||
load_native_plugins(cfg)
|
||||
signal.signal(signal.SIGINT, signal_handler)
|
||||
async_db_summery()
|
||||
|
||||
@ -1,26 +0,0 @@
|
||||
from typing import List, Optional
|
||||
import chardet
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.document_loaders.base import BaseLoader
|
||||
|
||||
|
||||
class EncodeTextLoader(BaseLoader):
|
||||
"""Load text files."""
|
||||
|
||||
def __init__(self, file_path: str, encoding: Optional[str] = None):
|
||||
"""Initialize with file path."""
|
||||
self.file_path = file_path
|
||||
self.encoding = encoding
|
||||
|
||||
def load(self) -> List[Document]:
|
||||
"""Load from file path."""
|
||||
with open(self.file_path, "rb") as f:
|
||||
raw_text = f.read()
|
||||
result = chardet.detect(raw_text)
|
||||
if result["encoding"] is None:
|
||||
text = raw_text.decode("utf-8")
|
||||
else:
|
||||
text = raw_text.decode(result["encoding"])
|
||||
metadata = {"source": self.file_path}
|
||||
return [Document(page_content=text, metadata=metadata)]
|
||||
@ -1,3 +0,0 @@
|
||||
from pilot.source_embedding.source_embedding import SourceEmbedding, register
|
||||
|
||||
__all__ = ["SourceEmbedding", "register"]
|
||||
@ -1,55 +0,0 @@
|
||||
import re
|
||||
from typing import List
|
||||
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
|
||||
|
||||
class CHNDocumentSplitter(CharacterTextSplitter):
|
||||
def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.pdf = pdf
|
||||
self.sentence_size = sentence_size
|
||||
|
||||
def split_text(self, text: str) -> List[str]:
|
||||
if self.pdf:
|
||||
text = re.sub(r"\n{3,}", r"\n", text)
|
||||
text = re.sub("\s", " ", text)
|
||||
text = re.sub("\n\n", "", text)
|
||||
|
||||
text = re.sub(r"([;；.!?。！？\?])([^”’])", r"\1\n\2", text)
|
||||
text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text)
|
||||
text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text)
|
||||
text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r"\1\n\2", text)
|
||||
text = text.rstrip()
|
||||
ls = [i for i in text.split("\n") if i]
|
||||
for ele in ls:
|
||||
if len(ele) > self.sentence_size:
|
||||
ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r"\1\n\2", ele)
|
||||
ele1_ls = ele1.split("\n")
|
||||
for ele_ele1 in ele1_ls:
|
||||
if len(ele_ele1) > self.sentence_size:
|
||||
ele_ele2 = re.sub(
|
||||
r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r"\1\n\2", ele_ele1
|
||||
)
|
||||
ele2_ls = ele_ele2.split("\n")
|
||||
for ele_ele2 in ele2_ls:
|
||||
if len(ele_ele2) > self.sentence_size:
|
||||
ele_ele3 = re.sub(
|
||||
'( ["’”」』]{0,2})([^ ])', r"\1\n\2", ele_ele2
|
||||
)
|
||||
ele2_id = ele2_ls.index(ele_ele2)
|
||||
ele2_ls = (
|
||||
ele2_ls[:ele2_id]
|
||||
+ [i for i in ele_ele3.split("\n") if i]
|
||||
+ ele2_ls[ele2_id + 1 :]
|
||||
)
|
||||
ele_id = ele1_ls.index(ele_ele1)
|
||||
ele1_ls = (
|
||||
ele1_ls[:ele_id]
|
||||
+ [i for i in ele2_ls if i]
|
||||
+ ele1_ls[ele_id + 1 :]
|
||||
)
|
||||
|
||||
id = ls.index(ele)
|
||||
ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1 :]
|
||||
return ls
|
||||
@ -1,36 +0,0 @@
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from langchain.document_loaders import CSVLoader
|
||||
from langchain.schema import Document
|
||||
|
||||
from pilot.source_embedding import SourceEmbedding, register
|
||||
|
||||
|
||||
class CSVEmbedding(SourceEmbedding):
|
||||
"""csv embedding for read csv document."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
file_path,
|
||||
vector_store_config,
|
||||
embedding_args: Optional[Dict] = None,
|
||||
):
|
||||
"""Initialize with csv path."""
|
||||
super().__init__(file_path, vector_store_config)
|
||||
self.file_path = file_path
|
||||
self.vector_store_config = vector_store_config
|
||||
self.embedding_args = embedding_args
|
||||
|
||||
@register
|
||||
def read(self):
|
||||
"""Load from csv path."""
|
||||
loader = CSVLoader(file_path=self.file_path)
|
||||
return loader.load()
|
||||
|
||||
@register
|
||||
def data_process(self, documents: List[Document]):
|
||||
i = 0
|
||||
for d in documents:
|
||||
documents[i].page_content = d.page_content.replace("\n", "")
|
||||
i += 1
|
||||
return documents
|
||||
@ -1,60 +0,0 @@
|
||||
from typing import Optional
|
||||
|
||||
from chromadb.errors import NotEnoughElementsException
|
||||
from langchain.embeddings import HuggingFaceEmbeddings
|
||||
|
||||
from pilot.configs.config import Config
|
||||
from pilot.embedding_engine.knowledge_type import get_knowledge_embedding
|
||||
from pilot.vector_store.connector import VectorStoreConnector
|
||||
|
||||
CFG = Config()
|
||||
|
||||
|
||||
class KnowledgeEmbedding:
|
||||
def __init__(
|
||||
self,
|
||||
model_name,
|
||||
vector_store_config,
|
||||
file_type: Optional[str] = "default",
|
||||
file_path: Optional[str] = None,
|
||||
):
|
||||
"""Initialize with Loader url, model_name, vector_store_config"""
|
||||
self.file_path = file_path
|
||||
self.model_name = model_name
|
||||
self.vector_store_config = vector_store_config
|
||||
self.file_type = file_type
|
||||
self.embeddings = HuggingFaceEmbeddings(model_name=self.model_name)
|
||||
self.vector_store_config["embeddings"] = self.embeddings
|
||||
|
||||
def knowledge_embedding(self):
|
||||
self.knowledge_embedding_client = self.init_knowledge_embedding()
|
||||
self.knowledge_embedding_client.source_embedding()
|
||||
|
||||
def knowledge_embedding_batch(self, docs):
|
||||
# docs = self.knowledge_embedding_client.read_batch()
|
||||
self.knowledge_embedding_client.index_to_store(docs)
|
||||
|
||||
def read(self):
|
||||
self.knowledge_embedding_client = self.init_knowledge_embedding()
|
||||
return self.knowledge_embedding_client.read_batch()
|
||||
|
||||
def init_knowledge_embedding(self):
|
||||
return get_knowledge_embedding(
|
||||
self.file_type.upper(), self.file_path, self.vector_store_config
|
||||
)
|
||||
|
||||
def similar_search(self, text, topk):
|
||||
vector_client = VectorStoreConnector(
|
||||
CFG.VECTOR_STORE_TYPE, self.vector_store_config
|
||||
)
|
||||
try:
|
||||
ans = vector_client.similar_search(text, topk)
|
||||
except NotEnoughElementsException:
|
||||
ans = vector_client.similar_search(text, 1)
|
||||
return ans
|
||||
|
||||
def vector_exist(self):
|
||||
vector_client = VectorStoreConnector(
|
||||
CFG.VECTOR_STORE_TYPE, self.vector_store_config
|
||||
)
|
||||
return vector_client.vector_name_exists()
|
||||
@ -1,51 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
import os
|
||||
from typing import List
|
||||
|
||||
import markdown
|
||||
from bs4 import BeautifulSoup
|
||||
from langchain.schema import Document
|
||||
from langchain.text_splitter import SpacyTextSplitter
|
||||
|
||||
from pilot.configs.config import Config
|
||||
from pilot.source_embedding import SourceEmbedding, register
|
||||
from pilot.source_embedding.EncodeTextLoader import EncodeTextLoader
|
||||
from pilot.source_embedding.chn_document_splitter import CHNDocumentSplitter
|
||||
|
||||
CFG = Config()
|
||||
|
||||
|
||||
class MarkdownEmbedding(SourceEmbedding):
|
||||
"""markdown embedding for read markdown document."""
|
||||
|
||||
def __init__(self, file_path, vector_store_config):
|
||||
"""Initialize with markdown path."""
|
||||
super().__init__(file_path, vector_store_config)
|
||||
self.file_path = file_path
|
||||
self.vector_store_config = vector_store_config
|
||||
# self.encoding = encoding
|
||||
|
||||
@register
|
||||
def read(self):
|
||||
"""Load from markdown path."""
|
||||
loader = EncodeTextLoader(self.file_path)
|
||||
textsplitter = SpacyTextSplitter(
|
||||
pipeline="zh_core_web_sm",
|
||||
chunk_size=CFG.KNOWLEDGE_CHUNK_SIZE,
|
||||
chunk_overlap=100,
|
||||
)
|
||||
return loader.load_and_split(textsplitter)
|
||||
|
||||
@register
|
||||
def data_process(self, documents: List[Document]):
|
||||
i = 0
|
||||
for d in documents:
|
||||
content = markdown.markdown(d.page_content)
|
||||
soup = BeautifulSoup(content, "html.parser")
|
||||
for tag in soup(["!doctype", "meta", "i.fa"]):
|
||||
tag.extract()
|
||||
documents[i].page_content = soup.get_text()
|
||||
documents[i].page_content = documents[i].page_content.replace("\n", " ")
|
||||
i += 1
|
||||
return documents
|
||||
@ -1,44 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
from typing import List
|
||||
|
||||
from langchain.document_loaders import PyPDFLoader
|
||||
from langchain.schema import Document
|
||||
from langchain.text_splitter import SpacyTextSplitter
|
||||
|
||||
from pilot.configs.config import Config
|
||||
from pilot.source_embedding import SourceEmbedding, register
|
||||
|
||||
CFG = Config()
|
||||
|
||||
|
||||
class PDFEmbedding(SourceEmbedding):
|
||||
"""pdf embedding for read pdf document."""
|
||||
|
||||
def __init__(self, file_path, vector_store_config):
|
||||
"""Initialize with pdf path."""
|
||||
super().__init__(file_path, vector_store_config)
|
||||
self.file_path = file_path
|
||||
self.vector_store_config = vector_store_config
|
||||
|
||||
@register
|
||||
def read(self):
|
||||
"""Load from pdf path."""
|
||||
loader = PyPDFLoader(self.file_path)
|
||||
# textsplitter = CHNDocumentSplitter(
|
||||
# pdf=True, sentence_size=CFG.KNOWLEDGE_CHUNK_SIZE
|
||||
# )
|
||||
textsplitter = SpacyTextSplitter(
|
||||
pipeline="zh_core_web_sm",
|
||||
chunk_size=CFG.KNOWLEDGE_CHUNK_SIZE,
|
||||
chunk_overlap=100,
|
||||
)
|
||||
return loader.load_and_split(textsplitter)
|
||||
|
||||
@register
|
||||
def data_process(self, documents: List[Document]):
|
||||
i = 0
|
||||
for d in documents:
|
||||
documents[i].page_content = d.page_content.replace("\n", "")
|
||||
i += 1
|
||||
return documents
|
||||
@ -1,55 +0,0 @@
|
||||
"""Loader that loads image files."""
|
||||
import os
|
||||
from typing import List
|
||||
|
||||
import fitz
|
||||
from langchain.document_loaders.unstructured import UnstructuredFileLoader
|
||||
from paddleocr import PaddleOCR
|
||||
|
||||
|
||||
class UnstructuredPaddlePDFLoader(UnstructuredFileLoader):
|
||||
"""Loader that uses unstructured to load image files, such as PNGs and JPGs."""
|
||||
|
||||
def _get_elements(self) -> List:
|
||||
def pdf_ocr_txt(filepath, dir_path="tmp_files"):
|
||||
full_dir_path = os.path.join(os.path.dirname(filepath), dir_path)
|
||||
if not os.path.exists(full_dir_path):
|
||||
os.makedirs(full_dir_path)
|
||||
filename = os.path.split(filepath)[-1]
|
||||
ocr = PaddleOCR(lang="ch", use_gpu=False, show_log=False)
|
||||
doc = fitz.open(filepath)
|
||||
txt_file_path = os.path.join(full_dir_path, "%s.txt" % (filename))
|
||||
img_name = os.path.join(full_dir_path, ".tmp.png")
|
||||
with open(txt_file_path, "w", encoding="utf-8") as fout:
|
||||
for i in range(doc.page_count):
|
||||
page = doc[i]
|
||||
text = page.get_text("")
|
||||
fout.write(text)
|
||||
fout.write("\n")
|
||||
|
||||
img_list = page.get_images()
|
||||
for img in img_list:
|
||||
pix = fitz.Pixmap(doc, img[0])
|
||||
|
||||
pix.save(img_name)
|
||||
|
||||
result = ocr.ocr(img_name)
|
||||
ocr_result = [i[1][0] for line in result for i in line]
|
||||
fout.write("\n".join(ocr_result))
|
||||
os.remove(img_name)
|
||||
return txt_file_path
|
||||
|
||||
txt_file_path = pdf_ocr_txt(self.file_path)
|
||||
from unstructured.partition.text import partition_text
|
||||
|
||||
return partition_text(filename=txt_file_path, **self.unstructured_kwargs)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
filepath = os.path.join(
|
||||
os.path.dirname(os.path.dirname(__file__)), "content", "samples", "test_py.pdf"
|
||||
)
|
||||
loader = UnstructuredPaddlePDFLoader(filepath, mode="elements")
|
||||
docs = loader.load()
|
||||
for doc in docs:
|
||||
print(doc)
|
||||
@ -1,41 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
from typing import List
|
||||
|
||||
from langchain.document_loaders import UnstructuredPowerPointLoader
|
||||
from langchain.schema import Document
|
||||
from langchain.text_splitter import SpacyTextSplitter
|
||||
|
||||
from pilot.configs.config import Config
|
||||
from pilot.source_embedding import SourceEmbedding, register
|
||||
|
||||
CFG = Config()
|
||||
|
||||
|
||||
class PPTEmbedding(SourceEmbedding):
|
||||
"""ppt embedding for read ppt document."""
|
||||
|
||||
def __init__(self, file_path, vector_store_config):
|
||||
"""Initialize with pdf path."""
|
||||
super().__init__(file_path, vector_store_config)
|
||||
self.file_path = file_path
|
||||
self.vector_store_config = vector_store_config
|
||||
|
||||
@register
|
||||
def read(self):
|
||||
"""Load from ppt path."""
|
||||
loader = UnstructuredPowerPointLoader(self.file_path)
|
||||
textsplitter = SpacyTextSplitter(
|
||||
pipeline="zh_core_web_sm",
|
||||
chunk_size=CFG.KNOWLEDGE_CHUNK_SIZE,
|
||||
chunk_overlap=200,
|
||||
)
|
||||
return loader.load_and_split(textsplitter)
|
||||
|
||||
@register
|
||||
def data_process(self, documents: List[Document]):
|
||||
i = 0
|
||||
for d in documents:
|
||||
documents[i].page_content = d.page_content.replace("\n", "")
|
||||
i += 1
|
||||
return documents
|
||||
@ -1,61 +0,0 @@
|
||||
# from langchain.embeddings import HuggingFaceEmbeddings
|
||||
# from langchain.vectorstores import Milvus
|
||||
# from pymilvus import Collection,utility
|
||||
# from pymilvus import connections, DataType, FieldSchema, CollectionSchema
|
||||
#
|
||||
# # milvus = connections.connect(
|
||||
# # alias="default",
|
||||
# # host='localhost',
|
||||
# # port="19530"
|
||||
# # )
|
||||
# # collection = Collection("book")
|
||||
#
|
||||
#
|
||||
# # Get an existing collection.
|
||||
# # collection.load()
|
||||
# #
|
||||
# # search_params = {"metric_type": "L2", "params": {}, "offset": 5}
|
||||
# #
|
||||
# # results = collection.search(
|
||||
# # data=[[0.1, 0.2]],
|
||||
# # anns_field="book_intro",
|
||||
# # param=search_params,
|
||||
# # limit=10,
|
||||
# # expr=None,
|
||||
# # output_fields=['book_id'],
|
||||
# # consistency_level="Strong"
|
||||
# # )
|
||||
# #
|
||||
# # # get the IDs of all returned hits
|
||||
# # results[0].ids
|
||||
# #
|
||||
# # # get the distances to the query vector from all returned hits
|
||||
# # results[0].distances
|
||||
# #
|
||||
# # # get the value of an output field specified in the search request.
|
||||
# # # vector fields are not supported yet.
|
||||
# # hit = results[0][0]
|
||||
# # hit.entity.get('title')
|
||||
#
|
||||
# # milvus = connections.connect(
|
||||
# # alias="default",
|
||||
# # host='localhost',
|
||||
# # port="19530"
|
||||
# # )
|
||||
# from pilot.vector_store.milvus_store import MilvusStore
|
||||
#
|
||||
# data = ["aaa", "bbb"]
|
||||
# model_name = "xx/all-MiniLM-L6-v2"
|
||||
# embeddings = HuggingFaceEmbeddings(model_name=model_name)
|
||||
#
|
||||
# # text_embeddings = Text2Vectors()
|
||||
# mivuls = MilvusStore(cfg={"url": "127.0.0.1", "port": "19530", "alias": "default", "table_name": "test_k"})
|
||||
#
|
||||
# mivuls.insert(["textc","tezt2"])
|
||||
# print("success")
|
||||
# ct
|
||||
# # mivuls.from_texts(texts=data, embedding=embeddings)
|
||||
# # docs,
|
||||
# # embedding=embeddings,
|
||||
# # connection_args={"host": "127.0.0.1", "port": "19530", "alias": "default"}
|
||||
# # )
|
||||
@ -1,101 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from chromadb.errors import NotEnoughElementsException
|
||||
from pilot.configs.config import Config
|
||||
from pilot.vector_store.connector import VectorStoreConnector
|
||||
|
||||
registered_methods = []
|
||||
CFG = Config()
|
||||
|
||||
|
||||
def register(method):
|
||||
registered_methods.append(method.__name__)
|
||||
return method
|
||||
|
||||
|
||||
class SourceEmbedding(ABC):
|
||||
"""base class for read data source embedding pipeline.
|
||||
include data read, data process, data split, data to vector, data index vector store
|
||||
Implementations should implement the method
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
file_path,
|
||||
vector_store_config,
|
||||
embedding_args: Optional[Dict] = None,
|
||||
):
|
||||
"""Initialize with Loader url, model_name, vector_store_config"""
|
||||
self.file_path = file_path
|
||||
self.vector_store_config = vector_store_config
|
||||
self.embedding_args = embedding_args
|
||||
self.embeddings = vector_store_config["embeddings"]
|
||||
|
||||
@abstractmethod
|
||||
@register
|
||||
def read(self) -> List[ABC]:
|
||||
"""read datasource into document objects."""
|
||||
|
||||
@register
|
||||
def data_process(self, text):
|
||||
"""pre process data."""
|
||||
|
||||
@register
|
||||
def text_split(self, text):
|
||||
"""text split chunk"""
|
||||
pass
|
||||
|
||||
@register
|
||||
def text_to_vector(self, docs):
|
||||
"""transform vector"""
|
||||
pass
|
||||
|
||||
@register
|
||||
def index_to_store(self, docs):
|
||||
"""index to vector store"""
|
||||
self.vector_client = VectorStoreConnector(
|
||||
CFG.VECTOR_STORE_TYPE, self.vector_store_config
|
||||
)
|
||||
self.vector_client.load_document(docs)
|
||||
|
||||
@register
|
||||
def similar_search(self, doc, topk):
|
||||
"""vector store similarity_search"""
|
||||
self.vector_client = VectorStoreConnector(
|
||||
CFG.VECTOR_STORE_TYPE, self.vector_store_config
|
||||
)
|
||||
try:
|
||||
ans = self.vector_client.similar_search(doc, topk)
|
||||
except NotEnoughElementsException:
|
||||
ans = self.vector_client.similar_search(doc, 1)
|
||||
return ans
|
||||
|
||||
def vector_name_exist(self):
|
||||
self.vector_client = VectorStoreConnector(
|
||||
CFG.VECTOR_STORE_TYPE, self.vector_store_config
|
||||
)
|
||||
return self.vector_client.vector_name_exists()
|
||||
|
||||
def source_embedding(self):
|
||||
if "read" in registered_methods:
|
||||
text = self.read()
|
||||
if "data_process" in registered_methods:
|
||||
text = self.data_process(text)
|
||||
if "text_split" in registered_methods:
|
||||
self.text_split(text)
|
||||
if "text_to_vector" in registered_methods:
|
||||
self.text_to_vector(text)
|
||||
if "index_to_store" in registered_methods:
|
||||
self.index_to_store(text)
|
||||
|
||||
def read_batch(self):
|
||||
if "read" in registered_methods:
|
||||
text = self.read()
|
||||
if "data_process" in registered_methods:
|
||||
text = self.data_process(text)
|
||||
if "text_split" in registered_methods:
|
||||
self.text_split(text)
|
||||
return text
|
||||
@ -1,29 +0,0 @@
|
||||
from typing import List
|
||||
|
||||
from langchain.schema import Document
|
||||
|
||||
from pilot import SourceEmbedding, register
|
||||
|
||||
|
||||
class StringEmbedding(SourceEmbedding):
|
||||
"""string embedding for read string document."""
|
||||
|
||||
def __init__(self, file_path, vector_store_config):
|
||||
"""Initialize with pdf path."""
|
||||
super().__init__(file_path, vector_store_config)
|
||||
self.file_path = file_path
|
||||
self.vector_store_config = vector_store_config
|
||||
|
||||
@register
|
||||
def read(self):
|
||||
"""Load from String path."""
|
||||
metadata = {"source": "db_summary"}
|
||||
return [Document(page_content=self.file_path, metadata=metadata)]
|
||||
|
||||
@register
|
||||
def data_process(self, documents: List[Document]):
|
||||
i = 0
|
||||
for d in documents:
|
||||
documents[i].page_content = d.page_content.replace("\n", "")
|
||||
i += 1
|
||||
return documents
|
||||
@ -1,49 +0,0 @@
|
||||
from typing import List
|
||||
|
||||
from bs4 import BeautifulSoup
|
||||
from langchain.document_loaders import WebBaseLoader
|
||||
from langchain.schema import Document
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
|
||||
from pilot.configs.config import Config
|
||||
from pilot.configs.model_config import KNOWLEDGE_CHUNK_SPLIT_SIZE
|
||||
from pilot.source_embedding import SourceEmbedding, register
|
||||
from pilot.source_embedding.chn_document_splitter import CHNDocumentSplitter
|
||||
|
||||
CFG = Config()
|
||||
|
||||
|
||||
class URLEmbedding(SourceEmbedding):
|
||||
"""url embedding for read url document."""
|
||||
|
||||
def __init__(self, file_path, vector_store_config):
|
||||
"""Initialize with url path."""
|
||||
super().__init__(file_path, vector_store_config)
|
||||
self.file_path = file_path
|
||||
self.vector_store_config = vector_store_config
|
||||
|
||||
@register
|
||||
def read(self):
|
||||
"""Load from url path."""
|
||||
loader = WebBaseLoader(web_path=self.file_path)
|
||||
if CFG.LANGUAGE == "en":
|
||||
text_splitter = CharacterTextSplitter(
|
||||
chunk_size=CFG.KNOWLEDGE_CHUNK_SIZE,
|
||||
chunk_overlap=20,
|
||||
length_function=len,
|
||||
)
|
||||
else:
|
||||
text_splitter = CHNDocumentSplitter(pdf=True, sentence_size=1000)
|
||||
return loader.load_and_split(text_splitter)
|
||||
|
||||
@register
|
||||
def data_process(self, documents: List[Document]):
|
||||
i = 0
|
||||
for d in documents:
|
||||
content = d.page_content.replace("\n", "")
|
||||
soup = BeautifulSoup(content, "html.parser")
|
||||
for tag in soup(["!doctype", "meta"]):
|
||||
tag.extract()
|
||||
documents[i].page_content = soup.get_text()
|
||||
i += 1
|
||||
return documents
|
||||
@ -1,39 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
from typing import List
|
||||
|
||||
from langchain.document_loaders import PyPDFLoader, UnstructuredWordDocumentLoader
|
||||
from langchain.schema import Document
|
||||
|
||||
from pilot.configs.config import Config
|
||||
from pilot.source_embedding import SourceEmbedding, register
|
||||
from pilot.source_embedding.chn_document_splitter import CHNDocumentSplitter
|
||||
|
||||
CFG = Config()
|
||||
|
||||
|
||||
class WordEmbedding(SourceEmbedding):
|
||||
"""word embedding for read word document."""
|
||||
|
||||
def __init__(self, file_path, vector_store_config):
|
||||
"""Initialize with word path."""
|
||||
super().__init__(file_path, vector_store_config)
|
||||
self.file_path = file_path
|
||||
self.vector_store_config = vector_store_config
|
||||
|
||||
@register
|
||||
def read(self):
|
||||
"""Load from word path."""
|
||||
loader = UnstructuredWordDocumentLoader(self.file_path)
|
||||
textsplitter = CHNDocumentSplitter(
|
||||
pdf=True, sentence_size=CFG.KNOWLEDGE_CHUNK_SIZE
|
||||
)
|
||||
return loader.load_and_split(textsplitter)
|
||||
|
||||
@register
|
||||
def data_process(self, documents: List[Document]):
|
||||
i = 0
|
||||
for d in documents:
|
||||
documents[i].page_content = d.page_content.replace("\n", "")
|
||||
i += 1
|
||||
return documents
|
||||
@ -49,6 +49,7 @@ class LocalKnowledgeInit:
|
||||
space = KnowledgeSpaceRequest
|
||||
space.name = self.vector_store_config["vector_store_name"]
|
||||
space.desc = "knowledge_init.py"
|
||||
space.vector_type = CFG.VECTOR_STORE_TYPE
|
||||
space.owner = "DB-GPT"
|
||||
knowledge_space_service.create_knowledge_space(space)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user