Feat: Change the corresponding prompt word according to the value of fieldName #9869 (#10291)

### What problem does this PR solve?

Feat: Change the corresponding prompt word according to the value of
fieldName #9869
### Type of change


- [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
balibabu
2025-09-25 19:53:37 +08:00
committed by GitHub
parent c1151519a0
commit abe7132630
7 changed files with 157 additions and 14 deletions

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@ -1712,6 +1712,53 @@ This delimiter is used to split the input text into several text pieces echo of
questions: 'Questions',
metadata: 'Metadata',
fieldName: 'Result Destination',
prompts: {
system: {
keywords: `Role
You are a text analyzer.
Task
Extract the most important keywords/phrases of a given piece of text content.
Requirements
- Summarize the text content, and give the top 5 important keywords/phrases.
- The keywords MUST be in the same language as the given piece of text content.
- The keywords are delimited by ENGLISH COMMA.
- Output keywords ONLY.`,
questions: `Role
You are a text analyzer.
Task
Propose 3 questions about a given piece of text content.
Requirements
- Understand and summarize the text content, and propose the top 3 important questions.
- The questions SHOULD NOT have overlapping meanings.
- The questions SHOULD cover the main content of the text as much as possible.
- The questions MUST be in the same language as the given piece of text content.
- One question per line.
- Output questions ONLY.`,
summary: `Act as a precise summarizer. Your task is to create a summary of the provided content that is both concise and faithful to the original.
Key Instructions:
1. Accuracy: Strictly base the summary on the information given. Do not introduce any new facts, conclusions, or interpretations that are not explicitly stated.
2. Language: Write the summary in the same language as the source text.
3. Objectivity: Present the key points without bias, preserving the original intent and tone of the content. Do not editorialize.
4. Conciseness: Focus on the most important ideas, omitting minor details and fluff.`,
metadata: `Extract important structured information from the given content. Output ONLY a valid JSON string with no additional text. If no important structured information is found, output an empty JSON object: {}.
Important structured information may include: names, dates, locations, events, key facts, numerical data, or other extractable entities.`,
},
user: {
keywords: `Text Content
[Insert text here]`,
questions: `Text Content
[Insert text here]`,
summary: `Text to Summarize:
[Insert text here]`,
metadata: `Content: [INSERT CONTENT HERE]`,
},
},
},
},
};

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@ -1630,6 +1630,53 @@ General实体和关系提取提示来自 GitHub - microsoft/graphrag基于
questions: '问题',
metadata: '元数据',
fieldName: '结果目的地',
prompts: {
system: {
keywords: `角色
你是一名文本分析员。
任务
从给定的文本内容中提取最重要的关键词/短语。
要求
- 总结文本内容并给出最重要的5个关键词/短语。
- 关键词必须与给定的文本内容使用相同的语言。
- 关键词之间用英文逗号分隔。
- 仅输出关键词。`,
questions: `角色
你是一名文本分析员。
任务
针对给定的文本内容提出3个问题。
要求
- 理解并总结文本内容并提出最重要的3个问题。
- 问题的含义不应重叠。
- 问题应尽可能涵盖文本的主要内容。
- 问题必须与给定的文本内容使用相同的语言。
- 每行一个问题。
- 仅输出问题。`,
summary: `扮演一个精准的摘要者。你的任务是为提供的内容创建一个简洁且忠实于原文的摘要。
关键说明:
1. 准确性:摘要必须严格基于所提供的信息。请勿引入任何未明确说明的新事实、结论或解释。
2. 语言:摘要必须使用与原文相同的语言。
3. 客观性:不带偏见地呈现要点,保留内容的原始意图和语气。请勿进行编辑。
4. 简洁性:专注于最重要的思想,省略细节和多余的内容。`,
metadata: `从给定内容中提取重要的结构化信息。仅输出有效的 JSON 字符串,不包含任何附加文本。如果未找到重要的结构化信息,则输出一个空的 JSON 对象:{}。
重要的结构化信息可能包括:姓名、日期、地点、事件、关键事实、数字数据或其他可提取实体。`,
},
user: {
keywords: `文本内容
[在此处插入文本]`,
questions: `文本内容
[在此处插入文本]`,
summary: `要总结的文本:
[在此处插入文本]`,
metadata: `内容:[在此处插入内容]`,
},
},
},
},
};

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@ -131,12 +131,12 @@ export default function VariablePickerMenuPlugin({
baseOptions,
}: VariablePickerMenuPluginProps): JSX.Element {
const [editor] = useLexicalComposerContext();
const isFirstRender = useRef(true);
const checkForTriggerMatch = useBasicTypeaheadTriggerMatch('/', {
minLength: 0,
});
const previousValue = useRef<string | undefined>();
const [queryString, setQueryString] = React.useState<string | null>('');
let options = useBuildQueryVariableOptions();
@ -280,8 +280,8 @@ export default function VariablePickerMenuPlugin({
);
useEffect(() => {
if (editor && value && isFirstRender.current) {
isFirstRender.current = false;
if (editor && value && value !== previousValue.current) {
previousValue.current = value;
editor.update(
() => {
parseTextToVariableNodes(value);

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@ -256,6 +256,23 @@ export const initialParserValues = {
fields: Object.values(ParserFields),
output_format: EmailOutputFormat.Text,
},
{
fileFormat: FileType.TextMarkdown,
output_format: TextMarkdownOutputFormat.Text,
},
{
fileFormat: FileType.Docx,
output_format: DocxOutputFormat.Json,
},
{
fileFormat: FileType.PowerPoint,
output_format: PptOutputFormat.Json,
},
{
fileFormat: FileType.Audio,
llm_id: '',
output_format: AudioOutputFormat.Text,
},
],
};
@ -289,7 +306,7 @@ export const initialHierarchicalMergerValues = {
],
};
export const initialContextValues = {
export const initialExtractorValues = {
...initialLlmBaseValues,
field_name: ContextGeneratorFieldName.Summary,
outputs: {},
@ -318,6 +335,7 @@ export const RestrictedUpstreamMap = {
[Operator.Splitter]: [Operator.Begin],
[Operator.HierarchicalMerger]: [Operator.Begin],
[Operator.Tokenizer]: [Operator.Begin],
[Operator.Extractor]: [Operator.Begin],
};
export const NodeMap = {

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@ -6,13 +6,13 @@ import { Form } from '@/components/ui/form';
import { PromptEditor } from '@/pages/agent/form/components/prompt-editor';
import { buildOptions } from '@/utils/form';
import { zodResolver } from '@hookform/resolvers/zod';
import { memo } from 'react';
import { memo, useCallback } from 'react';
import { useForm } from 'react-hook-form';
import { useTranslation } from 'react-i18next';
import { z } from 'zod';
import {
ContextGeneratorFieldName,
initialContextValues,
initialExtractorValues,
} from '../../constant';
import { useBuildNodeOutputOptions } from '../../hooks/use-build-options';
import { useFormValues } from '../../hooks/use-form-values';
@ -21,27 +21,48 @@ import { INextOperatorForm } from '../../interface';
import { FormWrapper } from '../components/form-wrapper';
export const FormSchema = z.object({
field_name: z.string(),
sys_prompt: z.string(),
prompts: z.string().optional(),
...LlmSettingSchema,
field_name: z.array(z.string()),
});
export type ExtractorFormSchemaType = z.infer<typeof FormSchema>;
const ExtractorForm = ({ node }: INextOperatorForm) => {
const defaultValues = useFormValues(initialContextValues, node);
const defaultValues = useFormValues(initialExtractorValues, node);
const { t } = useTranslation();
const form = useForm<ExtractorFormSchemaType>({
defaultValues,
resolver: zodResolver(FormSchema),
// mode: 'onChange',
});
const promptOptions = useBuildNodeOutputOptions(node?.id);
const options = buildOptions(ContextGeneratorFieldName, t, 'dataflow');
const setPromptValue = useCallback(
(field: keyof ExtractorFormSchemaType, key: string, value: string) => {
form.setValue(field, t(`dataflow.prompts.${key}.${value}`), {
shouldDirty: true,
shouldValidate: true,
});
},
[form, t],
);
const handleFieldNameChange = useCallback(
(value: string) => {
if (value) {
setPromptValue('sys_prompt', 'system', value);
setPromptValue('prompts', 'user', value);
}
},
[setPromptValue],
);
useWatchFormChange(node?.id, form);
return (
@ -51,7 +72,11 @@ const ExtractorForm = ({ node }: INextOperatorForm) => {
<RAGFlowFormItem label={t('dataflow.fieldName')} name="field_name">
{(field) => (
<SelectWithSearch
{...field}
onChange={(value) => {
field.onChange(value);
handleFieldNameChange(value);
}}
value={field.value}
placeholder={t('dataFlowPlaceholder')}
options={options}
></SelectWithSearch>

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@ -116,7 +116,7 @@ function ParserItem({ name, index, fieldLength, remove }: ParserItemProps) {
>
<div className="flex justify-between items-center">
<span className="text-text-primary text-sm font-medium">
Parser {index}
Parser {index + 1}
</span>
{index > 0 && (
<Button variant={'ghost'} onClick={() => remove(index)} ref={ref}>

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@ -9,7 +9,7 @@ import {
NodeMap,
Operator,
initialBeginValues,
initialContextValues,
initialExtractorValues,
initialHierarchicalMergerValues,
initialNoteValues,
initialParserValues,
@ -24,6 +24,7 @@ import {
export const useInitializeOperatorParams = () => {
const llmId = useFetchModelId();
const { t } = useTranslation();
const initialFormValuesMap = useMemo(() => {
return {
@ -33,9 +34,14 @@ export const useInitializeOperatorParams = () => {
[Operator.Tokenizer]: initialTokenizerValues,
[Operator.Splitter]: initialSplitterValues,
[Operator.HierarchicalMerger]: initialHierarchicalMergerValues,
[Operator.Extractor]: { ...initialContextValues, llm_id: llmId },
[Operator.Extractor]: {
...initialExtractorValues,
llm_id: llmId,
sys_prompt: t('dataflow.prompts.system.summary'),
prompts: t('dataflow.prompts.user.summary'),
},
};
}, [llmId]);
}, [llmId, t]);
const initializeOperatorParams = useCallback(
(operatorName: Operator) => {