dtamind-components
Version:
Apps integration for Dtamind. Contain Nodes and Credentials.
107 lines (104 loc) • 3.98 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.howToUseFileUpload = exports.addMMRInputParams = exports.resolveVectorStoreOrRetriever = void 0;
const resolveVectorStoreOrRetriever = (nodeData, vectorStore, metadataFilter) => {
const output = nodeData.outputs?.output;
const searchType = nodeData.outputs?.searchType;
const topK = nodeData.inputs?.topK;
const k = topK ? parseFloat(topK) : 4;
const alpha = nodeData.inputs?.alpha;
// If it is already pre-defined in lc_kwargs, then don't pass it again
const filter = vectorStore?.lc_kwargs?.filter ? undefined : metadataFilter;
if (output === 'retriever') {
const searchKwargs = {};
if (alpha !== undefined) {
searchKwargs.alpha = parseFloat(alpha);
}
if ('mmr' === searchType) {
const fetchK = nodeData.inputs?.fetchK;
const lambda = nodeData.inputs?.lambda;
const f = fetchK ? parseInt(fetchK) : 20;
const l = lambda ? parseFloat(lambda) : 0.5;
return vectorStore.asRetriever({
searchType: 'mmr',
k: k,
filter,
searchKwargs: {
//...searchKwargs,
fetchK: f,
lambda: l
}
});
}
else {
// "searchType" is "similarity"
return vectorStore.asRetriever({
k: k,
filter: filter,
searchKwargs: Object.keys(searchKwargs).length > 0 ? searchKwargs : undefined
});
}
}
else if (output === 'vectorStore') {
;
vectorStore.k = k;
vectorStore.filter = filter;
return vectorStore;
}
};
exports.resolveVectorStoreOrRetriever = resolveVectorStoreOrRetriever;
const addMMRInputParams = (inputs) => {
const mmrInputParams = [
{
label: 'Search Type',
name: 'searchType',
type: 'options',
default: 'similarity',
options: [
{
label: 'Similarity',
name: 'similarity'
},
{
label: 'Max Marginal Relevance',
name: 'mmr'
}
],
additionalParams: true,
optional: true
},
{
label: 'Fetch K (for MMR Search)',
name: 'fetchK',
description: 'Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR',
placeholder: '20',
type: 'number',
additionalParams: true,
optional: true
},
{
label: 'Lambda (for MMR Search)',
name: 'lambda',
description: 'Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR',
placeholder: '0.5',
type: 'number',
additionalParams: true,
optional: true
}
];
inputs.push(...mmrInputParams);
};
exports.addMMRInputParams = addMMRInputParams;
exports.howToUseFileUpload = `
**File Upload**
This allows file upload on the chat. Uploaded files will be upserted on the fly to the vector store.
**Note:**
- You can only turn on file upload for one vector store at a time.
- At least one Document Loader node should be connected to the document input.
- Document Loader should be file types like PDF, DOCX, TXT, etc.
**How it works**
- Uploaded files will have the metadata updated with the chatId.
- This will allow the file to be associated with the chatId.
- When querying, metadata will be filtered by chatId to retrieve files associated with the chatId.
`;
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