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https://github.com/arc53/DocsGPT
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Merge pull request #830 from arc53/feature/search-endpoint
Search docs not inside the /stream in the stream request
This commit is contained in:
commit
ad2221a677
@ -117,12 +117,9 @@ def complete_stream(question, docsearch, chat_history, api_key, prompt_id, conve
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source_log_docs = []
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for doc in docs:
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if doc.metadata:
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data = json.dumps({"type": "source", "doc": doc.page_content[:10], "metadata": doc.metadata})
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source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
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else:
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data = json.dumps({"type": "source", "doc": doc.page_content[:10]})
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source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
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yield f"data:{data}\n\n"
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if len(chat_history) > 1:
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tokens_current_history = 0
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@ -343,3 +340,32 @@ def api_answer():
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traceback.print_exc()
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print(str(e))
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return bad_request(500, str(e))
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@answer.route("/api/search", methods=["POST"])
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def api_search():
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data = request.get_json()
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# get parameter from url question
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question = data["question"]
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if not embeddings_key_set:
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embeddings_key = data["embeddings_key"]
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else:
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embeddings_key = settings.EMBEDDINGS_KEY
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if "active_docs" in data:
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vectorstore = get_vectorstore({"active_docs": data["active_docs"]})
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else:
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vectorstore = ""
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docsearch = VectorCreator.create_vectorstore(settings.VECTOR_STORE, vectorstore, embeddings_key)
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docs = docsearch.search(question, k=2)
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source_log_docs = []
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for doc in docs:
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if doc.metadata:
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source_log_docs.append({"title": doc.metadata['title'].split('/')[-1], "text": doc.page_content})
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else:
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source_log_docs.append({"title": doc.page_content, "text": doc.page_content})
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#yield f"data:{data}\n\n"
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return source_log_docs
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@ -12,20 +12,20 @@ export function fetchAnswerApi(
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promptId: string | null,
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): Promise<
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| {
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result: any;
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answer: any;
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sources: any;
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conversationId: any;
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query: string;
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}
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result: any;
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answer: any;
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sources: any;
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conversationId: any;
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query: string;
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}
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| {
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result: any;
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answer: any;
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sources: any;
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query: string;
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conversationId: any;
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title: any;
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}
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result: any;
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answer: any;
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sources: any;
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query: string;
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conversationId: any;
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title: any;
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}
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> {
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let namePath = selectedDocs.name;
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if (selectedDocs.language === namePath) {
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@ -128,7 +128,6 @@ export function fetchAnswerSteaming(
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conversation_id: conversationId,
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prompt_id: promptId,
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};
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fetch(apiHost + '/stream', {
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method: 'POST',
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headers: {
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@ -183,7 +182,58 @@ export function fetchAnswerSteaming(
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});
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});
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}
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export function searchEndpoint(
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question: string,
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apiKey: string,
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selectedDocs: Doc,
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conversation_id: string | null,
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history: Array<any> = [],
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) {
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/*
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"active_docs": "default",
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"question": "Summarise",
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"conversation_id": null,
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"history": "[]" */
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let namePath = selectedDocs.name;
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if (selectedDocs.language === namePath) {
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namePath = '.project';
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}
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let docPath = 'default';
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if (selectedDocs.location === 'local') {
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docPath = 'local' + '/' + selectedDocs.name + '/';
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} else if (selectedDocs.location === 'remote') {
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docPath =
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selectedDocs.language +
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'/' +
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namePath +
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'/' +
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selectedDocs.version +
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'/' +
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selectedDocs.model +
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'/';
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}
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const body = {
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question: question,
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active_docs: docPath,
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conversation_id,
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history
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};
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return fetch(`${apiHost}/api/search`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify(
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body
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),
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}).then((response) => response.json())
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.then((data) => {
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return data;
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})
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.catch(err => console.log(err))
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}
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export function sendFeedback(
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prompt: string,
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response: string,
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@ -1,6 +1,7 @@
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import { createAsyncThunk, createSlice, PayloadAction } from '@reduxjs/toolkit';
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import store from '../store';
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import { fetchAnswerApi, fetchAnswerSteaming } from './conversationApi';
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import { searchEndpoint } from './conversationApi';
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import { Answer, ConversationState, Query, Status } from './conversationModels';
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import { getConversations } from '../preferences/preferenceApi';
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import { setConversations } from '../preferences/preferenceSlice';
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@ -29,6 +30,7 @@ export const fetchAnswer = createAsyncThunk<Answer, { question: string }>(
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(event) => {
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const data = JSON.parse(event.data);
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// check if the 'end' event has been received
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if (data.type === 'end') {
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// set status to 'idle'
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@ -40,24 +42,22 @@ export const fetchAnswer = createAsyncThunk<Answer, { question: string }>(
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.catch((error) => {
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console.error('Failed to fetch conversations: ', error);
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});
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} else if (data.type === 'source') {
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// check if data.metadata exists
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let result;
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if (data.metadata && data.metadata.title) {
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const titleParts = data.metadata.title.split('/');
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result = {
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title: titleParts[titleParts.length - 1],
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text: data.doc,
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};
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} else {
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result = { title: data.doc, text: data.doc };
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}
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dispatch(
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updateStreamingSource({
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index: state.conversation.queries.length - 1,
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query: { sources: [result] },
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}),
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);
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searchEndpoint( //search for sources post streaming
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question,
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state.preference.apiKey,
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state.preference.selectedDocs!,
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state.conversation.conversationId,
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state.conversation.queries
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).then(sources => {
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//dispatch streaming sources
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dispatch(
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updateStreamingSource({
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index: state.conversation.queries.length - 1,
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query: { sources },
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}),
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);
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});
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} else if (data.type === 'id') {
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dispatch(
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updateConversationId({
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@ -165,9 +165,10 @@ export const conversationSlice = createSlice({
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state,
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action: PayloadAction<{ index: number; query: Partial<Query> }>,
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) {
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const { index, query } = action.payload;
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if (!state.queries[index].sources) {
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state.queries[index].sources = [query.sources![0]];
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state.queries[index].sources = query?.sources;
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} else {
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state.queries[index].sources!.push(query.sources![0]);
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}
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