mirror of https://github.com/FlowiseAI/Flowise.git
772 lines
35 KiB
JSON
772 lines
35 KiB
JSON
{
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"description": "Documents QnA using Retrieval Augmented Generation (RAG) with Mistral and FAISS for similarity search",
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"badge": "POPULAR",
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"usecases": ["Documents QnA"],
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"framework": ["Langchain"],
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"nodes": [
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{
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"width": 300,
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"height": 424,
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"id": "openAIEmbeddings_0",
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"id": "openAIEmbeddings_0",
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"label": "OpenAI Embeddings",
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"version": 4,
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"name": "openAIEmbeddings",
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"type": "OpenAIEmbeddings",
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"baseClasses": ["OpenAIEmbeddings", "Embeddings"],
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"category": "Embeddings",
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"description": "OpenAI API to generate embeddings for a given text",
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"inputParams": [
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{
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"label": "Connect Credential",
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"name": "credential",
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"type": "credential",
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"credentialNames": ["openAIApi"],
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"id": "openAIEmbeddings_0-input-credential-credential"
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},
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{
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"label": "Model Name",
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"name": "modelName",
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"type": "asyncOptions",
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"loadMethod": "listModels",
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"default": "text-embedding-ada-002",
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"id": "openAIEmbeddings_0-input-modelName-asyncOptions"
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},
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{
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"label": "Strip New Lines",
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"name": "stripNewLines",
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"type": "boolean",
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"optional": true,
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"additionalParams": true,
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"id": "openAIEmbeddings_0-input-stripNewLines-boolean"
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},
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{
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"label": "Batch Size",
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"name": "batchSize",
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"type": "number",
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"optional": true,
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"additionalParams": true,
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"id": "openAIEmbeddings_0-input-batchSize-number"
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},
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{
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"label": "Timeout",
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"name": "timeout",
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"type": "number",
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"optional": true,
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"additionalParams": true,
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"id": "openAIEmbeddings_0-input-timeout-number"
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},
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{
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"label": "BasePath",
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"name": "basepath",
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"type": "string",
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"optional": true,
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"additionalParams": true,
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"id": "openAIEmbeddings_0-input-basepath-string"
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},
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{
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"label": "Dimensions",
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"name": "dimensions",
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"type": "number",
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"optional": true,
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"additionalParams": true,
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"id": "openAIEmbeddings_0-input-dimensions-number"
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}
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],
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"inputAnchors": [],
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"inputs": {
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"modelName": "text-embedding-ada-002",
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"stripNewLines": "",
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"batchSize": "",
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"timeout": "",
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"basepath": "",
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"dimensions": ""
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},
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"outputAnchors": [
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{
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"id": "openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings",
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"name": "openAIEmbeddings",
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"label": "OpenAIEmbeddings",
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"description": "OpenAI API to generate embeddings for a given text",
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"type": "OpenAIEmbeddings | Embeddings"
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}
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],
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"outputs": {},
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"selected": false
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},
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"selected": false,
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"dragging": false
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},
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{
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"width": 300,
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"height": 430,
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"id": "recursiveCharacterTextSplitter_0",
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"position": {
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"x": 406.08456707531263,
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"y": 197.66460328693972
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},
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"type": "customNode",
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"data": {
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"id": "recursiveCharacterTextSplitter_0",
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"label": "Recursive Character Text Splitter",
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"version": 2,
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"name": "recursiveCharacterTextSplitter",
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"type": "RecursiveCharacterTextSplitter",
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"baseClasses": ["RecursiveCharacterTextSplitter", "TextSplitter"],
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"category": "Text Splitters",
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"description": "Split documents recursively by different characters - starting with \"\\n\\n\", then \"\\n\", then \" \"",
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"inputParams": [
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{
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"label": "Chunk Size",
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"name": "chunkSize",
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"type": "number",
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"default": 1000,
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"optional": true,
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"id": "recursiveCharacterTextSplitter_0-input-chunkSize-number"
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},
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{
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"label": "Chunk Overlap",
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"name": "chunkOverlap",
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"type": "number",
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"optional": true,
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"id": "recursiveCharacterTextSplitter_0-input-chunkOverlap-number"
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},
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{
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"label": "Custom Separators",
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"name": "separators",
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"type": "string",
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"rows": 4,
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"description": "Array of custom separators to determine when to split the text, will override the default separators",
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"placeholder": "[\"|\", \"##\", \">\", \"-\"]",
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"additionalParams": true,
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"optional": true,
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"id": "recursiveCharacterTextSplitter_0-input-separators-string"
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}
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],
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"inputAnchors": [],
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"inputs": {
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"chunkSize": 1000,
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"chunkOverlap": ""
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},
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"outputAnchors": [
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{
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"id": "recursiveCharacterTextSplitter_0-output-recursiveCharacterTextSplitter-RecursiveCharacterTextSplitter|TextSplitter",
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"name": "recursiveCharacterTextSplitter",
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"label": "RecursiveCharacterTextSplitter",
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"type": "RecursiveCharacterTextSplitter | TextSplitter"
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}
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],
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"outputs": {},
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"selected": false
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},
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"selected": false,
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"dragging": false
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},
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{
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"width": 300,
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"height": 421,
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"id": "textFile_0",
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"position": {
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"x": 786.5497697231324,
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"y": 140.09563157584407
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},
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"type": "customNode",
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"data": {
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"id": "textFile_0",
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"label": "Text File",
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"version": 3,
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"name": "textFile",
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"type": "Document",
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"baseClasses": ["Document"],
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"category": "Document Loaders",
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"description": "Load data from text files",
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"inputParams": [
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{
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"label": "Txt File",
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"name": "txtFile",
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"type": "file",
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"fileType": ".txt, .html, .aspx, .asp, .cpp, .c, .cs, .css, .go, .h, .java, .js, .less, .ts, .php, .proto, .python, .py, .rst, .ruby, .rb, .rs, .scala, .sc, .scss, .sol, .sql, .swift, .markdown, .md, .tex, .ltx, .vb, .xml",
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"id": "textFile_0-input-txtFile-file"
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},
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{
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"label": "Metadata",
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"name": "metadata",
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"type": "json",
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"optional": true,
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"additionalParams": true,
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"id": "textFile_0-input-metadata-json"
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}
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],
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"inputAnchors": [
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{
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"label": "Text Splitter",
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"name": "textSplitter",
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"type": "TextSplitter",
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"optional": true,
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"id": "textFile_0-input-textSplitter-TextSplitter"
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}
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],
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"inputs": {
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"textSplitter": "{{recursiveCharacterTextSplitter_0.data.instance}}",
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"metadata": ""
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},
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"outputAnchors": [
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{
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"name": "output",
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"label": "Output",
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"type": "options",
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"options": [
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{
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"id": "textFile_0-output-document-Document|json",
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"name": "document",
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"label": "Document",
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"type": "Document | json"
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},
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{
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"id": "textFile_0-output-text-string|json",
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"name": "text",
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"label": "Text",
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"type": "string | json"
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}
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],
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"default": "document"
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}
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],
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"outputs": {
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"output": "document"
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},
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"selected": false
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},
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"selected": false,
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"positionAbsolute": {
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},
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"dragging": false
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},
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{
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"width": 300,
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"height": 532,
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"id": "conversationalRetrievalQAChain_0",
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"position": {
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"x": 1558.6564094656787,
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"y": 386.60217819991124
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},
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"type": "customNode",
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"data": {
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"id": "conversationalRetrievalQAChain_0",
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"label": "Conversational Retrieval QA Chain",
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"version": 3,
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"name": "conversationalRetrievalQAChain",
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"type": "ConversationalRetrievalQAChain",
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"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
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"category": "Chains",
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"description": "Document QA - built on RetrievalQAChain to provide a chat history component",
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"inputParams": [
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{
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"label": "Return Source Documents",
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"name": "returnSourceDocuments",
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"type": "boolean",
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"optional": true,
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"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
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},
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{
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"label": "Rephrase Prompt",
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"name": "rephrasePrompt",
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"type": "string",
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"description": "Using previous chat history, rephrase question into a standalone question",
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"warning": "Prompt must include input variables: {chat_history} and {question}",
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"rows": 4,
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"additionalParams": true,
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"optional": true,
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"default": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
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"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
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},
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{
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"label": "Response Prompt",
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"name": "responsePrompt",
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"type": "string",
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"description": "Taking the rephrased question, search for answer from the provided context",
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"warning": "Prompt must include input variable: {context}",
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"rows": 4,
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"additionalParams": true,
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"optional": true,
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"default": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.",
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"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
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}
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],
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"inputAnchors": [
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{
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"label": "Chat Model",
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"name": "model",
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"type": "BaseChatModel",
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"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
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},
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{
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"label": "Vector Store Retriever",
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"name": "vectorStoreRetriever",
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"type": "BaseRetriever",
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"id": "conversationalRetrievalQAChain_0-input-vectorStoreRetriever-BaseRetriever"
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},
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{
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"label": "Memory",
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"name": "memory",
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"type": "BaseMemory",
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"optional": true,
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"description": "If left empty, a default BufferMemory will be used",
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"id": "conversationalRetrievalQAChain_0-input-memory-BaseMemory"
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},
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{
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"label": "Input Moderation",
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"description": "Detect text that could generate harmful output and prevent it from being sent to the language model",
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"name": "inputModeration",
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"type": "Moderation",
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"optional": true,
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"list": true,
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"id": "conversationalRetrievalQAChain_0-input-inputModeration-Moderation"
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}
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],
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"inputs": {
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"inputModeration": "",
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"model": "{{chatMistralAI_0.data.instance}}",
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"vectorStoreRetriever": "{{faiss_0.data.instance}}",
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"memory": "",
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"rephrasePrompt": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone Question:",
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"responsePrompt": "You are a helpful assistant. Using the provided context, answer the user's question to the best of your ability using the resources provided.\nIf there is nothing in the context relevant to the question at hand, just say \"Hmm, I'm not sure.\" Don't try to make up an answer.\n------------\n{context}\n------------\nREMEMBER: If there is no relevant information within the context, just say \"Hmm, I'm not sure.\" Don't try to make up an answer."
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},
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"outputAnchors": [
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{
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"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
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"name": "conversationalRetrievalQAChain",
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"label": "ConversationalRetrievalQAChain",
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"type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
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}
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],
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"outputs": {},
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"selected": false
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},
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"positionAbsolute": {
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},
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"selected": false,
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"dragging": false
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},
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{
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"id": "faiss_0",
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"position": {
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"x": 1193.61786387649,
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"y": 559.055052045731
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},
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"type": "customNode",
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"data": {
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"id": "faiss_0",
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"label": "Faiss",
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"version": 1,
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"name": "faiss",
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"type": "Faiss",
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"baseClasses": ["Faiss", "VectorStoreRetriever", "BaseRetriever"],
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"category": "Vector Stores",
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"description": "Upsert embedded data and perform similarity search upon query using Faiss library from Meta",
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"inputParams": [
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{
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"label": "Base Path to load",
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"name": "basePath",
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"description": "Path to load faiss.index file",
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"placeholder": "C:\\Users\\User\\Desktop",
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"type": "string",
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"id": "faiss_0-input-basePath-string"
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},
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{
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"label": "Top K",
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"name": "topK",
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"description": "Number of top results to fetch. Default to 4",
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"placeholder": "4",
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"type": "number",
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"additionalParams": true,
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"optional": true,
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"id": "faiss_0-input-topK-number"
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}
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],
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"inputAnchors": [
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{
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"label": "Document",
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"name": "document",
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"type": "Document",
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"list": true,
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"optional": true,
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"id": "faiss_0-input-document-Document"
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},
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{
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"label": "Embeddings",
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"name": "embeddings",
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"type": "Embeddings",
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"id": "faiss_0-input-embeddings-Embeddings"
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}
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],
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"inputs": {
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"document": ["{{textFile_0.data.instance}}", "{{documentStore_0.data.instance}}"],
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"embeddings": "{{openAIEmbeddings_0.data.instance}}",
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"basePath": "",
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"topK": ""
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},
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"outputAnchors": [
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{
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"name": "output",
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"label": "Output",
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"type": "options",
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"description": "",
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"options": [
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{
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"id": "faiss_0-output-retriever-Faiss|VectorStoreRetriever|BaseRetriever",
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"name": "retriever",
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"label": "Faiss Retriever",
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"description": "",
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"type": "Faiss | VectorStoreRetriever | BaseRetriever"
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},
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{
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"id": "faiss_0-output-vectorStore-Faiss|SaveableVectorStore|VectorStore",
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"name": "vectorStore",
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"label": "Faiss Vector Store",
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"description": "",
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"type": "Faiss | SaveableVectorStore | VectorStore"
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}
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],
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"default": "retriever"
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}
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],
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"outputs": {
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"output": "retriever"
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},
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"selected": false
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},
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"width": 300,
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"height": 459,
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"selected": false,
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"positionAbsolute": {
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"dragging": false
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},
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{
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"id": "documentStore_0",
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"position": {
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"x": 785.3020265031932,
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"y": -215.72424937010018
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},
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"type": "customNode",
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"data": {
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"id": "documentStore_0",
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"label": "Document Store",
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"version": 1,
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"name": "documentStore",
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"type": "Document",
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"baseClasses": ["Document"],
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"category": "Document Loaders",
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"description": "Load data from pre-configured document stores",
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"inputParams": [
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{
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"label": "Select Store",
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"name": "selectedStore",
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|
"type": "asyncOptions",
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"loadMethod": "listStores",
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"id": "documentStore_0-input-selectedStore-asyncOptions"
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}
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],
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"inputAnchors": [],
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"inputs": {
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"selectedStore": ""
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},
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"outputAnchors": [
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{
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"name": "output",
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"label": "Output",
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"type": "options",
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"description": "Array of document objects containing metadata and pageContent",
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"options": [
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{
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"id": "documentStore_0-output-document-Document|json",
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|
"name": "document",
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"label": "Document",
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"description": "Array of document objects containing metadata and pageContent",
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|
"type": "Document | json"
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},
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"description": "Concatenated string from pageContent of documents",
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"inputs": {
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"note": "Conversational Retrieval QA Chain composes of 2 chains:\n\n1. A chain to rephrase user question using previous conversations\n2. A chain to provide response based on the context fetched from vector store.\n\nWhy is the need for rephrasing question?\nThis is to ensure that a follow-up question can be asked. For example:\n\n- What is the address of the Bakery shop?\n- What about the opening time?\n\nA rephrased question will be:\n- What is the opening time of the Bakery shop?\n\nThis ensure a better search to vector store, hence better output quality.\n"
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