Files
Flowise/packages/server/marketplaces/chatflows/Local QnA.json
T
Henry 61dabbb7da Merge branch 'main' into feature/Credential
# Conflicts:
#	README.md
#	docker/.env.example
#	packages/components/nodes/documentloaders/Notion/NotionDB.ts
#	packages/components/nodes/memory/DynamoDb/DynamoDb.ts
#	packages/components/nodes/memory/MotorheadMemory/MotorheadMemory.ts
#	packages/components/nodes/memory/ZepMemory/ZepMemory.ts
#	packages/components/package.json
#	packages/components/src/utils.ts
#	packages/server/.env.example
#	packages/server/README.md
#	packages/server/marketplaces/chatflows/Conversational Retrieval QA Chain.json
#	packages/server/src/ChildProcess.ts
#	packages/server/src/DataSource.ts
#	packages/server/src/commands/start.ts
#	packages/server/src/index.ts
#	packages/server/src/utils/index.ts
#	packages/server/src/utils/logger.ts
2023-07-27 11:26:34 +01:00

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{
"description": "QnA chain using local LLM, Embedding models, and Faiss local vector store",
"nodes": [
{
"width": 300,
"height": 376,
"id": "recursiveCharacterTextSplitter_1",
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"type": "customNode",
"data": {
"id": "recursiveCharacterTextSplitter_1",
"label": "Recursive Character Text Splitter",
"name": "recursiveCharacterTextSplitter",
"type": "RecursiveCharacterTextSplitter",
"baseClasses": ["RecursiveCharacterTextSplitter", "TextSplitter"],
"category": "Text Splitters",
"description": "Split documents recursively by different characters - starting with \"\n\n\", then \"\n\", then \" \"",
"inputParams": [
{
"label": "Chunk Size",
"name": "chunkSize",
"type": "number",
"default": 1000,
"optional": true,
"id": "recursiveCharacterTextSplitter_1-input-chunkSize-number"
},
{
"label": "Chunk Overlap",
"name": "chunkOverlap",
"type": "number",
"optional": true,
"id": "recursiveCharacterTextSplitter_1-input-chunkOverlap-number"
}
],
"inputAnchors": [],
"inputs": {
"chunkSize": 1000,
"chunkOverlap": ""
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"outputAnchors": [
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"id": "recursiveCharacterTextSplitter_1-output-recursiveCharacterTextSplitter-RecursiveCharacterTextSplitter|TextSplitter",
"name": "recursiveCharacterTextSplitter",
"label": "RecursiveCharacterTextSplitter",
"type": "RecursiveCharacterTextSplitter | TextSplitter"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
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"dragging": false
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"id": "conversationalRetrievalQAChain_0",
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"type": "customNode",
"data": {
"id": "conversationalRetrievalQAChain_0",
"label": "Conversational Retrieval QA Chain",
"name": "conversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain",
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "BaseLangChain"],
"category": "Chains",
"description": "Document QA - built on RetrievalQAChain to provide a chat history component",
"inputParams": [
{
"label": "Return Source Documents",
"name": "returnSourceDocuments",
"type": "boolean",
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-returnSourceDocuments-boolean"
},
{
"label": "System Message",
"name": "systemMessagePrompt",
"type": "string",
"rows": 4,
"additionalParams": true,
"optional": true,
"placeholder": "I want you to act as a document that I am having a conversation with. Your name is \"AI Assistant\". You will provide me with answers from the given info. If the answer is not included, say exactly \"Hmm, I am not sure.\" and stop after that. Refuse to answer any question not about the info. Never break character.",
"id": "conversationalRetrievalQAChain_0-input-systemMessagePrompt-string"
},
{
"label": "Chain Option",
"name": "chainOption",
"type": "options",
"options": [
{
"label": "MapReduceDocumentsChain",
"name": "map_reduce",
"description": "Suitable for QA tasks over larger documents and can run the preprocessing step in parallel, reducing the running time"
},
{
"label": "RefineDocumentsChain",
"name": "refine",
"description": "Suitable for QA tasks over a large number of documents."
},
{
"label": "StuffDocumentsChain",
"name": "stuff",
"description": "Suitable for QA tasks over a small number of documents."
}
],
"additionalParams": true,
"optional": true,
"id": "conversationalRetrievalQAChain_0-input-chainOption-options"
}
],
"inputAnchors": [
{
"label": "Language Model",
"name": "model",
"type": "BaseLanguageModel",
"id": "conversationalRetrievalQAChain_0-input-model-BaseLanguageModel"
},
{
"label": "Vector Store Retriever",
"name": "vectorStoreRetriever",
"type": "BaseRetriever",
"id": "conversationalRetrievalQAChain_0-input-vectorStoreRetriever-BaseRetriever"
},
{
"label": "Memory",
"name": "memory",
"type": "BaseMemory",
"optional": true,
"description": "If left empty, a default BufferMemory will be used",
"id": "conversationalRetrievalQAChain_0-input-memory-BaseMemory"
}
],
"inputs": {
"model": "{{chatLocalAI_0.data.instance}}",
"vectorStoreRetriever": "{{faissUpsert_0.data.instance}}",
"memory": ""
},
"outputAnchors": [
{
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|BaseLangChain",
"name": "conversationalRetrievalQAChain",
"label": "ConversationalRetrievalQAChain",
"type": "ConversationalRetrievalQAChain | BaseChain | BaseLangChain"
}
],
"outputs": {},
"selected": false
},
"selected": false,
"positionAbsolute": {
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"dragging": false
},
{
"width": 300,
"height": 456,
"id": "faissUpsert_0",
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"type": "customNode",
"data": {
"id": "faissUpsert_0",
"label": "Faiss Upsert Document",
"name": "faissUpsert",
"type": "Faiss",
"baseClasses": ["Faiss", "VectorStoreRetriever", "BaseRetriever"],
"category": "Vector Stores",
"description": "Upsert documents to Faiss",
"inputParams": [
{
"label": "Base Path to store",
"name": "basePath",
"description": "Path to store faiss.index file",
"placeholder": "C:\\Users\\User\\Desktop",
"type": "string",
"id": "faissUpsert_0-input-basePath-string"
},
{
"label": "Top K",
"name": "topK",
"description": "Number of top results to fetch. Default to 4",
"placeholder": "4",
"type": "number",
"additionalParams": true,
"optional": true,
"id": "faissUpsert_0-input-topK-number"
}
],
"inputAnchors": [
{
"label": "Document",
"name": "document",
"type": "Document",
"list": true,
"id": "faissUpsert_0-input-document-Document"
},
{
"label": "Embeddings",
"name": "embeddings",
"type": "Embeddings",
"id": "faissUpsert_0-input-embeddings-Embeddings"
}
],
"inputs": {
"document": ["{{textFile_0.data.instance}}"],
"embeddings": "{{localAIEmbeddings_0.data.instance}}",
"basePath": "C:\\Users\\your-folder",
"topK": ""
},
"outputAnchors": [
{
"name": "output",
"label": "Output",
"type": "options",
"options": [
{
"id": "faissUpsert_0-output-retriever-Faiss|VectorStoreRetriever|BaseRetriever",
"name": "retriever",
"label": "Faiss Retriever",
"type": "Faiss | VectorStoreRetriever | BaseRetriever"
},
{
"id": "faissUpsert_0-output-vectorStore-Faiss|SaveableVectorStore|VectorStore",
"name": "vectorStore",
"label": "Faiss Vector Store",
"type": "Faiss | SaveableVectorStore | VectorStore"
}
],
"default": "retriever"
}
],
"outputs": {
"output": "retriever"
},
"selected": false
},
"selected": false,
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{
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"id": "chatLocalAI_0",
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"type": "customNode",
"data": {
"id": "chatLocalAI_0",
"label": "ChatLocalAI",
"name": "chatLocalAI",
"type": "ChatLocalAI",
"baseClasses": ["ChatLocalAI", "BaseChatModel", "LLM", "BaseLLM", "BaseLanguageModel", "BaseLangChain"],
"category": "Chat Models",
"description": "Use local LLMs like llama.cpp, gpt4all using LocalAI",
"inputParams": [
{
"label": "Base Path",
"name": "basePath",
"type": "string",
"placeholder": "http://localhost:8080/v1",
"id": "chatLocalAI_0-input-basePath-string"
},
{
"label": "Model Name",
"name": "modelName",
"type": "string",
"placeholder": "gpt4all-lora-quantized.bin",
"id": "chatLocalAI_0-input-modelName-string"
},
{
"label": "Temperature",
"name": "temperature",
"type": "number",
"default": 0.9,
"optional": true,
"id": "chatLocalAI_0-input-temperature-number"
},
{
"label": "Max Tokens",
"name": "maxTokens",
"type": "number",
"optional": true,
"additionalParams": true,
"id": "chatLocalAI_0-input-maxTokens-number"
},
{
"label": "Top Probability",
"name": "topP",
"type": "number",
"optional": true,
"additionalParams": true,
"id": "chatLocalAI_0-input-topP-number"
},
{
"label": "Timeout",
"name": "timeout",
"type": "number",
"optional": true,
"additionalParams": true,
"id": "chatLocalAI_0-input-timeout-number"
}
],
"inputAnchors": [],
"inputs": {
"basePath": "http://localhost:8080/v1",
"modelName": "ggml-gpt4all-j.bin",
"temperature": 0.9,
"maxTokens": "",
"topP": "",
"timeout": ""
},
"outputAnchors": [
{
"id": "chatLocalAI_0-output-chatLocalAI-ChatLocalAI|BaseChatModel|LLM|BaseLLM|BaseLanguageModel|BaseLangChain",
"name": "chatLocalAI",
"label": "ChatLocalAI",
"type": "ChatLocalAI | BaseChatModel | LLM | BaseLLM | BaseLanguageModel | BaseLangChain"
}
],
"outputs": {},
"selected": false
},
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"id": "textFile_0",
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"type": "customNode",
"data": {
"id": "textFile_0",
"label": "Text File",
"name": "textFile",
"type": "Document",
"baseClasses": ["Document"],
"category": "Document Loaders",
"description": "Load data from text files",
"inputParams": [
{
"label": "Txt File",
"name": "txtFile",
"type": "file",
"fileType": ".txt",
"id": "textFile_0-input-txtFile-file"
},
{
"label": "Metadata",
"name": "metadata",
"type": "json",
"optional": true,
"additionalParams": true,
"id": "textFile_0-input-metadata-json"
}
],
"inputAnchors": [
{
"label": "Text Splitter",
"name": "textSplitter",
"type": "TextSplitter",
"optional": true,
"id": "textFile_0-input-textSplitter-TextSplitter"
}
],
"inputs": {
"textSplitter": "{{recursiveCharacterTextSplitter_1.data.instance}}",
"metadata": ""
},
"outputAnchors": [
{
"id": "textFile_0-output-textFile-Document",
"name": "textFile",
"label": "Document",
"type": "Document"
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],
"outputs": {},
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},
"selected": false,
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"id": "localAIEmbeddings_0",
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"name": "localAIEmbeddings",
"type": "LocalAI Embeddings",
"baseClasses": ["LocalAI Embeddings", "Embeddings"],
"category": "Embeddings",
"description": "Use local embeddings models like llama.cpp",
"inputParams": [
{
"label": "Base Path",
"name": "basePath",
"type": "string",
"placeholder": "http://localhost:8080/v1",
"id": "localAIEmbeddings_0-input-basePath-string"
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{
"label": "Model Name",
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"placeholder": "text-embedding-ada-002",
"id": "localAIEmbeddings_0-input-modelName-string"
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],
"inputAnchors": [],
"inputs": {
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"modelName": "text-embedding-ada-002"
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{
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"name": "localAIEmbeddings",
"label": "LocalAI Embeddings",
"type": "LocalAI Embeddings | Embeddings"
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],
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"target": "conversationalRetrievalQAChain_0",
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"source": "chatLocalAI_0",
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},
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"target": "faissUpsert_0",
"targetHandle": "faissUpsert_0-input-document-Document",
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{
"source": "localAIEmbeddings_0",
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}