mirror of
https://github.com/farcasclaudiu/Flowise.git
synced 2026-06-28 21:00:58 +03:00
Merge branch 'main' of github.com:0xi4o/Flowise into feature/scrapped-links
This commit is contained in:
Ilango
@@ -26,3 +26,5 @@ PORT=3000
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||||
# LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
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# LANGCHAIN_API_KEY=your_api_key
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# LANGCHAIN_PROJECT=your_project
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# DISABLE_FLOWISE_TELEMETRY=true
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@@ -511,7 +511,7 @@
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||||
"type": "Pinecone",
|
||||
"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
|
||||
"category": "Vector Stores",
|
||||
"description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
|
||||
"description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
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||||
@@ -552,6 +552,45 @@
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||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "Search Type",
|
||||
"name": "searchType",
|
||||
"type": "options",
|
||||
"default": "similarity",
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||||
"options": [
|
||||
{
|
||||
"label": "Similarity",
|
||||
"name": "similarity"
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||||
},
|
||||
{
|
||||
"label": "Max Marginal Relevance",
|
||||
"name": "mmr"
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||||
}
|
||||
],
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||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-searchType-options"
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||||
},
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||||
{
|
||||
"label": "Fetch K (for MMR Search)",
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||||
"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,
|
||||
"id": "pinecone_0-input-fetchK-number"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-lambda-number"
|
||||
}
|
||||
],
|
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"inputAnchors": [
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||||
@@ -576,7 +615,10 @@
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"pineconeIndex": "",
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"pineconeNamespace": "",
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"pineconeMetadataFilter": "",
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||||
"topK": ""
|
||||
"topK": "",
|
||||
"searchType": "similarity",
|
||||
"fetchK": "",
|
||||
"lambda": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
|
||||
@@ -166,7 +166,7 @@
|
||||
"type": "Pinecone",
|
||||
"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
|
||||
"category": "Vector Stores",
|
||||
"description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
|
||||
"description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
@@ -207,6 +207,45 @@
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||||
"additionalParams": true,
|
||||
"optional": true,
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||||
"id": "pinecone_0-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "Search Type",
|
||||
"name": "searchType",
|
||||
"type": "options",
|
||||
"default": "similarity",
|
||||
"options": [
|
||||
{
|
||||
"label": "Similarity",
|
||||
"name": "similarity"
|
||||
},
|
||||
{
|
||||
"label": "Max Marginal Relevance",
|
||||
"name": "mmr"
|
||||
}
|
||||
],
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-searchType-options"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-fetchK-number"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-lambda-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -231,7 +270,10 @@
|
||||
"pineconeIndex": "",
|
||||
"pineconeNamespace": "",
|
||||
"pineconeMetadataFilter": "",
|
||||
"topK": ""
|
||||
"topK": "",
|
||||
"searchType": "similarity",
|
||||
"fetchK": "",
|
||||
"lambda": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
|
||||
@@ -6,15 +6,15 @@
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||||
"height": 376,
|
||||
"id": "bufferMemory_0",
|
||||
"position": {
|
||||
"x": 451.4449437285705,
|
||||
"y": 118.30026803362762
|
||||
"x": 240.5161028076149,
|
||||
"y": 165.35849026339048
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "bufferMemory_0",
|
||||
"label": "Buffer Memory",
|
||||
"name": "bufferMemory",
|
||||
"version": 1,
|
||||
"name": "bufferMemory",
|
||||
"type": "BufferMemory",
|
||||
"baseClasses": ["BufferMemory", "BaseChatMemory", "BaseMemory"],
|
||||
"category": "Memory",
|
||||
@@ -53,8 +53,8 @@
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 451.4449437285705,
|
||||
"y": 118.30026803362762
|
||||
"x": 240.5161028076149,
|
||||
"y": 165.35849026339048
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
@@ -63,17 +63,17 @@
|
||||
"height": 383,
|
||||
"id": "conversationChain_0",
|
||||
"position": {
|
||||
"x": 1176.1569322079652,
|
||||
"y": 303.56879146735974
|
||||
"x": 958.9887390513221,
|
||||
"y": 318.8734467468765
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "conversationChain_0",
|
||||
"label": "Conversation Chain",
|
||||
"version": 2,
|
||||
"name": "conversationChain",
|
||||
"version": 1,
|
||||
"type": "ConversationChain",
|
||||
"baseClasses": ["ConversationChain", "LLMChain", "BaseChain"],
|
||||
"baseClasses": ["ConversationChain", "LLMChain", "BaseChain", "Runnable"],
|
||||
"category": "Chains",
|
||||
"description": "Chat models specific conversational chain with memory",
|
||||
"inputParams": [
|
||||
@@ -82,9 +82,11 @@
|
||||
"name": "systemMessagePrompt",
|
||||
"type": "string",
|
||||
"rows": 4,
|
||||
"description": "If Chat Prompt Template is provided, this will be ignored",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"placeholder": "You are a helpful assistant that write codes",
|
||||
"default": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.",
|
||||
"placeholder": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.",
|
||||
"id": "conversationChain_0-input-systemMessagePrompt-string"
|
||||
}
|
||||
],
|
||||
@@ -102,27 +104,26 @@
|
||||
"id": "conversationChain_0-input-memory-BaseMemory"
|
||||
},
|
||||
{
|
||||
"label": "Document",
|
||||
"name": "document",
|
||||
"type": "Document",
|
||||
"description": "Include whole document into the context window",
|
||||
"label": "Chat Prompt Template",
|
||||
"name": "chatPromptTemplate",
|
||||
"type": "ChatPromptTemplate",
|
||||
"description": "Override existing prompt with Chat Prompt Template. Human Message must includes {input} variable",
|
||||
"optional": true,
|
||||
"list": true,
|
||||
"id": "conversationChain_0-input-document-Document"
|
||||
"id": "conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"model": "{{chatAnthropic_0.data.instance}}",
|
||||
"memory": "{{bufferMemory_0.data.instance}}",
|
||||
"document": ["{{pdfFile_0.data.instance}}"],
|
||||
"systemMessagePrompt": ""
|
||||
"chatPromptTemplate": "{{chatPromptTemplate_0.data.instance}}",
|
||||
"systemMessagePrompt": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain",
|
||||
"id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain|Runnable",
|
||||
"name": "conversationChain",
|
||||
"label": "ConversationChain",
|
||||
"type": "ConversationChain | LLMChain | BaseChain"
|
||||
"type": "ConversationChain | LLMChain | BaseChain | Runnable"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
@@ -130,27 +131,27 @@
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 1176.1569322079652,
|
||||
"y": 303.56879146735974
|
||||
"x": 958.9887390513221,
|
||||
"y": 318.8734467468765
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 523,
|
||||
"height": 574,
|
||||
"id": "chatAnthropic_0",
|
||||
"position": {
|
||||
"x": 800.5525382783799,
|
||||
"y": -130.7988221837009
|
||||
"x": 585.3308245972187,
|
||||
"y": -116.32789506560908
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "chatAnthropic_0",
|
||||
"label": "ChatAnthropic",
|
||||
"name": "chatAnthropic",
|
||||
"version": 3,
|
||||
"name": "chatAnthropic",
|
||||
"type": "ChatAnthropic",
|
||||
"baseClasses": ["ChatAnthropic", "BaseChatModel", "BaseLanguageModel"],
|
||||
"baseClasses": ["ChatAnthropic", "BaseChatModel", "BaseLanguageModel", "Runnable"],
|
||||
"category": "Chat Models",
|
||||
"description": "Wrapper around ChatAnthropic large language models that use the Chat endpoint",
|
||||
"inputParams": [
|
||||
@@ -226,7 +227,7 @@
|
||||
"name": "claude-instant-v1.1-100k"
|
||||
}
|
||||
],
|
||||
"default": "claude-v1",
|
||||
"default": "claude-2",
|
||||
"optional": true,
|
||||
"id": "chatAnthropic_0-input-modelName-options"
|
||||
},
|
||||
@@ -234,6 +235,7 @@
|
||||
"label": "Temperature",
|
||||
"name": "temperature",
|
||||
"type": "number",
|
||||
"step": 0.1,
|
||||
"default": 0.9,
|
||||
"optional": true,
|
||||
"id": "chatAnthropic_0-input-temperature-number"
|
||||
@@ -242,6 +244,7 @@
|
||||
"label": "Max Tokens",
|
||||
"name": "maxTokensToSample",
|
||||
"type": "number",
|
||||
"step": 1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatAnthropic_0-input-maxTokensToSample-number"
|
||||
@@ -250,6 +253,7 @@
|
||||
"label": "Top P",
|
||||
"name": "topP",
|
||||
"type": "number",
|
||||
"step": 0.1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatAnthropic_0-input-topP-number"
|
||||
@@ -258,6 +262,7 @@
|
||||
"label": "Top K",
|
||||
"name": "topK",
|
||||
"type": "number",
|
||||
"step": 0.1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatAnthropic_0-input-topK-number"
|
||||
@@ -273,6 +278,7 @@
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"cache": "",
|
||||
"modelName": "claude-2.1",
|
||||
"temperature": 0.9,
|
||||
"maxTokensToSample": "",
|
||||
@@ -281,10 +287,10 @@
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel",
|
||||
"id": "chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel|Runnable",
|
||||
"name": "chatAnthropic",
|
||||
"label": "ChatAnthropic",
|
||||
"type": "ChatAnthropic | BaseChatModel | BaseLanguageModel"
|
||||
"type": "ChatAnthropic | BaseChatModel | BaseLanguageModel | Runnable"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
@@ -292,61 +298,106 @@
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 800.5525382783799,
|
||||
"y": -130.7988221837009
|
||||
"x": 585.3308245972187,
|
||||
"y": -116.32789506560908
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 507,
|
||||
"id": "pdfFile_0",
|
||||
"height": 688,
|
||||
"id": "chatPromptTemplate_0",
|
||||
"position": {
|
||||
"x": 94.16886576108482,
|
||||
"y": 37.12056504707391
|
||||
"x": -106.44189698270114,
|
||||
"y": 20.133956087516538
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "pdfFile_0",
|
||||
"label": "Pdf File",
|
||||
"name": "pdfFile",
|
||||
"id": "chatPromptTemplate_0",
|
||||
"label": "Chat Prompt Template",
|
||||
"version": 1,
|
||||
"name": "chatPromptTemplate",
|
||||
"type": "ChatPromptTemplate",
|
||||
"baseClasses": ["ChatPromptTemplate", "BaseChatPromptTemplate", "BasePromptTemplate", "Runnable"],
|
||||
"category": "Prompts",
|
||||
"description": "Schema to represent a chat prompt",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "System Message",
|
||||
"name": "systemMessagePrompt",
|
||||
"type": "string",
|
||||
"rows": 4,
|
||||
"placeholder": "You are a helpful assistant that translates {input_language} to {output_language}.",
|
||||
"id": "chatPromptTemplate_0-input-systemMessagePrompt-string"
|
||||
},
|
||||
{
|
||||
"label": "Human Message",
|
||||
"name": "humanMessagePrompt",
|
||||
"type": "string",
|
||||
"rows": 4,
|
||||
"placeholder": "{text}",
|
||||
"id": "chatPromptTemplate_0-input-humanMessagePrompt-string"
|
||||
},
|
||||
{
|
||||
"label": "Format Prompt Values",
|
||||
"name": "promptValues",
|
||||
"type": "json",
|
||||
"optional": true,
|
||||
"acceptVariable": true,
|
||||
"list": true,
|
||||
"id": "chatPromptTemplate_0-input-promptValues-json"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [],
|
||||
"inputs": {
|
||||
"systemMessagePrompt": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\nThe AI has the following context:\n{context}",
|
||||
"humanMessagePrompt": "{input}",
|
||||
"promptValues": "{\"context\":\"{{plainText_0.data.instance}}\",\"input\":\"{{question}}\"}"
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable",
|
||||
"name": "chatPromptTemplate",
|
||||
"label": "ChatPromptTemplate",
|
||||
"type": "ChatPromptTemplate | BaseChatPromptTemplate | BasePromptTemplate | Runnable"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": -106.44189698270114,
|
||||
"y": 20.133956087516538
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 485,
|
||||
"id": "plainText_0",
|
||||
"position": {
|
||||
"x": -487.7511991135089,
|
||||
"y": 77.83838996645807
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "plainText_0",
|
||||
"label": "Plain Text",
|
||||
"version": 2,
|
||||
"name": "plainText",
|
||||
"type": "Document",
|
||||
"baseClasses": ["Document"],
|
||||
"category": "Document Loaders",
|
||||
"description": "Load data from PDF files",
|
||||
"description": "Load data from plain text",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Pdf File",
|
||||
"name": "pdfFile",
|
||||
"type": "file",
|
||||
"fileType": ".pdf",
|
||||
"id": "pdfFile_0-input-pdfFile-file"
|
||||
},
|
||||
{
|
||||
"label": "Usage",
|
||||
"name": "usage",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "One document per page",
|
||||
"name": "perPage"
|
||||
},
|
||||
{
|
||||
"label": "One document per file",
|
||||
"name": "perFile"
|
||||
}
|
||||
],
|
||||
"default": "perPage",
|
||||
"id": "pdfFile_0-input-usage-options"
|
||||
},
|
||||
{
|
||||
"label": "Use Legacy Build",
|
||||
"name": "legacyBuild",
|
||||
"type": "boolean",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "pdfFile_0-input-legacyBuild-boolean"
|
||||
"label": "Text",
|
||||
"name": "text",
|
||||
"type": "string",
|
||||
"rows": 4,
|
||||
"placeholder": "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua...",
|
||||
"id": "plainText_0-input-text-string"
|
||||
},
|
||||
{
|
||||
"label": "Metadata",
|
||||
@@ -354,7 +405,7 @@
|
||||
"type": "json",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "pdfFile_0-input-metadata-json"
|
||||
"id": "plainText_0-input-metadata-json"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -363,30 +414,45 @@
|
||||
"name": "textSplitter",
|
||||
"type": "TextSplitter",
|
||||
"optional": true,
|
||||
"id": "pdfFile_0-input-textSplitter-TextSplitter"
|
||||
"id": "plainText_0-input-textSplitter-TextSplitter"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"text": "Welcome to Skyworld Hotel, where your dreams take flight and your stay soars to new heights. Nestled amidst breathtaking cityscape views, our upscale establishment offers an unparalleled blend of luxury and comfort. Our rooms are elegantly appointed, featuring modern amenities and plush furnishings to ensure your relaxation.\n\nIndulge in culinary delights at our rooftop restaurant, offering a gastronomic journey with panoramic vistas. Skyworld Hotel boasts state-of-the-art conference facilities, perfect for business travelers, and an inviting spa for relaxation seekers. Our attentive staff is dedicated to ensuring your every need is met, making your stay memorable.\n\nCentrally located, we offer easy access to local attractions, making us an ideal choice for both leisure and business travelers. Experience the world of hospitality like never before at Skyworld Hotel.",
|
||||
"textSplitter": "",
|
||||
"usage": "perPage",
|
||||
"legacyBuild": "",
|
||||
"metadata": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "pdfFile_0-output-pdfFile-Document",
|
||||
"name": "pdfFile",
|
||||
"label": "Document",
|
||||
"type": "Document"
|
||||
"name": "output",
|
||||
"label": "Output",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"id": "plainText_0-output-document-Document",
|
||||
"name": "document",
|
||||
"label": "Document",
|
||||
"type": "Document"
|
||||
},
|
||||
{
|
||||
"id": "plainText_0-output-text-string|json",
|
||||
"name": "text",
|
||||
"label": "Text",
|
||||
"type": "string | json"
|
||||
}
|
||||
],
|
||||
"default": "document"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"outputs": {
|
||||
"output": "text"
|
||||
},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 94.16886576108482,
|
||||
"y": 37.12056504707391
|
||||
"x": -487.7511991135089,
|
||||
"y": 77.83838996645807
|
||||
},
|
||||
"dragging": false
|
||||
}
|
||||
@@ -398,32 +464,31 @@
|
||||
"target": "conversationChain_0",
|
||||
"targetHandle": "conversationChain_0-input-memory-BaseMemory",
|
||||
"type": "buttonedge",
|
||||
"id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
"id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory"
|
||||
},
|
||||
{
|
||||
"source": "chatAnthropic_0",
|
||||
"sourceHandle": "chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel",
|
||||
"sourceHandle": "chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel|Runnable",
|
||||
"target": "conversationChain_0",
|
||||
"targetHandle": "conversationChain_0-input-model-BaseChatModel",
|
||||
"type": "buttonedge",
|
||||
"id": "chatAnthropic_0-chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel-conversationChain_0-conversationChain_0-input-model-BaseChatModel",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
"id": "chatAnthropic_0-chatAnthropic_0-output-chatAnthropic-ChatAnthropic|BaseChatModel|BaseLanguageModel|Runnable-conversationChain_0-conversationChain_0-input-model-BaseChatModel"
|
||||
},
|
||||
{
|
||||
"source": "pdfFile_0",
|
||||
"sourceHandle": "pdfFile_0-output-pdfFile-Document",
|
||||
"target": "conversationChain_0",
|
||||
"targetHandle": "conversationChain_0-input-document-Document",
|
||||
"source": "plainText_0",
|
||||
"sourceHandle": "plainText_0-output-text-string|json",
|
||||
"target": "chatPromptTemplate_0",
|
||||
"targetHandle": "chatPromptTemplate_0-input-promptValues-json",
|
||||
"type": "buttonedge",
|
||||
"id": "pdfFile_0-pdfFile_0-output-pdfFile-Document-conversationChain_0-conversationChain_0-input-document-Document",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
"id": "plainText_0-plainText_0-output-text-string|json-chatPromptTemplate_0-chatPromptTemplate_0-input-promptValues-json"
|
||||
},
|
||||
{
|
||||
"source": "chatPromptTemplate_0",
|
||||
"sourceHandle": "chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable",
|
||||
"target": "conversationChain_0",
|
||||
"targetHandle": "conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate",
|
||||
"type": "buttonedge",
|
||||
"id": "chatPromptTemplate_0-chatPromptTemplate_0-output-chatPromptTemplate-ChatPromptTemplate|BaseChatPromptTemplate|BasePromptTemplate|Runnable-conversationChain_0-conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -301,7 +301,7 @@
|
||||
"type": "Pinecone",
|
||||
"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
|
||||
"category": "Vector Stores",
|
||||
"description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
|
||||
"description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
@@ -342,6 +342,45 @@
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "Search Type",
|
||||
"name": "searchType",
|
||||
"type": "options",
|
||||
"default": "similarity",
|
||||
"options": [
|
||||
{
|
||||
"label": "Similarity",
|
||||
"name": "similarity"
|
||||
},
|
||||
{
|
||||
"label": "Max Marginal Relevance",
|
||||
"name": "mmr"
|
||||
}
|
||||
],
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-searchType-options"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-fetchK-number"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-lambda-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -366,7 +405,10 @@
|
||||
"pineconeIndex": "",
|
||||
"pineconeNamespace": "",
|
||||
"pineconeMetadataFilter": "",
|
||||
"topK": ""
|
||||
"topK": "",
|
||||
"searchType": "similarity",
|
||||
"fetchK": "",
|
||||
"lambda": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
|
||||
@@ -541,7 +541,7 @@
|
||||
"type": "Pinecone",
|
||||
"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
|
||||
"category": "Vector Stores",
|
||||
"description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
|
||||
"description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
@@ -582,6 +582,45 @@
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "Search Type",
|
||||
"name": "searchType",
|
||||
"type": "options",
|
||||
"default": "similarity",
|
||||
"options": [
|
||||
{
|
||||
"label": "Similarity",
|
||||
"name": "similarity"
|
||||
},
|
||||
{
|
||||
"label": "Max Marginal Relevance",
|
||||
"name": "mmr"
|
||||
}
|
||||
],
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-searchType-options"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-fetchK-number"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-lambda-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -606,7 +645,10 @@
|
||||
"pineconeIndex": "",
|
||||
"pineconeNamespace": "",
|
||||
"pineconeMetadataFilter": "",
|
||||
"topK": ""
|
||||
"topK": "",
|
||||
"searchType": "similarity",
|
||||
"fetchK": "",
|
||||
"lambda": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
|
||||
@@ -625,7 +625,7 @@
|
||||
"type": "Pinecone",
|
||||
"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
|
||||
"category": "Vector Stores",
|
||||
"description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
|
||||
"description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
@@ -666,6 +666,45 @@
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "Search Type",
|
||||
"name": "searchType",
|
||||
"type": "options",
|
||||
"default": "similarity",
|
||||
"options": [
|
||||
{
|
||||
"label": "Similarity",
|
||||
"name": "similarity"
|
||||
},
|
||||
{
|
||||
"label": "Max Marginal Relevance",
|
||||
"name": "mmr"
|
||||
}
|
||||
],
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-searchType-options"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-fetchK-number"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-lambda-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -690,7 +729,10 @@
|
||||
"pineconeIndex": "",
|
||||
"pineconeNamespace": "",
|
||||
"pineconeMetadataFilter": "{\"id\":{\"$in\":[\"doc1\",\"doc2\"]}}",
|
||||
"topK": ""
|
||||
"topK": "",
|
||||
"searchType": "similarity",
|
||||
"fetchK": "",
|
||||
"lambda": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
|
||||
@@ -560,7 +560,7 @@
|
||||
"type": "Pinecone",
|
||||
"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
|
||||
"category": "Vector Stores",
|
||||
"description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
|
||||
"description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
@@ -601,6 +601,45 @@
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "Search Type",
|
||||
"name": "searchType",
|
||||
"type": "options",
|
||||
"default": "similarity",
|
||||
"options": [
|
||||
{
|
||||
"label": "Similarity",
|
||||
"name": "similarity"
|
||||
},
|
||||
{
|
||||
"label": "Max Marginal Relevance",
|
||||
"name": "mmr"
|
||||
}
|
||||
],
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-searchType-options"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-fetchK-number"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-lambda-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -625,7 +664,10 @@
|
||||
"pineconeIndex": "",
|
||||
"pineconeNamespace": "",
|
||||
"pineconeMetadataFilter": "",
|
||||
"topK": ""
|
||||
"topK": "",
|
||||
"searchType": "similarity",
|
||||
"fetchK": "",
|
||||
"lambda": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
@@ -840,6 +882,45 @@
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "supabase_0-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "Search Type",
|
||||
"name": "searchType",
|
||||
"type": "options",
|
||||
"default": "similarity",
|
||||
"options": [
|
||||
{
|
||||
"label": "Similarity",
|
||||
"name": "similarity"
|
||||
},
|
||||
{
|
||||
"label": "Max Marginal Relevance",
|
||||
"name": "mmr"
|
||||
}
|
||||
],
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-searchType-options"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-fetchK-number"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-lambda-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -865,7 +946,10 @@
|
||||
"tableName": "",
|
||||
"queryName": "",
|
||||
"supabaseMetadataFilter": "",
|
||||
"topK": ""
|
||||
"topK": "",
|
||||
"searchType": "similarity",
|
||||
"fetchK": "",
|
||||
"lambda": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
|
||||
@@ -2,20 +2,210 @@
|
||||
"description": "Basic example of Conversation Chain with built-in memory - works exactly like ChatGPT",
|
||||
"badge": "POPULAR",
|
||||
"nodes": [
|
||||
{
|
||||
"width": 300,
|
||||
"height": 574,
|
||||
"id": "chatOpenAI_0",
|
||||
"position": {
|
||||
"x": 579.0877964395976,
|
||||
"y": -138.68792413227874
|
||||
},
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||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "chatOpenAI_0",
|
||||
"label": "ChatOpenAI",
|
||||
"version": 2,
|
||||
"name": "chatOpenAI",
|
||||
"type": "ChatOpenAI",
|
||||
"baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
|
||||
"category": "Chat Models",
|
||||
"description": "Wrapper around OpenAI large language models that use the Chat endpoint",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
"name": "credential",
|
||||
"type": "credential",
|
||||
"credentialNames": ["openAIApi"],
|
||||
"id": "chatOpenAI_0-input-credential-credential"
|
||||
},
|
||||
{
|
||||
"label": "Model Name",
|
||||
"name": "modelName",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "gpt-4",
|
||||
"name": "gpt-4"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-1106-preview",
|
||||
"name": "gpt-4-1106-preview"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-vision-preview",
|
||||
"name": "gpt-4-vision-preview"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-0613",
|
||||
"name": "gpt-4-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-32k",
|
||||
"name": "gpt-4-32k"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-32k-0613",
|
||||
"name": "gpt-4-32k-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo",
|
||||
"name": "gpt-3.5-turbo"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-1106",
|
||||
"name": "gpt-3.5-turbo-1106"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-0613",
|
||||
"name": "gpt-3.5-turbo-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-16k",
|
||||
"name": "gpt-3.5-turbo-16k"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-16k-0613",
|
||||
"name": "gpt-3.5-turbo-16k-0613"
|
||||
}
|
||||
],
|
||||
"default": "gpt-3.5-turbo",
|
||||
"optional": true,
|
||||
"id": "chatOpenAI_0-input-modelName-options"
|
||||
},
|
||||
{
|
||||
"label": "Temperature",
|
||||
"name": "temperature",
|
||||
"type": "number",
|
||||
"step": 0.1,
|
||||
"default": 0.9,
|
||||
"optional": true,
|
||||
"id": "chatOpenAI_0-input-temperature-number"
|
||||
},
|
||||
{
|
||||
"label": "Max Tokens",
|
||||
"name": "maxTokens",
|
||||
"type": "number",
|
||||
"step": 1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-maxTokens-number"
|
||||
},
|
||||
{
|
||||
"label": "Top Probability",
|
||||
"name": "topP",
|
||||
"type": "number",
|
||||
"step": 0.1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-topP-number"
|
||||
},
|
||||
{
|
||||
"label": "Frequency Penalty",
|
||||
"name": "frequencyPenalty",
|
||||
"type": "number",
|
||||
"step": 0.1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-frequencyPenalty-number"
|
||||
},
|
||||
{
|
||||
"label": "Presence Penalty",
|
||||
"name": "presencePenalty",
|
||||
"type": "number",
|
||||
"step": 0.1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-presencePenalty-number"
|
||||
},
|
||||
{
|
||||
"label": "Timeout",
|
||||
"name": "timeout",
|
||||
"type": "number",
|
||||
"step": 1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-timeout-number"
|
||||
},
|
||||
{
|
||||
"label": "BasePath",
|
||||
"name": "basepath",
|
||||
"type": "string",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-basepath-string"
|
||||
},
|
||||
{
|
||||
"label": "BaseOptions",
|
||||
"name": "baseOptions",
|
||||
"type": "json",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-baseOptions-json"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Cache",
|
||||
"name": "cache",
|
||||
"type": "BaseCache",
|
||||
"optional": true,
|
||||
"id": "chatOpenAI_0-input-cache-BaseCache"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"cache": "",
|
||||
"modelName": "gpt-3.5-turbo-16k",
|
||||
"temperature": 0.9,
|
||||
"maxTokens": "",
|
||||
"topP": "",
|
||||
"frequencyPenalty": "",
|
||||
"presencePenalty": "",
|
||||
"timeout": "",
|
||||
"basepath": "",
|
||||
"baseOptions": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
|
||||
"name": "chatOpenAI",
|
||||
"label": "ChatOpenAI",
|
||||
"type": "ChatOpenAI | BaseChatModel | BaseLanguageModel | Runnable"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 579.0877964395976,
|
||||
"y": -138.68792413227874
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 376,
|
||||
"id": "bufferMemory_0",
|
||||
"position": {
|
||||
"x": 753.4300788823234,
|
||||
"y": 479.5336426526603
|
||||
"x": 220.30240896145915,
|
||||
"y": 351.61324070296877
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "bufferMemory_0",
|
||||
"label": "Buffer Memory",
|
||||
"name": "bufferMemory",
|
||||
"version": 1,
|
||||
"name": "bufferMemory",
|
||||
"type": "BufferMemory",
|
||||
"baseClasses": ["BufferMemory", "BaseChatMemory", "BaseMemory"],
|
||||
"category": "Memory",
|
||||
@@ -54,179 +244,8 @@
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 753.4300788823234,
|
||||
"y": 479.5336426526603
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 523,
|
||||
"id": "chatOpenAI_0",
|
||||
"position": {
|
||||
"x": 754.8942497823595,
|
||||
"y": -140
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "chatOpenAI_0",
|
||||
"label": "ChatOpenAI",
|
||||
"name": "chatOpenAI",
|
||||
"version": 2,
|
||||
"type": "ChatOpenAI",
|
||||
"baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel"],
|
||||
"category": "Chat Models",
|
||||
"description": "Wrapper around OpenAI large language models that use the Chat endpoint",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
"name": "credential",
|
||||
"type": "credential",
|
||||
"credentialNames": ["openAIApi"],
|
||||
"id": "chatOpenAI_0-input-credential-credential"
|
||||
},
|
||||
{
|
||||
"label": "Model Name",
|
||||
"name": "modelName",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "gpt-4",
|
||||
"name": "gpt-4"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-0613",
|
||||
"name": "gpt-4-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-32k",
|
||||
"name": "gpt-4-32k"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-32k-0613",
|
||||
"name": "gpt-4-32k-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo",
|
||||
"name": "gpt-3.5-turbo"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-0613",
|
||||
"name": "gpt-3.5-turbo-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-16k",
|
||||
"name": "gpt-3.5-turbo-16k"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-16k-0613",
|
||||
"name": "gpt-3.5-turbo-16k-0613"
|
||||
}
|
||||
],
|
||||
"default": "gpt-3.5-turbo",
|
||||
"optional": true,
|
||||
"id": "chatOpenAI_0-input-modelName-options"
|
||||
},
|
||||
{
|
||||
"label": "Temperature",
|
||||
"name": "temperature",
|
||||
"type": "number",
|
||||
"default": 0.9,
|
||||
"optional": true,
|
||||
"id": "chatOpenAI_0-input-temperature-number"
|
||||
},
|
||||
{
|
||||
"label": "Max Tokens",
|
||||
"name": "maxTokens",
|
||||
"type": "number",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-maxTokens-number"
|
||||
},
|
||||
{
|
||||
"label": "Top Probability",
|
||||
"name": "topP",
|
||||
"type": "number",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-topP-number"
|
||||
},
|
||||
{
|
||||
"label": "Frequency Penalty",
|
||||
"name": "frequencyPenalty",
|
||||
"type": "number",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-frequencyPenalty-number"
|
||||
},
|
||||
{
|
||||
"label": "Presence Penalty",
|
||||
"name": "presencePenalty",
|
||||
"type": "number",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-presencePenalty-number"
|
||||
},
|
||||
{
|
||||
"label": "Timeout",
|
||||
"name": "timeout",
|
||||
"type": "number",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-timeout-number"
|
||||
},
|
||||
{
|
||||
"label": "BasePath",
|
||||
"name": "basepath",
|
||||
"type": "string",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-basepath-string"
|
||||
},
|
||||
{
|
||||
"label": "BaseOptions",
|
||||
"name": "baseOptions",
|
||||
"type": "json",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-baseOptions-json"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Cache",
|
||||
"name": "cache",
|
||||
"type": "BaseCache",
|
||||
"optional": true,
|
||||
"id": "chatOpenAI_0-input-cache-BaseCache"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"modelName": "gpt-3.5-turbo",
|
||||
"temperature": 0.9,
|
||||
"maxTokens": "",
|
||||
"topP": "",
|
||||
"frequencyPenalty": "",
|
||||
"presencePenalty": "",
|
||||
"timeout": "",
|
||||
"basepath": "",
|
||||
"baseOptions": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
|
||||
"name": "chatOpenAI",
|
||||
"label": "ChatOpenAI",
|
||||
"type": "ChatOpenAI | BaseChatModel | BaseLanguageModel"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 754.8942497823595,
|
||||
"y": -140
|
||||
"x": 220.30240896145915,
|
||||
"y": 351.61324070296877
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
@@ -235,17 +254,17 @@
|
||||
"height": 383,
|
||||
"id": "conversationChain_0",
|
||||
"position": {
|
||||
"x": 1174.6496397666272,
|
||||
"y": 311.1052536740497
|
||||
"x": 958.9887390513221,
|
||||
"y": 318.8734467468765
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "conversationChain_0",
|
||||
"label": "Conversation Chain",
|
||||
"version": 2,
|
||||
"name": "conversationChain",
|
||||
"version": 1,
|
||||
"type": "ConversationChain",
|
||||
"baseClasses": ["ConversationChain", "LLMChain", "BaseChain"],
|
||||
"baseClasses": ["ConversationChain", "LLMChain", "BaseChain", "Runnable"],
|
||||
"category": "Chains",
|
||||
"description": "Chat models specific conversational chain with memory",
|
||||
"inputParams": [
|
||||
@@ -254,9 +273,11 @@
|
||||
"name": "systemMessagePrompt",
|
||||
"type": "string",
|
||||
"rows": 4,
|
||||
"description": "If Chat Prompt Template is provided, this will be ignored",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"placeholder": "You are a helpful assistant that write codes",
|
||||
"default": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.",
|
||||
"placeholder": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.",
|
||||
"id": "conversationChain_0-input-systemMessagePrompt-string"
|
||||
}
|
||||
],
|
||||
@@ -274,27 +295,26 @@
|
||||
"id": "conversationChain_0-input-memory-BaseMemory"
|
||||
},
|
||||
{
|
||||
"label": "Document",
|
||||
"name": "document",
|
||||
"type": "Document",
|
||||
"description": "Include whole document into the context window",
|
||||
"label": "Chat Prompt Template",
|
||||
"name": "chatPromptTemplate",
|
||||
"type": "ChatPromptTemplate",
|
||||
"description": "Override existing prompt with Chat Prompt Template. Human Message must includes {input} variable",
|
||||
"optional": true,
|
||||
"list": true,
|
||||
"id": "conversationChain_0-input-document-Document"
|
||||
"id": "conversationChain_0-input-chatPromptTemplate-ChatPromptTemplate"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"model": "{{chatOpenAI_0.data.instance}}",
|
||||
"memory": "{{bufferMemory_0.data.instance}}",
|
||||
"document": "",
|
||||
"systemMessagePrompt": ""
|
||||
"chatPromptTemplate": "",
|
||||
"systemMessagePrompt": "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain",
|
||||
"id": "conversationChain_0-output-conversationChain-ConversationChain|LLMChain|BaseChain|Runnable",
|
||||
"name": "conversationChain",
|
||||
"label": "ConversationChain",
|
||||
"type": "ConversationChain | LLMChain | BaseChain"
|
||||
"type": "ConversationChain | LLMChain | BaseChain | Runnable"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
@@ -302,8 +322,8 @@
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 1174.6496397666272,
|
||||
"y": 311.1052536740497
|
||||
"x": 958.9887390513221,
|
||||
"y": 318.8734467468765
|
||||
},
|
||||
"dragging": false
|
||||
}
|
||||
@@ -311,14 +331,11 @@
|
||||
"edges": [
|
||||
{
|
||||
"source": "chatOpenAI_0",
|
||||
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel",
|
||||
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
|
||||
"target": "conversationChain_0",
|
||||
"targetHandle": "conversationChain_0-input-model-BaseChatModel",
|
||||
"type": "buttonedge",
|
||||
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel-conversationChain_0-conversationChain_0-input-model-BaseChatModel",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationChain_0-conversationChain_0-input-model-BaseChatModel"
|
||||
},
|
||||
{
|
||||
"source": "bufferMemory_0",
|
||||
@@ -326,10 +343,7 @@
|
||||
"target": "conversationChain_0",
|
||||
"targetHandle": "conversationChain_0-input-memory-BaseMemory",
|
||||
"type": "buttonedge",
|
||||
"id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
"id": "bufferMemory_0-bufferMemory_0-output-bufferMemory-BufferMemory|BaseChatMemory|BaseMemory-conversationChain_0-conversationChain_0-input-memory-BaseMemory"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
@@ -1,461 +0,0 @@
|
||||
{
|
||||
"description": "A simple LLM chain that uses Vectara to enable conversations with uploaded files",
|
||||
"nodes": [
|
||||
{
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||||
"width": 300,
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||||
"height": 574,
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"id": "chatOpenAI_0",
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"label": "ChatOpenAI",
|
||||
"version": 2,
|
||||
"name": "chatOpenAI",
|
||||
"type": "ChatOpenAI",
|
||||
"baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
|
||||
"category": "Chat Models",
|
||||
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|
||||
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|
||||
{
|
||||
"label": "Connect Credential",
|
||||
"name": "credential",
|
||||
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|
||||
"credentialNames": ["openAIApi"],
|
||||
"id": "chatOpenAI_0-input-credential-credential"
|
||||
},
|
||||
{
|
||||
"label": "Model Name",
|
||||
"name": "modelName",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "gpt-4",
|
||||
"name": "gpt-4"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-0613",
|
||||
"name": "gpt-4-0613"
|
||||
},
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||||
{
|
||||
"label": "gpt-4-32k",
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||||
"name": "gpt-4-32k"
|
||||
},
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||||
{
|
||||
"label": "gpt-4-32k-0613",
|
||||
"name": "gpt-4-32k-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo",
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||||
"name": "gpt-3.5-turbo"
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||||
},
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{
|
||||
"label": "gpt-3.5-turbo-0613",
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||||
"name": "gpt-3.5-turbo-0613"
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},
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{
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||||
"label": "gpt-3.5-turbo-16k",
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||||
"name": "gpt-3.5-turbo-16k"
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||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-16k-0613",
|
||||
"name": "gpt-3.5-turbo-16k-0613"
|
||||
}
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||||
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||||
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||||
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},
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{
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
"optional": true,
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||||
"additionalParams": true,
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||||
"id": "chatOpenAI_0-input-frequencyPenalty-number"
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||||
},
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||||
{
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||||
"label": "Presence Penalty",
|
||||
"name": "presencePenalty",
|
||||
"type": "number",
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||||
"step": 0.1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-presencePenalty-number"
|
||||
},
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||||
{
|
||||
"label": "Timeout",
|
||||
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||||
"type": "number",
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||||
"step": 1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
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||||
"id": "chatOpenAI_0-input-timeout-number"
|
||||
},
|
||||
{
|
||||
"label": "BasePath",
|
||||
"name": "basepath",
|
||||
"type": "string",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-basepath-string"
|
||||
},
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||||
{
|
||||
"label": "BaseOptions",
|
||||
"name": "baseOptions",
|
||||
"type": "json",
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-baseOptions-json"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
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{
|
||||
"label": "Cache",
|
||||
"name": "cache",
|
||||
"type": "BaseCache",
|
||||
"optional": true,
|
||||
"id": "chatOpenAI_0-input-cache-BaseCache"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"modelName": "gpt-3.5-turbo",
|
||||
"temperature": "0.6",
|
||||
"maxTokens": "",
|
||||
"topP": "",
|
||||
"frequencyPenalty": "",
|
||||
"presencePenalty": "",
|
||||
"timeout": "",
|
||||
"basepath": "",
|
||||
"baseOptions": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
|
||||
"name": "chatOpenAI",
|
||||
"label": "ChatOpenAI",
|
||||
"type": "ChatOpenAI | BaseChatModel | BaseLanguageModel | Runnable"
|
||||
}
|
||||
],
|
||||
"outputs": {},
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"selected": false
|
||||
},
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"selected": false,
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"positionAbsolute": {
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},
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},
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{
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"width": 300,
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"height": 480,
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"id": "conversationalRetrievalQAChain_0",
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"position": {
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"y": 200.09513217589273
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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": 2,
|
||||
"name": "conversationalRetrievalQAChain",
|
||||
"type": "ConversationalRetrievalQAChain",
|
||||
"baseClasses": ["ConversationalRetrievalQAChain", "BaseChain", "Runnable"],
|
||||
"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": "Rephrase Prompt",
|
||||
"name": "rephrasePrompt",
|
||||
"type": "string",
|
||||
"description": "Using previous chat history, rephrase question into a standalone question",
|
||||
"warning": "Prompt must include input variables: {chat_history} and {question}",
|
||||
"rows": 4,
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
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|
||||
"id": "conversationalRetrievalQAChain_0-input-rephrasePrompt-string"
|
||||
},
|
||||
{
|
||||
"label": "Response Prompt",
|
||||
"name": "responsePrompt",
|
||||
"type": "string",
|
||||
"description": "Taking the rephrased question, search for answer from the provided context",
|
||||
"warning": "Prompt must include input variable: {context}",
|
||||
"rows": 4,
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"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.",
|
||||
"id": "conversationalRetrievalQAChain_0-input-responsePrompt-string"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Chat Model",
|
||||
"name": "model",
|
||||
"type": "BaseChatModel",
|
||||
"id": "conversationalRetrievalQAChain_0-input-model-BaseChatModel"
|
||||
},
|
||||
{
|
||||
"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": "{{chatOpenAI_0.data.instance}}",
|
||||
"vectorStoreRetriever": "{{vectara_0.data.instance}}",
|
||||
"memory": "",
|
||||
"returnSourceDocuments": true,
|
||||
"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:",
|
||||
"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."
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "conversationalRetrievalQAChain_0-output-conversationalRetrievalQAChain-ConversationalRetrievalQAChain|BaseChain|Runnable",
|
||||
"name": "conversationalRetrievalQAChain",
|
||||
"label": "ConversationalRetrievalQAChain",
|
||||
"type": "ConversationalRetrievalQAChain | BaseChain | Runnable"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
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}
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||||
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||||
{
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||||
"width": 300,
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"height": 535,
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||||
"id": "vectara_0",
|
||||
"position": {
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"x": 199.28476672510158,
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||||
"y": 177.63260741741112
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "vectara_0",
|
||||
"label": "Vectara",
|
||||
"version": 1,
|
||||
"name": "vectara",
|
||||
"type": "Vectara",
|
||||
"baseClasses": ["Vectara", "VectorStoreRetriever", "BaseRetriever"],
|
||||
"category": "Vector Stores",
|
||||
"description": "Upsert embedded data and perform similarity search upon query using Vectara, a LLM-powered search-as-a-service",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
"name": "credential",
|
||||
"type": "credential",
|
||||
"credentialNames": ["vectaraApi"],
|
||||
"id": "vectara_0-input-credential-credential"
|
||||
},
|
||||
{
|
||||
"label": "File",
|
||||
"name": "file",
|
||||
"description": "File to upload to Vectara. Supported file types: https://docs.vectara.com/docs/api-reference/indexing-apis/file-upload/file-upload-filetypes",
|
||||
"type": "file",
|
||||
"optional": true,
|
||||
"id": "vectara_0-input-file-file"
|
||||
},
|
||||
{
|
||||
"label": "Metadata Filter",
|
||||
"name": "filter",
|
||||
"description": "Filter to apply to Vectara metadata. Refer to the <a target=\"_blank\" href=\"https://docs.flowiseai.com/vector-stores/vectara\">documentation</a> on how to use Vectara filters with Flowise.",
|
||||
"type": "string",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_0-input-filter-string"
|
||||
},
|
||||
{
|
||||
"label": "Sentences Before",
|
||||
"name": "sentencesBefore",
|
||||
"description": "Number of sentences to fetch before the matched sentence. Defaults to 2.",
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_0-input-sentencesBefore-number"
|
||||
},
|
||||
{
|
||||
"label": "Sentences After",
|
||||
"name": "sentencesAfter",
|
||||
"description": "Number of sentences to fetch after the matched sentence. Defaults to 2.",
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_0-input-sentencesAfter-number"
|
||||
},
|
||||
{
|
||||
"label": "Lambda",
|
||||
"name": "lambda",
|
||||
"description": "Improves retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.",
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_0-input-lambda-number"
|
||||
},
|
||||
{
|
||||
"label": "Top K",
|
||||
"name": "topK",
|
||||
"description": "Number of top results to fetch. Defaults to 5",
|
||||
"placeholder": "5",
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_0-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "MMR K",
|
||||
"name": "mmrK",
|
||||
"description": "The number of results to rerank if MMR is enabled.",
|
||||
"placeholder": "50",
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_0-input-mmrK-number"
|
||||
},
|
||||
{
|
||||
"label": "MMR Diversity Bias",
|
||||
"name": "mmrDiversityBias",
|
||||
"step": 0.1,
|
||||
"description": "Diversity Bias parameter for MMR, if enabled. 0.0 means no diversiry bias, 1.0 means maximum diversity bias. Defaults to 0.0 (MMR disabled).",
|
||||
"placeholder": "0.0",
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_0-input-mmrDiversityBias-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Document",
|
||||
"name": "document",
|
||||
"type": "Document",
|
||||
"list": true,
|
||||
"optional": true,
|
||||
"id": "vectara_0-input-document-Document"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"document": "",
|
||||
"filter": "",
|
||||
"sentencesBefore": "",
|
||||
"sentencesAfter": "",
|
||||
"lambda": "",
|
||||
"topK": "",
|
||||
"mmrK": "",
|
||||
"mmrDiversityBias": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"name": "output",
|
||||
"label": "Output",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"id": "vectara_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever",
|
||||
"name": "retriever",
|
||||
"label": "Vectara Retriever",
|
||||
"type": "Vectara | VectorStoreRetriever | BaseRetriever"
|
||||
},
|
||||
{
|
||||
"id": "vectara_0-output-vectorStore-Vectara|VectorStore",
|
||||
"name": "vectorStore",
|
||||
"label": "Vectara Vector Store",
|
||||
"type": "Vectara | VectorStore"
|
||||
}
|
||||
],
|
||||
"default": "retriever"
|
||||
}
|
||||
],
|
||||
"outputs": {
|
||||
"output": "retriever"
|
||||
},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
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|
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|
||||
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|
||||
},
|
||||
"dragging": false
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "chatOpenAI_0",
|
||||
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
|
||||
"target": "conversationalRetrievalQAChain_0",
|
||||
"targetHandle": "conversationalRetrievalQAChain_0-input-model-BaseChatModel",
|
||||
"type": "buttonedge",
|
||||
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-model-BaseChatModel",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "vectara_0",
|
||||
"sourceHandle": "vectara_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever",
|
||||
"target": "conversationalRetrievalQAChain_0",
|
||||
"targetHandle": "conversationalRetrievalQAChain_0-input-vectorStoreRetriever-BaseRetriever",
|
||||
"type": "buttonedge",
|
||||
"id": "vectara_0-vectara_0-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever-conversationalRetrievalQAChain_0-conversationalRetrievalQAChain_0-input-vectorStoreRetriever-BaseRetriever",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,385 @@
|
||||
{
|
||||
"nodes": [
|
||||
{
|
||||
"width": 300,
|
||||
"height": 520,
|
||||
"id": "vectaraQAChain_0",
|
||||
"position": {
|
||||
"x": 740.28434119739,
|
||||
"y": 164.93261446841598
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "vectaraQAChain_0",
|
||||
"label": "Vectara QA Chain",
|
||||
"version": 1,
|
||||
"name": "vectaraQAChain",
|
||||
"type": "VectaraQAChain",
|
||||
"baseClasses": ["VectaraQAChain", "BaseChain", "Runnable"],
|
||||
"category": "Chains",
|
||||
"description": "QA chain for Vectara",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Summarizer Prompt Name",
|
||||
"name": "summarizerPromptName",
|
||||
"description": "Summarize the results fetched from Vectara. Read <a target=\"_blank\" href=\"https://docs.vectara.com/docs/learn/grounded-generation/select-a-summarizer\">more</a>",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "vectara-summary-ext-v1.2.0 (gpt-3.5-turbo)",
|
||||
"name": "vectara-summary-ext-v1.2.0"
|
||||
},
|
||||
{
|
||||
"label": "vectara-experimental-summary-ext-2023-10-23-small (gpt-3.5-turbo)",
|
||||
"name": "vectara-experimental-summary-ext-2023-10-23-small",
|
||||
"description": "In beta, available to both Growth and Scale Vectara users"
|
||||
},
|
||||
{
|
||||
"label": "vectara-summary-ext-v1.3.0 (gpt-4.0)",
|
||||
"name": "vectara-summary-ext-v1.3.0",
|
||||
"description": "Only available to paying Scale Vectara users"
|
||||
},
|
||||
{
|
||||
"label": "vectara-experimental-summary-ext-2023-10-23-med (gpt-4.0)",
|
||||
"name": "vectara-experimental-summary-ext-2023-10-23-med",
|
||||
"description": "In beta, only available to paying Scale Vectara users"
|
||||
}
|
||||
],
|
||||
"default": "vectara-summary-ext-v1.2.0",
|
||||
"id": "vectaraQAChain_0-input-summarizerPromptName-options"
|
||||
},
|
||||
{
|
||||
"label": "Response Language",
|
||||
"name": "responseLang",
|
||||
"description": "Return the response in specific language. If not selected, Vectara will automatically detects the language. Read <a target=\"_blank\" href=\"https://docs.vectara.com/docs/learn/grounded-generation/grounded-generation-response-languages\">more</a>",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "English",
|
||||
"name": "eng"
|
||||
},
|
||||
{
|
||||
"label": "German",
|
||||
"name": "deu"
|
||||
},
|
||||
{
|
||||
"label": "French",
|
||||
"name": "fra"
|
||||
},
|
||||
{
|
||||
"label": "Chinese",
|
||||
"name": "zho"
|
||||
},
|
||||
{
|
||||
"label": "Korean",
|
||||
"name": "kor"
|
||||
},
|
||||
{
|
||||
"label": "Arabic",
|
||||
"name": "ara"
|
||||
},
|
||||
{
|
||||
"label": "Russian",
|
||||
"name": "rus"
|
||||
},
|
||||
{
|
||||
"label": "Thai",
|
||||
"name": "tha"
|
||||
},
|
||||
{
|
||||
"label": "Dutch",
|
||||
"name": "nld"
|
||||
},
|
||||
{
|
||||
"label": "Italian",
|
||||
"name": "ita"
|
||||
},
|
||||
{
|
||||
"label": "Portuguese",
|
||||
"name": "por"
|
||||
},
|
||||
{
|
||||
"label": "Spanish",
|
||||
"name": "spa"
|
||||
},
|
||||
{
|
||||
"label": "Japanese",
|
||||
"name": "jpn"
|
||||
},
|
||||
{
|
||||
"label": "Polish",
|
||||
"name": "pol"
|
||||
},
|
||||
{
|
||||
"label": "Turkish",
|
||||
"name": "tur"
|
||||
},
|
||||
{
|
||||
"label": "Vietnamese",
|
||||
"name": "vie"
|
||||
},
|
||||
{
|
||||
"label": "Indonesian",
|
||||
"name": "ind"
|
||||
},
|
||||
{
|
||||
"label": "Czech",
|
||||
"name": "ces"
|
||||
},
|
||||
{
|
||||
"label": "Ukrainian",
|
||||
"name": "ukr"
|
||||
},
|
||||
{
|
||||
"label": "Greek",
|
||||
"name": "ell"
|
||||
},
|
||||
{
|
||||
"label": "Hebrew",
|
||||
"name": "heb"
|
||||
},
|
||||
{
|
||||
"label": "Farsi/Persian",
|
||||
"name": "fas"
|
||||
},
|
||||
{
|
||||
"label": "Hindi",
|
||||
"name": "hin"
|
||||
},
|
||||
{
|
||||
"label": "Urdu",
|
||||
"name": "urd"
|
||||
},
|
||||
{
|
||||
"label": "Swedish",
|
||||
"name": "swe"
|
||||
},
|
||||
{
|
||||
"label": "Bengali",
|
||||
"name": "ben"
|
||||
},
|
||||
{
|
||||
"label": "Malay",
|
||||
"name": "msa"
|
||||
},
|
||||
{
|
||||
"label": "Romanian",
|
||||
"name": "ron"
|
||||
}
|
||||
],
|
||||
"optional": true,
|
||||
"default": "eng",
|
||||
"id": "vectaraQAChain_0-input-responseLang-options"
|
||||
},
|
||||
{
|
||||
"label": "Max Summarized Results",
|
||||
"name": "maxSummarizedResults",
|
||||
"description": "Maximum results used to build the summarized response",
|
||||
"type": "number",
|
||||
"default": 7,
|
||||
"id": "vectaraQAChain_0-input-maxSummarizedResults-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Vectara Store",
|
||||
"name": "vectaraStore",
|
||||
"type": "VectorStore",
|
||||
"id": "vectaraQAChain_0-input-vectaraStore-VectorStore"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"vectaraStore": "{{vectara_1.data.instance}}",
|
||||
"summarizerPromptName": "vectara-experimental-summary-ext-2023-10-23-small",
|
||||
"responseLang": "eng",
|
||||
"maxSummarizedResults": 7
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"id": "vectaraQAChain_0-output-vectaraQAChain-VectaraQAChain|BaseChain|Runnable",
|
||||
"name": "vectaraQAChain",
|
||||
"label": "VectaraQAChain",
|
||||
"type": "VectaraQAChain | BaseChain | Runnable"
|
||||
}
|
||||
],
|
||||
"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 740.28434119739,
|
||||
"y": 164.93261446841598
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 536,
|
||||
"id": "vectara_1",
|
||||
"position": {
|
||||
"x": 139.43135627266395,
|
||||
"y": 189.3685569634871
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "vectara_1",
|
||||
"label": "Vectara",
|
||||
"version": 2,
|
||||
"name": "vectara",
|
||||
"type": "Vectara",
|
||||
"baseClasses": ["Vectara", "VectorStoreRetriever", "BaseRetriever"],
|
||||
"category": "Vector Stores",
|
||||
"description": "Upsert embedded data and perform similarity search upon query using Vectara, a LLM-powered search-as-a-service",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
"name": "credential",
|
||||
"type": "credential",
|
||||
"credentialNames": ["vectaraApi"],
|
||||
"id": "vectara_1-input-credential-credential"
|
||||
},
|
||||
{
|
||||
"label": "File",
|
||||
"name": "file",
|
||||
"description": "File to upload to Vectara. Supported file types: https://docs.vectara.com/docs/api-reference/indexing-apis/file-upload/file-upload-filetypes",
|
||||
"type": "file",
|
||||
"optional": true,
|
||||
"id": "vectara_1-input-file-file"
|
||||
},
|
||||
{
|
||||
"label": "Metadata Filter",
|
||||
"name": "filter",
|
||||
"description": "Filter to apply to Vectara metadata. Refer to the <a target=\"_blank\" href=\"https://docs.flowiseai.com/vector-stores/vectara\">documentation</a> on how to use Vectara filters with Flowise.",
|
||||
"type": "string",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_1-input-filter-string"
|
||||
},
|
||||
{
|
||||
"label": "Sentences Before",
|
||||
"name": "sentencesBefore",
|
||||
"description": "Number of sentences to fetch before the matched sentence. Defaults to 2.",
|
||||
"type": "number",
|
||||
"default": 2,
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_1-input-sentencesBefore-number"
|
||||
},
|
||||
{
|
||||
"label": "Sentences After",
|
||||
"name": "sentencesAfter",
|
||||
"description": "Number of sentences to fetch after the matched sentence. Defaults to 2.",
|
||||
"type": "number",
|
||||
"default": 2,
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_1-input-sentencesAfter-number"
|
||||
},
|
||||
{
|
||||
"label": "Lambda",
|
||||
"name": "lambda",
|
||||
"description": "Enable hybrid search to improve retrieval accuracy by adjusting the balance (from 0 to 1) between neural search and keyword-based search factors.A value of 0.0 means that only neural search is used, while a value of 1.0 means that only keyword-based search is used. Defaults to 0.0 (neural only).",
|
||||
"default": 0,
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_1-input-lambda-number"
|
||||
},
|
||||
{
|
||||
"label": "Top K",
|
||||
"name": "topK",
|
||||
"description": "Number of top results to fetch. Defaults to 5",
|
||||
"placeholder": "5",
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_1-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "MMR K",
|
||||
"name": "mmrK",
|
||||
"description": "Number of top results to fetch for MMR. Defaults to 50",
|
||||
"placeholder": "50",
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_1-input-mmrK-number"
|
||||
},
|
||||
{
|
||||
"label": "MMR diversity bias",
|
||||
"name": "mmrDiversityBias",
|
||||
"step": 0.1,
|
||||
"description": "The diversity bias to use for MMR. This is a value between 0.0 and 1.0Values closer to 1.0 optimize for the most diverse results.Defaults to 0 (MMR disabled)",
|
||||
"placeholder": "0.0",
|
||||
"type": "number",
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "vectara_1-input-mmrDiversityBias-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
{
|
||||
"label": "Document",
|
||||
"name": "document",
|
||||
"type": "Document",
|
||||
"list": true,
|
||||
"optional": true,
|
||||
"id": "vectara_1-input-document-Document"
|
||||
}
|
||||
],
|
||||
"inputs": {
|
||||
"document": "",
|
||||
"filter": "",
|
||||
"sentencesBefore": 2,
|
||||
"sentencesAfter": 2,
|
||||
"lambda": "",
|
||||
"topK": "",
|
||||
"mmrK": "",
|
||||
"mmrDiversityBias": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
"name": "output",
|
||||
"label": "Output",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"id": "vectara_1-output-retriever-Vectara|VectorStoreRetriever|BaseRetriever",
|
||||
"name": "retriever",
|
||||
"label": "Vectara Retriever",
|
||||
"type": "Vectara | VectorStoreRetriever | BaseRetriever"
|
||||
},
|
||||
{
|
||||
"id": "vectara_1-output-vectorStore-Vectara|VectorStore",
|
||||
"name": "vectorStore",
|
||||
"label": "Vectara Vector Store",
|
||||
"type": "Vectara | VectorStore"
|
||||
}
|
||||
],
|
||||
"default": "retriever"
|
||||
}
|
||||
],
|
||||
"outputs": {
|
||||
"output": "vectorStore"
|
||||
},
|
||||
"selected": false
|
||||
},
|
||||
"positionAbsolute": {
|
||||
"x": 139.43135627266395,
|
||||
"y": 189.3685569634871
|
||||
},
|
||||
"selected": false,
|
||||
"dragging": false
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "vectara_1",
|
||||
"sourceHandle": "vectara_1-output-vectorStore-Vectara|VectorStore",
|
||||
"target": "vectaraQAChain_0",
|
||||
"targetHandle": "vectaraQAChain_0-input-vectaraStore-VectorStore",
|
||||
"type": "buttonedge",
|
||||
"id": "vectara_1-vectara_1-output-vectorStore-Vectara|VectorStore-vectaraQAChain_0-vectaraQAChain_0-input-vectaraStore-VectorStore"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -643,7 +643,7 @@
|
||||
"type": "Pinecone",
|
||||
"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
|
||||
"category": "Vector Stores",
|
||||
"description": "Upsert embedded data and perform similarity search upon query using Pinecone, a leading fully managed hosted vector database",
|
||||
"description": "Upsert embedded data and perform similarity or mmr search using Pinecone, a leading fully managed hosted vector database",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
@@ -684,6 +684,45 @@
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-topK-number"
|
||||
},
|
||||
{
|
||||
"label": "Search Type",
|
||||
"name": "searchType",
|
||||
"type": "options",
|
||||
"default": "similarity",
|
||||
"options": [
|
||||
{
|
||||
"label": "Similarity",
|
||||
"name": "similarity"
|
||||
},
|
||||
{
|
||||
"label": "Max Marginal Relevance",
|
||||
"name": "mmr"
|
||||
}
|
||||
],
|
||||
"additionalParams": true,
|
||||
"optional": true,
|
||||
"id": "pinecone_0-input-searchType-options"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-fetchK-number"
|
||||
},
|
||||
{
|
||||
"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,
|
||||
"id": "pinecone_0-input-lambda-number"
|
||||
}
|
||||
],
|
||||
"inputAnchors": [
|
||||
@@ -708,7 +747,10 @@
|
||||
"pineconeIndex": "",
|
||||
"pineconeNamespace": "",
|
||||
"pineconeMetadataFilter": "",
|
||||
"topK": ""
|
||||
"topK": "",
|
||||
"searchType": "similarity",
|
||||
"fetchK": "",
|
||||
"lambda": ""
|
||||
},
|
||||
"outputAnchors": [
|
||||
{
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "flowise",
|
||||
"version": "1.4.10",
|
||||
"version": "1.4.11",
|
||||
"description": "Flowiseai Server",
|
||||
"main": "dist/index",
|
||||
"types": "dist/index.d.ts",
|
||||
@@ -60,6 +60,7 @@
|
||||
"multer": "^1.4.5-lts.1",
|
||||
"mysql": "^2.18.1",
|
||||
"pg": "^8.11.1",
|
||||
"posthog-node": "^3.5.0",
|
||||
"reflect-metadata": "^0.1.13",
|
||||
"sanitize-html": "^2.11.0",
|
||||
"socket.io": "^4.6.1",
|
||||
|
||||
@@ -39,7 +39,8 @@ export default class Start extends Command {
|
||||
LANGCHAIN_TRACING_V2: Flags.string(),
|
||||
LANGCHAIN_ENDPOINT: Flags.string(),
|
||||
LANGCHAIN_API_KEY: Flags.string(),
|
||||
LANGCHAIN_PROJECT: Flags.string()
|
||||
LANGCHAIN_PROJECT: Flags.string(),
|
||||
DISABLE_FLOWISE_TELEMETRY: Flags.string()
|
||||
}
|
||||
|
||||
async stopProcess() {
|
||||
@@ -113,6 +114,9 @@ export default class Start extends Command {
|
||||
if (flags.LANGCHAIN_API_KEY) process.env.LANGCHAIN_API_KEY = flags.LANGCHAIN_API_KEY
|
||||
if (flags.LANGCHAIN_PROJECT) process.env.LANGCHAIN_PROJECT = flags.LANGCHAIN_PROJECT
|
||||
|
||||
// Telemetry
|
||||
if (flags.DISABLE_FLOWISE_TELEMETRY) process.env.DISABLE_FLOWISE_TELEMETRY = flags.DISABLE_FLOWISE_TELEMETRY
|
||||
|
||||
await (async () => {
|
||||
try {
|
||||
logger.info('Starting Flowise...')
|
||||
|
||||
@@ -45,7 +45,9 @@ import {
|
||||
getSessionChatHistory,
|
||||
getAllConnectedNodes,
|
||||
clearSessionMemory,
|
||||
findMemoryNode
|
||||
findMemoryNode,
|
||||
getTelemetryFlowObj,
|
||||
getAppVersion
|
||||
} from './utils'
|
||||
import { cloneDeep, omit, uniqWith, isEqual } from 'lodash'
|
||||
import { getDataSource } from './DataSource'
|
||||
@@ -64,6 +66,7 @@ import { sanitizeMiddleware } from './utils/XSS'
|
||||
import axios from 'axios'
|
||||
import { Client } from 'langchainhub'
|
||||
import { parsePrompt } from './utils/hub'
|
||||
import { Telemetry } from './utils/telemetry'
|
||||
import { Variable } from './database/entities/Variable'
|
||||
|
||||
export class App {
|
||||
@@ -71,6 +74,7 @@ export class App {
|
||||
nodesPool: NodesPool
|
||||
chatflowPool: ChatflowPool
|
||||
cachePool: CachePool
|
||||
telemetry: Telemetry
|
||||
AppDataSource = getDataSource()
|
||||
|
||||
constructor() {
|
||||
@@ -105,6 +109,9 @@ export class App {
|
||||
|
||||
// Initialize cache pool
|
||||
this.cachePool = new CachePool()
|
||||
|
||||
// Initialize telemetry
|
||||
this.telemetry = new Telemetry()
|
||||
})
|
||||
.catch((err) => {
|
||||
logger.error('❌ [server]: Error during Data Source initialization:', err)
|
||||
@@ -294,7 +301,13 @@ export class App {
|
||||
const nodeModule = await import(nodeInstanceFilePath)
|
||||
const newNodeInstance = new nodeModule.nodeClass()
|
||||
|
||||
const returnData = await newNodeInstance.init(nodeData)
|
||||
const options: ICommonObject = {
|
||||
appDataSource: this.AppDataSource,
|
||||
databaseEntities,
|
||||
logger
|
||||
}
|
||||
|
||||
const returnData = await newNodeInstance.init(nodeData, '', options)
|
||||
const result = typeof returnData === 'string' ? handleEscapeCharacters(returnData, true) : returnData
|
||||
|
||||
return res.json(result)
|
||||
@@ -382,6 +395,12 @@ export class App {
|
||||
const chatflow = this.AppDataSource.getRepository(ChatFlow).create(newChatFlow)
|
||||
const results = await this.AppDataSource.getRepository(ChatFlow).save(chatflow)
|
||||
|
||||
await this.telemetry.sendTelemetry('chatflow_created', {
|
||||
version: await getAppVersion(),
|
||||
chatlowId: results.id,
|
||||
flowGraph: getTelemetryFlowObj(JSON.parse(results.flowData)?.nodes, JSON.parse(results.flowData)?.edges)
|
||||
})
|
||||
|
||||
return res.json(results)
|
||||
})
|
||||
|
||||
@@ -668,6 +687,12 @@ export class App {
|
||||
const tool = this.AppDataSource.getRepository(Tool).create(newTool)
|
||||
const results = await this.AppDataSource.getRepository(Tool).save(tool)
|
||||
|
||||
await this.telemetry.sendTelemetry('tool_created', {
|
||||
version: await getAppVersion(),
|
||||
toolId: results.id,
|
||||
toolName: results.name
|
||||
})
|
||||
|
||||
return res.json(results)
|
||||
})
|
||||
|
||||
@@ -874,6 +899,11 @@ export class App {
|
||||
const assistant = this.AppDataSource.getRepository(Assistant).create(newAssistant)
|
||||
const results = await this.AppDataSource.getRepository(Assistant).save(assistant)
|
||||
|
||||
await this.telemetry.sendTelemetry('assistant_created', {
|
||||
version: await getAppVersion(),
|
||||
assistantId: results.id
|
||||
})
|
||||
|
||||
return res.json(results)
|
||||
})
|
||||
|
||||
@@ -1461,6 +1491,11 @@ export class App {
|
||||
let chatId = incomingInput.chatId ?? ''
|
||||
let isUpsert = true
|
||||
|
||||
// Get session ID
|
||||
const memoryNode = findMemoryNode(nodes, edges)
|
||||
let sessionId = undefined
|
||||
if (memoryNode) sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal)
|
||||
|
||||
const vsNodes = nodes.filter(
|
||||
(node) =>
|
||||
node.data.category === 'Vector Stores' &&
|
||||
@@ -1498,6 +1533,7 @@ export class App {
|
||||
incomingInput.question,
|
||||
chatHistory,
|
||||
chatId,
|
||||
sessionId ?? '',
|
||||
chatflowid,
|
||||
this.AppDataSource,
|
||||
incomingInput?.overrideConfig,
|
||||
@@ -1509,6 +1545,15 @@ export class App {
|
||||
const startingNodes = nodes.filter((nd) => startingNodeIds.includes(nd.data.id))
|
||||
|
||||
this.chatflowPool.add(chatflowid, undefined, startingNodes, incomingInput?.overrideConfig)
|
||||
|
||||
await this.telemetry.sendTelemetry('vector_upserted', {
|
||||
version: await getAppVersion(),
|
||||
chatlowId: chatflowid,
|
||||
type: isInternal ? chatType.INTERNAL : chatType.EXTERNAL,
|
||||
flowGraph: getTelemetryFlowObj(nodes, edges),
|
||||
stopNodeId
|
||||
})
|
||||
|
||||
return res.status(201).send('Successfully Upserted')
|
||||
} catch (e: any) {
|
||||
logger.error('[server]: Error:', e)
|
||||
@@ -1575,6 +1620,12 @@ export class App {
|
||||
const nodes = parsedFlowData.nodes
|
||||
const edges = parsedFlowData.edges
|
||||
|
||||
// Get session ID
|
||||
const memoryNode = findMemoryNode(nodes, edges)
|
||||
const memoryType = memoryNode?.data.label
|
||||
let sessionId = undefined
|
||||
if (memoryNode) sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal)
|
||||
|
||||
/* Reuse the flow without having to rebuild (to avoid duplicated upsert, recomputation, reinitialization of memory) when all these conditions met:
|
||||
* - Node Data already exists in pool
|
||||
* - Still in sync (i.e the flow has not been modified since)
|
||||
@@ -1684,6 +1735,7 @@ export class App {
|
||||
incomingInput.question,
|
||||
chatHistory,
|
||||
chatId,
|
||||
sessionId ?? '',
|
||||
chatflowid,
|
||||
this.AppDataSource,
|
||||
incomingInput?.overrideConfig,
|
||||
@@ -1713,12 +1765,6 @@ export class App {
|
||||
|
||||
logger.debug(`[server]: Running ${nodeToExecuteData.label} (${nodeToExecuteData.id})`)
|
||||
|
||||
const memoryNode = findMemoryNode(nodes, edges)
|
||||
const memoryType = memoryNode?.data.label
|
||||
|
||||
let sessionId = undefined
|
||||
if (memoryNode) sessionId = getMemorySessionId(memoryNode, incomingInput, chatId, isInternal)
|
||||
|
||||
const nodeInstanceFilePath = this.nodesPool.componentNodes[nodeToExecuteData.name].filePath as string
|
||||
const nodeModule = await import(nodeInstanceFilePath)
|
||||
const nodeInstance = new nodeModule.nodeClass({ sessionId })
|
||||
@@ -1781,12 +1827,22 @@ export class App {
|
||||
if (result?.sourceDocuments) apiMessage.sourceDocuments = JSON.stringify(result.sourceDocuments)
|
||||
if (result?.usedTools) apiMessage.usedTools = JSON.stringify(result.usedTools)
|
||||
if (result?.fileAnnotations) apiMessage.fileAnnotations = JSON.stringify(result.fileAnnotations)
|
||||
await this.addChatMessage(apiMessage)
|
||||
const chatMessage = await this.addChatMessage(apiMessage)
|
||||
result.chatMessageId = chatMessage.id
|
||||
|
||||
logger.debug(`[server]: Finished running ${nodeToExecuteData.label} (${nodeToExecuteData.id})`)
|
||||
await this.telemetry.sendTelemetry('prediction_sent', {
|
||||
version: await getAppVersion(),
|
||||
chatlowId: chatflowid,
|
||||
chatId,
|
||||
type: isInternal ? chatType.INTERNAL : chatType.EXTERNAL,
|
||||
flowGraph: getTelemetryFlowObj(nodes, edges)
|
||||
})
|
||||
|
||||
// Only return ChatId when its Internal OR incoming input has ChatId, to avoid confusion when calling API
|
||||
if (incomingInput.chatId || isInternal) result.chatId = chatId
|
||||
// Prepare response
|
||||
result.chatId = chatId
|
||||
if (sessionId) result.sessionId = sessionId
|
||||
if (memoryType) result.memoryType = memoryType
|
||||
|
||||
return res.json(result)
|
||||
} catch (e: any) {
|
||||
@@ -1798,6 +1854,7 @@ export class App {
|
||||
async stopApp() {
|
||||
try {
|
||||
const removePromises: any[] = []
|
||||
removePromises.push(this.telemetry.flush())
|
||||
await Promise.all(removePromises)
|
||||
} catch (e) {
|
||||
logger.error(`❌[server]: Flowise Server shut down error: ${e}`)
|
||||
|
||||
@@ -273,6 +273,7 @@ export const buildLangchain = async (
|
||||
question: string,
|
||||
chatHistory: IMessage[],
|
||||
chatId: string,
|
||||
sessionId: string,
|
||||
chatflowid: string,
|
||||
appDataSource: DataSource,
|
||||
overrideConfig?: ICommonObject,
|
||||
@@ -317,6 +318,7 @@ export const buildLangchain = async (
|
||||
logger.debug(`[server]: Upserting ${reactFlowNode.data.label} (${reactFlowNode.data.id})`)
|
||||
await newNodeInstance.vectorStoreMethods!['upsert']!.call(newNodeInstance, reactFlowNodeData, {
|
||||
chatId,
|
||||
sessionId,
|
||||
chatflowid,
|
||||
chatHistory,
|
||||
logger,
|
||||
@@ -331,6 +333,7 @@ export const buildLangchain = async (
|
||||
logger.debug(`[server]: Initializing ${reactFlowNode.data.label} (${reactFlowNode.data.id})`)
|
||||
let outputResult = await newNodeInstance.init(reactFlowNodeData, question, {
|
||||
chatId,
|
||||
sessionId,
|
||||
chatflowid,
|
||||
chatHistory,
|
||||
logger,
|
||||
@@ -1079,3 +1082,60 @@ export const getAllValuesFromJson = (obj: any): any[] => {
|
||||
extractValues(obj)
|
||||
return values
|
||||
}
|
||||
|
||||
/**
|
||||
* Get only essential flow data items for telemetry
|
||||
* @param {IReactFlowNode[]} nodes
|
||||
* @param {IReactFlowEdge[]} edges
|
||||
*/
|
||||
export const getTelemetryFlowObj = (nodes: IReactFlowNode[], edges: IReactFlowEdge[]) => {
|
||||
const nodeData = nodes.map((node) => node.id)
|
||||
const edgeData = edges.map((edge) => ({ source: edge.source, target: edge.target }))
|
||||
return { nodes: nodeData, edges: edgeData }
|
||||
}
|
||||
|
||||
/**
|
||||
* Get user settings file
|
||||
* TODO: move env variables to settings json file, easier configuration
|
||||
*/
|
||||
export const getUserSettingsFilePath = () => {
|
||||
if (process.env.SECRETKEY_PATH) return path.join(process.env.SECRETKEY_PATH, 'settings.json')
|
||||
const checkPaths = [path.join(getUserHome(), '.flowise', 'settings.json')]
|
||||
for (const checkPath of checkPaths) {
|
||||
if (fs.existsSync(checkPath)) {
|
||||
return checkPath
|
||||
}
|
||||
}
|
||||
return ''
|
||||
}
|
||||
|
||||
/**
|
||||
* Get app current version
|
||||
*/
|
||||
export const getAppVersion = async () => {
|
||||
const getPackageJsonPath = (): string => {
|
||||
const checkPaths = [
|
||||
path.join(__dirname, '..', 'package.json'),
|
||||
path.join(__dirname, '..', '..', 'package.json'),
|
||||
path.join(__dirname, '..', '..', '..', 'package.json'),
|
||||
path.join(__dirname, '..', '..', '..', '..', 'package.json'),
|
||||
path.join(__dirname, '..', '..', '..', '..', '..', 'package.json')
|
||||
]
|
||||
for (const checkPath of checkPaths) {
|
||||
if (fs.existsSync(checkPath)) {
|
||||
return checkPath
|
||||
}
|
||||
}
|
||||
return ''
|
||||
}
|
||||
|
||||
const packagejsonPath = getPackageJsonPath()
|
||||
if (!packagejsonPath) return ''
|
||||
try {
|
||||
const content = await fs.promises.readFile(packagejsonPath, 'utf8')
|
||||
const parsedContent = JSON.parse(content)
|
||||
return parsedContent.version
|
||||
} catch (error) {
|
||||
return ''
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,52 @@
|
||||
import { v4 as uuidv4 } from 'uuid'
|
||||
import { PostHog } from 'posthog-node'
|
||||
import path from 'path'
|
||||
import fs from 'fs'
|
||||
import { getUserHome, getUserSettingsFilePath } from '.'
|
||||
|
||||
export class Telemetry {
|
||||
postHog?: PostHog
|
||||
|
||||
constructor() {
|
||||
if (process.env.DISABLE_FLOWISE_TELEMETRY !== 'true') {
|
||||
this.postHog = new PostHog('phc_jEDuFYnOnuXsws986TLWzuisbRjwFqTl9JL8tDMgqme')
|
||||
} else {
|
||||
this.postHog = undefined
|
||||
}
|
||||
}
|
||||
|
||||
async id(): Promise<string> {
|
||||
try {
|
||||
const settingsContent = await fs.promises.readFile(getUserSettingsFilePath(), 'utf8')
|
||||
const settings = JSON.parse(settingsContent)
|
||||
return settings.instanceId
|
||||
} catch (error) {
|
||||
const instanceId = uuidv4()
|
||||
const settings = {
|
||||
instanceId
|
||||
}
|
||||
const defaultLocation = process.env.SECRETKEY_PATH
|
||||
? path.join(process.env.SECRETKEY_PATH, 'settings.json')
|
||||
: path.join(getUserHome(), '.flowise', 'settings.json')
|
||||
await fs.promises.writeFile(defaultLocation, JSON.stringify(settings, null, 2))
|
||||
return instanceId
|
||||
}
|
||||
}
|
||||
|
||||
async sendTelemetry(event: string, properties = {}): Promise<void> {
|
||||
if (this.postHog) {
|
||||
const distinctId = await this.id()
|
||||
this.postHog.capture({
|
||||
event,
|
||||
distinctId,
|
||||
properties
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
async flush(): Promise<void> {
|
||||
if (this.postHog) {
|
||||
await this.postHog.shutdownAsync()
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user